| /* Copyright 2020 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/c/experimental/gradients/array_grad.h" |
| |
| #include "tensorflow/c/eager/abstract_context.h" |
| |
| namespace tensorflow { |
| namespace gradients { |
| namespace { |
| class IdentityNGradientFunction : public GradientFunction { |
| public: |
| Status Compute(AbstractContext* ctx, |
| absl::Span<AbstractTensorHandle* const> grad_outputs, |
| absl::Span<AbstractTensorHandle*> grad_inputs) override { |
| for (int i = 0; i < grad_outputs.size(); i++) { |
| auto grad_input = grad_outputs[i]; |
| // TODO(srbs): Should we add a copy contructor to AbstractTensorHandle |
| // that takes care of this similar to `Tensor`? |
| if (grad_input) { |
| grad_input->Ref(); |
| } |
| grad_inputs[i] = grad_input; |
| } |
| return OkStatus(); |
| } |
| ~IdentityNGradientFunction() override {} |
| }; |
| } // namespace |
| |
| GradientFunction* IdentityNRegisterer(const ForwardOperation& op) { |
| return new IdentityNGradientFunction; |
| } |
| |
| } // namespace gradients |
| } // namespace tensorflow |