100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Gradients for CuudnnRNN operators."""
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import gen_cudnn_rnn_ops
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@ops.RegisterGradient("CudnnRNN")
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def _cudnn_rnn_backward(op: ops.Operation, *grads):
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"""Gradients for the CudnnRNN op."""
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if not op.get_attr("is_training"):
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raise ValueError(
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"To use CudnnRNN in gradients, is_training must be set to True.")
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return gen_cudnn_rnn_ops.cudnn_rnn_backprop(
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input=op.inputs[0],
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input_h=op.inputs[1],
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input_c=op.inputs[2],
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params=op.inputs[3],
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output=op.outputs[0],
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output_h=op.outputs[1],
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output_c=op.outputs[2],
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output_backprop=grads[0],
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output_h_backprop=grads[1],
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output_c_backprop=grads[2],
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reserve_space=op.outputs[3],
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dropout=op.get_attr("dropout"),
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seed=op.get_attr("seed"),
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seed2=op.get_attr("seed2"),
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rnn_mode=op.get_attr("rnn_mode"),
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input_mode=op.get_attr("input_mode"),
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direction=op.get_attr("direction"))
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@ops.RegisterGradient("CudnnRNNV2")
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def _cudnn_rnn_backward_v2(op: ops.Operation, *grad):
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if not op.get_attr("is_training"):
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raise ValueError(
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"To use CudnnRNNV2 in gradients, is_training must be set to True.")
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return gen_cudnn_rnn_ops.cudnn_rnn_backprop_v2(
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input=op.inputs[0],
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input_h=op.inputs[1],
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input_c=op.inputs[2],
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params=op.inputs[3],
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output=op.outputs[0],
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output_h=op.outputs[1],
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output_c=op.outputs[2],
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output_backprop=grad[0],
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output_h_backprop=grad[1],
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output_c_backprop=grad[2],
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reserve_space=op.outputs[3],
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host_reserved=op.outputs[4],
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dropout=op.get_attr("dropout"),
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seed=op.get_attr("seed"),
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seed2=op.get_attr("seed2"),
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rnn_mode=op.get_attr("rnn_mode"),
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input_mode=op.get_attr("input_mode"),
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direction=op.get_attr("direction"))
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@ops.RegisterGradient("CudnnRNNV3")
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def _cudnn_rnn_backwardv3(op: ops.Operation, *grads):
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"""Gradients for the CudnnRNNV3 op."""
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if not op.get_attr("is_training"):
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raise ValueError(
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"To use CudnnRNNV3 in gradients, is_training must be set to True.")
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return gen_cudnn_rnn_ops.cudnn_rnn_backprop_v3(
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input=op.inputs[0],
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input_h=op.inputs[1],
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input_c=op.inputs[2],
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params=op.inputs[3],
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sequence_lengths=op.inputs[4],
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output=op.outputs[0],
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output_h=op.outputs[1],
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output_c=op.outputs[2],
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output_backprop=grads[0],
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output_h_backprop=grads[1],
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output_c_backprop=grads[2],
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reserve_space=op.outputs[3],
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host_reserved=op.outputs[4],
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dropout=op.get_attr("dropout"),
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seed=op.get_attr("seed"),
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seed2=op.get_attr("seed2"),
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time_major=op.get_attr("time_major"),
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num_proj=op.get_attr("num_proj"),
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rnn_mode=op.get_attr("rnn_mode"),
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input_mode=op.get_attr("input_mode"),
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direction=op.get_attr("direction")) + (None,)
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