mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 22:08:46 +01:00
260 lines
13 KiB
C++
260 lines
13 KiB
C++
#ifndef LLAMA_H
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#define LLAMA_H
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#include <stddef.h>
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#include <stdint.h>
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#include <stdbool.h>
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#ifdef LLAMA_SHARED
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# if defined(_WIN32) && !defined(__MINGW32__)
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# ifdef LLAMA_BUILD
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# define LLAMA_API __declspec(dllexport)
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# else
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# define LLAMA_API __declspec(dllimport)
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# endif
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# else
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# define LLAMA_API __attribute__ ((visibility ("default")))
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# endif
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#else
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# define LLAMA_API
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#endif
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#define LLAMA_FILE_VERSION 1
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#define LLAMA_FILE_MAGIC 'ggjt'
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#define LLAMA_FILE_MAGIC_UNVERSIONED 'ggml'
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#define LLAMA_SESSION_MAGIC 'ggsn'
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#define LLAMA_SESSION_VERSION 0
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#ifdef __cplusplus
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extern "C" {
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#endif
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//
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// C interface
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//
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// TODO: show sample usage
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//
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struct llama_context;
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typedef int llama_token;
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typedef struct llama_token_data {
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llama_token id; // token id
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float logit; // log-odds of the token
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float p; // probability of the token
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} llama_token_data;
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typedef struct llama_token_data_array {
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llama_token_data * data;
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size_t size;
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bool sorted;
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} llama_token_data_array;
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typedef void (*llama_progress_callback)(float progress, void *ctx);
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struct llama_context_params {
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int n_ctx; // text context
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int n_parts; // -1 for default
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int seed; // RNG seed, -1 for random
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bool f16_kv; // use fp16 for KV cache
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bool logits_all; // the llama_eval() call computes all logits, not just the last one
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bool vocab_only; // only load the vocabulary, no weights
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bool use_mmap; // use mmap if possible
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bool use_mlock; // force system to keep model in RAM
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bool embedding; // embedding mode only
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// called with a progress value between 0 and 1, pass NULL to disable
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llama_progress_callback progress_callback;
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// context pointer passed to the progress callback
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void * progress_callback_user_data;
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};
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// model file types
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enum llama_ftype {
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LLAMA_FTYPE_ALL_F32 = 0,
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LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
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// LLAMA_FTYPE_MOSTLY_Q4_3 (6) support has been removed
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LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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};
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LLAMA_API struct llama_context_params llama_context_default_params();
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LLAMA_API bool llama_mmap_supported();
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LLAMA_API bool llama_mlock_supported();
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// Various functions for loading a ggml llama model.
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// Allocate (almost) all memory needed for the model.
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// Return NULL on failure
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LLAMA_API struct llama_context * llama_init_from_file(
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const char * path_model,
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struct llama_context_params params);
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// Frees all allocated memory
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LLAMA_API void llama_free(struct llama_context * ctx);
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// TODO: not great API - very likely to change
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// Returns 0 on success
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// nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
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LLAMA_API int llama_model_quantize(
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const char * fname_inp,
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const char * fname_out,
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enum llama_ftype ftype,
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int nthread);
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// Apply a LoRA adapter to a loaded model
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// path_base_model is the path to a higher quality model to use as a base for
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// the layers modified by the adapter. Can be NULL to use the current loaded model.
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// The model needs to be reloaded before applying a new adapter, otherwise the adapter
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// will be applied on top of the previous one
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// Returns 0 on success
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LLAMA_API int llama_apply_lora_from_file(
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struct llama_context * ctx,
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const char * path_lora,
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const char * path_base_model,
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int n_threads);
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// Returns the number of tokens in the KV cache
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LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
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// Sets the current rng seed.
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LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
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// Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
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LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
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// Copies the state to the specified destination address.
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// Destination needs to have allocated enough memory.
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// Returns the number of bytes copied
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LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest);
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// Set the state reading from the specified address
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// Returns the number of bytes read
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LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src);
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// Save/load session file
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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);
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LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
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// Run the llama inference to obtain the logits and probabilities for the next token.
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// tokens + n_tokens is the provided batch of new tokens to process
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// n_past is the number of tokens to use from previous eval calls
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// Returns 0 on success
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LLAMA_API int llama_eval(
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struct llama_context * ctx,
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const llama_token * tokens,
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int n_tokens,
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int n_past,
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int n_threads);
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// Convert the provided text into tokens.
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// The tokens pointer must be large enough to hold the resulting tokens.
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// Returns the number of tokens on success, no more than n_max_tokens
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// Returns a negative number on failure - the number of tokens that would have been returned
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// TODO: not sure if correct
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LLAMA_API int llama_tokenize(
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struct llama_context * ctx,
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const char * text,
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llama_token * tokens,
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int n_max_tokens,
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bool add_bos);
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LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
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LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
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LLAMA_API int llama_n_embd (const struct llama_context * ctx);
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// Token logits obtained from the last call to llama_eval()
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// The logits for the last token are stored in the last row
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// Can be mutated in order to change the probabilities of the next token
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// Rows: n_tokens
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// Cols: n_vocab
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LLAMA_API float * llama_get_logits(struct llama_context * ctx);
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// Get the embeddings for the input
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// shape: [n_embd] (1-dimensional)
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LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
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// Token Id -> String. Uses the vocabulary in the provided context
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LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
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// Special tokens
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LLAMA_API llama_token llama_token_bos();
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LLAMA_API llama_token llama_token_eos();
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LLAMA_API llama_token llama_token_nl();
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// Sampling functions
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/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
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LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float penalty);
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/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
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LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
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/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
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LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
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/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep = 1);
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/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
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/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
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LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep = 1);
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/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
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LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
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LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
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/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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/// @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.
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/// @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.
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/// @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.
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/// @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.
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/// @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.
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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);
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/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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/// @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.
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/// @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.
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/// @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.
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/// @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.
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LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
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/// @details Selects the token with the highest probability.
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LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
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/// @details Randomly selects a token from the candidates based on their probabilities.
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LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
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// Performance information
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LLAMA_API void llama_print_timings(struct llama_context * ctx);
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LLAMA_API void llama_reset_timings(struct llama_context * ctx);
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// Print system information
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LLAMA_API const char * llama_print_system_info(void);
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#ifdef __cplusplus
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}
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#endif
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// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
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#ifdef LLAMA_API_INTERNAL
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#include <vector>
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#include <string>
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struct ggml_tensor;
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std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
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#endif
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#endif // LLAMA_H
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