mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-29 04:44:34 +00:00
2093 lines
74 KiB
C++
2093 lines
74 KiB
C++
#include "common.h"
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#include "llama.h"
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#include "build-info.h"
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#include "grammar-parser.h"
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#ifndef NDEBUG
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// crash the server in debug mode, otherwise send an http 500 error
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#define CPPHTTPLIB_NO_EXCEPTIONS 1
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#endif
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#include "httplib.h"
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#include "json.hpp"
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// auto generated files (update with ./deps.sh)
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#include "index.html.hpp"
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#include "index.js.hpp"
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#include "completion.js.hpp"
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#include "json-schema-to-grammar.mjs.hpp"
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#include <cstddef>
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#include <thread>
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#include <chrono>
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#ifndef SERVER_VERBOSE
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#define SERVER_VERBOSE 1
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#endif
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using namespace httplib;
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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::string public_path = "examples/server/public";
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int32_t port = 8040;
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int32_t read_timeout = 600;
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int32_t write_timeout = 600;
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};
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static bool server_verbose = false;
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#if SERVER_VERBOSE != 1
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#define LOG_VERBOSE(MSG, ...)
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#else
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#define LOG_VERBOSE(MSG, ...) \
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do \
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{ \
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if (server_verbose) \
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{ \
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server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
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} \
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} while (0)
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#endif
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#define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
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// parallel
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enum slot_state
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{
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IDLE,
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SLEEPING,
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PROCESSING
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};
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enum slot_command {
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NONE,
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LOAD_PROMPT,
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RELEASE
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};
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struct slot_params {
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bool stream = true;
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uint32_t seed = -1; // RNG seed
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int32_t n_predict = 128; // new tokens to predict
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// sampler params
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int32_t top_k = 40; // <= 0 to use vocab size
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float top_p = 0.95f; // 1.0 = disabled
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float tfs_z = 1.00f; // 1.0 = disabled
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float typical_p = 1.00f; // 1.0 = disabled
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float temp = 0.80f; // 1.0 = disabled
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float repeat_penalty = 1.10f; // 1.0 = disabled
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int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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float frequency_penalty = 0.00f; // 0.0 = disabled
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float presence_penalty = 0.00f; // 0.0 = disabled
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int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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float mirostat_tau = 5.00f; // target entropy
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float mirostat_eta = 0.10f; // learning rate
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int n_probs = 0;
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bool penalize_nl = false;
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std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
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std::string grammar = ""; // optional BNF-like grammar to constrain sampling
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bool remember_generation = false; // remember a part of the prompt to avoid reprocessing all prompt
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std::vector<std::string> antiprompt;
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};
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// completion token output with probabilities
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struct completion_token_output
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{
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struct token_prob
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{
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llama_token tok;
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float prob;
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};
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std::vector<token_prob> probs;
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llama_token tok;
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};
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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 void slot_params_to_gpt_params(const slot_params &src, gpt_params & dst)
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{
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dst.frequency_penalty = src.frequency_penalty;
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dst.temp = src.temp;
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dst.top_k = src.top_k;
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dst.top_p = src.top_p;
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dst.grammar = src.grammar;
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dst.logit_bias = src.logit_bias;
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dst.mirostat = src.mirostat;
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dst.mirostat_eta = src.mirostat_eta;
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dst.mirostat_tau = src.mirostat_tau;
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dst.typical_p = src.typical_p;
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dst.repeat_penalty = src.repeat_penalty;
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dst.repeat_last_n = src.repeat_last_n;
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dst.presence_penalty = src.presence_penalty;
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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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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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static void server_log(const char *level, const char *function, int line,
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const char *message, const nlohmann::ordered_json &extra)
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{
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nlohmann::ordered_json log{
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{"timestamp", time(nullptr)},
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{"level", level},
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{"function", function},
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{"line", line},
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{"message", message},
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};
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if (!extra.empty())
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{
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log.merge_patch(extra);
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}
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const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
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printf("%.*s\n", (int)str.size(), str.data());
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fflush(stdout);
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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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{"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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// generation props
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int32_t num_prompt_tokens = 0;
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int32_t n_decoded = 0;
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int32_t i_batch = -1;
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size_t n_past = 0;
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json prompt;
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std::string generated_text = "";
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int num_tokens_predicted = 0;
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llama_token sampled;
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std::vector<llama_token> context_tokens;
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std::vector<llama_token> last_n_tokens;
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std::vector<completion_token_output> generated_token_probs;
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int sent_tokens = 0;
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slot_state state = IDLE;
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slot_command command = NONE;
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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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std::string stopping_word;
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int32_t multibyte_pending = 0;
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slot_params params;
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// grammar props
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grammar_parser::parse_state parsed_grammar;
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llama_grammar *grammar = nullptr;
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void reset() {
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state = IDLE;
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command = NONE;
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num_prompt_tokens = 0;
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num_tokens_predicted = 0;
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generated_text = "";
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generated_token_probs.clear();
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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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multibyte_pending = 0;
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n_past = 0;
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if (grammar != nullptr) {
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llama_grammar_free(grammar);
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grammar = nullptr;
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}
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// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
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}
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bool loadGrammar()
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{
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if (!params.grammar.empty()) {
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parsed_grammar = grammar_parser::parse(params.grammar.c_str());
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// will be empty (default) if there are parse errors
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if (parsed_grammar.rules.empty()) {
