chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/meshgrid_grad_kernel_impl.h

@@ -0,0 +1,115 @@
+// 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/common/macros.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/meshgrid_grad_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context, int Rank>
+void MeshgridBackward(const Context& dev_ctx,
+                      const std::vector<const DenseTensor*>& ins UNUSED,
+                      const std::vector<const DenseTensor*>& out_grad,
+                      std::vector<DenseTensor*> outs) {
+  int n = out_grad.size();
+  auto out_dims = out_grad[0]->dims();
+  if (out_grad[0]->numel() == 0) {
+    for (size_t i = 0; i < outs.size(); i++) {
+      auto* out = outs[i];
+      dev_ctx.template Alloc<T>(out);
+      if (out->numel() != 0) {
+        Full<T, Context>(dev_ctx, out->dims(), 0, out);
+      }
+    }
+    return;
+  }
+
+  for (int i = 0; i < n; i++) {
+    dev_ctx.template Alloc<T>(outs[i]);
+    auto out_grad_tmp = EigenVector<T>::Flatten(*out_grad[i]);
+    auto in_grad = EigenVector<T>::Flatten(*outs[i]);
+
+    std::vector<int> reduce_dims_vec;
+    std::vector<int64_t> reshape_dims_vec;
+    for (int j = 0; j < n; j++) {
+      reduce_dims_vec.push_back(reshape_dims_vec.size());
+      if (j == i) {
+        reshape_dims_vec.push_back(1);
+        reshape_dims_vec.push_back(out_dims[j]);
+      } else {
+        reshape_dims_vec.push_back(out_dims[j]);
+        reshape_dims_vec.push_back(1);
+      }
+    }
+
+    Eigen::DSizes<int64_t, Rank> reduce_dims;
+    for (int k = 0; k < n; k++) {
+      reduce_dims[k] = reduce_dims_vec[k];
+    }
+
+    Eigen::DSizes<int64_t, Rank * 2> reshape_dims;
+    for (int k = 0; k < n * 2; k++) {
+      reshape_dims[k] = reshape_dims_vec[k];
+    }
+
+    auto& place = *dev_ctx.eigen_device();
+    funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, T, Rank>::Eval(
+        place, in_grad, out_grad_tmp, reduce_dims, reshape_dims);
+  }
+}
+
+template <typename T, typename Context>
+void MeshgridGradKernel(const Context& dev_ctx,
+                        const std::vector<const DenseTensor*>& inputs,
+                        const std::vector<const DenseTensor*>& outputs_grad,
+                        std::vector<DenseTensor*> inputs_grad) {
+  int n = outputs_grad.size();
+  switch (n) {
+    case 1:
+      MeshgridBackward<T, Context, 1>(
+          dev_ctx, inputs, outputs_grad, inputs_grad);
+      break;
+    case 2:
+      MeshgridBackward<T, Context, 2>(
+          dev_ctx, inputs, outputs_grad, inputs_grad);
+      break;
+    case 3:
+      MeshgridBackward<T, Context, 3>(
+          dev_ctx, inputs, outputs_grad, inputs_grad);
+      break;
+    case 4:
+      MeshgridBackward<T, Context, 4>(
+          dev_ctx, inputs, outputs_grad, inputs_grad);
+      break;
+    case 5:
+      MeshgridBackward<T, Context, 5>(
+          dev_ctx, inputs, outputs_grad, inputs_grad);
+      break;
+    case 6:
+      MeshgridBackward<T, Context, 6>(
+          dev_ctx, inputs, outputs_grad, inputs_grad);
+      break;
+    default:
+      PADDLE_THROW(common::errors::InvalidArgument(
+          "Excepted Tensor numbers between 1 and 6, but only received %d.", n));
+  }
+}
+
+}  // namespace phi