import json import os import random import shutil import sys import tempfile import time import unittest from collections import Counter from unittest.mock import MagicMock import numpy as np import pytest import ray from ray import tune from ray.air.constants import TRAINING_ITERATION from ray.train._internal.checkpoint_manager import _CheckpointManager from ray.train._internal.session import _FutureTrainingResult, _TrainingResult from ray.train._internal.storage import StorageContext from ray.tune import Checkpoint, CheckpointConfig, PlacementGroupFactory, Trainable from ray.tune.experiment import Trial from ray.tune.experiment.trial import _TemporaryTrialState from ray.tune.schedulers import ( AsyncHyperBandScheduler, FIFOScheduler, HyperBandForBOHB, HyperBandScheduler, MedianStoppingRule, PopulationBasedTraining, TrialScheduler, ) from ray.tune.schedulers.pbt import PopulationBasedTrainingReplay, _explore from ray.tune.search import ConcurrencyLimiter from ray.tune.search._mock import _MockSearcher from ray.tune.trainable.metadata import _TrainingRunMetadata from ray.tune.utils.mock_trainable import MOCK_TRAINABLE_NAME, register_mock_trainable def result(t, rew): return dict(time_total_s=t, episode_reward_mean=rew, training_iteration=int(t)) def mock_tune_controller(trials=None): tune_controller = MagicMock() tune_controller.get_trials.return_value = trials or [] return tune_controller class EarlyStoppingSuite(unittest.TestCase): def setUp(self): ray.init(num_cpus=2) register_mock_trainable() def tearDown(self): ray.shutdown() def basicSetup(self, rule): t1 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 900, t_max=10 t2 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 450, t_max=5 runner = mock_tune_controller() for i in range(10): r1 = result(i, i * 100) print("basicSetup:", i) self.assertEqual( rule.on_trial_result(runner, t1, r1), TrialScheduler.CONTINUE ) for i in range(5): r2 = result(i, 450) self.assertEqual( rule.on_trial_result(runner, t2, r2), TrialScheduler.CONTINUE ) return t1, t2 def testMedianStoppingConstantPerf(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=1, ) t1, t2 = self.basicSetup(rule) runner = mock_tune_controller() rule.on_trial_complete(runner, t1, result(10, 1000)) self.assertEqual( rule.on_trial_result(runner, t2, result(5, 450)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t2, result(6, 0)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t2, result(10, 450)), TrialScheduler.STOP ) def testMedianStoppingOnCompleteOnly(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=1, ) t1, t2 = self.basicSetup(rule) runner = mock_tune_controller() self.assertEqual( rule.on_trial_result(runner, t2, result(100, 0)), TrialScheduler.CONTINUE ) rule.on_trial_complete(runner, t1, result(101, 1000)) self.assertEqual( rule.on_trial_result(runner, t2, result(101, 0)), TrialScheduler.STOP ) def testMedianStoppingGracePeriod(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=2.5, min_samples_required=1, ) t1, t2 = self.basicSetup(rule) runner = mock_tune_controller() rule.on_trial_complete(runner, t1, result(10, 1000)) rule.on_trial_complete(runner, t2, result(10, 1000)) t3 = Trial(MOCK_TRAINABLE_NAME) self.assertEqual( rule.on_trial_result(runner, t3, result(1, 10)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t3, result(2, 10)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t3, result(3, 10)), TrialScheduler.STOP ) def testMedianStoppingMinSamples(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=2, ) t1, t2 = self.basicSetup(rule) runner = mock_tune_controller() rule.on_trial_complete(runner, t1, result(10, 1000)) t3 = Trial(MOCK_TRAINABLE_NAME) # Insufficient samples to evaluate t3 self.assertEqual( rule.on_trial_result(runner, t3, result(5, 