server : parallel decoding and multimodal (#3677)
* implementing parallel decoding in server example * crash fixed * save dev progress * refactored sampling function * completion endpoint working * multiple client support * grammar + no stream completion * cached prompt support * chat.mjs support cached prompt + some fixes * server ui now support multiple clients * unused change reverted * fixed timings per slot * add context swap * add changes to README.md * llava multimodal integration * fixed tokens probs * add multimodal input - alfa * refactor code + remove unused comments + improved README.md * fix compilation errors with llvm * notify the user from server ui that multimodality is unavialable * some ci fixes * fix ci make build undefined ref errors * fix long prompt than ctx proposed in #3639 * fixed premature end due stop word * context shift fixed * fix llava implementation * sync README.md changes * readme change * update api like OpenAI * multimodal support enabled by default * fix make bui;d errors * fix multiple clients * fix zig build * new sampling API * latest changes of sampling API * server : coding-style normalization * server : coding-style normalization (part 2) * server : remove beam-search functionality * server : bug fix in ingest_images n_tokens is incremented internally by llama_batch_add * server : use refs + use llama_batch_clear() * server : snake case * server : minor sync * added thread safe pipeline * server : bach has to be allocated for n_parallel sequences * server : no need for atomic int - already using mutex * server : logs + minor code style * server : fix multibyte handle in partial response (#3706) * fix image load + view image in chat * make : silence stb warnings * clip : link to ggml, not to llama * server : fix switch fallthrough * server : fix crash in Debug on macOS (I have no idea why this fixes it!?) * server : refactor ctx_sampling init + n_ctx + names * server : bug fix for prompt caching * Do not save/load image_data to localStorage * editorconfig : new line in index.html * server : completion requests remember slot_id * Update readme to document multimodal in server * server : minor style * Update readme to document multimodal in server * server : hide ctx_sampling->prev behind API (#3696) * server : apply fix from #3722 * server : fix slot reuse * server : add comment about changing slot_state to bool --------- Co-authored-by: FSSRepo <go778sgt@gmail.com> Co-authored-by: Damian Stewart <d@damianstewart.com> Co-authored-by: Steward Garcia <57494570+FSSRepo@users.noreply.github.com> Co-authored-by: Jhen-Jie Hong <iainst0409@gmail.com> Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
This commit is contained in:
Georgi Gerganov
+1733
-962
@@ -1,6 +1,11 @@
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#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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#include "../llava/clip.h"
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#include "stb_image.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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@@ -17,12 +22,14 @@
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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 <mutex>
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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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@@ -34,6 +41,165 @@ struct server_params
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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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//
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// base64 utils (TODO: move to common in the future)
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//
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static const std::string base64_chars =
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"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
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"abcdefghijklmnopqrstuvwxyz"
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"0123456789+/";
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static inline bool is_base64(uint8_t c)
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{
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return (isalnum(c) || (c == '+') || (c == '/'));
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}
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static std::vector<uint8_t> base64_decode(std::string const &encoded_string)
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{
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int i = 0;
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int j = 0;
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int in_ = 0;
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int in_len = encoded_string.size();
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uint8_t char_array_4[4];
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uint8_t char_array_3[3];
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std::vector<uint8_t> ret;
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while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
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{
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char_array_4[i++] = encoded_string[in_]; in_++;
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if (i == 4)
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{
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for (i = 0; i <4; i++)
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{
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char_array_4[i] = base64_chars.find(char_array_4[i]);
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}
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char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
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char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
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char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
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for (i = 0; (i < 3); i++)
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{
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ret.push_back(char_array_3[i]);
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}
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i = 0;
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}
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}
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if (i)
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{
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for (j = i; j <4; j++)
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{
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char_array_4[j] = 0;
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}
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for (j = 0; j <4; j++)
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{
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char_array_4[j] = base64_chars.find(char_array_4[j]);
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}
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char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
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char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
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char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
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for (j = 0; (j < i - 1); j++)
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{
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ret.push_back(char_array_3[j]);
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}
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}
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return ret;
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}
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//
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// parallel
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//
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enum task_type {
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COMPLETION_TASK,
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CANCEL_TASK
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};
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struct task_server {
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int id;
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int target_id;
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task_type type;
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json data;
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bool infill_mode = false;
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};
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struct task_result {
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int id;
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bool stop;
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bool error;
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json result_json;
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};
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// TODO: can become bool if we can't find use of more states
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enum slot_state
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{
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IDLE,
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PROCESSING,
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};
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enum slot_command
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{
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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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{
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bool stream = true;
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bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
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uint32_t seed = -1; // RNG seed
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_predict = -1; // new tokens to predict
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std::vector<std::string> antiprompt;
