chore: import upstream snapshot with attribution

paddle/phi/kernels/array_grad_kernel.cc

@@ -0,0 +1,85 @@
+// 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/array_grad_kernel.h"
+
+#include "paddle/common/layout.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/concat_grad_kernel.h"
+#include "paddle/phi/kernels/stack_grad_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void TensorToArrayKernel(const Context& dev_ctx,
+                         const TensorArray& x,
+                         const DenseTensor& out_grad,
+                         int axis,
+                         bool use_stack,
+                         TensorArray* x_grad) {
+  std::vector<DenseTensor> tmp_inputs(x.size());
+  std::vector<const DenseTensor*> inputs;
+
+  std::vector<DenseTensor*> inputs_grad;
+  std::vector<DenseTensor> tmp_inputs_grad(x.size());
+
+  for (size_t i = 0; i < x.size(); i++) {
+    tmp_inputs[i].ShareDataWith(x[i]);
+    inputs.push_back(&tmp_inputs[i]);
+    inputs_grad.push_back(&tmp_inputs_grad[i]);
+    inputs_grad[i]->set_meta(x[i].meta());
+  }
+
+  if (use_stack) {
+    StackGradKernel<T, Context>(dev_ctx, out_grad, axis, inputs_grad);
+  } else {
+    ConcatGradKernel<T, Context>(dev_ctx, inputs, out_grad, axis, inputs_grad);
+  }
+
+  for (size_t i = 0; i < tmp_inputs_grad.size(); i++) {
+    inputs_grad[i] = nullptr;
+    x_grad->push_back(tmp_inputs_grad[i]);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(tensor_to_array,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::TensorToArrayKernel,
+                   bool,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::complex64,
+                   phi::complex128) {}
+
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_REGISTER_KERNEL(tensor_to_array,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::TensorToArrayKernel,
+                   bool,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::complex64,
+                   phi::complex128) {}
+#endif