wehub-resource-sync
158 lines
5.1 KiB
Python
158 lines
5.1 KiB
Python
# Copyright (c) 2023 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 logging
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import os
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from abc import ABC, abstractmethod
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from .prune import _PRUNE_HISTORY_FUNC
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from .utils import (
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gbs_search_all,
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load_configs_from_csv,
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search_all,
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search_by_dp_estimation,
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)
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logger = logging.getLogger('auto_tuner')
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class SearchAlgo(ABC):
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def __init__(self, tuner_cfg):
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self.tuner_cfg = tuner_cfg
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self.pruned_cfgs = []
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@abstractmethod
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def search_once(self, history_cfgs):
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pass
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def prune(self, tuner_cfg, cur_cfg, history_cfgs, pruned_cfgs):
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for func in _PRUNE_HISTORY_FUNC:
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result = func(tuner_cfg, cur_cfg, history_cfgs, pruned_cfgs)
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if result:
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return True
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return False
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class GridSearch(SearchAlgo):
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def __init__(self, tuner_cfg):
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super().__init__(tuner_cfg)
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self.idx = 0
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self.all_tasks = search_all(tuner_cfg)
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need_baseline = self.tuner_cfg.get("need_baseline", False)
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self.baseline = None
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if need_baseline:
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from .utils import memory_sort
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self.all_tasks.sort(key=memory_sort)
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self.previous_cfg = None
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def search_once(self, history_cfgs):
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new_cfg = None
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stop = False
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if history_cfgs:
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if history_cfgs[-1].get("time", -1) > 0:
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if self.baseline is None and self.tuner_cfg.get(
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"need_baseline", False
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):
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from .utils import performance_sort
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self.baseline = history_cfgs[-1]
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self.all_tasks[self.idx :] = sorted(
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self.all_tasks[self.idx : len(self.all_tasks)],
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key=performance_sort,
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)
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if self.tuner_cfg.get("schedule_prior", False):
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from .utils import sort_by_special
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self.all_tasks[self.idx :] = sort_by_special(
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self.all_tasks[self.idx :], self.tuner_cfg
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)
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while not stop:
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if self.idx < len(self.all_tasks):
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new_cfg = self.all_tasks[self.idx]
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self.idx += 1
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stop = not self.prune(
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self.tuner_cfg, new_cfg, history_cfgs, self.pruned_cfgs
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)
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self.pruned_cfgs.append(new_cfg)
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else:
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return None
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return new_cfg
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class DpEstimationSearch(SearchAlgo):
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def __init__(self, tuner_cfg):
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super().__init__(tuner_cfg)
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self.idx = 0
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if tuner_cfg["candidates"]["dp_degree"] != [1]:
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logger.warning(
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"dp_degree should be [1] in dp estimation search mode. Modify it to [1] automatically."
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)
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tuner_cfg["candidates"]["dp_degree"] = [1]
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self.all_tasks = search_by_dp_estimation(tuner_cfg)
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assert len(self.all_tasks) > 0, (
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"Unable to perform single dp estimation search."
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)
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def search_once(self, history_cfgs):
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new_cfg = None
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stop = False
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while not stop:
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if self.idx < len(self.all_tasks):
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new_cfg = self.all_tasks[self.idx]
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self.idx += 1
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stop = not self.prune(self.tuner_cfg, new_cfg, history_cfgs)
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else:
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return None
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return new_cfg
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class GBSSearch(SearchAlgo):
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def __init__(self, tuner_cfg):
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super().__init__(tuner_cfg)
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self.idx = 0
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self.all_tasks = gbs_search_all(tuner_cfg)
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def search_once(self, history_cfgs):
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new_cfg = None
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stop = False
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while not stop:
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if self.idx < len(self.all_tasks):
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new_cfg = self.all_tasks[self.idx]
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self.idx += 1
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glb = new_cfg.get("global_batch_size", None)
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self.tuner_cfg["model_cfg"]["global_batch_size"] = glb
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stop = not self.prune(self.tuner_cfg, new_cfg, history_cfgs)
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else:
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return None
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return new_cfg
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class CustomizeSearch(SearchAlgo):
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def __init__(self, tuner_cfg):
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super().__init__(tuner_cfg)
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self.idx = 0
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self.configs_csv = tuner_cfg.get("configs_csv", None)
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assert os.path.exists(self.configs_csv), (
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"configs_csv file is necessary in CustomizeSearch mode."
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)
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self.all_tasks = load_configs_from_csv(self.configs_csv)
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def search_once(self, history_cfgs):
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new_cfg = self.all_tasks[self.idx]
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self.idx += 1
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return new_cfg
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