clang-format the llama.* files
This commit is contained in:
Will Findley
parent
bcc0eb4591
commit
92887c4684
2 changed files with 10010 additions and 9127 deletions
509
llama.h
509
llama.h
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@ -8,10 +8,10 @@
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#else
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#define LLAMA_MAX_DEVICES 1
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#endif // GGML_USE_CUBLAS
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#include <stdbool.h>
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#include <stddef.h>
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#include <stdint.h>
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#include <stdio.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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@ -44,8 +44,10 @@
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#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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#define LLAMA_SESSION_VERSION 3
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#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
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// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
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#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || \
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defined(GGML_USE_METAL)
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// Defined when llama.cpp is compiled with support for offloading model layers
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// to GPU.
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#define LLAMA_SUPPORTS_GPU_OFFLOAD
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#endif
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@ -87,7 +89,8 @@ extern "C" {
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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_1_SOME_F16 =
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4, // tok_embeddings.weight and output.weight are F16
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// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
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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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@ -133,7 +136,8 @@ extern "C" {
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// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
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//
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// - token : the token ids of the input (used when embd is NULL)
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// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
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// - embd : token embeddings (i.e. float vector of size n_embd) (used when
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// token is NULL)
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// - pos : the positions of the respective token in the sequence
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// - seq_id : the sequence to which the respective token belongs
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// - logits : if zero, the logits for the respective token will not be output
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@ -149,7 +153,8 @@ extern "C" {
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int8_t *logits;
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// NOTE: helpers for smooth API transition - can be deprecated in the future
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// for future-proof code, use the above fields instead and ignore everything below
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// for future-proof code, use the above fields instead and ignore
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// everything below
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//
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// pos[i] = all_pos_0 + i*all_pos_1
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//
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@ -177,7 +182,8 @@ extern "C" {
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struct llama_model_params {
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int32_t n_gpu_layers; // number of layers to store in VRAM
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int32_t main_gpu; // the GPU that is used for scratch and small tensors
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const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
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const float *tensor_split; // how to split layers across multiple GPUs (size:
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// LLAMA_MAX_DEVICES)
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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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@ -200,7 +206,8 @@ extern "C" {
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uint32_t n_batch; // prompt processing maximum batch size
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uint32_t n_threads; // number of threads to use for generation
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uint32_t n_threads_batch; // number of threads to use for batch processing
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int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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int8_t rope_scaling_type; // RoPE scaling type, from `enum
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// llama_rope_scaling_type`
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// ref: https://github.com/ggerganov/llama.cpp/pull/2054
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float rope_freq_base; // RoPE base frequency, 0 = from model
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@ -215,20 +222,26 @@ extern "C" {
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enum ggml_type type_v; // data type for V cache
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// Keep the booleans together to avoid misalignment during copy-by-value.
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bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
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bool logits_all; // the llama_eval() call computes all logits, not just the last one
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bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED -
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// always true)
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bool logits_all; // the llama_eval() call computes all logits, not just the
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// last one
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bool embedding; // embedding mode only
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bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
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bool offload_kqv; // whether to offload the KQV ops (including the KV cache)
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// to GPU
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};
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// model quantization parameters
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typedef struct llama_model_quantize_params {
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int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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int nthread; // number of threads to use for quantizing, if <=0 will use
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// std::thread::hardware_concurrency()
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enum llama_ftype ftype; // quantize to this llama_ftype
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bool allow_requantize; // allow quantizing non-f32/f16 tensors
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bool quantize_output_tensor; // quantize output.weight
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bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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bool pure; // disable k-quant mixtures and quantize all tensors to the same type
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bool only_copy; // only copy tensors - ftype, allow_requantize and
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// quantize_output_tensor are ignored
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bool pure; // disable k-quant mixtures and quantize all tensors to the same
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// type
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} llama_model_quantize_params;
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// grammar types
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@ -256,7 +269,8 @@ extern "C" {
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LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
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// modifies a preceding LLAMA_GRETYPE_CHAR or
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// LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
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// LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab],
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// [a-zA])
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LLAMA_GRETYPE_CHAR_ALT = 6,
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};
