mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
1439 lines
51 KiB
C++
1439 lines
51 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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#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 = 8080;
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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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// 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 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_str(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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fprintf(stdout, "%.*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_str(ctx, token);
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// if first bit is 1, meaning it's a partial character
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if (out.size() > 0 && (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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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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struct llama_server_context
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{
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bool stream = false;
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bool has_next_token = false;
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std::string generated_text;
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std::vector<completion_token_output> generated_token_probs;
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size_t num_prompt_tokens = 0;
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size_t num_tokens_predicted = 0;
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size_t n_past = 0;
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size_t n_remain = 0;
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std::vector<llama_token> embd;
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std::vector<llama_token> last_n_tokens;
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llama_model *model = nullptr;
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llama_context *ctx = nullptr;
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gpt_params params;
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grammar_parser::parse_state parsed_grammar;
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llama_grammar *grammar = nullptr;
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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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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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}
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void rewind()
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{
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params.antiprompt.clear();
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params.grammar.clear();
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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_text.reserve(params.n_ctx);
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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_remain = 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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}
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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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last_n_tokens.resize(params.n_ctx);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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return true;
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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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{
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auto it = params.logit_bias.find(llama_token_eos());
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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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void loadPrompt()
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{
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params.prompt.insert(0, 1, ' '); // always add a first space
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std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
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num_prompt_tokens = prompt_tokens.size();
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if (params.n_keep < 0)
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{
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params.n_keep = (int)num_prompt_tokens;
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}
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params.n_keep = std::min(params.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)params.n_ctx)
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{
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const int n_left = (params.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() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
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LOG_VERBOSE("input truncated", {
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{"n_ctx", params.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
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n_past = common_part(embd, prompt_tokens);
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embd = prompt_tokens;
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if (n_past == num_prompt_tokens)
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{
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// we have to evaluate at least 1 token to generate logits.
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n_past--;
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}
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LOG_VERBOSE("prompt ingested", {
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{"n_past", n_past},
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{"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)},
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{"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
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});
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has_next_token = true;
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}
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void beginCompletion()
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{
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// number of tokens to keep when resetting context
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n_remain = params.n_predict;
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llama_set_rng_seed(ctx, params.seed);
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}
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completion_token_output nextToken()
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{
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completion_token_output result;
