Yu Yang
8 years ago
commit
3a6f20025a
@ -0,0 +1,91 @@
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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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#include <glog/logging.h>
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#include "unsupported/Eigen/CXX11/Tensor"
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namespace paddle {
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template <class T>
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struct EigenBlasGemm {
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typedef Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, int>,
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Eigen::Aligned>
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Matrix;
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static void compute(const bool transA,
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const bool transB,
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const int M,
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const int N,
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const int K,
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const T alpha,
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const T* A,
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const int lda,
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const T* B,
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const int ldb,
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const T beta,
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T* C,
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const int ldc) {
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Eigen::array<int, 2> sizeA;
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if (transA) {
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sizeA[0] = K;
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sizeA[1] = M;
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CHECK_EQ(M, lda);
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} else {
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sizeA[0] = M;
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sizeA[1] = K;
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CHECK_EQ(K, lda);
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}
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Eigen::array<int, 2> sizeB;
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if (transB) {
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sizeB[0] = N;
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sizeB[1] = K;
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CHECK_EQ(K, ldb);
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} else {
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sizeB[0] = K;
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sizeB[1] = N;
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CHECK_EQ(N, ldb);
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}
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Eigen::array<int, 2> sizeC;
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sizeC[0] = M;
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sizeC[1] = N;
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CHECK_EQ(N, ldc);
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const Matrix a(const_cast<T*>(A), sizeA);
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const Matrix b(const_cast<T*>(B), sizeB);
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Matrix c(C, sizeC);
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typedef typename Eigen::Tensor<T, 2>::DimensionPair DimPair;
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Eigen::array<DimPair, 1> dims;
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dims[0] = DimPair(1, 0);
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dims[0].first = transA ? 0 : 1;
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dims[0].second = transB ? 1 : 0;
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Eigen::DefaultDevice device;
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if (alpha == T(1) && beta == T(0)) {
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c.device(device) = a.contract(b, dims);
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} else if (alpha == T(1) && beta == T(1)) {
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c.device(device) += a.contract(b, dims);
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} else {
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c.device(device) = alpha * a.contract(b, dims) + beta * c;
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}
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}
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};
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#ifdef PADDLE_TYPE_DOUBLE
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template class EigenBlasGemm<double>;
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#else
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template class EigenBlasGemm<float>;
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#endif
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} // namespace paddle
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@ -0,0 +1,90 @@
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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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#include "GemmFunctor.h"
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#include "paddle/math/MathFunctions.h"
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namespace paddle {
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template <class T>
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struct BlasGemm<DEVICE_TYPE_CPU, T> {
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static void compute(const bool transA,
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const bool transB,
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const int M,
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const int N,
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const int K,
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const T alpha,
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const T* A,
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const int lda,
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const T* B,
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const int ldb,
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const T beta,
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T* C,
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const int ldc) {
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#ifdef PADDLE_USE_EIGEN_FOR_BLAS
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EigenBlasGemm<T>::compute(
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transA, transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
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#else
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gemm<T>(transA == false ? CblasNoTrans : CblasTrans,
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transB == false ? CblasNoTrans : CblasTrans,
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M,
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N,
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K,
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alpha,
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A,
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lda,
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B,
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ldb,
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beta,
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C,
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ldc);
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#endif
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}
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};
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template <class T>
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struct BlasGemm<DEVICE_TYPE_GPU, T> {
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static void compute(const bool transA,
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const bool transB,
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const int M,
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const int N,
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const int K,
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const T alpha,
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const T* A,
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const int lda,
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const T* B,
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const int ldb,
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const T beta,
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T* C,
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const int ldc) {
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hl_matrix_mul((T*)A,
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transA == false ? HPPL_OP_N : HPPL_OP_T,
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(T*)B,
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transB == false ? HPPL_OP_N : HPPL_OP_T,
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C,
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M,
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N,
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K,
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alpha,
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beta,
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lda,
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ldb,
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ldc);
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}
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};
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template struct BlasGemm<DEVICE_TYPE_CPU, real>;
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template struct BlasGemm<DEVICE_TYPE_GPU, real>;
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} // namespace paddle
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@ -0,0 +1,107 @@
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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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#include "Layer.h"
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namespace paddle {
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/**
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* A layer applies a linear transformation to each element in each row of
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* the input matrix. For each element, the layer first re-scale it and then
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* adds a bias to it.
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*
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* \f[
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* y = wx + b
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* \f]
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*
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* Here, w is the scale and b is the bias. Both w and b are trainable scalars.
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*
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*/
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class ScaleShiftLayer : public Layer {
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protected:
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std::unique_ptr<Weight> scale_;
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std::unique_ptr<Weight> offset_;
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public:
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explicit ScaleShiftLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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REGISTER_LAYER(scale_shift, ScaleShiftLayer);
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bool ScaleShiftLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 1U);
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scale_.reset(new Weight(1, 1, parameters_[0]));
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if (biasParameter_.get() != NULL) {
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offset_ = std::unique_ptr<Weight>(new Weight(1, 1, biasParameter_));
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}
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return true;
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}
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void ScaleShiftLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV = getInputValue(0);
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resetOutput(inV->getHeight(), inV->getWidth());
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MatrixPtr outV = getOutputValue();
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real scaleValue = scale_->getW()->getElement(0, 0);
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outV->mulScalar(*inV, scaleValue);
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if (offset_) {
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real offsetValue = offset_->getW()->getElement(0, 0);
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outV->add(offsetValue);
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}
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}
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void ScaleShiftLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV = getInputValue(0);
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MatrixPtr inG = getInputGrad(0);
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MatrixPtr outV = getOutputValue();
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MatrixPtr outG = getOutputGrad();
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/* Calculate the parameter gradient for the current layer */
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if (scale_->getWGrad()) {
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MatrixPtr rowSumMtx;
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Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_);
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// this_i = scaleDest * this_i + scaleSum * \sum_j b_{ij} * c_{ij}
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rowSumMtx->sumOfProducts(
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/* b= */ *inV, /* c= */ *outG, /* scaleSum= */ 1, /* scaleDest= */ 0.);
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// this_i = scaleDest * this_i + scaleSum * \sum_j b_{ji}
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scale_->getWGrad()->sumCols(
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/* b= */ *rowSumMtx, /* scaleSum= */ 1., /* scaleDest= */ 1.);
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scale_->getParameterPtr()->incUpdate(callback);
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}
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if (offset_ && offset_->getWGrad()) {
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MatrixPtr rowSumMtx;
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Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_);
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rowSumMtx->sumRows(*outG, 1., 0.);
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offset_->getWGrad()->sumCols(*rowSumMtx, 1., 1.);
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offset_->getParameterPtr()->incUpdate(callback);
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}
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/* Calculate the input layers error */
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if (inG) {
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real scaleValue = scale_->getW()->getElement(0, 0);
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inG->add(*outG, scaleValue);
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}
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}
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} // namespace paddle
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