// 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 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, MaxGradDy>( dev_ctx, x, y, dout, dout, axis, dx, dy, MaxGradDx(), MaxGradDy()); } template 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, MinGradDy>( dev_ctx, x, y, dout, dout, axis, dx, dy, MinGradDx(), MinGradDy()); } } // namespace phi 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_pow_grad, CPU, ALL_LAYOUT, phi::ElementwisePowGradKernel, float, double, int, int64_t) {}