// Various helper functions and utilities #pragma once #include "llama.h" #define LOG_NO_FILE_LINE_FUNCTION #include "log.h" #include #include #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) #define print_build_info() do { \ fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) #define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf" struct llama_lora_adapter_info { std::string path; float scale; }; struct llama_lora_adapter_container : llama_lora_adapter_info { struct llama_lora_adapter * adapter; }; // build info extern int LLAMA_BUILD_NUMBER; extern char const * LLAMA_COMMIT; extern char const * LLAMA_COMPILER; extern char const * LLAMA_BUILD_TARGET; struct llama_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_COMMON, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, 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_LLAVA, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_PARALLEL, LLAMA_EXAMPLE_COUNT, }; enum gpt_sampler_type { GPT_SAMPLER_TYPE_NONE = 0, GPT_SAMPLER_TYPE_TOP_K = 1, GPT_SAMPLER_TYPE_TOP_P = 2, GPT_SAMPLER_TYPE_MIN_P = 3, GPT_SAMPLER_TYPE_TFS_Z = 4, GPT_SAMPLER_TYPE_TYPICAL_P = 5, GPT_SAMPLER_TYPE_TEMPERATURE = 6, }; // dimensionality reduction methods, used by cvector-generator enum dimre_method { DIMRE_METHOD_PCA, DIMRE_METHOD_MEAN, }; // sampler parameters struct gpt_sampler_params { 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 tfs_z = 1.00f; // 1.0 = disabled 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 int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate bool penalize_nl = false; // consider newlines as a repeatable token bool ignore_eos = false; std::vector samplers = { GPT_SAMPLER_TYPE_TOP_K, GPT_SAMPLER_TYPE_TFS_Z, GPT_SAMPLER_TYPE_TYPICAL_P, GPT_SAMPLER_TYPE_TOP_P, GPT_SAMPLER_TYPE_MIN_P, GPT_SAMPLER_TYPE_TEMPERATURE }; std::string grammar; // optional BNF-like grammar to constrain sampling std::vector logit_bias; // logit biases to apply // print the parameters into a string std::string print() const; }; struct gpt_params { int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 0; // context size 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_draft = 5; // number of tokens to draft during speculative decoding 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 float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) 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 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 = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold struct cpu_params cpuparams; struct cpu_params cpuparams_batch; struct cpu_params draft_cpuparams; struct cpu_params draft_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_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs 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 struct gpt_sampler_params sparams; std::string model = ""; // model path // NOLINT std::string model_draft = ""; // draft model for speculative decoding // NOLINT std::string model_alias = "unknown"; // model alias // NOLINT std::string model_url = ""; // model url to download // NOLINT std::string hf_token = ""; // HF token // NOLINT std::string hf_repo = ""; // HF repo // NOLINT std::string hf_file = ""; // HF file // NOLINT std::string 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 logdir = ""; // directory in which to save YAML log files // NOLINT 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 std::string logits_file = ""; // file for saving *all* logits // NOLINT std::string rpc_servers = ""; // comma separated list of RPC servers // NOLINT std::vector in_files; // all input files std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector kv_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_lora_adapter_apply) std::vector lora_adapters; // lora adapter path with user defined scale std::vector control_vectors; // control vector with user defined scale int32_t verbosity = 0; int32_t control_vector_layer_start = -1; // layer range for control vector int32_t control_vector_layer_end = -1; // layer range for control vector 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 usage = false; // print usage 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 conversation = false; // conversation mode (does not print special tokens and suffix/prefix) 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 flash_attn = false; // flash attention bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool logits_all = false; // return logits for all tokens in the batch bool use_mmap = true; // use mmap for faster loads 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 dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading bool warmup = true; // warmup run bool check_tensors = false; // validate tensor data std::string cache_type_k = "f16"; // KV cache data type for the K std::string cache_type_v = "f16"; // KV cache data type for the V // multimodal models (see examples/llava) std::string mmproj = ""; // path to multimodal projector // NOLINT std::vector image; // path to image file(s) // embedding bool embedding = false; // get only sentence embedding int32_t embd_normalize = 2; // normalisation for embendings (-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 embendings // server params int32_t port = 8080; // server listens on this network port int32_t timeout_read = 600; // http read timeout in seconds int32_t timeout_write = timeout_read; // http write timeout in seconds int n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT std::string chat_template = ""; // NOLINT std::string system_prompt = ""; // NOLINT bool enable_chat_template = true; std::vector api_keys; std::string ssl_file_key = ""; // NOLINT