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@ -282,7 +296,8 @@ extern "C" {
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// Helpers for getting default parameters
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LLAMA_API struct llama_model_params llama_model_default_params(void);
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LLAMA_API struct llama_context_params llama_context_default_params(void);
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LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
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LLAMA_API struct llama_model_quantize_params
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llama_model_quantize_default_params(void);
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// Initialize the llama + ggml backend
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// If numa is true, use NUMA optimizations
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@ -292,14 +307,14 @@ extern "C" {
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// Call once at the end of the program - currently only used for MPI
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LLAMA_API void llama_backend_free(void);
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LLAMA_API struct llama_model * llama_load_model_from_file(
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const char * path_model,
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LLAMA_API struct llama_model *
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llama_load_model_from_file(const char *path_model,
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struct llama_model_params params);
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LLAMA_API void llama_free_model(struct llama_model *model);
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LLAMA_API struct llama_context * llama_new_context_with_model(
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struct llama_model * model,
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LLAMA_API struct llama_context *
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llama_new_context_with_model(struct llama_model *model,
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struct llama_context_params params);
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// Frees all allocated memory
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@ -311,11 +326,13 @@ extern "C" {
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LLAMA_API bool llama_mmap_supported(void);
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LLAMA_API bool llama_mlock_supported(void);
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LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
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LLAMA_API const struct llama_model *
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llama_get_model(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 enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
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LLAMA_API enum llama_vocab_type
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llama_vocab_type(const struct llama_model *model);
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LLAMA_API int llama_n_vocab(const struct llama_model *model);
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LLAMA_API int llama_n_ctx_train(const struct llama_model *model);
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@ -330,19 +347,25 @@ extern "C" {
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// - GGUF array values are not supported by these functions
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// Get metadata value as a string by key name
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LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
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LLAMA_API int llama_model_meta_val_str(const struct llama_model *model,
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const char *key, char *buf,
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size_t buf_size);
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// Get the number of metadata key/value pairs
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LLAMA_API int llama_model_meta_count(const struct llama_model *model);
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// Get metadata key name by index
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LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
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LLAMA_API int llama_model_meta_key_by_index(const struct llama_model *model,
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int i, char *buf, size_t buf_size);
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// Get metadata value as a string by index
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LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
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LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model *model,
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int i, char *buf,
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size_t buf_size);
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// Get a string describing the model type
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LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
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LLAMA_API int llama_model_desc(const struct llama_model *model, char *buf,
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size_t buf_size);
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// Returns the total size of all the tensors in the model in bytes
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LLAMA_API uint64_t llama_model_size(const struct llama_model *model);
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@ -351,30 +374,27 @@ extern "C" {
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LLAMA_API uint64_t llama_model_n_params(const struct llama_model *model);
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// Get a llama model tensor
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LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
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LLAMA_API struct ggml_tensor *llama_get_model_tensor(struct llama_model *model,
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const char *name);
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// Returns 0 on success
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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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LLAMA_API int llama_model_quantize(const char *fname_inp, const char *fname_out,
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const llama_model_quantize_params *params);
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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 DEPRECATED(int llama_apply_lora_from_file(
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struct llama_context * ctx,
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// the layers modified by the adapter. Can be NULL to use the current loaded
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// model. The model needs to be reloaded before applying a new adapter,
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// otherwise the adapter will be applied on top of the previous one Returns 0 on
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// success
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LLAMA_API DEPRECATED(int llama_apply_lora_from_file(struct llama_context *ctx,
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const char *path_lora,
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float scale,
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const char *path_base_model,
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int n_threads),
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"use llama_model_apply_lora_from_file instead");
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LLAMA_API int llama_model_apply_lora_from_file(
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const struct llama_model * model,
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LLAMA_API int llama_model_apply_lora_from_file(const struct llama_model *model,
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const char *path_lora,
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float scale,
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const char *path_base_model,
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@ -424,61 +444,54 @@ extern "C" {
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};
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// Create an empty KV cache view. (use only for debugging purposes)
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LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
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LLAMA_API struct llama_kv_cache_view
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llama_kv_cache_view_init(const struct llama_context *ctx, int32_t n_max_seq);
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// Free a KV cache view. (use only for debugging purposes)
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LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view *view);
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// Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
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LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
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// Update the KV cache view structure with the current state of the KV cache.
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// (use only for debugging purposes)
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LLAMA_API void llama_kv_cache_view_update(const struct llama_context *ctx,
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struct llama_kv_cache_view *view);
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// Returns the number of tokens in the KV cache (slow, use only for debug)
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// If a KV cell has multiple sequences assigned to it, it will be counted multiple times
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// If a KV cell has multiple sequences assigned to it, it will be counted
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// multiple times
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LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context *ctx);
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// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
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// Returns the number of used KV cells (i.e. have at least one sequence assigned
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// to them)
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LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context *ctx);
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// Clear the KV cache
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LLAMA_API void llama_kv_cache_clear(
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struct llama_context * ctx);
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LLAMA_API void llama_kv_cache_clear(struct llama_context *ctx);
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// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
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// seq_id < 0 : match any sequence
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// p0 < 0 : [0, p1]
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// p1 < 0 : [p0, inf)
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LLAMA_API void llama_kv_cache_seq_rm(
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struct llama_context * ctx,
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llama_seq_id seq_id,
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llama_pos p0,
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// Removes all tokens that belong to the specified sequence and have positions
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// in [p0, p1) seq_id < 0 : match any sequence p0 < 0 : [0, p1] p1 < 0 :
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// [p0, inf)
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LLAMA_API void llama_kv_cache_seq_rm(struct llama_context *ctx,
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llama_seq_id seq_id, llama_pos p0,
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llama_pos p1);
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// Copy all tokens that belong to the specified sequence to another sequence
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// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
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// p0 < 0 : [0, p1]
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// p1 < 0 : [p0, inf)
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LLAMA_API void llama_kv_cache_seq_cp(
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struct llama_context * ctx,
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// Note that this does not allocate extra KV cache memory - it simply assigns
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// the tokens to the new sequence p0 < 0 : [0, p1] p1 < 0 : [p0, inf)
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LLAMA_API void llama_kv_cache_seq_cp(struct llama_context *ctx,
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llama_seq_id seq_id_src,
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llama_seq_id seq_id_dst,
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llama_pos p0,
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llama_seq_id seq_id_dst, llama_pos p0,
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llama_pos p1);
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// Removes all tokens that do not belong to the specified sequence
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LLAMA_API void llama_kv_cache_seq_keep(
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struct llama_context * ctx,
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LLAMA_API void llama_kv_cache_seq_keep(struct llama_context *ctx,
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llama_seq_id seq_id);
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// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
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// If the KV cache is RoPEd, the KV data is updated accordingly
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// p0 < 0 : [0, p1]
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// p1 < 0 : [p0, inf)
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LLAMA_API void llama_kv_cache_seq_shift(
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struct llama_context * ctx,
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llama_seq_id seq_id,
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llama_pos p0,
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llama_pos p1,
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llama_pos delta);
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// Adds relative position "delta" to all tokens that belong to the specified
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// sequence and have positions in [p0, p1) If the KV cache is RoPEd, the KV data
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// is updated accordingly p0 < 0 : [0, p1] p1 < 0 : [p0, inf)
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LLAMA_API void llama_kv_cache_seq_shift(struct llama_context *ctx,
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llama_seq_id seq_id, llama_pos p0,
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llama_pos p1, llama_pos delta);
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//
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// State / sessions
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@ -491,26 +504,20 @@ extern "C" {
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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(
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struct llama_context * ctx,
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uint8_t * dst);
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LLAMA_API size_t llama_copy_state_data(struct llama_context *ctx, uint8_t *dst);
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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(
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struct llama_context * ctx,
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uint8_t * src);
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LLAMA_API size_t llama_set_state_data(struct llama_context *ctx, uint8_t *src);
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// Save/load session file
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LLAMA_API bool llama_load_session_file(
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struct llama_context * ctx,
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LLAMA_API bool llama_load_session_file(struct llama_context *ctx,
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const char *path_session,
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llama_token *tokens_out,
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size_t n_token_capacity,
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size_t *n_token_count_out);
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LLAMA_API bool llama_save_session_file(
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struct llama_context * ctx,
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LLAMA_API bool llama_save_session_file(struct llama_context *ctx,
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const char *path_session,
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const llama_token *tokens,
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size_t n_token_count);
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@ -519,33 +526,28 @@ extern "C" {
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// Decoding
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//
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// Run the llama inference to obtain the logits and probabilities for the next token(s).
