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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
#define EIGEN_USE_THREADS
#if TENSORFLOW_USE_SYCL
#define EIGEN_USE_SYCL
#endif
#include "tensorflow/core/kernels/dense_update_ops.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/assign_op.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/types.h"
namespace tensorflow {
template <typename Device, typename T>
class AssignOpT : public AssignOp {
public:
using AssignOp::AssignOp;
void Copy(OpKernelContext* context, Tensor* lhs, const Tensor& rhs) override {
functor::DenseUpdate<Device, T, ASSIGN> copy;
copy(context->eigen_device<Device>(), lhs->flat<T>(), rhs.flat<T>());
}
};
// TODO(jeff): Get rid of use_exclusive_lock_ option
template <typename Device, typename T, DenseUpdateType OP>
class DenseUpdateOp : public OpKernel {
public:
explicit DenseUpdateOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context,
context->GetAttr("use_locking", &use_exclusive_lock_));
const DataType dt = DataTypeToEnum<T>::v();
OP_REQUIRES_OK(context, context->MatchSignature({MakeRefType(dt), dt},
{MakeRefType(dt)}));
}
void Compute(OpKernelContext* context) override {
// We always return the input ref.
context->forward_ref_input_to_ref_output(0, 0);
if (use_exclusive_lock_) {
mutex_lock l(*context->input_ref_mutex(0));
DoUpdate(context);
} else {
DoUpdate(context);
}
}
private:
void DoUpdate(OpKernelContext* context) {
Tensor Tparams = context->mutable_input(0, use_exclusive_lock_);
const Tensor& Tupdate = context->input(1);
OP_REQUIRES(context, Tparams.IsInitialized(),
errors::FailedPrecondition("Attempting to use uninitialized "
"parameters: ",
def().input(0)));
OP_REQUIRES(
context, Tparams.IsSameSize(Tupdate),
errors::InvalidArgument("Parameters and update must be the same size"));
functor::DenseUpdate<Device, T, OP> update_functor;
update_functor(context->eigen_device<Device>(), Tparams.flat<T>(),
Tupdate.flat<T>());
}
bool use_exclusive_lock_;
};
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
#define REGISTER_KERNELS(type) \
REGISTER_KERNEL_BUILDER( \
Name("Assign").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
AssignOpT<CPUDevice, type>);
TF_CALL_ALL_TYPES(REGISTER_KERNELS);
TF_CALL_QUANTIZED_TYPES(REGISTER_KERNELS);
#undef REGISTER_KERNELS
#if TENSORFLOW_USE_SYCL
typedef Eigen::SyclDevice SYCLDevice;
#define REGISTER_SYCL_KERNEL(type) \
REGISTER_KERNEL_BUILDER( \
Name("Assign") \
.Device(DEVICE_SYCL) \
.TypeConstraint<type>("T"), \
AssignOpT<SYCLDevice, type>); \
REGISTER_KERNEL_BUILDER( \
Name("AssignAdd").Device(DEVICE_SYCL).TypeConstraint<type>("T"), \
DenseUpdateOp<SYCLDevice, type, DenseUpdateType::ADD>); \
REGISTER_KERNEL_BUILDER( \
Name("AssignSub").Device(DEVICE_SYCL).TypeConstraint<type>("T"), \
DenseUpdateOp<SYCLDevice, type, DenseUpdateType::SUB>);
REGISTER_SYCL_KERNEL(float);
#undef REGISTER_SYCL_KERNEL
#endif
#if GOOGLE_CUDA
// Only register 'Assign' on GPU for the subset of types also supported by
// 'Variable' (see variable_ops.cc.)
#define REGISTER_GPU_KERNELS(type) \
namespace functor { \
template <> \
void DenseUpdate<GPUDevice, type, ASSIGN>::operator()( \
const GPUDevice& d, typename TTypes<type>::Flat lhs, \
typename TTypes<type>::ConstFlat rhs); \
extern template struct DenseUpdate<GPUDevice, type, ASSIGN>; \
} \
REGISTER_KERNEL_BUILDER( \
Name("Assign").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
AssignOpT<GPUDevice, type>);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
#undef REGISTER_GPU_KERNELS
#endif // GOOGLE_CUDA
#define REGISTER_KERNELS(type) \
REGISTER_KERNEL_BUILDER( \
Name("AssignAdd").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
DenseUpdateOp<CPUDevice, type, DenseUpdateType::ADD>); \
REGISTER_KERNEL_BUILDER( \
Name("AssignSub").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
DenseUpdateOp<CPUDevice, type, DenseUpdateType::SUB>);
TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
#undef REGISTER_KERNELS
#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC_FOR_OP(T, OP) \
template <> \
void DenseUpdate<GPUDevice, T, OP>::operator()( \
const GPUDevice& d, typename TTypes<T>::Flat params, \
typename TTypes<T>::ConstFlat update); \
extern template struct DenseUpdate<GPUDevice, T, OP>;
#define DECLARE_GPU_SPEC(T) \
DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::ADD); \
DECLARE_GPU_SPEC_FOR_OP(T, DenseUpdateType::SUB)
TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
#undef DECLARE_GPU_SPEC
#undef DECLARE_GPU_SPEC_FOR_OP
} // namespace functor
#define REGISTER_GPU_KERNELS(type) \
REGISTER_KERNEL_BUILDER( \
Name("AssignAdd").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
DenseUpdateOp<GPUDevice, type, DenseUpdateType::ADD>); \
REGISTER_KERNEL_BUILDER( \
Name("AssignSub").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
DenseUpdateOp<GPUDevice, type, DenseUpdateType::SUB>);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
#undef REGISTER_GPU_KERNELS
#endif // end GOOGLE_CUDA
} // namespace tensorflow
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