paddlenlp.utils.profiler 源代码

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import sys

import paddle.profiler as profiler

# A global variable to record the number of calling times for profiler
# functions. It is used to specify the tracing range of training steps.
_profiler_step_id = 0

# A global variable to avoid parsing from string every time.
_profiler_options = None
_prof = None

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