llama.cpp/ggml/src/ggml-blas/mmf.cpp

60 lines
1.8 KiB
C++

#include "ggml.h"
#include "mmf.hpp"
void ggml_blas_mul_mat_f(
const ggml_backend_blas_context * ctx,
const ggml_tensor * src0,
const ggml_tensor * src1,
ggml_tensor * dst) {
GGML_TENSOR_BINARY_OP_LOCALS
const ggml_type type = src0->type;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const int64_t ne_plane = ne01*ne00;
const ggml_backend_blas_buffer * extra = (ggml_backend_blas_buffer *)src0->extra;
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
// switch to dequantized F32 data
if (type != GGML_TYPE_F32) {
x = (float *)extra->data + i02*ne_plane + i03*ne02*ne_plane;
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne1, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
}
}
GGML_UNUSED(ctx);
}