1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #ifndef TENSORFLOW_KERNELS_TRAINING_OP_HELPERS_H_ 17 #define TENSORFLOW_KERNELS_TRAINING_OP_HELPERS_H_ 18 19 #include "tensorflow/core/framework/op_kernel.h" 20 #include "tensorflow/core/kernels/dense_update_functor.h" 21 #include "tensorflow/core/kernels/variable_ops.h" 22 23 namespace tensorflow { 24 25 mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input); 26 27 std::vector<mutex_lock> MaybeLockVariableInputMutexesInOrder( 28 OpKernelContext* ctx, bool do_lock, const std::vector<int>& input_ids); 29 30 void MaybeForwardRefInputToRefOutput(OpKernelContext* ctx, int input, 31 int output); 32 33 // This is for use with ResourceVariables to ensure *tensor has a 34 // reference count of 1 before you update it. 35 // REQUIRES: If you pass in variable->tensor(), *variable->mu() must be held. 36 template <typename Device, typename T> 37 Status PrepareToUpdateVariable(OpKernelContext* ctx, Tensor* tensor) { 38 if (!tensor->RefCountIsOne()) { 39 // Tensor's buffer is in use by some read, so we need to copy before 40 // updating. 41 PersistentTensor unused; 42 Tensor* tmp; 43 AllocatorAttributes attr; 44 attr.set_gpu_compatible(true); 45 attr.set_nic_compatible(true); 46 TF_RETURN_IF_ERROR(ctx->allocate_persistent( 47 tensor->dtype(), tensor->shape(), &unused, &tmp, attr)); 48 functor::DenseUpdate<Device, T, ASSIGN> copy_functor; 49 copy_functor(ctx->eigen_device<Device>(), tmp->flat<T>(), 50 const_cast<const Tensor*>(tensor)->flat<T>()); 51 *tensor = *tmp; 52 } 53 return Status::OK(); 54 } 55 56 // This gives you `*out`, a tensor you can update, corresponding to a 57 // variable passed as input index `input`. This handles the 58 // differences between reference and resource variables. For resource 59 // variables, we ensure `*out` has a reference count of 1 (using 60 // PrepareToUpdateVariable() to copy if necessary) unless 61 // sparse && !lock_held, in which case it never copies. 62 template <typename Device, typename T> 63 Status GetInputTensorFromVariable(OpKernelContext* ctx, int input, 64 bool lock_held, bool sparse, Tensor* out) { 65 if (ctx->input_dtype(input) == DT_RESOURCE) { 66 Var* var; 67 if (LookupResource(ctx, HandleFromInput(ctx, input), &var).ok()) { 68 core::ScopedUnref unref_var(var); 69 if (lock_held) { 70 TF_RETURN_IF_ERROR( 71 PrepareToUpdateVariable<Device, T>(ctx, var->tensor())); 72 *out = *var->tensor(); 73 } else { 74 mutex_lock ml(*var->mu()); 75 if (!sparse) { 76 TF_RETURN_IF_ERROR( 77 PrepareToUpdateVariable<Device, T>(ctx, var->tensor())); 78 } 79 *out = *var->tensor(); 80 } 81 return Status::OK(); 82 } else { 83 return errors::Internal("Invalid variable reference."); 84 } 85 } 86 *out = ctx->mutable_input(input, lock_held); 87 return Status::OK(); 88 } 89 90 } // end namespace tensorflow 91 92 #endif // TENSORFLOW_KERNELS_TRAINING_OP_HELPERS_H_ 93