10)), TrialScheduler.CONTINUE ) rule.on_trial_complete(runner, t2, result(5, 1000)) # Sufficient samples to evaluate t3 self.assertEqual( rule.on_trial_result(runner, t3, result(5, 10)), TrialScheduler.STOP ) def testMedianStoppingUsesMedian(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=1, ) t1, t2 = self.basicSetup(rule) runner = mock_tune_controller() rule.on_trial_complete(runner, t1, result(10, 1000)) rule.on_trial_complete(runner, t2, result(10, 1000)) t3 = Trial(MOCK_TRAINABLE_NAME) self.assertEqual( rule.on_trial_result(runner, t3, result(1, 260)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t3, result(2, 260)), TrialScheduler.STOP ) def testMedianStoppingSoftStop(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=1, hard_stop=False, ) t1, t2 = self.basicSetup(rule) runner = mock_tune_controller() rule.on_trial_complete(runner, t1, result(10, 1000)) rule.on_trial_complete(runner, t2, result(10, 1000)) t3 = Trial(MOCK_TRAINABLE_NAME) self.assertEqual( rule.on_trial_result(runner, t3, result(1, 260)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t3, result(2, 260)), TrialScheduler.PAUSE ) def _test_metrics(self, result_func, metric, mode): rule = MedianStoppingRule( grace_period=0, min_samples_required=1, time_attr="training_iteration", metric=metric, mode=mode, ) t1 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 900, t_max=10 t2 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 450, t_max=5 runner = mock_tune_controller() for i in range(10): self.assertEqual( rule.on_trial_result(runner, t1, result_func(i, i * 100)), TrialScheduler.CONTINUE, ) for i in range(5): self.assertEqual( rule.on_trial_result(runner, t2, result_func(i, 450)), TrialScheduler.CONTINUE, ) rule.on_trial_complete(runner, t1, result_func(10, 1000)) self.assertEqual( rule.on_trial_result(runner, t2, result_func(5, 450)), TrialScheduler.CONTINUE, ) self.assertEqual( rule.on_trial_result(runner, t2, result_func(6, 0)), TrialScheduler.CONTINUE ) def testAlternateMetrics(self): def result2(t, rew): return dict(training_iteration=t, neg_mean_loss=rew) self._test_metrics(result2, "neg_mean_loss", "max") def testAlternateMetricsMin(self): def result2(t, rew): return dict(training_iteration=t, mean_loss=-rew) self._test_metrics(result2, "mean_loss", "min") class _FakeFutureResult(_FutureTrainingResult): def __init__(self, result): self.result = result def resolve(self, block: bool = True): return self.result class _MockTrialRunner: def __init__(self, scheduler): self._scheduler_alg = scheduler self.search_alg = None self.trials = [] def process_action(self, trial, action): if action == TrialScheduler.CONTINUE: pass elif action == TrialScheduler.PAUSE: self.pause_trial(trial) elif action == TrialScheduler.STOP: self.stop_trial(trial) def pause_trial(self, trial, should_checkpoint: bool = True): if should_checkpoint: self._schedule_trial_save(trial, None) trial.status = Trial.PAUSED def stop_trial(self, trial, error=False, error_msg=None): if trial.status in [Trial.ERROR, Trial.TERMINATED]: return elif trial.status in [Trial.PENDING, Trial.PAUSED]: self._scheduler_alg.on_trial_remove(self, trial) else: self._scheduler_alg.on_trial_complete(self, trial, result(100, 10)) trial.status = Trial.ERROR if error else Trial.TERMINATED def add_trial(self, trial): self.trials.append(trial) self._scheduler_alg.on_trial_add(self, trial) def get_trials(self): return self.trials def get_live_trials(self): return {t for t in self.trials if t.status != Trial.TERMINATED} def _launch_trial(self, trial): trial.status = Trial.RUNNING def _set_trial_status(self, trial, status): trial.status = status def start_trial(self, trial, checkpoint_obj=None, train=True): trial.logger_running = True if checkpoint_obj: