extern/whisper.cpp
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ggml : sync latest ggml repo

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- new Q4 and Q8 quantization
- updated CUDA
This commit is contained in:
Georgi Gerganov 2023-05-20 18:56:30 +03:00
parent bc89f285d8
commit e410cfc3ce
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GPG Key ID: 449E073F9DC10735
5 changed files with 502 additions and 295 deletions
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3
examples/common.cpp
View File

@ -26,7 +26,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
} else if (arg == "-n" || arg == "--n_predict") {
params.n_predict = std::stoi(argv[++i]);
} else if (arg == "--top_k") {
params.top_k = std::stoi(argv[++i]);
params.top_k = std::max(1, std::stoi(argv[++i]));
} else if (arg == "--top_p") {
params.top_p = std::stof(argv[++i]);
} else if (arg == "--temp") {
@ -259,6 +259,7 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
if (it != vocab.token_to_id.end()) {
tokens.push_back(it->second);
i = j;
j = n;
break;
}
--j;

291
ggml-cuda.cu
View File

@ -42,19 +42,19 @@ typedef void (*dequantize_mul_mat_vec_cuda_t)(const void * vx, const float * y,
#define QK4_0 32
#define QR4_0 2
typedef struct {
float d; // delta
half d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
#define QR4_1 2
typedef struct {
float d; // delta
float m; // min
half d; // delta
half m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK5_0 32
#define QR5_0 2
@ -78,12 +78,23 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
#define QK8_0 32
#define QR8_0 1
typedef struct {
float d; // delta
half d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
#define CUDA_DMMV_BLOCK_SIZE 32
#define CUDA_MUL_BLOCK_SIZE 256
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
#define CUDA_DMMV_BLOCK_SIZE 32 // dmmv = dequantize_mul_mat_vec
static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= kx) {
return;
}
dst[i] = x[i] * y[i%ky];
}
static __device__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){
const block_q4_0 * x = (const block_q4_0 *) vx;
@ -170,104 +181,23 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
v1 = __half2float(x[ib + 1]);
}
static __global__ void dequantize_block_q4_0(const void * vx, float * y) {
static const int qk = QK4_0;
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
static __global__ void dequantize_block(const void * vx, float * y, const int k) {
const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
const block_q4_0 * x = (const block_q4_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0xf) - 8;
const int x1 = (x[i].qs[j] >> 4) - 8;
y[i*qk + j + 0 ] = x0*d;
y[i*qk + j + qk/2] = x1*d;
if (i >= k) {
return;
}
}
static __global__ void dequantize_block_q4_1(const void * vx, float * y) {
static const int qk = QK4_1;
const int ib = i/qk; // block index
const int iqs = (i%qk)/qr; // quant index
const int iybs = i - i%qk; // y block start index
const int y_offset = qr == 1 ? 1 : qk/2;
const block_q4_1 * x = (const block_q4_1 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0xf);
const int x1 = (x[i].qs[j] >> 4);
y[i*qk + j + 0 ] = x0*d + m;
y[i*qk + j + qk/2] = x1*d + m;
}
}
static __global__ void dequantize_block_q5_0(const void * vx, float * y) {
static const int qk = QK5_0;
const block_q5_0 * x = (const block_q5_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int j = 0; j < qk/2; ++j) {
const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;
y[i*qk + j + 0 ] = x0*d;
y[i*qk + j + qk/2] = x1*d;
}
}
static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
static const int qk = QK5_1;
const block_q5_1 * x = (const block_q5_1 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int j = 0; j < qk/2; ++j) {
const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
const int x0 = (x[i].qs[j] & 0xf) | xh_0;
const int x1 = (x[i].qs[j] >> 4) | xh_1;
y[i*qk + j + 0 ] = x0*d + m;
y[i*qk + j + qk/2] = x1*d + m;
}
}
static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
static const int qk = QK8_0;
const block_q8_0 * x = (const block_q8_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
for (int j = 0; j < qk; ++j) {
y[i*qk + j] = x[i].qs[j]*d;
}
// dequantize
float & v0 = y[iybs + iqs + 0];
float & v1 = y[iybs + iqs + y_offset];
dequantize_kernel(vx, ib, iqs, v0, v1);
}
template <int block_size, int qk, int qr, dequantize_kernel_t dequantize_kernel>
@ -308,29 +238,34 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y,
}
}