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// tokens + n_tokens is the provided batch of new tokens to process
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// Run the llama inference to obtain the logits and probabilities for the next
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// token(s). 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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// DEPRECATED: use llama_decode() instead
|
||||
LLAMA_API DEPRECATED(int llama_eval(
|
||||
struct llama_context * ctx,
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens,
|
||||
LLAMA_API DEPRECATED(int llama_eval(struct llama_context *ctx,
|
||||
llama_token *tokens, int32_t n_tokens,
|
||||
int n_past),
|
||||
"use llama_decode() instead");
|
||||
|
||||
// Same as llama_eval, but use float matrix input directly.
|
||||
// DEPRECATED: use llama_decode() instead
|
||||
LLAMA_API DEPRECATED(int llama_eval_embd(
|
||||
struct llama_context * ctx,
|
||||
float * embd,
|
||||
int32_t n_tokens,
|
||||
int n_past),
|
||||
LLAMA_API DEPRECATED(int llama_eval_embd(struct llama_context *ctx, float *embd,
|
||||
int32_t n_tokens, int n_past),
|
||||
"use llama_decode() instead");
|
||||
|
||||
// Return batch for single sequence of tokens starting at pos_0
|
||||
//
|
||||
// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
|
||||
// NOTE: this is a helper function to facilitate transition to the new batch API
|
||||
// - avoid using it
|
||||
//
|
||||
LLAMA_API struct llama_batch llama_batch_get_one(
|
||||
llama_token * tokens,
|
||||
LLAMA_API struct llama_batch llama_batch_get_one(llama_token *tokens,
|
||||
int32_t n_tokens,
|
||||
llama_pos pos_0,
|
||||
llama_seq_id seq_id);
|
||||
|
|
@ -553,13 +555,11 @@ extern "C" {
|
|||
// Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
|
||||
// Each token can be assigned up to n_seq_max sequence ids
|
||||
// The batch has to be freed with llama_batch_free()
|
||||
// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
|
||||
// Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
|
||||
// The rest of the llama_batch members are allocated with size n_tokens
|
||||
// All members are left uninitialized
|
||||
LLAMA_API struct llama_batch llama_batch_init(
|
||||
int32_t n_tokens,
|
||||
int32_t embd,
|
||||
// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd
|
||||
// * sizeof(float) Otherwise, llama_batch.token will be allocated to store
|
||||
// n_tokens llama_token The rest of the llama_batch members are allocated with
|
||||
// size n_tokens All members are left uninitialized
|
||||
LLAMA_API struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd,
|
||||
int32_t n_seq_max);
|
||||
|
||||
// Frees a batch of tokens allocated with llama_batch_init()
|
||||
|
|
@ -567,16 +567,18 @@ extern "C" {
|
|||
|
||||
// Positive return values does not mean a fatal error, but rather a warning.