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json input_prefix;
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json input_suffix;
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};
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struct slot_image
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{
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int32_t id;
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bool request_encode_image = false;
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float* image_embedding = nullptr;
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int32_t image_tokens = 0;
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clip_image_u8 img_data;
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std::string prefix_prompt; // before of this image
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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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@@ -45,6 +211,7 @@ struct completion_token_output
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std::vector<token_prob> probs;
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llama_token tok;
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std::string text_to_send;
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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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@@ -89,6 +256,7 @@ static size_t find_partial_stop_string(const std::string &stop,
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return std::string::npos;
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}
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// TODO: reuse llama_detokenize
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template <class Iter>
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static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
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{
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@@ -103,12 +271,13 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
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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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nlohmann::ordered_json log
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{
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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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{"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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@@ -138,7 +307,7 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c
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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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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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@@ -147,76 +316,211 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector<com
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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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probs_for_token.push_back(json
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{
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{"tok_str", tok_str},
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{"prob", p.prob},
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{"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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{"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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template <typename T>
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static T json_value(const json &body, const std::string &key, const T &default_value)
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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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// Fallback null to default value
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return body.contains(key) && !body.at(key).is_null()
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? body.value(key, default_value)
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: default_value;
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}
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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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struct llama_client_slot
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{
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int id;
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int task_id = -1;
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struct slot_params params;
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slot_state state = IDLE;
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slot_command command = NONE;
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// used to determine the slot that has been used the longest
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int64_t t_last_used = -1;
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// generation props
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int32_t n_ctx = 0; // context size per slot
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int32_t n_past = 0;
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int32_t n_decoded = 0;
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int32_t n_remaining = -1;
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int32_t i_batch = -1;
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int32_t num_prompt_tokens = 0;
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int32_t num_prompt_tokens_processed = 0;
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int32_t multibyte_pending = 0;
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json prompt;
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std::vector<llama_token> embd;
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gpt_params params;
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llama_model *model = nullptr;
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llama_context *ctx = nullptr;
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llama_sampling_context *ctx_sampling = nullptr;
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int n_ctx;
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std::string generated_text;
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llama_token sampled;
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std::vector<llama_token> cache_tokens;
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std::vector<completion_token_output> generated_token_probs;
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bool infill = false;
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bool has_next_token = true;
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bool truncated = false;
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bool stopped_eos = false;
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bool stopped_word = false;
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bool stopped_limit = false;
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std::string stopping_word;
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int32_t multibyte_pending = 0;
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std::mutex mutex;
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// sampling
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struct llama_sampling_params sparams;
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llama_sampling_context *ctx_sampling = nullptr;
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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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// multimodal
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std::vector<slot_image> images;
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// stats
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size_t sent_count = 0;
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size_t sent_token_probs_index = 0;
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int64_t t_start_process_prompt;
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int64_t t_start_genereration;
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double t_prompt_processing; // ms
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double t_token_generation; // ms
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void reset() {
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num_prompt_tokens = 0;
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generated_text = "";
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truncated = false;
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stopped_eos = false;
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stopped_word = false;
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stopped_limit = false;
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stopping_word = "";
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multibyte_pending = 0;
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n_past = 0;
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sent_count = 0;
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sent_token_probs_index = 0;
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infill = false;
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generated_token_probs.clear();
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for (slot_image &img : images)
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{
|
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free(img.image_embedding);
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delete[] img.img_data.data;
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img.prefix_prompt = "";
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}
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|
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images.clear();
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// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
|
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}
|
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|
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bool has_budget(gpt_params &global_params) {
