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#define EIGEN_USE_THREADS
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/assign_op.h"
#include "tensorflow/core/kernels/dense_update_ops.h"
#include "tensorflow/core/platform/port.h"
#include "tensorflow/core/lib/core/errors.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);
#undef REGISTER_KERNELS
#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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