wehub-resource-sync
116 lines
3.8 KiB
C++
116 lines
3.8 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/common/macros.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/meshgrid_grad_kernel.h"
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namespace phi {
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template <typename T, typename Context, int Rank>
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void MeshgridBackward(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& ins UNUSED,
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const std::vector<const DenseTensor*>& out_grad,
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std::vector<DenseTensor*> outs) {
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int n = out_grad.size();
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auto out_dims = out_grad[0]->dims();
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if (out_grad[0]->numel() == 0) {
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for (size_t i = 0; i < outs.size(); i++) {
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auto* out = outs[i];
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dev_ctx.template Alloc<T>(out);
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if (out->numel() != 0) {
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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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}
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}
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return;
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}
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for (int i = 0; i < n; i++) {
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dev_ctx.template Alloc<T>(outs[i]);
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auto out_grad_tmp = EigenVector<T>::Flatten(*out_grad[i]);
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auto in_grad = EigenVector<T>::Flatten(*outs[i]);
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std::vector<int> reduce_dims_vec;
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std::vector<int64_t> reshape_dims_vec;
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for (int j = 0; j < n; j++) {
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reduce_dims_vec.push_back(reshape_dims_vec.size());
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if (j == i) {
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reshape_dims_vec.push_back(1);
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reshape_dims_vec.push_back(out_dims[j]);
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} else {
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reshape_dims_vec.push_back(out_dims[j]);
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reshape_dims_vec.push_back(1);
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}
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}
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Eigen::DSizes<int64_t, Rank> reduce_dims;
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for (int k = 0; k < n; k++) {
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reduce_dims[k] = reduce_dims_vec[k];
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}
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Eigen::DSizes<int64_t, Rank * 2> reshape_dims;
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for (int k = 0; k < n * 2; k++) {
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reshape_dims[k] = reshape_dims_vec[k];
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}
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auto& place = *dev_ctx.eigen_device();
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funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, T, Rank>::Eval(
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place, in_grad, out_grad_tmp, reduce_dims, reshape_dims);
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}
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}
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template <typename T, typename Context>
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void MeshgridGradKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& inputs,
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const std::vector<const DenseTensor*>& outputs_grad,
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std::vector<DenseTensor*> inputs_grad) {
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int n = outputs_grad.size();
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switch (n) {
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case 1:
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MeshgridBackward<T, Context, 1>(
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dev_ctx, inputs, outputs_grad, inputs_grad);
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break;
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case 2:
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MeshgridBackward<T, Context, 2>(
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dev_ctx, inputs, outputs_grad, inputs_grad);
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break;
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case 3:
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MeshgridBackward<T, Context, 3>(
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dev_ctx, inputs, outputs_grad, inputs_grad);
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break;
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case 4:
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MeshgridBackward<T, Context, 4>(
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dev_ctx, inputs, outputs_grad, inputs_grad);
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break;
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case 5:
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MeshgridBackward<T, Context, 5>(
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dev_ctx, inputs, outputs_grad, inputs_grad);
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break;
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case 6:
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MeshgridBackward<T, Context, 6>(
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dev_ctx, inputs, outputs_grad, inputs_grad);
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break;
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default:
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PADDLE_THROW(common::errors::InvalidArgument(
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"Excepted Tensor numbers between 1 and 6, but only received %d.", n));
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}
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}
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} // namespace phi
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