|
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n_remaining = -1;
|
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if(params.n_predict != -1)
|
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{
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n_remaining = params.n_predict - n_decoded;
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}
|
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else if (global_params.n_predict != -1)
|
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{
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n_remaining = global_params.n_predict - n_decoded;
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}
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return n_remaining > 0 || n_remaining == -1; // no budget || limitless
|
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}
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|
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bool available() const {
|
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return state == IDLE && command == NONE;
|
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}
|
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|
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bool is_processing() const {
|
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return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
|
||||
}
|
||||
|
||||
void add_token_string(const completion_token_output &token) {
|
||||
if (command == RELEASE)
|
||||
{
|
||||
return;
|
||||
}
|
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cache_tokens.push_back(token.tok);
|
||||
generated_token_probs.push_back(token);
|
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}
|
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|
||||
void release() {
|
||||
if (state == PROCESSING)
|
||||
{
|
||||
t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
|
||||
command = RELEASE;
|
||||
}
|
||||
}
|
||||
|
||||
json get_formated_timings() {
|
||||
return json
|
||||
{
|
||||
{"prompt_n", num_prompt_tokens_processed},
|
||||
{"prompt_ms", t_prompt_processing},
|
||||
{"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed},
|
||||
{"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed},
|
||||
|
||||
{"predicted_n", n_decoded},
|
||||
{"predicted_ms", t_token_generation},
|
||||
{"predicted_per_token_ms", t_token_generation / n_decoded},
|
||||
{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
|
||||
};
|
||||
}
|
||||
|
||||
void print_timings() {
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
|
||||
__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
|
||||
LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
|
||||
__func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
|
||||
LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
|
||||
}
|
||||
};
|
||||
|
||||
struct llama_server_context
|
||||
{
|
||||
llama_model *model = nullptr;
|
||||
llama_context *ctx = nullptr;
|
||||
|
||||
clip_ctx *clp_ctx = nullptr;
|
||||
|
||||
gpt_params params;
|
||||
|
||||
llama_batch batch;
|
||||
|
||||
bool multimodal = false;
|
||||
bool clean_kv_cache = true;
|
||||
bool all_slots_are_idle = false;
|
||||
|
||||
int32_t id_gen;
|
||||
int32_t n_ctx; // total context for all clients / slots
|
||||
|
||||
// system prompt
|
||||
bool system_need_update = false;
|
||||
|
||||
std::string system_prompt;
|
||||
std::vector<llama_token> system_tokens;
|
||||
|
||||
std::string name_user; // this should be the antiprompt
|
||||
std::string name_assistant;
|
||||
|
||||
// slots / clients
|
||||
std::vector<llama_client_slot> slots;
|
||||
|
||||
std::vector<task_server> queue_tasks;
|
||||
std::vector<task_result> queue_results;
|
||||
std::mutex mutex_tasks;
|
||||
std::mutex mutex_results;
|
||||
|
||||
~llama_server_context()
|
||||
{
|
||||
if (ctx)
|
||||
@@ -231,46 +535,74 @@ struct llama_server_context
|
||||
}
|
||||
}
|
||||
|
||||
void rewind()
|
||||
{
|
||||
params.antiprompt.clear();
|
||||
params.sparams.grammar.clear();
|
||||
num_prompt_tokens = 0;
|
||||
num_tokens_predicted = 0;
|
||||
generated_text = "";
|
||||
generated_text.reserve(n_ctx);
|
||||
generated_token_probs.clear();
|
||||
truncated = false;
|
||||
stopped_eos = false;
|
||||
stopped_word = false;
|
||||
stopped_limit = false;
|
||||
stopping_word = "";
|
||||
multibyte_pending = 0;
|
||||
n_remain = 0;
|
||||
n_past = 0;
|
||||
params.sparams.n_prev = n_ctx;
|
||||
}
|
||||
|
||||
void initSampling() {
|
||||
if (ctx_sampling != nullptr) {
|
||||
llama_sampling_free(ctx_sampling);
|
||||
}
|
||||
ctx_sampling = llama_sampling_init(params.sparams);
|
||||
}
|
||||
|
||||
bool loadModel(const gpt_params ¶ms_)
|
||||
bool load_model(const gpt_params ¶ms_)
|
||||
{
|
||||
params = params_;
|
||||
if (!params.mmproj.empty()) {
|
||||
multimodal = true;
|
||||
LOG_TEE("Multi Modal Mode Enabled");
|
||||
clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
|
||||
if(clp_ctx == nullptr) {
|
||||
LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
|
||||
return false;
|
||||
}
|
||||
|
||||
if (params.n_ctx < 2048) { // request larger context for the image embedding
|
||||
params.n_ctx = 2048;
|
||||
}
|
||||
}
|
||||
|
||||
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
||||
if (model == nullptr)
|
||||
{
|
||||
LOG_ERROR("unable to load model", {{"model", params_.model}});
|
||||
LOG_ERROR("unable to load model", {{"model", params.model}});
|
||||
return false;
|
||||
}
|
||||
|
||||
if (multimodal) {
|
||||
const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
|
||||
const int n_embd_llm = llama_n_embd(model);
|
||||
if (n_embd_clip != n_embd_llm) {
|
||||
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
n_ctx = llama_n_ctx(ctx);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void initialize() {
|
||||
id_gen = 0;
|
||||
|
||||
// create slots
|
||||
all_slots_are_idle = true;
|
||||
|
||||
const int32_t n_ctx_slot = n_ctx / params.n_parallel;
|
||||
|
||||
LOG_TEE("Available slots:\n");
|
||||
for (int i = 0; i < params.n_parallel; i++)
|
||||
{
|
||||
llama_client_slot slot;
|
||||
|
||||
slot.id = i;
|
||||
slot.n_ctx = n_ctx_slot;
|
||||
slot.reset();
|
||||
|
||||
LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
|
||||
slots.push_back(slot);
|
||||
}
|
||||
|
||||
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
|
||||
|
||||
// empty system prompt
|
||||
system_prompt = "";
|
||||
system_tokens.clear();
|
||||
}
|
||||
|
||||
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
|
||||
{
|
||||
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
|
||||
@@ -316,260 +648,301 @@ struct llama_server_context
|
||||
return prompt_tokens;
|
||||
}
|
||||
|
||||
void truncatePrompt(std::vector<llama_token> &prompt_tokens) {
|
||||
const int n_left = n_ctx - params.n_keep;
|
||||
const int n_block_size = n_left / 2;
|
||||
const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_block_size) / n_block_size;
|
||||
llama_client_slot* get_slot(int id) {
|
||||
int64_t t_last = ggml_time_us();
|
||||
llama_client_slot *last_used = nullptr;
|
||||
|
||||
// Keep n_keep tokens at start of prompt (at most n_ctx - 4)
|
||||
std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
|
||||
for (llama_client_slot & slot : slots)
|
||||
{
|
||||
if (slot.id == id && slot.available())
|
||||
{
|
||||
return &slot;
|
||||
}
|
||||
|
||||
new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
|
||||
if (slot.available() && slot.t_last_used < t_last)
|
||||
{
|
||||
last_used = &slot;
|
||||
t_last = slot.t_last_used;
|
||||
}
|
||||
}
|
||||
|
||||
LOG_VERBOSE("input truncated", {
|
||||
{"n_ctx", n_ctx},
|
||||
{"n_keep", params.n_keep},
|
||||
{"n_left", n_left},
|
||||
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
|
||||
{"num_prompt_tokens", new_tokens.size()}
|
||||
});
|
||||
|
||||
truncated = true;
|
||||
prompt_tokens = new_tokens;
|
||||
return last_used;
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
bool launch_slot_with_data(llama_client_slot* &slot, json data) {
|
||||
slot_params default_params;
|
||||
llama_sampling_params default_sparams;
|
||||
|
||||
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));
|
||||
slot->params.stream = json_value(data, "stream", false);
|
||||
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
|
||||
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
|
||||
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
|
||||
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
|
||||
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
|
||||
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
|
||||
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
|
||||
slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
|
||||
slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
|
||||
slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
|
||||
slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
|
||||
slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
|
||||
slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
|
||||
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
|
||||
slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
|
||||
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
|
||||
slot->params.seed = json_value(data, "seed", default_params.seed);
|
||||
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
|
||||
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
|
||||
|
||||
auto prompt_tokens = prefix_tokens;
|
||||
|
||||
num_prompt_tokens = prompt_tokens.size();
|
||||
|
||||
if (params.n_keep < 0)
|
||||
// infill
|
||||
if (data.count("input_prefix") != 0)
|
||||
{
|
||||
params.n_keep = (int)num_prompt_tokens;
|
||||
slot->params.input_prefix = data["input_prefix"];
|
||||
}
|
||||
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) n_ctx)
|
||||
else
|
||||
{
|
||||
truncatePrompt(prompt_tokens);
|
||||
num_prompt_tokens = prompt_tokens.size();
|
||||
|
||||
GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx);
|
||||
slot->params.input_prefix = "";
|
||||
}
|
||||
|
||||
// push the prompt into the sampling context (do not apply grammar)
|
||||
for (auto & token : prompt_tokens)
|
||||
if (data.count("input_suffix") != 0)
|
||||
{
|
||||
llama_sampling_accept(ctx_sampling, ctx, token, false);
|
||||
slot->params.input_suffix = data["input_suffix"];
|
||||
}
|
||||
|
||||
