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4 changed files with 3210 additions and 26 deletions
10
ggml.c
10
ggml.c
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@ -138,14 +138,14 @@ inline static void* ggml_aligned_malloc(size_t size) {
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#if defined(GGML_USE_ACCELERATE)
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#include <Accelerate/Accelerate.h>
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#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
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#include "ggml_v2-opencl.h"
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#include "ggml-opencl.h"
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#endif
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#elif defined(GGML_USE_OPENBLAS)
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#include <cblas.h>
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#elif defined(GGML_USE_CUBLAS)
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#include "ggml-cuda.h"
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#elif defined(GGML_USE_CLBLAST)
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#include "ggml_v2-opencl.h"
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#include "ggml-opencl.h"
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#endif
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#undef MIN
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@ -512,7 +512,7 @@ static inline int hsum_i32_4(const __m128i a) {
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return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
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}
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#if __AVX2__ || __AVX512F__
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#if defined(__AVX2__) || defined(__AVX512F__)
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// spread 32 bits to 32 bytes { 0x00, 0xFF }
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static inline __m256i bytes_from_bits_32(const uint8_t * x) {
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uint32_t x32;
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@ -688,7 +688,7 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
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#endif // __AVX__ || __AVX2__ || __AVX512F__
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#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
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#if __ARM_NEON
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#if defined(__ARM_NEON)
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#if !defined(__aarch64__)
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@ -2481,7 +2481,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
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sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
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}
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sumf += (GGML_FP16_TO_FP32(x[i]).d*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
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sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
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}
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*s = sumf;
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27
ggml.h
27
ggml.h
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@ -190,7 +190,7 @@
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#define GGML_FILE_MAGIC 0x67676d6c // "ggml"
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#define GGML_FILE_VERSION 1
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#define GGML_QNT_VERSION 1 // bump this on quantization format changes
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#define GGML_QNT_VERSION 2 // bump this on quantization format changes
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#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
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#define GGML_MAX_DIMS 4
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@ -234,8 +234,8 @@ extern "C" {
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GGML_TYPE_F16 = 1,
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GGML_TYPE_Q4_0 = 2,
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GGML_TYPE_Q4_1 = 3,
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GGML_TYPE_Q4_2 = 4, //support has been removed
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GGML_TYPE_Q4_3 = 5, //support has been removed
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// GGML_TYPE_Q4_2 = 4, support has been removed
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// GGML_TYPE_Q4_3 (5) support has been removed
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GGML_TYPE_Q5_0 = 6,
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GGML_TYPE_Q5_1 = 7,
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GGML_TYPE_Q8_0 = 8,
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@ -243,14 +243,12 @@ extern "C" {
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GGML_TYPE_I8,
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GGML_TYPE_I16,
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GGML_TYPE_I32,
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GGML_TYPE_Q8_1B = 13, //legacy q8_1
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GGML_TYPE_COUNT,
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};
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enum ggml_backend {
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GGML_BACKEND_CPU = 0,
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GGML_BACKEND_CUDA = 1,
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GGML_BACKEND_CL = 2,
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};
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// model file types
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@ -261,8 +259,6 @@ extern "C" {
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GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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GGML_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q4_3 = 6, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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@ -853,7 +849,7 @@ extern "C" {
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int n_past);
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// in-place, returns view(a)
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GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
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GGML_API struct ggml_tensor * gml_diag_mask_zero_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past);
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@ -1073,28 +1069,17 @@ extern "C" {
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//
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GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q4_0_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q4_1_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q4_2_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q4_3_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q5_0_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q5_1_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_q8_0_v2(const float * src, void * dst, int n, int k, int64_t * hist);
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GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
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GGML_API size_t ggml_quantize_chunk_v2(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
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//
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// system info
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//
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void SetQuantsUnshuffled(bool unshuffled);
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bool GetQuantsUnshuffled();
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GGML_API int ggml_cpu_has_avx (void);
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GGML_API int ggml_cpu_has_avx2 (void);
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GGML_API int ggml_cpu_has_avx512 (void);
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260
llama.h
Normal file
260
llama.h
Normal file
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@ -0,0 +1,260 @@
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#ifndef LLAMA_H
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#define LLAMA_H
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#include <stddef.h>
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#include <stdint.h>
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#include <stdbool.h>
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#ifdef LLAMA_SHARED
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# if defined(_WIN32) && !defined(__MINGW32__)
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# ifdef LLAMA_BUILD
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# define LLAMA_API __declspec(dllexport)
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# else
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# define LLAMA_API __declspec(dllimport)
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# endif
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# else
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# define LLAMA_API __attribute__ ((visibility ("default")))
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# endif
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#else
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# define LLAMA_API
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#endif
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#define LLAMA_FILE_VERSION 3
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#define LLAMA_FILE_MAGIC 'ggjt'
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#define LLAMA_FILE_MAGIC_UNVERSIONED 'ggml'
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#define LLAMA_SESSION_MAGIC 'ggsn'
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#define LLAMA_SESSION_VERSION 1
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#ifdef __cplusplus
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extern "C" {
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#endif
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//
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// C interface
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//
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// TODO: show sample usage
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//
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struct llama_context;
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typedef int llama_token;
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typedef struct llama_token_data {
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llama_token id; // token id
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float logit; // log-odds of the token
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float p; // probability of the token
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} llama_token_data;
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typedef struct llama_token_data_array {
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llama_token_data * data;
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size_t size;
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bool sorted;
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} llama_token_data_array;
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typedef void (*llama_progress_callback)(float progress, void *ctx);
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struct llama_context_params {
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int n_ctx; // text context
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int n_gpu_layers; // number of layers to store in VRAM
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int seed; // RNG seed, -1 for random
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bool f16_kv; // use fp16 for KV cache
