mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2025-06-17 06:18:08 +00:00
sync : ggml (#2001)
* sync : update scripts * sync : ggml * talk-llama : sync llama.cpp * make : WHISPER_CUBLAS -> WHISPER_CUDA * ci : try to fix sycl build * talk-llama : fix make build
This commit is contained in:
Georgi Gerganov
GitHub
263
ggml.c
263
ggml.c
@ -3,6 +3,7 @@
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#include "ggml-impl.h"
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#include "ggml-quants.h"
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#include "ggml.h"
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#if defined(_MSC_VER) || defined(__MINGW32__)
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#include <malloc.h> // using malloc.h with MSC/MINGW
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@ -43,6 +44,10 @@
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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typedef volatile LONG atomic_int;
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@ -282,14 +287,10 @@ inline static void * ggml_calloc(size_t num, size_t size) {
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#else
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#include <cblas.h>
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#endif
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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-opencl.h"
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#elif defined(GGML_USE_VULKAN)
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#include "ggml-vulkan.h"
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#elif defined(GGML_USE_SYCL)
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#include "ggml-sycl.h"
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#endif
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// floating point type used to accumulate sums
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@ -432,6 +433,57 @@ int64_t ggml_cycles_per_ms(void) {
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#define ggml_perf_cycles_per_ms() 0
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#endif
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//
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// cross-platform UTF-8 file paths
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//
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#ifdef _WIN32
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static wchar_t * ggml_mbstowcs(const char * mbs) {
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int wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, NULL, 0);
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if (!wlen) {
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errno = EINVAL;
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return NULL;
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}
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wchar_t * wbuf = GGML_MALLOC(wlen * sizeof(wchar_t));
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wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, wbuf, wlen);
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if (!wlen) {
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GGML_FREE(wbuf);
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errno = EINVAL;
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return NULL;
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}
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return wbuf;
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}
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#endif
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FILE * ggml_fopen(const char * fname, const char * mode) {
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#ifdef _WIN32
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FILE * file = NULL;
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// convert fname (UTF-8)
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wchar_t * wfname = ggml_mbstowcs(fname);
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if (wfname) {
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// convert mode (ANSI)
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wchar_t * wmode = GGML_MALLOC((strlen(mode) + 1) * sizeof(wchar_t));
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wchar_t * wmode_p = wmode;
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do {
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*wmode_p++ = (wchar_t)*mode;
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} while (*mode++);
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// open file
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file = _wfopen(wfname, wmode);
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GGML_FREE(wfname);
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GGML_FREE(wmode);
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}
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return file;
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#else
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return fopen(fname, mode);
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#endif
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}
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//
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// cache line
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//
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@ -470,6 +522,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
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.type_size = sizeof(int32_t),
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.is_quantized = false,
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},
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[GGML_TYPE_I64] = {
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.type_name = "i64",
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.blck_size = 1,
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.type_size = sizeof(int64_t),
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.is_quantized = false,
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},
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[GGML_TYPE_F64] = {
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.type_name = "f64",
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.blck_size = 1,
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.type_size = sizeof(double),
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.is_quantized = false,
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.nrows = 1,
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},
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[GGML_TYPE_F32] = {
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.type_name = "f32",
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.blck_size = 1,
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@ -729,6 +794,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
