131 lines
5.2 KiB
Python
131 lines
5.2 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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"""Support for ragged tensors."""
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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.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.ragged import ragged_config
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.ops.ragged import ragged_util
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from tensorflow.python.util import dispatch
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("ragged.map_flat_values")
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@dispatch.add_dispatch_support
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def map_flat_values(op, *args, **kwargs):
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"""Applies `op` to the values of one or more RaggedTensors.
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Replaces any `RaggedTensor` in `args` or `kwargs` with its `flat_values`
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tensor, and then calls `op`. Returns a `RaggedTensor` that is constructed
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from the input `RaggedTensor`s' `nested_row_splits` and the value returned by
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the `op`.
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If the input arguments contain multiple `RaggedTensor`s, then they must have
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identical `nested_row_splits`.
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Examples:
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>>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])
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>>> map_flat_values(tf.ones_like, rt).to_list()
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[[1, 1, 1], [], [1, 1], [1]]
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>>> map_flat_values(tf.multiply, rt, rt).to_list()
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[[1, 4, 9], [], [16, 25], [36]]
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>>> map_flat_values(tf.add, rt, 5).to_list()
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[[6, 7, 8], [], [9, 10], [11]]
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Args:
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op: The operation that should be applied to the RaggedTensor `flat_values`.
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`op` is typically an element-wise operation (such as math_ops.add), but
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any operation that preserves the size of the outermost dimension can be
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used. I.e., `shape[0]` of the value returned by `op` must match
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`shape[0]` of the `RaggedTensor`s' `flat_values` tensors.
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*args: Arguments for `op`.
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**kwargs: Keyword arguments for `op`.
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Returns:
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A `RaggedTensor` whose `ragged_rank` matches the `ragged_rank` of all
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input `RaggedTensor`s.
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Raises:
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ValueError: If args contains no `RaggedTensors`, or if the `nested_splits`
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of the input `RaggedTensor`s are not identical.
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"""
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# Replace RaggedTensors with their values; and collect the splits tensors
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# from each RaggedTensor.
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nested_splits_lists = []
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inner_args = _replace_ragged_with_flat_values(args, nested_splits_lists)
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inner_kwargs = _replace_ragged_with_flat_values(kwargs, nested_splits_lists)
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if not nested_splits_lists:
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return op(*args, **kwargs)
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split_dtypes = set(splits[0].dtype for splits in nested_splits_lists)
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if len(split_dtypes) > 1:
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if not ragged_config.auto_cast_partition_dtype():
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raise ValueError("Input RaggedTensors have mismatched row_splits dtypes; "
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"use RaggedTensor.with_row_splits_dtype() to convert "
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"them to compatible dtypes.")
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nested_splits_lists = [
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[math_ops.cast(s, dtypes.int64) for s in nested_splits] # pylint: disable=g-complex-comprehension
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for nested_splits in nested_splits_lists]
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with ops.control_dependencies(
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ragged_util.assert_splits_match(nested_splits_lists)):
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# Delegate to op, and then compose the result from the transformed values
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# and the splits.
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return ragged_tensor.RaggedTensor.from_nested_row_splits(
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op(*inner_args, **inner_kwargs), nested_splits_lists[0], validate=False)
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def _replace_ragged_with_flat_values(value, nested_splits_lists):
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"""Replace RaggedTensors with their flat_values, and record their splits.
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Returns a copy of `value`, with any nested `RaggedTensor`s replaced by their
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`flat_values` tensor. Looks inside lists, tuples, and dicts.
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Appends each `RaggedTensor`'s `nested_splits` to `nested_splits_lists`.
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Args:
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value: The value that should be transformed by replacing `RaggedTensors`.
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nested_splits_lists: An output parameter used to record the `nested_splits`
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for any `RaggedTensors` that were replaced.
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Returns:
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A copy of `value` with nested `RaggedTensors` replaced by their `values`.
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"""
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# Base case
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if ragged_tensor.is_ragged(value):
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value = ragged_tensor.convert_to_tensor_or_ragged_tensor(value)
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nested_splits_lists.append(value.nested_row_splits)
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return value.flat_values
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# Recursion cases
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def recurse(v):
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return _replace_ragged_with_flat_values(v, nested_splits_lists)
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if isinstance(value, list):
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return [recurse(v) for v in value]
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elif isinstance(value, tuple):
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return tuple(recurse(v) for v in value)
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elif isinstance(value, dict):
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return dict((k, recurse(v)) for (k, v) in value.items())
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else:
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return value
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