1026 lines
40 KiB
Python
1026 lines
40 KiB
Python
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# 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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"""Type specifications for TensorFlow APIs."""
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import abc
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import functools
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from typing import Any, List, Optional, Sequence, Type
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import warnings
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import numpy as np
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from tensorflow.core.function import trace_type
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from tensorflow.core.protobuf import struct_pb2
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from tensorflow.python.framework import composite_tensor
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import type_spec_registry
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.types import internal
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from tensorflow.python.types import trace
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from tensorflow.python.util import _pywrap_utils
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from tensorflow.python.util import compat
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from tensorflow.python.util import deprecation
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from tensorflow.python.util import nest
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from tensorflow.python.util import tf_decorator
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from tensorflow.python.util.lazy_loader import LazyLoader
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from tensorflow.python.util.tf_export import tf_export
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from tensorflow.tools.docs import doc_controls
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# Use LazyLoader to avoid circular dependencies.
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ops = LazyLoader("ops", globals(),
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"tensorflow.python.framework.ops")
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# TODO(b/238903802): Remove this dependency.
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nested_structure_coder = LazyLoader(
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"nested_structure_coder", globals(),
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"tensorflow.python.saved_model.nested_structure_coder")
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@tf_export("TypeSpec", v1=["TypeSpec", "data.experimental.Structure"])
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class TypeSpec(
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internal.TypeSpec,
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trace.TraceType,
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trace_type.Serializable,
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metaclass=abc.ABCMeta,
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):
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"""Specifies a TensorFlow value type.
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A `tf.TypeSpec` provides metadata describing an object accepted or returned
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by TensorFlow APIs. Concrete subclasses, such as `tf.TensorSpec` and
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`tf.RaggedTensorSpec`, are used to describe different value types.
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For example, `tf.function`'s `input_signature` argument accepts a list
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(or nested structure) of `TypeSpec`s.
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Creating new subclasses of `TypeSpec` (outside of TensorFlow core) is not
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currently supported. In particular, we may make breaking changes to the
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private methods and properties defined by this base class.
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Example:
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>>> spec = tf.TensorSpec(shape=[None, None], dtype=tf.int32)
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>>> @tf.function(input_signature=[spec])
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... def double(x):
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... return x * 2
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>>> double(tf.constant([[1, 2], [3, 4]]))
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<tf.Tensor: shape=(2, 2), dtype=int32,
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numpy=array([[2, 4], [6, 8]], dtype=int32)>
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"""
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# === Subclassing ===
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#
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# Each `TypeSpec` subclass must define:
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#
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# * A "component encoding" for values.
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# * A "serialization" for types.
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#
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# The component encoding for a value is a nested structure of `tf.Tensor`
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# or `CompositeTensor` that can be used by the `TypeSpec` to reconstruct
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# the value. Each individual `TypeSpec` must use the same nested structure
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# for all values -- this structure is defined by the `component_specs`
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# attribute. Decomposing values into components, and reconstructing them
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# from those components, should be inexpensive. In particular, it should
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# *not* require any TensorFlow ops.
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#
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# The serialization for a `TypeSpec` is a nested tuple of values that can
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# be used to reconstruct the `TypeSpec`. See the documentation for
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# `_serialize()` for more information.
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__slots__ = []
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@abc.abstractproperty
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def value_type(self):
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"""The Python type for values that are compatible with this TypeSpec.
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In particular, all values that are compatible with this TypeSpec must be an
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instance of this type.
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"""
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raise NotImplementedError("%s.value_type" % type(self).__name__)
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def is_subtype_of(self, other: trace.TraceType) -> bool:
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"""Returns True if `self` is a subtype of `other`.
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Implements the tf.types.experimental.func.TraceType interface.
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If not overridden by a subclass, the default behavior is to assume the
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TypeSpec is covariant upon attributes that implement TraceType and
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invariant upon rest of the attributes as well as the structure and type
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of the TypeSpec.
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Args:
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other: A TraceType object.
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"""
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if type(self) is not type(other):
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return False
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is_subtype = True
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def check_attribute(attribute_self, attribute_other):
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nonlocal is_subtype
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if not is_subtype:
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return
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if isinstance(attribute_self, trace.TraceType):
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if not attribute_self.is_subtype_of(attribute_other):
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is_subtype = False
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return
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else:
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if attribute_self != attribute_other:
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is_subtype = False
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try:
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# TODO(b/217959193): Replace _serialize with parameter decomposition.
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nest.map_structure(check_attribute, self._serialize(), other._serialize()) # pylint: disable=protected-access
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except (ValueError, TypeError):
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return False
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return is_subtype
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def most_specific_common_supertype(
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self, others: Sequence[trace.TraceType]) -> Optional["TypeSpec"]:
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"""Returns the most specific supertype TypeSpec of `self` and `others`.
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Implements the tf.types.experimental.func.TraceType interface.
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If not overridden by a subclass, the default behavior is to assume the
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TypeSpec is covariant upon attributes that implement TraceType and
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invariant upon rest of the attributes as well as the structure and type
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of the TypeSpec.
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Args:
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others: A sequence of TraceTypes.
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"""
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if any(type(self) is not type(other) for other in others):
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return None
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has_supertype = True
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def make_supertype_attribute(attribute_self, *attribute_others):
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nonlocal has_supertype
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if not has_supertype:
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return
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if isinstance(attribute_self, trace.TraceType):
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attribute_supertype = attribute_self.most_specific_common_supertype(
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attribute_others)
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if attribute_supertype is None:
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has_supertype = False
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return
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return attribute_supertype
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else:
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if not all(attribute_self == attribute_other
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for attribute_other in attribute_others):
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has_supertype = False
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return
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return attribute_self
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try:
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# TODO(b/217959193): Replace _serialize with parameter decomposition.