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.vec_dot_type = GGML_TYPE_Q8_K,
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.nrows = 1,
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},
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[GGML_TYPE_IQ1_M] = {
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.type_name = "iq1_m",
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.blck_size = QK_K,
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.type_size = sizeof(block_iq1_m),
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.is_quantized = true,
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.to_float = (ggml_to_float_t) dequantize_row_iq1_m,
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.from_float = NULL,
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.from_float_reference = NULL,
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.vec_dot = ggml_vec_dot_iq1_m_q8_K,
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.vec_dot_type = GGML_TYPE_Q8_K,
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.nrows = 1,
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},
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[GGML_TYPE_IQ4_NL] = {
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.type_name = "iq4_nl",
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.blck_size = QK4_NL,
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@ -918,6 +995,101 @@ inline static float vaddvq_f32(float32x4_t v) {
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#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE
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#endif
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#elif defined(__AVX512F__)
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#define GGML_SIMD
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// F32 AVX512
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#define GGML_F32_STEP 64
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#define GGML_F32_EPR 16
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#define GGML_F32x16 __m512
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#define GGML_F32x16_ZERO _mm512_setzero_ps()
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#define GGML_F32x16_SET1(x) _mm512_set1_ps(x)
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#define GGML_F32x16_LOAD _mm512_loadu_ps
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#define GGML_F32x16_STORE _mm512_storeu_ps
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// _mm512_fmadd_ps is defined in AVX512F so no guard is required
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#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
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#define GGML_F32x16_ADD _mm512_add_ps
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#define GGML_F32x16_MUL _mm512_mul_ps
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#define GGML_F32x16_REDUCE(res, x) \
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do { \
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int offset = GGML_F32_ARR >> 1; \
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for (int i = 0; i < offset; ++i) { \
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x[i] = _mm512_add_ps(x[i], x[offset+i]); \
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} \
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offset >>= 1; \
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for (int i = 0; i < offset; ++i) { \
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x[i] = _mm512_add_ps(x[i], x[offset+i]); \
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} \
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offset >>= 1; \
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for (int i = 0; i < offset; ++i) { \
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x[i] = _mm512_add_ps(x[i], x[offset+i]); \
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} \
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res = _mm512_reduce_add_ps(x[0]); \
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} while (0)
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// TODO: is this optimal ?
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#define GGML_F32_VEC GGML_F32x16
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#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO
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#define GGML_F32_VEC_SET1 GGML_F32x16_SET1
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#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD
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#define GGML_F32_VEC_STORE GGML_F32x16_STORE
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#define GGML_F32_VEC_FMA GGML_F32x16_FMA
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#define GGML_F32_VEC_ADD GGML_F32x16_ADD
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#define GGML_F32_VEC_MUL GGML_F32x16_MUL
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#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE
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// F16 AVX512
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// F16 AVX
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#define GGML_F16_STEP 64
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#define GGML_F16_EPR 16
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// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead
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#define GGML_F32Cx16 __m512
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#define GGML_F32Cx16_ZERO _mm512_setzero_ps()
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#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x)
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// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F
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// so F16C guard isn't required
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#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x)))
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#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0))
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#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
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#define GGML_F32Cx16_ADD _mm512_add_ps
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#define GGML_F32Cx16_MUL _mm512_mul_ps
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#define GGML_F32Cx16_REDUCE(res, x) \
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do { \
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int offset = GGML_F32_ARR >> 1; \
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for (int i = 0; i < offset; ++i) { \
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x[i] = _mm512_add_ps(x[i], x[offset+i]); \
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} \
