Merge pull request #2953 from wangkuiyi/tensor_type_to_eigen
Refactorize Tensor to Eigen convesioncblas_new
QI JUN
8 years ago
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GitHub
commit
d81084939b
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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/tensor.h"
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#include "unsupported/Eigen/CXX11/Tensor"
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namespace paddle {
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namespace framework {
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// EigenDim converts paddle::platform::DDim into Eigen::DSizes.
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template <int D>
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struct EigenDim {
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using Type = Eigen::DSizes<Eigen::DenseIndex, D>;
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static Type From(const DDim& dims) {
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PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
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Type ret;
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for (int d = 0; d < arity(dims); d++) {
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ret[d] = dims[d];
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}
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return ret;
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}
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};
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// Interpret paddle::platform::Tensor as EigenTensor and EigenConstTensor.
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template <typename T, size_t D, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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struct EigenTensor {
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// TODO(qijun) Now, default type in unaligned, and we will make a benchmark on
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// the speed of aligned and unaligned version in future.
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using Type = Eigen::TensorMap<Eigen::Tensor<T, D, MajorType, IndexType>>;
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using ConstType =
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Eigen::TensorMap<Eigen::Tensor<const T, D, MajorType, IndexType>>;
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static Type From(Tensor& tensor, DDim dims) {
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return Type(tensor.data<T>(), EigenDim<D>::From(dims));
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}
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static Type From(Tensor& tensor) { return From(tensor, tensor.dims_); }
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static ConstType From(const Tensor& tensor, DDim dims) {
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return ConstType(tensor.data<T>(), EigenDim<D>::From(dims));
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}
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static ConstType From(const Tensor& tensor) {
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return From(tensor, tensor.dims_);
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}
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};
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
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// Flatten is to reshape a Tensor into a one dimension EigenVector
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static typename EigenTensor<T, 1>::Type Flatten(Tensor& tensor) {
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return EigenTensor<T, 1>::From(
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tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
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}
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static typename EigenTensor<T, 1>::ConstType Flatten(const Tensor& tensor) {
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return EigenTensor<T, 1>::From(
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tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
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}
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};
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = EigenTensor<T, 2, MajorType, IndexType>;
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,101 @@
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/*
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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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*/
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#include "paddle/framework/eigen.h"
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#include <gtest/gtest.h>
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namespace paddle {
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namespace framework {
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TEST(EigenDim, From) {
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EigenDim<3>::Type ed = EigenDim<3>::From(make_ddim({1, 2, 3}));
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ASSERT_EQ(1, ed[0]);
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ASSERT_EQ(2, ed[1]);
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ASSERT_EQ(3, ed[2]);
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}
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TEST(Eigen, Tensor) {
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Tensor t;
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float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
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for (int i = 0; i < 1 * 2 * 3; i++) {
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p[i] = static_cast<float>(i);
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}
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EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
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ASSERT_EQ(1, et.dimension(0));
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ASSERT_EQ(2, et.dimension(1));
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ASSERT_EQ(3, et.dimension(2));
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for (int i = 0; i < 1; i++) {
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for (int j = 0; j < 2; j++) {
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for (int k = 0; k < 3; k++) {
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ASSERT_NEAR((i * 2 + j) * 3 + k, et(i, j, k), 1e-6f);
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}
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}
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}
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}
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TEST(Eigen, VectorFrom) {
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Tensor t;
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float* p = t.mutable_data<float>(make_ddim({6}), platform::CPUPlace());
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for (int i = 0; i < 6; i++) {
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p[i] = static_cast<float>(i);
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}
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EigenVector<float>::Type ev = EigenVector<float>::From(t);
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ASSERT_EQ(6, ev.dimension(0));
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for (int i = 0; i < 6; i++) {
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ASSERT_NEAR(i, ev(i), 1e-6f);
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}
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}
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TEST(Eigen, VectorFlatten) {
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Tensor t;
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float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
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for (int i = 0; i < 1 * 2 * 3; i++) {
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p[i] = static_cast<float>(i);
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}
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EigenVector<float>::Type ev = EigenVector<float>::Flatten(t);
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ASSERT_EQ(1 * 2 * 3, ev.dimension(0));
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for (int i = 0; i < 1 * 2 * 3; i++) {
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ASSERT_NEAR(i, ev(i), 1e-6f);
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}
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}
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TEST(Eigen, Matrix) {
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Tensor t;
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float* p = t.mutable_data<float>(make_ddim({2, 3}), platform::CPUPlace());
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for (int i = 0; i < 2 * 3; i++) {
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p[i] = static_cast<float>(i);
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}
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EigenMatrix<float>::Type em = EigenMatrix<float>::From(t);
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ASSERT_EQ(2, em.dimension(0));
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ASSERT_EQ(3, em.dimension(1));
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for (int i = 0; i < 2; i++) {
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for (int j = 0; j < 3; j++) {
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ASSERT_NEAR(i * 3 + j, em(i, j), 1e-6f);
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}
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}
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}
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} // namespace framework
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} // namespace paddle
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@ -1,67 +0,0 @@
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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 "unsupported/Eigen/CXX11/Tensor"
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namespace paddle {
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namespace framework {
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// Helper to define Tensor types given that the scalar is of type T.
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template <typename T, int NDIMS = 1, typename IndexType = Eigen::DenseIndex>
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struct TTypes {
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// Rank-<NDIMS> tensor of scalar type T.
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typedef Eigen::TensorMap<Eigen::Tensor<T, NDIMS, Eigen::RowMajor, IndexType>,
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Eigen::Aligned>
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Tensor;
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typedef Eigen::TensorMap<
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Eigen::Tensor<const T, NDIMS, Eigen::RowMajor, IndexType>, Eigen::Aligned>
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ConstTensor;
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// Scalar tensor (implemented as a rank-0 tensor) of scalar type T.
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typedef Eigen::TensorMap<
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Eigen::TensorFixedSize<T, Eigen::Sizes<>, Eigen::RowMajor, IndexType>,
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Eigen::Aligned>
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Scalar;
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typedef Eigen::TensorMap<Eigen::TensorFixedSize<const T, Eigen::Sizes<>,
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Eigen::RowMajor, IndexType>,
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Eigen::Aligned>
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ConstScalar;
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// Rank-1 tensor (vector) of scalar type T.
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typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>,
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Eigen::Aligned>
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Flat;
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typedef Eigen::TensorMap<
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Eigen::Tensor<const T, 1, Eigen::RowMajor, IndexType>, Eigen::Aligned>
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ConstFlat;
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typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>,
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Eigen::Aligned>
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Vec;
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typedef Eigen::TensorMap<
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Eigen::Tensor<const T, 1, Eigen::RowMajor, IndexType>, Eigen::Aligned>
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ConstVec;
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// Rank-2 tensor (matrix) of scalar type T.
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typedef Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, IndexType>,
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Eigen::Aligned>
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Matrix;
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typedef Eigen::TensorMap<
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Eigen::Tensor<const T, 2, Eigen::RowMajor, IndexType>, Eigen::Aligned>
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ConstMatrix;
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};
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} // namespace framework
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} // namespace paddle
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