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serialized_supertype = nest.map_structure(
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make_supertype_attribute, self._serialize(),
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*(o._serialize() for o in others)) # pylint: disable=protected-access
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except (ValueError, TypeError):
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return None
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return self._deserialize(serialized_supertype) if has_supertype else None
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@classmethod
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def experimental_type_proto(cls) -> Type[struct_pb2.TypeSpecProto]:
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"""Returns the type of proto associated with TypeSpec serialization.
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Do NOT override for custom non-TF types.
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"""
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return struct_pb2.TypeSpecProto
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@classmethod
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def experimental_from_proto(cls,
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proto: struct_pb2.TypeSpecProto) -> "TypeSpec":
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"""Returns a TypeSpec instance based on the serialized proto.
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Do NOT override for custom non-TF types.
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Args:
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proto: Proto generated using 'experimental_as_proto'.
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"""
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return nested_structure_coder.decode_proto(
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struct_pb2.StructuredValue(type_spec_value=proto))
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def experimental_as_proto(self) -> struct_pb2.TypeSpecProto:
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"""Returns a proto representation of the TypeSpec instance.
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Do NOT override for custom non-TF types.
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"""
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return nested_structure_coder.encode_structure(self).type_spec_value
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@doc_controls.do_not_doc_inheritable
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def placeholder_value(self, placeholder_context):
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"""Value used for tracing a function signature with this TraceType.
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WARNING: Do not override.
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Args:
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placeholder_context: A class container for context information when
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creating a placeholder value.
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Returns:
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A `CompositeTensor` placeholder whose components are recursively composed
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of placeholders themselves.
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"""
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if placeholder_context.unnest_only:
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return self
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component_placeholders = nest.map_structure(
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lambda x: x.placeholder_value(placeholder_context),
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self._component_specs)
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return self._from_components(component_placeholders)
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def _to_tensors(self, value):
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value_spec = type_spec_from_value(value)
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assert value_spec.is_subtype_of(self)
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return [arg for arg in nest.flatten(value, expand_composites=True)
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if isinstance(arg, ops.Tensor)]
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def _cast(self, value, casting_context):
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cast_components = nest.map_structure(
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lambda spec, v: spec._cast(v, casting_context), # pylint: disable=protected-access
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self._component_specs,
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self._to_components(value))
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return self._from_components(cast_components)
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# TODO(b/225058047): Reconsider semantics.
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def is_compatible_with(self, spec_or_value):
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"""Returns true if `spec_or_value` is compatible with this TypeSpec.
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Prefer using "is_subtype_of" and "most_specific_common_supertype" wherever
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possible.
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Args:
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spec_or_value: A TypeSpec or TypeSpec associated value to compare against.
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"""
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# === Subclassing ===
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# If not overridden by subclasses, the default behavior is to convert
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# `spec_or_value` to a `TypeSpec` (if it isn't already); and then to
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# consider two `TypeSpec`s compatible if they have the same type, and
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# the values returned by `_serialize` are compatible (where
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# `tf.TensorShape`, `tf.TensorSpec`, and `tf.DType` are checked for
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# compatibility using their `is_compatible_with` method; and all other
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# types are considered compatible if they are equal).
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if not isinstance(spec_or_value, TypeSpec):
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spec_or_value = type_spec_from_value(spec_or_value)
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if type(self) is not type(spec_or_value):
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return False
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return self.__is_compatible(self._serialize(), spec_or_value._serialize()) # pylint: disable=protected-access
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@deprecation.deprecated(None, "Use most_specific_common_supertype instead.")
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def most_specific_compatible_type(self, other: "TypeSpec") -> "TypeSpec":
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"""Returns the most specific TypeSpec compatible with `self` and `other`.
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Deprecated. Please use `most_specific_common_supertype` instead.
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Do not override this function.
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Args:
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other: A `TypeSpec`.
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Raises:
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ValueError: If there is no TypeSpec that is compatible with both `self`
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and `other`.
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"""
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result = self.most_specific_common_supertype([other])
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if result is None:
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raise ValueError("No TypeSpec is compatible with both %s and %s" %
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(self, other))
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return result
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# TODO(b/226395276): Delete after removing usages.
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def _with_tensor_ranks_only(self) -> "TypeSpec":
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"""Returns a TypeSpec compatible with `self`, with tensor shapes relaxed.
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Returns:
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A `TypeSpec` that is compatible with `self`, where any `TensorShape`
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information has been relaxed to include only tensor rank (and not
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the dimension sizes for individual axes).
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"""
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# === Subclassing ===
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# If not overridden by a subclass, the default behavior is to serialize
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# this TypeSpec, relax any TensorSpec or TensorShape values, and
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# deserialize the result.
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def relax(value):
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if isinstance(value, TypeSpec):
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return value._with_tensor_ranks_only() # pylint: disable=protected-access
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elif (isinstance(value, tensor_shape.TensorShape) and
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value.rank is not None):
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return tensor_shape.TensorShape([None] * value.rank)
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else:
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return value
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return self._deserialize(nest.map_structure(relax, self._serialize()))
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# TODO(b/206014848): Helper function to support logic that does not consider
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# Tensor name. Will be removed once load-bearing usages of Tensor name are
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# fixed.
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def _without_tensor_names(self) -> "TypeSpec":
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"""Returns a TypeSpec compatible with `self`, with tensor names removed.
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Returns:
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A `TypeSpec` that is compatible with `self`, where the name of any
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`TensorSpec` is set to `None`.
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"""
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# === Subclassing ===
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# If not overridden by a subclass, the default behavior is to serialize
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# this TypeSpec, set the TensorSpecs' names to None, and deserialize the
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# result.
