chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/concat_grad_kernel_impl.h

@@ -0,0 +1,74 @@
+// 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/concat_grad_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
+#include "paddle/phi/kernels/funcs/concat_funcs.h"
+#include "paddle/phi/kernels/funcs/strided_memcpy.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ConcatGradKernel(const Context& dev_ctx,
+                      const std::vector<const DenseTensor*>& x,
+                      const DenseTensor& out_grad,
+                      const Scalar& axis_scalar,
+                      std::vector<DenseTensor*> x_grad) {
+  auto outs = x_grad;
+  {
+    auto dx = x_grad;
+    for (size_t i = 0; i < dx.size(); ++i) {
+      if (dx[i] != nullptr) {
+        dx[i]->set_lod(x[i]->lod());
+      }
+    }
+  }
+  PADDLE_ENFORCE_NOT_NULL(
+      x[0],
+      common::errors::NotFound("The first input tensor is not initialized."));
+
+  auto axis = axis_scalar.to<int>();
+  axis = funcs::ComputeAxis(static_cast<int64_t>(axis),
+                            static_cast<int64_t>(x[0]->dims().size()));
+  // get output tensor that the name is not kEmptyVarName
+  std::vector<DenseTensor*> outputs;
+  for (size_t j = 0; j < outs.size(); ++j) {
+    if (outs[j]) {
+      dev_ctx.template Alloc<T>(outs[j]);
+      outputs.push_back(outs[j]);
+    } else {
+      outputs.push_back(nullptr);
+    }
+  }
+  // if the out_grad.numel() == 0 ,the all x and x_grad must be zero size
+  // tensor, so just return
+  if (out_grad.numel() == 0) {
+    return;
+  }
+
+  // Sometimes direct copies will be faster, this maybe need deeply analysis.
+  if (axis == 0 && outs.size() < 10) {
+    std::vector<const DenseTensor*> ref_shape;
+    ref_shape.insert(ref_shape.begin(), x.begin(), x.end());
+    funcs::StridedMemcpyWithAxis0<T, Context>(
+        dev_ctx, out_grad, ref_shape, &outputs);
+  } else {
+    funcs::SplitFunctor<Context, T> split_functor;
+    split_functor(dev_ctx, out_grad, x, static_cast<int>(axis), &outputs);
+  }
+}
+
+}  // namespace phi