145 lines
5.6 KiB
Python
145 lines
5.6 KiB
Python
# Copyright 2019 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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"""Tensor-like objects that are composed from tf.Tensors."""
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import abc
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from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
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from tensorflow.python.util import _pywrap_utils
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from tensorflow.python.util import nest
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("__internal__.CompositeTensor", v1=[])
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class CompositeTensor(metaclass=abc.ABCMeta):
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"""Abstract base class for Tensor-like objects that are composed from Tensors.
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Each `CompositeTensor` can be decomposed into a structured collection of
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component `tf.Tensor`s, and reconstructed from those components.
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The `tensorflow.python.util.nest` module has support for treating composite
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tensors as structure, which makes it easy to flatten and reconstruct
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composite tensors (or larger structures that contain composite tensors).
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E.g.:
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```python
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ct = ... # Create a composite tensor.
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flat_list_of_tensors = nest.flatten(ct, expand_composites=True)
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transformed_list_of_tensors = ... # do something with the flat tensors.
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result = nest.pack_sequence_as(ct, transformed_list_of_tensors,
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expand_composites=True)
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```
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"""
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@abc.abstractproperty
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def _type_spec(self):
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"""A `TypeSpec` describing the type of this value."""
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raise NotImplementedError(f"{type(self).__name__}._type_spec()")
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def _shape_invariant_to_type_spec(self, shape):
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"""Returns a TypeSpec given a shape invariant (used by `tf.while_loop`).
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Args:
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shape: A `tf.TensorShape` object. The shape invariant for this
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`CompositeTensor`, or `None` if a default shape invariant should be used
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(based on the value of this `CompositeTensor`).
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Returns:
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A nested structure whose values are `tf.TensorShape` objects, specifying
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the shape invariants for the tensors that comprise this `CompositeTensor`.
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"""
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# New TypeSpec subclasses generally do not need to implement this --
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# this method is used for backwards compatibility. Users of tf.while_loop
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# can specify a type by passing in TypeSpec instead.
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raise NotImplementedError(
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f"{type(self).__name__}._shape_invariant_to_type_spec")
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def _consumers(self):
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"""Returns a list of `Operation`s that consume this `CompositeTensor`.
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Returns:
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A list of `Operation`s.
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Raises:
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RuntimeError: If this method is called while executing eagerly.
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"""
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consumers = nest.flatten([
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component.consumers()
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for component in nest.flatten(self, expand_composites=True)
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if getattr(component, "graph", None) is not None
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])
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return list(set(consumers))
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def __tf_tracing_type__(self, context):
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return self._type_spec.__tf_tracing_type__(context)
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def _convert_variables_to_tensors(self):
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"""Converts ResourceVariable components to Tensors.
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Override this method to explicitly convert ResourceVariables embedded in the
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CompositeTensor to Tensors. By default, it returns the CompositeTensor
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unchanged.
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Returns:
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A CompositeTensor with all its ResourceVariable components converted to
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Tensors.
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"""
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return self
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_pywrap_utils.RegisterType("CompositeTensor", CompositeTensor)
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def replace_composites_with_components(structure):
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"""Recursively replaces CompositeTensors with their components.
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Args:
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structure: A `nest`-compatible structure, possibly containing composite
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tensors.
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Returns:
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A copy of `structure`, where each composite tensor has been replaced by
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its components. The result will contain no composite tensors.
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Note that `nest.flatten(replace_composites_with_components(structure))`
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returns the same value as `nest.flatten(structure)`.
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"""
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if isinstance(structure, CompositeTensor):
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return replace_composites_with_components(
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structure._type_spec._to_components(structure)) # pylint: disable=protected-access
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elif not nest.is_nested(structure):
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return structure
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else:
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return nest.map_structure(
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replace_composites_with_components, structure, expand_composites=False)
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def convert_variables_to_tensors(composite_tensor):
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return composite_tensor._convert_variables_to_tensors() # pylint: disable=protected-access
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# @TODO(edloper): Can we replace convert_to_tensor_or_xyz with just
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# convert_to_tensor_or_composite? Alternatively, should composite tensors
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# register a dispatch override for tf.convert_to_tensor?
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# Note about the internal encoding of composite tensors when they are "lowered"
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# from Python objects to tensors. The usual encoding is "component encoding"
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# which uses the dense tensors that represent a composite tensor.
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# A second encoding, "batchable tensor list encoding", is used by datasets
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# and map_fn which in addition to supporting batching also can use ops
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# for encoding and decoding, e.g. for encoding/decoding to/from a
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# single variant that represents a composite tensor. Some internal properties
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# for type specs for composite tensors use `flat` as a nickname for
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# "batchable tensor list encoding". (e.g. `flat_tensor_specs`).
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