|
||||
// 0 - success
|
||||
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||||
// 1 - could not find a KV slot for the batch (try reducing the size of the
|
||||
// batch or increase the context)
|
||||
// < 0 - error
|
||||
LLAMA_API int llama_decode(
|
||||
struct llama_context * ctx,
|
||||
struct llama_batch batch);
|
||||
LLAMA_API int llama_decode(struct llama_context *ctx, struct llama_batch batch);
|
||||
|
||||
// Set the number of threads used for decoding
|
||||
// n_threads is the number of threads used for generation (single token)
|
||||
// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
|
||||
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
|
||||
// n_threads_batch is the number of threads used for prompt and batch processing
|
||||
// (multiple tokens)
|
||||
LLAMA_API void llama_set_n_threads(struct llama_context *ctx,
|
||||
uint32_t n_threads,
|
||||
uint32_t n_threads_batch);
|
||||
|
||||
// Token logits obtained from the last call to llama_eval()
|
||||
// The logits for the last token are stored in the last row
|
||||
|
|
@ -597,16 +599,22 @@ extern "C" {
|
|||
// Vocab
|
||||
//
|
||||
|
||||
LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
|
||||
LLAMA_API const char *llama_token_get_text(const struct llama_model *model,
|
||||
llama_token token);
|
||||
|
||||
LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
|
||||
LLAMA_API float llama_token_get_score(const struct llama_model *model,
|
||||
llama_token token);
|
||||
|
||||
LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
|
||||
LLAMA_API enum llama_token_type
|
||||
llama_token_get_type(const struct llama_model *model, llama_token token);
|
||||
|
||||
// Special tokens
|
||||
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
||||
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
||||
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
||||
LLAMA_API llama_token
|
||||
llama_token_bos(const struct llama_model *model); // beginning-of-sentence
|
||||
LLAMA_API llama_token
|
||||
llama_token_eos(const struct llama_model *model); // end-of-sentence
|
||||
LLAMA_API llama_token
|
||||
llama_token_nl(const struct llama_model *model); // next-line
|
||||
|
||||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||||
LLAMA_API int llama_add_bos_token(const struct llama_model *model);
|
||||
|
|
@ -615,52 +623,52 @@ extern "C" {
|
|||
LLAMA_API int llama_add_eos_token(const struct llama_model *model);
|
||||
|
||||
// codellama infill tokens
|
||||
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
||||
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
||||
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
||||
LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle
|
||||
LLAMA_API llama_token llama_token_prefix(
|
||||
const struct llama_model *model); // Beginning of infill prefix
|
||||
LLAMA_API llama_token llama_token_middle(
|
||||
const struct llama_model *model); // Beginning of infill middle
|
||||
LLAMA_API llama_token llama_token_suffix(
|
||||
const struct llama_model *model); // Beginning of infill suffix
|
||||
LLAMA_API llama_token
|
||||
llama_token_eot(const struct llama_model *model); // End of infill middle
|
||||
|
||||
//
|
||||
// Tokenization
|
||||
//
|
||||
|
||||
/// @details Convert the provided text into tokens.
|
||||
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
|
||||
/// @param tokens The tokens pointer must be large enough to hold the resulting
|
||||
/// tokens.
|
||||
/// @return Returns the number of tokens on success, no more than n_max_tokens
|
||||
/// @return Returns a negative number on failure - the number of tokens that would have been returned
|
||||
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
|
||||
/// @return Returns a negative number on failure - the number of tokens that
|
||||
/// would have been returned
|
||||
/// @param special Allow tokenizing special and/or control tokens which
|
||||
/// otherwise are not exposed and treated as plaintext.
|
||||
/// Does not insert a leading space.
|
||||
LLAMA_API int llama_tokenize(
|
||||
const struct llama_model * model,
|
||||
const char * text,
|
||||
int text_len,
|
||||
llama_token * tokens,
|
||||
int n_max_tokens,
|
||||
bool add_bos,
|
||||
bool special);
|
||||
LLAMA_API int llama_tokenize(const struct llama_model *model, const char *text,
|
||||
int text_len, llama_token *tokens,
|
||||
int n_max_tokens, bool add_bos, bool special);
|
||||
|
||||
// Token Id -> Piece.
|
||||
// Uses the vocabulary in the provided context.
|
||||
// Does not write null terminator to the buffer.
|
||||
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||||
LLAMA_API int llama_token_to_piece(
|
||||
const struct llama_model * model,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
int length);
|
||||
// User code is responsible to remove the leading whitespace of the first
|
||||
// non-BOS token when decoding multiple tokens.
|
||||
LLAMA_API int llama_token_to_piece(const struct llama_model *model,
|
||||
llama_token token, char *buf, int length);
|
||||
|
||||
//
|
||||
// Grammar
|
||||
//
|
||||
|
||||
LLAMA_API struct llama_grammar * llama_grammar_init(
|
||||
const llama_grammar_element ** rules,
|
||||
size_t n_rules,
|
||||
LLAMA_API struct llama_grammar *
|
||||
llama_grammar_init(const llama_grammar_element **rules, size_t n_rules,
|
||||
size_t start_rule_index);
|
||||
|
||||
LLAMA_API void llama_grammar_free(struct llama_grammar *grammar);
|
||||
|
||||
LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
|
||||
LLAMA_API struct llama_grammar *
|
||||
llama_grammar_copy(const struct llama_grammar *grammar);
|
||||
|
||||
//
|
||||
// Sampling functions
|
||||
|
|
@ -669,124 +677,138 @@ extern "C" {
|
|||
// Sets the current rng seed.