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LOG_ERROR("grammar parse error", {{"grammar", params.grammar}});
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return false;
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}
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grammar_parser::print_grammar(stderr, parsed_grammar);
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// TODO: fix this comment
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// {
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// auto it = params.logit_bias.find(llama_token_eos(ctx));
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// if (it != params.logit_bias.end() && it->second == -INFINITY) {
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// LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
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// }
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// }
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std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
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grammar = llama_grammar_init(
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grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
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}
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return true;
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}
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bool hasNewToken() {
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return generated_token_probs.size() > sent_tokens;
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}
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bool available() {
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return state == IDLE &&
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command == NONE && !params.remember_generation;
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}
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bool isProcessing() {
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return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
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}
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completion_token_output next() {
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completion_token_output tkn = generated_token_probs.at(sent_tokens);
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sent_tokens++;
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return tkn;
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}
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void addTokenString(completion_token_output token) {
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if(command == RELEASE) {
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generated_token_probs.clear();
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sent_tokens = 0;
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return;
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}
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context_tokens.push_back(token.tok);
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generated_token_probs.push_back(token);
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num_tokens_predicted++;
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}
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void release() {
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if(state == PROCESSING) {
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command = RELEASE;
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}
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}
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};
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struct llama_server_context
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{
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std::vector<llama_client_slot> slots;
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// system prompt
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std::string system_prompt = "";
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bool update_system_prompt = false;
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std::vector<llama_token> tokens_system;
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int32_t n_tokens_system = 0;
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// broadcast to all clients to keep the same prompt format
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std::string user_name = ""; // this should be the anti prompt
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std::string assistant_name = ""; // this is for generate the prompt
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llama_model *model = nullptr;
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llama_context *ctx = nullptr;
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llama_batch batch;
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std::vector<llama_token_data> candidates;
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bool all_slots_are_idle = false;
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gpt_params params;
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int n_ctx;
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int n_vocab;
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bool clean_kv_cache = true;
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std::mutex mutex;
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std::unique_lock<std::mutex> lock()
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{
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return std::unique_lock<std::mutex>(mutex);
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}
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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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for(auto &slot : slots) {
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if(slot.grammar) {
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llama_grammar_free(slot.grammar);
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}
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}
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}
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bool loadModel(const gpt_params ¶ms_)
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{
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params = params_;
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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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n_ctx = llama_n_ctx(ctx);
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n_vocab = llama_n_vocab(model);
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candidates.reserve(n_vocab);
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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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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.last_n_tokens.resize(params.n_predict); // max prediction per slot
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slot.reset();
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std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0);
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LOG_TEE(" - slot %i\n", slot.id);
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slots.push_back(slot);
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}
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batch = llama_batch_init(params.n_ctx, 0);
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// empty system prompt
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system_prompt = "";
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all_slots_are_idle = true;
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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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// 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);
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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);
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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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printf("----------------------\nprompt:\n%s-----------------------\n", s.c_str());
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prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
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}
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return prompt_tokens;
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}
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void processPrompt() {
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//params.n_keep = std::min(n_ctx - 4, params.n_keep);
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// if input prompt is too big, truncate like normal
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// if (num_prompt_tokens >= (size_t)n_ctx)
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// {
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// const int n_left = (n_ctx - params.n_keep) / 2;
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// std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
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// const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
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// new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
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// std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin());
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// LOG_VERBOSE("input truncated", {
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// {"n_ctx", n_ctx},
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// {"n_keep", params.n_keep},
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// {"n_left", n_left},
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// {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
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// });
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// truncated = true;
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// prompt_tokens = new_tokens;
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// }
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// else
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// {
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// const size_t ps = num_prompt_tokens;
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// std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
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// std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
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// }
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// compare the evaluated prompt with the new prompt
|
|
}
|
|
|
|
|
|
llama_client_slot* getSlot(int id) {
|
|
for (llama_client_slot & slot : slots)
|
|
{
|
|
if ((id == -1 && slot.available()) || slot.id == id)
|
|
{
|
|
return &slot;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
bool launchSlot(llama_client_slot* &slot) {
|
|
if(!slot->loadGrammar()) {
|
|
return false;
|
|
}
|
|
all_slots_are_idle = false;
|
|
slot->command = LOAD_PROMPT;
|
|
LOG_TEE("slot %i is processing\n", slot->id);
|
|
return true;
|
|
}
|
|
|
|
void loadInfill()
|
|
{
|
|
// 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(params.input_prefix, false);
|
|
// auto suffix_tokens = tokenize(params.input_suffix, false);
|
|
// const int space_token = 29871;
|
|
// if (suff_rm_leading_spc && suffix_tokens[0] == space_token) {
|
|
// suffix_tokens.erase(suffix_tokens.begin());
|
|
// }
|
|
// prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx));
|
|
// prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS
|
|
// prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx));
|
|
// prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
|
|
// prefix_tokens.push_back(llama_token_middle(ctx));
|
|
// auto prompt_tokens = prefix_tokens;
|
|
|
|
// num_prompt_tokens = prompt_tokens.size();
|
|
|
|
// if (params.n_keep < 0)
|
|
// {
|
|
// params.n_keep = (int)num_prompt_tokens;
|
|
// }
|
|
// params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
|
|
|
|
// // if input prompt is too big, truncate like normal
|
|
// if (num_prompt_tokens >= (size_t)params.n_ctx)
|
|
// {
|
|
// printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens);
|
|
// // todo we probably want to cut from both sides
|
|
// const int n_left = (params.n_ctx - params.n_keep) / 2;
|
|
// std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
|
|
// const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
|
|
// new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
|
|
// std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
|
|
|
|
// LOG_VERBOSE("input truncated", {
|
|
// {"n_ctx", params.n_ctx},
|
|
// {"n_keep", params.n_keep},
|
|
// {"n_left", n_left},
|
|
// {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
|
|
// });
|
|
|
|
// truncated = true;
|
|
// prompt_tokens = new_tokens;
|
|
// }
|
|
// else
|
|
// {
|
|
// const size_t ps = num_prompt_tokens;
|
|
// std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
|
|
// std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
|
|
// }
|
|
|
|
// // compare the evaluated prompt with the new prompt
|
|
// n_past = common_part(embd, prompt_tokens);
|
|
// embd = prompt_tokens;
|
|
// if (n_past == num_prompt_tokens)
|
|
// {
|
|
// // we have to evaluate at least 1 token to generate logits.