trial.restored_checkpoint = checkpoint_obj.dir_or_data trial.status = Trial.RUNNING return True def _schedule_trial_restore(self, trial): pass def _schedule_trial_save(self, trial, result=None): return _FakeFutureResult( _TrainingResult( checkpoint=Checkpoint.from_directory(trial.trainable_name), metrics=result, ) ) class HyperbandSuite(unittest.TestCase): def setUp(self): ray.init(object_store_memory=int(1e8)) register_mock_trainable() def tearDown(self): ray.shutdown() def schedulerSetup(self, num_trials, max_t=81, **kwargs): """Setup a scheduler and Runner with max Iter = 9. Bracketing is placed as follows: (5, 81); (8, 27) -> (3, 54); (15, 9) -> (5, 27) -> (2, 45); (34, 3) -> (12, 9) -> (4, 27) -> (2, 42); (81, 1) -> (27, 3) -> (9, 9) -> (3, 27) -> (1, 41);""" sched = HyperBandScheduler( metric="episode_reward_mean", mode="max", max_t=max_t, **kwargs ) runner = _MockTrialRunner(sched) for i in range(num_trials): t = Trial(MOCK_TRAINABLE_NAME, trial_id=f"ft_{i:04d}", stub=True) runner.add_trial(t) return sched, runner def default_statistics(self): """Default statistics for HyperBand.""" sched = HyperBandScheduler() res = { str(s): {"n": sched._get_n0(s), "r": sched._get_r0(s)} # noqa for s in range(sched._s_max_1) } res["max_trials"] = sum(v["n"] for v in res.values()) res["brack_count"] = sched._s_max_1 res["s_max"] = sched._s_max_1 - 1 return res def downscale(self, n, sched): return int(np.ceil(n / sched._eta)) def basicSetup(self): """Setup and verify full band.""" stats = self.default_statistics() sched, _ = self.schedulerSetup(stats["max_trials"]) self.assertEqual(len(sched._hyperbands), 1) self.assertEqual(sched._cur_band_filled(), True) filled_band = sched._hyperbands[0] for bracket in filled_band: self.assertEqual(bracket.filled(), True) return sched def advancedSetup(self): sched = self.basicSetup() for i in range(4): t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, t) self.assertEqual(sched._cur_band_filled(), False) unfilled_band = sched._hyperbands[-1] self.assertEqual(len(unfilled_band), 2) bracket = unfilled_band[-1] self.assertEqual(bracket.filled(), False) self.assertEqual(len(bracket.current_trials()), 7) return sched def testConfigSameEta(self): sched = HyperBandScheduler(metric="episode_reward_mean", mode="max") i = 0 while not sched._cur_band_filled(): t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, t) i += 1 self.assertEqual(len(sched._hyperbands[0]), 5) self.assertEqual(sched._hyperbands[0][0]._n, 5) self.assertEqual(sched._hyperbands[0][0]._r, 81) self.assertEqual(sched._hyperbands[0][-1]._n, 81) self.assertEqual(sched._hyperbands[0][-1]._r, 1) reduction_factor = 10 sched = HyperBandScheduler( metric="episode_reward_mean", mode="max", max_t=1000, reduction_factor=reduction_factor, ) i = 0 while not sched._cur_band_filled(): t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, t) i += 1 self.assertEqual(len(sched._hyperbands[0]), 4) self.assertEqual(sched._hyperbands[0][0]._n, 4) self.assertEqual(sched._hyperbands[0][0]._r, 1000) self.assertEqual(sched._hyperbands[0][-1]._n, 1000) self.assertEqual(sched._hyperbands[0][-1]._r, 1) def testConfigSameEtaSmall(self): sched = HyperBandScheduler(metric="episode_reward_mean", mode="max", max_t=1) i = 0 while len(sched._hyperbands) < 2: t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, t) i += 1 self.assertEqual(len(sched._hyperbands[0]), 1) def testSuccessiveHalving(self): """Setup full band, then iterate through last bracket (n=81) to make sure successive halving is correct.""" stats = self.default_statistics() sched, mock_runner = self.schedulerSetup(stats["max_trials"]) big_bracket = sched._state["bracket"] cur_units = stats[str(stats["s_max"])]["r"] # The last bracket will downscale 4 times for x in