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llama.cpp
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common/common.h
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Sascha Rogmann
server : speculative checkpointing (#19493)
19 апр 2026, 10:24
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19 апр 2026, 10:24
455d8e4
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// Various helper functions and utilities #pragma once #include "llama-cpp.h" #include "ggml-opt.h" #include "ggml.h" #include <set> #include <sstream> #include <string> #include <string_view> #include <vector> #include <map> #if defined(_WIN32) && !defined(_WIN32_WINNT) #define _WIN32_WINNT 0x0A00 #endif #ifdef _WIN32 #define DIRECTORY_SEPARATOR '\\' #else #define DIRECTORY_SEPARATOR '/' #endif // _WIN32 #define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) struct common_time_meas { common_time_meas(int64_t & t_acc, bool disable = false); ~common_time_meas(); const int64_t t_start_us; int64_t & t_acc; }; struct common_adapter_lora_info { std::string path; float scale; std::string task_name; std::string prompt_prefix; struct llama_adapter_lora * ptr; }; using llama_tokens = std::vector<llama_token>; struct common_control_vector_load_info; // // CPU utils // struct cpu_params { int n_threads = -1; bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask. bool mask_valid = false; // Default: any CPU enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime) bool strict_cpu = false; // Use strict CPU placement uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling) }; int32_t cpu_get_num_physical_cores(); int32_t cpu_get_num_math(); // // Common params // enum llama_example { LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_DEBUG, LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PASSKEY, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_PARALLEL, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_FINETUNE, LLAMA_EXAMPLE_FIT_PARAMS, LLAMA_EXAMPLE_RESULTS, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS, LLAMA_EXAMPLE_COUNT, }; enum common_sampler_type { COMMON_SAMPLER_TYPE_NONE = 0, COMMON_SAMPLER_TYPE_DRY = 1, COMMON_SAMPLER_TYPE_TOP_K = 2, COMMON_SAMPLER_TYPE_TOP_P = 3, COMMON_SAMPLER_TYPE_MIN_P = 4, //COMMON_SAMPLER_TYPE_TFS_Z = 5, COMMON_SAMPLER_TYPE_TYPICAL_P = 6, COMMON_SAMPLER_TYPE_TEMPERATURE = 7, COMMON_SAMPLER_TYPE_XTC = 8, COMMON_SAMPLER_TYPE_INFILL = 9, COMMON_SAMPLER_TYPE_PENALTIES = 10, COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11, COMMON_SAMPLER_TYPE_ADAPTIVE_P = 12, }; // dimensionality reduction methods, used by cvector-generator enum dimre_method { DIMRE_METHOD_PCA, DIMRE_METHOD_MEAN, }; enum common_conversation_mode { COMMON_CONVERSATION_MODE_DISABLED = 0, COMMON_CONVERSATION_MODE_ENABLED = 1, COMMON_CONVERSATION_MODE_AUTO = 2, }; enum common_grammar_trigger_type { COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN, COMMON_GRAMMAR_TRIGGER_TYPE_WORD, COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, }; struct common_grammar_trigger { common_grammar_trigger_type type; std::string value; llama_token token = LLAMA_TOKEN_NULL; }; enum common_params_sampling_config : uint64_t { COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS = 1 << 0, COMMON_PARAMS_SAMPLING_CONFIG_TOP_K = 1 << 1, COMMON_PARAMS_SAMPLING_CONFIG_TOP_P = 1 << 2, COMMON_PARAMS_SAMPLING_CONFIG_MIN_P = 1 << 3, COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY = 1 << 4, COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD = 1 << 5, COMMON_PARAMS_SAMPLING_CONFIG_TEMP = 1 << 6, COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N = 1 << 7, COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT = 1 << 8, COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT = 1 << 9, COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU = 1 << 10, COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA = 1 << 11, }; enum common_speculative_type { COMMON_SPECULATIVE_TYPE_NONE, // no speculative decoding COMMON_SPECULATIVE_TYPE_DRAFT, // draft model COMMON_SPECULATIVE_TYPE_EAGLE3, // eagle draft model COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, // simple self-speculative decoding COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, // self-speculative decoding with n-gram keys only COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values COMMON_SPECULATIVE_TYPE_NGRAM_MOD, COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, // self-speculative decoding with 3-level n-gram cache COMMON_SPECULATIVE_TYPE_COUNT // number of types, unknown type }; // Grammar type enumeration enum common_grammar_type { COMMON_GRAMMAR_TYPE_NONE, // no grammar set COMMON_GRAMMAR_TYPE_USER, // user-provided GBNF (--grammar / "grammar" API field) COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, // auto-generated from JSON schema (--json-schema / "json_schema" API field) COMMON_GRAMMAR_TYPE_TOOL_CALLS, // auto-generated by chat template parser for function calling }; // Grammar variant struct with type and grammar string struct common_grammar { common_grammar_type type = COMMON_GRAMMAR_TYPE_NONE; std::string grammar; // Default constructor - no grammar common_grammar() = default; // Constructor with type and grammar string common_grammar(common_grammar_type t, std::string g) : type(t), grammar(std::move(g)) { GGML_ASSERT(type != COMMON_GRAMMAR_TYPE_NONE || !grammar.empty()); } // Check if a grammar is set bool empty() const { return type == COMMON_GRAMMAR_TYPE_NONE || grammar.empty(); } }; // Returns the raw grammar string, or empty string if no grammar is set. inline const std::string & common_grammar_value(const common_grammar & g) { return g.grammar; } // Returns true when the generation_prompt should be prefilled into the grammar sampler. // Only output-format and tool-call grammars need prefill; user-supplied grammars must not be prefilled. inline bool common_grammar_needs_prefill(const common_grammar & g) { return g.type == COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT || g.type == COMMON_GRAMMAR_TYPE_TOOL_CALLS; } // sampling parameters struct common_params_sampling { uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler int32_t n_prev = 64; // number of previous tokens to remember int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens int32_t top_k = 40; // <= 0 to use vocab size float top_p = 0.95f; // 1.0 = disabled float min_p = 0.05f; // 0.0 = disabled float xtc_probability = 0.00f; // 0.0 = disabled float xtc_threshold = 0.10f; // > 0.5 disables XTC float typ_p = 1.00f; // typical_p, 1.0 = disabled float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities float dynatemp_range = 0.00f; // 0.0 = disabled float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) float penalty_repeat = 1.00f; // 1.0 = disabled float penalty_freq = 0.00f; // 0.0 = disabled float penalty_present = 0.00f; // 0.0 = disabled float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) float adaptive_target = -1.0f; // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) float adaptive_decay = 0.90f; // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99) int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float top_n_sigma = -1.00f; // -1.0 = disabled float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate bool ignore_eos = false; bool no_perf = false; // disable performance metrics bool timing_per_token = false; uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY std::vector<enum common_sampler_type> samplers = { COMMON_SAMPLER_TYPE_PENALTIES, COMMON_SAMPLER_TYPE_DRY, COMMON_SAMPLER_TYPE_TOP_N_SIGMA, COMMON_SAMPLER_TYPE_TOP_K, COMMON_SAMPLER_TYPE_TYPICAL_P, COMMON_SAMPLER_TYPE_TOP_P, COMMON_SAMPLER_TYPE_MIN_P, COMMON_SAMPLER_TYPE_XTC, COMMON_SAMPLER_TYPE_TEMPERATURE, }; common_grammar grammar; // optional grammar constraint (user / output-format / tool-calls) bool grammar_lazy = false; std::vector<common_grammar_trigger> grammar_triggers; // optional triggers (for lazy grammars) std::set<llama_token> preserved_tokens; std::vector<llama_logit_bias> logit_bias; // logit biases to apply std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens // The assistant generation prompt already prefilled into the prompt. // Fed to the grammar sampler (to advance past pre-existing tokens) and used // to determine the reasoning budget sampler's initial state. // Only applied when the grammar is of output-format or tool-calls type. std::string generation_prompt; // reasoning budget sampler parameters // these are populated by the server/CLI based on chat template params int32_t reasoning_budget_tokens = -1; // -1 = disabled, >= 0 = token budget std::vector<llama_token> reasoning_budget_start; // start tag token sequence std::vector<llama_token> reasoning_budget_end; // end tag token sequence std::vector<llama_token> reasoning_budget_forced; // forced sequence (message + end tag) bool backend_sampling = false; bool has_logit_bias() const { return !logit_bias.empty(); } // print the parameters into a string std::string print() const; }; struct common_params_model { std::string path = ""; // model local path // NOLINT std::string url = ""; // model url to download // NOLINT std::string hf_repo = ""; // HF repo // NOLINT std::string hf_file = ""; // HF file // NOLINT std::string docker_repo = ""; // Docker repo // NOLINT std::string name = ""; // in format <user>/<model>[:<tag>] (tag is optional) // NOLINT }; struct common_ngram_mod; struct common_params_speculative { common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding // general-purpose speculative decoding parameters int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding float p_split = 0.1f; // speculative decoding split probability float p_min = 0.75f; // minimum speculative decoding probability (greedy) // ngram-based speculative decoding uint16_t ngram_size_n = 12; // ngram size for lookup uint16_t ngram_size_m = 48; // mgram size for speculative tokens uint16_t ngram_min_hits = 1; // minimum hits at ngram/mgram lookup for mgram to be proposed bool use_checkpoints = false; // use checkpoints to rewind in token history of recurrent models std::shared_ptr<common_ngram_mod> ngram_mod; std::string lookup_cache_static; // path of static ngram cache file for lookup decoding // NOLINT std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding // NOLINT // draft-model speculative decoding struct common_params_model mparams_dft; llama_model * model_dft = nullptr; // a llama_model that can be shared by multiple speculative contexts llama_context_params cparams_dft; // these are the parameters for the draft llama_context int32_t n_ctx = 0; // draft context