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result.tok = -1;
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if (embd.size() >= (size_t)params.n_ctx)
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{
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// Reset context
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const int n_left = (params.n_ctx - params.n_keep) / 2;
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std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
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new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
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embd = new_tokens;
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n_past = params.n_keep;
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truncated = true;
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LOG_VERBOSE("input truncated", {
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{"n_ctx", params.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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}
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while (n_past < embd.size())
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{
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int n_eval = (int)embd.size() - n_past;
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if (n_eval > params.n_batch)
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{
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n_eval = params.n_batch;
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}
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if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
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{
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LOG_ERROR("failed to eval", {
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{"n_eval", n_eval},
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{"n_past", n_past},
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{"n_threads", params.n_threads},
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{"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
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});
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has_next_token = false;
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return result;
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}
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n_past += n_eval;
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}
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if (params.n_predict == 0)
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{
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has_next_token = false;
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result.tok = llama_token_eos();
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return result;
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}
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// out of user input, sample next token
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const float temp = params.temp;
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const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
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const float top_p = params.top_p;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
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const float repeat_penalty = params.repeat_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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const int32_t n_probs = params.n_probs;
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{
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auto *logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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// Apply params.logit_bias map
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for (const auto &it : params.logit_bias)
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{
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logits[it.first] += it.second;
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}
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++)
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{
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
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// Apply penalties
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float nl_logit = logits[llama_token_nl()];
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auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
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llama_sample_repetition_penalty(ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, repeat_penalty);
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llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, alpha_frequency, alpha_presence);
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if (!penalize_nl)
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{
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logits[llama_token_nl()] = nl_logit;
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}
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if (grammar != nullptr) {
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llama_sample_grammar(ctx, &candidates_p, grammar);
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}
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if (temp <= 0)
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{
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// Greedy sampling
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result.tok = llama_sample_token_greedy(ctx, &candidates_p);
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if (n_probs > 0)
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{
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llama_sample_softmax(ctx, &candidates_p);
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}
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}
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else
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{
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if (mirostat == 1)
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{
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static float mirostat_mu = 2.0f * mirostat_tau;
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const int mirostat_m = 100;
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llama_sample_temperature(ctx, &candidates_p, temp);
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result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
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}
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else if (mirostat == 2)
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{
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static float mirostat_mu = 2.0f * mirostat_tau;
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llama_sample_temperature(ctx, &candidates_p, temp);
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result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