std::string ssl_file_cert = ""; // NOLINT bool endpoint_slots = true; bool endpoint_metrics = false; bool log_json = false; std::string slot_save_path; float slot_prompt_similarity = 0.5f; // batched-bench params bool is_pp_shared = false; std::vector n_pp; std::vector n_tg; std::vector n_pl; // retrieval params std::vector 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 std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file 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 bool process_output = false; // collect data for the output tensor bool compute_ppl = true; // whether to compute perplexity // cvector-generator params int n_pca_batch = 100; int n_pca_iterations = 1000; dimre_method cvector_dimre_method = DIMRE_METHOD_PCA; std::string cvector_outfile = "control_vector.gguf"; std::string cvector_positive_file = "examples/cvector-generator/positive.txt"; std::string cvector_negative_file = "examples/cvector-generator/negative.txt"; bool spm_infill = false; // suffix/prefix/middle pattern for infill std::string lora_outfile = "ggml-lora-merged-f16.gguf"; // batched-bench params bool batched_bench_output_jsonl = false; }; std::string gpt_params_get_system_info(const gpt_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 // std::vector string_split(std::string input, char separator); std::string string_strip(const std::string & str); std::string string_get_sortable_timestamp(); void string_replace_all(std::string & s, const std::string & search, const std::string & replace); template static std::vector string_split(const std::string & str, char delim) { std::vector 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; } bool string_parse_kv_override(const char * data, std::vector & overrides); void string_process_escapes(std::string & input); // // Filesystem utils // bool fs_validate_filename(const std::string & filename); bool fs_create_directory_with_parents(const std::string & path); std::string fs_get_cache_directory(); std::string fs_get_cache_file(const std::string & filename); // // Model utils // struct llama_init_result { struct llama_model * model = nullptr; struct llama_context * context = nullptr; std::vector lora_adapters; }; struct llama_init_result llama_init_from_gpt_params(gpt_params & params); struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); struct llama_context_params llama_context_params_from_gpt_params (const gpt_params & params); struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params); struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params); // clear LoRA adapters from context, then apply new list of adapters void llama_lora_adapters_apply(struct llama_context * ctx, std::vector & lora_adapters); // Batch utils void llama_batch_clear(struct llama_batch & batch); void llama_batch_add( struct llama_batch & batch, llama_token id, llama_pos pos, const std::vector & seq_ids, bool logits); // // Vocab utils // // tokenizes a string into a vector of tokens // should work similar to Python's `tokenizer.encode` std::vector llama_tokenize( const struct llama_context * ctx, const std::string & text, bool add_special, bool parse_special = false); std::vector llama_tokenize( const struct llama_model * model, 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 llama_token_to_piece( const struct llama_context * ctx, 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 llama_detokenize( llama_context * ctx, const std::vector & tokens, bool special = true); // // Chat template utils // // same with llama_chat_message, but uses std::string struct llama_chat_msg { std::string role; std::string content; }; // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid bool llama_chat_verify_template(const std::string & tmpl); // CPP wrapper for llama_chat_apply_template // If the built-in template is not supported, we default to chatml // If the custom "tmpl" is not supported, we throw an error std::string llama_chat_apply_template(const struct llama_model * model, const std::string & tmpl, const std::vector & chat, bool add_ass); // Format single message, while taking into account the position of that message in chat history std::string llama_chat_format_single(const struct llama_model * model, const std::string & tmpl, const std::vector & past_msg, const llama_chat_msg & new_msg, bool add_ass); // Returns an example of formatted chat std::string llama_chat_format_example(const struct llama_model * model, const std::string & tmpl); // // KV cache utils // // Dump the KV cache view with the number of sequences per cell. void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40); // // Embedding utils // void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2); float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n); // // Control vector utils // struct llama_control_vector_data { int n_embd; // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd std::vector data; }; struct llama_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} llama_control_vector_data llama_control_vector_load(const std::vector & load_infos); // // Split utils // static const char * const LLM_KV_SPLIT_NO = "split.no"; static const char * const LLM_KV_SPLIT_COUNT = "split.count"; static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; // // YAML utils // void yaml_dump_vector_float (FILE * stream, const char * prop_name, const std::vector & data); void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector & data); void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data); void yaml_dump_non_result_info( FILE * stream, const gpt_params & params, const llama_context * lctx, const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc);