996 lines
44 KiB
C++
996 lines
44 KiB
C++
#ifndef LLAMA_H
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#define LLAMA_H
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#include "ggml.h"
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#include "ggml-backend.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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# ifdef LLAMA_BUILD
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# define LLAMA_API __declspec(dllexport)
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# else
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# define LLAMA_API __declspec(dllimport)
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# endif
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# else
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# define LLAMA_API __attribute__ ((visibility ("default")))
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# endif
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#else
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# define LLAMA_API
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#endif
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#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_DEFAULT_SEED 0xFFFFFFFF
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#define LLAMA_MAX_RNG_STATE (64*1024)
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#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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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 4
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#ifdef __cplusplus
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extern "C" {
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#endif
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//
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// C interface
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//
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// TODO: show sample usage
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//
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struct llama_model;
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struct llama_context;
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typedef int32_t llama_pos;
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typedef int32_t llama_token;
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typedef int32_t llama_seq_id;
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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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LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
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};
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// note: these values should be synchronized with ggml_rope
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// TODO: maybe move this enum to ggml.h (ggml_rope_type)
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enum llama_rope_type {
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LLAMA_ROPE_TYPE_NONE = -1,
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LLAMA_ROPE_TYPE_NORM = 0,
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LLAMA_ROPE_TYPE_NEOX = 2,
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LLAMA_ROPE_TYPE_GLM = 4,
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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_MOSTLY_IQ2_XXS = 19, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // 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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enum llama_rope_scaling_type {
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LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
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LLAMA_ROPE_SCALING_TYPE_NONE = 0,
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LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
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LLAMA_ROPE_SCALING_TYPE_YARN = 2,
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LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
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};
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enum llama_pooling_type {
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LLAMA_POOLING_TYPE_NONE = 0,
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LLAMA_POOLING_TYPE_MEAN = 1,
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LLAMA_POOLING_TYPE_CLS = 2,
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};
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enum llama_split_mode {
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LLAMA_SPLIT_MODE_NONE = 0, // single GPU
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LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
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LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
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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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float p; // probability of the token
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} llama_token_data;
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typedef struct llama_token_data_array {
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llama_token_data * data;
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size_t size;
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bool sorted;
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} llama_token_data_array;
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typedef bool (*llama_progress_callback)(float progress, void *ctx);
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// Input data for llama_decode
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// A llama_batch object can contain input about one or many sequences
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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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// - 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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//
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typedef struct llama_batch {
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int32_t n_tokens;
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llama_token * token;
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float * embd;
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llama_pos * pos;
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int32_t * n_seq_id;
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llama_seq_id ** seq_id;
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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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//
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// pos[i] = all_pos_0 + i*all_pos_1
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//
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llama_pos all_pos_0; // used if pos == NULL
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llama_pos all_pos_1; // used if pos == NULL
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llama_seq_id all_seq_id; // used if seq_id == NULL
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} llama_batch;
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enum llama_model_kv_override_type {
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LLAMA_KV_OVERRIDE_TYPE_INT,
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LLAMA_KV_OVERRIDE_TYPE_FLOAT,
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LLAMA_KV_OVERRIDE_TYPE_BOOL,
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};
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struct llama_model_kv_override {
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char key[128];
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enum llama_model_kv_override_type tag;
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union {
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int64_t int_value;
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double float_value;
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bool bool_value;
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};
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};
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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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enum llama_split_mode split_mode; // how to split the model across multiple GPUs
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// main_gpu interpretation depends on split_mode:
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// LLAMA_SPLIT_NONE: the GPU that is used for the entire model
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// LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results
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// LLAMA_SPLIT_LAYER: ignored
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int32_t main_gpu;
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// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
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const float * tensor_split;
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// Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
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// If the provided progress_callback returns true, model loading continues.
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// If it returns false, model loading is immediately aborted.
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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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// override key-value pairs of the model meta data
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const struct llama_model_kv_override * kv_overrides;
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// Keep the booleans together to avoid misalignment during copy-by-value.