// compare the evaluated prompt with the new prompt
|
||||
n_past = common_part(embd, prompt_tokens);
|
||||
embd = prompt_tokens;
|
||||
|
||||
if (n_past == num_prompt_tokens)
|
||||
else
|
||||
{
|
||||
// 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--;
|
||||
slot->params.input_suffix = "";
|
||||
}
|
||||
|
||||
// since #3228 we now have to manually manage the KV cache
|
||||
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
|
||||
|
||||
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 loadPrompt()
|
||||
{
|
||||
auto prompt_tokens = tokenize(prompt, true); // always add BOS
|
||||
|
||||
num_prompt_tokens = prompt_tokens.size();
|
||||
|
||||
if (params.n_keep < 0)
|
||||
if (data.count("prompt") != 0)
|
||||
{
|
||||
params.n_keep = (int)num_prompt_tokens;
|
||||
slot->prompt = data["prompt"];
|
||||
}
|
||||
params.n_keep = std::min(n_ctx - 4, params.n_keep);
|
||||
|
||||
// if input prompt is too big, truncate like normal
|
||||
if (num_prompt_tokens >= (size_t) n_ctx)
|
||||
else
|
||||
{
|
||||
truncatePrompt(prompt_tokens);
|
||||
num_prompt_tokens = prompt_tokens.size();
|
||||
|
||||
GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx);
|
||||
slot->prompt = "";
|
||||
}
|
||||
|
||||
// push the prompt into the sampling context (do not apply grammar)
|
||||
for (auto & token : prompt_tokens)
|
||||
slot->sparams.logit_bias.clear();
|
||||
|
||||
if (json_value(data, "ignore_eos", false))
|
||||
{
|
||||
llama_sampling_accept(ctx_sampling, ctx, token, false);
|
||||
slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY;
|
||||
}
|
||||
|
||||
// compare the evaluated prompt with the new prompt
|
||||
n_past = common_part(embd, prompt_tokens);
|
||||
|
||||
embd = prompt_tokens;
|
||||
if (n_past == num_prompt_tokens)
|
||||
const auto &logit_bias = data.find("logit_bias");
|
||||
if (logit_bias != data.end() && logit_bias->is_array())
|
||||
{
|
||||
// we have to evaluate at least 1 token to generate logits.
|
||||
n_past--;
|
||||
const int n_vocab = llama_n_vocab(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->sparams.logit_bias[tok] = el[1].get<float>();
|
||||
}
|
||||
else if (el[1].is_boolean() && !el[1].get<bool>())
|
||||
{
|
||||
slot->sparams.logit_bias[tok] = -INFINITY;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// since #3228 we now have to manually manage the KV cache
|
||||
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
|
||||
slot->params.antiprompt.clear();
|
||||
const auto &stop = data.find("stop");
|
||||
if (stop != data.end() && stop->is_array())
|
||||
{
|
||||
for (const auto &word : *stop)
|
||||
{
|
||||
if (!word.empty())
|
||||
{
|
||||
slot->params.antiprompt.push_back(word);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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())},
|
||||
});
|
||||
if (multimodal)
|
||||
{
|
||||
const auto &images_data = data.find("image_data");
|
||||
if (images_data != data.end() && images_data->is_array())
|
||||
{
|
||||
for (const auto &img : *images_data)
|
||||
{
|
||||
std::string data_b64 = img["data"].get<std::string>();
|
||||
slot_image img_sl;
|
||||
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
|
||||
int width, height, channels;
|
||||
std::vector<uint8_t> image_buffer = base64_decode(data_b64);
|
||||
data_b64.clear();
|
||||
auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3);
|
||||
if (!data) {
|
||||
LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
|
||||
return false;
|
||||
}
|
||||
LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height);
|
||||
img_sl.img_data.nx = width;
|
||||
img_sl.img_data.ny = height;
|
||||
img_sl.img_data.size = width * height * 3;
|
||||
img_sl.img_data.data = new uint8_t[width * height * 3]();
|
||||
memcpy(img_sl.img_data.data, data, width * height * 3);
|
||||
stbi_image_free(data);
|
||||
img_sl.request_encode_image = true;
|
||||
slot->images.push_back(img_sl);
|
||||
}
|
||||
// process prompt
|
||||
// example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
|
||||
if (slot->images.size() > 0 && !slot->prompt.is_array())
|
||||
{
|
||||
std::string prompt = slot->prompt.get<std::string>();
|
||||
size_t pos = 0, begin_prefix = 0;
|
||||
std::string pattern = "[img-";
|
||||
while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
|
||||
size_t end_prefix = pos;
|
||||
pos += pattern.length();
|
||||
size_t end_pos = prompt.find("]", pos);
|
||||
if (end_pos != std::string::npos)
|
||||
{
|
||||
std::string image_id = prompt.substr(pos, end_pos - pos);
|
||||
try
|
||||
{
|
||||
int img_id = std::stoi(image_id);
|
||||
bool found = false;
|
||||
for (slot_image &img : slot->images)
|
||||
{
|
||||
if (img.id == img_id) {
|
||||
found = true;
|
||||
img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
|
||||
begin_prefix = end_pos + 1;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!found) {
|
||||
LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
|
||||
slot->images.clear();
|
||||
return false;
|
||||
}
|
||||
} catch (const std::invalid_argument& e) {
|
||||
LOG_TEE("Invalid image number id in prompt\n");
|
||||
slot->images.clear();
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
slot->prompt = "";
|
||||
slot->params.input_suffix = prompt.substr(begin_prefix);
|
||||
slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
has_next_token = true;
|
||||
if (slot->ctx_sampling != nullptr)
|
||||
{
|
||||
llama_sampling_free(slot->ctx_sampling);
|
||||
}
|
||||
slot->ctx_sampling = llama_sampling_init(slot->sparams);
|
||||
slot->command = LOAD_PROMPT;
|
||||
|
||||
all_slots_are_idle = false;
|
||||
|
||||
LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void beginCompletion()
|
||||
{
|
||||
// number of tokens to keep when resetting context
|
||||
n_remain = params.n_predict;
|
||||
llama_set_rng_seed(ctx, params.seed);
|
||||
void kv_cache_clear() {
|
||||
// clear the entire KV cache
|
||||
llama_kv_cache_tokens_rm(ctx, -1, -1);
|
||||
clean_kv_cache = false;
|
||||
}
|
||||
|
||||
completion_token_output nextToken()
|
||||
{
|
||||
completion_token_output result;
|
||||
result.tok = -1;
|
||||
void update_system_prompt() {
|
||||
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
|
||||
|
||||
if (embd.size() >= (size_t)n_ctx)
|
||||
llama_batch_clear(batch);
|
||||
|
||||
kv_cache_clear();
|
||||
|
||||
for (int32_t i = 0; i < batch.n_tokens; ++i)
|
||||
{
|
||||
// Shift context
|
||||
|
||||
const int n_left = n_past - params.n_keep - 1;
|
||||
const int n_discard = n_left/2;
|
||||
|
||||
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
|
||||
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
|
||||
|
||||
for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++)
|
||||
{
|
||||
embd[i - n_discard] = embd[i];
|
||||
}
|
||||
embd.resize(embd.size() - n_discard);
|
||||
|
||||
n_past -= n_discard;
|
||||
|
||||
truncated = true;
|
||||
LOG_VERBOSE("input truncated", {
|
||||
{"n_ctx", n_ctx},
|
||||
{"n_keep", params.n_keep},
|
||||
{"n_left", n_left},
|
||||
});
|
||||
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
|
||||
}
|
||||
|
||||
bool tg = true;
|
||||
while (n_past < embd.size())
|
||||
if (llama_decode(ctx, batch) != 0)
|
||||
{
|
||||
int n_eval = (int)embd.size() - n_past;
|
||||
tg = n_eval == 1;
|
||||
if (n_eval > params.n_batch)
|
||||
{
|
||||
n_eval = params.n_batch;
|
||||
}
|
||||
|
||||
if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0)))
|
||||
{
|
||||
LOG_ERROR("failed to eval", {
|
||||
{"n_eval", n_eval},
|
||||
{"n_past", n_past},
|
||||
{"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
|
||||
});
|
||||
has_next_token = false;
|
||||
return result;
|
||||
}
|
||||
n_past += n_eval;
|
||||
LOG_TEE("%s: llama_decode() failed\n", __func__);
|
||||
return;
|
||||
}
|
||||
|
||||
if (params.n_predict == 0)
|
||||
// assign the system KV cache to all parallel sequences
|
||||
for (int32_t i = 1; i < params.n_parallel; ++i)
|
||||
{
|
||||
has_next_token = false;
|
||||
result.tok = llama_token_eos(ctx);
|
||||
return result;
|
||||
llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
|
||||
}
|
||||
|
||||
{
|
||||
// out of user input, sample next token
|
||||
result.tok = llama_sampling_sample(ctx_sampling, ctx, NULL);
|
||||
|
||||
llama_token_data_array cur_p = { ctx_sampling->cur.data(), ctx_sampling->cur.size(), false };
|
||||
|
||||
const int32_t n_probs = params.sparams.n_probs;
|
||||
if (params.sparams.temp <= 0 && n_probs > 0)
|
||||
{
|
||||
// For llama_sample_token_greedy we need to sort candidates
|
||||
llama_sample_softmax(ctx, &cur_p);
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
|
||||
{
|
||||
result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
|
||||
}
|
||||
|
||||
llama_sampling_accept(ctx_sampling, ctx, result.tok, true);
|
||||
|
||||
if (tg) {
|
||||
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(ctx))
|
||||
{
|
||||
// stopping_word = llama_token_to_piece(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;
|
||||
LOG_TEE("system prompt updated\n");
|
||||
system_need_update = false;
|
||||
}
|
||||
|
||||
size_t findStoppingStrings(const std::string &text, const size_t last_token_size,
|
||||
const stop_type type)
|
||||
void notify_system_prompt_changed() {
|
||||
// release all slots
|
||||
for (llama_client_slot &slot : slots)
|
||||
{
|
||||
slot.release();
|
||||
}
|
||||
wait_all_are_idle();
|
||||
all_slots_are_idle = true;
|
||||
|
||||
// wait until system prompt load
|
||||
system_need_update = true;
|
||||
while (system_need_update)
|
||||
{
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(5));
|
||||
}
|
||||
// system prompt loaded, continue
|
||||
}
|
||||
|
||||
void process_system_prompt_data(const json &sys_props) {
|
||||
system_prompt = sys_props.value("prompt", "");
|
||||
name_user = sys_props.value("anti_prompt", "");
|
||||
name_assistant = sys_props.value("assistant_name", "");
|
||||
|
||||
if (slots.size() > 0)
|
||||
{
|
||||
notify_system_prompt_changed();
|
||||
}
|
||||
else
|
||||
{
|
||||
system_need_update = true;
|
||||
}
|
||||
}
|
||||
|
||||
void wait_all_are_idle() {
|
||||
bool wait = true;
|
||||
while (wait)
|
||||
{
|
||||
wait = false;
|
||||
for (auto &slot : slots)
|
||||
{
|
||||
if (!slot.available())
|
||||
{
|
||||
wait = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
|
||||
const stop_type type, llama_client_slot &slot)
|
||||
{
|
||||
size_t stop_pos = std::string::npos;
