提交 3208914b 编写于 作者: Q QI JUN 提交者: GitHub

Merge pull request #2805 from QiJune/tensor_to_EigenTensor

Add method converting Tensor to Eigen TensorMap
# ddim lib
cc_library(enforce SRCS enforce.cc DEPS glog)
cc_test(enforce_test SRCS enforce_test.cc DEPS enforce)
cc_library(ddim SRCS ddim.cc)
cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_library(tensor SRCS tensor.cc DEPS ddim place enforce paddle_memory)
......
/* 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 <boost/variant.hpp>
#include <initializer_list>
#include <stdexcept>
#include <vector>
#include "paddle/framework/dim.h"
#include "paddle/framework/enforce.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
......@@ -104,6 +119,17 @@ int arity(const DDim& 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 paddle
......
......@@ -19,6 +19,20 @@ limitations under the License. */
namespace paddle {
namespace framework {
template <>
Eigen::DefaultDevice* KernelContext::GetEigenDevice<
platform::CPUPlace, Eigen::DefaultDevice>() const {
return device_context_.get_eigen_device<Eigen::DefaultDevice>();
}
#ifndef PADDLE_ONLY_CPU
template <>
Eigen::GpuDevice*
KernelContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
return device_context_.get_eigen_device<Eigen::GpuDevice>();
}
#endif
const std::string& OperatorBase::Input(const std::string& name) const {
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
......
......@@ -31,6 +31,21 @@ limitations under the License. */
namespace paddle {
namespace framework {
template <typename T>
struct EigenDeviceConverter;
template <>
struct EigenDeviceConverter<platform::CPUPlace> {
using EigenDeviceType = Eigen::DefaultDevice;
};
#ifndef PADDLE_ONLY_CPU
template <>
struct EigenDeviceConverter<platform::GPUPlace> {
using EigenDeviceType = Eigen::GpuDevice;
};
#endif
class OperatorBase;
using OperatorPtr = std::shared_ptr<OperatorBase>;
/**
......@@ -131,6 +146,13 @@ class KernelContext {
return res;
}
template <typename PlaceType,
typename DeviceType =
typename EigenDeviceConverter<PlaceType>::EigenDeviceType>
DeviceType* GetEigenDevice() const;
platform::Place GetPlace() const { return device_context_.GetPlace(); }
const OperatorBase& op_;
const std::shared_ptr<Scope>& scope_;
const platform::DeviceContext& device_context_;
......@@ -144,6 +166,7 @@ class OpKernel {
* device resource such as CUDA stream, cublas handle, etc. from
* KernelContext. User should construct it before run the Operator.
*/
virtual void Compute(const KernelContext& context) const = 0;
virtual ~OpKernel() {}
......
......@@ -20,8 +20,10 @@ limitations under the License. */
#include <typeindex>
#include "paddle/framework/ddim.h"
#include "paddle/framework/enforce.h"
#include "paddle/framework/tensor_types.h"
#include "paddle/memory/memory.h"
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace pybind {
......@@ -43,6 +45,13 @@ class Tensor {
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
template <typename T>
T* raw_data() const {
CheckDims<T>();
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
template <typename T>
T* mutable_data(DDim dims, platform::Place place) {
set_dims(dims);
......@@ -77,6 +86,66 @@ class Tensor {
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>
void ShareDataFrom(const Tensor& src) {
src.CheckDims<T>();
......
/* 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
/* 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
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
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. */
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/op_registry.h>
#include <paddle/framework/tensor.h>
#include <paddle/operators/add_op.h>
#include "paddle/operators/add_op.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
......@@ -53,5 +53,6 @@ The equation is: Out = X + Y
} // namespace paddle
REGISTER_OP(add_two, paddle::operators::AddOp, paddle::operators::AddOpMaker);
REGISTER_OP_CPU_KERNEL(
add_two, ::paddle::operators::AddKernel<::paddle::platform::CPUPlace>);
typedef paddle::operators::AddKernel<::paddle::platform::CPUPlace, float>
AddKernel_CPU_float;
REGISTER_OP_CPU_KERNEL(add_two, AddKernel_CPU_float);
#include <paddle/operators/add_op.h>
#include <paddle/framework/op_registry.h>
#include "paddle/operators/add_op.h"
#include "paddle/framework/op_registry.h"
typedef paddle::operators::AddKernel<::paddle::platform::GPUPlace, float> AddKernel_GPU_float;
REGISTER_OP_GPU_KERNEL(add_two,
paddle::operators::AddKernel<paddle::platform::GPUPlace>);
\ No newline at end of file
AddKernel_GPU_float);
\ No newline at end of file
/* 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 <glog/logging.h>
#include <paddle/framework/operator.h>
#include "glog/logging.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place>
template <typename Place, typename T>
class AddKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Add kernel in " << typeid(Place).name();
void Compute(const framework::KernelContext& context) const override {
auto input0 = context.Input(0)->Get<framework::Tensor>();
auto input1 = context.Input(1)->Get<framework::Tensor>();
auto* output = context.Output(0)->GetMutable<framework::Tensor>();
output->mutable_data<T>(context.GetPlace());
output->flat<T>().device(*(context.GetEigenDevice<Place>())) =
input0.flat<T>() + input1.flat<T>();
}
};
......
/* 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 <gtest/gtest.h>
#define private public
#include <paddle/framework/op_registry.h>
......
......@@ -15,14 +15,15 @@ namespace paddle {
namespace platform {
template <>
Eigen::DefaultDevice* DeviceContext::get_eigen_device<Eigen::DefaultDevice>() {
return reinterpret_cast<CPUDeviceContext*>(this)->eigen_device();
Eigen::DefaultDevice* DeviceContext::get_eigen_device<Eigen::DefaultDevice>()
const {
return reinterpret_cast<const CPUDeviceContext*>(this)->eigen_device();
}
#ifndef PADDLE_ONLY_CPU
template <>
Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() {
return reinterpret_cast<CUDADeviceContext*>(this)->eigen_device();
Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
return reinterpret_cast<const CUDADeviceContext*>(this)->eigen_device();
}
#endif
......
......@@ -20,9 +20,9 @@ limitations under the License. */
#include "paddle/platform/gpu_info.h"
#define EIGEN_USE_GPU
#endif
#include <paddle/platform/place.h>
#include <memory>
#include <unsupported/Eigen/CXX11/Tensor>
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace platform {
......@@ -33,17 +33,14 @@ class DeviceContext {
virtual Place GetPlace() const = 0;
template <typename DeviceType>
DeviceType* get_eigen_device();
DeviceType* get_eigen_device() const;
};
class CPUDeviceContext : public DeviceContext {
public:
Eigen::DefaultDevice* eigen_device() {
if (!eigen_device_) {
eigen_device_.reset(new Eigen::DefaultDevice());
}
return eigen_device_.get();
}
CPUDeviceContext() { eigen_device_.reset(new Eigen::DefaultDevice()); }
Eigen::DefaultDevice* eigen_device() const { return eigen_device_.get(); }
Place GetPlace() const override {
Place retv = CPUPlace();
......@@ -92,7 +89,7 @@ class CUDADeviceContext : public DeviceContext {
cudaStream_t stream() { return stream_; }
Eigen::GpuDevice* eigen_device() { return eigen_device_.get(); }
Eigen::GpuDevice* eigen_device() const { return eigen_device_.get(); }
cublasHandle_t cublas_handle() {
if (!blas_handle_) {
......
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