wehub-resource-sync
85 lines
4.1 KiB
C++
85 lines
4.1 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/elementwise_kernel.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/funcs/elementwise_functor.h"
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#if defined(__NVCC__) || defined(__HIPCC__) || defined(__xpu__)
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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#endif
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namespace phi {
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#define DEFINE_CPU_ELEMENTWISE_OP(name) \
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template <typename T, typename Context> \
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void name##RawKernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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const DenseTensor& y, \
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int axis, \
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DenseTensor* out) { \
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dev_ctx.template Alloc<T>(out); \
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if (x.dims() == y.dims()) { \
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SameDimsElementwiseCompute<SameDims##name##Functor<CPUContext, T>>()( \
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dev_ctx, x, y, out); \
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} else { \
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auto x_dims = x.dims(); \
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auto y_dims = y.dims(); \
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if (x_dims.size() >= y_dims.size()) { \
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funcs::ElementwiseCompute<funcs::name##Functor<T>, T>( \
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dev_ctx, x, y, funcs::name##Functor<T>(), out, axis); \
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} else { \
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funcs::ElementwiseCompute<funcs::Inverse##name##Functor<T>, T>( \
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dev_ctx, x, y, funcs::Inverse##name##Functor<T>(), out, axis); \
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} \
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} \
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}
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#define DEFINE_CUDA_ELEMENTWISE_OP(name) \
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template <typename T, typename Context> \
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void name##RawKernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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const DenseTensor& y, \
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int axis, \
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DenseTensor* out) { \
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std::vector<const DenseTensor*> inputs = {&x, &y}; \
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std::vector<DenseTensor*> outputs = {out}; \
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dev_ctx.template Alloc<T>(out); \
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funcs::BroadcastKernel<T>( \
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dev_ctx, inputs, &outputs, funcs::name##Functor<T>(), axis); \
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}
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template <typename T, typename Context>
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void FMaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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funcs::ElementwiseCompute<funcs::FMaxFunctor<T>, T>(
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dev_ctx, x, y, funcs::FMaxFunctor<T>(), out);
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}
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template <typename T, typename Context>
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void FMinKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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funcs::ElementwiseCompute<funcs::FMinFunctor<T>, T>(
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dev_ctx, x, y, funcs::FMinFunctor<T>(), out);
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}
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} // namespace phi
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