#ifndef LLAMA_H #define LLAMA_H #include #include #include #ifdef LLAMA_SHARED # if defined(_WIN32) && !defined(__MINGW32__) # ifdef LLAMA_BUILD # define LLAMA_API __declspec(dllexport) # else # define LLAMA_API __declspec(dllimport) # endif # else # define LLAMA_API __attribute__ ((visibility ("default"))) # endif #else # define LLAMA_API #endif #define LLAMA_FILE_VERSION 2 #define LLAMA_FILE_MAGIC 'ggjt' #define LLAMA_FILE_MAGIC_UNVERSIONED 'ggml' #define LLAMA_SESSION_MAGIC 'ggsn' #define LLAMA_SESSION_VERSION 1 #ifdef __cplusplus extern "C" { #endif // // C interface // // TODO: show sample usage // struct llama_context; typedef int llama_token; typedef struct llama_token_data { llama_token id; // token id float logit; // log-odds of the token float p; // probability of the token } llama_token_data; typedef struct llama_token_data_array { llama_token_data * data; size_t size; bool sorted; } llama_token_data_array; typedef void (*llama_progress_callback)(float progress, void *ctx); struct llama_context_params { int n_ctx; // text context int n_parts; // -1 for default int n_gpu_layers; // number of layers to store in VRAM int seed; // RNG seed, -1 for random bool f16_kv; // use fp16 for KV cache bool logits_all; // the llama_eval() call computes all logits, not just the last one bool vocab_only; // only load the vocabulary, no weights bool use_mmap; // use mmap if possible bool use_mlock; // force system to keep model in RAM bool embedding; // embedding mode only // called with a progress value between 0 and 1, pass NULL to disable llama_progress_callback progress_callback; // context pointer passed to the progress callback void * progress_callback_user_data; }; // model file types enum llama_ftype { LLAMA_FTYPE_ALL_F32 = 0, LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed // LLAMA_FTYPE_MOSTLY_Q4_3 (6) support has been removed LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors }; LLAMA_API struct llama_context_params llama_context_default_params(); LLAMA_API bool llama_mmap_supported(); LLAMA_API bool llama_mlock_supported(); // Various functions for loading a ggml llama model. // Allocate (almost) all memory needed for the model. // Return NULL on failure LLAMA_API struct llama_context * llama_init_from_file( const char * path_model, struct llama_context_params params); // Frees all allocated memory LLAMA_API void llama_free(struct llama_context * ctx); // TODO: not great API - very likely to change // Returns 0 on success // nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given LLAMA_API int llama_model_quantize( const char * fname_inp, const char * fname_out, enum llama_ftype ftype, int nthread); // Apply a LoRA adapter to a loaded model // path_base_model is the path to a higher quality model to use as a base for // the layers modified by the adapter. Can be NULL to use the current loaded model. // The model needs to be reloaded before applying a new adapter, otherwise the adapter // will be applied on top of the previous one // Returns 0 on success LLAMA_API int llama_apply_lora_from_file( struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads); // Returns the number of tokens in the KV cache LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx); // Sets the current rng seed. LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed); // Returns the maximum size in bytes of the state (rng, logits, embedding // and kv_cache) - will often be smaller after compacting tokens LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx); // Copies the state to the specified destination address. // Destination needs to have allocated enough memory. // Returns the number of bytes copied LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst); // Set the state reading from the specified address // Returns the number of bytes read LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src); // Save/load session file LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out); LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count); // Run the llama inference to obtain the logits and probabilities for the next token. // tokens + n_tokens is the provided batch of new tokens to process // n_past is the number of tokens to use from previous eval calls // Returns 0 on success LLAMA_API int llama_eval( struct llama_context * ctx, const llama_token * tokens, int n_tokens, int n_past, int n_threads); // Convert the provided text into tokens. // The tokens pointer must be large enough to hold the resulting tokens. // Returns the number of tokens on success, no more than n_max_tokens // Returns a negative number on failure - the number of tokens that would have been returned // TODO: not sure if correct LLAMA_API int llama_tokenize( struct llama_context * ctx, const char * text, llama_token * tokens, int n_max_tokens, bool add_bos); LLAMA_API int llama_n_vocab(const struct llama_context * ctx); LLAMA_API int llama_n_ctx (const struct llama_context * ctx); LLAMA_API int llama_n_embd (const struct llama_context * ctx); // Token logits obtained from the last call to llama_eval() // The logits for the last token are stored in the last row // Can be mutated in order to change the probabilities of the next token // Rows: n_tokens // Cols: n_vocab LLAMA_API float * llama_get_logits(struct llama_context * ctx); // Get the embeddings for the input // shape: [n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); // Token Id -> String. Uses the vocabulary in the provided context LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token); // Special tokens LLAMA_API llama_token llama_token_bos(); LLAMA_API llama_token llama_token_eos(); LLAMA_API llama_token llama_token_nl(); LLAMA_API void llama_set_steering_write(struct llama_context * ctx, int layer, float mul); LLAMA_API void llama_set_steering_read(struct llama_context * ctx, int layer, float mul); // Sampling functions /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix. LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty); /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details. LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence); /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates); /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep); /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep); /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/. LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep); /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep); LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp); /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm. /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu); /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu); /// @details Selects the token with the highest probability. LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates); /// @details Randomly selects a token from the candidates based on their probabilities. LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates); // Performance information LLAMA_API void llama_print_timings(struct llama_context * ctx); LLAMA_API void llama_reset_timings(struct llama_context * ctx); // Print system information LLAMA_API const char * llama_print_system_info(void); #ifdef __cplusplus } #endif // Internal API to be implemented by llama.cpp and used by tests/benchmarks only #ifdef LLAMA_API_INTERNAL #include #include struct ggml_tensor; std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx); #endif #endif // LLAMA_H