size int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default) ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V struct cpu_params cpuparams; struct cpu_params cpuparams_batch; std::vector<ggml_backend_dev_t> devices; // devices to use for offloading std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements std::vector<llama_model_tensor_buft_override> tensor_buft_overrides; bool has_dft() const { return !mparams_dft.path.empty() || !mparams_dft.hf_repo.empty(); } }; struct common_params_vocoder { struct common_params_model model; std::string speaker_file = ""; // speaker file path // NOLINT bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT }; struct common_params_diffusion { int32_t steps = 128; bool visual_mode = false; float eps = 0; // epsilon for timesteps int32_t block_length = 0; // block length for generation int32_t algorithm = 4; // default algorithm: low-confidence float alg_temp = 0.0f; // algorithm temperature float cfg_scale = 0; // classifier-free guidance scale bool add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0 }; // reasoning API response format (not to be confused as chat template's reasoning format) // only used by server enum common_reasoning_format { COMMON_REASONING_FORMAT_NONE, COMMON_REASONING_FORMAT_AUTO, // Same as deepseek, using `message.reasoning_content` COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas. // do not extend this enum unless you absolutely have to // in most cases, use COMMON_REASONING_FORMAT_AUTO // see: https://github.com/ggml-org/llama.cpp/pull/15408 }; struct lr_opt { float lr0 = 1e-5; // learning rate at first epoch float lr_min = -1; float decay_epochs = -1; // if >0, the learning rate starts at lr0 and decays to lr_min after this many epochs float scale_epoch = 0; float wd = 0; unsigned epochs = 2; unsigned epoch; // set by optimizer outer (epochs) loop // learning rate decay - constant LR per epoch only for now float get_lr(float e) const; float get_lr() const { return get_lr(epoch); } // must call after arg parse, before get_lr void init(); }; struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata); struct common_params { int32_t n_predict = -1; // max. number of new tokens to predict, -1 == no limit int32_t n_ctx = 0; // context size, 0 == context the model was trained with int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode int32_t grp_attn_n = 1; // group-attention factor int32_t grp_attn_w = 512; // group-attention width int32_t n_print = -1; // print token count every n tokens (-1 = disabled) float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor float yarn_attn_factor = -1.0f; // YaRN magnitude scaling factor float yarn_beta_fast = -1.0f; // YaRN low correction dim float yarn_beta_slow = -1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length // offload params std::vector<ggml_backend_dev_t> devices; // devices to use for offloading int32_t n_gpu_layers = -1; // number of layers to store in VRAM, -1 is auto, <= -2 is all int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs bool fit_params = true; // whether to fit unset model/context parameters to free device memory int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use // margin per device in bytes for fitting parameters to free memory: std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024); enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs struct cpu_params cpuparams; struct cpu_params cpuparams_batch; ggml_backend_sched_eval_callback cb_eval = nullptr; void * cb_eval_user_data = nullptr; ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings enum llama_flash_attn_type flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO; // whether to use Flash Attention struct common_params_sampling sampling; struct common_params_speculative speculative; struct common_params_vocoder vocoder; struct common_params_diffusion diffusion; struct common_params_model model; std::set<std::string> model_alias; // model aliases // NOLINT std::set<std::string> model_tags; // model tags (informational, not used for routing) // NOLINT std::string hf_token = ""; // HF token // NOLINT std::string prompt = ""; // NOLINT std::string system_prompt = ""; // NOLINT std::string prompt_file = ""; // store the external prompt file name // NOLINT std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT std::string input_prefix = ""; // string to prefix user inputs with // NOLINT std::string input_suffix = ""; // string to suffix user inputs with // NOLINT std::string logits_file = ""; // file for saving *all* logits // NOLINT // llama-debug specific options std::string logits_output_dir = "data"; // directory for saving logits output files // NOLINT bool save_logits = false; // whether to save logits to files // NOLINT std::vector<std::string> tensor_filter; // filter tensor names for debug output (regex) // NOLINT std::vector<std::string> in_files; // all input files std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector<llama_model_kv_override> kv_overrides; std::vector<llama_model_tensor_buft_override> tensor_buft_overrides; bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply) std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale int32_t verbosity = 3; // LOG_LEVEL_INFO int32_t control_vector_layer_start = -1; // layer range for control vector