|
|
// printf("we have to evaluate at least 1 token to generate logits\n");
|
|
// n_past--;
|
|
// }
|
|
|
|
// LOG_VERBOSE("prompt ingested", {
|
|
// {"n_past", n_past},
|
|
// {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)},
|
|
// {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
|
|
// });
|
|
|
|
// has_next_token = true;
|
|
}
|
|
|
|
void cleanKVCache() {
|
|
// clear the entire KV cache
|
|
for (int i = 0; i < params.n_parallel; ++i)
|
|
{
|
|
llama_kv_cache_seq_rm(ctx, i, 0, -1);
|
|
}
|
|
clean_kv_cache = false;
|
|
}
|
|
|
|
void updateSystemPrompt() {
|
|
tokens_system = ::llama_tokenize(ctx, system_prompt, true);
|
|
n_tokens_system = tokens_system.size();
|
|
|
|
batch.n_tokens = n_tokens_system;
|
|
|
|
cleanKVCache();
|
|
|
|
for (int32_t i = 0; i < batch.n_tokens; ++i)
|
|
{
|
|
batch.token[i] = tokens_system[i];
|
|
batch.pos[i] = i;
|
|
batch.seq_id[i] = 0;
|
|
batch.logits[i] = 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, n_tokens_system);
|
|
}
|
|
|
|
LOG_TEE("system prompt updated\n");
|
|
update_system_prompt = false;
|
|
}
|
|
|
|
void notifySystemPromptChanged() {
|
|
// release all slots
|
|
for (llama_client_slot &slot : slots)
|
|
{
|
|
slot.release();
|
|
}
|
|
waitAllAreIdle();
|
|
all_slots_are_idle = true;
|
|
// wait until system prompt load
|
|
update_system_prompt = true;
|
|
while(update_system_prompt) {
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(5));
|
|
}
|
|
// system prompt loaded, continue
|
|
}
|
|
|
|
void processSystemPromptData(json sys_props) {
|
|
system_prompt = sys_props.value("system_prompt", "");
|
|
user_name = sys_props.value("anti_prompt", "");
|
|
assistant_name = sys_props.value("assistant_name", "");
|
|
notifySystemPromptChanged();
|
|
}
|
|
|
|
void waitAllAreIdle() {
|
|
bool wait = true;
|
|
while(wait) {
|
|
wait = false;
|
|
for (auto &slot : slots)
|
|
{
|
|
if (!slot.available())
|
|
{
|
|
wait = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
size_t findStoppingStrings(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 = slot.generated_text.size() > tmp ? slot.generated_text.size() - tmp : 0;
|
|
pos = slot.generated_text.find(word, from_pos);
|
|
}
|
|
else
|
|
{
|
|
pos = find_partial_stop_string(word, slot.generated_text);
|
|
}
|
|
if (pos != std::string::npos &&
|
|
(stop_pos == std::string::npos || pos < stop_pos))
|
|
{
|
|
if (type == STOP_FULL)
|
|
{
|
|
slot.stopping_word = word;
|
|
slot.stopped_word = true;
|
|
}
|
|
stop_pos = pos;
|
|
}
|
|
}
|
|
return stop_pos;
|
|
}
|
|
|
|
bool processToken(completion_token_output & result, llama_client_slot & slot) {
|
|
// remember which tokens were sampled - used for repetition penalties during sampling
|
|
slot.last_n_tokens.erase(slot.last_n_tokens.begin());
|
|
slot.last_n_tokens.push_back(result.tok);
|
|
const std::string token_str = llama_token_to_piece(ctx, result.tok);
|
|
printf("%s", token_str.c_str());
|
|
slot.sampled = result.tok;
|
|
|
|
size_t stop_pos =
|
|
findStoppingStrings(token_str.size(), STOP_FULL, slot);
|
|
|
|
slot.addTokenString(result);
|
|
|
|
slot.generated_text += token_str;
|
|
|
|
bool has_next_token = !(slot.n_decoded > 2 &&
|
|
(result.tok == llama_token_eos(ctx) ||
|
|
(slot.n_decoded + slot.n_past >=
|
|
params.n_predict) ||
|
|
stop_pos != std::string::npos));
|
|
|
|
if (slot.params.n_probs > 0)
|
|
{
|
|
slot.generated_token_probs.push_back(result);
|
|
}
|
|
|
|
if (slot.multibyte_pending > 0)
|
|
{
|
|
slot.multibyte_pending -= token_str.size();
|
|
}
|
|
else if (token_str.size() == 1)
|
|
{
|
|
const char c = token_str[0];
|
|
// 2-byte characters: 110xxxxx 10xxxxxx
|
|
if ((c & 0xE0) == 0xC0)
|
|
{
|
|
slot.multibyte_pending = 1;
|
|
// 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
|
|
}
|
|
else if ((c & 0xF0) == 0xE0)
|
|
{
|
|
slot.multibyte_pending = 2;
|
|
// 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
|
|
}
|
|
else if ((c & 0xF8) == 0xF0)
|
|
{
|
|
slot.multibyte_pending = 3;
|
|
}
|
|
else
|
|
{
|
|
slot.multibyte_pending = 0;
|
|
}
|
|
}
|
|
|
|
if (slot.multibyte_pending > 0 && !has_next_token)
|
|
{
|
|
has_next_token = true;
|
|
}
|
|
|
|
if (!has_next_token && (slot.n_decoded + slot.n_past >= params.n_predict))
|
|
{
|
|
slot.stopped_limit = true;
|
|
}
|
|
|
|
if (!slot.context_tokens.empty() && result.tok == llama_token_eos(ctx)){
|
|
slot.stopped_eos = true;
|
|
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", has_next_token},
|
|
{"n_remain", (params.n_predict - slot.n_decoded + slot.n_past)},
|
|
{"num_tokens_predicted", slot.num_tokens_predicted},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
});
|
|
return has_next_token; // continue
|
|
}
|
|
|
|
bool updateSlots() {
|
|
// update the system prompt wait until all slots are idle state
|
|
if(update_system_prompt) {
|
|
updateSystemPrompt();
|
|
}
|
|
|
|
batch.n_tokens = 0;
|
|
int kv_cache_free = (n_ctx - n_tokens_system);
|
|
|
|
if(all_slots_are_idle) {
|
|
if(system_prompt.empty() && clean_kv_cache) {
|
|
cleanKVCache();
|
|
}
|
|
// avoid 100% usage of cpu all time
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(5));
|
|
}
|
|
|
|
// decode any currently ongoing sequences
|
|
for (auto & slot : slots) {
|
|
// release the slot
|
|
if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken())
|
|
{
|
|
LOG_TEE("slot %i released\n", slot.id);
|
|
if(!slot.params.remember_generation) {
|
|
llama_kv_cache_seq_rm(ctx, slot.id, n_tokens_system, n_ctx);
|
|
slot.state = IDLE;
|
|
slot.command = NONE;
|
|
slot.num_prompt_tokens = 0;
|
|
slot.num_tokens_predicted = 0;
|
|
} else {
|
|
slot.state = SLEEPING;
|
|
slot.command = NONE;
|
|
}
|
|
continue;
|
|
}
|
|
|
|
kv_cache_free -= slot.num_prompt_tokens;
|
|
|
|
if (slot.state == IDLE || slot.command == RELEASE) {
|
|
continue;
|
|
}
|
|
|
|
batch.token [batch.n_tokens] = slot.sampled;
|
|
batch.pos [batch.n_tokens] = n_tokens_system + slot.n_past + slot.n_decoded;
|
|
batch.seq_id[batch.n_tokens] = slot.id;
|
|
batch.logits[batch.n_tokens] = true;
|
|
|
|
slot.n_decoded += 1;
|
|
slot.i_batch = batch.n_tokens;
|
|
|
|
batch.n_tokens += 1;
|
|
}
|
|
|
|
// assign workload to the slots
|
|
if (params.cont_batching || batch.n_tokens == 0) {
|
|
for (auto & slot : slots) {
|
|
// need process the prompt
|
|
bool keep_gen = slot.state == SLEEPING; // remember generation
|
|
if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) {
|
|
LOG_TEE("processing prompt\n");
|
|
slot.state = PROCESSING;
|
|
slot.command = NONE;
|
|
|
|
auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
|
|
slot.num_prompt_tokens = prompt_tokens.size();
|
|
|
|
slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0;
|
|
|
|
slot.context_tokens = prompt_tokens;
|
|
|
|
LOG_VERBOSE("prompt ingested", {
|
|
{"n_past", slot.n_past},
|
|
