chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/overlap_add_grad_kernel.cc

@@ -0,0 +1,170 @@
+// 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.
+
+#include "paddle/phi/kernels/overlap_add_grad_kernel.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/overlap_add_functor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void OverlapAddGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x UNUSED,
+                          const DenseTensor& out_grad,
+                          int hop_length,
+                          int axis,
+                          DenseTensor* x_grad) {
+  dev_ctx.template Alloc<T>(x_grad);
+  const size_t out_grad_rank = out_grad.dims().size();
+  const size_t x_grad_rank = x_grad->dims().size();
+
+  const int n_frames = static_cast<int>(
+      (axis == 0) ? x_grad->dims()[0]
+                  : x_grad->dims()[static_cast<int>(x_grad_rank) - 1]);
+  const int frame_length = static_cast<int>(
+      (axis == 0) ? x_grad->dims()[1]
+                  : x_grad->dims()[static_cast<int>(x_grad_rank) - 2]);
+  const int seq_length = static_cast<int>(
+      (axis == 0) ? out_grad.dims()[0]
+                  : out_grad.dims()[static_cast<int>(out_grad_rank) - 1]);
+
+  // When the number of input dims is larger than 2, it needs to copy
+  // from x to resize input into 2d and output into 3d. Moreover, output
+  // dims will be restored at the last step.
+  DenseTensor out_grad_(out_grad.type());
+  out_grad_ = out_grad;
+
+  DDim preserved_dims;
+  if (out_grad_rank > 2) {
+    // Save dims used to flatten both input and output tensors and restore
+    // output tensor.
+    DDim x_grad_resized_dims;
+    DDim out_grad_resized_dims;
+    if (axis == 0) {
+      preserved_dims =
+          slice_ddim(out_grad_.dims(), 1, static_cast<int>(out_grad_rank));
+      x_grad_resized_dims = {
+          n_frames, frame_length, common::product(preserved_dims)};
+      out_grad_resized_dims = {seq_length, common::product(preserved_dims)};
+    } else {
+      preserved_dims =
+          slice_ddim(out_grad_.dims(), 0, static_cast<int>(out_grad_rank) - 1);
+      x_grad_resized_dims = {
+          common::product(preserved_dims), frame_length, n_frames};
+      out_grad_resized_dims = {common::product(preserved_dims), seq_length};
+    }
+    x_grad->Resize(x_grad_resized_dims);
+    out_grad_.Resize(out_grad_resized_dims);
+  }
+
+  DenseTensor trans_x_grad(x_grad->type());
+  DenseTensor trans_out_grad(out_grad_.type());
+
+  // Transpose input and output in case that axis is 0.
+  if (axis == 0) {
+    if (out_grad_rank == 1U) {
+      trans_out_grad = out_grad_;
+
+      std::vector<int> perm_x_grad{1, 0};
+      auto x_grad_dims_vec = vectorize(x_grad->dims());
+      for (int i = 0; i < x_grad->dims().size(); ++i) {
+        x_grad_dims_vec[i] = x_grad->dims()[perm_x_grad[i]];
+      }
+      trans_x_grad.Resize(x_grad_dims_vec);
+      dev_ctx.template Alloc<T>(&trans_x_grad);
+      funcs::TransCompute<Context, T>(
+          perm_x_grad.size(), dev_ctx, *x_grad, &trans_x_grad, perm_x_grad);
+    } else {
+      std::vector<int> perm_d_out{1, 0};
+      auto out_grad_dims_vec = vectorize(out_grad_.dims());
+      for (int i = 0; i < out_grad_.dims().size(); ++i) {
+        out_grad_dims_vec[i] = out_grad_.dims()[perm_d_out[i]];
+      }
+      trans_out_grad.Resize(out_grad_dims_vec);
+      dev_ctx.template Alloc<T>(&trans_out_grad);
+      funcs::TransCompute<Context, T>(
+          perm_d_out.size(), dev_ctx, out_grad_, &trans_out_grad, perm_d_out);
+
+      std::vector<int> perm_x_grad{2, 1, 0};
+      auto x_grad_dims_vec = vectorize(x_grad->dims());
+      for (int i = 0; i < x_grad->dims().size(); ++i) {
+        x_grad_dims_vec[i] = x_grad->dims()[perm_x_grad[i]];
+      }
+      trans_x_grad.Resize(x_grad_dims_vec);
+      dev_ctx.template Alloc<T>(&trans_x_grad);
+      funcs::TransCompute<Context, T>(
+          perm_x_grad.size(), dev_ctx, *x_grad, &trans_x_grad, perm_x_grad);
+    }
+  } else {
+    trans_x_grad = *x_grad;
+    trans_out_grad = out_grad_;
+  }
+
+  OverlapAddFunctor<Context, T>()(dev_ctx,
+                                  &trans_out_grad,
+                                  &trans_x_grad,
+                                  seq_length,
+                                  frame_length,
+                                  n_frames,
+                                  hop_length,
+                                  /*is_grad*/ true);
+
+  // Transpose output in case axis is 0.
+  if (axis == 0) {
+    if (out_grad_rank == 1U) {
+      std::vector<int> perm_x_grad{1, 0};
+      funcs::TransCompute<Context, T>(
+          perm_x_grad.size(), dev_ctx, trans_x_grad, x_grad, perm_x_grad);
+    } else {
+      std::vector<int> perm_x_grad{2, 1, 0};
+      funcs::TransCompute<Context, T>(
+          perm_x_grad.size(), dev_ctx, trans_x_grad, x_grad, perm_x_grad);
+    }
+  }
+
+  // Restore output dims when the number of dims is larger than 2.
+  if (out_grad_rank > 2) {
+    std::vector<int64_t> restored_x_grad_shape;
+    restored_x_grad_shape.reserve(preserved_dims.size());
+    for (int i = 0; i < preserved_dims.size(); i++) {
+      restored_x_grad_shape.push_back(preserved_dims[i]);
+    }
+
+    if (axis == 0) {
+      // (n_frames, frame_length, ...)
+      restored_x_grad_shape.insert(restored_x_grad_shape.begin(), frame_length);
+      restored_x_grad_shape.insert(restored_x_grad_shape.begin(), n_frames);
+    } else {
+      // (..., frame_length, n_frames)
+      restored_x_grad_shape.push_back(frame_length);
+      restored_x_grad_shape.push_back(n_frames);
+    }
+
+    x_grad->Resize(restored_x_grad_shape);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(overlap_add_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::OverlapAddGradKernel,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::complex64,
+                   phi::complex128) {}