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129 lines
5.1 KiB
129 lines
5.1 KiB
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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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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import sys
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import paddle.profiler as profiler
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# A global variable to record the number of calling times for profiler
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# functions. It is used to specify the tracing range of training steps.
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_profiler_step_id = 0
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# A global variable to avoid parsing from string every time.
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_profiler_options = None
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_prof = None
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class ProfilerOptions(object):
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'''
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Use a string to initialize a ProfilerOptions.
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The string should be in the format: "key1=value1;key2=value;key3=value3".
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For example:
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"profile_path=model.profile"
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"batch_range=[50, 60]; profile_path=model.profile"
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"batch_range=[50, 60]; tracer_option=OpDetail; profile_path=model.profile"
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ProfilerOptions supports following key-value pair:
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batch_range - a integer list, e.g. [100, 110].
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state - a string, the optional values are 'CPU', 'GPU' or 'All'.
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sorted_key - a string, the optional values are 'calls', 'total',
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'max', 'min' or 'ave.
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tracer_option - a string, the optional values are 'Default', 'OpDetail',
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'AllOpDetail'.
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profile_path - a string, the path to save the serialized profile data,
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which can be used to generate a timeline.
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exit_on_finished - a boolean.
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'''
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def __init__(self, options_str):
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assert isinstance(options_str, str)
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self._options = {
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'batch_range': [10, 20],
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'state': 'All',
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'sorted_key': 'total',
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'tracer_option': 'Default',
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'profile_path': '/tmp/profile',
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'exit_on_finished': True,
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'timer_only': True
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}
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self._parse_from_string(options_str)
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def _parse_from_string(self, options_str):
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for kv in options_str.replace(' ', '').split(';'):
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key, value = kv.split('=')
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if key == 'batch_range':
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value_list = value.replace('[', '').replace(']', '').split(',')
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value_list = list(map(int, value_list))
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if len(value_list) >= 2 and value_list[0] >= 0 and value_list[
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1] > value_list[0]:
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self._options[key] = value_list
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elif key == 'exit_on_finished':
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self._options[key] = value.lower() in ("yes", "true", "t", "1")
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elif key in [
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'state', 'sorted_key', 'tracer_option', 'profile_path'
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]:
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self._options[key] = value
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elif key == 'timer_only':
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self._options[key] = value
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def __getitem__(self, name):
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if self._options.get(name, None) is None:
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raise ValueError(
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"ProfilerOptions does not have an option named %s." % name)
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return self._options[name]
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def add_profiler_step(options_str=None):
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'''
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Enable the operator-level timing using PaddlePaddle's profiler.
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The profiler uses a independent variable to count the profiler steps.
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One call of this function is treated as a profiler step.
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Args:
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profiler_options - a string to initialize the ProfilerOptions.
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Default is None, and the profiler is disabled.
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'''
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if options_str is None:
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return
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global _prof
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global _profiler_step_id
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global _profiler_options
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if _profiler_options is None:
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_profiler_options = ProfilerOptions(options_str)
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# profile : https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/performance_improving/profiling_model.html#chakanxingnengshujudetongjibiaodan
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# timer_only = True only the model's throughput and time overhead are displayed
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# timer_only = False calling summary can print a statistical form that presents performance data from different perspectives.
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# timer_only = False the output Timeline information can be found in the profiler_log directory
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if _prof is None:
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_timer_only = str(_profiler_options['timer_only']) == str(True)
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_prof = profiler.Profiler(
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scheduler = (_profiler_options['batch_range'][0], _profiler_options['batch_range'][1]),
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on_trace_ready = profiler.export_chrome_tracing('./profiler_log'),
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timer_only = _timer_only)
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_prof.start()
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else:
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_prof.step()
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if _profiler_step_id == _profiler_options['batch_range'][1]:
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_prof.stop()
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_prof.summary(
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op_detail=True,
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thread_sep=False,
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time_unit='ms')
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_prof = None
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if _profiler_options['exit_on_finished']:
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sys.exit(0)
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_profiler_step_id += 1
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