| /* 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. |
| ==============================================================================*/ |
| |
| #include "tensorflow/core/framework/op_kernel.h" |
| #include "tensorflow/core/kernels/variable_ops.h" |
| #include "tensorflow/core/lib/core/errors.h" |
| #include "tensorflow/core/lib/core/refcount.h" |
| #include "tensorflow/core/platform/mutex.h" |
| #include "tensorflow/core/platform/types.h" |
| |
| namespace tensorflow { |
| |
| template <class T> |
| class CountUpToOp : public OpKernel { |
| public: |
| explicit CountUpToOp(OpKernelConstruction* context) : OpKernel(context) { |
| OP_REQUIRES_OK(context, context->GetAttr("limit", &limit_)); |
| } |
| |
| void Compute(OpKernelContext* context) override { |
| T before_increment; |
| { |
| mutex_lock l(*context->input_ref_mutex(0)); |
| Tensor tensor = context->mutable_input(0, true); |
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(tensor.shape()), |
| errors::InvalidArgument("input is not a scalar: ", |
| tensor.shape().DebugString())); |
| T* ptr = &tensor.scalar<T>()(); |
| before_increment = *ptr; |
| if (*ptr >= limit_) { |
| context->SetStatus(errors::OutOfRange("Reached limit of ", limit_)); |
| return; |
| } |
| ++*ptr; |
| } |
| // Output if no error. |
| Tensor* out_tensor; |
| OP_REQUIRES_OK(context, context->allocate_output("output", TensorShape({}), |
| &out_tensor)); |
| out_tensor->scalar<T>()() = before_increment; |
| } |
| |
| private: |
| T limit_; |
| }; |
| |
| template <class T> |
| class ResourceCountUpToOp : public OpKernel { |
| public: |
| explicit ResourceCountUpToOp(OpKernelConstruction* context) |
| : OpKernel(context) { |
| OP_REQUIRES_OK(context, context->GetAttr("limit", &limit_)); |
| OP_REQUIRES_OK(context, context->GetAttr("T", &dtype_)); |
| } |
| |
| void Compute(OpKernelContext* context) override { |
| core::RefCountPtr<Var> variable; |
| OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0), |
| &variable)); |
| mutex_lock l(*variable->mu()); |
| Tensor before_increment = *variable->tensor(); |
| OP_REQUIRES( |
| context, TensorShapeUtils::IsScalar(before_increment.shape()), |
| errors::InvalidArgument("input is not a scalar: ", |
| before_increment.shape().DebugString())); |
| if (before_increment.scalar<T>()() >= limit_) { |
| context->SetStatus(errors::OutOfRange("Reached limit of ", limit_)); |
| return; |
| } |
| // Allocate new buffer |
| AllocatorAttributes attr; |
| attr.set_gpu_compatible(true); |
| attr.set_nic_compatible(true); |
| PersistentTensor unused; |
| Tensor* tmp; |
| OP_REQUIRES_OK(context, context->allocate_persistent( |
| dtype_, TensorShape({}), &unused, &tmp, attr)); |
| *variable->tensor() = *tmp; |
| tmp->scalar<T>()() = before_increment.scalar<T>()() + 1; |
| context->set_output(0, before_increment); |
| } |
| |
| private: |
| T limit_; |
| DataType dtype_; |
| }; |
| |
| #define REGISTER(TYPE) \ |
| REGISTER_KERNEL_BUILDER( \ |
| Name("CountUpTo").TypeConstraint<TYPE>("T").Device(DEVICE_CPU), \ |
| CountUpToOp<TYPE>) \ |
| REGISTER_KERNEL_BUILDER( \ |
| Name("ResourceCountUpTo").TypeConstraint<TYPE>("T").Device(DEVICE_CPU), \ |
| ResourceCountUpToOp<TYPE>) |
| |
| REGISTER(int32); |
| REGISTER(int64); |
| |
| #undef REGISTER |
| |
| } // namespace tensorflow |