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205 lines
8.3 KiB
205 lines
8.3 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/operators/math/vol2col.h"
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#include "paddle/platform/cuda_helper.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <class T>
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__global__ void vol2col(int num_kernels, const T* data_vol, int depth,
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int height, int width, int filter_depth,
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int filter_height, int filter_width, int stride_depth,
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int stride_height, int stride_width, int padding_depth,
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int padding_height, int padding_width, int output_detph,
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int output_height, int output_width, T* data_col) {
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for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
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index += blockDim.x * gridDim.x) {
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int w_out = index % output_width;
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int h_out = (index / output_width) % output_height;
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int d_out = (index / output_width / output_height) % output_detph;
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int channel_in = index / output_width / output_height / output_detph;
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int channel_out = channel_in * filter_depth * filter_height * filter_width;
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int w_in = w_out * stride_width - padding_width;
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int h_in = h_out * stride_height - padding_height;
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int d_in = d_out * stride_depth - padding_depth;
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data_col += ((channel_out * output_detph + d_out) * output_height + h_out) *
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output_width +
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w_out;
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data_vol += ((channel_in * depth + d_in) * height + h_in) * width + w_in;
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for (int k = 0; k < filter_depth; ++k) {
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for (int i = 0; i < filter_height; ++i) {
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for (int j = 0; j < filter_width; ++j) {
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int d = d_in + k;
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int h = h_in + i;
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int w = w_in + j;
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*data_col = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 &&
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w < width)
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? data_vol[(k * height + i) * width + j]
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: 0;
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data_col += output_detph * output_height * output_width;
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}
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}
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}
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}
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}
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/*
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* im = [input_channels,intpu_depth, input_height, input_width]
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* col =
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* [input_channels, filter_depth, filter_height, filter_width,
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* output_depth, output_height, output_width]
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*/
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template <class T>
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class Vol2ColFunctor<platform::GPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& vol, framework::Tensor& col,
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int stride_depth, int stride_height, int stride_width,
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int padding_depth, int padding_height,
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int padding_width) const {
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PADDLE_ENFORCE(vol.dims().size() == 4);
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PADDLE_ENFORCE(col.dims().size() == 7);
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int input_channels = vol.dims()[0];
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int input_depth = vol.dims()[1];
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int input_height = vol.dims()[2];
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int input_width = vol.dims()[3];
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int filter_depth = col.dims()[1];
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int filter_height = col.dims()[2];
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int filter_width = col.dims()[3];
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int output_depth = col.dims()[4];
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int output_height = col.dims()[5];
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int output_width = col.dims()[6];
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int num_outputs =
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input_channels * output_depth * output_height * output_width;
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const int threads = 1024;
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const int blocks = (num_outputs + 1024 - 1) / 1024;
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vol2col<T><<<blocks, threads, 0,
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.stream()>>>(
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num_outputs, vol.data<T>(), input_depth, input_height, input_width,
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filter_depth, filter_height, filter_width, stride_depth, stride_height,
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stride_width, padding_depth, padding_height, padding_width,
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output_depth, output_height, output_width, col.data<T>());
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}
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};
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template <class T>
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__global__ void col2vol(int num_kernels, const T* data_col, int depth,
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int height, int width, int filter_depth,
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int filter_height, int filter_width, int stride_depth,
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int stride_height, int stride_width, int padding_depth,
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int padding_height, int padding_width, int output_detph,
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int output_height, int output_width, T* data_vol) {
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for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
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index += blockDim.x * gridDim.x) {
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T src_val = 0;
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int w = index % width + padding_width;
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int h = (index / width) % height + padding_height;
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int d = (index / width / height) % depth + padding_depth;
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int c = index / width / height / depth;
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// compute the start and end of the output
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int w_col_start =
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(w < filter_width) ? 0 : (w - filter_width) / stride_width + 1;
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int w_col_end = min(w / stride_width + 1, output_width);
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int h_col_start =
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(h < filter_height) ? 0 : (h - filter_height) / stride_height + 1;
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int h_col_end = min(h / stride_height + 1, output_height);
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int d_col_start =
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(d < filter_depth) ? 0 : (d - filter_depth) / stride_depth + 1;
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int d_col_end = min(d / stride_depth + 1, output_detph);
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int offset = (c * filter_depth * filter_height * filter_width +
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d * filter_width * filter_height + h * filter_width + w) *
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output_detph * output_height * output_width;
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int coeff_d_col =
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(1 - stride_depth * filter_width * filter_height * output_detph) *
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output_height * output_width;
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int coeff_h_col =
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(1 - stride_height * filter_width * output_detph * output_height) *
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output_width;
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int coeff_w_col =
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(1 - stride_width * output_detph * output_height * output_width);
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for (int d_col = d_col_start; d_col < d_col_end; ++d_col) {
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for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
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for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
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src_val += data_col[offset + d_col * coeff_d_col +
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h_col * coeff_h_col + w_col * coeff_w_col];
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}
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}
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}
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data_vol[index] = src_val;
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}
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}
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/*
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* im = [input_channels, input_depth, input_height, input_width]
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* col =
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* [input_channels, filter_depth, filter_height, filter_width,
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* output_depth, output_height, output_width]
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*/
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template <class T>
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class Col2VolFunctor<platform::GPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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framework::Tensor& vol, const framework::Tensor& col,
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int stride_depth, int stride_height, int stride_width,
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int padding_depth, int padding_height,
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int padding_width) const {
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PADDLE_ENFORCE(vol.dims().size() == 4);
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PADDLE_ENFORCE(col.dims().size() == 7);
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int input_channels = vol.dims()[0];
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int input_depth = vol.dims()[1];
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int input_height = vol.dims()[2];
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int input_width = vol.dims()[3];
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int filter_depth = col.dims()[1];
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int filter_height = col.dims()[2];
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int filter_width = col.dims()[3];
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int output_depth = col.dims()[4];
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int output_height = col.dims()[5];
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int output_width = col.dims()[6];
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int num_kernels = input_channels * input_depth * input_height * input_width;
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const int threads = 1024;
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const int blocks = (num_kernels + 1024 - 1) / 1024;
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col2vol<T><<<blocks, threads, 0,
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.stream()>>>(
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num_kernels, col.data<T>(), input_depth, input_height, input_width,
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filter_depth, filter_height, filter_width, stride_depth, stride_height,
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stride_width, padding_depth, padding_height, padding_width,
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output_depth, output_height, output_width, vol.data<T>());
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}
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};
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template class Vol2ColFunctor<platform::GPUPlace, float>;
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template class Vol2ColFunctor<platform::GPUPlace, double>;
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template class Col2VolFunctor<platform::GPUPlace, float>;
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template class Col2VolFunctor<platform::GPUPlace, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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