chore: import upstream snapshot with attribution

This commit is contained in:
wehub-resource-sync
2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
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// Copyright (c) 2021 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.
#pragma once
#include "paddle/phi/kernels/funcs/broadcast_function.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/funcs/for_range.h"
namespace phi {
template <typename T, typename Context>
void ConjKernel(const Context& dev_ctx,
const DenseTensor& x,
DenseTensor* out) {
if (out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
auto numel = x.numel();
auto* x_data = x.data<T>();
auto* out_data = dev_ctx.template Alloc<T>(out);
funcs::ForRange<Context> for_range(dev_ctx, numel);
funcs::ConjFunctor<T> functor(x_data, numel, out_data);
for_range(functor);
}
template <typename T, typename Context>
void RealKernel(const Context& dev_ctx,
const DenseTensor& x,
DenseTensor* out) {
if (out->numel() == 0) {
dev_ctx.template Alloc<dtype::Real<T>>(out);
return;
}
auto numel = x.numel();
auto* x_data = x.data<T>();
auto* out_data = dev_ctx.template Alloc<dtype::Real<T>>(
out, static_cast<size_t>(numel * sizeof(dtype::Real<T>)));
funcs::ForRange<Context> for_range(dev_ctx, numel);
funcs::RealFunctor<T> functor(x_data, out_data, numel);
for_range(functor);
}
template <typename T, typename Context>
void ImagKernel(const Context& dev_ctx,
const DenseTensor& x,
DenseTensor* out) {
if (out->numel() == 0) {
dev_ctx.template Alloc<dtype::Real<T>>(out);
return;
}
auto numel = x.numel();
auto* x_data = x.data<T>();
auto* out_data = dev_ctx.template Alloc<dtype::Real<T>>(
out, static_cast<size_t>(numel * sizeof(dtype::Real<T>)));
funcs::ForRange<Context> for_range(dev_ctx, numel);
funcs::ImagFunctor<T> functor(x_data, out_data, numel);
for_range(functor);
}
// functors to use with ElementwiseComputeEx
template <typename T>
struct RealAndImagToComplexFunctor {
inline HOSTDEVICE dtype::complex<T> operator()(const T x, const T y) {
return dtype::complex<T>(x, y);
}
};
template <typename T>
struct ImagAndRealToComplexFunctor {
inline HOSTDEVICE dtype::complex<T> operator()(const T y, const T x) {
return dtype::complex<T>(x, y);
}
};
template <typename T, typename Context>
void ComplexKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
using C = dtype::complex<T>;
if (out->numel() == 0) {
dev_ctx.template Alloc<C>(out);
return;
}
dev_ctx.template Alloc<C>(out);
// NOTE(chenfeiyu): be careful of the caveats of calling elementwise-related
// facility functions
#if defined(__NVCC__) || defined(__HIPCC__)
funcs::ElementwiseCompute<RealAndImagToComplexFunctor<T>, T, C>(
dev_ctx, x, y, RealAndImagToComplexFunctor<T>(), out);
#else
auto x_dims = x.dims();
auto y_dims = y.dims();
if (x_dims.size() >= y_dims.size()) {
funcs::ElementwiseCompute<RealAndImagToComplexFunctor<T>, T, C>(
dev_ctx, x, y, RealAndImagToComplexFunctor<T>(), out);
} else {
funcs::ElementwiseCompute<ImagAndRealToComplexFunctor<T>, T, C>(
dev_ctx, x, y, ImagAndRealToComplexFunctor<T>(), out);
}
#endif
}
} // namespace phi