|
||||
for (const std::string &word : params.antiprompt)
|
||||
|
||||
for (const std::string &word : slot.params.antiprompt)
|
||||
{
|
||||
size_t pos;
|
||||
if (type == STOP_FULL)
|
||||
@@ -587,95 +960,803 @@ struct llama_server_context
|
||||
{
|
||||
if (type == STOP_FULL)
|
||||
{
|
||||
stopping_word = word;
|
||||
stopped_word = true;
|
||||
has_next_token = false;
|
||||
slot.stopped_word = true;
|
||||
slot.stopping_word = word;
|
||||
slot.has_next_token = false;
|
||||
}
|
||||
stop_pos = pos;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
return stop_pos;
|
||||
}
|
||||
|
||||
completion_token_output doCompletion()
|
||||
{
|
||||
auto token_with_probs = nextToken();
|
||||
bool process_token(completion_token_output &result, llama_client_slot &slot) {
|
||||
// remember which tokens were sampled - used for repetition penalties during sampling
|
||||
const std::string token_str = llama_token_to_piece(ctx, result.tok);
|
||||
slot.sampled = result.tok;
|
||||
|
||||
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
|
||||
generated_text += token_text;
|
||||
// search stop word and delete it
|
||||
slot.generated_text += token_str;
|
||||
slot.has_next_token = true;
|
||||
|
||||
if (params.sparams.n_probs > 0)
|
||||
if (slot.multibyte_pending > 0)
|
||||
{
|
||||
generated_token_probs.push_back(token_with_probs);
|
||||
slot.multibyte_pending -= token_str.size();
|
||||
}
|
||||
|
||||
if (multibyte_pending > 0)
|
||||
else if (token_str.size() == 1)
|
||||
{
|
||||
multibyte_pending -= token_text.size();
|
||||
}
|
||||
else if (token_text.size() == 1)
|
||||
{
|
||||
const char c = token_text[0];
|
||||
const char c = token_str[0];
|
||||
// 2-byte characters: 110xxxxx 10xxxxxx
|
||||
if ((c & 0xE0) == 0xC0)
|
||||
{
|
||||
multibyte_pending = 1;
|
||||
slot.multibyte_pending = 1;
|
||||
// 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
|
||||
}
|
||||
else if ((c & 0xF0) == 0xE0)
|
||||
{
|
||||
multibyte_pending = 2;
|
||||
slot.multibyte_pending = 2;
|
||||
// 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
|
||||
}
|
||||
else if ((c & 0xF8) == 0xF0)
|
||||
{
|
||||
multibyte_pending = 3;
|
||||
slot.multibyte_pending = 3;
|
||||
}
|
||||
else
|
||||
{
|
||||
multibyte_pending = 0;
|
||||
slot.multibyte_pending = 0;
|
||||
}
|
||||
}
|
||||
|
||||
if (multibyte_pending > 0 && !has_next_token)
|
||||
if (slot.multibyte_pending == 0)
|
||||
{
|
||||
has_next_token = true;
|
||||
n_remain++;
|
||||
size_t pos = std::min(slot.sent_count, slot.generated_text.size());
|
||||
const std::string str_test = slot.generated_text.substr(pos);
|
||||
bool is_stop_full = false;
|
||||
size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
|
||||
if (stop_pos != std::string::npos)
|
||||
{
|
||||
is_stop_full = true;
|
||||
slot.generated_text.erase(
|
||||
slot.generated_text.begin() + pos + stop_pos,
|
||||
slot.generated_text.end());
|
||||
pos = std::min(slot.sent_count, slot.generated_text.size());
|
||||
}
|
||||
else
|
||||
{
|
||||
is_stop_full = false;
|
||||
stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
|
||||
}
|
||||
|
||||
// check if there is any token to predict
|
||||
if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
|
||||
{
|
||||
// no send the stop word in the response
|
||||
result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
|
||||
slot.sent_count += result.text_to_send.size();
|
||||
// add the token to slot queue and cache
|
||||
}
|
||||
slot.add_token_string(result);
|
||||
if (slot.params.stream)
|
||||
{
|
||||
send_partial_response(slot, result);
|
||||
}
|
||||
}
|
||||
|
||||
if (!has_next_token && n_remain == 0)
|
||||
if (slot.multibyte_pending > 0 && !slot.has_next_token)
|
||||
{
|
||||
stopped_limit = true;
|
||||
slot.has_next_token = true;
|
||||
}
|
||||
|
||||
// check the limits
|
||||
if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
|
||||
{
|
||||
slot.stopped_limit = true;
|
||||
slot.has_next_token = false;
|
||||
}
|
||||
|
||||
if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx))
|
||||
{
|
||||
slot.stopped_eos = true;
|
||||
slot.has_next_token = false;
|
||||
LOG_VERBOSE("eos token found", {});
|
||||
}
|
||||
|
||||
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},
|
||||
{"token", result.tok},
|
||||
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
|
||||
{"has_next_token", slot.has_next_token},
|
||||
{"n_remain", slot.n_remaining},
|
||||
{"num_tokens_predicted", slot.n_decoded},
|
||||
{"stopped_eos", slot.stopped_eos},
|
||||
{"stopped_word", slot.stopped_word},
|
||||
{"stopped_limit", slot.stopped_limit},
|
||||
{"stopping_word", slot.stopping_word},
|
||||
});
|
||||
|
||||
return token_with_probs;
|
||||
return slot.has_next_token; // continue
|
||||
}
|
||||
|
||||
std::vector<float> getEmbedding()
|
||||
bool process_images(llama_client_slot &slot) const
|
||||
{
|
||||
static const int n_embd = llama_n_embd(model);
|
||||
for (slot_image &img : slot.images)
|
||||
{
|
||||
if (!img.request_encode_image)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
clip_image_f32 img_res;
|
||||
if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true))
|
||||
{
|
||||
LOG_TEE("Error processing the given image");
|
||||
clip_free(clp_ctx);
|
||||
return false;
|
||||
}
|
||||
img.image_tokens = clip_n_patches(clp_ctx);
|
||||
img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
|
||||
if (!img.image_embedding)
|
||||
{
|
||||
LOG_TEE("Unable to allocate memory for image embeddings\n");
|
||||
clip_free(clp_ctx);
|
||||
return false;
|
||||
}
|
||||
LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
|
||||
if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding))
|
||||
{
|
||||
LOG_TEE("Unable to encode image\n");
|
||||
return false;
|
||||
}
|
||||
img.request_encode_image = false;
|
||||
}
|
||||
|
||||
return slot.images.size() > 0;
|
||||
}
|
||||
|
||||
void send_error(int id, std::string error)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = id;
|
||||
res.error = true;
|
||||
res.result_json = { { "content", error } };
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
json get_model_props()
|
||||
{
|
||||
return get_formated_generation(slots[0]);
|
||||
}
|
||||
|
||||
json get_formated_generation(llama_client_slot &slot)
|
||||
{
|
||||
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx));
|
||||
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
|
||||
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
||||
return json {
|
||||
{"n_ctx", slot.n_ctx},
|
||||
{"model", params.model_alias},
|
||||
{"seed", slot.params.seed},
|
||||
{"temp", slot.sparams.temp},
|
||||
{"top_k", slot.sparams.top_k},
|
||||
{"top_p", slot.sparams.top_p},
|
||||
{"tfs_z", slot.sparams.tfs_z},
|
||||
{"typical_p", slot.sparams.typical_p},
|
||||
{"repeat_last_n", slot.sparams.penalty_last_n},
|
||||
{"repeat_penalty", slot.sparams.penalty_repeat},
|
||||
{"presence_penalty", slot.sparams.penalty_present},
|
||||
{"frequency_penalty", slot.sparams.penalty_freq},
|
||||
{"mirostat", slot.sparams.mirostat},
|
||||
{"mirostat_tau", slot.sparams.mirostat_tau},
|
||||
{"mirostat_eta", slot.sparams.mirostat_eta},
|
||||
{"penalize_nl", slot.sparams.penalize_nl},
|
||||
{"stop", slot.params.antiprompt},
|
||||
{"n_predict", slot.params.n_predict},
|
||||
{"n_keep", params.n_keep},
|
||||
{"ignore_eos", ignore_eos},
|
||||
{"stream", slot.params.stream},
|
||||
{"logit_bias", slot.sparams.logit_bias},
|
||||
{"n_probs", slot.sparams.n_probs},
|
||||
{"grammar", slot.sparams.grammar},
|
||||
};
|
||||
}
|
||||
|
||||
void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = slot.task_id;
|
||||
res.error = false;
|
||||
res.stop = false;
|
||||
|
||||
res.result_json = json
|
||||
{
|
||||
{"content", tkn.text_to_send},
|
||||
{"stop", false},
|
||||
{"slot_id", slot.id},
|
||||
{"multimodal", multimodal}
|
||||
};
|
||||
|
||||
if (slot.sparams.n_probs > 0)
|
||||
{
|
||||
std::vector<completion_token_output> probs_output = {};
|
||||
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
|
||||
size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
|
||||
size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
|
||||
if (probs_pos < probs_stop_pos)
|
||||
{
|
||||
probs_output = std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos);
|
||||
}
|
||||
slot.sent_token_probs_index = probs_stop_pos;
|
||||
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
|
||||
}
|
||||
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
void send_final_response(llama_client_slot &slot)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = slot.task_id;
|
||||
res.error = false;
|
||||
res.stop = true;
|
||||
|
||||
res.result_json = json
|
||||
{
|
||||
{"content", !slot.params.stream ? slot.generated_text : ""},
|
||||
{"slot_id", slot.id},
|
||||
{"stop", true},
|
||||
{"model", params.model_alias},
|
||||
{"tokens_predicted", slot.n_decoded},
|
||||
{"tokens_evaluated", slot.num_prompt_tokens},
|
||||
{"generation_settings", get_formated_generation(slot)},
|
||||
{"prompt", slot.prompt},
|
||||
{"truncated", slot.truncated},
|
||||
{"stopped_eos", slot.stopped_eos},
|
||||
{"stopped_word", slot.stopped_word},
|
||||
{"stopped_limit", slot.stopped_limit},
|
||||
{"stopping_word", slot.stopping_word},
|
||||
{"tokens_cached", slot.n_past},
|
||||
{"timings", slot.get_formated_timings()}
|
||||
};
|
||||
|
||||
if (slot.sparams.n_probs > 0)
|
||||
{
|
||||
std::vector<completion_token_output> probs = {};
|
||||
if (!slot.params.stream && slot.stopped_word)
|
||||
{
|
||||
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
|
||||
probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size());
|
||||
}
|
||||
else
|
||||
{
|
||||
probs = std::vector<completion_token_output>(
|
||||
slot.generated_token_probs.begin(),
|
||||
slot.generated_token_probs.begin() + slot.sent_token_probs_index);
|
||||
}
|
||||
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
|
||||
}
|
||||
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
void send_embedding(llama_client_slot &slot)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = slot.task_id;
|
||||
res.error = false;
|
||||
res.stop = true;
|
||||
|
||||
const int n_embd = llama_n_embd(model);
|
||||
if (!params.embedding)
|
||||
{
|
||||
LOG_WARNING("embedding disabled", {
|
||||
{"params.embedding", params.embedding},
|
||||
});
|
||||
return std::vector<float>(n_embd, 0.0f);
|
||||