|
||||
LLAMA_API void llama_set_rng_seed(struct llama_context *ctx, uint32_t seed);
|
||||
|
||||
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||||
/// @details Repetition penalty described in CTRL academic paper
|
||||
/// https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
/// @details Frequency and presence penalties described in OpenAI API
|
||||
/// https://platform.openai.com/docs/api-reference/parameter-details.
|
||||
LLAMA_API void llama_sample_repetition_penalties(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
const llama_token * last_tokens,
|
||||
size_t penalty_last_n,
|
||||
float penalty_repeat,
|
||||
float penalty_freq,
|
||||
float penalty_present);
|
||||
struct llama_context *ctx, llama_token_data_array *candidates,
|
||||
const llama_token *last_tokens, size_t penalty_last_n, float penalty_repeat,
|
||||
float penalty_freq, float penalty_present);
|
||||
|
||||
/// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
|
||||
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
|
||||
/// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
|
||||
/// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
||||
/// @details Apply classifier-free guidance to the logits as described in
|
||||
/// academic paper "Stay on topic with Classifier-Free Guidance"
|
||||
/// https://arxiv.org/abs/2306.17806
|
||||
/// @param candidates A vector of `llama_token_data` containing the candidate
|
||||
/// tokens, the logits must be directly extracted from the original generation
|
||||
/// context without being sorted.
|
||||
/// @params guidance_ctx A separate context from the same model. Other than a
|
||||
/// negative prompt at the beginning, it should have all generated and user
|
||||
/// input tokens copied from the main context.
|
||||
/// @params scale Guidance strength. 1.0f means no guidance. Higher values mean
|
||||
/// stronger guidance.
|
||||
LLAMA_API void llama_sample_classifier_free_guidance(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
struct llama_context * guidance_ctx,
|
||||
float scale);
|
||||
struct llama_context *ctx, llama_token_data_array *candidates,
|
||||
struct llama_context *guidance_ctx, float scale);
|
||||
|
||||
/// @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,
|
||||
/// @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,
|
||||
/// @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,
|
||||
/// @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 Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
|
||||
LLAMA_API void llama_sample_min_p(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float p,
|
||||
/// @details Minimum P sampling as described in
|
||||
/// https://github.com/ggerganov/llama.cpp/pull/3841
|
||||
LLAMA_API void llama_sample_min_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,
|
||||
/// @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,
|
||||
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);
|
||||
|
||||
/// @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_temp(
|
||||
struct llama_context * ctx,
|
||||
LLAMA_API void llama_sample_temp(struct llama_context *ctx,
|
||||
llama_token_data_array *candidates,
|
||||
float temp);
|
||||
|
||||
LLAMA_API DEPRECATED(void llama_sample_temperature(
|
||||
struct llama_context * ctx,
|
||||
LLAMA_API
|
||||
DEPRECATED(void llama_sample_temperature(struct llama_context *ctx,
|
||||
llama_token_data_array *candidates,
|
||||
float temp),
|
||||
"use llama_sample_temp instead");
|
||||
|
||||
/// @details Apply constraints from grammar
|
||||
LLAMA_API void llama_sample_grammar(
|
||||
struct llama_context * ctx,
|
||||
LLAMA_API void llama_sample_grammar(struct llama_context *ctx,
|
||||
llama_token_data_array *candidates,
|
||||
const struct llama_grammar *grammar);
|
||||
|
||||
/// @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.
|
||||
/// @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);
|
||||
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.
|
||||
/// @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);
|
||||
struct llama_context *ctx, llama_token_data_array *candidates, float tau,
|
||||
float eta, float *mu);
|
||||
|
||||
/// @details Selects the token with the highest probability.
|
||||
/// Does not compute the token probabilities. Use llama_sample_softmax() instead.
|
||||
/// Does not compute the token probabilities. Use
|
||||
/// llama_sample_softmax() instead.
