mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 22:08:46 +01:00
b6e7f9b09e
* reserve correct size for logits * add functions to get and set the whole llama state: including rng, logits, embedding and kv_cache * remove unused variables * remove trailing whitespace * fix comment
222 lines
8.0 KiB
C++
222 lines
8.0 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 0x67676a74 // 'ggjt' in hex
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#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
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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 p; // probability of the token
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float plog; // log probability of the token
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} llama_token_data;
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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, 0 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, // 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 KV cache that will contain the context for the
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// ongoing prediction with the model.
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LLAMA_API const uint8_t * llama_get_kv_cache(struct llama_context * ctx);
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// Returns the size of the KV cache
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LLAMA_API size_t llama_get_kv_cache_size(struct llama_context * ctx);
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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(struct llama_context * ctx);
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// Sets the KV cache containing the current context for the model
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LLAMA_API void llama_set_kv_cache(
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struct llama_context * ctx,
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const uint8_t * kv_cache,
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size_t n_size,
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int n_token_count);
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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(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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// 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(struct llama_context * ctx);
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LLAMA_API int llama_n_ctx (struct llama_context * ctx);
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LLAMA_API int llama_n_embd (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(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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// TODO: improve the last_n_tokens interface ?
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LLAMA_API llama_token llama_sample_top_p_top_k(
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struct llama_context * ctx,
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const llama_token * last_n_tokens_data,
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int last_n_tokens_size,
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int top_k,
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float top_p,
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float temp,
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float repeat_penalty);
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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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