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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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};
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struct llama_context_params {
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uint32_t seed; // RNG seed, -1 for random
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uint32_t n_ctx; // text context, 0 = from model
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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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int32_t rope_scaling_type; // RoPE scaling type, from `enum 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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float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
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float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
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float yarn_attn_factor; // YaRN magnitude scaling factor
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float yarn_beta_fast; // YaRN low correction dim
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float yarn_beta_slow; // YaRN high correction dim
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uint32_t yarn_orig_ctx; // YaRN original context size
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float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default)
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ggml_backend_sched_eval_callback cb_eval;
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void * cb_eval_user_data;
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enum ggml_type type_k; // data type for K cache
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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 (DEPRECATED - set llama_batch.logits instead)
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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 do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
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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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int32_t 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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bool pure; // disable k-quant mixtures and quantize all tensors to the same type
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void * imatrix; // pointer to importance matrix data
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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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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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// 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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// used in chat template
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typedef struct llama_chat_message {
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const char * role;
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const char * content;
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} llama_chat_message;
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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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// 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_backend_init(void);
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//optional:
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LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
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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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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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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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LLAMA_API int64_t llama_time_us(void);
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LLAMA_API size_t llama_max_devices(void);
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LLAMA_API bool llama_supports_mmap (void);
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LLAMA_API bool llama_supports_mlock (void);
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LLAMA_API bool llama_supports_gpu_offload(void);
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LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead");
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LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");
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LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
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LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
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LLAMA_API uint32_t llama_n_batch (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_rope_type llama_rope_type (const struct llama_model * model);
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LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
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LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
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LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
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// Get the model's RoPE frequency scaling factor
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LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
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// Functions to access the model's GGUF metadata scalar values
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// - The functions return the length of the string on success, or -1 on failure
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// - The output string is always null-terminated and cleared on failure
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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 int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
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// Get the number of metadata key/value pairs
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LLAMA_API int32_t 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 int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t 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 int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
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// Get a string describing the model type
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LLAMA_API int32_t 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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// 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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// Returns 0 on success
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LLAMA_API uint32_t llama_model_quantize(
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const char * fname_inp,
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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(int32_t 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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float scale,
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const char * path_base_model,
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int32_t n_threads),
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"use llama_model_apply_lora_from_file instead");
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LLAMA_API int32_t llama_model_apply_lora_from_file(
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const struct llama_model * model,
|
||
const char * path_lora,
|
||
float scale,
|
||
const char * path_base_model,
|
||
int32_t n_threads);
|
||
|
||
//
|
||
// KV cache
|
||
//
|
||
|
||
// Information associated with an individual cell in the KV cache view.
|
||
struct llama_kv_cache_view_cell {
|
||
// The position for this cell. Takes KV cache shifts into account.
|
||
// May be negative if the cell is not populated.
|
||
llama_pos pos;
|
||
};
|
||
|
||
// An updateable view of the KV cache.
|
||
struct llama_kv_cache_view {
|
||
// Number of KV cache cells. This will be the same as the context size.
|
||
int32_t n_cells;
|
||
|
||
// Maximum number of sequences that can exist in a cell. It's not an error
|
||
// if there are more sequences in a cell than this value, however they will
|
||
// not be visible in the view cells_sequences.
|
||
int32_t n_max_seq;
|
||
|
||
// Number of tokens in the cache. For example, if there are two populated
|
||
// cells, the first with 1 sequence id in it and the second with 2 sequence
|
||
// ids then you'll have 3 tokens.
|
||
int32_t token_count;
|
||
|
||
// Number of populated cache cells.
|
||
int32_t used_cells;
|
||
|
||
// Maximum contiguous empty slots in the cache.
|
||
int32_t max_contiguous;
|
||
|
||
// Index to the start of the max_contiguous slot range. Can be negative
|
||
// when cache is full.
|
||
int32_t max_contiguous_idx;
|
||
|
||
// Information for an individual cell.
|
||
struct llama_kv_cache_view_cell * cells;
|
||
|
||
// The sequences for each cell. There will be n_max_seq items per cell.