|
||||
LLAMA_API llama_token llama_sample_token_greedy(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates);
|
||||
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,
|
||||
/// @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);
|
||||
|
||||
/// @details Accepts the sampled token into the grammar
|
||||
LLAMA_API void llama_grammar_accept_token(
|
||||
struct llama_context * ctx,
|
||||
LLAMA_API void llama_grammar_accept_token(struct llama_context *ctx,
|
||||
struct llama_grammar *grammar,
|
||||
llama_token token);
|
||||
|
||||
|
|
@ -803,36 +825,39 @@ extern "C" {
|
|||
};
|
||||
|
||||
// Passed to beam_search_callback function.
|
||||
// Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
|
||||
// (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
|
||||
// These pointers are valid only during the synchronous callback, so should not be saved.
|
||||
// Whenever 0 < common_prefix_length, this number of tokens should be copied
|
||||
// from any of the beams (e.g. beams[0]) as they will be removed (shifted) from
|
||||
// all beams in all subsequent callbacks. These pointers are valid only during
|
||||
// the synchronous callback, so should not be saved.
|
||||
struct llama_beams_state {
|
||||
struct llama_beam_view *beam_views;
|
||||
|
||||
size_t n_beams; // Number of elements in beam_views[].
|
||||
size_t common_prefix_length; // Current max length of prefix tokens shared by all beams.
|
||||
size_t common_prefix_length; // Current max length of prefix tokens shared by
|
||||
// all beams.
|
||||
bool last_call; // True iff this is the last callback invocation.
|
||||
};
|
||||
|
||||
// Type of pointer to the beam_search_callback function.
|
||||
// void* callback_data is any custom data passed to llama_beam_search, that is subsequently
|
||||
// passed back to beam_search_callback. This avoids having to use global variables in the callback.
|
||||
typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
|
||||
// void* callback_data is any custom data passed to llama_beam_search, that is
|
||||
// subsequently passed back to beam_search_callback. This avoids having to use
|
||||
// global variables in the callback.
|
||||
typedef void (*llama_beam_search_callback_fn_t)(void *callback_data,
|
||||
struct llama_beams_state);
|
||||
|
||||
/// @details Deterministically returns entire sentence constructed by a beam search.
|
||||
/// @details Deterministically returns entire sentence constructed by a beam
|
||||
/// search.
|
||||
/// @param ctx Pointer to the llama_context.
|
||||
/// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
|
||||
/// @param callback Invoked for each iteration of the beam_search loop, passing
|
||||
/// in beams_state.
|
||||
/// @param callback_data A pointer that is simply passed back to callback.
|
||||
/// @param n_beams Number of beams to use.
|
||||
/// @param n_past Number of tokens already evaluated.
|
||||
/// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
|
||||
LLAMA_API void llama_beam_search(
|
||||
struct llama_context * ctx,
|
||||
LLAMA_API void llama_beam_search(struct llama_context *ctx,
|
||||
llama_beam_search_callback_fn_t callback,
|
||||
void * callback_data,
|
||||
size_t n_beams,
|
||||
int n_past,
|
||||
int n_predict);
|
||||
void *callback_data, size_t n_beams,
|
||||
int n_past, int n_predict);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API struct llama_timings llama_get_timings(struct llama_context *ctx);
|
||||
|
|
@ -847,7 +872,8 @@ extern "C" {
|
|||
// If this is not called, or NULL is supplied, everything is output on stderr.
|
||||
LLAMA_API void llama_log_set(ggml_log_callback log_callback, void *user_data);
|
||||
|
||||
LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
|
||||
LLAMA_API void llama_dump_timing_info_yaml(FILE *stream,
|
||||
const struct llama_context *ctx);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
@ -856,14 +882,13 @@ extern "C" {
|
|||
// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
|
||||
#ifdef LLAMA_API_INTERNAL
|
||||
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
struct ggml_tensor;
|
||||
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
|
||||
struct llama_context * ctx
|
||||
);
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>> &
|
||||
llama_internal_get_tensor_map(struct llama_context *ctx);
|
||||
|
||||
#endif // LLAMA_API_INTERNAL
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue