提交 d8108493 编写于 作者: Q QI JUN 提交者: GitHub

Merge pull request #2953 from wangkuiyi/tensor_type_to_eigen

Refactorize Tensor to Eigen convesion
...@@ -4,8 +4,11 @@ cc_test(enforce_test SRCS enforce_test.cc DEPS enforce) ...@@ -4,8 +4,11 @@ cc_test(enforce_test SRCS enforce_test.cc DEPS enforce)
cc_library(ddim SRCS ddim.cc DEPS eigen3) cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim) cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim) nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_library(tensor SRCS tensor.cc DEPS ddim place enforce paddle_memory) cc_library(tensor SRCS tensor.cc DEPS ddim place enforce paddle_memory)
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor) cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_test(variable_test SRCS variable_test.cc) cc_test(variable_test SRCS variable_test.cc)
cc_test(scope_test SRCS scope_test.cc) cc_test(scope_test SRCS scope_test.cc)
proto_library(attr_type SRCS attr_type.proto) proto_library(attr_type SRCS attr_type.proto)
......
...@@ -119,17 +119,6 @@ int arity(const DDim& ddim); ...@@ -119,17 +119,6 @@ int arity(const DDim& ddim);
std::ostream& operator<<(std::ostream&, const DDim&); std::ostream& operator<<(std::ostream&, const DDim&);
template <int NDIMS>
Eigen::DSizes<Eigen::DenseIndex, NDIMS> ToEigenDSizes(const DDim& dims) {
int rank = arity(dims);
PADDLE_ENFORCE(rank == NDIMS, "DDim and NDIMS must be same");
Eigen::DSizes<Eigen::DenseIndex, NDIMS> dsizes;
for (int d = 0; d < rank; d++) {
dsizes[d] = dims[d];
}
return dsizes;
}
} // namespace framework } // namespace framework
} // namespace paddle } // namespace paddle
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/tensor.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
// EigenDim converts paddle::platform::DDim into Eigen::DSizes.
template <int D>
struct EigenDim {
using Type = Eigen::DSizes<Eigen::DenseIndex, D>;
static Type From(const DDim& dims) {
PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
Type ret;
for (int d = 0; d < arity(dims); d++) {
ret[d] = dims[d];
}
return ret;
}
};
// Interpret paddle::platform::Tensor as EigenTensor and EigenConstTensor.
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenTensor {
// TODO(qijun) Now, default type in unaligned, and we will make a benchmark on
// the speed of aligned and unaligned version in future.
using Type = Eigen::TensorMap<Eigen::Tensor<T, D, MajorType, IndexType>>;
using ConstType =
Eigen::TensorMap<Eigen::Tensor<const T, D, MajorType, IndexType>>;
static Type From(Tensor& tensor, DDim dims) {
return Type(tensor.data<T>(), EigenDim<D>::From(dims));
}
static Type From(Tensor& tensor) { return From(tensor, tensor.dims_); }
static ConstType From(const Tensor& tensor, DDim dims) {
return ConstType(tensor.data<T>(), EigenDim<D>::From(dims));
}
static ConstType From(const Tensor& tensor) {
return From(tensor, tensor.dims_);
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
// Flatten is to reshape a Tensor into a one dimension EigenVector
static typename EigenTensor<T, 1>::Type Flatten(Tensor& tensor) {
return EigenTensor<T, 1>::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
static typename EigenTensor<T, 1>::ConstType Flatten(const Tensor& tensor) {
return EigenTensor<T, 1>::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = EigenTensor<T, 2, MajorType, IndexType>;
} // namespace framework
} // namespace paddle
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "paddle/framework/eigen.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
TEST(EigenDim, From) {
EigenDim<3>::Type ed = EigenDim<3>::From(make_ddim({1, 2, 3}));
ASSERT_EQ(1, ed[0]);
ASSERT_EQ(2, ed[1]);
ASSERT_EQ(3, ed[2]);
}
TEST(Eigen, Tensor) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
ASSERT_EQ(1, et.dimension(0));
ASSERT_EQ(2, et.dimension(1));
ASSERT_EQ(3, et.dimension(2));
for (int i = 0; i < 1; i++) {
for (int j = 0; j < 2; j++) {
for (int k = 0; k < 3; k++) {
ASSERT_NEAR((i * 2 + j) * 3 + k, et(i, j, k), 1e-6f);
}
}
}
}
TEST(Eigen, VectorFrom) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({6}), platform::CPUPlace());
for (int i = 0; i < 6; i++) {
p[i] = static_cast<float>(i);
}
EigenVector<float>::Type ev = EigenVector<float>::From(t);
ASSERT_EQ(6, ev.dimension(0));
for (int i = 0; i < 6; i++) {
ASSERT_NEAR(i, ev(i), 1e-6f);
}
}
TEST(Eigen, VectorFlatten) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenVector<float>::Type ev = EigenVector<float>::Flatten(t);
ASSERT_EQ(1 * 2 * 3, ev.dimension(0));
for (int i = 0; i < 1 * 2 * 3; i++) {
ASSERT_NEAR(i, ev(i), 1e-6f);
}
}
TEST(Eigen, Matrix) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({2, 3}), platform::CPUPlace());
for (int i = 0; i < 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenMatrix<float>::Type em = EigenMatrix<float>::From(t);
ASSERT_EQ(2, em.dimension(0));
ASSERT_EQ(3, em.dimension(1));
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 3; j++) {
ASSERT_NEAR(i * 3 + j, em(i, j), 1e-6f);
}
}
}
} // namespace framework
} // namespace paddle
...@@ -20,7 +20,6 @@ limitations under the License. */ ...@@ -20,7 +20,6 @@ limitations under the License. */
#include <typeindex> #include <typeindex>
#include "paddle/framework/ddim.h" #include "paddle/framework/ddim.h"
#include "paddle/framework/enforce.h" #include "paddle/framework/enforce.h"
#include "paddle/framework/tensor_types.h"