{"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)},
|
|
{"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())},
|
|
});
|
|
|
|
if(system_prompt.empty()) {
|
|
LOG_TEE("cleaning kv: %i\n", slot.n_past);
|
|
llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
|
|
}
|
|
|
|
std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0);
|
|
for (size_t i = slot.n_past; i < slot.context_tokens.size(); ++i) {
|
|
batch.token [batch.n_tokens] = slot.context_tokens[i];
|
|
batch.pos [batch.n_tokens] = i + n_tokens_system;
|
|
batch.seq_id[batch.n_tokens] = slot.id;
|
|
batch.logits[batch.n_tokens] = false;
|
|
batch.n_tokens += 1;
|
|
}
|
|
|
|
// 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;
|
|
}
|
|
|
|
// process in chunks of params.n_batch
|
|
int32_t n_batch = params.n_batch;
|
|
|
|
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.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("%s : failed to decode the batch, retrying with 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;
|
|
}
|
|
|
|
slot_params_to_gpt_params(slot.params, params);
|
|
completion_token_output result;
|
|
const llama_token id = llama_sample_token(ctx, NULL, NULL, params, slot.last_n_tokens, candidates, slot.i_batch - i);
|
|
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
|
result.tok = id;
|
|
const int32_t n_probs = params.n_probs;
|
|
if (params.temp <= 0 && n_probs > 0)
|
|
{
|
|
// For llama_sample_token_greedy we need to sort candidates
|
|
llama_sample_softmax(ctx, &candidates_p);
|
|
}
|
|
|
|
for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
|
|
{
|
|
result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
|
|
}
|
|
|
|
if (!processToken(result, slot)) {
|
|
slot.generated_text.clear();
|
|
slot.release();
|
|
}
|
|
kv_cache_free -= slot.num_tokens_predicted;
|
|
slot.i_batch = -1;
|
|
}
|
|
}
|
|
|
|
if(kv_cache_free < 0) {
|
|
LOG_TEE("\nError: kv cache is full, increase context size.");
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
std::vector<float> getEmbedding()
|
|
{
|
|
static const int n_embd = llama_n_embd(model);
|
|
if (!params.embedding)
|
|
{
|
|
LOG_WARNING("embedding disabled", {
|
|
{"params.embedding", params.embedding},
|
|
});
|
|
return std::vector<float>(n_embd, 0.0f);
|
|
}
|
|
const float *data = llama_get_embeddings(ctx);
|
|
std::vector<float> embedding(data, data + n_embd);
|
|
return embedding;
|
|
}
|
|
};
|
|
|
|
static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
|
const server_params &sparams)
|
|
{
|
|
printf("usage: %s [options]\n", argv0);
|
|
printf("\n");
|
|
printf("options:\n");
|
|
printf(" -h, --help show this help message and exit\n");
|
|
printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
|
|
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
|
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n");
|
|
printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n");
|
|
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
|
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
|
|
if (llama_mlock_supported())
|
|
{
|
|
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_mmap_supported())
|
|
{
|
|
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
printf(" --numa attempt optimizations that help on some NUMA systems\n");
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
printf(" -ngl N, --n-gpu-layers N\n");
|
|
printf(" number of layers to store in VRAM\n");
|
|
printf(" -ts SPLIT --tensor-split SPLIT\n");
|
|
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
|
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
|
printf(" -nommq, --no-mul-mat-q\n");
|
|
printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
|
|
printf(" Not recommended since this is both slower and uses more VRAM.\n");
|
|
#endif
|
|
printf(" -m FNAME, --model FNAME\n");
|
|
printf(" model path (default: %s)\n", params.model.c_str());
|
|
printf(" -a ALIAS, --alias ALIAS\n");
|
|
printf(" set an alias for the model, will be added as `model` field in completion response\n");
|
|
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
|
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
|
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
|
|
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
|
|
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
|
|
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
|
|
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
|
printf("\n");
|
|
}
|
|
|
|
static void server_params_parse(int argc, char **argv, server_params &sparams,
|
|
gpt_params ¶ms)
|
|
{
|
|
gpt_params default_params;
|
|
server_params default_sparams;
|
|
std::string arg;
|
|
bool invalid_param = false;
|
|
|
|
for (int i = 1; i < argc; i++)
|
|
{
|
|
arg = argv[i];
|
|
if (arg == "--port")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.port = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--host")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.hostname = argv[i];
|
|
}
|
|
else if (arg == "--path")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.public_path = argv[i];
|
|
}
|
|
else if (arg == "--timeout" || arg == "-to")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.read_timeout = std::stoi(argv[i]);
|
|
sparams.write_timeout = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-m" || arg == "--model")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model = argv[i];
|
|
}
|
|
else if (arg == "-a" || arg == "--alias")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model_alias = argv[i];
|
|
}
|
|
else if (arg == "-h" || arg == "--help")
|
|
{
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(0);
|
|
}
|
|
else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_ctx = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--rope-freq-base")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_base = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--rope-freq-scale")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_scale = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--memory-f32" || arg == "--memory_f32")
|
|
{
|
|
params.memory_f16 = false;
|
|
}
|
|
else if (arg == "--threads" || arg == "-t")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-b" || arg == "--batch-size")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_batch = std::stoi(argv[i]);
|
|
params.n_batch = std::min(512, params.n_batch);
|
|
}
|
|
else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
#else
|
|
LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
|
|
"See main README.md for information on enabling GPU BLAS support",
|
|
{{"n_gpu_layers", params.n_gpu_layers}});
|
|
#endif
|
|
}
|
|
else if (arg == "--tensor-split" || arg == "-ts")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifdef GGML_USE_CUBLAS
|
|
std::string arg_next = argv[i];
|
|
|
|