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}
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else
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{
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// Temperature sampling
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size_t min_keep = std::max(1, n_probs);
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llama_sample_top_k(ctx, &candidates_p, top_k, min_keep);
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llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep);
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llama_sample_typical(ctx, &candidates_p, typical_p, min_keep);
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llama_sample_top_p(ctx, &candidates_p, top_p, min_keep);
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llama_sample_temperature(ctx, &candidates_p, temp);
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result.tok = llama_sample_token(ctx, &candidates_p);
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}
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}
|
|
|
|
if (grammar != nullptr) {
|
|
llama_grammar_accept_token(ctx, grammar, result.tok);
|
|
}
|
|
|
|
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});
|
|
}
|
|
|
|
last_n_tokens.erase(last_n_tokens.begin());
|
|
last_n_tokens.push_back(result.tok);
|
|
num_tokens_predicted++;
|
|
}
|
|
|
|
// add it to the context
|
|
embd.push_back(result.tok);
|
|
// decrement remaining sampling budget
|
|
--n_remain;
|
|
|
|
if (!embd.empty() && embd.back() == llama_token_eos())
|
|
{
|
|
// stopping_word = llama_token_to_str(ctx, embd.back());
|
|
has_next_token = false;
|
|
stopped_eos = true;
|
|
LOG_VERBOSE("eos token found", {});
|
|
return result;
|
|
}
|
|
|
|
has_next_token = params.n_predict == -1 || n_remain != 0;
|
|
return result;
|
|
}
|
|
|
|
size_t findStoppingStrings(const std::string &text, const size_t last_token_size,
|
|
const stop_type type)
|
|
{
|
|
size_t stop_pos = std::string::npos;
|
|
for (const std::string &word : 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)
|
|
{
|
|
stopping_word = word;
|
|
stopped_word = true;
|
|
has_next_token = false;
|
|
}
|
|
stop_pos = pos;
|
|
}
|
|
}
|
|
return stop_pos;
|
|
}
|
|
|
|
completion_token_output doCompletion()
|
|
{
|
|
const completion_token_output token_with_probs = nextToken();
|
|
|
|
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
|
|
generated_text += token_text;
|
|
|
|
if (params.n_probs > 0)
|
|
{
|
|
generated_token_probs.push_back(token_with_probs);
|
|
}
|
|
|
|
if (multibyte_pending > 0)
|
|
{
|
|
multibyte_pending -= token_text.size();
|
|
}
|
|
else if (token_text.size() == 1)
|
|
{
|
|
const char c = token_text[0];
|
|
// 2-byte characters: 110xxxxx 10xxxxxx
|
|
if ((c & 0xE0) == 0xC0)
|
|
{
|
|
multibyte_pending = 1;
|
|
// 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
|
|
}
|
|
else if ((c & 0xF0) == 0xE0)
|
|
{
|
|
multibyte_pending = 2;
|
|
// 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
|
|
}
|
|
else if ((c & 0xF8) == 0xF0)
|
|
{
|
|
multibyte_pending = 3;
|
|
}
|
|
else
|
|
{
|
|
multibyte_pending = 0;
|
|
}
|
|
}
|
|
|
|
if (multibyte_pending > 0 && !has_next_token)
|
|
{
|
|
has_next_token = true;
|
|
n_remain++;
|
|
}
|
|
|
|
if (!has_next_token && n_remain == 0)
|
|
{
|
|
stopped_limit = true;
|
|
}
|
|
|
|
LOG_VERBOSE("next token", {
|
|
{"token", token_with_probs.tok},
|
|
{"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)},
|
|
{"has_next_token", has_next_token},
|
|
{"n_remain", n_remain},
|
|
{"num_tokens_predicted", num_tokens_predicted},
|
|
{"stopped_eos", stopped_eos},
|
|
{"stopped_word", stopped_word},
|
|
{"stopped_limit", stopped_limit},
|
|
{"stopping_word", stopping_word},
|
|
});
|
|
|
|
return token_with_probs;
|
|
}
|
|
|
|
std::vector<float> getEmbedding()
|
|
{
|
|
static const int n_embd = llama_n_embd(ctx);
|
|
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)
|
|
{
|
|
fprintf(stdout, "usage: %s [options]\n", argv0);
|
|
fprintf(stdout, "\n");
|
|
fprintf(stdout, "options:\n");
|
|
fprintf(stdout, " -h, --help show this help message and exit\n");
|
|
fprintf(stdout, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
|
|
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
|
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
|
|
fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps);
|
|
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
|
|
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
|
|
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
|
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
|
|
if (llama_mlock_supported())
|
|
{
|
|
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_mmap_supported())
|
|
{
|
|
fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n");
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
|
|
fprintf(stdout, " number of layers to store in VRAM\n");
|
|
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
|
|
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
|
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
|
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
|
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
|
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
|
|
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
|
|
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
|
|
#endif
|
|
fprintf(stdout, " -m FNAME, --model FNAME\n");
|
|
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
|
|
fprintf(stdout, " -a ALIAS, --alias ALIAS\n");
|
|
fprintf(stdout, " set an alias for the model, will be added as `model` field in completion response\n");
|
|
fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
|
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
|
fprintf(stdout, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
|
|
fprintf(stdout, " --port PORT port to listen (default (default: %d)\n", sparams.port);
|
|
fprintf(stdout, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
|
|
fprintf(stdout, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
|
|
fprintf(stdout, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
|
fprintf(stdout, "\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 == "-gqa" || arg == "--gqa")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_gqa = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-eps" || arg == "--rms-norm-eps") {
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rms_norm_eps = std::stof(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 == "--low-vram" || arg == "-lv")
|
|
{
|
|
#ifdef GGML_USE_CUBLAS
|
|
params.low_vram = true;
|
|
#else
|
|
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
|
|
#endif // GGML_USE_CUBLAS
|
|
}
|
|
else if (arg == "--mul-mat-q" || arg == "-mmq")
|
|
{
|
|
#ifdef GGML_USE_CUBLAS
|
|
params.mul_mat_q = true;
|
|
#else
|
|
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\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 = 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
|
|
{
|
|
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)
|
|
{
|
|
const auto eos_bias = llama.params.logit_bias.find(llama_token_eos());
|
|
const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
