wehub-resource-sync
75 lines
2.6 KiB
C++
75 lines
2.6 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/phi/kernels/concat_grad_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/phi/kernels/funcs/concat_funcs.h"
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#include "paddle/phi/kernels/funcs/strided_memcpy.h"
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namespace phi {
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template <typename T, typename Context>
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void ConcatGradKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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const DenseTensor& out_grad,
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const Scalar& axis_scalar,
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std::vector<DenseTensor*> x_grad) {
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auto outs = x_grad;
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{
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auto dx = x_grad;
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for (size_t i = 0; i < dx.size(); ++i) {
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if (dx[i] != nullptr) {
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dx[i]->set_lod(x[i]->lod());
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}
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}
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}
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PADDLE_ENFORCE_NOT_NULL(
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x[0],
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common::errors::NotFound("The first input tensor is not initialized."));
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auto axis = axis_scalar.to<int>();
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axis = funcs::ComputeAxis(static_cast<int64_t>(axis),
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static_cast<int64_t>(x[0]->dims().size()));
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// get output tensor that the name is not kEmptyVarName
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std::vector<DenseTensor*> outputs;
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for (size_t j = 0; j < outs.size(); ++j) {
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if (outs[j]) {
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dev_ctx.template Alloc<T>(outs[j]);
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outputs.push_back(outs[j]);
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} else {
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outputs.push_back(nullptr);
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}
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}
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// if the out_grad.numel() == 0 ,the all x and x_grad must be zero size
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// tensor, so just return
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if (out_grad.numel() == 0) {
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return;
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}
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// Sometimes direct copies will be faster, this maybe need deeply analysis.
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if (axis == 0 && outs.size() < 10) {
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std::vector<const DenseTensor*> ref_shape;
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ref_shape.insert(ref_shape.begin(), x.begin(), x.end());
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funcs::StridedMemcpyWithAxis0<T, Context>(
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dev_ctx, out_grad, ref_shape, &outputs);
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} else {
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funcs::SplitFunctor<Context, T> split_functor;
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split_functor(dev_ctx, out_grad, x, static_cast<int>(axis), &outputs);
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}
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}
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} // namespace phi
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