// 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;
|
|
}
|
|
}
|
|
#else
|
|
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
|
|
#endif // GGML_USE_CUBLAS
|
|
}
|
|
else if (arg == "--no-mul-mat-q" || arg == "-nommq")
|
|
{
|
|
#ifdef GGML_USE_CUBLAS
|
|
params.mul_mat_q = false;
|
|
#else
|
|
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
|
|
#endif // GGML_USE_CUBLAS
|
|
}
|
|
else if (arg == "--main-gpu" || arg == "-mg")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifdef GGML_USE_CUBLAS
|
|
params.main_gpu = std::stoi(argv[i]);
|
|
#else
|
|
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
|
|
#endif
|
|
}
|
|
else if (arg == "--lora")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
|
|
params.use_mmap = false;
|
|
}
|
|
else if (arg == "--lora-scaled")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
const char * lora_adapter = argv[i];
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
|
|
params.use_mmap = false;
|
|
}
|
|
else if (arg == "--lora-base")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_base = argv[i];
|
|
}
|
|
else if (arg == "-v" || arg == "--verbose")
|
|
{
|
|
#if SERVER_VERBOSE != 1
|
|
LOG_WARNING("server.cpp is not built with verbose logging.", {});
|
|
#else
|
|
server_verbose = true;
|
|
#endif
|
|
}
|
|
else if (arg == "--mlock")
|
|
{
|
|
params.use_mlock = true;
|
|
}
|
|
else if (arg == "--no-mmap")
|
|
{
|
|
params.use_mmap = false;
|
|
}
|
|
else if (arg == "--numa")
|
|
{
|
|
params.numa = true;
|
|
}
|
|
else if (arg == "--embedding")
|
|
{
|
|
params.embedding = true;
|
|
} else if (arg == "-cb" || arg == "--cont-batching")
|
|
{
|
|
params.cont_batching = true;
|
|
}
|
|
else if (arg == "-np" || arg == "--parallel")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_parallel = std::stoi(argv[i]);
|
|
} else if (arg == "-n" || arg == "--n-predict")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_predict = std::stoi(argv[i]);
|
|
if(params.n_predict <= 128) { // this example don't support long prompts
|
|
params.n_predict = 128;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(1);
|
|
}
|
|
}
|
|
|
|
if (invalid_param)
|
|
{
|
|
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(1);
|
|
}
|
|
}
|
|
|
|
static json format_generation_settings(llama_server_context &llama, llama_client_slot* &slot)
|
|
{
|
|
const auto eos_bias = slot->params.logit_bias.find(llama_token_eos(llama.ctx));
|
|
const bool ignore_eos = eos_bias != slot->params.logit_bias.end() &&
|
|
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
|
|
return json{
|
|
{"n_ctx", llama.n_ctx},
|
|
{"model", llama.params.model_alias},
|
|
{"seed", slot->params.seed},
|
|
{"temp", slot->params.temp},
|
|
{"top_k", slot->params.top_k},
|
|
{"top_p", slot->params.top_p},
|
|
{"tfs_z", slot->params.tfs_z},
|
|
{"typical_p", slot->params.typical_p},
|
|
{"repeat_last_n", slot->params.repeat_last_n},
|
|
{"repeat_penalty", slot->params.repeat_penalty},
|
|
{"presence_penalty",slot->params.presence_penalty},
|
|
{"frequency_penalty", slot->params.frequency_penalty},
|
|
{"mirostat", slot->params.mirostat},
|
|
{"mirostat_tau", slot->params.mirostat_tau},
|
|
{"mirostat_eta", slot->params.mirostat_eta},
|
|
{"penalize_nl", slot->params.penalize_nl},
|
|
{"stop", slot->params.antiprompt},
|
|
{"n_predict", slot->params.n_predict},
|
|
// {"n_keep", slot.params.n_keep},
|
|
{"ignore_eos", ignore_eos},
|
|
{"stream", slot->params.stream},
|
|
{"logit_bias", slot->params.logit_bias},
|
|
{"n_probs", slot->params.n_probs},
|
|
{"grammar", slot->params.grammar},
|
|
};
|
|
}
|
|
|
|
static json format_embedding_response(llama_server_context &llama)
|
|
{
|
|
return json{
|
|
{"embedding", llama.getEmbedding()},
|
|
};
|
|
}
|
|
|
|
static json format_timings(llama_server_context &llama)
|
|
{
|
|
const auto timings = llama_get_timings(llama.ctx);
|
|
|
|
return json{
|
|
{"prompt_n", timings.n_p_eval},
|
|
{"prompt_ms", timings.t_p_eval_ms},
|
|
{"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval},
|
|
{"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval},
|
|
|
|
{"predicted_n", timings.n_eval},
|
|
{"predicted_ms", timings.t_eval_ms},
|
|
{"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval},
|
|
{"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval},
|
|
};
|
|
}
|
|
|
|
static json format_final_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", true},
|
|
{"model", llama.params.model_alias},
|
|
{"tokens_predicted", slot->num_tokens_predicted},
|
|
{"tokens_evaluated", slot->num_prompt_tokens},
|
|
{"generation_settings", format_generation_settings(llama, 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", format_timings(llama)},
|
|
};
|
|
|
|
if (slot->params.n_probs > 0)
|
|
{
|
|
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
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 }
|
|
};
|
|
|
|
if (slot->params.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}};
|
|
}
|
|
|
|
template <typename T>
|
|
static T json_value(const json &body, const std::string &key, const T &default_value)
|
|
{
|
|
// Fallback null to default value
|
|
return body.contains(key) && !body.at(key).is_null()
|
|
? body.value(key, default_value)
|
|
: default_value;
|
|
}
|
|
|
|
static void parse_options_completion(const json &body, llama_client_slot* &slot, llama_server_context &llama)
|
|
{
|
|
slot_params default_params;
|
|
|
|
slot->params.stream = json_value(body, "stream", false);
|
|
slot->params.n_predict = json_value(body, "n_predict", default_params.n_predict);
|
|
slot->params.top_k = json_value(body, "top_k", default_params.top_k);
|
|
slot->params.top_p = json_value(body, "top_p", default_params.top_p);
|
|
slot->params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z);
|
|
slot->params.typical_p = json_value(body, "typical_p", default_params.typical_p);
|
|
slot->params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n);
|
|
slot->params.temp = json_value(body, "temperature", default_params.temp);
|
|
slot->params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty);
|
|
slot->params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty);
|
|
slot->params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty);
|
|
slot->params.mirostat = json_value(body, "mirostat", default_params.mirostat);
|
|
slot->params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau);
|
|
slot->params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta);
|
|
slot->params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl);
|
|
//llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep);
|
|
slot->params.seed = json_value(body, "seed", default_params.seed);
|
|
slot->params.grammar = json_value(body, "grammar", default_params.grammar);
|
|
slot->params.n_probs = json_value(body, "n_probs", default_params.n_probs);