range(stats["brack_count"] - 1): trials = big_bracket.current_trials() current_length = len(trials) for trl in trials: mock_runner._launch_trial(trl) # Provides results from 0 to 8 in order, keeping last one running for i, trl in enumerate(trials): action = sched.on_trial_result(mock_runner, trl, result(cur_units, i)) if i < current_length - 1: self.assertEqual(action, TrialScheduler.PAUSE) mock_runner.process_action(trl, action) self.assertEqual(action, TrialScheduler.CONTINUE) new_length = len(big_bracket.current_trials()) self.assertEqual(new_length, self.downscale(current_length, sched)) cur_units = int(cur_units * sched._eta) self.assertEqual(len(big_bracket.current_trials()), 1) def testHalvingStop(self): stats = self.default_statistics() num_trials = stats[str(0)]["n"] + stats[str(1)]["n"] sched, mock_runner = self.schedulerSetup(num_trials) big_bracket = sched._state["bracket"] for trl in big_bracket.current_trials(): mock_runner._launch_trial(trl) # # Provides result in reverse order, killing the last one cur_units = stats[str(1)]["r"] for i, trl in reversed(list(enumerate(big_bracket.current_trials()))): action = sched.on_trial_result(mock_runner, trl, result(cur_units, i)) mock_runner.process_action(trl, action) self.assertEqual(action, TrialScheduler.STOP) def testStopsLastOne(self): stats = self.default_statistics() num_trials = stats[str(0)]["n"] # setup one bracket sched, mock_runner = self.schedulerSetup(num_trials) big_bracket = sched._state["bracket"] for trl in big_bracket.current_trials(): mock_runner._launch_trial(trl) # # Provides result in reverse order, killing the last one cur_units = stats[str(0)]["r"] for i, trl in enumerate(big_bracket.current_trials()): action = sched.on_trial_result(mock_runner, trl, result(cur_units, i)) mock_runner.process_action(trl, action) self.assertEqual(action, TrialScheduler.STOP) def testTrialErrored(self): """If a trial errored, make sure successive halving still happens""" stats = self.default_statistics() trial_count = stats[str(0)]["n"] + 3 sched, mock_runner = self.schedulerSetup(trial_count) t1, t2, t3 = sched._state["bracket"].current_trials() for t in [t1, t2, t3]: mock_runner._launch_trial(t) sched.on_trial_error(mock_runner, t3) self.assertEqual( TrialScheduler.PAUSE, sched.on_trial_result(mock_runner, t1, result(stats[str(1)]["r"], 10)), ) self.assertEqual( TrialScheduler.CONTINUE, sched.on_trial_result(mock_runner, t2, result(stats[str(1)]["r"], 10)), ) def testTrialErrored2(self): """Check successive halving happened even when last trial failed""" stats = self.default_statistics() trial_count = stats[str(0)]["n"] + stats[str(1)]["n"] sched, mock_runner = self.schedulerSetup(trial_count) trials = sched._state["bracket"].current_trials() for t in trials[:-1]: mock_runner._launch_trial(t) sched.on_trial_result(mock_runner, t, result(stats[str(1)]["r"], 10)) mock_runner._launch_trial(trials[-1]) sched.on_trial_error(mock_runner, trials[-1]) self.assertEqual( len(sched._state["bracket"].current_trials()), self.downscale(stats[str(1)]["n"], sched), ) def testTrialEndedEarly(self): """Check successive halving happened even when one trial failed""" stats = self.default_statistics() trial_count = stats[str(0)]["n"] + 3 sched, mock_runner = self.schedulerSetup(trial_count) t1, t2, t3 = sched._state["bracket"].current_trials() for t in [t1, t2, t3]: mock_runner._launch_trial(t) sched.on_trial_complete(mock_runner, t3, result(1, 12)) self.assertEqual( TrialScheduler.PAUSE, sched.on_trial_result(mock_runner, t1, result(stats[str(1)]["r"], 10)), ) self.assertEqual( TrialScheduler.CONTINUE, sched.on_trial_result(mock_runner, t2, result(stats[str(1)]["r"], 10)), ) def testTrialEndedEarly2(self): """Check successive halving happened even when last trial