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def rename(value):
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if isinstance(value, TypeSpec):
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return value._without_tensor_names() # pylint: disable=protected-access
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return value
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return self._deserialize(nest.map_structure(rename, self._serialize()))
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# === Component encoding for values ===
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@abc.abstractmethod
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def _to_components(self, value):
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"""Encodes `value` as a nested structure of `Tensor` or `CompositeTensor`.
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Args:
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value: A value compatible with this `TypeSpec`. (Caller is responsible
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for ensuring compatibility.)
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Returns:
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A nested structure of `tf.Tensor` or `tf.CompositeTensor` compatible with
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`self._component_specs`, which can be used to reconstruct `value`.
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"""
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# === Subclassing ===
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# This method must be inexpensive (do not call TF ops).
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raise NotImplementedError("%s._to_components()" % type(self).__name__)
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@abc.abstractmethod
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def _from_components(self, components):
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"""Reconstructs a value from a nested structure of Tensor/CompositeTensor.
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|
Args:
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components: A nested structure of `tf.Tensor` or `tf.CompositeTensor`,
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compatible with `self._component_specs`. (Caller is responsible for
|
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ensuring compatibility.)
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Returns:
|
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A value that is compatible with this `TypeSpec`.
|
||
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"""
|
||
|
# === Subclassing ===
|
||
|
# This method must be inexpensive (do not call TF ops).
|
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raise NotImplementedError("%s._from_components()" % type(self).__name__)
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|
||
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@abc.abstractproperty
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def _component_specs(self):
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"""A nested structure of TypeSpecs for this type's components.
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Returns:
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A nested structure describing the component encodings that are returned
|
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by this TypeSpec's `_to_components` method. In particular, for a
|
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TypeSpec `spec` and a compatible value `value`:
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```
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nest.map_structure(lambda t, c: assert t.is_compatible_with(c),
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spec._component_specs, spec._to_components(value))
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```
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"""
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raise NotImplementedError("%s._component_specs()" % type(self).__name__)
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||
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# === Tensor list encoding for values ===
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def _to_tensor_list(self, value) -> List["ops.Tensor"]:
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"""Encodes `value` as a flat list of `tf.Tensor`.
|
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||
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By default, this just flattens `self._to_components(value)` using
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`nest.flatten`. However, subclasses may override this to return a
|
||
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different tensor encoding for values. In particular, some subclasses
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of `BatchableTypeSpec` override this method to return a "boxed" encoding
|
||
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for values, which then can be batched or unbatched. See
|
||
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`BatchableTypeSpec` for more details.
|
||
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|
||
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Args:
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value: A value with compatible this `TypeSpec`. (Caller is responsible
|
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for ensuring compatibility.)
|
||
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|
||
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Returns:
|
||
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A list of `tf.Tensor`, compatible with `self._flat_tensor_specs`, which
|
||
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can be used to reconstruct `value`.
|
||
|
"""
|
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return nest.flatten(self._to_components(value), expand_composites=True)
|
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def _from_tensor_list(self, tensor_list: List["ops.Tensor"]) -> Any:
|
||
|
"""Reconstructs a value from a flat list of `tf.Tensor`.
|
||
|
|
||
|
Args:
|
||
|
tensor_list: A flat list of `tf.Tensor`, compatible with
|
||
|
`self._flat_tensor_specs`.
|
||
|
|
||
|
Returns:
|
||
|
A value that is compatible with this `TypeSpec`.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If `tensor_list` is not compatible with
|
||
|
`self._flat_tensor_specs`.
|
||
|
"""
|
||
|
self.__check_tensor_list(tensor_list)
|
||
|
return self._from_compatible_tensor_list(tensor_list)
|
||
|
|
||
|
def _from_compatible_tensor_list(self,
|
||
|
tensor_list: List["ops.Tensor"]) -> Any:
|
||
|
"""Reconstructs a value from a compatible flat list of `tf.Tensor`.
|
||
|
|
||
|
Args:
|
||
|
tensor_list: A flat list of `tf.Tensor`, compatible with
|
||
|
`self._flat_tensor_specs`. (Caller is responsible for ensuring
|
||
|
compatibility.)
|
||
|
|
||
|
Returns:
|
||
|
A value that is compatible with this `TypeSpec`.
|
||
|
"""
|
||
|
return self._from_components(
|
||
|
nest.pack_sequence_as(
|
||
|
self._component_specs, tensor_list, expand_composites=True))
|
||
|
|
||
|
@property
|
||
|
def _flat_tensor_specs(self):
|
||
|
"""A list of TensorSpecs compatible with self._to_tensor_list(v)."""
|
||
|
return nest.flatten(self._component_specs, expand_composites=True)
|
||
|
|
||
|
# === Serialization for types ===
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def _serialize(self):
|
||
|
"""Returns a nested tuple containing the state of this TypeSpec.
|
||
|
|
||
|
The serialization may contain the following value types: boolean,
|
||
|
integer, string, float, None, `TensorSpec`, `tf.TensorShape`, `tf.DType`,
|
||
|
`np.ndarray`, `TypeSpec`, and nested tuples, namedtuples, dicts, and
|
||
|
OrderedDicts of any of the above.
|
||
|
|
||
|
This method is used to provide default definitions for: equality
|
||
|
testing (__eq__, __ne__), hashing (__hash__), pickling (__reduce__),
|
||
|
string representation (__repr__), `self.is_compatible_with()`,
|
||
|
`self.most_specific_compatible_type()`, and protobuf serialization
|
||
|
(e.g. TensorInfo and StructuredValue).