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bool logits_all; // the llama_eval() call computes all logits, not just the last one
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bool vocab_only; // only load the vocabulary, no weights
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bool use_mmap; // use mmap if possible
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bool use_mlock; // force system to keep model in RAM
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bool embedding; // embedding mode only
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// called with a progress value between 0 and 1, pass NULL to disable
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llama_progress_callback progress_callback;
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// context pointer passed to the progress callback
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void * progress_callback_user_data;
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};
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// model file types
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enum llama_ftype {
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LLAMA_FTYPE_ALL_F32 = 0,
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LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // 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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};
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LLAMA_API struct llama_context_params llama_context_default_params();
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LLAMA_API bool llama_mmap_supported();
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LLAMA_API bool llama_mlock_supported();
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// Various functions for loading a ggml llama model.
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// Allocate (almost) all memory needed for the model.
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// Return NULL on failure
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LLAMA_API struct llama_context * llama_init_from_file(
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const char * path_model,
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struct llama_context_params params);
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// Frees all allocated memory
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LLAMA_API void llama_free(struct llama_context * ctx);
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// TODO: not great API - very likely to change
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// Returns 0 on success
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// nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
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LLAMA_API int llama_model_quantize(
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const char * fname_inp,
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const char * fname_out,
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enum llama_ftype ftype,
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int nthread);
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// Apply a LoRA adapter to a loaded model
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// path_base_model is the path to a higher quality model to use as a base for
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// the layers modified by the adapter. Can be NULL to use the current loaded model.
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// The model needs to be reloaded before applying a new adapter, otherwise the adapter
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// will be applied on top of the previous one
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// Returns 0 on success
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LLAMA_API int llama_apply_lora_from_file(
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struct llama_context * ctx,
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const char * path_lora,
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const char * path_base_model,
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int n_threads);
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// Returns the number of tokens in the KV cache
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LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
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// Sets the current rng seed.
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LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
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// Returns the 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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LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
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// Copies the state to the specified destination address.
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// Destination needs to have allocated enough memory.
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// Returns the number of bytes copied
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LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
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// Set the state reading from the specified address
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// Returns the number of bytes read
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LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
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// Save/load session file
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LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
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LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
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// Run the llama inference to obtain the logits and probabilities for the next token.
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// tokens + n_tokens is the provided batch of new tokens to process
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// n_past is the number of tokens to use from previous eval calls
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// Returns 0 on success
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LLAMA_API int llama_eval(
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struct llama_context * ctx,
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const llama_token * tokens,
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int n_tokens,
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int n_past,
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int n_threads);
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// Convert the provided text into tokens.
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// The tokens pointer must be large enough to hold the resulting tokens.
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// Returns the number of tokens on success, no more than n_max_tokens
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// Returns a negative number on failure - the number of tokens that would have been returned
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// TODO: not sure if correct
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LLAMA_API int llama_tokenize(
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struct llama_context * ctx,
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const char * text,
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llama_token * tokens,
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int n_max_tokens,
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bool add_bos);
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LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
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LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
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LLAMA_API int llama_n_embd (const struct llama_context * ctx);
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// Token logits obtained from the last call to llama_eval()
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// The logits for the last token are stored in the last row
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// Can be mutated in order to change the probabilities of the next token
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// Rows: n_tokens
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// Cols: n_vocab
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LLAMA_API float * llama_get_logits(struct llama_context * ctx);
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// Get the embeddings for the input
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// shape: [n_embd] (1-dimensional)
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LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
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// Token Id -> String. Uses the vocabulary in the provided context
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LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
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// Special tokens
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LLAMA_API llama_token llama_token_bos();
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LLAMA_API llama_token llama_token_eos();
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LLAMA_API llama_token llama_token_nl();
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// Sampling functions
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/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
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LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
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/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
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LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
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/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
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LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
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/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
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/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
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/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
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LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
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/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
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LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
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LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
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/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
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/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
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/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
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/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
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/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
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LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
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/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||||
/// @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.
|
||||
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);
|
||||
|
||||
// Performance information
|
||||
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);
|
||||
|
||||
#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;
|
||||
|
||||
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
||||
|
||||
#endif
|
||||
|
||||
#endif // LLAMA_H
|
Loading…
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Reference in a new issue