wehub-resource-sync
122 lines
4.0 KiB
C++
122 lines
4.0 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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#include "paddle/phi/kernels/concat_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/axis_utils.h"
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#include "paddle/phi/kernels/funcs/concat_funcs.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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using XPUType = typename XPUTypeTrait<T>::Type;
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auto outs = x_grad;
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{
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auto dx = outs;
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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_NE(
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x[0],
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nullptr,
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common::errors::InvalidArgument("The input should not be null."));
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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<XPUType*> ptrs(outs.size());
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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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ptrs[j] = reinterpret_cast<XPUType*>(outs[j]->data<T>());
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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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PADDLE_ENFORCE_GE(axis,
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0,
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common::errors::InvalidArgument(
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"concat_grad: axis should be larger than or "
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"equal to 0, but received axis is %d.",
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axis));
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PADDLE_ENFORCE_LT(axis,
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out_grad.dims().size(),
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common::errors::InvalidArgument(
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"concat_grad: axis should be less than x[0]->dims()!"
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"But received axis is %d, while x[0]->dims()"
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"size is %d.",
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axis,
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out_grad.dims().size()));
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auto input_dims = x[0]->dims();
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std::vector<int64_t> split_list(x.size());
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std::vector<int64_t> xdims_list(input_dims.size());
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int64_t total_length = 0;
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for (size_t i = 0; i < x.size(); ++i) {
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split_list[i] = x[i]->dims()[axis];
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total_length += x[i]->dims()[axis];
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}
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for (int i = 0; i < input_dims.size(); ++i) {
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if (i == axis) {
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continue;
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}
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xdims_list[i] = input_dims[i];
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}
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xdims_list[axis] = total_length;
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std::vector<XPUType*> ptrs_nozero;
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std::vector<int64_t> split_list_nozero;
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for (size_t i = 0; i < x.size(); i++) {
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if (split_list[i] != 0) {
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ptrs_nozero.push_back(ptrs[i]);
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split_list_nozero.push_back(split_list[i]);
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}
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}
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if (ptrs_nozero.size() != 0) {
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int r = xpu::split<XPUType>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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ptrs_nozero,
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xdims_list,
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split_list_nozero,
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axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "concat_grad");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(concat_grad,
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XPU,
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ALL_LAYOUT,
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phi::ConcatGradKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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