#include "common.h" #include "llama.h" #include "build-info.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 #endif #include "httplib.h" #include "json.hpp" // auto generated files (update with ./deps.sh) #include "index.html.hpp" #include "index.js.hpp" #include "completion.js.hpp" #include "json-schema-to-grammar.mjs.hpp" #include #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif using namespace httplib; using json = nlohmann::json; struct server_params { std::string hostname = "127.0.0.1"; std::string public_path = "examples/server/public"; int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; }; // completion token output with probabilities struct completion_token_output { struct token_prob { llama_token tok; float prob; }; std::vector probs; llama_token tok; }; static size_t common_part(const std::vector &a, const std::vector &b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) { } return i; } enum stop_type { STOP_FULL, STOP_PARTIAL, }; static bool ends_with(const std::string &str, const std::string &suffix) { return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { if (!text.empty() && !stop.empty()) { const char text_last_char = text.back(); for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { if (stop[char_index] == text_last_char) { const std::string current_partial = stop.substr(0, char_index + 1); if (ends_with(text, current_partial)) { return text.size() - char_index - 1; } } } } return std::string::npos; } template static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) { std::string ret; for (; begin != end; ++begin) { ret += llama_token_to_piece(ctx, *begin); } return ret; } static void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { nlohmann::ordered_json log{ {"timestamp", time(nullptr)}, {"level", level}, {"function", function}, {"line", line}, {"message", message}, }; if (!extra.empty()) { log.merge_patch(extra); } const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace); printf("%.*s\n", (int)str.size(), str.data()); fflush(stdout); } // format incomplete utf-8 multibyte character for output static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) { std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) if (out.size() == 1 && (out[0] & 0x80) == 0x80) { std::stringstream ss; ss << std::hex << (out[0] & 0xff); std::string res(ss.str()); out = "byte: \\x" + res; } return out; } // convert a vector of completion_token_output to json static json probs_vector_to_json(const llama_context *ctx, const std::vector & probs) { json out = json::array(); for (const auto &prob : probs) { json probs_for_token = json::array(); for (const auto &p : prob.probs) { std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); probs_for_token.push_back(json{ {"tok_str", tok_str}, {"prob", p.prob}, }); } std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); out.push_back(json{ {"content", tok_str}, {"probs", probs_for_token}, }); } return out; } static bool server_verbose = false; #if SERVER_VERBOSE != 1 #define LOG_VERBOSE(MSG, ...) #else #define LOG_VERBOSE(MSG, ...) \ do \ { \ if (server_verbose) \ { \ server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ } \ } while (0) #endif #define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) struct llama_server_context { bool stream = false; bool has_next_token = false; std::string generated_text; std::vector generated_token_probs; size_t num_prompt_tokens = 0; size_t num_tokens_predicted = 0; size_t n_past = 0; size_t n_remain = 0; json prompt; std::vector embd; llama_model *model = nullptr; llama_context *ctx = nullptr; gpt_params params; llama_sampling_context *ctx_sampling; int n_ctx; bool truncated = false; bool stopped_eos = false; bool stopped_word = false; bool stopped_limit = false; std::string stopping_word; int32_t multibyte_pending = 0; std::mutex mutex; std::unique_lock lock() { return std::unique_lock(mutex); } ~llama_server_context() { if (ctx) { llama_free(ctx); ctx = nullptr; } if (model) { llama_free_model(model); model = nullptr; } } void rewind() { params.antiprompt.clear(); params.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; if (ctx_sampling != nullptr) { llama_sampling_free(ctx_sampling); } ctx_sampling = llama_sampling_init(params); } bool loadModel(const gpt_params ¶ms_) { params = params_; std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params_.model}}); return false; } n_ctx = llama_n_ctx(ctx); return true; } std::vector tokenize(const json & json_prompt, bool add_bos) const { // If `add_bos` is true, we only add BOS, when json_prompt is a string, // or the first element of the json_prompt array is a string. std::vector prompt_tokens; if (json_prompt.is_array()) { bool first = true; for (const auto& p : json_prompt) { if (p.is_string()) { auto s = p.template get(); std::vector p; if (first) { p = ::llama_tokenize(ctx, s, add_bos); first = false; } else { p = ::llama_tokenize(ctx, s, false); } prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); } else { if (first) { first = false; } prompt_tokens.push_back(p.template get()); } } } else { auto s = json_prompt.template get(); prompt_tokens = ::llama_tokenize(ctx, s, add_bos); } return prompt_tokens; } bool loadGrammar() { ctx_sampling = llama_sampling_init(params); return true; } void loadInfill() { bool suff_rm_leading_spc = true; if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { params.input_suffix.erase(0, 1); suff_rm_leading_spc = false; } auto prefix_tokens = tokenize(params.input_prefix, false); auto suffix_tokens = tokenize(params.input_suffix, false); const int space_token = 29871; if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { suffix_tokens.erase(suffix_tokens.begin()); } prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(ctx)); auto prompt_tokens = prefix_tokens; num_prompt_tokens = prompt_tokens.size(); if (params.n_keep < 0) { params.n_keep = (int)num_prompt_tokens; } params.n_keep = std::min(params.n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal if (num_prompt_tokens >= (size_t)params.n_ctx) { printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens); // todo we probably want to cut from both sides const int n_left = (params.n_ctx - params.n_keep) / 2; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), ctx_sampling->prev.begin()); LOG_VERBOSE("input truncated", { {"n_ctx", params.n_ctx}, {"n_keep", params.n_keep}, {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, }); truncated = true; prompt_tokens = new_tokens; } else { const size_t ps = num_prompt_tokens; std::fill(ctx_sampling->prev.begin(), ctx_sampling->prev.end() - ps, 0); std::copy(prompt_tokens.begin(), prompt_tokens.end(), ctx_sampling->prev.end() - ps); } // compare the evaluated prompt with the new prompt n_past = common_part(embd, prompt_tokens); embd = prompt_tokens; if (n_past == num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. printf("we have to evaluate at least 1 token to generate logits\n"); n_past--; } // 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) { params.n_keep = (int)num_prompt_tokens; } 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) { const int n_left = (n_ctx - params.n_keep) / 2; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), ctx_sampling->prev.begin()); 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())}, }); truncated = true; prompt_tokens = new_tokens; } else { const size_t ps = num_prompt_tokens; std::fill(ctx_sampling->prev.begin(), ctx_sampling->prev.end() - ps, 0); std::copy(prompt_tokens.begin(), prompt_tokens.end(), ctx_sampling->prev.end() - ps); } // compare the evaluated prompt with the new prompt n_past = common_part(embd, prompt_tokens); embd = prompt_tokens; if (n_past == num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. n_past--; } // 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 beginCompletion() { // number of tokens to keep when resetting context n_remain = params.n_predict; llama_set_rng_seed(ctx, params.seed); } completion_token_output nextToken() { completion_token_output result; result.tok = -1; if (embd.size() >= (size_t)n_ctx) { // 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}, }); } bool tg = true; while (n_past < embd.size()) { 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; } if (params.n_predict == 0) { has_next_token = false; result.tok = llama_token_eos(ctx); return result; } { // 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.sampling_params.n_probs; if (params.sampling_params.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); 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; } size_t findStoppingStrings(const std::string &text, const size_t last_token_size, const stop_type type) { size_t stop_pos = std::string::npos; for (const std::string &word : params.antiprompt) { size_t pos; if (type == STOP_FULL) { const size_t tmp = word.size() + last_token_size; const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; pos = text.find(word, from_pos); } else { pos = find_partial_stop_string(word, text); } if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { if (type == STOP_FULL) { stopping_word = word; stopped_word = true; has_next_token = false; } stop_pos = pos; } } return stop_pos; } completion_token_output doCompletion() { auto token_with_probs = nextToken(); const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); generated_text += token_text; if (params.sampling_params.n_probs > 0) { generated_token_probs.push_back(token_with_probs); } if (multibyte_pending > 0) { multibyte_pending -= token_text.size(); } else if (token_text.size() == 1) { const char c = token_text[0]; // 2-byte characters: 110xxxxx 10xxxxxx if ((c & 0xE0) == 0xC0) { multibyte_pending = 1; // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx } else if ((c & 0xF0) == 0xE0) { multibyte_pending = 2; // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx } else if ((c & 0xF8) == 0xF0) { multibyte_pending = 3; } else { multibyte_pending = 0; } } if (multibyte_pending > 0 && !has_next_token) { has_next_token = true; n_remain++; } if (!has_next_token && n_remain == 0) { stopped_limit = true; } LOG_VERBOSE("next token", { {"token", token_with_probs.tok}, {"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)}, {"has_next_token", has_next_token}, {"n_remain", n_remain}, {"num_tokens_predicted", num_tokens_predicted}, {"stopped_eos", stopped_eos}, {"stopped_word", stopped_word}, {"stopped_limit", stopped_limit}, {"stopping_word", stopping_word}, }); return token_with_probs; } std::vector getEmbedding() { static const int n_embd = llama_n_embd(model); if (!params.embedding) { LOG_WARNING("embedding disabled", { {"params.embedding", params.embedding}, }); return std::vector(n_embd, 0.0f); } const float *data = llama_get_embeddings(ctx); std::vector embedding(data, data + n_embd); return embedding; } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, const server_params &sparams) { printf("usage: %s [options]\n", argv0); printf("\n"); printf("options:\n"); printf(" -h, --help show this help message and exit\n"); printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -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"); if (llama_mlock_supported()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); printf(" number of layers to store in VRAM\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); printf(" -nommq, --no-mul-mat-q\n"); printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n"); printf(" Not recommended since this is both slower and uses more VRAM.\n"); #endif printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); printf(" set an alias for the model, will be added as `model` field in completion response\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf("\n"); } static void server_params_parse(int argc, char **argv, server_params &sparams, gpt_params ¶ms) { gpt_params default_params; server_params default_sparams; std::string arg; bool invalid_param = false; for (int i = 1; i < argc; i++) { arg = argv[i]; if (arg == "--port") { if (++i >= argc) { invalid_param = true; break; } sparams.port = std::stoi(argv[i]); } else if (arg == "--host") { if (++i >= argc) { invalid_param = true; break; } sparams.hostname = argv[i]; } else if (arg == "--path") { if (++i >= argc) { invalid_param = true; break; } sparams.public_path = argv[i]; } else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) { invalid_param = true; break; } sparams.read_timeout = std::stoi(argv[i]); sparams.write_timeout = std::stoi(argv[i]); } else if (arg == "-m" || arg == "--model") { if (++i >= argc) { invalid_param = true; break; } params.model = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; break; } params.model_alias = argv[i]; } else if (arg == "-h" || arg == "--help") { server_print_usage(argv[0], default_params, default_sparams); exit(0); } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { if (++i >= argc) { invalid_param = true; break; } params.n_ctx = std::stoi(argv[i]); } else if (arg == "--rope-freq-base") { if (++i >= argc) { invalid_param = true; break; } params.rope_freq_base = std::stof(argv[i]); } else if (arg == "--rope-freq-scale") { if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = std::stof(argv[i]); } else if (arg == "--memory-f32" || arg == "--memory_f32") { params.memory_f16 = false; } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) { invalid_param = true; break; } params.n_threads = std::stoi(argv[i]); } else if (arg == "--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) { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); params.n_batch = std::min(512, params.n_batch); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; break; } #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD params.n_gpu_layers = std::stoi(argv[i]); #else LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); #endif } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; break; } #ifdef GGML_USE_CUBLAS std::string arg_next = argv[i]; // split string by , and / const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) { if (i_device < split_arg.size()) { params.tensor_split[i_device] = std::stof(split_arg[i_device]); } else { params.tensor_split[i_device] = 0.0f; } } #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--no-mul-mat-q" || arg == "-nommq") { #ifdef GGML_USE_CUBLAS params.mul_mat_q = false; #else LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; break; } #ifdef GGML_USE_CUBLAS params.main_gpu = std::stoi(argv[i]); #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); #endif } else if (arg == "--lora") { if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); params.use_mmap = false; } else if (arg == "--lora-scaled") { if (++i >= argc) { invalid_param = true; break; } const char * lora_adapter = argv[i]; if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); params.use_mmap = false; } else if (arg == "--lora-base") { if (++i >= argc) { invalid_param = true; break; } params.lora_base = argv[i]; } else if (arg == "-v" || arg == "--verbose") { #if SERVER_VERBOSE != 1 LOG_WARNING("server.cpp is not built with verbose logging.", {}); #else server_verbose = true; #endif } else if (arg == "--mlock") { params.use_mlock = true; } else if (arg == "--no-mmap") { params.use_mmap = false; } else if (arg == "--numa") { params.numa = true; } else if (arg == "--embedding") { params.embedding = true; } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } static json format_generation_settings(llama_server_context &llama) { const auto & sparams = llama.params.sampling_params; 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.repeat_last_n}, {"repeat_penalty", sparams.repeat_penalty}, {"presence_penalty", sparams.presence_penalty}, {"frequency_penalty", sparams.frequency_penalty}, {"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.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 &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.sampling_params.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } return res; } static json format_partial_response( llama_server_context &llama, const std::string &content, const std::vector &probs ) { json res = json{ {"content", content}, {"stop", false}, }; if (llama.params.sampling_params.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } return res; } static json format_tokenizer_response(const std::vector &tokens) { return json{ {"tokens", tokens}}; } static json format_detokenized_response(std::string content) { return json{ {"content", content}}; } template 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.sampling_params; auto & sparams = llama.params.sampling_params; llama.stream = json_value(body, "stream", false); llama.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.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n); sparams.temp = json_value(body, "temperature", default_sparams.temp); sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty); sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty); sparams.frequency_penalty = json_value(body, "frequency_penalty", default_sparams.frequency_penalty); 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); llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep); llama.params.seed = json_value(body, "seed", default_params.seed); llama.params.grammar = json_value(body, "grammar", default_params.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(); if (tok >= 0 && tok < n_vocab) { if (el[1].is_number()) { sparams.logit_bias[tok] = el[1].get(); } else if (el[1].is_boolean() && !el[1].get()) { 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); } } } llama.ctx_sampling = llama_sampling_init(llama.params); 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) { LOG_INFO("request", { {"remote_addr", req.remote_addr}, {"remote_port", req.remote_port}, {"status", res.status}, {"method", req.method}, {"path", req.path}, {"params", req.params}, }); LOG_VERBOSE("request", { {"request", req.body}, {"response", res.body}, }); } static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) { return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx); } // Function matching type llama_beam_search_callback_fn_t. // Custom callback example is called each time the beams lengths increase: // * Show progress by printing ',' following by number of convergent beam tokens if any. // * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. // This is also called when the stop condition is met. // Collect tokens into std::vector 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(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["<(&index_html), index_html_len, "text/html"); return false; }); // this is only called if no index.js is found in the public --path svr.Get("/index.js", [](const Request &, Response &res) { res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); return false; }); // this is only called if no index.html is found in the public --path svr.Get("/completion.js", [](const Request &, Response &res) { res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); return false; }); // this is only called if no index.html is found in the public --path svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res) { res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); return false; }); svr.Post("/completion", [&llama](const Request &req, Response &res) { auto lock = llama.lock(); llama.rewind(); llama_reset_timings(llama.ctx); parse_options_completion(json::parse(req.body), llama); if (!llama.loadGrammar()) { res.status = 400; return; } llama.loadPrompt(); llama.beginCompletion(); if (!llama.stream) { 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.sampling_params.n_probs > 0 && llama.stopped_word) { const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); probs = std::vector(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; } 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 probs_output = {}; if (llama.params.sampling_params.n_probs > 0) { const std::vector 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(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(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); if (!llama.loadGrammar()) { res.status = 400; return; } llama.loadInfill(); llama.beginCompletion(); const auto chunked_content_provider = [&](size_t, DataSink & sink) { size_t sent_count = 0; size_t sent_token_probs_index = 0; while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { continue; } const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); size_t pos = std::min(sent_count, llama.generated_text.size()); const std::string str_test = llama.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); if (stop_pos != std::string::npos) { is_stop_full = true; llama.generated_text.erase( llama.generated_text.begin() + pos + stop_pos, llama.generated_text.end()); pos = std::min(sent_count, llama.generated_text.size()); } else { is_stop_full = false; stop_pos = llama.findStoppingStrings(str_test, token_text.size(), STOP_PARTIAL); } if ( stop_pos == std::string::npos || // Send rest of the text if we are at the end of the generation (!llama.has_next_token && !is_stop_full && stop_pos > 0) ) { const std::string to_send = llama.generated_text.substr(pos, std::string::npos); sent_count += to_send.size(); std::vector probs_output = {}; if (llama.params.sampling_params.n_probs > 0) { const std::vector 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(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(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) ); const std::string str = "data: " + data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", { { "to_send", str } }); if (!sink.write(str.data(), str.size())) { LOG_VERBOSE("stream closed", {}); llama_print_timings(llama.ctx); return false; } } } llama_print_timings(llama.ctx); sink.done(); return true; }; const auto on_complete = [&](bool) { llama.mutex.unlock(); }; lock.release(); res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); }); svr.Get("/model.json", [&llama](const Request &, Response &res) { const json data = format_generation_settings(llama); return res.set_content(data.dump(), "application/json"); }); svr.Options(R"(/.*)", [](const Request &, Response &res) { return res.set_content("", "application/json"); }); svr.Post("/tokenize", [&llama](const Request &req, Response &res) { auto lock = llama.lock(); const json body = json::parse(req.body); std::vector tokens; if (body.count("content") != 0) { tokens = llama.tokenize(body["content"], false); } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json"); }); svr.Post("/detokenize", [&llama](const Request &req, Response &res) { auto lock = llama.lock(); const json body = json::parse(req.body); std::string content; if (body.count("tokens") != 0) { const std::vector tokens = body["tokens"]; content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); } const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json"); }); svr.Post("/embedding", [&llama](const Request &req, Response &res) { auto lock = llama.lock(); const json body = json::parse(req.body); llama.rewind(); llama_reset_timings(llama.ctx); if (body.count("content") != 0) { llama.prompt = body["content"]; } else { llama.prompt = ""; } llama.params.n_predict = 0; llama.loadPrompt(); llama.beginCompletion(); llama.doCompletion(); const json data = format_embedding_response(llama); return res.set_content(data.dump(), "application/json"); }); svr.set_logger(log_server_request); svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep) { const char fmt[] = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; try { std::rethrow_exception(std::move(ep)); } catch (std::exception & e) { snprintf(buf, sizeof(buf), fmt, e.what()); } catch (...) { snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); } res.set_content(buf, "text/plain"); res.status = 500; }); svr.set_error_handler([](const Request &, Response &res) { if (res.status == 400) { res.set_content("Invalid request", "text/plain"); } else if (res.status != 500) { res.set_content("File Not Found", "text/plain"); res.status = 404; } }); // set timeouts and change hostname and port svr.set_read_timeout(sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); if (!svr.bind_to_port(sparams.hostname, sparams.port)) { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } // Set the base directory for serving static files svr.set_base_dir(sparams.public_path); // to make it ctrl+clickable: printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); LOG_INFO("HTTP server listening", { {"hostname", sparams.hostname}, {"port", sparams.port}, }); if (!svr.listen_after_bind()) { return 1; } llama_sampling_free(llama.ctx_sampling); llama_backend_free(); return 0; }