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/
elementwise_grad_kernel.cc
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/
elementwise_grad_kernel.cc
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// 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/elementwise_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/copy_kernel.h"
#include "paddle/phi/kernels/cpu/elementwise_grad.h"
#include "paddle/phi/kernels/funcs/elementwise_functor.h"
#include "paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h"
namespace phi {
template <typename T>
void AddGradFunc(const CPUContext& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& out,
const DenseTensor& dout,
DenseTensor* dx,
DenseTensor* dy,
int axis = -1) {
if (dx != nullptr && dy != nullptr && (dx->dims() == dy->dims())) {
ElementwiseAddGrad<T>(dev_ctx, x, y, out, dout, dx, dy);
} else {
ElemwiseExplicitGradCompute<T, IdentityGrad<T>, IdentityGrad<T>>(
dev_ctx,
x,
y,
out,
dout,
axis,
dx,
dy,
IdentityGrad<T>(),
IdentityGrad<T>());
}
}
template <typename T, typename Context>
void AddGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
phi::AddGradImpl<T>(dev_ctx, x, y, dout, axis, dx, dy, AddGradFunc<T>);
}
template <typename T, typename Context>
void AddDoubleGradKernel(const Context& dev_ctx,
const DenseTensor& y,
const DenseTensor& dout,
paddle::optional<const DenseTensor&> ddx,
paddle::optional<const DenseTensor&> ddy,
int axis,
DenseTensor* ddout) {
phi::AddDoubleGradImpl<T>(dev_ctx, y, ddx, ddy, dout, axis, ddout);
}
template <typename T, typename Context>
void AddTripleGradKernel(const Context& dev_ctx,
const DenseTensor& ddx,
const DenseTensor& ddy,
const DenseTensor& d_ddout,
int axis,
DenseTensor* d_ddx,
DenseTensor* d_ddy) {
phi::AddGradImpl<T>(
dev_ctx, ddx, ddy, d_ddout, axis, d_ddx, d_ddy, AddGradFunc<T>);
}
template <typename T, typename Context>
void SubtractGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
// skip out
auto* out = &dout;
ElementwiseSubGrad<T>(dev_ctx, x, y, *out, dout, dx, dy, axis);
}
template <typename T, typename Context>
void SubtractDoubleGradKernel(const Context& dev_ctx,
const DenseTensor& y,
paddle::optional<const DenseTensor&> ddx,
paddle::optional<const DenseTensor&> ddy,
const DenseTensor& dout,
int axis,
DenseTensor* ddout) {
phi::SubtractDoubleGradImpl<T>(dev_ctx, y, ddx, ddy, dout, axis, ddout);
}
template <typename T, typename Context>
void DivideGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& out,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
funcs::ElementwiseGradPreProcess(dout, dx);
phi::funcs::ElemwiseGradCompute<Context, T, DivGradDX<T>, DivGradDY<T>>(
dev_ctx, x, y, out, dout, axis, dx, dy, DivGradDX<T>(), DivGradDY<T>());
}
template <typename T, typename Context>
void MultiplyGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
funcs::ElementwiseGradPreProcess(dout, dx);
auto* out = &dout; // out is not necessary
phi::funcs::ElemwiseGradCompute<Context, T, MulGradDX<T>, MulGradDY<T>>(
dev_ctx, x, y, *out, dout, axis, dx, dy, MulGradDX<T>(), MulGradDY<T>());
}
template <typename T, typename Context>
void MaximumGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
funcs::ElementwiseGradPreProcess(dout, dx);
phi::funcs::ElemwiseGradCompute<Context, T, MaxGradDx<T>, MaxGradDy<T>>(
dev_ctx, x, y, dout, dout, axis, dx, dy, MaxGradDx<T>(), MaxGradDy<T>());
}
template <typename T, typename Context>
void MinimumGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
funcs::ElementwiseGradPreProcess(dout, dx);
phi::funcs::ElemwiseGradCompute<Context, T, MinGradDx<T>, MinGradDy<T>>(
dev_ctx, x, y, dout, dout, axis, dx, dy, MinGradDx<T>(), MinGradDy<T>());
}
} // namespace phi
PD_REGISTER_KERNEL(add_grad,
CPU,
ALL_LAYOUT,
phi::AddGradKernel,
float,
double,
int16_t,
int,
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(add_double_grad,
CPU,
ALL_LAYOUT,
phi::AddDoubleGradKernel,
float,
double,
int16_t,
int,
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(add_triple_grad,
CPU,
ALL_LAYOUT,
phi::AddTripleGradKernel,
float,
double,
int16_t,
int,
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(subtract_grad,
CPU,
ALL_LAYOUT,
phi::SubtractGradKernel,
float,
double,
int16_t,
int,
int64_t,
phi::dtype::bfloat16,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(subtract_double_grad,
CPU,
ALL_LAYOUT,
phi::SubtractDoubleGradKernel,
float,
double,
int16_t,
int,
int64_t,
phi::dtype::bfloat16,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(divide_grad,
CPU,
ALL_LAYOUT,
phi::DivideGradKernel,
float,
double,
int,
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(divide_double_grad,
CPU,
ALL_LAYOUT,
phi::DivideDoubleGradKernel,
float,
double,
int,
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(multiply_grad,
CPU,
ALL_LAYOUT,
phi::MultiplyGradKernel,
float,
double,
int,
int64_t,
bool,
phi::dtype::bfloat16,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(multiply_double_grad,
CPU,
ALL_LAYOUT,
phi::MultiplyDoubleGradKernel,
float,
double,
int,
int64_t,
bool,
phi::dtype::bfloat16,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(multiply_triple_grad,
CPU,
ALL_LAYOUT,
phi::MultiplyTripleGradKernel,
float,
double,
int,
int64_t,
bool,
phi::dtype::bfloat16,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
PD_REGISTER_KERNEL(fmax_grad,
CPU,
ALL_LAYOUT,
phi::ElementwiseFMaxGradKernel,
float,
double,
int,
int64_t) {}
PD_REGISTER_KERNEL(fmin_grad,
CPU,
ALL_LAYOUT,
phi::ElementwiseFMinGradKernel,
float,
double,
int,
int64_t) {}
PD_REGISTER_KERNEL(maximum_grad,
CPU,
ALL_LAYOUT,
phi::MaximumGradKernel,
float,
double,
int,
int64_t,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(minimum_grad,
CPU,
ALL_LAYOUT,
phi::MinimumGradKernel,
float,
double,
int,
int64_t,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(elementwise_heaviside_grad,
CPU,
ALL_LAYOUT,
phi::ElementwiseHeavisideGradKernel,
float,
double,
int,
int64_t) {}
PD_REGISTER_KERNEL(elementwise_pow_grad,
CPU,
ALL_LAYOUT,
phi::ElementwisePowGradKernel,
float,
double,
int,
int64_t) {}