Files
2026-07-13 12:40:42 +08:00

83 lines
2.3 KiB
C++

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/mv_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
namespace phi {
template <typename T, typename Context>
void MvGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& vec,
const DenseTensor& out_grad,
DenseTensor* x_grad,
DenseTensor* vec_grad) {
auto dout = out_grad;
auto dx = x_grad;
auto dvec = vec_grad;
if (x.numel() == 0 || vec.numel() == 0) {
if (dx) {
Full<T, Context>(dev_ctx, dx->dims(), static_cast<T>(0), dx);
}
if (dvec) {
Full<T, Context>(dev_ctx, dvec->dims(), static_cast<T>(0), dvec);
}
return;
}
const auto& dim_x = x.dims();
int m = static_cast<int>(dim_x[0]);
int n = static_cast<int>(dim_x[1]);
// get data ptr
const T* x_data = x.data<T>();
const T* vec_data = vec.data<T>();
const T* dout_data = dout.data<T>();
if (dx) {
T* dx_data = dev_ctx.template Alloc<T>(dx);
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
dx_data[i * n + j] = dout_data[i] * vec_data[j];
}
}
}
if (dvec) {
T* dvec_data = dev_ctx.template Alloc<T>(dvec);
auto blas = funcs::GetBlas<Context, T>(dev_ctx);
blas.GEMV(true,
dim_x[0],
dim_x[1],
static_cast<T>(1),
x_data,
dout_data,
static_cast<T>(0),
dvec_data);
}
}
} // namespace phi
PD_REGISTER_KERNEL(mv_grad, CPU, ALL_LAYOUT, phi::MvGradKernel, float, double) {
}