int32_t control_vector_layer_end = -1; // layer range for control vector bool offline = false; int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line // (which is more convenient to use for plotting) // bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed bool kl_divergence = false; // compute KL divergence bool check = false; // check rather than generate results for llama-results bool usage = false; // print usage bool completion = false; // print source-able completion script bool use_color = false; // use color to distinguish generations and inputs bool special = false; // enable special token output bool interactive = false; // interactive mode bool interactive_first = false; // wait for user input immediately bool prompt_cache_all = false; // save user input and generations to prompt cache bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it bool escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\" bool multiline_input = false; // reverse the usage of `\` bool simple_io = false; // improves compatibility with subprocesses and limited consoles bool cont_batching = true; // insert new sequences for decoding on-the-fly bool no_perf = false; // disable performance metrics bool show_timings = true; // show timing information on CLI bool ctx_shift = false; // context shift on infinite text generation bool swa_full = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) bool kv_unified = false; // enable unified KV cache bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool use_mmap = true; // enable mmap to use filesystem cache bool use_direct_io = false; // read from disk without buffering bool use_mlock = false; // use mlock to keep model in memory bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation bool no_kv_offload = false; // disable KV offloading bool warmup = true; // warmup run bool check_tensors = false; // validate tensor data bool no_op_offload = false; // globally disable offload host tensor operations to device bool no_extra_bufts = false; // disable extra buffer types (used for weight repacking) bool no_host = false; // bypass host buffer allowing extra buffers to be used bool single_turn = false; // single turn chat conversation ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO; // multimodal models (see tools/mtmd) struct common_params_model mmproj; bool mmproj_use_gpu = true; // use GPU for multimodal model bool no_mmproj = false; // explicitly disable multimodal model std::vector<std::string> image; // path to image file(s) int image_min_tokens = -1; int image_max_tokens = -1; // finetune struct lr_opt lr; enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW; float val_split = 0.05f; // fraction of the data used for the validation set // embedding bool embedding = false; // get only sentence embedding int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix std::string embd_sep = "\n"; // separator of embeddings std::string cls_sep = "\t"; // separator of classification sequences // server params int32_t port = 8080; // server listens on this network port bool reuse_port = false; // allow multiple sockets to bind to the same port int32_t timeout_read = 600; // http read timeout in seconds int32_t timeout_write = timeout_read; // http write timeout in seconds int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting bool cache_prompt = true; // whether to enable prompt caching bool clear_idle = true; // save and clear idle slots upon starting a new task int32_t n_ctx_checkpoints = 32; // max number of context checkpoints per slot int32_t checkpoint_every_nt = 8192; // make a checkpoint every n tokens during prefill int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc. std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT std::string api_prefix = ""; // NOLINT std::string chat_template = ""; // NOLINT bool use_jinja = true; // NOLINT bool enable_chat_template = true; bool force_pure_content_parser = false; common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; int enable_reasoning = -1; // -1 = auto, 0 = disable, 1 = enable int reasoning_budget = -1; std::string reasoning_budget_message; // message injected before end tag when budget exhausted bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response int sleep_idle_seconds = -1; // if >0, server will sleep after this many seconds of idle time std::vector<std::string> api_keys; std::string ssl_file_key = ""; // NOLINT std::string ssl_file_cert = ""; // NOLINT std::map<std::string, std::string> default_template_kwargs; // webui configs bool webui = true; bool webui_mcp_proxy = false; std::string webui_config_json; // "advanced" endpoints are disabled by default for better security bool endpoint_slots = true; bool endpoint_props = false; // only control POST requests, not GET bool endpoint_metrics = false; // enable built-in tools std::vector<std::string> server_tools; // router server configs std::string models_dir = ""; // directory containing models for the router server std::string models_preset = ""; // directory containing model presets for the router server int models_max = 4; // maximum number of models to load simultaneously bool models_autoload = true; // automatically load models when requested via the router server bool log_json = false; std::string slot_save_path; std::string media_path; // path to