112 lines
4.4 KiB
Python
112 lines
4.4 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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"""Provides a lazy wrapper for deferring Tensor creation."""
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import threading
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from tensorboard.compat import tf2 as tf
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# Sentinel used for LazyTensorCreator._tensor to indicate that a value is
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# currently being computed, in order to fail hard on reentrancy.
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_CALL_IN_PROGRESS_SENTINEL = object()
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class LazyTensorCreator:
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"""Lazy auto-converting wrapper for a callable that returns a `tf.Tensor`.
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This class wraps an arbitrary callable that returns a `Tensor` so that it
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will be automatically converted to a `Tensor` by any logic that calls
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`tf.convert_to_tensor()`. This also memoizes the callable so that it is
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called at most once.
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The intended use of this class is to defer the construction of a `Tensor`
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(e.g. to avoid unnecessary wasted computation, or ensure any new ops are
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created in a context only available later on in execution), while remaining
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compatible with APIs that expect to be given an already materialized value
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that can be converted to a `Tensor`.
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This class is thread-safe.
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"""
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def __init__(self, tensor_callable):
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"""Initializes a LazyTensorCreator object.
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Args:
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tensor_callable: A callable that returns a `tf.Tensor`.
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"""
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if not callable(tensor_callable):
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raise ValueError("Not a callable: %r" % tensor_callable)
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self._tensor_callable = tensor_callable
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self._tensor = None
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self._tensor_lock = threading.RLock()
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_register_conversion_function_once()
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def __call__(self):
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if self._tensor is None or self._tensor is _CALL_IN_PROGRESS_SENTINEL:
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with self._tensor_lock:
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if self._tensor is _CALL_IN_PROGRESS_SENTINEL:
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raise RuntimeError(
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"Cannot use LazyTensorCreator with reentrant callable"
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)
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elif self._tensor is None:
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self._tensor = _CALL_IN_PROGRESS_SENTINEL
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self._tensor = self._tensor_callable()
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return self._tensor
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def _lazy_tensor_creator_converter(value, dtype=None, name=None, as_ref=False):
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del name # ignored
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if not isinstance(value, LazyTensorCreator):
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raise RuntimeError("Expected LazyTensorCreator, got %r" % value)
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if as_ref:
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raise RuntimeError("Cannot use LazyTensorCreator to create ref tensor")
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tensor = value()
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if dtype not in (None, tensor.dtype):
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raise RuntimeError(
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"Cannot convert LazyTensorCreator returning dtype %s to dtype %s"
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% (tensor.dtype, dtype)
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)
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return tensor
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# Use module-level bit and lock to ensure that registration of the
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# LazyTensorCreator conversion function happens only once.
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_conversion_registered = False
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_conversion_registered_lock = threading.Lock()
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def _register_conversion_function_once():
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"""Performs one-time registration of `_lazy_tensor_creator_converter`.
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This helper can be invoked multiple times but only registers the conversion
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function on the first invocation, making it suitable for calling when
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constructing a LazyTensorCreator.
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Deferring the registration is necessary because doing it at at module import
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time would trigger the lazy TensorFlow import to resolve, and that in turn
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would break the delicate `tf.summary` import cycle avoidance scheme.
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"""
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global _conversion_registered
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if not _conversion_registered:
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with _conversion_registered_lock:
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if not _conversion_registered:
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_conversion_registered = True
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tf.register_tensor_conversion_function(
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base_type=LazyTensorCreator,
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conversion_func=_lazy_tensor_creator_converter,
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priority=0,
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)
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