chore: import upstream snapshot with attribution
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wehub-resource-sync
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# 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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from __future__ import annotations
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import copy
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import csv
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import os
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import pandas as pd
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class HistoryRecorder:
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# NOTE increase extenable ablitity
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def __init__(self, tuner_cfg) -> None:
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self.tuner_cfg = tuner_cfg
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self.search_algo = self.tuner_cfg['search_algo']['name']
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self.history = []
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self.store_path = None
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self.additional_metric_key = None
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def add_cfg(self, **kwargs):
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cur_configs = {}
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for key, val in kwargs.items():
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cur_configs[key] = val
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self.history.append(cur_configs)
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def sort_metric(self, direction, metric_name) -> None:
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if direction == 'Maximize':
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self.history.sort(
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key=lambda x: (
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x[metric_name]
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if x[metric_name] is not None
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else float('-inf')
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),
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reverse=True,
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)
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else:
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self.history.sort(
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key=lambda x: (
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x[metric_name]
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if x[metric_name] is not None
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else float('inf')
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),
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reverse=False,
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)
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def get_best(
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self, metric, direction, buffer=None, max_mem_usage=None
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) -> tuple[dict, bool]:
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self.sort_metric(direction=direction, metric_name=metric)
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if len(self.history) == 0:
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return (None, True)
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best_cfg = self.history[0]
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if isinstance(best_cfg["max_mem_usage"], str) or best_cfg["time"] == -1:
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return (best_cfg, True)
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if buffer is not None:
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if buffer < 0:
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raise ValueError("The buffer should be not less than 0.")
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assert max_mem_usage is not None, (
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"max_mem_usage cannot be None when buffer is greater than 0."
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)
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if max_mem_usage <= 0:
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raise ValueError("max_mem_usage should be greater than 0.")
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for cfg in self.history:
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if (
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not best_cfg["max_mem_usage"]
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and cfg["max_mem_usage"]
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and not isinstance(cfg["max_mem_usage"], str)
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and cfg["time"] != -1
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):
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best_cfg = cfg
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continue
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if (
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not isinstance(cfg["max_mem_usage"], str)
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and cfg["max_mem_usage"]
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and cfg["max_mem_usage"] < best_cfg["max_mem_usage"]
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and cfg["time"] != -1
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):
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best_cfg = cfg
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if (
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not isinstance(cfg["max_mem_usage"], str)
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and cfg["max_mem_usage"]
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and cfg["max_mem_usage"] < max_mem_usage - buffer
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and cfg["time"] != -1
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):
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break
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return (best_cfg, False)
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return (self.history[0], False)
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def _store_history_impl(self, data, path="./history.csv"):
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"""Store history to csv file."""
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# convert to pd dataframe
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df = pd.DataFrame(data)
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# move 'job_id' to the first column
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cols = df.columns.tolist()
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cols.insert(0, cols.pop(cols.index('job_id')))
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df = df.reindex(columns=cols)
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# check if 'time' exists
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if 'time' in df.columns:
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df = df.drop(columns=['time'])
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if 'has_error' in df.columns:
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df = df.drop(columns=['has_error'])
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# write to csv
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df.to_csv(path, index=False)
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def store_history(self, path="./history.csv"):
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# get enhanced report in dp-estimation mode
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if self.search_algo == "dp_estimation":
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metric_name = self.tuner_cfg['metric_cfg']['name']
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if self.additional_metric_key:
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_history = []
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for cfg in self.history:
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if (
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"sharding_overlap" not in cfg.keys()
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or cfg["sharding_overlap"] is None
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) and cfg["error_info"] is None:
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_history.append(copy.deepcopy(cfg))
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_history.sort(
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key=lambda x: (
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x[self.additional_metric_key]
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if x[self.additional_metric_key] is not None
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else float('-inf')
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),
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reverse=True,
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)
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self._store_history_impl(
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data=_history, path=path.split('.csv')[0] + '_enhanced.csv'
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)
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"""Store history to csv file."""
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self.store_path = path
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self._store_history_impl(data=self.history, path=path)
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def load_history(self, path="./history.csv") -> tuple[list, bool]:
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"""Load history from csv file."""
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err = False
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if self.store_path is None:
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self.store_path = path
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if not os.path.exists(self.store_path):
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err = True
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else:
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with open(self.store_path, "r") as f:
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reader = csv.reader(f)
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self.history = list(reader)
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return (self.history, err)
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def clean_history(self) -> None:
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"""Clean history."""
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self.history = []
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