res.result_json = json
|
||||
{
|
||||
{"embedding", std::vector<float>(n_embd, 0.0f)},
|
||||
};
|
||||
}
|
||||
const float *data = llama_get_embeddings(ctx);
|
||||
std::vector<float> embedding(data, data + n_embd);
|
||||
return embedding;
|
||||
else
|
||||
{
|
||||
const float *data = llama_get_embeddings(ctx);
|
||||
std::vector<float> embedding(data, data + n_embd);
|
||||
res.result_json = json
|
||||
{
|
||||
{"embedding", embedding },
|
||||
};
|
||||
}
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
int request_completion(json data, bool infill)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
task_server task;
|
||||
task.id = id_gen++;
|
||||
task.data = data;
|
||||
task.infill_mode = infill;
|
||||
task.type = COMPLETION_TASK;
|
||||
queue_tasks.push_back(task);
|
||||
return task.id;
|
||||
}
|
||||
|
||||
task_result next_result(int task_id)
|
||||
{
|
||||
while (true)
|
||||
{
|
||||
std::this_thread::sleep_for(std::chrono::microseconds(5));
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
|
||||
if (queue_results.empty())
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
for (int i = 0; i < (int) queue_results.size(); i++)
|
||||
{
|
||||
if (queue_results[i].id == task_id)
|
||||
{
|
||||
task_result res = queue_results[i];
|
||||
queue_results.erase(queue_results.begin() + i);
|
||||
return res;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// never reached
|
||||
//return task_result{-1, false, false, {}};
|
||||
}
|
||||
|
||||
// for multiple images processing
|
||||
bool ingest_images(llama_client_slot &slot, int n_batch)
|
||||
{
|
||||
int image_idx = 0;
|
||||
|
||||
while (image_idx < (int) slot.images.size())
|
||||
{
|
||||
slot_image &img = slot.images[image_idx];
|
||||
|
||||
// process prefix prompt
|
||||
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
|
||||
{
|
||||
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
|
||||
llama_batch batch_view = {
|
||||
n_tokens,
|
||||
batch.token + i,
|
||||
nullptr,
|
||||
batch.pos + i,
|
||||
batch.n_seq_id + i,
|
||||
batch.seq_id + i,
|
||||
batch.logits + i,
|
||||
0, 0, 0, // unused
|
||||
};
|
||||
if (llama_decode(ctx, batch_view))
|
||||
{
|
||||
LOG_TEE("%s : failed to eval\n", __func__);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
// process image with llm
|
||||
for (int i = 0; i < img.image_tokens; i += n_batch)
|
||||
{
|
||||
int n_eval = img.image_tokens - i;
|
||||
if (n_eval > n_batch)
|
||||
{
|
||||
n_eval = n_batch;
|
||||
}
|
||||
|
||||
const int n_embd = llama_n_embd(model);
|
||||
llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
|
||||
if (llama_decode(ctx, batch_img))
|
||||
{
|
||||
LOG_TEE("%s : failed to eval image\n", __func__);
|
||||
return false;
|
||||
}
|
||||
slot.n_past += n_eval;
|
||||
}
|
||||
image_idx++;
|
||||
|
||||
llama_batch_clear(batch);
|
||||
|
||||
// append prefix of next image
|
||||
const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
|
||||
slot.params.input_suffix : // no more images, then process suffix prompt
|
||||
(json)(slot.images[image_idx].prefix_prompt);
|
||||
|
||||
std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
|
||||
for (int i = 0; i < (int) append_tokens.size(); ++i)
|
||||
{
|
||||
llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true);
|
||||
slot.n_past += 1;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void request_cancel(int task_id)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
task_server task;
|
||||
task.id = id_gen++;
|
||||
task.type = CANCEL_TASK;
|
||||
task.target_id = task_id;
|
||||
queue_tasks.push_back(task);
|
||||
}
|
||||
|
||||
void process_tasks()
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
while (!queue_tasks.empty())
|
||||
{
|
||||
task_server task = queue_tasks.front();
|
||||
queue_tasks.erase(queue_tasks.begin());
|
||||
switch (task.type)
|
||||
{
|
||||
case COMPLETION_TASK: {
|
||||
llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
|
||||
if (slot == nullptr)
|
||||
{
|
||||
LOG_TEE("slot unavailable\n");
|
||||
// send error result
|
||||
send_error(task.id, "slot unavaliable");
|
||||
return;
|
||||
}
|
||||
|
||||
if (task.data.contains("system_prompt"))
|
||||
{
|
||||
process_system_prompt_data(task.data["system_prompt"]);
|
||||
}
|
||||
|
||||
slot->reset();
|
||||
|
||||
slot->infill = task.infill_mode;
|
||||
slot->task_id = task.id;
|
||||
|
||||
if (!launch_slot_with_data(slot, task.data))
|
||||
{
|
||||
// send error result
|
||||
send_error(task.id, "internal_error");
|
||||
break;
|
||||
}
|
||||
} break;
|
||||
case CANCEL_TASK: { // release slot linked with the task id
|
||||
for (auto & slot : slots)
|
||||
{
|
||||
if (slot.task_id == task.target_id)
|
||||
{
|
||||
slot.release();
|
||||
break;
|
||||
}
|
||||
}
|
||||
} break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool update_slots() {
|
||||
// attend tasks
|
||||
process_tasks();
|
||||
|
||||
// update the system prompt wait until all slots are idle state
|
||||
if (system_need_update)
|
||||
{
|
||||
LOG_TEE("updating system prompt\n");
|
||||
update_system_prompt();
|
||||
}
|
||||
|
||||
llama_batch_clear(batch);
|
||||
|
||||
if (all_slots_are_idle)
|
||||
{
|
||||
if (system_prompt.empty() && clean_kv_cache)
|
||||
{
|
||||
LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
|
||||
kv_cache_clear();
|
||||
}
|
||||
// avoid 100% usage of cpu all time
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(5));
|
||||
}
|
||||
|
||||
for (llama_client_slot &slot : slots)
|
||||
{
|
||||
if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
|
||||
{
|
||||
// Shift context
|
||||
const int n_left = slot.n_past - slot.params.n_keep - 1;
|
||||
const int n_discard = n_left / 2;
|
||||
|
||||
LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
|
||||
llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1);
|
||||
llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);
|
||||
|
||||
for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
|
||||
{
|
||||
slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
|
||||
}
|
||||
|
||||
slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
|
||||
|
||||
slot.n_past -= n_discard;
|
||||
|
||||
slot.truncated = true;
|
||||
|
||||
LOG_VERBOSE("context shift", {
|
||||
{"n_ctx", n_ctx},
|
||||
{"n_keep", params.n_keep},
|
||||
{"n_left", n_left},
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// decode any currently ongoing sequences
|
||||
for (auto & slot : slots)
|
||||
{
|
||||
// release the slot
|
||||
if (slot.state == PROCESSING && slot.command == RELEASE)
|
||||
{
|
||||
slot.state = IDLE;
|
||||
slot.command = NONE;
|
||||
slot.t_last_used = ggml_time_us();
|
||||
|
||||
LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());
|
||||
|
||||
continue;
|
||||
}
|
||||
|
||||
if (slot.state == IDLE || slot.command == RELEASE)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
slot.i_batch = batch.n_tokens;
|
||||
|
||||
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
|
||||
|
||||
slot.n_decoded += 1;
|
||||
slot.n_past += 1;
|
||||
}
|
||||
|
||||
// process in chunks of params.n_batch
|
||||
int32_t n_batch = params.n_batch;
|
||||
|
||||
// assign workload to the slots
|
||||
if (params.cont_batching || batch.n_tokens == 0)
|
||||
{
|
||||
for (auto & slot : slots)
|
||||
{
|
||||
// need process the prompt
|
||||
if (slot.state == IDLE && slot.command == LOAD_PROMPT)
|
||||
{
|
||||
slot.state = PROCESSING;
|
||||
slot.command = NONE;
|
||||
std::vector<llama_token> prompt_tokens;
|
||||
slot.t_start_process_prompt = ggml_time_us();
|
||||
slot.t_start_genereration = 0;
|
||||
|
||||
if (slot.infill)
|
||||
{
|
||||
bool suff_rm_leading_spc = true;
|
||||
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
|
||||
{
|
||||
params.input_suffix.erase(0, 1);
|
||||
suff_rm_leading_spc = false;
|
||||
}
|
||||
auto prefix_tokens = tokenize(slot.params.input_prefix, false);
|
||||
auto suffix_tokens = tokenize(slot.params.input_suffix, false);
|
||||
|
||||
const int space_token = 29871; // TODO: this should not be hardcoded
|
||||
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
|
||||
suffix_tokens.erase(suffix_tokens.begin());
|
||||
}
|
||||
|
||||
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(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));
|
||||
prompt_tokens = prefix_tokens;
|
||||
}
|
||||
else
|
||||
{
|
||||
prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
|
||||
}
|
||||
|
||||
slot.num_prompt_tokens = prompt_tokens.size();
|
||||
|
||||
if (!slot.params.cache_prompt)
|
||||
{
|
||||
llama_sampling_reset(slot.ctx_sampling);
|
||||
|
||||
slot.n_past = 0;
|
||||
slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (slot.params.n_keep < 0)
|
||||
{
|
||||
slot.params.n_keep = slot.num_prompt_tokens;
|
||||
}
|
||||
slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
|
||||
|
||||
// if input prompt is too big, truncate it
|
||||
if (slot.num_prompt_tokens >= slot.n_ctx)
|
||||
{
|
||||
const int n_left = slot.n_ctx - slot.params.n_keep;
|
||||
const int n_block_size = n_left / 2;
|
||||
const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
|
||||
|
||||
std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
|
||||
new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
|
||||
|
||||
LOG_VERBOSE("input truncated", {
|
||||
{"n_ctx", slot.n_ctx},
|
||||
{"n_keep", slot.params.n_keep},
|
||||
{"n_left", n_left},
|
||||
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
|
||||
});
|
||||
slot.truncated = true;
|
||||
prompt_tokens = new_tokens;
|
||||
|
||||
slot.num_prompt_tokens = prompt_tokens.size();
|
||||
GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
|
||||
}
|
||||
|
||||
// push the prompt into the sampling context (do not apply grammar)
|
||||
for (auto &token : prompt_tokens)
|
||||
{
|
||||
llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
|
||||
}
|
||||
|
||||
slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
|
||||
slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
|
||||
|
||||
LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
|
||||
}
|
||||
|
||||
LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
|
||||
|
||||
llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
|
||||
|
||||
slot.cache_tokens = prompt_tokens;
|
||||
|
||||
if (slot.n_past == slot.num_prompt_tokens)
|
||||
{
|
||||
// we have to evaluate at least 1 token to generate logits.