directory for loading media files float slot_prompt_similarity = 0.1f; // batched-bench params bool is_pp_shared = false; bool is_tg_separate = false; std::vector<int32_t> n_pp; std::vector<int32_t> n_tg; std::vector<int32_t> n_pl; // retrieval params std::vector<std::string> context_files; // context files to embed int32_t chunk_size = 64; // chunk size for context embedding std::string chunk_separator = "\n"; // chunk separator for context embedding // passkey params int32_t n_junk = 250; // number of times to repeat the junk text int32_t i_pos = -1; // position of the passkey in the junk text // imatrix params int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations int32_t i_chunk = 0; // start processing from this chunk int8_t imat_dat = 0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat) bool process_output = false; // collect data for the output tensor bool compute_ppl = true; // whether to compute perplexity bool show_statistics = false; // show imatrix statistics per tensor bool parse_special = false; // whether to parse special tokens during imatrix tokenization // cvector-generator params int n_pca_batch = 100; int n_pca_iterations = 1000; dimre_method cvector_dimre_method = DIMRE_METHOD_PCA; std::string cvector_positive_file = "tools/cvector-generator/positive.txt"; std::string cvector_negative_file = "tools/cvector-generator/negative.txt"; bool spm_infill = false; // suffix/prefix/middle pattern for infill // batched-bench params bool batched_bench_output_jsonl = false; // common params std::string out_file; // output filename for all example programs // optional callback for model loading progress and cancellation: // called with a progress value between 0.0 and 1.0. // return false from callback to abort model loading or true to continue llama_progress_callback load_progress_callback = NULL; void * load_progress_callback_user_data = NULL; bool no_alloc = false; // Don't allocate model buffers }; // call once at the start of a program if it uses libcommon // initializes the logging system and prints info about the build void common_init(); std::string common_params_get_system_info(const common_params & params); bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]); bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]); void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr); bool set_process_priority(enum ggml_sched_priority prio); // // String utils // #ifdef __GNUC__ # if defined(__MINGW32__) && !defined(__clang__) # define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) # else # define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) # endif #else # define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) #endif LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) std::string string_format(const char * fmt, ...); std::string string_strip(const std::string & str); std::string string_get_sortable_timestamp(); std::string string_join(const std::vector<std::string> & values, const std::string & separator); std::vector<std::string> string_split(const std::string & str, const std::string & delimiter); std::string string_repeat(const std::string & str, size_t n); void string_replace_all(std::string & s, const std::string & search, const std::string & replace); std::string regex_escape(const std::string & s); template<class T> static std::vector<T> string_split(const std::string & str, char delim) { static_assert(!std::is_same<T, std::string>::value, "Please use the specialized version for std::string"); std::vector<T> values; std::istringstream str_stream(str); std::string token; while (std::getline(str_stream, token, delim)) { T value; std::istringstream token_stream(token); token_stream >> value; values.push_back(value); } return values; } template<> inline std::vector<std::string> string_split<std::string>(const std::string & str, char delim) { std::vector<std::string> parts; size_t begin_pos = 0; size_t delim_pos = str.find(delim); while (delim_pos != std::string::npos) { std::string part = str.substr(begin_pos, delim_pos - begin_pos); parts.emplace_back(part); begin_pos = delim_pos + 1; delim_pos = str.find(delim, begin_pos); } parts.emplace_back(str.substr(begin_pos)); return parts; } // remove when moving to c++20 inline bool string_starts_with(std::string_view str, std::string_view prefix) { return str.size() >= prefix.size() && str.compare(0, prefix.size(), prefix) == 0; } // remove when moving to c++20 inline bool string_ends_with(std::string_view str, std::string_view suffix) { return str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), suffix.size(), suffix) == 0; } inline bool string_remove_suffix(std::string & str, std::string_view suffix) { if (string_ends_with(str, suffix)) { str.resize(str.size() - suffix.size()); return true; } return false; } inline size_t string_find_partial_stop(std::string_view str, std::string_view stop) { if (!str.empty() && !stop.empty()) { const size_t max_len = std::min(str.size(), stop.size()); const char last_char = str.back(); for (size_t len = max_len; len > 0; --len) { if (stop[len - 1] == last_char) { if (string_ends_with(str, stop.substr(0, len))) { return str.size() - len; } } } } return std::string::npos; } bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides); void string_process_escapes(std::string & input); std::string