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offset >>= 1; \
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for (int i = 0; i < offset; ++i) { \
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x[i] = _mm512_add_ps(x[i], x[offset+i]); \
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} \
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offset >>= 1; \
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for (int i = 0; i < offset; ++i) { \
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x[i] = _mm512_add_ps(x[i], x[offset+i]); \
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} \
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res = _mm512_reduce_add_ps(x[0]); \
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} while (0)
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#define GGML_F16_VEC GGML_F32Cx16
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#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO
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#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1
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#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p)
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#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i])
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#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA
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#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD
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#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL
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#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE
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#elif defined(__AVX__)
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#define GGML_SIMD
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@ -2379,6 +2551,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
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case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
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case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
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case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break;
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case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
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case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
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case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
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@ -2434,6 +2607,16 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
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tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
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}
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GGML_CALL bool ggml_is_empty(const struct ggml_tensor * tensor) {
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for (int i = 0; i < GGML_MAX_DIMS; ++i) {
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if (tensor->ne[i] == 0) {
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// empty if any dimension has no elements
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return true;
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}
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}
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return false;
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}
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bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
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static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
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@ -2448,7 +2631,7 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor
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static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
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static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
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return
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return ggml_is_empty(t0) ? ggml_is_empty(t1) :
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(t1->ne[0]%t0->ne[0] == 0) &&
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(t1->ne[1]%t0->ne[1] == 0) &&
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(t1->ne[2]%t0->ne[2] == 0) &&
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@ -2532,14 +2715,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
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GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
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}
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#if defined(GGML_USE_CUBLAS)
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ggml_init_cublas();
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#elif defined(GGML_USE_CLBLAST)
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#if defined(GGML_USE_CLBLAST)
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ggml_cl_init();
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#elif defined(GGML_USE_VULKAN)
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ggml_vk_init_cpu_assist();
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#elif defined(GGML_USE_SYCL)
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ggml_init_sycl();
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#endif
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ggml_setup_op_has_task_pass();
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@ -7979,6 +8158,7 @@ static void ggml_compute_forward_add(
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ3_S:
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@ -8261,6 +8441,7 @@ static void ggml_compute_forward_add1(
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ1_S:
|
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case GGML_TYPE_IQ1_M:
|
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case GGML_TYPE_IQ4_NL:
|
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ3_S:
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@ -8388,6 +8569,7 @@ static void ggml_compute_forward_acc(
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case GGML_TYPE_IQ2_XS:
|
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case GGML_TYPE_IQ3_XXS:
|
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case GGML_TYPE_IQ1_S:
|
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case GGML_TYPE_IQ1_M:
|
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case GGML_TYPE_IQ4_NL:
|
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ3_S:
|
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@ -10997,7 +11179,6 @@ static void ggml_compute_forward_out_prod_f32(
