| # 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. |
| # ============================================================================== |
| """Gradients for CuudnnRNN operators.""" |
| from tensorflow.python.framework import ops |
| from tensorflow.python.ops import gen_cudnn_rnn_ops |
| |
| |
| @ops.RegisterGradient("CudnnRNN") |
| def _cudnn_rnn_backward(op, *grads): |
| """Gradients for the CudnnRNN op.""" |
| if not op.get_attr("is_training"): |
| raise ValueError( |
| "To use CudnnRNN in gradients, is_training must be set to True.") |
| return gen_cudnn_rnn_ops.cudnn_rnn_backprop( |
| input=op.inputs[0], |
| input_h=op.inputs[1], |
| input_c=op.inputs[2], |
| params=op.inputs[3], |
| output=op.outputs[0], |
| output_h=op.outputs[1], |
| output_c=op.outputs[2], |
| output_backprop=grads[0], |
| output_h_backprop=grads[1], |
| output_c_backprop=grads[2], |
| reserve_space=op.outputs[3], |
| dropout=op.get_attr("dropout"), |
| seed=op.get_attr("seed"), |
| seed2=op.get_attr("seed2"), |
| rnn_mode=op.get_attr("rnn_mode"), |
| input_mode=op.get_attr("input_mode"), |
| direction=op.get_attr("direction")) |
| |
| |
| @ops.RegisterGradient("CudnnRNNV2") |
| def _cudnn_rnn_backward_v2(op, *grad): |
| if not op.get_attr("is_training"): |
| raise ValueError( |
| "To use CudnnRNNV2 in gradients, is_training must be set to True.") |
| return gen_cudnn_rnn_ops.cudnn_rnn_backprop_v2( |
| input=op.inputs[0], |
| input_h=op.inputs[1], |
| input_c=op.inputs[2], |
| params=op.inputs[3], |
| output=op.outputs[0], |
| output_h=op.outputs[1], |
| output_c=op.outputs[2], |
| output_backprop=grad[0], |
| output_h_backprop=grad[1], |
| output_c_backprop=grad[2], |
| reserve_space=op.outputs[3], |
| host_reserved=op.outputs[4], |
| dropout=op.get_attr("dropout"), |
| seed=op.get_attr("seed"), |
| seed2=op.get_attr("seed2"), |
| rnn_mode=op.get_attr("rnn_mode"), |
| input_mode=op.get_attr("input_mode"), |
| direction=op.get_attr("direction")) |
| |
| |
| @ops.RegisterGradient("CudnnRNNV3") |
| def _cudnn_rnn_backwardv3(op, *grads): |
| """Gradients for the CudnnRNNV3 op.""" |
| if not op.get_attr("is_training"): |
| raise ValueError( |
| "To use CudnnRNNV3 in gradients, is_training must be set to True.") |
| return gen_cudnn_rnn_ops.cudnn_rnn_backprop_v3( |
| input=op.inputs[0], |
| input_h=op.inputs[1], |
| input_c=op.inputs[2], |
| params=op.inputs[3], |
| sequence_lengths=op.inputs[4], |
| output=op.outputs[0], |
| output_h=op.outputs[1], |
| output_c=op.outputs[2], |
| output_backprop=grads[0], |
| output_h_backprop=grads[1], |
| output_c_backprop=grads[2], |
| reserve_space=op.outputs[3], |
| host_reserved=op.outputs[4], |
| dropout=op.get_attr("dropout"), |
| seed=op.get_attr("seed"), |
| seed2=op.get_attr("seed2"), |
| time_major=op.get_attr("time_major"), |
| num_proj=op.get_attr("num_proj"), |
| rnn_mode=op.get_attr("rnn_mode"), |
| input_mode=op.get_attr("input_mode"), |
| direction=op.get_attr("direction")) + (None,) |