126 lines
4.3 KiB
Python
126 lines
4.3 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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"""Trace allows the profiler to trace Python events."""
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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.profiler.internal import _pywrap_traceme
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from tensorflow.python.util.tf_export import tf_export
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# This variable is modified by PythonHooks::Start/Stop() in C++. Such
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# arrangement will reduce the number of calls through pybind11.
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enabled = False
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@tf_export('profiler.experimental.Trace', v1=[])
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class Trace(object):
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"""Context manager that generates a trace event in the profiler.
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A trace event will start when entering the context, and stop and save the
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result to the profiler when exiting the context. Open TensorBoard Profile tab
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and choose trace viewer to view the trace event in the timeline.
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Trace events are created only when the profiler is enabled. More information
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on how to use the profiler can be found at
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https://tensorflow.org/guide/profiler
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Example usage:
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```python
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tf.profiler.experimental.start('logdir')
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for step in range(num_steps):
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# Creates a trace event for each training step with the step number.
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with tf.profiler.experimental.Trace("Train", step_num=step):
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train_fn()
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tf.profiler.experimental.stop()
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```
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"""
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def __init__(self, name, **kwargs):
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"""Creates a trace event in the profiler.
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Args:
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name: The name of the trace event.
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**kwargs: Keyword arguments added to the trace event.
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Both the key and value are of types that
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can be converted to strings, which will be
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interpreted by the profiler according to the
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traceme name.
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Example usage:
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```python
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tf.profiler.experimental.start('logdir')
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for step in range(num_steps):
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# Creates a trace event for each training step with the
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# step number.
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with tf.profiler.experimental.Trace("Train", step_num=step):
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train_fn()
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tf.profiler.experimental.stop()
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```
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The example above uses the keyword argument "step_num" to specify the
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training step being traced.
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"""
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if enabled:
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# Creating _pywrap_traceme.TraceMe starts the clock.
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self._traceme = _pywrap_traceme.TraceMe(name, **kwargs)
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else:
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self._traceme = None
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def __enter__(self):
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# Starting the TraceMe clock here would require an extra Python->C++ call.
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return self
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def set_metadata(self, **kwargs):
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"""Sets metadata in this trace event.
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Args:
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**kwargs: metadata in key-value pairs.
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This method enables setting metadata in a trace event after it is
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created.
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Example usage:
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```python
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def call(function):
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with tf.profiler.experimental.Trace("call",
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function_name=function.name) as tm:
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binary, in_cache = jit_compile(function)
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tm.set_metadata(in_cache=in_cache)
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execute(binary)
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```
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In this example, we want to trace how much time spent on
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calling a function, which includes compilation and execution.
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The compilation can be either getting a cached copy of the
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binary or actually generating the binary, which is indicated
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by the boolean "in_cache" returned by jit_compile(). We need
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to use set_metadata() to pass in_cache because we did not know
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the in_cache value when the trace was created (and we cannot
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create the trace after jit_compile(), because we want
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to measure the entire duration of call()).
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"""
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if self._traceme and kwargs:
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self._traceme.SetMetadata(**kwargs)
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self._traceme:
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self._traceme.Stop()
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