|
||
|
"""
|
||
|
raise NotImplementedError("%s._serialize()" % type(self).__name__)
|
||
|
|
||
|
@classmethod
|
||
|
def _deserialize(cls, serialization):
|
||
|
"""Reconstructs a TypeSpec from a value returned by `serialize`.
|
||
|
|
||
|
Args:
|
||
|
serialization: A value returned by _serialize. In some contexts,
|
||
|
`namedtuple`s in `serialization` may not have the identical type that
|
||
|
was returned by `_serialize` (but its type will still be a `namedtuple`
|
||
|
type with the same type name and field names). For example, the code
|
||
|
that loads a SavedModel does not have access to the original
|
||
|
`namedtuple` type, so it dynamically creates a new `namedtuple` type
|
||
|
with the same type name and field names as the original one. If
|
||
|
necessary, you can check `serialization` for these duck-typed
|
||
|
`nametuple` types, and restore them to the original type. (E.g., this
|
||
|
would be necessary if you rely on type checks such as `isinstance` for
|
||
|
this `TypeSpec`'s member variables).
|
||
|
|
||
|
Returns:
|
||
|
A `TypeSpec` of type `cls`.
|
||
|
"""
|
||
|
return cls(*serialization) # pytype: disable=not-instantiable # trace-all-classes
|
||
|
|
||
|
# === Operators ===
|
||
|
|
||
|
def __eq__(self, other) -> bool:
|
||
|
# pylint: disable=protected-access
|
||
|
return (type(other) is type(self) and
|
||
|
self.__get_cmp_key() == other.__get_cmp_key())
|
||
|
|
||
|
def __ne__(self, other) -> bool:
|
||
|
return not self == other
|
||
|
|
||
|
def __hash__(self) -> int:
|
||
|
return hash(self.__get_cmp_key())
|
||
|
|
||
|
def __reduce__(self):
|
||
|
return type(self), self._serialize()
|
||
|
|
||
|
def __repr__(self) -> str:
|
||
|
return "%s%r" % (type(self).__name__, self._serialize())
|
||
|
|
||
|
# === Legacy Output ===
|
||
|
# TODO(b/133606651) Document and/or deprecate the legacy_output methods.
|
||
|
# (These are used by tf.data.)
|
||
|
|
||
|
def _to_legacy_output_types(self):
|
||
|
raise NotImplementedError("%s._to_legacy_output_types()" %
|
||
|
type(self).__name__)
|
||
|
|
||
|
def _to_legacy_output_shapes(self):
|
||
|
raise NotImplementedError("%s._to_legacy_output_shapes()" %
|
||
|
type(self).__name__)
|
||
|
|
||
|
def _to_legacy_output_classes(self):
|
||
|
return self.value_type
|
||
|
|
||
|
# === Private Helper Methods ===
|
||
|
|
||
|
# TODO(b/154541175): Currently this usage is used to represent a Tensor
|
||
|
# argument not a TensorSpec argument as it should be.
|
||
|
def __tf_tracing_type__(self,
|
||
|
context: trace.TracingContext) -> trace.TraceType:
|
||
|
return self
|
||
|
|
||
|
def __check_tensor_list(self, tensor_list):
|
||
|
"""Raises an exception if tensor_list incompatible w/ flat_tensor_specs."""
|
||
|
expected = self._flat_tensor_specs
|
||
|
specs = [type_spec_from_value(t) for t in tensor_list]
|
||
|
if len(specs) != len(expected):
|
||
|
raise ValueError(f"Cannot create a {self.value_type.__name__} from the "
|
||
|
f"tensor list because the TypeSpec expects "
|
||
|
f"{len(expected)} items, but the provided tensor list "
|
||
|
f"has {len(specs)} items.")
|
||
|
for i, (s1, s2) in enumerate(zip(specs, expected)):
|
||
|
if not s1.is_compatible_with(s2):
|
||
|
raise ValueError(f"Cannot create a {self.value_type.__name__} from the "
|
||
|
f"tensor list because item {i} ({tensor_list[i]!r}) "
|
||
|
f"is incompatible with the expected TypeSpec {s2}.")
|
||
|
|
||
|
def __get_cmp_key(self):
|
||
|
"""Returns a hashable eq-comparable key for `self`."""
|
||
|
# TODO(b/133606651): Decide whether to cache this value.
|
||
|
return (type(self), self.__make_cmp_key(self._serialize()))
|
||
|
|
||
|
def __make_cmp_key(self, value):
|
||
|
"""Converts `value` to a hashable key."""
|
||
|
if isinstance(value, (int, float, bool, np.generic, dtypes.DType, TypeSpec,
|
||
|
tensor_shape.TensorShape)):
|
||
|
return value
|
||
|
if isinstance(value, compat.bytes_or_text_types):
|
||
|
return value
|
||
|
if value is None:
|
||
|
return value
|
||
|
if isinstance(value, dict):
|
||
|
return tuple([
|
||
|
tuple([self.__make_cmp_key(key),
|
||
|
self.__make_cmp_key(value[key])])
|
||
|
for key in sorted(value.keys())
|
||
|
])
|
||
|
if isinstance(value, tuple):
|
||
|
return tuple([self.__make_cmp_key(v) for v in value])
|
||
|
if isinstance(value, list):
|
||
|
return (list, tuple([self.__make_cmp_key(v) for v in value]))
|
||
|
if isinstance(value, np.ndarray):
|
||
|
return (np.ndarray, value.shape,
|
||
|
TypeSpec.__nested_list_to_tuple(value.tolist()))
|
||
|
raise ValueError(f"Cannot generate a hashable key for {self} because "
|
||
|
f"the _serialize() method "
|
||
|
f"returned an unsupproted value of type {type(value)}")
|
||
|
|
||
|
@staticmethod
|
||
|
def __nested_list_to_tuple(value):
|
||
|
"""Converts a nested list to a corresponding nested tuple."""
