mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 11:54:35 +00:00
261 lines
13 KiB
C++
261 lines
13 KiB
C++
#ifndef LLAMA_H
|
|
#define LLAMA_H
|
|
|
|
#include <stddef.h>
|
|
#include <stdint.h>
|
|
#include <stdbool.h>
|
|
|
|
#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 1
|
|
#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 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 * dest);
|
|
|
|
// 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();
|
|
|
|
// 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 <vector>
|
|
#include <string>
|
|
struct ggml_tensor;
|
|
|
|
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
|
|
|
#endif
|
|
|
|
#endif // LLAMA_H
|