// Various helper functions and utilities #pragma once #include "llama.h" #include "sampling.h" #define LOG_NO_FILE_LINE_FUNCTION #include "log.h" #include #include #include #include #include #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) // 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; int32_t get_num_physical_cores(); // // CLI argument parsing // struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed int32_t n_threads = get_num_physical_cores(); int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_threads_batch_draft = -1; int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 512; // 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) llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs 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 n_beams = 0; // if non-zero then use beam search of given width. 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 ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings // // sampling parameters struct llama_sampling_params sparams; std::string model = "models/7B/ggml-model-f16.gguf"; // model path std::string model_url = ""; // model url to download std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string prompt = ""; std::string prompt_file = ""; // store the external prompt file name std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state std::string input_prefix = ""; // string to prefix user inputs with std::string input_suffix = ""; // string to suffix user inputs with std::vector antiprompt; // string upon seeing which more user input is prompted std::string logdir = ""; // directory in which to save YAML log files std::string logits_file = ""; // file for saving *all* logits std::vector kv_overrides; // TODO: avoid tuple, use struct std::vector> lora_adapter; // lora adapter path with user defined scale std::string lora_base = ""; // base model path for the lora adapter std::vector control_vectors; // control vector with user defined scale int32_t control_vector_layer_start = -1; // layer range for control vector int32_t control_vector_layer_end = -1; // layer range for control vector int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. int 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 random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode bool chatml = false; // chatml mode (used for models trained on chatml syntax) 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 embedding = false; // get only sentence embedding bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\" bool interactive_first = false; // wait for user input immediately 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 input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool ignore_eos = false; // ignore generated EOS tokens bool instruct = false; // instruction mode (used for Alpaca models) 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 infill = false; // use infill mode bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading 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 std::string image = ""; // path to an image file }; bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); bool gpt_params_parse(int argc, char ** argv, gpt_params & params); void gpt_print_usage(int argc, char ** argv, const gpt_params & params); std::string get_system_info(const gpt_params & params); std::string gpt_random_prompt(std::mt19937 & rng); void process_escapes(std::string& input); // // String utils // std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); std::vector sampler_types_from_chars(const std::string & names_string); std::vector string_split(std::string input, char separator); std::string sampler_type_to_name_string(llama_sampler_type sampler_type); // // Model utils // // TODO: avoid tuplue, use struct std::tuple 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 llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, struct llama_model_params params); // 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_bos, bool special = false); std::vector llama_tokenize( const struct llama_model * model, const std::string & text, bool add_bos, bool special = false); // tokenizes a token into a piece // should work similar to Python's `tokenizer.id_to_piece` std::string llama_token_to_piece( const struct llama_context * ctx, llama_token token); // TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function // that takes into account the tokenizer type and decides how to handle the leading space // // detokenizes a vector of tokens into a string // should work similar to Python's `tokenizer.decode` // removes the leading space from the first non-BOS token std::string llama_detokenize_spm( llama_context * ctx, const std::vector & tokens); // detokenizes a vector of tokens into a string // should work similar to Python's `tokenizer.decode` std::string llama_detokenize_bpe( llama_context * ctx, const std::vector & tokens); // Uses the value from the model metadata if possible, otherwise // defaults to true when model type is SPM, otherwise false. bool llama_should_add_bos_token(const llama_model * model); // // YAML utils // bool create_directory_with_parents(const std::string & path); void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); std::string get_sortable_timestamp(); void dump_non_result_info_yaml( FILE * stream, const gpt_params & params, const llama_context * lctx, const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); // // KV cache utils // // Dump the KV cache view with the number of sequences per cell. void dump_kv_cache_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 dump_kv_cache_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); 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);