|
||
|
if isinstance(value, list):
|
||
|
return tuple(TypeSpec.__nested_list_to_tuple(v) for v in value)
|
||
|
return value
|
||
|
|
||
|
@staticmethod
|
||
|
def __same_types(a, b):
|
||
|
"""Returns whether a and b have the same type, up to namedtuple equivalence.
|
||
|
|
||
|
Consistent with tf.nest.assert_same_structure(), two namedtuple types
|
||
|
are considered the same iff they agree in their class name (without
|
||
|
qualification by module name) and in their sequence of field names.
|
||
|
This makes namedtuples recreated by nested_structure_coder compatible with
|
||
|
their original Python definition.
|
||
|
|
||
|
Args:
|
||
|
a: a Python object.
|
||
|
b: a Python object.
|
||
|
|
||
|
Returns:
|
||
|
A boolean that is true iff type(a) and type(b) are the same object
|
||
|
or equivalent namedtuple types.
|
||
|
"""
|
||
|
if nest.is_namedtuple(a) and nest.is_namedtuple(b):
|
||
|
return nest.same_namedtuples(a, b)
|
||
|
else:
|
||
|
return type(a) is type(b)
|
||
|
|
||
|
@staticmethod
|
||
|
def __is_compatible(a, b):
|
||
|
"""Returns true if the given type serializations compatible."""
|
||
|
if isinstance(a, TypeSpec):
|
||
|
return a.is_compatible_with(b)
|
||
|
if not TypeSpec.__same_types(a, b):
|
||
|
return False
|
||
|
if isinstance(a, (list, tuple)):
|
||
|
return (len(a) == len(b) and
|
||
|
all(TypeSpec.__is_compatible(x, y) for (x, y) in zip(a, b)))
|
||
|
if isinstance(a, dict):
|
||
|
return (len(a) == len(b) and sorted(a.keys()) == sorted(b.keys()) and
|
||
|
all(TypeSpec.__is_compatible(a[k], b[k]) for k in a.keys()))
|
||
|
if isinstance(a, (tensor_shape.TensorShape, dtypes.DType)):
|
||
|
return a.is_compatible_with(b)
|
||
|
return a == b
|
||
|
|
||
|
trace_type.register_serializable(TypeSpec)
|
||
|
|
||
|
|
||
|
class TypeSpecBatchEncoder(object, metaclass=abc.ABCMeta):
|
||
|
"""Class used to encode and decode composite tensor values for batching.
|
||
|
|
||
|
In order to be batched and unbatched by APIs such as `tf.data.Dataset` and
|
||
|
`tf.map_fn`, composite tensors must be encoded using flat tensors that can
|
||
|
themselves be batched or unbatched. `TypeSpecBatchEncoder`s are
|
||
|
responsible for implementing this encoding.
|
||
|
|
||
|
If a composite tensor's shape is a prefix of the shape of all of its
|
||
|
component tensors, then this encoding can usually be performed by just
|
||
|
returning those component tensors as a list. But if the composite tensor
|
||
|
has components whose shape has a more complex relationship to the shape
|
||
|
of the composite tensor, then a custom `TypeSpecBatchEncoder` may
|
||
|
need to be implemented.
|
||
|
"""
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def batch(self, spec, batch_size):
|
||
|
"""Returns the TypeSpec representing a batch of values described by `spec`.
|
||
|
|
||
|
Args:
|
||
|
spec: The `TypeSpec` for an individual value.
|
||
|
batch_size: An `int` indicating the number of values that are batched
|
||
|
together, or `None` if the batch size is not known.
|
||
|
|
||
|
Returns:
|
||
|
A `TypeSpec` for a batch of values.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}.batch")
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def unbatch(self, spec):
|
||
|
"""Returns the TypeSpec for a single unbatched element in `spec`.
|
||
|
|
||
|
Args:
|
||
|
spec: The `TypeSpec` for a batch of values.
|
||
|
|
||
|
Returns:
|
||
|
A `TypeSpec` for an individual value.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}.unbatch")
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def encode(self, spec, value, minimum_rank=0):
|
||
|
"""Encodes `value` as a nest of batchable `Tensor` or `CompositeTensor`.
|
||
|
|
||
|
Args:
|
||
|
spec: The TypeSpec of the value to encode.
|
||
|
value: A value compatible with `spec`.
|
||
|
minimum_rank: The minimum rank for the returned Tensors, CompositeTensors,
|
||
|
and ExtensionType values. This can be used to ensure that the encoded
|
||
|
values can be unbatched this number of times. If `minimum_rank>0`,
|
||
|
then `t.shape[:minimum_rank]` must be compatible for all values `t`
|
||
|
returned by `encode`.
|
||
|
|
||
|
Returns:
|
||
|
A nest (as defined by `tf.nest`) of `tf.Tensor`s, batchable
|
||
|
`tf.CompositeTensor`s, or `tf.ExtensionType`s. Stacking, unstacking, or
|
||
|
concatenating these encoded values and then decoding the result must be
|
||
|
equivalent to stacking, unstacking, or concatenating the original values.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}.encode")
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def decode(self, spec, encoded_value):
|
||
|
"""Decodes `value` from a batchable tensor encoding.
|
||
|
|
||
|
Args:
|
||
|
spec: The TypeSpec for the result value. If encoded values with spec `s`
|
||
|
were batched, then `spec` should be `s.batch(batch_size)`; or if encoded
|
||
|
values with spec `s` were unbatched, then `spec` should be
|
||
|
`s.unbatch()`.