|
|
|
|
if (body.count("prompt") != 0)
|
|
{
|
|
slot->prompt = body["prompt"];
|
|
}
|
|
else
|
|
{
|
|
slot->prompt = "";
|
|
}
|
|
|
|
slot->params.logit_bias.clear();
|
|
if (json_value(body, "ignore_eos", false))
|
|
{
|
|
slot->params.logit_bias[llama_token_eos(llama.ctx)] = -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())
|
|
{
|
|
slot->params.logit_bias[tok] = el[1].get<float>();
|
|
}
|
|
else if (el[1].is_boolean() && !el[1].get<bool>())
|
|
{
|
|
slot->params.logit_bias[tok] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
slot->params.antiprompt.clear();
|
|
const auto &stop = body.find("stop");
|
|
if (stop != body.end() && stop->is_array())
|
|
{
|
|
for (const auto &word : *stop)
|
|
{
|
|
if (!word.empty())
|
|
{
|
|
slot->params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot));
|
|
}
|
|
|
|
// static void parse_options_infill(const json &body, llama_server_context &llama)
|
|
// {
|
|
// if (body.count("input_prefix") != 0)
|
|
// {
|
|
// llama.params.input_prefix = body["input_prefix"];
|
|
// }
|
|
// else
|
|
// {
|
|
// llama.params.input_prefix = "";
|
|
// }
|
|
// if (body.count("input_suffix") != 0)
|
|
// {
|
|
// llama.params.input_suffix = body["input_suffix"];
|
|
// }
|
|
// else
|
|
// {
|
|
// llama.params.input_suffix = "";
|
|
// }
|
|
// parse_options_completion(body, slot, llama);
|
|
// }
|
|
|
|
static void log_server_request(const Request &req, const Response &res)
|
|
{
|
|
LOG_INFO("request", {
|
|
{"remote_addr", req.remote_addr},
|
|
{"remote_port", req.remote_port},
|
|
{"status", res.status},
|
|
{"method", req.method},
|
|
{"path", req.path},
|
|
{"params", req.params},
|
|
});
|
|
|
|
LOG_VERBOSE("request", {
|
|
{"request", req.body},
|
|
{"response", res.body},
|
|
});
|
|
}
|
|
|
|
static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) {
|
|
return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx);
|
|
}
|
|
|
|
// Function matching type llama_beam_search_callback_fn_t.
|
|
// Custom callback example is called each time the beams lengths increase:
|
|
// * Show progress by printing ',' following by number of convergent beam tokens if any.
|
|
// * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
|
|
// This is also called when the stop condition is met.
|
|
// Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
|
|
|
|
// AVOID HEADACHES unnecessaries
|
|
|
|
// static void beam_search_callback(void *callback_data, llama_beams_state beams_state) {
|
|
// auto & llama = *static_cast<llama_server_context*>(callback_data);
|
|
// // Mark beams as EOS as needed.
|
|
// for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
|
|
// llama_beam_view& beam_view = beams_state.beam_views[i];
|
|
// if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) {
|
|
// beam_view.eob = true;
|
|
// }
|
|
// }
|
|
// printf(","); // Show progress
|
|
// if (const size_t n = beams_state.common_prefix_length) {
|
|
// llama.generated_token_probs.resize(llama.generated_token_probs.size() + n);
|
|
// assert(0u < beams_state.n_beams);
|
|
// const llama_token * tokens = beams_state.beam_views[0].tokens;
|
|
// const auto map = [](llama_token tok) { return completion_token_output{{},tok}; };
|
|
// std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map);
|
|
// printf("%zu", n);
|
|
// }
|
|
// fflush(stdout);
|
|
// #if 0 // DEBUG: print current beams for this iteration
|
|
// std::cout << "\n\nCurrent beams:\n";
|
|
// for (size_t i=0 ; i < beams_state.n_beams ; ++i) {
|
|
// std::cout << "beams["<<i<<"]: " << ostream_beam_view{state.ctx,beams_state.beam_views[i]} << std::endl;
|
|
// }
|
|
// #endif
|
|
// }
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
int main(int argc, char **argv)
|
|
{
|
|
// own arguments required by this example
|
|
gpt_params params;
|
|
server_params sparams;
|
|
|
|
// struct that contains llama context and inference
|
|
llama_server_context llama;
|
|
|
|
server_params_parse(argc, argv, sparams, params);
|
|
|
|
if (params.model_alias == "unknown")
|
|
{
|
|
params.model_alias = params.model;
|
|
}
|
|
|
|
llama_backend_init(params.numa);
|
|
|
|
LOG_INFO("build info", {{"build", BUILD_NUMBER},
|
|
{"commit", BUILD_COMMIT}});
|
|
LOG_INFO("system info", {
|
|
{"n_threads", params.n_threads},
|
|
{"n_threads_batch", params.n_threads_batch},
|
|
{"total_threads", std::thread::hardware_concurrency()},
|
|
{"system_info", llama_print_system_info()},
|
|
});
|
|
|
|
// load the model
|
|
if (!llama.loadModel(params))
|
|
{
|
|
return 1;
|
|
}
|
|
|
|
llama.initialize();
|
|
|
|
Server svr;
|
|
|
|
svr.set_default_headers({{"Server", "llama.cpp"},
|
|
{"Access-Control-Allow-Origin", "*"},
|
|
{"Access-Control-Allow-Headers", "content-type"}});
|
|
|
|
// this is only called if no index.html is found in the public --path
|
|
svr.Get("/", [](const Request &, Response &res)
|
|
{
|
|
res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
|
|
return false; });
|
|
|
|
// this is only called if no index.js is found in the public --path
|
|
svr.Get("/index.js", [](const Request &, Response &res)
|
|
{
|
|
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
|
|
return false; });
|
|
|
|
// this is only called if no index.html is found in the public --path
|
|
svr.Get("/completion.js", [](const Request &, Response &res)
|
|
{
|
|
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
|
|
return false; });
|
|
|
|
// this is only called if no index.html is found in the public --path
|
|
svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res)
|
|
{
|
|
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
|
|
return false; });
|
|
|
|
svr.Get("/props", [&llama](const Request & /*req*/, Response &res)
|
|
{
|
|
res.set_header("Access-Control-Allow-Origin", "*");
|
|
json data = {
|
|
{ "user_name", llama.user_name.c_str() },
|
|
{ "assistant_name", llama.assistant_name.c_str() }
|
|
};
|
|
res.set_content(data.dump(), "application/json"); });
|
|
|
|
svr.Post("/completion", [&llama](const Request &req, Response &res)
|
|
{
|
|
//auto lock = llama.lock();
|
|
|
|
json data = json::parse(req.body);
|
|
|
|
llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1));
|
|
|
|
if(slot == nullptr) {
|
|
LOG_TEE("slot unavailable\n");
|
|
res.status = 404;
|
|
res.set_content("slot_error", "text/plain");
|
|
return;
|
|
}
|
|
|
|
if(data.contains("system_prompt")) {
|
|
llama.processSystemPromptData(data["system_prompt"]);
|
|
}
|
|
|
|
// llama_reset_timings(llama.ctx);
|
|
|
|
slot->reset();
|
|
|
|
parse_options_completion(json::parse(req.body), slot, llama);
|
|
|
|
if (!llama.launchSlot(slot))
|
|
{
|
|
res.status = 400;
|
|
return;
|
|
}
|
|
|
|
if (!slot->params.stream) {
|
|
// if (llama.params.n_beams) {
|
|
// // Fill llama.generated_token_probs vector with final beam.
