77 lines
2.6 KiB
Python
77 lines
2.6 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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"""Executor for eager execution."""
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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 import pywrap_tfe
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class Executor(object):
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"""A class for handling eager execution.
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The default behavior for asynchronous execution is to serialize all ops on
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a single thread. Having different `Executor` objects in different threads
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enables executing ops asynchronously in parallel:
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```python
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def thread_function():
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executor = executor.Executor(enable_async=True):
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context.set_executor(executor)
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a = threading.Thread(target=thread_function)
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a.start()
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b = threading.Thread(target=thread_function)
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b.start()
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```
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"""
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def __init__(self, handle):
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self._handle = handle
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def __del__(self):
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try:
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# pywrap_tfe.TFE_ExecutorWaitForAllPendingNodes(self._handle)
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pywrap_tfe.TFE_DeleteExecutor(self._handle)
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except TypeError:
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# Suppress some exceptions, mainly for the case when we're running on
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# module deletion. Things that can go wrong include the pywrap module
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# already being unloaded, self._handle. no longer being
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# valid, and so on. Printing warnings in these cases is silly
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# (exceptions raised from __del__ are printed as warnings to stderr).
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pass # 'NoneType' object is not callable when the handle has been
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# partially unloaded.
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def is_async(self):
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return pywrap_tfe.TFE_ExecutorIsAsync(self._handle)
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def handle(self):
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return self._handle
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def wait(self):
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"""Waits for ops dispatched in this executor to finish."""
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pywrap_tfe.TFE_ExecutorWaitForAllPendingNodes(self._handle)
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def clear_error(self):
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"""Clears errors raised in this executor during execution."""
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pywrap_tfe.TFE_ExecutorClearError(self._handle)
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def new_executor(enable_async):
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handle = pywrap_tfe.TFE_NewExecutor(enable_async)
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return Executor(handle)
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