// 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/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 void ConcatGradKernel(const Context& dev_ctx, const std::vector& x, const DenseTensor& out_grad, const Scalar& axis_scalar, std::vector 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], phi::errors::NotFound("The first input tensor is not initalized.")); auto axis = axis_scalar.to(); axis = funcs::ComputeAxis(static_cast(axis), static_cast(x[0]->dims().size())); // get output tensor that the name is not kEmptyVarName std::vector outputs; for (size_t j = 0; j < outs.size(); ++j) { if (outs[j] && outs[j]->numel() != 0UL) { dev_ctx.template Alloc(outs[j]); outputs.push_back(outs[j]); } else { outputs.push_back(nullptr); } } // Sometimes direct copies will be faster, this maybe need deeply analysis. if (axis == 0 && outs.size() < 10) { std::vector ref_shape; ref_shape.insert(ref_shape.begin(), x.begin(), x.end()); phi::funcs::StridedMemcpyWithAxis0( dev_ctx, out_grad, ref_shape, &outputs); } else { phi::funcs::SplitFunctor split_functor; split_functor(dev_ctx, out_grad, x, static_cast(axis), &outputs); } } } // namespace phi