|
|
// llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams,
|
|
// llama.n_past, llama.n_remain);
|
|
// // Translate llama.generated_token_probs to llama.generated_text.
|
|
// append_to_generated_text_from_generated_token_probs(llama);
|
|
// } else {
|
|
// size_t stop_pos = std::string::npos;
|
|
|
|
// while (llama.has_next_token) {
|
|
// const completion_token_output token_with_probs = llama.doCompletion();
|
|
// const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);
|
|
|
|
// stop_pos = llama.findStoppingStrings(llama.generated_text,
|
|
// token_text.size(), STOP_FULL);
|
|
// }
|
|
|
|
// if (stop_pos == std::string::npos) {
|
|
// stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
|
|
// }
|
|
// if (stop_pos != std::string::npos) {
|
|
// llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
|
|
// llama.generated_text.end());
|
|
// }
|
|
// }
|
|
|
|
// auto probs = llama.generated_token_probs;
|
|
// if (llama.params.n_probs > 0 && llama.stopped_word) {
|
|
// const std::vector<llama_token> stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false);
|
|
// probs = std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size());
|
|
// }
|
|
|
|
// const json data = format_final_response(llama, llama.generated_text, probs);
|
|
|
|
// llama_print_timings(llama.ctx);
|
|
|
|
// res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace),
|
|
// "application/json");
|
|
} else {
|
|
printf("processing -> %s\n", slot->isProcessing() ? "true" : "false");
|
|
const auto chunked_content_provider = [slot](size_t, DataSink & sink) {
|
|
size_t sent_count = 0;
|
|
size_t sent_token_probs_index = 0;
|
|
while(slot->isProcessing()) {
|
|
if(slot->hasNewToken()) { // new token notification
|
|
// const completion_token_output token = slot->next();
|
|
// std::string token_str = llama_token_to_piece(llama.ctx, token.tok);
|
|
|
|
// std::vector<completion_token_output> probs_output = {};
|
|
|
|
// const json data = format_partial_response(llama, slot, token_str, probs_output);
|
|
// const std::string str =
|
|
// "data: " +
|
|
// data.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())) {
|
|
// slot->release();
|
|
// return false;
|
|
// }
|
|
} else {
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(5));
|
|
}
|
|
}
|
|
// const json data = format_final_response(
|
|
// llama, slot,
|
|
// "",
|
|
// std::vector<completion_token_output>(
|
|
// slot->generated_token_probs.begin(),
|
|
// slot->generated_token_probs.begin() + sent_token_probs_index)
|
|
// );
|
|
|
|
// const std::string str =
|
|
// "data: " +
|
|
// data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
// "\n\n";
|
|
|
|
// LOG_VERBOSE("data stream", {
|
|
// { "to_send", str }
|
|
// });
|
|
|
|
// if (!sink.write(str.data(), str.size())) {
|
|
// LOG_VERBOSE("stream closed", {});
|
|
// llama_print_timings(llama.ctx);
|
|
// return false;
|
|
// }
|
|
sink.done();
|
|
return true;
|
|
};
|
|
auto on_complete = [&] (bool) {
|
|
//llama.mutex.unlock();
|
|
slot->sent_tokens = 0;
|
|
slot->generated_token_probs.clear();
|
|
slot->release();
|
|
};
|
|
//lock.release();
|
|
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
} });
|
|
|
|
// svr.Post("/infill", [&llama](const Request &req, Response &res)
|
|
// {
|
|
// auto lock = llama.lock();
|
|
|
|
// llama.rewind();
|
|
|
|
// llama_reset_timings(llama.ctx);
|
|
|
|
// parse_options_infill(json::parse(req.body), llama);
|
|
|
|
// if (!llama.loadGrammar())
|
|
// {
|
|
// res.status = 400;
|
|
// return;
|
|
// }
|
|
// llama.loadInfill();
|
|
// llama.beginCompletion();
|
|
// const auto chunked_content_provider = [&](size_t, DataSink & sink) {
|
|
// size_t sent_count = 0;
|
|
// size_t sent_token_probs_index = 0;
|
|
|
|
// while (llama.has_next_token) {
|
|
// const completion_token_output token_with_probs = llama.doCompletion();
|
|
// if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
|
|
// continue;
|
|
// }
|
|
// const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);
|
|
|
|
// size_t pos = std::min(sent_count, llama.generated_text.size());
|
|
|
|
// const std::string str_test = llama.generated_text.substr(pos);
|
|
// bool is_stop_full = false;
|
|
// size_t stop_pos =
|
|
// llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
|
|
// if (stop_pos != std::string::npos) {
|
|
// is_stop_full = true;
|
|
// llama.generated_text.erase(
|
|
// llama.generated_text.begin() + pos + stop_pos,
|
|
// llama.generated_text.end());
|
|
// pos = std::min(sent_count, llama.generated_text.size());
|
|
// } else {
|
|
// is_stop_full = false;
|
|
// stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
|
|
// STOP_PARTIAL);
|
|
// }
|
|
|
|
// if (
|
|
// stop_pos == std::string::npos ||
|
|
// // Send rest of the text if we are at the end of the generation
|
|
// (!llama.has_next_token && !is_stop_full && stop_pos > 0)
|
|
// ) {
|
|
// const std::string to_send = llama.generated_text.substr(pos, std::string::npos);
|
|
|
|
// sent_count += to_send.size();
|
|
|
|
// std::vector<completion_token_output> probs_output = {};
|
|
|
|
// if (llama.params.n_probs > 0) {
|
|
// const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
|
|
// size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
|
|
// size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
|
|
// if (probs_pos < probs_stop_pos) {
|
|
// probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
|
|
// }
|
|
// sent_token_probs_index = probs_stop_pos;
|
|
// }
|
|
|
|
// const json data = format_partial_response(llama, to_send, probs_output);
|
|
|
|
// const std::string str =
|
|
// "data: " +
|
|
// data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
// "\n\n";
|
|
|
|
// LOG_VERBOSE("data stream", {
|
|
// { "to_send", str }
|
|
// });
|
|
|
|
// if (!sink.write(str.data(), str.size())) {
|
|
// LOG_VERBOSE("stream closed", {});
|
|
// llama_print_timings(llama.ctx);
|
|
// return false;
|
|
// }
|
|
// }
|
|
|
|
// if (!llama.has_next_token) {
|
|
// // Generation is done, send extra information.
