chore: import upstream snapshot with attribution

paddle/phi/infermeta/sparse/binary.cc

@@ -0,0 +1,210 @@
+/* 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/infermeta/sparse/binary.h"
+
+namespace phi {
+namespace sparse {
+
+inline void GetOutShape(const DDim& x_dims,
+                        const std::vector<int>& kernel_sizes,
+                        const std::vector<int>& paddings,
+                        const std::vector<int>& dilations,
+                        const std::vector<int>& strides,
+                        DDim* out_dims) {
+  const bool is2D = out_dims->size() == 4 ? true : false;
+  if (is2D) {
+    PADDLE_ENFORCE_EQ(x_dims.size(),
+                      4,
+                      common::errors::InvalidArgument(
+                          "the shape of x should be (N, H, W, C)"));
+    PADDLE_ENFORCE_EQ(kernel_sizes.size(),
+                      4,
+                      common::errors::InvalidArgument(
+                          "the shape of kernel should be (H, W, C, OC)"));
+
+    // infer out shape
+    (*out_dims)[0] = x_dims[0];
+    (*out_dims)[3] = kernel_sizes[3];
+    for (int i = 1; i < 3; i++) {
+      (*out_dims)[i] = (x_dims[i] + 2 * paddings[i - 1] -
+                        dilations[i - 1] * (kernel_sizes[i - 1] - 1) - 1) /
+                           strides[i - 1] +
+                       1;
+    }
+  } else {
+    PADDLE_ENFORCE_EQ(x_dims.size(),
+                      5,
+                      common::errors::InvalidArgument(
+                          "the shape of x should be (N, D, H, W, C)"));
+    PADDLE_ENFORCE_EQ(kernel_sizes.size(),
+                      5,
+                      common::errors::InvalidArgument(
+                          "the shape of kernel should be (D, H, W, C, OC)"));
+
+    // infer out shape
+    (*out_dims)[0] = x_dims[0];
+    (*out_dims)[4] = kernel_sizes[4];
+    for (int i = 1; i < 4; i++) {
+      (*out_dims)[i] = (x_dims[i] + 2 * paddings[i - 1] -
+                        dilations[i - 1] * (kernel_sizes[i - 1] - 1) - 1) /
+                           strides[i - 1] +
+                       1;
+    }
+  }
+}
+
+inline void ResetSubmKernelSizeAndStrides(const DDim& kernel_dims,
+                                          std::vector<int>* paddings,
+                                          std::vector<int>* strides) {
+  for (uint64_t i = 0; i < paddings->size(); i++) {
+    (*paddings)[i] = kernel_dims[i] / 2;  // NOLINT
+    (*strides)[i] = 1;
+  }
+}
+
+void Conv3dInferMeta(const MetaTensor& x,
+                     const MetaTensor& kernel,
+                     const std::vector<int>& paddings,
+                     const std::vector<int>& dilations,
+                     const std::vector<int>& strides,
+                     const int groups,
+                     const bool subm,
+                     const std::string& key,
+                     MetaTensor* out,
+                     MetaTensor* rulebook,
+                     MetaTensor* counter) {
+  const auto& x_dims = x.dims();
+  const bool is2D = x_dims.size() == 4 ? true : false;
+  const auto& kernel_dims = kernel.dims();
+
+  int rank = is2D ? 4 : 5;
+  std::vector<int> out_dims_vec(rank, 1);
+  DDim out_dims = make_ddim(out_dims_vec);
+
+  std::vector<int> kernel_sizes(kernel_dims.size());
+  for (int i = 0; i < kernel_dims.size(); i++) {
+    kernel_sizes[i] = static_cast<int>(kernel_dims[i]);
+  }
+
+  std::vector<int> subm_paddings(paddings), subm_strides(strides);
+  if (subm) {
+    // the out shape of subm_conv is same as input shape
+    // reset the padding=kernel_size/2 and strides=1
