wehub-resource-sync
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
# 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 operators defined in data_flow_ops.py."""
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import indexed_slices
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import data_flow_ops
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from tensorflow.python.ops import math_ops
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@ops.RegisterGradient("DynamicPartition")
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def _DynamicPartitionGrads(op, *grads):
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"""Gradients for DynamicPartition."""
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data = op.inputs[0]
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indices = op.inputs[1]
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num_partitions = op.get_attr("num_partitions")
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prefix_shape = array_ops.shape(indices)
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original_indices = array_ops.reshape(
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math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
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partitioned_indices = data_flow_ops.dynamic_partition(
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original_indices, indices, num_partitions)
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reconstructed = data_flow_ops.parallel_dynamic_stitch(partitioned_indices,
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grads)
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reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
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return [reconstructed, None]
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@ops.RegisterGradient("DynamicStitch")
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@ops.RegisterGradient("ParallelDynamicStitch")
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def _DynamicStitchGrads(op, grad):
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"""Gradients for DynamicStitch and ParallelDynamicStitch."""
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num_values = len(op.inputs) // 2
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indices_grad = [None] * num_values
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def AsInt32(x):
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return (x if op.inputs[0].dtype == dtypes.int32 else
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math_ops.cast(x, dtypes.int32))
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inputs = [AsInt32(op.inputs[i]) for i in range(num_values)]
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if isinstance(grad, indexed_slices.IndexedSlices):
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output_shape = array_ops.shape(op.outputs[0])
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output_rows = output_shape[0]
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grad = math_ops.unsorted_segment_sum(grad.values, grad.indices, output_rows)
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ids = []
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current_size = array_ops.zeros([], dtype=dtypes.int32)
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for inp in inputs:
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num_elements = math_ops.cast(array_ops.size(inp), current_size.dtype)
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flat_id = math_ops.range(current_size, current_size + num_elements)
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ids.append(array_ops.reshape(flat_id, array_ops.shape(inp)))
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current_size += num_elements
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stitch_op = (
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data_flow_ops.parallel_dynamic_stitch
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if op.type == "ParallelDynamicStitch"
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else data_flow_ops.dynamic_stitch
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)
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stitched_ids = stitch_op(inputs, ids)
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values_grad = []
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num_inner_dims = array_ops.rank(grad) - 1
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for inp, single_id in zip(inputs, ids):
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value_grad = array_ops.gather(grad, inp)
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winning_ids = array_ops.gather(stitched_ids, inp)
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is_winner = math_ops.equal(winning_ids, single_id)
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mask = math_ops.cast(is_winner, value_grad.dtype)
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winner_shape = array_ops.shape(is_winner)
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mask_shape = array_ops.concat(
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[
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winner_shape,
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array_ops.ones([num_inner_dims], dtype=winner_shape.dtype),
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],
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axis=0,
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)
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values_grad.append(value_grad * array_ops.reshape(mask, mask_shape))
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return indices_grad + values_grad
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ops.NotDifferentiable("Queue")
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ops.NotDifferentiable("QueueEnqueue")
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ops.NotDifferentiable("QueueEnqueueMany")
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ops.NotDifferentiable("QueueDequeue")
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ops.NotDifferentiable("QueueDequeueMany")
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ops.NotDifferentiable("QueueDequeueUpTo")
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ops.NotDifferentiable("QueueClose")
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ops.NotDifferentiable("QueueSize")
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ops.NotDifferentiable("Stack")
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ops.NotDifferentiable("StackPush")
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ops.NotDifferentiable("StackPop")
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ops.NotDifferentiable("StackClose")
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ops.NotDifferentiable("GetSessionHandle")
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ops.NotDifferentiable("GetSessionHandleV2")
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ops.NotDifferentiable("GetSessionTensor")
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ops.NotDifferentiable("DeleteSessionTensor")
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