|
|
// const json data = format_final_response(
|
|
// llama,
|
|
// "",
|
|
// std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index)
|
|
// );
|
|
|
|
// const std::string str =
|
|
// "data: " +
|
|
// data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
// "\n\n";
|
|
|
|
// LOG_VERBOSE("data stream", {
|
|
// { "to_send", str }
|
|
// });
|
|
|
|
// if (!sink.write(str.data(), str.size())) {
|
|
// LOG_VERBOSE("stream closed", {});
|
|
// llama_print_timings(llama.ctx);
|
|
// return false;
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
// llama_print_timings(llama.ctx);
|
|
// sink.done();
|
|
// return true;
|
|
// };
|
|
// const auto on_complete = [&](bool) {
|
|
// llama.mutex.unlock();
|
|
// };
|
|
// lock.release();
|
|
// res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
// });
|
|
|
|
// svr.Get("/model.json", [&llama](const Request &, Response &res)
|
|
// {
|
|
// const json data = format_generation_settings(llama);
|
|
// return res.set_content(data.dump(), "application/json"); });
|
|
|
|
svr.Options(R"(/.*)", [](const Request &, Response &res)
|
|
{ return res.set_content("", "application/json"); });
|
|
|
|
svr.Post("/tokenize", [&llama](const Request &req, Response &res)
|
|
{
|
|
auto lock = llama.lock();
|
|
|
|
const json body = json::parse(req.body);
|
|
std::vector<llama_token> tokens;
|
|
if (body.count("content") != 0)
|
|
{
|
|
tokens = llama.tokenize(body["content"], false);
|
|
}
|
|
const json data = format_tokenizer_response(tokens);
|
|
return res.set_content(data.dump(), "application/json"); });
|
|
|
|
svr.Post("/detokenize", [&llama](const Request &req, Response &res)
|
|
{
|
|
auto lock = llama.lock();
|
|
|
|
const json body = json::parse(req.body);
|
|
std::string content;
|
|
if (body.count("tokens") != 0)
|
|
{
|
|
const std::vector<llama_token> tokens = body["tokens"];
|
|
content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
|
|
}
|
|
|
|
const json data = format_detokenized_response(content);
|
|
return res.set_content(data.dump(), "application/json"); });
|
|
|
|
// svr.Post("/embedding", [&llama](const Request &req, Response &res)
|
|
// {
|
|
// auto lock = llama.lock();
|
|
|
|
// const json body = json::parse(req.body);
|
|
|
|
// llama.rewind();
|
|
// llama_reset_timings(llama.ctx);
|
|
// if (body.count("content") != 0)
|
|
// {
|
|
// llama.prompt = body["content"];
|
|
// }
|
|
// else
|
|
// {
|
|
// llama.prompt = "";
|
|
// }
|
|
// llama.params.n_predict = 0;
|
|
// llama.loadPrompt();
|
|
// llama.beginCompletion();
|
|
// llama.doCompletion();
|
|
|
|
// const json data = format_embedding_response(llama);
|
|
// return res.set_content(data.dump(), "application/json"); });
|
|
|
|
svr.set_logger(log_server_request);
|
|
|
|
svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep)
|
|
{
|
|
const char fmt[] = "500 Internal Server Error\n%s";
|
|
char buf[BUFSIZ];
|
|
try {
|
|
std::rethrow_exception(std::move(ep));
|
|
} catch (std::exception & e) {
|
|
snprintf(buf, sizeof(buf), fmt, e.what());
|
|
} catch (...) {
|
|
snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
|
|
}
|
|
res.set_content(buf, "text/plain");
|
|
res.status = 500; });
|
|
|
|
svr.set_error_handler([](const Request &, Response &res)
|
|
{
|
|
if (res.status == 400) {
|
|
res.set_content("Invalid request", "text/plain");
|
|
} else if (res.status != 500) {
|
|
res.set_content("File Not Found", "text/plain");
|
|
res.status = 404;
|
|
} });
|
|
|
|
// set timeouts and change hostname and port
|
|
svr.set_read_timeout(sparams.read_timeout);
|
|
svr.set_write_timeout(sparams.write_timeout);
|
|
|
|
if (!svr.bind_to_port(sparams.hostname, sparams.port))
|
|
{
|
|
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
|
|
return 1;
|
|
}
|
|
|
|
// Set the base directory for serving static files
|
|
svr.set_base_dir(sparams.public_path);
|
|
|
|
// to make it ctrl+clickable:
|
|
printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
|
|
|
|
LOG_INFO("HTTP server listening", {
|
|
{"hostname", sparams.hostname},
|
|
{"port", sparams.port},
|
|
});
|
|
std::thread t([&llama]()
|
|
{
|
|
bool running = true;
|
|
while (running)
|
|
{
|
|
running = llama.updateSlots();
|
|
} });
|
|
if (!svr.listen_after_bind())
|
|
{
|
|
return 1;
|
|
}
|
|
llama_backend_free();
|
|
return 0;
|
|
}
|