|
||
|
encoded_value: A nest of values returned by `encode`; or a nest of values
|
||
|
that was formed by stacking, unstacking, or concatenating the
|
||
|
corresponding elements of values returned by `encode`.
|
||
|
|
||
|
Returns:
|
||
|
A value compatible with `type_spec`.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}.decode")
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def encoding_specs(self, spec):
|
||
|
"""Returns a nest of `TypeSpec`(s) describing the encoding for `spec`.
|
||
|
|
||
|
Args:
|
||
|
spec: The TypeSpec whose encoding should be described.
|
||
|
|
||
|
Returns:
|
||
|
A nest (as defined by `tf.nest) of `tf.TypeSpec`, describing the values
|
||
|
that are returned by `self.encode(spec, ...)`. All TypeSpecs in this
|
||
|
nest must be batchable.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}.encoding_specs")
|
||
|
|
||
|
|
||
|
class LegacyTypeSpecBatchEncoder(TypeSpecBatchEncoder):
|
||
|
"""TypeSpecBatchEncoder for legacy composite tensor classes.
|
||
|
|
||
|
TODO(edloper): Update existing composite tensors to use non-legacy
|
||
|
CompositTensorBatchEncoders.
|
||
|
"""
|
||
|
|
||
|
def batch(self, type_spec, batch_size):
|
||
|
return type_spec._batch(batch_size) # pylint: disable=protected-access
|
||
|
|
||
|
def unbatch(self, type_spec):
|
||
|
return type_spec._unbatch() # pylint: disable=protected-access
|
||
|
|
||
|
def encode(self, type_spec, value, minimum_rank=0):
|
||
|
if minimum_rank == 0:
|
||
|
return type_spec._to_tensor_list(value) # pylint: disable=protected-access
|
||
|
elif minimum_rank == 1:
|
||
|
if not isinstance(type_spec, BatchableTypeSpec):
|
||
|
raise ValueError(f"{type_spec.__name__}.encode does not support "
|
||
|
"minimum_rank>0.")
|
||
|
return type_spec._to_batched_tensor_list(value) # pylint: disable=protected-access
|
||
|
else:
|
||
|
raise ValueError(f"{type_spec.__name__}.encode does not support "
|
||
|
"minimum_rank>1.")
|
||
|
|
||
|
def decode(self, type_spec, encoded_value):
|
||
|
return type_spec._from_tensor_list(encoded_value) # pylint: disable=protected-access
|
||
|
|
||
|
def encoding_specs(self, spec):
|
||
|
return spec._flat_tensor_specs # pylint: disable=protected-access
|
||
|
|
||
|
|
||
|
class BatchableTypeSpec(TypeSpec, metaclass=abc.ABCMeta):
|
||
|
"""TypeSpec with a batchable tensor encoding.
|
||
|
|
||
|
The batchable tensor encoding is a list of `tf.Tensor`s that supports
|
||
|
batching and unbatching. In particular, stacking (or unstacking)
|
||
|
values with the same `TypeSpec` must be equivalent to stacking (or
|
||
|
unstacking) each of their tensor lists. Unlike the component encoding
|
||
|
(returned by `self._to_components)`, the batchable tensor encoding
|
||
|
may require using encoding/decoding ops.
|
||
|
|
||
|
If a subclass's batchable tensor encoding is not simply a flattened version
|
||
|
of the component encoding, then the subclass must override `_to_tensor_list`,
|
||
|
`_from_tensor_list`, and _flat_tensor_specs`.
|
||
|
"""
|
||
|
|
||
|
__slots__ = []
|
||
|
|
||
|
__batch_encoder__ = LegacyTypeSpecBatchEncoder()
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def _batch(self, batch_size) -> TypeSpec:
|
||
|
"""Returns a TypeSpec representing a batch of objects with this TypeSpec.
|
||
|
|
||
|
Args:
|
||
|
batch_size: An `int` representing the number of elements in a batch, or
|
||
|
`None` if the batch size may vary.
|
||
|
|
||
|
Returns:
|
||
|
A `TypeSpec` representing a batch of objects with this TypeSpec.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}._batch")
|
||
|
|
||
|
@abc.abstractmethod
|
||
|
def _unbatch(self) -> TypeSpec:
|
||
|
"""Returns a TypeSpec representing a single element this TypeSpec.
|
||
|
|
||
|
Returns:
|
||
|
A `TypeSpec` representing a single element of objects with this TypeSpec.
|
||
|
"""
|
||
|
raise NotImplementedError(f"{type(self).__name__}._unbatch")
|
||
|
|
||
|
# LINT.IfChange
|
||
|
@property
|
||
|
def _flat_tensor_specs(self) -> List[TypeSpec]:
|
||
|
"""A list of TensorSpecs compatible with self._to_tensor_list(v)."""
|
||
|
component_flat_tensor_specs = nest.map_structure(
|
||
|
functools.partial(get_batchable_flat_tensor_specs, context_spec=self),
|
||
|
self._component_specs)
|
||
|
return nest.flatten(component_flat_tensor_specs)
|
||
|
# LINT.ThenChange(//tensorflow/python/framework/type_utils.py:_specs_for_flat_tensors)
|
||
|
# Note that _specs_for_flat_tensors in type_utils.py must correspond
|
||
|
# _flat_tensor_specs in this class and any derived classes.
|
||
|
|
||
|
def _to_tensor_list(
|
||
|
self, value: composite_tensor.CompositeTensor) -> List["ops.Tensor"]:
|
||
|
"""Encodes `value` as a flat list of `ops.Tensor`."""
