mirror of
https://github.com/ggerganov/whisper.cpp.git
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talk-llama : update to latest llama.cpp
This commit is contained in:
@ -1,8 +1,16 @@
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#ifndef LLAMA_H
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#define LLAMA_H
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#include "ggml.h"
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#ifdef GGML_USE_CUBLAS
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#include "ggml-cuda.h"
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#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
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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 <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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@ -19,17 +27,25 @@
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# define LLAMA_API
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#endif
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#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
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#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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#define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
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#define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
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#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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#ifdef __GNUC__
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# define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
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#elif defined(_MSC_VER)
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# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
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#else
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# define DEPRECATED(func, hint) func
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#endif
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#define LLAMA_FILE_VERSION 3
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#define LLAMA_FILE_MAGIC LLAMA_FILE_MAGIC_GGJT
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#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
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#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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#define LLAMA_SESSION_VERSION 1
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#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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#define LLAMA_SESSION_VERSION 1
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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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#define LLAMA_SUPPORTS_GPU_OFFLOAD
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#endif
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#ifdef __cplusplus
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extern "C" {
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@ -41,10 +57,57 @@ extern "C" {
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// TODO: show sample usage
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//
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struct llama_model;
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struct llama_context;
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typedef int llama_token;
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enum llama_log_level {
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LLAMA_LOG_LEVEL_ERROR = 2,
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LLAMA_LOG_LEVEL_WARN = 3,
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LLAMA_LOG_LEVEL_INFO = 4
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};
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enum llama_vocab_type {
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LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
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LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
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};
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enum llama_token_type {
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LLAMA_TOKEN_TYPE_UNDEFINED = 0,
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LLAMA_TOKEN_TYPE_NORMAL = 1,
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LLAMA_TOKEN_TYPE_UNKNOWN = 2,
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LLAMA_TOKEN_TYPE_CONTROL = 3,
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LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
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LLAMA_TOKEN_TYPE_UNUSED = 5,
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LLAMA_TOKEN_TYPE_BYTE = 6,
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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, // 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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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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LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
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LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
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};
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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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@ -60,67 +123,152 @@ extern "C" {
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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_gpu_layers; // number of layers to store in VRAM
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int seed; // RNG seed, -1 for random
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uint32_t seed; // RNG seed, -1 for random
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int32_t n_ctx; // text context
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int32_t n_batch; // prompt processing batch size
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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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// ref: https://github.com/ggerganov/llama.cpp/pull/2054
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float rope_freq_base; // RoPE base frequency
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float rope_freq_scale; // RoPE frequency scaling factor
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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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// Keep the booleans together to avoid misalignment during copy-by-value.
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bool low_vram; // if true, reduce VRAM usage at the cost of performance
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bool mul_mat_q; // if true, use experimental mul_mat_q kernels
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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, // 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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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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// Signature for logging events
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// Note that text includes the new line character at the end for most events.
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// If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
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// if it exists.
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// It might not exist for progress report where '.' is output repeatedly.
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typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
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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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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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} llama_model_quantize_params;
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// grammar types
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struct llama_grammar;
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// grammar element type
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enum llama_gretype {
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// end of rule definition
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LLAMA_GRETYPE_END = 0,
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// start of alternate definition for rule
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LLAMA_GRETYPE_ALT = 1,
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// non-terminal element: reference to rule
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LLAMA_GRETYPE_RULE_REF = 2,
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// terminal element: character (code point)
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LLAMA_GRETYPE_CHAR = 3,
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// inverse char(s) ([^a], [^a-b] [^abc])
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LLAMA_GRETYPE_CHAR_NOT = 4,
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// modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
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// be an inclusive range ([a-z])
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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_ALT = 6,
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};
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LLAMA_API struct llama_context_params llama_context_default_params();
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typedef struct llama_grammar_element {
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enum llama_gretype type;
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uint32_t value; // Unicode code point or rule ID
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} llama_grammar_element;
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LLAMA_API bool llama_mmap_supported();
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LLAMA_API bool llama_mlock_supported();
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// performance timing information
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struct llama_timings {
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double t_start_ms;
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double t_end_ms;