|
|
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
|
|
return json{
|
|
{"n_ctx", llama.params.n_ctx},
|
|
{"model", llama.params.model_alias},
|
|
{"seed", llama.params.seed},
|
|
{"temp", llama.params.temp},
|
|
{"top_k", llama.params.top_k},
|
|
{"top_p", llama.params.top_p},
|
|
{"tfs_z", llama.params.tfs_z},
|
|
{"typical_p", llama.params.typical_p},
|
|
{"repeat_last_n", llama.params.repeat_last_n},
|
|
{"repeat_penalty", llama.params.repeat_penalty},
|
|
{"presence_penalty", llama.params.presence_penalty},
|
|
{"frequency_penalty", llama.params.frequency_penalty},
|
|
{"mirostat", llama.params.mirostat},
|
|
{"mirostat_tau", llama.params.mirostat_tau},
|
|
{"mirostat_eta", llama.params.mirostat_eta},
|
|
{"penalize_nl", llama.params.penalize_nl},
|
|
{"stop", llama.params.antiprompt},
|
|
{"n_predict", llama.params.n_predict},
|
|
{"n_keep", llama.params.n_keep},
|
|
{"ignore_eos", ignore_eos},
|
|
{"stream", llama.stream},
|
|
{"logit_bias", llama.params.logit_bias},
|
|
{"n_probs", llama.params.n_probs},
|
|
{"grammar", llama.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);
|
|
|
|
assert(timings.n_eval == llama.num_tokens_predicted);
|
|
|
|
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, 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", llama.num_tokens_predicted},
|
|
{"tokens_evaluated", llama.num_prompt_tokens},
|
|
{"generation_settings", format_generation_settings(llama)},
|
|
{"prompt", llama.params.prompt},
|
|
{"truncated", llama.truncated},
|
|
{"stopped_eos", llama.stopped_eos},
|
|
{"stopped_word", llama.stopped_word},
|
|
{"stopped_limit", llama.stopped_limit},
|
|
{"stopping_word", llama.stopping_word},
|
|
{"tokens_cached", llama.n_past},
|
|
{"timings", format_timings(llama)},
|
|
};
|
|
|
|
if (llama.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, const std::string &content, const std::vector<completion_token_output> &probs)
|
|
{
|
|
json res = json{
|
|
{"content", content},
|
|
{"stop", false},
|
|
};
|
|
|
|
if (llama.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 void parse_options_completion(const json &body, llama_server_context &llama)
|
|
{
|
|
gpt_params default_params;
|
|
|
|
llama.stream = body.value("stream", false);
|
|
llama.params.n_predict = body.value("n_predict", default_params.n_predict);
|
|
llama.params.top_k = body.value("top_k", default_params.top_k);
|
|
llama.params.top_p = body.value("top_p", default_params.top_p);
|
|
llama.params.tfs_z = body.value("tfs_z", default_params.tfs_z);
|
|
llama.params.typical_p = body.value("typical_p", default_params.typical_p);
|
|
llama.params.repeat_last_n = body.value("repeat_last_n", default_params.repeat_last_n);
|
|
llama.params.temp = body.value("temperature", default_params.temp);
|
|
llama.params.repeat_penalty = body.value("repeat_penalty", default_params.repeat_penalty);
|
|
llama.params.presence_penalty = body.value("presence_penalty", default_params.presence_penalty);
|
|
llama.params.frequency_penalty = body.value("frequency_penalty", default_params.frequency_penalty);
|
|
llama.params.mirostat = body.value("mirostat", default_params.mirostat);
|
|
llama.params.mirostat_tau = body.value("mirostat_tau", default_params.mirostat_tau);
|
|
llama.params.mirostat_eta = body.value("mirostat_eta", default_params.mirostat_eta);
|
|
llama.params.penalize_nl = body.value("penalize_nl", default_params.penalize_nl);
|
|
llama.params.n_keep = body.value("n_keep", default_params.n_keep);
|
|
llama.params.seed = body.value("seed", default_params.seed);
|
|
llama.params.prompt = body.value("prompt", default_params.prompt);
|
|
llama.params.grammar = body.value("grammar", default_params.grammar);
|
|
llama.params.n_probs = body.value("n_probs", default_params.n_probs);
|
|
|
|
llama.params.logit_bias.clear();
|
|
if (body.value("ignore_eos", false))
|
|
{
|
|
llama.params.logit_bias[llama_token_eos()] = -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.ctx);
|
|
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();
|
|
const auto &stop = body.find("stop");
|
|
if (stop != body.end() && stop->is_array())
|
|
{
|
|
for (const auto &word : *stop)
|
|
{
|
|
if (!word.empty())
|
|
{
|
|
llama.params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("completion parameters parsed", format_generation_settings(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},
|
|
});
|
|
}
|
|
|
|
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},
|
|
{"total_threads", std::thread::hardware_concurrency()},
|
|
{"system_info", llama_print_system_info()},
|
|
});
|
|
|
|
// load the model
|
|
if (!llama.loadModel(params))
|
|
{
|
|
return 1;
|
|
}
|
|
|
|
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.Post("/completion", [&llama](const Request &req, Response &res)
|
|
{
|
|
auto lock = llama.lock();
|
|
|
|
llama.rewind();
|
|
|
|
llama_reset_timings(llama.ctx);
|
|
|
|
parse_options_completion(json::parse(req.body), llama);
|
|
|
|
if (!llama.loadGrammar())
|
|
{
|
|
res.status = 400;
|
|
return;
|
|
}
|
|
|
|
llama.loadPrompt();
|
|
llama.beginCompletion();
|
|
|
|
if (!llama.stream) {
|
|
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_str(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());
|
|
}
|
|
|
|
const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs);
|
|
|
|
llama_print_timings(llama.ctx);
|
|
|
|
res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace),
|
|
"application/json");
|
|
} else {
|
|
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();
|
|
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
|
|
if (llama.multibyte_pending > 0) {
|
|
continue;
|
|
}
|
|
|
|
size_t pos = std::min(sent_count, llama.generated_text.size());
|
|
|
|
const std::string str_test = llama.generated_text.substr(pos);
|
|
size_t stop_pos =
|
|
llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
|
|
if (stop_pos != std::string::npos) {
|
|
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 {
|
|
stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
|
|
STOP_PARTIAL);
|
|
}
|
|
|
|
const std::string to_send = llama.generated_text.substr(pos, stop_pos);
|
|
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 = llama.has_next_token
|
|
? format_partial_response(llama, to_send, probs_output)
|
|
// Generation is done, send extra information.
|
|
: format_final_response(llama, to_send, llama.generated_token_probs);
|
|
|
|
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);
|
|
const std::string content = body.value("content", "");
|
|
const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
|
|
const json data = format_tokenizer_response(tokens);
|
|
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);
|
|
llama.params.prompt = body.value("content", "");
|
|
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 auto * 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 {
|
|
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:
|
|
fprintf(stdout, "\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},
|
|
});
|
|
|
|
if (!svr.listen_after_bind())
|
|
{
|
|
return 1;
|
|
}
|
|
|
|
if (llama.grammar != nullptr) {
|
|
llama_grammar_free(llama.grammar);
|
|
}
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|