static void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_0;
dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
}
static void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_1;
dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_0;
dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
static void dequantize_row_q4_1_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<QK4_1, QR4_1, dequantize_q4_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_1;
dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
static void dequantize_row_q5_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<QK5_0, QR5_0, dequantize_q5_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK8_0;
dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
static void dequantize_row_q5_1_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<QK5_1, QR5_1, dequantize_q5_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<QK8_0, QR8_0, dequantize_q8_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
@ -363,17 +298,9 @@ static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const float * y, f
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
}
// TODO: optimize
static __global__ void convert_fp16_to_fp32(const void * vx, float * y) {
const half * x = (const half *) vx;
const int i = blockIdx.x;
y[i] = __half2float(x[i]);
}
static void convert_fp16_to_fp32_cuda(const void * x, float * y, int k, cudaStream_t stream) {
convert_fp16_to_fp32<<<k, 1, 0, stream>>>(x, y);
static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<32, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static void convert_mul_mat_vec_f16_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
@ -555,6 +482,67 @@ static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor
}
}
static void ggml_cuda_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src1->backend == GGML_BACKEND_CUDA);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[2];
const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
size_t x_size, d_size;
float * d_X = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &x_size); // src0
float * d_Y = (float *) src1->data; // src1 is already on device, broadcasted.
float * d_D = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &d_size); // dst
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const int i0 = i03*ne02 + i02;
float * c_X2 = d_X + i0*ne01*ne00;
float * c_D2 = d_D + i0*ne01*ne00;
cudaStream_t cudaStream = g_cudaStreams[i0 % GGML_CUDA_MAX_STREAMS];
cudaStream_t cudaStream2 = g_cudaStreams2[i0 % GGML_CUDA_MAX_STREAMS];
cudaEvent_t cudaEvent = g_cudaEvents[i0 % GGML_CUDA_MAX_EVENTS];
// copy src0 to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X2, src0, i03, i02, cudaStream2));
CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));
// wait for data
CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));
for (int64_t i01 = 0; i01 < ne01; i01++) {
const int64_t i13 = i03%ne13;
const int64_t i12 = i02%ne12;
const int64_t i11 = i01%ne11;
const int i1 = i13*ne12*ne11 + i12*ne11 + i11;
float * c_X1 = c_X2 + i01*ne00;
float * c_Y = d_Y + i1*ne10;
float * c_D1 = c_D2 + i01*ne00;
// compute
mul_f32_cuda(c_X1, c_Y, c_D1, ne00, ne10, cudaStream);
CUDA_CHECK(cudaGetLastError());
}
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D2, sizeof(float)*ne00*ne01, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_D, d_size);
}
static void ggml_cuda_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
@ -812,6 +800,11 @@ static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor
ggml_cuda_pool_free(d_Q, q_size);
}
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cuda_mul_f32(src0, src1, dst);
}
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
const int64_t ne10 = src1->ne[0];
@ -885,14 +878,48 @@ void ggml_cuda_transform_tensor(ggml_tensor * tensor) {
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
size_t q_size;
char * d_Q = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);
char * dst = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);
cudaStream_t cudaStream2 = g_cudaStreams2[0];
// copy tensor to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, tensor, 0, 0, cudaStream2));
CUDA_CHECK(cudaDeviceSynchronize());
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = 0; i2 < ne2; i2++) {
int i = i3*ne2 + i2;
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(dst + i*ne0*ne1, tensor, i3, i2, cudaStream2));
}
}
tensor->data = d_Q;
tensor->data = dst;
tensor->backend = GGML_BACKEND_CUDA;