|
||
|
component_tensor_lists = nest.map_structure(batchable_to_tensor_list,
|
||
|
self._component_specs,
|
||
|
self._to_components(value))
|
||
|
return nest.flatten(component_tensor_lists)
|
||
|
|
||
|
def _to_batched_tensor_list(
|
||
|
self, value: composite_tensor.CompositeTensor) -> List["ops.Tensor"]:
|
||
|
"""Encodes `value` as a flat list of `ops.Tensor` each with rank>0."""
|
||
|
get_spec_tensor_list = lambda spec, v: ( # pylint: disable=g-long-lambda
|
||
|
batchable_to_tensor_list(spec, v, minimum_rank=1)
|
||
|
if isinstance(spec, BatchableTypeSpec) else spec._to_tensor_list(v)) # pylint: disable=protected-access
|
||
|
component_batched_tensor_lists = nest.map_structure(
|
||
|
get_spec_tensor_list, self._component_specs, self._to_components(value))
|
||
|
tensor_list = nest.flatten(component_batched_tensor_lists)
|
||
|
if any(t.shape.ndims == 0 for t in tensor_list):
|
||
|
raise ValueError(
|
||
|
f"While converting {value} to a list of tensors for batching, "
|
||
|
f"found a scalar item which cannot be batched.")
|
||
|
return tensor_list
|
||
|
|
||
|
def _from_compatible_tensor_list(
|
||
|
self,
|
||
|
tensor_list: List["ops.Tensor"]) -> composite_tensor.CompositeTensor:
|
||
|
"""Reconstructs a value from a compatible flat list of `ops.Tensor`."""
|
||
|
flat_specs = nest.map_structure(
|
||
|
functools.partial(get_batchable_flat_tensor_specs, context_spec=self),
|
||
|
self._component_specs)
|
||
|
nested_tensor_list = nest.pack_sequence_as(flat_specs, tensor_list)
|
||
|
components = nest.map_structure_up_to(self._component_specs,
|
||
|
batchable_from_tensor_list,
|
||
|
self._component_specs,
|
||
|
nested_tensor_list)
|
||
|
return self._from_components(components)
|
||
|
|
||
|
|
||
|
def get_batchable_flat_tensor_specs(spec, context_spec=None):
|
||
|
"""Returns the flat tensor specs for `spec`."""
|
||
|
if isinstance(spec, internal.TensorSpec):
|
||
|
return [spec]
|
||
|
elif hasattr(spec, "__batch_encoder__"):
|
||
|
encoding_specs = nest.map_structure(
|
||
|
functools.partial(
|
||
|
get_batchable_flat_tensor_specs, context_spec=context_spec),
|
||
|
spec.__batch_encoder__.encoding_specs(spec))
|
||
|
return nest.flatten(encoding_specs)
|
||
|
else:
|
||
|
# TODO(edloper) Fix existing CompositeTensors that permit this, and
|
||
|
# then turn this warning into an error.
|
||
|
warnings.warn(f"Batchable type {context_spec} contains non-batchable "
|
||
|
f"field or component with type {spec}.")
|
||
|
return spec._flat_tensor_specs # pylint: disable=protected-access
|
||
|
|
||
|
|
||
|
def batchable_to_tensor_list(spec, value, minimum_rank=0):
|
||
|
"""Returns a list of tensors encoding `value`, whose type is `spec`."""
|
||
|
if isinstance(spec, internal.TensorSpec):
|
||
|
return [value]
|
||
|
elif hasattr(spec, "__batch_encoder__"):
|
||
|
encoded_value = spec.__batch_encoder__.encode(spec, value, minimum_rank)
|
||
|
encoded_specs = spec.__batch_encoder__.encoding_specs(spec)
|
||
|
encoded_flats = nest.map_structure(
|
||
|
functools.partial(batchable_to_tensor_list, minimum_rank=minimum_rank),
|
||
|
encoded_specs, encoded_value)
|
||
|
return nest.flatten(encoded_flats)
|
||
|
else:
|
||
|
return spec._to_tensor_list(value) # pylint: disable=protected-access
|
||
|
|
||
|
|
||
|
def batchable_from_tensor_list(spec, tensor_list):
|
||
|
"""Returns a value with type `spec` decoded from `tensor_list`."""
|
||
|
if isinstance(spec, internal.TensorSpec):
|
||
|
assert len(tensor_list) == 1
|
||
|
return tensor_list[0]
|
||
|
elif hasattr(spec, "__batch_encoder__"):
|
||
|
encoded_specs = spec.__batch_encoder__.encoding_specs(spec)
|
||
|
flat_specs = nest.map_structure(get_batchable_flat_tensor_specs,
|
||
|
encoded_specs)
|
||
|
encoded_flats = nest.pack_sequence_as(flat_specs, tensor_list)
|
||
|
encoded_value = nest.map_structure_up_to(encoded_specs,
|
||
|
batchable_from_tensor_list,
|
||
|
encoded_specs, encoded_flats)
|
||
|
return spec.__batch_encoder__.decode(spec, encoded_value)
|
||
|
else:
|
||
|
return spec._from_compatible_tensor_list(tensor_list) # pylint: disable=protected-access
|
||
|
|
||
|
|
||
|
@tf_export("type_spec_from_value")
|
||
|
def type_spec_from_value(value) -> TypeSpec:
|
||
|
"""Returns a `tf.TypeSpec` that represents the given `value`.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> tf.type_spec_from_value(tf.constant([1, 2, 3]))
|
||
|
TensorSpec(shape=(3,), dtype=tf.int32, name=None)
|
||
|
>>> tf.type_spec_from_value(np.array([4.0, 5.0], np.float64))
|
||
|
TensorSpec(shape=(2,), dtype=tf.float64, name=None)
|
||
|
>>> tf.type_spec_from_value(tf.ragged.constant([[1, 2], [3, 4, 5]]))
|
||
|
RaggedTensorSpec(TensorShape([2, None]), tf.int32, 1, tf.int64)
|
||
|
|
||
|
>>> example_input = tf.ragged.constant([[1, 2], [3]])
|
||
|
>>> @tf.function(input_signature=[tf.type_spec_from_value(example_input)])
|
||
|
... def f(x):
|
||
|
... return tf.reduce_sum(x, axis=1)
|
||
|
|
||
|
Args:
|
||
|
value: A value that can be accepted or returned by TensorFlow APIs. Accepted
|
||
|
types for `value` include `tf.Tensor`, any value that can be converted to
|
||
|
`tf.Tensor` using `tf.convert_to_tensor`, and any subclass of
|
||
|
`CompositeTensor` (such as `tf.RaggedTensor`).