|
||||
LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
|
||||
slot.n_past--;
|
||||
}
|
||||
|
||||
LOG_VERBOSE("prompt ingested", {
|
||||
{"n_past", slot.n_past},
|
||||
{"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
|
||||
{"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
|
||||
});
|
||||
|
||||
const bool has_images = process_images(slot);
|
||||
|
||||
// process the prefix of first image
|
||||
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens;
|
||||
for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
|
||||
{
|
||||
llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false);
|
||||
}
|
||||
|
||||
if (has_images && !ingest_images(slot, n_batch))
|
||||
{
|
||||
LOG_TEE("failed processing images\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
// extract the logits only for the last token
|
||||
if (batch.n_tokens > 0)
|
||||
{
|
||||
batch.logits[batch.n_tokens - 1] = true;
|
||||
}
|
||||
|
||||
slot.n_decoded = 0;
|
||||
slot.i_batch = batch.n_tokens - 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (batch.n_tokens == 0)
|
||||
{
|
||||
all_slots_are_idle = true;
|
||||
return true;
|
||||
}
|
||||
|
||||
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
|
||||
{
|
||||
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
|
||||
llama_batch batch_view =
|
||||
{
|
||||
n_tokens,
|
||||
batch.token + i,
|
||||
nullptr,
|
||||
batch.pos + i,
|
||||
batch.n_seq_id + i,
|
||||
batch.seq_id + i,
|
||||
batch.logits + i,
|
||||
0, 0, 0, // unused
|
||||
};
|
||||
|
||||
const int ret = llama_decode(ctx, batch_view);
|
||||
if (ret != 0)
|
||||
{
|
||||
if (n_batch == 1 || ret < 0)
|
||||
{
|
||||
// if you get here, it means the KV cache is full - try increasing it via the context size
|
||||
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
|
||||
return false;
|
||||
}
|
||||
|
||||
LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
|
||||
|
||||
// retry with half the batch size to try to find a free slot in the KV cache
|
||||
n_batch /= 2;
|
||||
i -= n_batch;
|
||||
continue;
|
||||
}
|
||||
|
||||
for (auto & slot : slots)
|
||||
{
|
||||
if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
// prompt evaluated for embedding
|
||||
if (params.embedding)
|
||||
{
|
||||
send_embedding(slot);
|
||||
slot.release();
|
||||
slot.i_batch = -1;
|
||||
return true;
|
||||
}
|
||||
|
||||
completion_token_output result;
|
||||
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);
|
||||
|
||||
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
||||
|
||||
if (slot.n_decoded == 1)
|
||||
{
|
||||
slot.t_start_genereration = ggml_time_us();
|
||||
slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
|
||||
}
|
||||
|
||||
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
|
||||
result.tok = id;
|
||||
|
||||
const int32_t n_probs = slot.sparams.n_probs;
|
||||
if (slot.sparams.temp <= 0 && n_probs > 0)
|
||||
{
|
||||
// for llama_sample_token_greedy we need to sort candidates
|
||||
llama_sample_softmax(ctx, &cur_p);
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
|
||||
{
|
||||
result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
|
||||
}
|
||||
|
||||
if (!process_token(result, slot))
|
||||
{
|
||||
slot.release();
|
||||
send_final_response(slot);
|
||||
slot.print_timings();
|
||||
}
|
||||
|
||||
slot.i_batch = -1;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
};
|
||||
|
||||
@@ -685,16 +1766,15 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
||||
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(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n");
|
||||
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");
|
||||
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");
|
||||
@@ -725,11 +1805,16 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
||||
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(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
|
||||
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
|
||||
printf(" -spf FNAME, --system-prompt-file FNAME\n");
|
||||
printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
|
||||
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n");
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
gpt_params ¶ms)
|
||||
gpt_params ¶ms, llama_server_context& llama)
|
||||
{
|
||||
gpt_params default_params;
|
||||
server_params default_sparams;
|
||||
@@ -839,15 +1924,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
}
|
||||
params.n_threads = std::stoi(argv[i]);
|
||||
}
|
||||
else if (arg == "--threads-batch" || arg == "-tb")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.n_threads_batch = std::stoi(argv[i]);
|
||||
}
|
||||
else if (arg == "-b" || arg == "--batch-size")
|
||||
{
|
||||
if (++i >= argc)
|
||||
@@ -984,6 +2060,56 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
{
|
||||
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]);
|
||||
} else if (arg == "-spf" || arg == "--system-prompt-file")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
std::ifstream file(argv[i]);
|
||||
if (!file) {
|
||||
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
std::string systm_content;
|
||||
std::copy(
|
||||
std::istreambuf_iterator<char>(file),
|
||||
std::istreambuf_iterator<char>(),
|
||||
std::back_inserter(systm_content)
|
||||
);
|
||||
llama.process_system_prompt_data(json::parse(systm_content));
|
||||
}
|
||||
else if(arg == "--mmproj")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.mmproj = argv[i];
|
||||
}
|
||||
else
|
||||
{
|
||||
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
||||
@@ -1000,102 +2126,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
}
|
||||
}
|
||||
|
||||
static json format_generation_settings(llama_server_context &llama)
|
||||
{
|
||||
const auto & sparams = llama.params.sparams;
|
||||
const auto eos_bias = sparams.logit_bias.find(llama_token_eos(llama.ctx));
|
||||
const bool ignore_eos = eos_bias != sparams.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", llama.params.seed},
|
||||
{"temp", sparams.temp},
|
||||
{"top_k", sparams.top_k},
|
||||
{"top_p", sparams.top_p},
|
||||
{"tfs_z", sparams.tfs_z},
|
||||
{"typical_p", sparams.typical_p},
|
||||
{"repeat_last_n", sparams.penalty_last_n},
|
||||
{"repeat_penalty", sparams.penalty_repeat},
|
||||
{"frequency_penalty", sparams.penalty_freq},
|
||||
{"presence_penalty", sparams.penalty_present},
|
||||
{"mirostat", sparams.mirostat},
|
||||
{"mirostat_tau", sparams.mirostat_tau},
|
||||
{"mirostat_eta", sparams.mirostat_eta},
|
||||
{"penalize_nl", sparams.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", sparams.logit_bias},
|
||||
{"n_probs", sparams.n_probs},
|
||||
{"grammar", llama.params.sparams.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, 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.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.sparams.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
|
||||
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},
|
||||
json res = json
|
||||
{
|
||||
{"content", content },
|
||||
{"stop", false},
|
||||
{"slot_id", slot->id },
|
||||
{"multimodal", llama.multimodal }
|
||||
};
|
||||
|
||||
if (llama.params.sparams.n_probs > 0)
|
||||
if (slot->sparams.n_probs > 0)
|
||||
{
|
||||
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
|
||||
}
|
||||
@@ -1115,120 +2157,8 @@ static json format_detokenized_response(std::string content)
|
||||
{"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_server_context &llama)
|
||||
{
|
||||
gpt_params default_params;
|
||||
const auto & default_sparams = default_params.sparams;
|
||||
|
||||
auto & params = llama.params;
|
||||
auto & sparams = llama.params.sparams;
|
||||
|
||||
llama.stream = json_value(body, "stream", false);
|
||||
params.n_predict = json_value(body, "n_predict", default_params.n_predict);
|
||||
sparams.top_k = json_value(body, "top_k", default_sparams.top_k);
|
||||
sparams.top_p = json_value(body, "top_p", default_sparams.top_p);
|
||||
sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z);
|
||||
sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p);
|
||||
sparams.temp = json_value(body, "temperature", default_sparams.temp);
|
||||
sparams.penalty_last_n = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
|
||||
sparams.penalty_repeat = json_value(body, "repeat_penalty", default_sparams.penalty_repeat);
|
||||
sparams.penalty_freq = json_value(body, "frequency_penalty", default_sparams.penalty_freq);
|
||||
sparams.penalty_present = json_value(body, "presence_penalty", default_sparams.penalty_present);
|
||||
sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat);
|
||||
sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
|
||||
sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
|
||||
sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl);
|
||||
params.n_keep = json_value(body, "n_keep", default_params.n_keep);
|
||||
params.seed = json_value(body, "seed", default_params.seed);
|
||||
sparams.grammar = json_value(body, "grammar", default_sparams.grammar);
|
||||
sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs);
|
||||
|
||||
if (body.count("prompt") != 0)
|
||||
{
|
||||
llama.prompt = body["prompt"];
|
||||
}
|
||||
else
|
||||
{
|
||||
llama.prompt = "";
|
||||
}
|
||||
|
||||
sparams.logit_bias.clear();
|
||||
if (json_value(body, "ignore_eos", false))
|
||||
{
|
||||
sparams.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())
|
||||
{
|
||||
sparams.logit_bias[tok] = el[1].get<float>();
|
||||
}
|
||||
else if (el[1].is_boolean() && !el[1].get<bool>())
|
||||
{
|
||||
sparams.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 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, llama);
|
||||
}
|
||||
|
||||
static void log_server_request(const Request &req, const Response &res)
|
||||
static void log_server_request(const httplib::Request &req, const httplib::Response &res)
|
||||
{
|
||||
LOG_INFO("request", {
|
||||
{"remote_addr", req.remote_addr},
|
||||
@@ -1245,60 +2175,26 @@ static void log_server_request(const Request &req, const Response &res)
|
||||
});
|
||||
}
|
||||
|
||||
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.
