wehub-resource-sync
170 lines
4.6 KiB
Python
170 lines
4.6 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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# Notice that the following codes are modified from KerasTuner to implement our own tuner.
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# Please refer to https://github.com/keras-team/keras-tuner/blob/master/keras_tuner/engine/trial.py.
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import hashlib
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import random
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import time
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from .recorder import MetricsRecorder
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from .storable import Storable
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from .tunable_space import TunableSpace
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class TrialStatus:
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RUNNING = "RUNNING"
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COMPLETED = "COMPLETED"
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STOPPED = "STOPPED"
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INVALID = "INVALID"
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class Trial(Storable):
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def __init__(
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self, tunable_space, trial_id=None, status=TrialStatus.RUNNING
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):
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self._id = _generate_trial_id() if trial_id is None else trial_id
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self._space = tunable_space
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self._recorder = MetricsRecorder()
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self._score = None
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self._best_step = None
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self._status = status
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@property
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def id(self):
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return self._id
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@property
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def space(self):
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return self._space
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@property
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def recorder(self):
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return self._recorder
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@property
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def score(self):
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return self._score
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@score.setter
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def score(self, score):
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self._score = score
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@property
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def best_step(self):
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return self._best_step
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@best_step.setter
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def best_step(self, best_step):
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self._best_step = best_step
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@property
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def status(self):
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return self._status
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@status.setter
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def status(self, status):
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self._status = status
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def summary(self):
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print("Tunable space:")
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if self.space.values:
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for tv, value in self.space.values.items():
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print(tv + ":", value)
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if self.score is not None:
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print(f"Score: {self.score}")
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def get_state(self):
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return {
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"id": self.id,
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"space": self.space.get_state(),
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"recorder": self.recorder.get_state(),
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"score": self.score,
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"best_step": self.best_step,
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"status": self.status,
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}
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def set_state(self, state):
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self._id = state["id"]
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self._space = TunableSpace.from_state(state["space"])
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self._recorder = MetricsRecorder.from_state(state["recorder"])
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self._score = state["score"]
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self._best_step = state["best_step"]
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self._status = state["status"]
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@classmethod
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def from_state(cls, state):
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trial = cls(tunable_space=None)
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trial.set_state(state)
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return trial
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class OptimizationTunerTrial(Trial):
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def __init__(
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self,
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config,
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name,
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changed_configs,
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trial_id=None,
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status=TrialStatus.RUNNING,
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):
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super().__init__(config, trial_id, status)
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self._name = name
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self._changed_configs = changed_configs
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@property
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def name(self):
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return self._name
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def summary(self):
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spacing = 2
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max_k = 38
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max_v = 38
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length = max_k + max_v + spacing
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h1_format = " " + f"|{{:^{length}s}}|\n"
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h2_format = " " + "|{{:>{}s}}{}{{:^{}s}}|\n".format(
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max_k, " " * spacing, max_v
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)
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border = " +" + "".join(["="] * length) + "+"
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line = " +" + "".join(["-"] * length) + "+"
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draws = border + "\n"
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draws += h1_format.format("")
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draws += h1_format.format("Tuned Configurations Overview")
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draws += h1_format.format("")
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for name in self._changed_configs:
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draws += border + "\n"
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draws += h1_format.format(f"{name} auto=True <-> {name}")
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draws += line + "\n"
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my_configs = getattr(self.space, name)
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keys = my_configs.to_dict().keys()
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for key in keys:
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draws += h2_format.format(
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key, str(my_configs.to_dict().get(key, None))
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)
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result_res = draws + border
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return result_res
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def _generate_trial_id():
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s = str(time.time()) + str(random.randint(1, int(1e7)))
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return hashlib.sha256(s.encode("utf-8")).hexdigest()[:32]
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