wehub-resource-sync
142 lines
4.2 KiB
C++
142 lines
4.2 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/kernels/abs_grad_kernel.h"
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#include "paddle/phi/kernels/funcs/complex_functors.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/funcs/for_range.h"
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namespace phi {
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#if defined(__NVCC__)
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template <typename T>
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struct AbsGradCUDAFunctor {
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HOSTDEVICE inline AbsGradCUDAFunctor() {}
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HOSTDEVICE inline T operator()(const T x, const T dout) const {
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T output;
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if (x == T(0)) {
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output = T(0);
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} else {
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output = T(dout) * (x / T(std::abs(x)));
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}
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return output;
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}
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};
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template <>
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struct AbsGradCUDAFunctor<bfloat16> {
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HOSTDEVICE inline AbsGradCUDAFunctor() {}
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HOSTDEVICE inline bfloat16 operator()(const bfloat16 x,
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const bfloat16 dout) const {
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bfloat16 output;
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if (x == bfloat16(0)) {
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output = static_cast<bfloat16>(0);
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} else {
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output = (dout) * (x / abs(x));
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}
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return output;
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}
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};
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template <>
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struct AbsGradCUDAFunctor<complex64> {
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HOSTDEVICE inline AbsGradCUDAFunctor() {}
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HOSTDEVICE inline complex64 operator()(const complex64 x,
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const float dout) const {
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complex64 output;
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if (x == complex64(0)) {
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output = complex64(0);
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} else {
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output = complex64(dout) * (x / complex64(abs(x)));
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}
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return output;
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}
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};
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template <>
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struct AbsGradCUDAFunctor<complex128> {
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HOSTDEVICE inline AbsGradCUDAFunctor() {}
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HOSTDEVICE inline complex128 operator()(const complex128 x,
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const double dout) const {
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complex128 output;
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if (x == complex128(0)) {
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output = complex128(0);
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} else {
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output = complex128(dout) * (x / complex128(abs(x)));
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}
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return output;
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}
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};
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template <typename T>
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void AbsGradKernelImpl(const GPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& dout,
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DenseTensor* dx) {
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std::vector<const DenseTensor*> ins = {&x, &dout};
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std::vector<DenseTensor*> outs = {dx};
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dev_ctx.Alloc<T>(dx);
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AbsGradCUDAFunctor<T> abs_grad_cuda_functor;
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funcs::ElementwiseKernel<T>(dev_ctx, ins, &outs, abs_grad_cuda_functor);
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}
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template <typename T, typename Context>
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void AbsGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& dout,
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DenseTensor* dx) {
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AbsGradKernelImpl<T>(dev_ctx, x, dout, dx);
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}
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#else
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template <typename T, typename Context>
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void AbsGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& dout,
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DenseTensor* dx) {
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auto numel = dout.numel();
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auto* dout_data = dout.data<dtype::Real<T>>();
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auto* x_data = x.data<T>();
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dev_ctx.template Alloc<T>(dx, static_cast<size_t>(numel * sizeof(T)));
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auto* dx_data = dx->data<T>();
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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funcs::AbsGradFunctor<T> functor(dout_data, x_data, dx_data, numel);
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for_range(functor);
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}
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#endif
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template <typename T, typename Context>
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void AbsDoubleGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& ddx,
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DenseTensor* ddout) {
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auto numel = ddx.numel();
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auto* ddx_data = ddx.data<T>();
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auto* x_data = x.data<T>();
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dev_ctx.template Alloc<T>(ddout, static_cast<size_t>(numel * sizeof(T)));
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auto* ddout_data = ddout->data<T>();
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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funcs::AbsGradGradFunctor<T> functor(ddx_data, x_data, ddout_data, numel);
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for_range(functor);
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}
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} // namespace phi
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