|
||||
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 {
|
||||
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); }
|
||||
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)
|
||||
static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot)
|
||||
{
|
||||
auto & gtps = llama.generated_token_probs;
|
||||
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 (llama.generated_text.capacity() < llama.generated_text.size() + len) {
|
||||
llama.generated_text.reserve(llama.generated_text.size() + len);
|
||||
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) {
|
||||
llama.generated_text += translator(cto);
|
||||
for (const completion_token_output & cto : gtps)
|
||||
{
|
||||
slot->generated_text += translator(cto);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1311,7 +2207,7 @@ int main(int argc, char **argv)
|
||||
// struct that contains llama context and inference
|
||||
llama_server_context llama;
|
||||
|
||||
server_params_parse(argc, argv, sparams, params);
|
||||
server_params_parse(argc, argv, sparams, params, llama);
|
||||
|
||||
if (params.model_alias == "unknown")
|
||||
{
|
||||
@@ -1322,6 +2218,7 @@ int main(int argc, char **argv)
|
||||
|
||||
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},
|
||||
@@ -1330,405 +2227,261 @@ int main(int argc, char **argv)
|
||||
});
|
||||
|
||||
// load the model
|
||||
if (!llama.loadModel(params))
|
||||
if (!llama.load_model(params))
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
|
||||
Server svr;
|
||||
llama.initialize();
|
||||
|
||||
httplib::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)
|
||||
svr.Get("/", [](const httplib::Request &, httplib::Response &res)
|
||||
{
|
||||
res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
|
||||
return false; });
|
||||
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)
|
||||
svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
|
||||
{
|
||||
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
|
||||
return false; });
|
||||
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)
|
||||
svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
|
||||
{
|
||||
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
|
||||
return false; });
|
||||
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)
|
||||
svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
|
||||
{
|
||||
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
|
||||
return false; });
|
||||
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();
|
||||
svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res)
|
||||
{
|
||||
res.set_header("Access-Control-Allow-Origin", "*");
|
||||
json data = {
|
||||
{ "user_name", llama.name_user.c_str() },
|
||||
{ "assistant_name", llama.name_assistant.c_str() }
|
||||
};
|
||||
res.set_content(data.dump(), "application/json");
|
||||
});
|
||||
|
||||
llama.rewind();
|
||||
|
||||
llama_reset_timings(llama.ctx);
|
||||
parse_options_completion(json::parse(req.body), llama);
|
||||
|
||||
llama.initSampling();
|
||||
llama.loadPrompt();
|
||||
llama.beginCompletion();
|
||||
|
||||
if (!llama.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.sparams.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 {
|
||||
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;
|
||||
svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
json data = json::parse(req.body);
|
||||
const int task_id = llama.request_completion(data, false);
|
||||
if (!json_value(data, "stream", false)) {
|
||||
std::string completion_text;
|
||||
task_result result = llama.next_result(task_id);
|
||||
if(!result.error && result.stop) {
|
||||
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
|
||||
}
|
||||
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);
|
||||
else
|
||||
{
|
||||
res.status = 404;
|
||||
res.set_content(result.result_json["content"], "text/plain");
|
||||
return;
|
||||
}
|
||||
|
||||
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.sparams.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.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);
|
||||
|
||||
llama.initSampling();
|
||||
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.sparams.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);
|
||||
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink)
|
||||
{
|
||||
while (true)
|
||||
{
|
||||
task_result result = llama.next_result(task_id);
|
||||
if (!result.error) {
|
||||
const std::string str =
|
||||
"data: " +
|
||||
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
LOG_VERBOSE("data stream", {
|
||||
{ "to_send", str }
|
||||
});
|
||||
if (!sink.write(str.c_str(), str.size()))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
if(result.stop) {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
sent_token_probs_index = probs_stop_pos;
|
||||
}
|
||||
sink.done();
|
||||
return true;
|
||||
};
|
||||
|
||||
const json data = format_partial_response(llama, to_send, probs_output);
|
||||
auto on_complete = [task_id, &llama] (bool)
|
||||
{
|
||||
// cancel
|
||||
llama.request_cancel(task_id);
|
||||
};
|
||||
|
||||
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;
|
||||
}
|
||||
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
||||
}
|
||||
});
|
||||
|
||||
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)
|
||||
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
const json data = format_generation_settings(llama);
|
||||
return res.set_content(data.dump(), "application/json"); });
|
||||
json data = json::parse(req.body);
|
||||
const int task_id = llama.request_completion(data, true);
|
||||
if (!json_value(data, "stream", false)) {
|
||||
std::string completion_text;
|
||||
task_result result = llama.next_result(task_id);
|
||||
if (!result.error && result.stop)
|
||||
{
|
||||
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
|
||||
}
|
||||
else
|
||||
{
|
||||
res.status = 404;
|
||||
res.set_content(result.result_json["content"], "text/plain");
|
||||
return;
|
||||
}
|
||||
} else {
|
||||
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) {
|
||||
while (true)
|
||||
{
|
||||
task_result result = llama.next_result(task_id);
|
||||
if (!result.error) {
|
||||
const std::string str =
|
||||
"data: " +
|
||||
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
LOG_VERBOSE("data stream", {
|
||||
{ "to_send", str }
|
||||
});
|
||||
if (!sink.write(str.c_str(), str.size()))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
if (result.stop)
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
svr.Options(R"(/.*)", [](const Request &, Response &res)
|
||||
sink.done();
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
auto on_complete = [task_id, &llama] (bool)
|
||||
{
|
||||
// cancel
|
||||
llama.request_cancel(task_id);
|
||||
};
|
||||
|
||||
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
||||
}
|
||||
});
|
||||
|
||||
svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
|
||||
{
|
||||
const json data = llama.get_model_props();
|
||||
return res.set_content(data.dump(), "application/json");
|
||||
});
|
||||
|
||||
svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
|
||||
{ return res.set_content("", "application/json"); });
|
||||
|
||||
svr.Post("/tokenize", [&llama](const Request &req, Response &res)
|
||||
{
|
||||
auto lock = llama.lock();
|
||||
svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
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");
|
||||
});
|
||||
|
||||
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 httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
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());
|
||||
}
|
||||
|
||||
svr.Post("/detokenize", [&llama](const Request &req, Response &res)
|
||||
{
|
||||
auto lock = llama.lock();
|
||||
const json data = format_detokenized_response(content);
|
||||
return res.set_content(data.dump(), "application/json");
|
||||
});
|
||||
|
||||
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.initSampling();
|
||||
llama.loadPrompt();
|
||||
llama.beginCompletion();
|
||||
llama.doCompletion();
|
||||
|
||||
const json data = format_embedding_response(llama);
|
||||
return res.set_content(data.dump(), "application/json"); });
|
||||
svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
const json body = json::parse(req.body);
|
||||
json prompt;
|
||||
if (body.count("content") != 0)
|
||||
{
|
||||
prompt = body["content"];
|
||||
}
|
||||
else
|
||||
{
|
||||
prompt = "";
|
||||
}
|
||||
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false);
|
||||
task_result result = llama.next_result(task_id);
|
||||
return res.set_content(result.result_json.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_exception_handler([](const httplib::Request &, httplib::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;
|
||||
} });
|
||||
svr.set_error_handler([](const httplib::Request &, httplib::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_read_timeout (sparams.read_timeout);
|
||||
svr.set_write_timeout(sparams.write_timeout);
|
||||
|
||||
if (!svr.bind_to_port(sparams.hostname, sparams.port))
|
||||
@@ -1741,20 +2494,38 @@ int main(int argc, char **argv)
|
||||
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_TEE("\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())
|
||||
// run the HTTP server in a thread - see comment below
|
||||
std::thread t([&]()
|
||||
{
|
||||
if (!svr.listen_after_bind())
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
});
|
||||
|
||||
// GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
|
||||
// "Bus error: 10" - this is on macOS, it does not crash on Linux
|
||||
//std::thread t2([&]()
|
||||
{
|
||||
return 1;
|
||||
bool running = true;
|
||||
while (running)
|
||||
{
|
||||
running = llama.update_slots();
|
||||
}
|
||||
}
|
||||
//);
|
||||
|
||||
t.join();
|
||||
|
||||
llama_sampling_free(llama.ctx_sampling);
|
||||
llama_backend_free();
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user