|
||
llama_seq_id * cells_sequences;
|
||
};
|
||
|
||
// Create an empty KV cache view. (use only for debugging purposes)
|
||
LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
|
||
|
||
// Free a KV cache view. (use only for debugging purposes)
|
||
LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
|
||
|
||
// Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
|
||
LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
|
||
|
||
// Returns the number of tokens in the KV cache (slow, use only for debug)
|
||
// If a KV cell has multiple sequences assigned to it, it will be counted multiple times
|
||
LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
||
|
||
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
||
LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
||
|
||
// Clear the KV cache
|
||
LLAMA_API void llama_kv_cache_clear(
|
||
struct llama_context * ctx);
|
||
|
||
// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
// seq_id < 0 : match any sequence
|
||
// p0 < 0 : [0, p1]
|
||
// p1 < 0 : [p0, inf)
|
||
LLAMA_API void llama_kv_cache_seq_rm(
|
||
struct llama_context * ctx,
|
||
llama_seq_id seq_id,
|
||
llama_pos p0,
|
||
llama_pos p1);
|
||
|
||
// Copy all tokens that belong to the specified sequence to another sequence
|
||
// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
|
||
// p0 < 0 : [0, p1]
|
||
// p1 < 0 : [p0, inf)
|
||
LLAMA_API void llama_kv_cache_seq_cp(
|
||
struct llama_context * ctx,
|
||
llama_seq_id seq_id_src,
|
||
llama_seq_id seq_id_dst,
|
||
llama_pos p0,
|
||
llama_pos p1);
|
||
|
||
// Removes all tokens that do not belong to the specified sequence
|
||
LLAMA_API void llama_kv_cache_seq_keep(
|
||
struct llama_context * ctx,
|
||
llama_seq_id seq_id);
|
||
|
||
// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
// If the KV cache is RoPEd, the KV data is updated accordingly:
|
||
// - lazily on next llama_decode()
|
||
// - explicitly with llama_kv_cache_update()
|
||
// p0 < 0 : [0, p1]
|
||
// p1 < 0 : [p0, inf)
|
||
LLAMA_API void llama_kv_cache_seq_add(
|
||
struct llama_context * ctx,
|
||
llama_seq_id seq_id,
|
||
llama_pos p0,
|
||
llama_pos p1,
|
||
llama_pos delta);
|
||
|
||
// Integer division of the positions by factor of `d > 1`
|
||
// If the KV cache is RoPEd, the KV data is updated accordingly:
|
||
// - lazily on next llama_decode()
|
||
// - explicitly with llama_kv_cache_update()
|
||
// p0 < 0 : [0, p1]
|
||
// p1 < 0 : [p0, inf)
|
||
LLAMA_API void llama_kv_cache_seq_div(
|
||
struct llama_context * ctx,
|
||
llama_seq_id seq_id,
|
||
llama_pos p0,
|
||
llama_pos p1,
|
||
int d);
|
||
|
||
// Returns the largest position present in the KV cache for the specified sequence
|
||
LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
|
||
struct llama_context * ctx,
|
||
llama_seq_id seq_id);
|
||
|
||
// Defragment the KV cache
|
||
// This will be applied:
|
||
// - lazily on next llama_decode()
|
||
// - explicitly with llama_kv_cache_update()
|
||
LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
|
||
|
||
// Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
|
||
LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
|
||
|
||
//
|
||
// State / sessions
|
||
//
|
||
|
||
// Returns the maximum size in bytes of the state (rng, logits, embedding
|
||
// and kv_cache) - will often be smaller after compacting tokens
|
||
LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
|
||
|
||
// Copies the state to the specified destination address.
|
||
// Destination needs to have allocated enough memory.
|
||
// Returns the number of bytes copied
|
||
LLAMA_API size_t llama_copy_state_data(
|
||
struct llama_context * ctx,
|
||
uint8_t * dst);
|
||
|
||
// Set the state reading from the specified address
|
||
// Returns the number of bytes read
|
||
LLAMA_API size_t llama_set_state_data(
|
||
struct llama_context * ctx,
|
||
uint8_t * src);
|
||
|
||
// Save/load session file
|
||
LLAMA_API bool llama_load_session_file(
|
||
struct llama_context * ctx,
|
||
const char * path_session,
|
||
llama_token * tokens_out,
|
||
size_t n_token_capacity,
|
||
size_t * n_token_count_out);
|
||
|
||
LLAMA_API bool llama_save_session_file(
|
||
struct llama_context * ctx,
|
||
const char * path_session,
|
||
const llama_token * tokens,
|
||
size_t n_token_count);
|
||
|
||
//
|
||
// Decoding
|
||
//
|
||
|
||
// Run the llama inference to obtain the logits and probabilities for the next token(s).
|
||
// tokens + n_tokens is the provided batch of new tokens to process
|
||
// n_past is the number of tokens to use from previous eval calls
|
||
// Returns 0 on success
|
||
// DEPRECATED: use llama_decode() instead
|
||
LLAMA_API DEPRECATED(int llama_eval(
|
||
struct llama_context * ctx,
|
||
llama_token * tokens,
|
||
int32_t n_tokens,
|
||
int32_t 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,
|
||
int32_t 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
|
||
//
|
||
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);
|
||
|
||
// 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,
|
||
int32_t n_seq_max);
|
||
|
||
// Frees a batch of tokens allocated with llama_batch_init()
|
||
LLAMA_API void llama_batch_free(struct llama_batch batch);
|
||
|
||
// 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)
|
||
// < 0 - error
|
||
LLAMA_API int32_t 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);
|
||
|
||
// Token logits obtained from the last call to llama_eval()
|
||
// The logits for the last token are stored in the last row
|
||
// Logits for which llama_batch.logits[i] == 0 are undefined
|
||
// Rows: n_tokens provided with llama_batch
|
||
// Cols: n_vocab
|
||
LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
||
|
||
// Logits for the ith token. Equivalent to:
|
||
// llama_get_logits(ctx) + i*n_vocab
|
||
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
|
||
|
||
// Get the embeddings for the input
|
||
// shape: [n_embd] (1-dimensional)
|
||
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
||
|
||
// Get the embeddings for the ith sequence
|
||
// llama_get_embeddings(ctx) + i*n_embd
|
||
LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
|
||
|
||
//
|
||
// Vocab
|
||
//
|
||
|
||
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 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
|
||
|
||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||
LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);
|
||
|
||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||
LLAMA_API int32_t 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
|
||
|
||
//
|
||
// Tokenization
|
||
//
|
||
|
||
/// @details Convert the provided text into 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.
