chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/frame_kernel_impl.h

@@ -0,0 +1,147 @@
+// 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/funcs/frame_functor.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+template <typename T, typename Context>
+void FrameKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 int frame_length,
+                 int hop_length,
+                 int axis,
+                 DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  const size_t x_rank = x.dims().size();
+  const size_t out_rank = out->dims().size();
+  const int64_t n_frames =
+      (axis == 0) ? out->dims()[0] : out->dims()[out_rank - 1];
+  const int64_t seq_length = (axis == 0) ? x.dims()[0] : x.dims()[x_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 x_tmp = x;
+
+  DDim preserved_dims;
+  if (x_rank > 2) {
+    // Save dims used to flatten both input and output tensors and restore
+    // output tensor.
+    DDim x_resized_dims;
+    DDim out_resized_dims;
+    if (axis == 0) {
+      preserved_dims = slice_ddim(x_tmp.dims(), 1, x_rank);
+      x_resized_dims = {seq_length, common::product(preserved_dims)};
+      out_resized_dims = {
+          n_frames, frame_length, common::product(preserved_dims)};
+    } else {
+      preserved_dims = slice_ddim(x_tmp.dims(), 0, x_rank - 1);
+      x_resized_dims = {common::product(preserved_dims), seq_length};
+      out_resized_dims = {
+          common::product(preserved_dims), frame_length, n_frames};
+    }
+    x_tmp.Resize(x_resized_dims);
+    out->Resize(out_resized_dims);
+  }
+
+  DenseTensor trans_x;
+  DenseTensor trans_out;
+
+  // Transpose input and output in case that axis is 0.
+  if (axis == 0) {
+    if (x_rank == 1U) {
+      trans_x = x_tmp;
+
+      std::vector<int> perm_out{1, 0};
+      auto out_dims_vec = vectorize(out->dims());
+      for (int i = 0; i < out->dims().size(); ++i) {
+        out_dims_vec[i] = out->dims()[perm_out[i]];
+      }
+      trans_out.Resize(out_dims_vec);
+
+      dev_ctx.template Alloc<T>(&trans_out);
+      funcs::TransCompute<Context, T>(
+          perm_out.size(), dev_ctx, *out, &trans_out, perm_out);
+    } else {
+      std::vector<int> perm_x{1, 0};
+      auto x_dims_vec = vectorize(x_tmp.dims());
+      for (int i = 0; i < x_tmp.dims().size(); ++i) {
+        x_dims_vec[i] = x_tmp.dims()[perm_x[i]];
+      }
+      trans_x.Resize(x_dims_vec);
+      dev_ctx.template Alloc<T>(&trans_x);
+      funcs::TransCompute<Context, T>(
+          perm_x.size(), dev_ctx, x_tmp, &trans_x, perm_x);
+
+      std::vector<int> perm_out{2, 1, 0};
+      auto out_dims_vec = vectorize(out->dims());
+      for (int i = 0; i < out->dims().size(); ++i) {
+        out_dims_vec[i] = out->dims()[perm_out[i]];
+      }
+      trans_out.Resize(out_dims_vec);
+      dev_ctx.template Alloc<T>(&trans_out);
+      funcs::TransCompute<Context, T>(
+          perm_out.size(), dev_ctx, *out, &trans_out, perm_out);
+    }
+  } else {
+    trans_x = x_tmp;
+    trans_out = *out;
+  }
+
+  funcs::FrameFunctor<Context, T>()(dev_ctx,
+                                    &trans_x,
+                                    &trans_out,
+                                    seq_length,
+                                    frame_length,
+                                    n_frames,
+                                    hop_length,
+                                    /*is_grad*/ false);
+
+  // Transpose output in case axis is 0.
+  if (axis == 0) {
+    if (x_rank == 1U) {
+      std::vector<int> perm_out{1, 0};
+      funcs::TransCompute<Context, T>(
+          perm_out.size(), dev_ctx, trans_out, out, perm_out);
+    } else {
+      std::vector<int> perm_out{2, 1, 0};
+      funcs::TransCompute<Context, T>(
+          perm_out.size(), dev_ctx, trans_out, out, perm_out);
+    }
+  }
+
+  // Restore output dims when the number of dims is larger than 2.
+  if (x_rank > 2) {
+    std::vector<int64_t> restored_out_shape;
+    for (int i = 0; i < preserved_dims.size(); i++) {
+      restored_out_shape.push_back(preserved_dims[i]);
+    }
+
+    if (axis == 0) {
+      // (n_frames, frame_length, ...)
+      restored_out_shape.insert(restored_out_shape.begin(), frame_length);
+      restored_out_shape.insert(restored_out_shape.begin(), n_frames);
+    } else {
+      // (..., frame_length, n_frames)
+      restored_out_shape.push_back(frame_length);
+      restored_out_shape.push_back(n_frames);
+    }
+
+    out->Resize(restored_out_shape);
+  }
+}
+
+}  // namespace phi