add tile

fastdeploy/function/tile.cc

@@ -0,0 +1,105 @@
+// 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 "fastdeploy/function/tile.h"
+#include "fastdeploy/function/eigen.h"
+
+namespace fastdeploy {
+namespace function {
+
+template <typename T, int Rank>
+void TileFunctor(const FDTensor& x, std::vector<int64_t> repeat_times,
+                 FDTensor* out) {
+  auto x_shape = x.Shape();
+  for (size_t i = 0; i < repeat_times.size(); ++i) {
+    FDASSERT(repeat_times[i] > 0,
+             "All elements of the input 'repeat_times' "
+             "for tile op must be positive integers, but "
+             "the value received is %d.",
+             repeat_times[i]);
+  }
+  if (repeat_times.size() < x_shape.size()) {
+    int diff = x_shape.size() - repeat_times.size();
+    repeat_times.insert(repeat_times.begin(), diff, 1);
+  } else {
+    int diff = repeat_times.size() - x_shape.size();
+    x_shape.insert(x_shape.begin(), diff, 1);
+  }
+  FDASSERT(repeat_times.size() == x_shape.size(),
+           "The rank (%d) of the input 'x' and the rank (%d) of the input "
+           "'repeat_times' for tile op must match after promotion.",
+           x_shape.size(), repeat_times.size());
+
+  if (Rank == 0) {
+    // Deep copy
+    *out = x;
+    return;
+  }
+
+  Eigen::DSizes<Eigen::DenseIndex, Rank> bcast_dims;
+  for (size_t i = 0; i < repeat_times.size(); ++i) {
+    bcast_dims[i] = repeat_times[i];
+  }
+
+  std::vector<int64_t> out_shape(x_shape);
+  for (size_t i = 0; i < repeat_times.size(); ++i) {
+    out_shape[i] *= repeat_times[i];
+  }
+
+  out->Allocate(out_shape, x.Dtype());
+  auto eigen_x = EigenTensor<T, Rank>::From(x, x_shape);
+  auto eigen_out = EigenTensor<T, Rank>::From(*out, out_shape);
+
+  const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
+  eigen_out.device(dev) = eigen_x.broadcast(bcast_dims);
+}
+
+template <typename T>
+void TileKernel(const FDTensor& x, std::vector<int64_t> repeat_times,
+                FDTensor* out) {
+  auto rank = x.Shape().size();
+  auto repeat_times_size = repeat_times.size();
+  rank = (std::max)(rank, repeat_times_size);
+  switch (rank) {
+  case 0:
+    *out = x;
+    break;
+  case 1:
+    TileFunctor<T, 1>(x, repeat_times, out);
+    break;
+  case 2:
+    TileFunctor<T, 2>(x, repeat_times, out);
+    break;
+  case 3:
+    TileFunctor<T, 3>(x, repeat_times, out);
+    break;
+  case 4:
+    TileFunctor<T, 4>(x, repeat_times, out);
+    break;
+  case 5:
+    TileFunctor<T, 5>(x, repeat_times, out);
+    break;
+  case 6:
+    TileFunctor<T, 6>(x, repeat_times, out);
+    break;
+  }
+}
+
+void Tile(const FDTensor& x, std::vector<int64_t> repeat_times, FDTensor* out) {
+  FD_VISIT_ALL_TYPES(x.dtype, "TileKernel",
+                     ([&] { TileKernel<data_t>(x, repeat_times, out); }));
+}
+
+}  // namespace function
+}  // namespace fastdeploy