wehub-resource-sync
86 lines
2.9 KiB
C++
86 lines
2.9 KiB
C++
// Copyright (c) 2024 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 <algorithm>
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#include <vector>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void ShuffleChannelOpKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int group,
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DenseTensor* out) {
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const auto& input_dims = x.dims();
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auto num = input_dims[0];
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auto channel = input_dims[1];
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auto height = input_dims[2];
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auto weight = input_dims[3];
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auto feature_map_size = channel * height * weight;
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auto sp_sz = height * weight;
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int group_row = group;
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int group_column = channel / group_row;
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const T* input_data = x.data<T>();
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T* output_data = dev_ctx.template Alloc<T>(out);
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for (int n = 0; n < num; ++n) {
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for (int i = 0; i < group_row; ++i) {
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for (int j = 0; j < group_column; ++j) {
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const T* p_i =
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input_data + n * feature_map_size + (i * group_column + j) * sp_sz;
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T* p_o =
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output_data + n * feature_map_size + (j * group_row + i) * sp_sz;
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memcpy(p_o, p_i, sizeof(int) * sp_sz);
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}
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}
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}
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}
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template <typename T, typename Context>
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void ShuffleChannelGradOpKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int group,
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DenseTensor* x_grad) {
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const auto& input_dims = x_grad->dims();
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auto num = input_dims[0];
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auto channel = input_dims[1];
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auto height = input_dims[2];
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auto weight = input_dims[3];
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auto feature_map_size = channel * height * weight;
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auto sp_sz = height * weight;
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int group_row = group;
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int group_column = channel / group_row;
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T* input_grad_data = dev_ctx.template Alloc<T>(x_grad);
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const T* output_grad_data = out_grad.data<T>();
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for (int n = 0; n < num; ++n) {
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for (int i = 0; i < group_row; ++i) {
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for (int j = 0; j < group_column; ++j) {
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const T* p_i = output_grad_data + n * feature_map_size +
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(i * group_column + j) * sp_sz;
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T* p_o = input_grad_data + n * feature_map_size +
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(j * group_row + i) * sp_sz;
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memcpy(p_o, p_i, sizeof(int) * sp_sz);
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}
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}
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}
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}
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} // namespace phi
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