wehub-resource-sync
123 lines
3.8 KiB
C++
123 lines
3.8 KiB
C++
// Copyright (c) 2021 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/funcs/broadcast_function.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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template <typename T, typename Context>
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void ConjKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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if (out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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auto numel = x.numel();
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auto* x_data = x.data<T>();
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auto* out_data = dev_ctx.template Alloc<T>(out);
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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funcs::ConjFunctor<T> functor(x_data, numel, out_data);
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for_range(functor);
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}
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template <typename T, typename Context>
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void RealKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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if (out->numel() == 0) {
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dev_ctx.template Alloc<dtype::Real<T>>(out);
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return;
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}
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auto numel = x.numel();
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auto* x_data = x.data<T>();
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auto* out_data = dev_ctx.template Alloc<dtype::Real<T>>(
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out, static_cast<size_t>(numel * sizeof(dtype::Real<T>)));
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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funcs::RealFunctor<T> functor(x_data, out_data, numel);
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for_range(functor);
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}
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template <typename T, typename Context>
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void ImagKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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if (out->numel() == 0) {
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dev_ctx.template Alloc<dtype::Real<T>>(out);
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return;
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}
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auto numel = x.numel();
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auto* x_data = x.data<T>();
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auto* out_data = dev_ctx.template Alloc<dtype::Real<T>>(
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out, static_cast<size_t>(numel * sizeof(dtype::Real<T>)));
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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funcs::ImagFunctor<T> functor(x_data, out_data, numel);
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for_range(functor);
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}
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// functors to use with ElementwiseComputeEx
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template <typename T>
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struct RealAndImagToComplexFunctor {
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inline HOSTDEVICE dtype::complex<T> operator()(const T x, const T y) {
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return dtype::complex<T>(x, y);
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}
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};
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template <typename T>
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struct ImagAndRealToComplexFunctor {
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inline HOSTDEVICE dtype::complex<T> operator()(const T y, const T x) {
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return dtype::complex<T>(x, y);
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}
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};
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template <typename T, typename Context>
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void ComplexKernel(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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using C = dtype::complex<T>;
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if (out->numel() == 0) {
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dev_ctx.template Alloc<C>(out);
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return;
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}
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dev_ctx.template Alloc<C>(out);
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// NOTE(chenfeiyu): be careful of the caveats of calling elementwise-related
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// facility functions
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#if defined(__NVCC__) || defined(__HIPCC__)
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funcs::ElementwiseCompute<RealAndImagToComplexFunctor<T>, T, C>(
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dev_ctx, x, y, RealAndImagToComplexFunctor<T>(), 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<RealAndImagToComplexFunctor<T>, T, C>(
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dev_ctx, x, y, RealAndImagToComplexFunctor<T>(), out);
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} else {
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funcs::ElementwiseCompute<ImagAndRealToComplexFunctor<T>, T, C>(
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dev_ctx, x, y, ImagAndRealToComplexFunctor<T>(), out);
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}
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#endif
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}
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} // namespace phi
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