wehub-resource-sync
122 lines
4.6 KiB
C++
122 lines
4.6 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/fft_grad_kernel.h"
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#include <string>
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#include <vector>
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#include "paddle/common/ddim.h"
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_meta.h"
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#include "paddle/phi/kernels/complex_kernel.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/fft.h"
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#include "paddle/phi/kernels/funcs/fft_fill_conj.h"
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#include "paddle/phi/kernels/funcs/for_range.h"
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#include "paddle/phi/kernels/pad_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void FFTC2CGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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const std::vector<int64_t>& axes,
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const std::string& normalization,
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bool forward,
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DenseTensor* x_grad) {
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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auto norm_type = funcs::get_norm_from_string(normalization, forward);
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funcs::FFTC2CFunctor<Context, T, T> fft_c2c_func;
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fft_c2c_func(dev_ctx, out_grad, x_grad, axes, norm_type, !forward);
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}
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template <typename T, typename Context>
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void FFTR2CGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const std::vector<int64_t>& axes,
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const std::string& normalization,
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bool forward,
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bool onesided,
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DenseTensor* x_grad) {
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using R = typename T::value_type;
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DenseTensor complex_x_grad = EmptyLike<T>(dev_ctx, x);
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dev_ctx.template Alloc<R>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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auto norm_type = funcs::get_norm_from_string(normalization, forward);
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funcs::FFTC2CFunctor<Context, T, T> fft_c2c_func;
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if (!onesided) {
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fft_c2c_func(dev_ctx, out_grad, &complex_x_grad, axes, norm_type, !forward);
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} else {
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DenseTensor full_dy;
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DenseTensorMeta full_dy_meta(out_grad.type(), x_grad->dims());
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full_dy.set_meta(full_dy_meta);
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auto zero_length = static_cast<int>(full_dy.dims().at(axes.back()) -
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out_grad.dims().at(axes.back()));
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auto rank = out_grad.dims().size();
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std::vector<int> pads(rank * 2, 0);
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pads[axes.back() * 2 + 1] = zero_length;
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PadKernel<T>(dev_ctx, out_grad, pads, static_cast<float>(0.0), &full_dy);
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fft_c2c_func(dev_ctx, full_dy, &complex_x_grad, axes, norm_type, !forward);
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}
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RealKernel<T>(dev_ctx, complex_x_grad, x_grad);
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}
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template <typename T, typename Context>
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void FFTC2RGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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const std::vector<int64_t>& axes,
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const std::string& normalization,
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bool forward,
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int64_t last_dim_size UNUSED,
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DenseTensor* x_grad) {
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using C = dtype::complex<T>;
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dev_ctx.template Alloc<C>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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auto norm_type = funcs::get_norm_from_string(normalization, forward);
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funcs::FFTR2CFunctor<Context, T, C> fft_r2c_func;
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fft_r2c_func(dev_ctx, out_grad, x_grad, axes, norm_type, !forward);
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const int64_t double_length =
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out_grad.dims()[axes.back()] - x_grad->dims()[axes.back()];
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int64_t stride_to_last_axis = 1;
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auto ddim = x_grad->dims();
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for (int i = ddim.size() - 2; i >= axes.back(); --i) {
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stride_to_last_axis *= ddim[i + 1];
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}
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int64_t stride_second_to_last_axis = stride_to_last_axis * ddim[axes.back()];
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funcs::FFTFillConjGradFunctor<C> func(x_grad->data<C>(),
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axes.back(),
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stride_second_to_last_axis,
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stride_to_last_axis,
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double_length);
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size_t limit = x_grad->numel();
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funcs::ForRange<Context> for_range(dev_ctx, limit);
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for_range(func);
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}
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} // namespace phi
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