wehub-resource-sync
160 lines
5.8 KiB
C++
160 lines
5.8 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/elementwise_grad_base.h"
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namespace phi {
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// NOTE(dzhwinter): Only used in elementwise_add, elementwise_sub.
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// explicit gradient can cut off X, Y, Out from gradient op
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// In elementwise_add, elementwise_sub, we use dout as fake X, Y, Out to reuse
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// elementwise code.
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template <typename T, typename DX_OP, typename DY_OP>
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void ElemwiseExplicitGradCompute(const CPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& out,
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const DenseTensor& dout,
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int axis,
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DenseTensor* dx,
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DenseTensor* dy,
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DX_OP dx_op,
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DY_OP dy_op) {
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const DDim& x_dim = x.dims();
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const DDim& y_dim = y.dims();
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if (x.dims() == y.dims()) {
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funcs::ElemwiseGradComputeNoBroadcast<CPUContext, T, DX_OP, DY_OP>(dev_ctx,
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x_dim,
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y_dim,
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dout,
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dout,
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out,
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dout,
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axis,
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dx,
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dy,
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dx_op,
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dy_op);
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} else {
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funcs::ElemwiseGradComputeWithBroadcast<T, DX_OP, DY_OP>(dev_ctx,
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x_dim,
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y_dim,
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dout,
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dout,
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out,
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dout,
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axis,
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dx,
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dy,
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dx_op,
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dy_op);
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}
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}
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/*
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******************************
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Add Grad
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******************************
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*/
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template <typename T>
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struct IdentityGrad {
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HOSTDEVICE T operator()(T x UNUSED, T y UNUSED, T out UNUSED, T dout) const {
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return dout;
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}
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};
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template <typename T>
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typename std::enable_if<std::is_floating_point<T>::value>::type
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ElementwiseAddGrad(const CPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& out,
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const DenseTensor& dout,
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DenseTensor* dx,
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DenseTensor* dy,
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int axis = -1) {
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auto blas = funcs::GetBlas<CPUContext, T>(dev_ctx);
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if (dx) {
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blas.VCOPY(dout.numel(), dout.data<T>(), dev_ctx.template Alloc<T>(dx));
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}
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if (dy) {
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blas.VCOPY(dout.numel(), dout.data<T>(), dev_ctx.template Alloc<T>(dy));
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}
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}
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template <typename T>
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typename std::enable_if<!std::is_floating_point<T>::value>::type
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ElementwiseAddGrad(const CPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& out,
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const DenseTensor& dout,
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DenseTensor* dx,
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DenseTensor* dy,
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int axis = -1) {
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ElemwiseExplicitGradCompute<T, IdentityGrad<T>, IdentityGrad<T>>(
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dev_ctx,
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x,
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y,
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out,
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dout,
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axis,
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dx,
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dy,
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IdentityGrad<T>(),
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IdentityGrad<T>());
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}
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/*
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******************************
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Sub Grad
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******************************
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*/
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template <typename T>
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struct SubGradDX {
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HOSTDEVICE T operator()(T x UNUSED, T y UNUSED, T out UNUSED, T dout) const {
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return dout;
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}
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};
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template <typename T>
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struct SubGradDY {
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HOSTDEVICE T operator()(T x UNUSED, T y UNUSED, T out UNUSED, T dout) const {
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return -dout;
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}
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};
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template <typename T>
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void ElementwiseSubGrad(const CPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& out,
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const DenseTensor& dout,
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DenseTensor* dx,
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DenseTensor* dy,
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int axis = -1) {
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ElemwiseExplicitGradCompute<T, SubGradDX<T>, SubGradDY<T>>(
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dev_ctx, x, y, out, dout, axis, dx, dy, SubGradDX<T>(), SubGradDY<T>());
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}
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} // namespace phi
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