+    ResetSubmKernelSizeAndStrides(kernel.dims(), &subm_paddings, &subm_strides);
+  }
+
+  GetOutShape(
+      x_dims, kernel_sizes, subm_paddings, dilations, subm_strides, &out_dims);
+
+  out->set_dtype(x.dtype());
+  out->set_dims(out_dims);
+  out->set_layout(x.layout());
+
+  rulebook->set_dtype(DataType::INT32);
+  rulebook->set_layout(DataLayout::NCHW);
+  rulebook->set_dims({1});
+
+  counter->set_dtype(DataType::INT32);
+  counter->set_layout(DataLayout::NCHW);
+  counter->set_dims({1});
+}
+
+void Conv3dImplicitGemmInferMeta(const MetaTensor& x,
+                                 const MetaTensor& kernel,
+                                 const std::vector<int>& paddings,
+                                 const std::vector<int>& dilations,
+                                 const std::vector<int>& strides,
+                                 const int groups,
+                                 const bool subm,
+                                 const std::string& key,
+                                 MetaTensor* out) {
+  const auto& x_dims = x.dims();
+  const bool is2D = x_dims.size() == 4 ? true : false;
+  const auto& kernel_dims = kernel.dims();
+
+  int rank = is2D ? 4 : 5;
+  std::vector<int> out_dims_vec(rank, 1);
+  DDim out_dims = make_ddim(out_dims_vec);
+
+  std::vector<int> kernel_sizes(kernel_dims.size());
+  for (int i = 0; i < kernel_dims.size(); i++) {
+    kernel_sizes[i] = static_cast<int>(kernel_dims[i]);
+  }
+
+  std::vector<int> subm_paddings(paddings), subm_strides(strides);
+  if (subm) {
+    // the out shape of subm_conv is same as input shape
+    // reset the padding=kernel_size/2 and strides=1
+    ResetSubmKernelSizeAndStrides(kernel.dims(), &subm_paddings, &subm_strides);
+  }
+
+  GetOutShape(
+      x_dims, kernel_sizes, subm_paddings, dilations, subm_strides, &out_dims);
+
+  out->set_dtype(x.dtype());
+  out->set_dims(out_dims);
+  out->set_layout(x.layout());
+}
+
+inline const std::vector<int> PoolResetKernel(
+    const std::vector<int>& kernel_sizes,
+    const int in_channels,
+    const int out_channels) {
+  std::vector<int> res(kernel_sizes);
+  res.resize(5);
+  res[3] = in_channels;
+  res[4] = out_channels;
+  return res;
+}
+
+void Pool3dInferMeta(const MetaTensor& x,
+                     const std::vector<int>& kernel_sizes,
+                     const std::vector<int>& paddings,
+                     const std::vector<int>& dilations,
+                     const std::vector<int>& strides,
+                     MetaTensor* out,
+                     MetaTensor* rulebook,
+                     MetaTensor* counter) {
+  const auto& x_dims = x.dims();
+  DDim out_dims = {1, 1, 1, 1, 1};
+
+  const std::vector<int>& real_kernel_sizes = PoolResetKernel(
+      kernel_sizes, static_cast<int>(x_dims[4]), static_cast<int>(x_dims[4]));
+  GetOutShape(
+      x_dims, real_kernel_sizes, paddings, dilations, strides, &out_dims);
+  out->set_dtype(x.dtype());
+  out->set_dims(out_dims);
+  out->set_layout(x.layout());
+
+  rulebook->set_dtype(DataType::INT32);
+  rulebook->set_layout(DataLayout::NCHW);
+  rulebook->set_dims({1});
+
+  counter->set_dtype(DataType::INT32);
+  counter->set_layout(DataLayout::NCHW);
+  counter->set_dims({1});
+}
+
+void SparseCooTensorInferMeta(const MetaTensor& values,
+                              const MetaTensor& indices,
+                              const std::vector<int64_t>& shape,
+                              MetaTensor* out) {
+  out->set_dims(make_ddim(shape));
+  out->set_dtype(values.dtype());
+  out->set_layout(values.layout());
+}
+
+}  // namespace sparse
+}  // namespace phi