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double t_load_ms;
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double t_sample_ms;
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double t_p_eval_ms;
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double t_eval_ms;
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int32_t n_sample;
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int32_t n_p_eval;
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int32_t n_eval;
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};
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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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// TODO: not great API - very likely to change
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// Initialize the llama + ggml backend
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// If numa is true, use NUMA optimizations
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// Call once at the start of the program
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LLAMA_API void llama_init_backend();
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LLAMA_API void llama_backend_init(bool numa);
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LLAMA_API int64_t llama_time_us();
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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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// 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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LLAMA_API struct llama_model * llama_load_model_from_file(
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const char * path_model,
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struct llama_context_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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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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LLAMA_API int64_t llama_time_us(void);
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LLAMA_API int llama_max_devices (void);
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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 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_ctx_train(const struct llama_context * ctx);
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LLAMA_API int llama_n_embd (const struct llama_context * ctx);
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LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
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LLAMA_API int llama_model_n_vocab (const struct llama_model * model);
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LLAMA_API int llama_model_n_ctx (const struct llama_model * model);
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LLAMA_API int llama_model_n_ctx_train(const struct llama_model * model);
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LLAMA_API int llama_model_n_embd (const struct llama_model * model);
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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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// 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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// Returns the total number of parameters in the model
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LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
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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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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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@ -128,17 +276,24 @@ extern "C" {
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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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LLAMA_API DEPRECATED(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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int n_threads),
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"please 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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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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LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
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// Returns the maximum size in bytes of the state (rng, logits, embedding
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// and kv_cache) - will often be smaller after compacting tokens
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@ -168,21 +323,19 @@ extern "C" {
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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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// Same as llama_eval, but use float matrix input directly.
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LLAMA_API int llama_eval_embd(
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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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const float * embd,
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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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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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// Export a static computation graph for context of 511 and batch size of 1
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// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
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// parameters here to keep things simple
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// IMPORTANT: do not use for anything else other than debugging and testing!
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LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
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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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@ -195,15 +348,75 @@ extern "C" {
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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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//
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// Vocab
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//
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LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
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LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
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LLAMA_API enum llama_token_type llama_token_get_type(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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LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx); // beginning-of-sentence
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LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx); // end-of-sentence
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LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx); // next-line
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//
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// Tokenization
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//
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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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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_tokenize_with_model(
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const struct llama_model * model,
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const char * text,
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llama_token * tokens,
|
||||
int n_max_tokens,
|
||||
bool add_bos);
|
||||
|
||||
// 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_context * ctx,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
int length);
|
||||
|
||||
LLAMA_API int llama_token_to_piece_with_model(
|
||||
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,
|
||||
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);
|
||||
|
||||
//
|
||||
// Sampling functions
|
||||
//
|
||||
|
||||
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
|
||||
@ -211,6 +424,16 @@ extern "C" {
|
||||
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||||
LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
|
||||
|
||||
/// @details 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);
|
||||
|
||||
/// @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);
|
||||
|
||||
@ -227,6 +450,9 @@ extern "C" {
|
||||
LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
|
||||
LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
|
||||
|
||||
/// @details Apply constraints from grammar
|
||||
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.
|
||||
@ -248,13 +474,60 @@ extern "C" {
|
||||
/// @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, struct llama_grammar * grammar, llama_token token);
|
||||
|
||||
//
|
||||
// Beam search
|
||||
//
|
||||
|
||||
struct llama_beam_view {
|
||||
const llama_token * tokens;
|
||||
size_t n_tokens;
|
||||
float p; // Cumulative beam probability (renormalized relative to all beams)
|
||||
bool eob; // Callback should set this to true when a beam is at end-of-beam.
|
||||
};
|
||||
|
||||
// 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.
|
||||
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.
|
||||
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);
|
||||
|
||||
/// @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_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.
|
||||
/// @param n_threads Number of threads as passed to llama_eval().
|
||||
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, int n_threads);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
|
||||
LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||||
LLAMA_API void llama_reset_timings(struct llama_context * ctx);
|
||||
|
||||
// Print system information
|
||||
LLAMA_API const char * llama_print_system_info(void);
|
||||
|
||||
// Set callback for all future logging events.
|
||||
// If this is not called, or NULL is supplied, everything is output on stderr.
|
||||
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
|
||||
|
||||
LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
@ -264,10 +537,11 @@ extern "C" {
|
||||
|
||||
#include <vector>
|
||||
#include <string>
|
||||
|
||||
struct ggml_tensor;
|
||||
|
||||
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
||||
|
||||
#endif
|
||||
#endif // LLAMA_API_INTERNAL
|
||||
|
||||
#endif // LLAMA_H
|
||||
|
Reference in New Issue
Block a user