102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
# Copyright 2018 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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"""Input-pipeline utilities for Distribution strategies."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.data.experimental.ops import distribute
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from tensorflow.python.data.experimental.ops.distribute_options import AutoShardPolicy
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.util import traverse
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from tensorflow.python.framework import op_def_registry
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from tensorflow.python.framework import ops
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# pylint: disable=protected-access
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def auto_shard_dataset(dataset, num_shards, index):
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"""Shard the input pipeline by sharding the underlying list of files.
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Args:
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dataset: A `tf.data.Dataset` instance, typically the result of a bunch of
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dataset transformations.
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num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of
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shards operating in parallel. Same usage as in `tf.data.Dataset.shard`.
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index: A `tf.int64` scalar `tf.Tensor`, representing the worker index.
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Same usage as in `tf.data.Dataset.shard`.
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Returns:
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A modified `Dataset` obtained by updating the pipeline sharded by the
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files. The input dataset will be returned if we cannot automatically
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determine a good way to shard the input dataset.
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"""
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if (dataset.options().experimental_distribute.auto_shard_policy !=
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AutoShardPolicy.OFF):
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if isinstance(dataset, dataset_ops.DatasetV1):
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return distribute._AutoShardDatasetV1(dataset, num_shards, index)
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else:
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return distribute._AutoShardDataset(dataset, num_shards, index)
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else:
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return dataset
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def _clone_dataset(dataset):
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"""Returns a cloned version of `dataset`."""
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variant_tensor_ops = traverse.obtain_all_variant_tensor_ops(dataset)
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remap_dict = _clone_helper(dataset._variant_tensor.op, variant_tensor_ops)
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new_variant_tensor = remap_dict[dataset._variant_tensor.op].outputs[0]
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return dataset_ops._VariantDataset(new_variant_tensor, dataset.element_spec)
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def _get_op_def(op):
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return op.op_def or op_def_registry.get(op.type)
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def _clone_helper(op_to_clone, variant_tensor_ops):
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"""Helper method that recursively clones `op_to_clone`.
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Args:
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op_to_clone: The op we want to clone.
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variant_tensor_ops: A list of ops that we have to clone along the way.
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Returns:
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A dictionary mapping old_ops to new_ops created. Includes op_to_clone
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as a key.
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"""
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remap_dict = {}
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for input_tensor in op_to_clone.inputs:
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input_tensor_op = input_tensor.op
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if input_tensor_op in variant_tensor_ops:
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recursive_map = _clone_helper(input_tensor_op, variant_tensor_ops)
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remap_dict.update(recursive_map)
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inputs_list = []
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for input_tensor in op_to_clone.inputs:
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input_tensor_op = input_tensor.op
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if input_tensor_op in remap_dict:
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remapped_input = remap_dict[input_tensor_op].outputs[0]
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inputs_list.append(remapped_input)
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else:
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inputs_list.append(input_tensor_op.outputs[input_tensor.value_index])
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g = ops.get_default_graph()
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new_op = g.create_op(
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op_to_clone.type,
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inputs_list, [o.dtype for o in op_to_clone.outputs],
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name=op_to_clone.name,
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attrs=op_to_clone.node_def.attr,
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op_def=_get_op_def(op_to_clone))
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remap_dict[op_to_clone] = new_op
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return remap_dict
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