|
||
|
|
||
|
Returns:
|
||
|
A `TypeSpec` that is compatible with `value`.
|
||
|
|
||
|
Raises:
|
||
|
TypeError: If a TypeSpec cannot be built for `value`, because its type
|
||
|
is not supported.
|
||
|
"""
|
||
|
spec = _type_spec_from_value(value)
|
||
|
if spec is not None:
|
||
|
return spec
|
||
|
|
||
|
# Fallback: try converting value to a tensor.
|
||
|
try:
|
||
|
tensor = ops.convert_to_tensor(value)
|
||
|
spec = _type_spec_from_value(tensor)
|
||
|
if spec is not None:
|
||
|
return spec
|
||
|
except (ValueError, TypeError) as e:
|
||
|
logging.vlog(
|
||
|
3, "Failed to convert %r to tensor: %s" % (type(value).__name__, e))
|
||
|
|
||
|
raise TypeError(f"Could not build a TypeSpec for {value} of "
|
||
|
f"unsupported type {type(value)}.")
|
||
|
|
||
|
|
||
|
def _type_spec_from_value(value) -> TypeSpec:
|
||
|
"""Returns a `TypeSpec` that represents the given `value`."""
|
||
|
if isinstance(value, ops.Tensor):
|
||
|
# Note: we do not include Tensor names when constructing TypeSpecs.
|
||
|
return trace_type.from_value(value)
|
||
|
|
||
|
if isinstance(value, composite_tensor.CompositeTensor):
|
||
|
return value._type_spec # pylint: disable=protected-access
|
||
|
|
||
|
# If `value` is a list and all of its elements can be represented by the same
|
||
|
# batchable type spec, then we can represent the entire list using a single
|
||
|
# type spec that captures the type accurately (unlike the `convert_to_tensor`
|
||
|
# fallback).
|
||
|
if isinstance(value, list) and value:
|
||
|
subspecs = [_type_spec_from_value(v) for v in value]
|
||
|
if isinstance(subspecs[0], BatchableTypeSpec):
|
||
|
merged_subspec = subspecs[0].most_specific_common_supertype(subspecs[1:])
|
||
|
if merged_subspec is not None:
|
||
|
return merged_subspec._batch(len(subspecs)) # pylint: disable=protected-access
|
||
|
|
||
|
for entry in reversed(_TYPE_CONVERSION_FUNCTION_REGISTRY):
|
||
|
type_object, converter_fn, allow_subclass = entry
|
||
|
if ((type(value) is type_object) or # pylint: disable=unidiomatic-typecheck
|
||
|
(allow_subclass and isinstance(value, type_object))):
|
||
|
return converter_fn(value)
|
||
|
|
||
|
return None
|
||
|
|
||
|
|
||
|
_TYPE_CONVERSION_FUNCTION_REGISTRY = []
|
||
|
|
||
|
|
||
|
def register_type_spec_from_value_converter(type_object,
|
||
|
converter_fn,
|
||
|
allow_subclass=False):
|
||
|
"""Registers a function for converting values with a given type to TypeSpecs.
|
||
|
|
||
|
If multiple registered `type_object`s match a value, then the most recent
|
||
|
registration takes precedence. Custom converters should not be defined for
|
||
|
`CompositeTensor`s; use `CompositeTensor._type_spec` instead.
|
||
|
|
||
|
Args:
|
||
|
type_object: A Python `type` object representing the type of values accepted
|
||
|
by `converter_fn`.
|
||
|
converter_fn: A function that takes one argument (an instance of the type
|
||
|
represented by `type_object`) and returns a `TypeSpec`.
|
||
|
allow_subclass: If true, then use `isinstance(value, type_object)` to check
|
||
|
for matches. If false, then use `type(value) is type_object`.
|
||
|
"""
|
||
|
_, type_object = tf_decorator.unwrap(type_object)
|
||
|
_TYPE_CONVERSION_FUNCTION_REGISTRY.append(
|
||
|
(type_object, converter_fn, allow_subclass))
|
||
|
|
||
|
|
||
|
_pywrap_utils.RegisterType("TypeSpec", TypeSpec)
|
||
|
|
||
|
# TODO(flang) delete type_spec.py's dependency on type_spec_registry.py once
|
||
|
# all legacy references type_spec's registry functions below have migrated
|
||
|
_TYPE_SPEC_TO_NAME = type_spec_registry._TYPE_SPEC_TO_NAME # pylint: disable=protected-access
|
||
|
_NAME_TO_TYPE_SPEC = type_spec_registry._NAME_TO_TYPE_SPEC # pylint: disable=protected-access
|
||
|
lookup = type_spec_registry.lookup
|
||
|
get_name = type_spec_registry.get_name
|
||
|
register = type_spec_registry.register
|
||
|
_REGISTERED_NAME_RE = type_spec_registry._REGISTERED_NAME_RE # pylint: disable=protected-access
|