wehub-resource-sync
123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
# Copyright (c) 2022 PaddlePaddle 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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import copy
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import os
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from ...strategy import Strategy
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_tuning_supported_passes = ["sharding", "recompute"]
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def _get_pass_config(strategy, pass_name):
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config = getattr(strategy, pass_name)
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return config
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class TuningConfig:
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"""
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A uniform config wrap:
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distributed strategy: the user defined configuration for optimization pass
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tuning config: configuration for the tuning process: mode (profile or cost model), log dir, extra tuning config for optimization like search range for specific
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"""
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def __init__(self, strategy):
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if not isinstance(strategy, Strategy):
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raise TypeError("'strategy' must be object of class `Strategy`.")
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self._tuning_passes_name = set()
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self._dist_strategy = copy.deepcopy(strategy)
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self._mode = None
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self._profile_start_step = None
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self._profile_end_step = None
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self._project_dir = None
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self._max_num_trial = None
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self._early_stop = None
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self._debug = None
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self._initialize()
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@property
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def mode(self):
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return self._mode
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@property
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def profile_start_step(self):
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return self._profile_start_step
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@property
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def profile_end_step(self):
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return self._profile_end_step
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@property
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def project_dir(self):
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return self._project_dir
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@property
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def tuning_passes_name(self):
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return self._tuning_passes_name
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@property
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def max_num_trial(self):
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return self._max_num_trial
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@property
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def early_stop(self):
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return self._early_stop
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@property
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def debug(self):
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return self._debug
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@property
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def dist_strategy(self):
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return self._dist_strategy
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# initialize config with user define value or default value
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def _initialize(self):
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tuning_strategy = self._dist_strategy.tuning
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self._mode = tuning_strategy.get("mode", "PROFILE")
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self._profile_start_step = tuning_strategy.get("profile_start_step", 10)
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self._profile_end_step = tuning_strategy.get("profile_end_step", 30)
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self._max_num_trial = tuning_strategy.get("max_num_trial", 50)
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self._early_stop = tuning_strategy.get("early_stop", None)
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self._debug = tuning_strategy.get("debug", False)
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project_dir = tuning_strategy.get("project_dir", None)
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if not project_dir:
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project_dir = os.path.join(os.getcwd(), "OptimizationTuning")
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self._project_dir = project_dir
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for p in _tuning_supported_passes:
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if (
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getattr(self._dist_strategy, p)
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and _get_pass_config(self._dist_strategy, p).enable_tuning
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):
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# TODO distinguish different args of each passes
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self._tuning_passes_name.add(p)
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p_strategy = getattr(self._dist_strategy, p)
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self.__dict__[p] = p_strategy
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# # TODO verify the user defined configs
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# tuning_config_for_pass = tuning_strategy.get(p, None)
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# if tuning_config_for_pass:
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# for k, v in tuning_config_for_pass.items():
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# self.__dict__[p][k] = v
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# (NOTE)tuning config ONLY wraps dist strategy for pass config which is to be tuned
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def __getattr__(self, item):
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return getattr(self._dist_strategy, item)
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