chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/fft_grad_kernel_impl.h

@@ -0,0 +1,121 @@
+// 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.
+
+#pragma once
+#include "paddle/phi/kernels/fft_grad_kernel.h"
+
+#include <string>
+#include <vector>
+
+#include "paddle/common/ddim.h"
+#include "paddle/phi/common/data_type.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_meta.h"
+#include "paddle/phi/kernels/complex_kernel.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/funcs/fft.h"
+#include "paddle/phi/kernels/funcs/fft_fill_conj.h"
+#include "paddle/phi/kernels/funcs/for_range.h"
+#include "paddle/phi/kernels/pad_kernel.h"
+
+namespace phi {
+template <typename T, typename Context>
+void FFTC2CGradKernel(const Context& dev_ctx,
+                      const DenseTensor& out_grad,
+                      const std::vector<int64_t>& axes,
+                      const std::string& normalization,
+                      bool forward,
+                      DenseTensor* x_grad) {
+  dev_ctx.template Alloc<T>(x_grad);
+  if (x_grad && x_grad->numel() == 0) {
+    return;
+  }
+  auto norm_type = funcs::get_norm_from_string(normalization, forward);
+  funcs::FFTC2CFunctor<Context, T, T> fft_c2c_func;
+  fft_c2c_func(dev_ctx, out_grad, x_grad, axes, norm_type, !forward);
+}
+
+template <typename T, typename Context>
+void FFTR2CGradKernel(const Context& dev_ctx,
+                      const DenseTensor& x,
+                      const DenseTensor& out_grad,
+                      const std::vector<int64_t>& axes,
+                      const std::string& normalization,
+                      bool forward,
+                      bool onesided,
+                      DenseTensor* x_grad) {
+  using R = typename T::value_type;
+  DenseTensor complex_x_grad = EmptyLike<T>(dev_ctx, x);
+  dev_ctx.template Alloc<R>(x_grad);
+  if (x_grad && x_grad->numel() == 0) {
+    return;
+  }
+
+  auto norm_type = funcs::get_norm_from_string(normalization, forward);
+  funcs::FFTC2CFunctor<Context, T, T> fft_c2c_func;
+
+  if (!onesided) {
+    fft_c2c_func(dev_ctx, out_grad, &complex_x_grad, axes, norm_type, !forward);
+  } else {
+    DenseTensor full_dy;
+    DenseTensorMeta full_dy_meta(out_grad.type(), x_grad->dims());
+    full_dy.set_meta(full_dy_meta);
+    auto zero_length = static_cast<int>(full_dy.dims().at(axes.back()) -
+                                        out_grad.dims().at(axes.back()));
+    auto rank = out_grad.dims().size();
+    std::vector<int> pads(rank * 2, 0);
+    pads[axes.back() * 2 + 1] = zero_length;
+    PadKernel<T>(dev_ctx, out_grad, pads, static_cast<float>(0.0), &full_dy);
+    fft_c2c_func(dev_ctx, full_dy, &complex_x_grad, axes, norm_type, !forward);
+  }
+  RealKernel<T>(dev_ctx, complex_x_grad, x_grad);
+}
+
+template <typename T, typename Context>
+void FFTC2RGradKernel(const Context& dev_ctx,
+                      const DenseTensor& out_grad,
+                      const std::vector<int64_t>& axes,
+                      const std::string& normalization,
+                      bool forward,
+                      int64_t last_dim_size UNUSED,
+                      DenseTensor* x_grad) {
+  using C = dtype::complex<T>;
+  dev_ctx.template Alloc<C>(x_grad);
+  if (x_grad && x_grad->numel() == 0) {
+    return;
+  }
+
+  auto norm_type = funcs::get_norm_from_string(normalization, forward);
+
+  funcs::FFTR2CFunctor<Context, T, C> fft_r2c_func;
+  fft_r2c_func(dev_ctx, out_grad, x_grad, axes, norm_type, !forward);
+
+  const int64_t double_length =
+      out_grad.dims()[axes.back()] - x_grad->dims()[axes.back()];
+  int64_t stride_to_last_axis = 1;
+  auto ddim = x_grad->dims();
+  for (int i = ddim.size() - 2; i >= axes.back(); --i) {
+    stride_to_last_axis *= ddim[i + 1];
+  }
+  int64_t stride_second_to_last_axis = stride_to_last_axis * ddim[axes.back()];
+  funcs::FFTFillConjGradFunctor<C> func(x_grad->data<C>(),
+                                        axes.back(),
+                                        stride_second_to_last_axis,
+                                        stride_to_last_axis,
+                                        double_length);
+  size_t limit = x_grad->numel();
+  funcs::ForRange<Context> for_range(dev_ctx, limit);
+  for_range(func);
+}
+}  // namespace phi