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// nb01 >= nb00 - src0 is not transposed
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// compute by src0 rows
|
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|
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// TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
|
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// TODO: #if defined(GGML_USE_CLBLAST)
|
||||
|
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
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@ -11197,7 +11378,6 @@ static void ggml_compute_forward_out_prod_q_f32(
|
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// nb01 >= nb00 - src0 is not transposed
|
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// compute by src0 rows
|
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|
||||
// TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
|
||||
// TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT) {
|
||||
@ -11293,6 +11473,7 @@ static void ggml_compute_forward_out_prod(
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
@ -11484,6 +11665,7 @@ static void ggml_compute_forward_set(
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
@ -11707,6 +11889,7 @@ static void ggml_compute_forward_get_rows(
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
@ -12410,6 +12593,7 @@ static void ggml_compute_forward_alibi(
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
@ -12418,6 +12602,8 @@ static void ggml_compute_forward_alibi(
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
case GGML_TYPE_I64:
|
||||
case GGML_TYPE_F64:
|
||||
case GGML_TYPE_COUNT:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
@ -12496,6 +12682,7 @@ static void ggml_compute_forward_clamp(
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
@ -12504,6 +12691,8 @@ static void ggml_compute_forward_clamp(
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
case GGML_TYPE_I64:
|
||||
case GGML_TYPE_F64:
|
||||
case GGML_TYPE_COUNT:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
@ -15935,18 +16124,11 @@ static void ggml_compute_forward_cross_entropy_loss_back(
|
||||
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
GGML_ASSERT(params);
|
||||
|
||||
if (tensor->op == GGML_OP_NONE) {
|
||||
if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) {
|
||||
return;
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
|
||||
if (skip_cpu) {
|
||||
return;
|
||||
}
|
||||
GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU);
|
||||
GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU);
|
||||
#elif defined(GGML_USE_VULKAN)
|
||||
#if defined(GGML_USE_VULKAN)
|
||||
const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
|
||||
#ifdef GGML_VULKAN_CHECK_RESULTS
|
||||
if (skip_cpu) {
|
||||
@ -15958,14 +16140,8 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
||||
}
|
||||
GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU);
|
||||
GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU);
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#endif // GGML_USE_VULKAN
|
||||
|
||||
#ifdef GGML_USE_SYCL
|
||||
bool skip_cpu = ggml_sycl_compute_forward(params, tensor);
|
||||
if (skip_cpu) {
|
||||
return;
|
||||
}
|
||||
#endif // GGML_USE_SYCL
|
||||
switch (tensor->op) {
|
||||
case GGML_OP_DUP:
|
||||
{
|
||||
@ -17817,6 +17993,12 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const
|
||||
static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
|
||||
int n_tasks = 0;
|
||||
|
||||
if (ggml_is_empty(node)) {
|
||||
// no need to multi-thread a no-op
|
||||
n_tasks = 1;
|
||||
return n_tasks;
|
||||
}
|
||||
|
||||
switch (node->op) {
|
||||
case GGML_OP_CPY:
|
||||
case GGML_OP_DUP:
|
||||
@ -18640,7 +18822,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
|
||||
|
||||
// write binary data
|
||||
{
|
||||
FILE * fout = fopen(fname, "wb");
|
||||
FILE * fout = ggml_fopen(fname, "wb");
|
||||
|
||||
if (!fout) {
|
||||
fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
|
||||
@ -18778,7 +18960,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
|
||||
|
||||
// read file into data
|
||||
{
|
||||
FILE * fin = fopen(fname, "rb");
|
||||
FILE * fin = ggml_fopen(fname, "rb");
|
||||
if (!fin) {
|
||||
fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
|
||||
return result;
|
||||
@ -19114,7 +19296,7 @@ static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node,
|
||||
void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) {
|
||||
char color[16];
|
||||
|
||||
FILE * fp = fopen(filename, "w");
|
||||
FILE * fp = ggml_fopen(filename, "w");
|
||||
GGML_ASSERT(fp);
|
||||
|
||||
fprintf(fp, "digraph G {\n");
|
||||
@ -20161,7 +20343,8 @@ void ggml_quantize_init(enum ggml_type type) {
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ2_S:
|
||||
case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break;
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break;
|
||||
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
|
||||
case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
|
||||
default: // nothing
|
||||
@ -20186,7 +20369,8 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) {
|
||||
return
|
||||
type == GGML_TYPE_IQ2_XXS ||
|
||||
type == GGML_TYPE_IQ2_XS ||
|
||||
type == GGML_TYPE_IQ1_S;
|
||||
type == GGML_TYPE_IQ1_S;// ||
|
||||
//type == GGML_TYPE_IQ1_M;
|
||||
}
|
||||
|
||||
size_t ggml_quantize_chunk(
|
||||
@ -20230,6 +20414,7 @@ size_t ggml_quantize_chunk(
|
||||
case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
#if QK_K == 64
|
||||
case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
@ -20432,7 +20617,7 @@ struct gguf_context * gguf_init_empty(void) {
|
||||
}
|
||||
|
||||
struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
|
||||
FILE * file = fopen(fname, "rb");
|
||||
FILE * file = ggml_fopen(fname, "rb");
|
||||
if (!file) {
|
||||
return NULL;
|
||||
}
|
||||
@ -21387,7 +21572,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
|
||||
}
|
||||
|
||||
void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
|
||||
FILE * file = fopen(fname, "wb");
|
||||
FILE * file = ggml_fopen(fname, "wb");
|
||||
if (!file) {
|
||||
GGML_ASSERT(false && "failed to open file for writing");
|
||||
}
|
||||
@ -21529,15 +21714,15 @@ int ggml_cpu_has_wasm_simd(void) {
|
||||
}
|
||||
|
||||
int ggml_cpu_has_blas(void) {
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
|
||||
return 1;
|
||||
#else
|
||||
return 0;
|
||||
#endif
|
||||
}
|
||||
|
||||
int ggml_cpu_has_cublas(void) {
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
int ggml_cpu_has_cuda(void) {
|
||||
#if defined(GGML_USE_CUDA)
|
||||
return 1;
|
||||
#else
|
||||
return 0;
|
||||
@ -21577,7 +21762,7 @@ int ggml_cpu_has_sycl(void) {
|
||||
}
|
||||
|
||||
int ggml_cpu_has_gpublas(void) {
|
||||
return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
|
||||
return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
|
||||
ggml_cpu_has_sycl();
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user