}
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
FILE * fp = fopen(fname, "rb");
const size_t size = ggml_nbytes(tensor);
void * buf;
CUDA_CHECK(cudaMalloc(&buf, size));
void * buf_host = malloc(size);
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
#else
int ret = fseek(fp, (long) offset, SEEK_SET);
#endif
GGML_ASSERT(ret == 0); // same
size_t ret2 = fread(buf_host, size, 1, fp);
if (ret2 != 1) {
fprintf(stderr, "unexpectedly reached end of file");
exit(1);
}
cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
tensor->data = buf;
free(buf_host);
fclose(fp);
}

2
ggml-cuda.h
View File

@ -6,6 +6,7 @@ extern "C" {
void ggml_init_cublas(void);
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
@ -15,6 +16,7 @@ void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void ggml_cuda_transform_tensor(struct ggml_tensor * tensor);
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
#ifdef __cplusplus
}

485
ggml.c
View File

@ -512,7 +512,7 @@ static inline int hsum_i32_4(const __m128i a) {
return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
}
#if __AVX2__ || __AVX512F__
#if defined(__AVX2__) || defined(__AVX512F__)
// spread 32 bits to 32 bytes { 0x00, 0xFF }
static inline __m256i bytes_from_bits_32(const uint8_t * x) {
uint32_t x32;
@ -543,12 +543,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) {
return _mm256_cvtepi32_ps(summed_pairs);
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
#if __AVXVNNI__
const __m256i zero = _mm256_setzero_si256();
const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
@ -560,6 +555,21 @@ static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#endif
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#if __AVXVNNIINT8__
const __m256i zero = _mm256_setzero_si256();
const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
return _mm256_cvtepi32_ps(summed_pairs);
#else
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
return mul_sum_us8_pairs_float(ax, sy);
#endif
}
static inline __m128i packNibbles( __m256i bytes )
{
// Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
@ -619,6 +629,17 @@ static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
return _mm256_cvtepi32_ps(summed_pairs);
}
static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
const __m128i axl = _mm256_castsi256_si128(ax);
const __m128i axh = _mm256_extractf128_si256(ax, 1);
const __m128i syl = _mm256_castsi256_si128(sy);
const __m128i syh = _mm256_extractf128_si256(sy, 1);
// Perform multiplication and create 16-bit values
const __m128i dotl = _mm_maddubs_epi16(axl, syl);
const __m128i doth = _mm_maddubs_epi16(axh, syh);
return sum_i16_pairs_float(doth, dotl);
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
const __m128i xl = _mm256_castsi256_si128(x);
@ -667,7 +688,7 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
#endif // __AVX__ || __AVX2__ || __AVX512F__
#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
#if __ARM_NEON
#if defined(__ARM_NEON)
#if !defined(__aarch64__)
@ -748,18 +769,18 @@ int32x4_t vcvtnq_s32_f32(float32x4_t v) {
#define QK4_0 32
typedef struct {
float d; // delta
ggml_fp16_t d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
float d; // delta
float m; // min
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == 2 * sizeof(float) + QK4_1 / 2, "wrong q4_1 block size/padding");
static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK5_0 32
typedef struct {
@ -780,16 +801,16 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
#define QK8_0 32
typedef struct {
float d; // delta
int8_t qs[QK8_0]; // quants
ggml_fp16_t d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
#define QK8_1 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_1]; // quants
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_1]; // quants
} block_q8_1;
static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
@ -816,7 +837,7 @@ static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * r
const float d = max / -8;
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
for (int j = 0; j < qk/2; ++j) {
const float x0 = x[i*qk + 0 + j]*id;
@ -856,8 +877,8 @@ static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * r
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].m = min;
y[i].d = GGML_FP32_TO_FP16(d);
y[i].m = GGML_FP32_TO_FP16(min);
for (int j = 0; j < qk/2; ++j) {
const float x0 = (x[i*qk + 0 + j] - min)*id;
@ -988,7 +1009,7 @@ static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * r
