120 lines
4.6 KiB
Python
120 lines
4.6 KiB
Python
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# Copyright 2022 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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"""The implementation of `tf.data.Dataset.snapshot`."""
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import multiprocessing
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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def _snapshot(input_dataset, # pylint: disable=unused-private-name
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path,
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compression="AUTO",
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reader_func=None,
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shard_func=None,
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name=None):
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"""See `Dataset.snapshot()` for details."""
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project_func = None
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if shard_func is None:
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input_dataset = input_dataset.enumerate(name=name)
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# This sets the amount of parallelism based on the number of CPU cores on
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# the machine where this Python code is executed, which may differ from
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# the number of CPU cores where the input pipeline graph is actually
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# executed (e.g. remote Cloud TPU workers).
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local_shard_func = lambda index, _: index % multiprocessing.cpu_count()
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project_func = lambda _, elem: elem
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else:
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local_shard_func = shard_func
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dataset = _SnapshotDataset(
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input_dataset=input_dataset,
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path=path,
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compression=compression,
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reader_func=reader_func,
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# This will not do the right thing where the graph is built on a
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# different machine than the executor (e.g. Cloud TPUs).
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shard_func=local_shard_func,
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name=name)
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if project_func is not None:
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dataset = dataset.map(project_func, name=name)
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return dataset
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class _SnapshotDataset(dataset_ops.UnaryUnchangedStructureDataset):
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"""A dataset that allows saving and re-use of already processed data."""
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def __init__(self,
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input_dataset,
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path,
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shard_func,
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compression=None,
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reader_func=None,
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pending_snapshot_expiry_seconds=None,
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use_legacy_function=False,
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name=None):
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if reader_func is None:
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reader_func = lambda datasets: datasets.interleave( # pylint:disable=g-long-lambda
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lambda x: x,
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cycle_length=multiprocessing.cpu_count(),
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num_parallel_calls=dataset_ops.AUTOTUNE)
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self._input_dataset = input_dataset
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self._path = path
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self._compression = compression
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self._reader_func = structured_function.StructuredFunctionWrapper(
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reader_func,
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self._transformation_name() + ".reader_func",
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# Dataset of datasets of input elements
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input_structure=dataset_ops.DatasetSpec(
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dataset_ops.DatasetSpec(input_dataset.element_spec)),
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use_legacy_function=use_legacy_function)
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self._shard_func = structured_function.StructuredFunctionWrapper(
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shard_func,
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self._transformation_name() + ".shard_func",
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dataset=input_dataset,
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use_legacy_function=use_legacy_function)
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if ((not self._shard_func.output_structure.is_compatible_with(
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tensor_spec.TensorSpec([], dtypes.int32))) and
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(not self._shard_func.output_structure.is_compatible_with(
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tensor_spec.TensorSpec([], dtypes.int64)))):
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raise TypeError(f"Invalid `shard_func`. `shard_func` must return "
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f"`tf.int64` scalar tensor but its return type is "
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f"{self._shard_func.output_structure}.")
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self._name = name
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variant_tensor = ged_ops.snapshot_dataset_v2(
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input_dataset._variant_tensor, # pylint: disable=protected-access
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path,
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self._reader_func.function.captured_inputs,
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self._shard_func.function.captured_inputs,
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compression=compression,
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reader_func=self._reader_func.function,
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shard_func=self._shard_func.function,
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**self._common_args)
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super().__init__(input_dataset, variant_tensor)
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def _functions(self):
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return [self._reader_func, self._shard_func]
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def _transformation_name(self):
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return "Dataset.snapshot()"
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