#include "paddle/memory/memory.h" #include "paddle/memory/memory.h"
#include "paddle/platform/place.h" #include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor" #include "unsupported/Eigen/CXX11/Tensor"
...@@ -35,6 +34,15 @@ struct CastToPyBufferImpl; ...@@ -35,6 +34,15 @@ struct CastToPyBufferImpl;
namespace framework { namespace framework {
class Tensor { class Tensor {
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
template <typename T, int MajorType, typename IndexType>
friend struct EigenVector;
public: public:
Tensor() : offset_(0) {} Tensor() : offset_(0) {}
...@@ -46,7 +54,7 @@ class Tensor { ...@@ -46,7 +54,7 @@ class Tensor {
} }
template <typename T> template <typename T>
T* raw_data() const { T* data() {
CheckDims<T>(); CheckDims<T>();
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) + return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_); offset_);
...@@ -86,66 +94,6 @@ class Tensor { ...@@ -86,66 +94,6 @@ class Tensor {
offset_); offset_);
} }
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::Tensor shaped(DDim new_dims) {
Eigen::array<Eigen::DenseIndex, NDIMS> dims =
paddle::framework::ToEigenDSizes<NDIMS>(new_dims);
return typename TTypes<T, NDIMS>::Tensor(raw_data<T>(), dims);
}
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::Tensor tensor() {
return typename TTypes<T, NDIMS>::Tensor(
raw_data<T>(), paddle::framework::ToEigenDSizes<NDIMS>(dims_));
}
// flat to rank = 1
template <typename T>
typename TTypes<T>::Flat flat() {
return shaped<T, 1>(make_ddim({static_cast<int>(product(dims_))}));
}
// to TensorType Vec
template <typename T>
typename TTypes<T>::Vec vec() {
return tensor<T, 1>();
}
// to TensorType Matrix
template <typename T>
typename TTypes<T>::Matrix matrix() {
return tensor<T, 2>();
}
// const versions of all the methods above.
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::Tensor shaped(DDim new_dims) const {
Eigen::array<Eigen::DenseIndex, NDIMS> dims =
paddle::framework::ToEigenDSizes<NDIMS>(new_dims);
return typename TTypes<T, NDIMS>::Tensor(data<T>(), dims);
}
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::ConstantTensor tensor() const {
return typename TTypes<T, NDIMS>::Tensor(
data<T>(), paddle::framework::ToEigenDSizes<NDIMS>(dims_));
}
template <typename T>
typename TTypes<T>::ConstFlat flat() const {
return shaped<T, 1>(make_ddim({static_cast<int>(product(dims_))}));
}
template <typename T>
typename TTypes<T>::ConstVec vec() const {
return tensor<T, 1>();
}
template <typename T>
typename TTypes<T>::ConstMatrix matrix() const {
return tensor<T, 2>();
}
template <typename T> template <typename T>
void ShareDataFrom(const Tensor& src) { void ShareDataFrom(const Tensor& src) {
src.CheckDims<T>(); src.CheckDims<T>();
...@@ -251,8 +199,6 @@ class Tensor { ...@@ -251,8 +199,6 @@ class Tensor {
std::shared_ptr<Placeholder> holder_; // holds the memory block if allocated. std::shared_ptr<Placeholder> holder_; // holds the memory block if allocated.
DDim dims_; DDim dims_;
size_t offset_; // marks the begin of tensor data area. size_t offset_; // marks the begin of tensor data area.
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
}; };
} // namespace framework } // namespace framework
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
// Helper to define Tensor types given that the scalar is of type T.
template <typename T, int NDIMS = 1, typename IndexType = Eigen::DenseIndex>
struct TTypes {
// Rank-<NDIMS> tensor of scalar type T.
typedef Eigen::TensorMap<Eigen::Tensor<T, NDIMS, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Tensor;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, NDIMS, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstTensor;
// Scalar tensor (implemented as a rank-0 tensor) of scalar type T.
typedef Eigen::TensorMap<
Eigen::TensorFixedSize<T, Eigen::Sizes<>, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Scalar;
typedef Eigen::TensorMap<Eigen::TensorFixedSize<const T, Eigen::Sizes<>,
Eigen::RowMajor, IndexType>,
Eigen::Aligned>
ConstScalar;
// Rank-1 tensor (vector) of scalar type T.
typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Flat;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstFlat;
typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Vec;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstVec;
// Rank-2 tensor (matrix) of scalar type T.
typedef Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Matrix;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, 2, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstMatrix;
};
} // namespace framework
} // namespace paddle
...@@ -14,6 +14,7 @@ limitations under the License. */ ...@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once #pragma once
#include "glog/logging.h" #include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h" #include "paddle/framework/operator.h"
namespace paddle { namespace paddle {
...@@ -29,8 +30,10 @@ public: ...@@ -29,8 +30,10 @@ public:
output->mutable_data<T>(context.GetPlace()); output->mutable_data<T>(context.GetPlace());
output->flat<T>().device(*(context.GetEigenDevice<Place>())) = framework::EigenVector<T>::Flatten(*output).device(
input0.flat<T>() + input1.flat<T>(); *(context.GetEigenDevice<Place>())) =
framework::EigenVector<T>::Flatten(input0) +
framework::EigenVector<T>::Flatten(input1);
} }
}; };
......
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