const float d = amax / ((1 << 7) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
for (int j = 0; j < QK8_0; ++j) {
const float x0 = x[i*QK8_0 + j]*id;
@ -1023,7 +1044,7 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
const float d = amax / ((1 << 7) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
for (int j = 0; j < 8; j++) {
const float32x4_t v = vmulq_n_f32(srcv[j], id);
@ -1058,7 +1079,7 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
// Quantize these floats
const float d = maxScalar / 127.f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
const __m256 mul = _mm256_set1_ps( id );
@ -1157,7 +1178,7 @@ static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * r
sum += y[i].qs[QK8_1/2 + j];
}
y[i].s = d * sum;
y[i].s = sum*d;
}
}
@ -1309,7 +1330,7 @@ static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict
const int nb = k / qk;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float d = GGML_FP16_TO_FP32(x[i].d);
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0x0F) - 8;
@ -1329,8 +1350,8 @@ static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict
const int nb = k / qk;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float m = x[i].m;
const float d = GGML_FP16_TO_FP32(x[i].d);
const float m = GGML_FP16_TO_FP32(x[i].m);
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0x0F);
@ -1405,7 +1426,7 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in
const block_q8_0 * restrict x = vx;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float d = GGML_FP16_TO_FP32(x[i].d);
for (int j = 0; j < qk; ++j) {
y[i*qk + j] = x[i].qs[j]*d;
@ -1669,8 +1690,9 @@ quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
float tmp[8];
for (int i = 0; i < 8; i++)
for (int i = 0; i < 8; i++) {
tmp[i] = GGML_FP16_TO_FP32(x[i]);
}
return _mm256_loadu_ps(tmp);
}
@ -2090,8 +2112,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
const uint8x16_t v0_1 = vld1q_u8(x1->qs);
@ -2119,8 +2141,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
@ -2137,8 +2159,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#endif
}
@ -2150,7 +2172,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
__m256i bx = bytes_from_nibbles_32(x[i].qs);
@ -2174,7 +2196,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
// Compute combined scale for the block
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
const __m128i lowMask = _mm_set1_epi8(0xF);
const __m128i off = _mm_set1_epi8(8);
@ -2216,7 +2238,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 0 and 1
const __m128 d_0_1 = _mm_mul_ps( _mm_set1_ps( x[0].d ), _mm_set1_ps( y[0].d ) );
const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
@ -2234,7 +2256,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 2 and 3
const __m128 d_2_3 = _mm_mul_ps( _mm_set1_ps( x[1].d ), _mm_set1_ps( y[1].d ) );
const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
@ -2267,7 +2289,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 0 and 1
const __m128 d_0_1 = _mm_mul_ps( _mm_set1_ps( x[i].d ), _mm_set1_ps( y[i].d ) );
const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
@ -2285,7 +2307,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 2 and 3
const __m128 d_2_3 = _mm_mul_ps( _mm_set1_ps( x[i + 1].d ), _mm_set1_ps( y[i + 1].d ) );
const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
@ -2333,7 +2355,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
}
sumf += (x[i].d*y[i].d)*sumi;
sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
}
*s = sumf;
@ -2363,7 +2385,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const block_q8_1 * restrict y0 = &y[i + 0];
const block_q8_1 * restrict y1 = &y[i + 1];
summs += x0->m * y0->s + x1->m * y1->s;
summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
const uint8x16_t m4b = vdupq_n_u8(0x0F);
@ -2387,8 +2409,8 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
@ -2405,8 +2427,8 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
#endif
}
@ -2419,13 +2441,13 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
const float * d0 = &x[i].d;
const float * d1 = &y[i].d;
const float d0 = GGML_FP16_TO_FP32(x[i].d);
const float d1 = y[i].d;
summs += x[i].m * y[i].s;
summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
const __m256 d0v = _mm256_broadcast_ss( d0 );
const __m256 d1v = _mm256_broadcast_ss( d1 );
const __m256 d0v = _mm256_set1_ps( d0 );
const __m256 d1v = _mm256_set1_ps( d1 );
// Compute combined scales
const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
