chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/fft_kernel_impl.h

@@ -0,0 +1,102 @@
+// 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_kernel.h"
+
+#include <string>
+#include <vector>
+
+#include "paddle/common/ddim.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/fft.h"
+#include "paddle/phi/kernels/funcs/fft_fill_conj.h"
+namespace phi {
+template <typename T, typename Context>
+void FFTC2CKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const std::vector<int64_t>& axes,
+                  const std::string& normalization,
+                  bool forward,
+                  DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  if (x.numel() == 0) {
+    /*
+    This will return 0:
+    >>> scipy.fft.fft2(np.random.random([3, 0, 1, 2]), s=(1, 2), axes=(0, 1),
+norm='backward')
+    array([[[[0.-0.j, 0.-0.j]],
+        [[0.-0.j, 0.-0.j]]]])
+    */
+    Full<T, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+  const auto norm_type = funcs::get_norm_from_string(normalization, forward);
+  funcs::FFTC2CFunctor<Context, T, T> fft_c2c_func;
+  fft_c2c_func(dev_ctx, x, out, axes, norm_type, forward);
+}
+
+template <typename T, typename Context>
+void FFTC2RKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const std::vector<int64_t>& axes,
+                  const std::string& normalization,
+                  bool forward,
+                  int64_t last_dim_size UNUSED,
+                  DenseTensor* out) {
+  using R = typename T::value_type;  // get real type
+  dev_ctx.template Alloc<R>(out);
+  if (x.numel() == 0) {
+    Full<R, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+  const auto norm_type = funcs::get_norm_from_string(normalization, forward);
+  funcs::FFTC2RFunctor<Context, T, R> fft_c2r_func;
+  fft_c2r_func(dev_ctx, x, out, axes, norm_type, forward);
+}
+
+template <typename T, typename Context>
+void FFTR2CKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const std::vector<int64_t>& axes,
+                  const std::string& normalization,
+                  bool forward,
+                  bool onesided,
+                  DenseTensor* out) {
+  using C = dtype::complex<T>;
+  dev_ctx.template Alloc<C>(out);
+  if (x.numel() == 0) {
+    Full<C, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+  auto norm_type = funcs::get_norm_from_string(normalization, forward);
+  funcs::FFTR2CFunctor<Context, T, C> fft_r2c_func;
+
+  if (onesided) {
+    fft_r2c_func(dev_ctx, x, out, axes, norm_type, forward);
+  } else {
+    DDim onesided_out_shape = x.dims();
+    const int64_t last_fft_axis = axes.back();
+    const int64_t onesided_last_axis_size =
+        out->dims().at(last_fft_axis) / 2 + 1;
+    onesided_out_shape[last_fft_axis] = onesided_last_axis_size;
+    DenseTensor onesided_out =
+        Empty<C, Context>(dev_ctx, vectorize(onesided_out_shape));
+    fft_r2c_func(dev_ctx, x, &onesided_out, axes, norm_type, forward);
+    funcs::FFTFillConj<Context, C>(dev_ctx, &onesided_out, out, axes);
+  }
+}
+}  // namespace phi