Merge pull request #5573 from qingqing01/cmake_speed
[Speed Compiling]: Reduce NVCC compiling files.updateWriteDocsCN
Tao Luo
7 years ago
committed by
GitHub
commit
3edd833174
@ -0,0 +1,83 @@
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/* 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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#pragma once
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#include "paddle/framework/data_type.h"
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#include "paddle/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <typename Place, typename T>
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void SetConstant<Place, T>::operator()(const platform::DeviceContext& context,
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framework::Tensor* tensor, T num) {
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auto t = framework::EigenVector<T>::Flatten(*tensor);
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t.device(*context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(num));
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}
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template <typename Place, typename T, int Rank>
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void Transpose<Place, T, Rank>::operator()(
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const platform::DeviceContext& context, const framework::Tensor& in,
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framework::Tensor* out, const std::vector<int>& axis) {
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Eigen::array<int, Rank> permute;
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for (int i = 0; i < Rank; i++) {
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permute[i] = axis[i];
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}
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auto in_dim = in.dims();
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auto out_dim = out->dims();
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auto eigen_in = framework::EigenTensor<T, Rank>::From(in);
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auto eigen_out = framework::EigenTensor<T, Rank>::From(*out);
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auto* dev = context.GetEigenDevice<Place>();
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eigen_out.device(*dev) = eigen_in.shuffle(permute);
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}
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template <typename Place, typename T>
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void RowwiseAdd<Place, T>::operator()(const platform::DeviceContext& context,
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const framework::Tensor& input,
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const framework::Tensor& vector,
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framework::Tensor* output) {
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auto in_dims = input.dims();
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auto size = input.numel() / in_dims[0];
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PADDLE_ENFORCE_EQ(vector.numel(), size);
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PADDLE_ENFORCE_EQ(output->dims(), in_dims);
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auto in = framework::EigenMatrix<T>::From(input);
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auto vec = framework::EigenMatrix<T>::From(vector);
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auto out = framework::EigenMatrix<T>::From(*output);
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Eigen::array<int, 2> shape({{1, static_cast<int>(size)}});
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Eigen::array<int, 2> bcast({{static_cast<int>(in_dims[0]), 1}});
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out.device(*context.GetEigenDevice<Place>()) =
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in + vec.reshape(shape).broadcast(bcast);
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}
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template <typename Place, typename T>
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void ColwiseSum<Place, T>::operator()(const platform::DeviceContext& context,
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const framework::Tensor& input,
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framework::Tensor* vector) {
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auto in_dims = input.dims();
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auto size = input.numel() / in_dims[0];
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PADDLE_ENFORCE_EQ(vector->numel(), size);
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auto vec = framework::EigenMatrix<T>::From(*vector);
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auto in = framework::EigenMatrix<T>::From(input);
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Eigen::array<int, 2> shape({{1, static_cast<int>(size)}});
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vec.reshape(shape).device(*context.GetEigenDevice<Place>()) =
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in.sum(Eigen::array<int, 1>({{0}})).reshape(shape);
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}
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} // namespace math
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} // namespace operators
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} // namespace paddle
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