chore: import upstream snapshot with attribution

python/paddle/distributed/auto_parallel/static/tuner/trial.py

@@ -0,0 +1,169 @@
+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Notice that the following codes are modified from KerasTuner to implement our own tuner.
+# Please refer to https://github.com/keras-team/keras-tuner/blob/master/keras_tuner/engine/trial.py.
+
+import hashlib
+import random
+import time
+
+from .recorder import MetricsRecorder
+from .storable import Storable
+from .tunable_space import TunableSpace
+
+
+class TrialStatus:
+    RUNNING = "RUNNING"
+    COMPLETED = "COMPLETED"
+    STOPPED = "STOPPED"
+    INVALID = "INVALID"
+
+
+class Trial(Storable):
+    def __init__(
+        self, tunable_space, trial_id=None, status=TrialStatus.RUNNING
+    ):
+        self._id = _generate_trial_id() if trial_id is None else trial_id
+        self._space = tunable_space
+        self._recorder = MetricsRecorder()
+        self._score = None
+        self._best_step = None
+        self._status = status
+
+    @property
+    def id(self):
+        return self._id
+
+    @property
+    def space(self):
+        return self._space
+
+    @property
+    def recorder(self):
+        return self._recorder
+
+    @property
+    def score(self):
+        return self._score
+
+    @score.setter
+    def score(self, score):
+        self._score = score
+
+    @property
+    def best_step(self):
+        return self._best_step
+
+    @best_step.setter
+    def best_step(self, best_step):
+        self._best_step = best_step
+
+    @property
+    def status(self):
+        return self._status
+
+    @status.setter
+    def status(self, status):
+        self._status = status
+
+    def summary(self):
+        print("Tunable space:")
+        if self.space.values:
+            for tv, value in self.space.values.items():
+                print(tv + ":", value)
+
+        if self.score is not None:
+            print(f"Score: {self.score}")
+
+    def get_state(self):
+        return {
+            "id": self.id,
+            "space": self.space.get_state(),
+            "recorder": self.recorder.get_state(),
+            "score": self.score,
+            "best_step": self.best_step,
+            "status": self.status,
+        }
+
+    def set_state(self, state):
+        self._id = state["id"]
+        self._space = TunableSpace.from_state(state["space"])
+        self._recorder = MetricsRecorder.from_state(state["recorder"])
+        self._score = state["score"]
+        self._best_step = state["best_step"]
+        self._status = state["status"]
+
+    @classmethod
+    def from_state(cls, state):
+        trial = cls(tunable_space=None)
+        trial.set_state(state)
+        return trial
+
+
+class OptimizationTunerTrial(Trial):
+    def __init__(
+        self,
+        config,
+        name,
+        changed_configs,
+        trial_id=None,
+        status=TrialStatus.RUNNING,
+    ):
+        super().__init__(config, trial_id, status)
+        self._name = name
+        self._changed_configs = changed_configs
+
+    @property
+    def name(self):
+        return self._name
+
+    def summary(self):
+        spacing = 2
+        max_k = 38
+        max_v = 38
+
+        length = max_k + max_v + spacing
+
+        h1_format = "    " + f"|{{:^{length}s}}|\n"
+        h2_format = "    " + "|{{:>{}s}}{}{{:^{}s}}|\n".format(
+            max_k, " " * spacing, max_v
+        )
+
+        border = "    +" + "".join(["="] * length) + "+"
+        line = "    +" + "".join(["-"] * length) + "+"
+
+        draws = border + "\n"
+        draws += h1_format.format("")
+        draws += h1_format.format("Tuned Configurations Overview")
+        draws += h1_format.format("")
+
+        for name in self._changed_configs:
+            draws += border + "\n"
+            draws += h1_format.format(f"{name} auto=True <-> {name}")
+            draws += line + "\n"
+            my_configs = getattr(self.space, name)
+            keys = my_configs.to_dict().keys()
+            for key in keys:
+                draws += h2_format.format(
+                    key, str(my_configs.to_dict().get(key, None))
+                )
+
+        result_res = draws + border
+        return result_res
+
+
+def _generate_trial_id():
+    s = str(time.time()) + str(random.randint(1, int(1e7)))
+    return hashlib.sha256(s.encode("utf-8")).hexdigest()[:32]