string_from(bool value); std::string string_from(const std::vector<int> & values); std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens); std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch); bool glob_match(const std::string & pattern, const std::string & str); // // Filesystem utils // bool fs_validate_filename(const std::string & filename, bool allow_subdirs = false); bool fs_create_directory_with_parents(const std::string & path); bool fs_is_directory(const std::string & path); std::string fs_get_cache_directory(); std::string fs_get_cache_file(const std::string & filename); struct common_file_info { std::string path; std::string name; size_t size = 0; // in bytes bool is_dir = false; }; std::vector<common_file_info> fs_list(const std::string & path, bool include_directories); // // TTY utils // // Auto-detect if colors can be enabled based on terminal and environment bool tty_can_use_colors(); // // Model utils // struct common_sampler; // note: defines the model, context, samplers, ets. lifetimes struct common_init_result { common_init_result(common_params & params); ~common_init_result(); llama_model * model(); llama_context * context(); common_sampler * sampler(llama_seq_id seq_id); void reset_samplers(); std::vector<llama_adapter_lora_ptr> & lora(); private: struct impl; std::unique_ptr<impl> pimpl; }; using common_init_result_ptr = std::unique_ptr<common_init_result>; common_init_result_ptr common_init_from_params(common_params & params); struct llama_model_params common_model_params_to_llama ( common_params & params); struct llama_context_params common_context_params_to_llama(const common_params & params); struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); // clear LoRA adapters from context, then apply new list of adapters void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora); std::string get_model_endpoint(); // // Batch utils // void common_batch_clear(struct llama_batch & batch); void common_batch_add( struct llama_batch & batch, llama_token id, llama_pos pos, const std::vector<llama_seq_id> & seq_ids, bool logits); // decodes a single batch of tokens for a prompt and manages session tokens // // Note: We save state before the last token so that we can replay it to ensure // compatibility with all memory types. Recurrent/hybrid models cannot remove // tokens from memory, so this approach works across all model architectures. bool common_prompt_batch_decode( struct llama_context * ctx, const std::vector<llama_token> & embd, int & n_past, int n_batch, std::string_view state_path, bool save_state); // replays the last token after loading state to regenerate logits // used after loading session state to ensure the sampling context has valid logits bool common_replay_last_token(struct llama_context * ctx, llama_token last_token, int32_t pos); // // Vocab utils // // tokenizes a string into a vector of tokens // should work similar to Python's `tokenizer.encode` std::vector<llama_token> common_tokenize( const struct llama_context * ctx, const std::string & text, bool add_special, bool parse_special = false); std::vector<llama_token> common_tokenize( const struct llama_vocab * vocab, const std::string & text, bool add_special, bool parse_special = false); // tokenizes a token into a piece, optionally renders special/control tokens // should work similar to Python's `tokenizer.id_to_piece` std::string common_token_to_piece( const struct llama_context * ctx, llama_token token, bool special = true); std::string common_token_to_piece( const struct llama_vocab * vocab, llama_token token, bool special = true); // detokenizes a vector of tokens into a string // should work similar to Python's `tokenizer.decode` // optionally renders special/control tokens std::string common_detokenize( const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special = true); std::string common_detokenize( const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special = true); // // Embedding utils // // TODO: replace embd_norm with an enum void common_embd_normalize(const float * inp, float * out, int n, int embd_norm); float common_embd_similarity_cos(const float * embd1, const float * embd2, int n); // // Control vector utils // struct common_control_vector_data { int n_embd; // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd std::vector<float> data; }; struct common_control_vector_load_info { float strength; std::string fname; }; // Load control vectors, scale each by strength, and add them together. // On error, returns {-1, empty} common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos); // // Split utils // namespace { const char * const LLM_KV_SPLIT_NO = "split.no"; const char * const LLM_KV_SPLIT_COUNT = "split.count"; const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; } // // MoE utils // const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps"; inline std::string llm_ffn_exps_block_regex(int idx) { return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX); } inline llama_model_tensor_buft_override llm_ffn_exps_cpu_override() { return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() }; } // // training utils // ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride); // "adamw" or "sgd" (case insensitive) enum ggml_opt_optimizer_type common_opt_get_optimizer(const char *);