@ -2434,7 +2456,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const __m256i bx = bytes_from_nibbles_32(x[i].qs);
const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
const __m256 xy = mul_sum_i8_pairs_float(bx, by);
const __m256 xy = mul_sum_us8_pairs_float(bx, by);
// Accumulate d0*d1*x*y
#if defined(__AVX2__)
@ -2459,7 +2481,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
}
sumf += (x[i].d*y[i].d)*sumi + x[i].m*y[i].s;
sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
}
*s = sumf;
@ -2535,16 +2557,13 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
const int8x16_t v1_1l = vld1q_s8(y1->qs);
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
const float x0d = GGML_FP16_TO_FP32(x0->d);
const float x1d = GGML_FP16_TO_FP32(x1->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), x0d*y0->d);
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), x1d*y1->d);
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
@ -2561,8 +2580,8 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#endif
}
@ -2637,7 +2656,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; i++) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)), _mm256_broadcast_ss(&y[i].d));
const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
__m256i bx = bytes_from_nibbles_32(x[i].qs);
__m256i bxhi = bytes_from_bits_32(x[i].qh);
@ -2661,7 +2680,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; i++) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)), _mm256_broadcast_ss(&y[i].d));
const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
__m256i bx = bytes_from_nibbles_32(x[i].qs);
const __m256i bxhi = bytes_from_bits_32(x[i].qh);
@ -2704,7 +2723,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
}
sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi;
sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
}
*s = sumf;
@ -2786,16 +2805,13 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const int8x16_t v1_1l = vld1q_s8(y1->qs);
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
const float x0d = GGML_FP16_TO_FP32(x0->d);
const float x1d = GGML_FP16_TO_FP32(x1->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), x0d*y0->d);
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), x1d*y1->d);
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
@ -2812,8 +2828,8 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
#endif
}
@ -2873,15 +2889,14 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
const float x0d = GGML_FP16_TO_FP32(x0->d);
// dot product
sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(
wasm_i32x4_add(
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), wasm_f32x4_splat(x0d*y0->d)));
sumv = wasm_f32x4_add(sumv,
wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add(
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d));
}
*s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
@ -2903,10 +2918,10 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
bx = _mm256_or_si256(bx, bxhi);
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256 dy = _mm256_set1_ps(y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
const __m256 q = mul_sum_us8_pairs_float(bx, by);
acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
}
@ -2937,10 +2952,10 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
bxh = _mm_or_si128(bxh, bxhih);
bx = _mm256_set_m128i(bxh, bxl);
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256 dy = _mm256_set1_ps(y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
const __m256 q = mul_sum_us8_pairs_float(bx, by);
acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
}
@ -3007,11 +3022,11 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), x0->d*y0->d);
vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), x1->d*y1->d);
vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#else
const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
@ -3029,8 +3044,8 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#endif
}
@ -3042,7 +3057,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
// Compute combined scale for the block
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
__m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
__m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
@ -3068,7 +3083,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
sumi += x[i].qs[j]*y[i].qs[j];
}
sumf += (x[i].d*y[i].d)*sumi;
sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
}
*s = sumf;
@ -3457,6 +3472,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"ROPE",
