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/* Copyright 2017 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.
==============================================================================*/
#ifndef TENSORFLOW_KERNELS_TRAINING_OP_HELPERS_H_
#define TENSORFLOW_KERNELS_TRAINING_OP_HELPERS_H_
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/variant_op_registry.h"
#include "tensorflow/core/kernels/dense_update_functor.h"
#include "tensorflow/core/kernels/variable_ops.h"
namespace tensorflow {
mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input);
std::vector<mutex_lock> MaybeLockVariableInputMutexesInOrder(
OpKernelContext* ctx, bool do_lock, const std::vector<int>& input_ids);
void MaybeForwardRefInputToRefOutput(OpKernelContext* ctx, int input,
int output);
// This is for use with ResourceVariables to ensure *tensor has a
// reference count of 1 before you update it.
// REQUIRES: If you pass in variable->tensor(), *variable->mu() must be held.
template <typename Device, typename T>
Status PrepareToUpdateVariable(OpKernelContext* ctx, Tensor* tensor) {
if (!tensor->RefCountIsOne()) {
// Tensor's buffer is in use by some read, so we need to copy before
// updating.
PersistentTensor unused;
Tensor* tmp;
if (std::is_same<T, Variant>::value) {
AllocatorAttributes attr;
attr.set_on_host(true);
TF_RETURN_IF_ERROR(ctx->allocate_persistent(
tensor->dtype(), tensor->shape(), &unused, &tmp, attr));
const auto elements_in = tensor->flat<Variant>();
auto elements_out = tmp->flat<Variant>();
for (int64 i = 0; i < elements_in.size(); ++i) {
elements_out(i) = elements_in(i);
}
} else {
AllocatorAttributes attr;
attr.set_gpu_compatible(true);
attr.set_nic_compatible(true);
TF_RETURN_IF_ERROR(ctx->allocate_persistent(
tensor->dtype(), tensor->shape(), &unused, &tmp, attr));
functor::DenseUpdate<Device, T, ASSIGN> copy_functor;
copy_functor(ctx->eigen_device<Device>(), tmp->flat<T>(),
const_cast<const Tensor*>(tensor)->flat<T>());
}
*tensor = *tmp;
}
return Status::OK();
}
// This gives you `*out`, a tensor you can update, corresponding to a
// variable passed as input index `input`. This handles the
// differences between reference and resource variables. For resource
// variables, we ensure `*out` has a reference count of 1 (using
// PrepareToUpdateVariable() to copy if necessary) unless
// sparse && !lock_held, in which case it never copies.
template <typename Device, typename T>
Status GetInputTensorFromVariable(OpKernelContext* ctx, int input,
bool lock_held, bool sparse, Tensor* out) {
if (ctx->input_dtype(input) == DT_RESOURCE) {
Var* var;
TF_RETURN_IF_ERROR(LookupResource(ctx, HandleFromInput(ctx, input), &var));
core::ScopedUnref unref_var(var);
if (lock_held) {
TF_RETURN_IF_ERROR(
PrepareToUpdateVariable<Device, T>(ctx, var->tensor()));
*out = *var->tensor();
} else {
mutex_lock ml(*var->mu());
if (!sparse) {
TF_RETURN_IF_ERROR(
PrepareToUpdateVariable<Device, T>(ctx, var->tensor()));
}
*out = *var->tensor();
}
return Status::OK();
}
*out = ctx->mutable_input(input, lock_held);
return Status::OK();
}
} // end namespace tensorflow
#endif // TENSORFLOW_KERNELS_TRAINING_OP_HELPERS_H_
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