|
||
/// Does not insert a leading space.
|
||
LLAMA_API int32_t llama_tokenize(
|
||
const struct llama_model * model,
|
||
const char * text,
|
||
int32_t text_len,
|
||
llama_token * tokens,
|
||
int32_t 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 int32_t llama_token_to_piece(
|
||
const struct llama_model * model,
|
||
llama_token token,
|
||
char * buf,
|
||
int32_t length);
|
||
|
||
/// Apply chat template. Inspired by hf apply_chat_template() on python.
|
||
/// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
|
||
/// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
|
||
/// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
|
||
/// @param chat Pointer to a list of multiple llama_chat_message
|
||
/// @param n_msg Number of llama_chat_message in this chat
|
||
/// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
|
||
/// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
|
||
/// @param length The size of the allocated buffer
|
||
/// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
|
||
LLAMA_API int32_t llama_chat_apply_template(
|
||
const struct llama_model * model,
|
||
const char * tmpl,
|
||
const struct llama_chat_message * chat,
|
||
size_t n_msg,
|
||
bool add_ass,
|
||
char * buf,
|
||
int32_t 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
|
||
//
|
||
|
||
// 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.
|
||
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);
|
||
|
||
/// @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 logits Logits extracted from the original generation context.
|
||
/// @param logits_guidance Logits extracted from 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.
|
||
/// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
||
LLAMA_API void llama_sample_apply_guidance(
|
||
struct llama_context * ctx,
|
||
float * logits,
|
||
float * logits_guidance,
|
||
float scale);
|
||
|
||
LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance(
|
||
struct llama_context * ctx,
|
||
llama_token_data_array * candidates,
|
||
struct llama_context * guidance_ctx,
|
||
float scale),
|
||
"use llama_sample_apply_guidance() instead");
|
||
|
||
/// @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,
|
||
int32_t k,
|
||
size_t min_keep);
|
||
|
||
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||
LLAMA_API void llama_sample_top_p(
|
||
struct llama_context * ctx,
|
||
llama_token_data_array * candidates,
|
||
float p,
|
||
size_t min_keep);
|
||
|
||
/// @details 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,
|
||
llama_token_data_array * candidates,
|
||
float z,
|
||
size_t min_keep);
|
||
|
||
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
|
||
LLAMA_API void llama_sample_typical(
|
||
struct llama_context * ctx,
|
||
llama_token_data_array * candidates,
|
||
float p,
|
||
size_t min_keep);
|
||
|
||
/// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772.
|
||
LLAMA_API void llama_sample_entropy(
|
||
struct llama_context * ctx,
|
||
llama_token_data_array * candidates_p,
|
||
float min_temp,
|
||
float max_temp,
|
||
float exponent_val);
|
||
|
||
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_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_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.
|
||
LLAMA_API llama_token llama_sample_token_mirostat(
|
||
struct llama_context * ctx,
|
||
llama_token_data_array * candidates,
|
||
float tau,
|
||
float eta,
|
||
int32_t m,
|
||
float * mu);
|
||
|
||
/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||
LLAMA_API llama_token llama_sample_token_mirostat_v2(
|
||
struct llama_context * ctx,
|
||
llama_token_data_array * candidates,
|
||
float tau,
|
||
float eta,
|
||
float * mu);
|
||
|
||
/// @details Selects the token with the highest probability.
|
||
/// 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);
|
||
|
||
/// @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.
|
||
LLAMA_API void llama_beam_search(
|
||
struct llama_context * ctx,
|
||
llama_beam_search_callback_fn_t callback,
|
||
void * callback_data,
|
||
size_t n_beams,
|
||
int32_t n_past,
|
||
int32_t n_predict);
|
||
|
||
// 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(ggml_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
|
||
|
||
// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
|
||
#ifdef LLAMA_API_INTERNAL
|
||
|
||
#include <vector>
|
||
#include <string>
|
||
|
||
struct ggml_tensor;
|
||
|
||
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
|
||
struct llama_context * ctx
|
||
);
|
||
|
||
#endif // LLAMA_API_INTERNAL
|
||
|
||
#endif // LLAMA_H
|