"ROPE_BACK",
"ALIBI",
"CLAMP",
"CONV_1D_1S",
"CONV_1D_2S",
@ -3467,7 +3483,8 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"MAP_BINARY",
};
static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -3517,6 +3534,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"rope(x)",
"rope_back(x)",
"alibi(x)",
"clamp(x)",
"conv_1d_1s(x)",
"conv_1d_2s(x)",
@ -3527,7 +3545,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"f(x,y)",
};
static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@ -3761,6 +3779,12 @@ static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct g
(t1->ne[3]%t0->ne[3] == 0);
}
static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1);
}
static inline int ggml_up32(int n) {
return (n + 31) & ~31;
}
@ -4643,11 +4667,15 @@ struct ggml_tensor * ggml_mul_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
bool is_node = false;
if (!inplace && (a->grad || b->grad)) {
// TODO: support backward pass for broadcasting
GGML_ASSERT(ggml_are_same_shape(a, b));
is_node = true;
}
@ -6189,7 +6217,8 @@ struct ggml_tensor * ggml_alibi(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_head) {
int n_head,
float bias_max) {
GGML_ASSERT(n_past >= 0);
bool is_node = false;
@ -6208,6 +6237,8 @@ struct ggml_tensor * ggml_alibi(
((int32_t *) b->data)[0] = n_past;
((int32_t *) b->data)[1] = n_head;
GGML_ASSERT(sizeof(float) == sizeof(int32_t));
(((float *) b->data)[2]) = bias_max;
ggml_scratch_load(ctx);
@ -6219,6 +6250,40 @@ struct ggml_tensor * ggml_alibi(
return result;
}
// ggml_clamp
struct ggml_tensor * ggml_clamp(
struct ggml_context * ctx,
struct ggml_tensor * a,
float min,
float max) {
bool is_node = false;
if (a->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
// TODO: when implement backward, fix this:
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
((float *) b->data)[0] = min;
((float *) b->data)[1] = max;
ggml_scratch_load(ctx);
result->op = GGML_OP_CLAMP;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_conv_1d_1s
struct ggml_tensor * ggml_conv_1d_1s(
@ -7945,7 +8010,7 @@ static void ggml_compute_forward_mul_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@ -7953,10 +8018,25 @@ static void ggml_compute_forward_mul_f32(
const int ith = params->ith;
const int nth = params->nth;
const int nr = ggml_nrows(src0);
const int64_t ne0 = src0->ne[0];
const int64_t ne1 = src0->ne[1];
const int64_t ne2 = src0->ne[2];
#ifdef GGML_USE_CUBLAS
if (src1->backend == GGML_BACKEND_CUDA) {
if (ith == 0) {
ggml_cuda_mul(src0, src1, dst);
}
return;
}
#endif
const int64_t nr = ggml_nrows(src0);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
@ -7975,44 +8055,51 @@ static void ggml_compute_forward_mul_f32(
GGML_ASSERT( nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(ne00 == ne10);
if (nb10 == sizeof(float)) {
for (int ir = ith; ir < nr; ir += nth) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
#ifdef GGML_USE_ACCELERATE
UNUSED(ggml_vec_mul_f32);
vDSP_vmul(
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1,
ne0);
vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
#else
ggml_vec_mul_f32(ne0,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ),
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
// }
// }
}
} else {
// src1 is not contiguous
for (int ir = ith; ir < nr; ir += nth) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
// src1 is broadcastable across src0 and dst in i1, i2, i3
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int64_t i0 = 0; i0 < ne00; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
}
@ -10501,34 +10588,29 @@ static void ggml_compute_forward_diag_mask_f32(
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
const int n_past = ((int32_t *) src1->data)[0];
const bool inplace = (bool)((int32_t *) src1->data)[1];
if (params->type == GGML_TASK_INIT) {
// TODO: this hack is not good, need a better way to handle this
if (!inplace) {
// use the init task to copy src -> dst
struct ggml_compute_params params_cpy = *params;
params_cpy.ith = 0;
params_cpy.nth = 1;
params_cpy.type = GGML_TASK_COMPUTE;
ggml_compute_forward_dup_same_cont(&params_cpy, src0, dst);
}
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
const int ith = params->ith;
const int nth = params->nth;
const int n_past = ((int32_t *) src1->data)[0];
const bool inplace = (bool)((int32_t *) src1->data)[1];
assert(n_past >= 0);
if (!inplace && (params->type == GGML_TASK_INIT)) {
// memcpy needs to be synchronized across threads to avoid race conditions.
// => do it in INIT phase
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
memcpy(
((char *) dst->data),
((char *) src0->data),
ggml_nbytes(dst));
}
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
// TODO: handle transposed/permuted matrices
const int n = ggml_nrows(src0);
@ -10682,14 +10764,15 @@ static void ggml_compute_forward_alibi_f32(
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
assert(n_past >= 0);
@ -10712,8 +10795,8 @@ static void ggml_compute_forward_alibi_f32(
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
for (int i = 0; i < ne0; i++) {
for (int j = 0; j < ne1; j++) {
@ -10731,13 +10814,13 @@ static void ggml_compute_forward_alibi_f32(
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
}
pdst[0] = i * m_k + src[0];
pdst[0] = (i-ne0+1) * m_k + src[0];
}
}
}
}
static void ggml_compute_forward_alibi_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
@ -10745,14 +10828,15 @@ static void ggml_compute_forward_alibi_f16(
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
assert(n_past >= 0);
@ -10775,8 +10859,8 @@ static void ggml_compute_forward_alibi_f16(
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
for (int i = 0; i < ne0; i++) {
for (int j = 0; j < ne1; j++) {
@ -10795,7 +10879,7 @@ static void ggml_compute_forward_alibi_f16(
}
// we return F32
pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
pdst[0] = (i-ne0+1) * m_k + GGML_FP16_TO_FP32(src[0]);
}
}
}
@ -10831,6 +10915,77 @@ static void ggml_compute_forward_alibi(
}
}
// ggml_compute_forward_clamp
static void ggml_compute_forward_clamp_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int min = ((float *) src1->data)[0];
const int max = ((float *) src1->data)[1];
const int ith = params->ith;
const int nth = params->nth;
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
const size_t nb0 = dst->nb[0];
const size_t nb1 = dst->nb[1];
GGML_ASSERT( nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
for (int j = ith; j < n; j += nth) {
float * dst_ptr = (float *) ((char *) dst->data + j*nb1);
float * src0_ptr = (float *) ((char *) src0->data + j*nb01);
for (int i = 0; i < nc; i++) {
dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min);
}
}
}
static void ggml_compute_forward_clamp(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_clamp_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q8_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_rope
static void ggml_compute_forward_rope_f32(
@ -12812,6 +12967,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_alibi(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CLAMP:
{
ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CONV_1D_1S:
{
ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor);
@ -13119,6 +13278,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CLAMP:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_SILU:
{
// necessary for llama
@ -13998,6 +14161,10 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
{
node->n_tasks = 1; //TODO
} break;
case GGML_OP_CLAMP:
{
node->n_tasks = 1; //TODO
} break;
case GGML_OP_CONV_1D_1S:
case GGML_OP_CONV_1D_2S:
{

16
ggml.h
View File

@ -190,7 +190,7 @@
#define GGML_FILE_MAGIC 0x67676d6c // "ggml"
#define GGML_FILE_VERSION 1
#define GGML_QNT_VERSION 1 // bump this on quantization format changes
#define GGML_QNT_VERSION 2 // bump this on quantization format changes
#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
#define GGML_MAX_DIMS 4
@ -313,6 +313,7 @@ extern "C" {
GGML_OP_ROPE,
GGML_OP_ROPE_BACK,
GGML_OP_ALIBI,
GGML_OP_CLAMP,
GGML_OP_CONV_1D_1S,
GGML_OP_CONV_1D_2S,
@ -849,7 +850,7 @@ extern "C" {
int n_past);
// in-place, returns view(a)
GGML_API struct ggml_tensor * gml_diag_mask_zero_inplace(
GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
@ -897,7 +898,16 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_head);
int n_head,
float bias_max);
// clamp
// in-place, returns view(a)
struct ggml_tensor * ggml_clamp(
struct ggml_context * ctx,
struct ggml_tensor * a,
float min,
float max);
// padding = 1
// TODO: we don't support extra parameters for now