failed""" stats = self.default_statistics() trial_count = stats[str(0)]["n"] + stats[str(1)]["n"] sched, mock_runner = self.schedulerSetup(trial_count) trials = sched._state["bracket"].current_trials() for t in trials[:-1]: mock_runner._launch_trial(t) sched.on_trial_result(mock_runner, t, result(stats[str(1)]["r"], 10)) mock_runner._launch_trial(trials[-1]) sched.on_trial_complete(mock_runner, trials[-1], result(100, 12)) self.assertEqual( len(sched._state["bracket"].current_trials()), self.downscale(stats[str(1)]["n"], sched), ) def testAddAfterHalving(self): stats = self.default_statistics() trial_count = stats[str(0)]["n"] + 1 sched, mock_runner = self.schedulerSetup(trial_count) bracket_trials = sched._state["bracket"].current_trials() init_units = stats[str(1)]["r"] for t in bracket_trials: mock_runner._launch_trial(t) for i, t in enumerate(bracket_trials): action = sched.on_trial_result(mock_runner, t, result(init_units, i)) self.assertEqual(action, TrialScheduler.CONTINUE) t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, t) mock_runner._launch_trial(t) self.assertEqual(len(sched._state["bracket"].current_trials()), 2) # Make sure that newly added trial gets fair computation (not just 1) self.assertEqual( TrialScheduler.CONTINUE, sched.on_trial_result(mock_runner, t, result(init_units, 12)), ) new_units = init_units + int(init_units * sched._eta) self.assertEqual( TrialScheduler.PAUSE, sched.on_trial_result(mock_runner, t, result(new_units, 12)), ) def _test_metrics(self, result_func, metric, mode): sched = HyperBandScheduler(time_attr="time_total_s", metric=metric, mode=mode) stats = self.default_statistics() for i in range(stats["max_trials"]): t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, t) runner = _MockTrialRunner(sched) big_bracket = sched._hyperbands[0][-1] for trl in big_bracket.current_trials(): runner._launch_trial(trl) current_length = len(big_bracket.current_trials()) # Provides results from 0 to 8 in order, keeping the last one running for i, trl in enumerate(big_bracket.current_trials()): action = sched.on_trial_result(runner, trl, result_func(1, i)) runner.process_action(trl, action) new_length = len(big_bracket.current_trials()) self.assertEqual(action, TrialScheduler.CONTINUE) self.assertEqual(new_length, self.downscale(current_length, sched)) def testAlternateMetrics(self): """Checking that alternate metrics will pass.""" def result2(t, rew): return dict(time_total_s=t, neg_mean_loss=rew) self._test_metrics(result2, "neg_mean_loss", "max") def testAlternateMetricsMin(self): """Checking that alternate metrics will pass.""" def result2(t, rew): return dict(time_total_s=t, mean_loss=-rew) self._test_metrics(result2, "mean_loss", "min") def testJumpingTime(self): sched, mock_runner = self.schedulerSetup(81) big_bracket = sched._hyperbands[0][-1] for trl in big_bracket.current_trials(): mock_runner._launch_trial(trl) # Provides results from 0 to 8 in order, keeping the last one running main_trials = big_bracket.current_trials()[:-1] jump = big_bracket.current_trials()[-1] for i, trl in enumerate(main_trials): action = sched.on_trial_result(mock_runner, trl, result(1, i)) mock_runner.process_action(trl, action) action = sched.on_trial_result(mock_runner, jump, result(4, i)) self.assertEqual(action, TrialScheduler.PAUSE) current_length = len(big_bracket.current_trials()) self.assertLess(current_length, 27) def testRemove(self): """Test with 4: start 1, remove 1 pending, add 2, remove 1 pending.""" sched, runner = self.schedulerSetup(4) trials = sorted(sched._trial_info, key=lambda t: t.trial_id) runner._launch_trial(trials[0]) sched.on_trial_result(runner, trials[0], result(1, 5)) self.assertEqual(trials[0].status, Trial.RUNNING) self.assertEqual(trials[1].status, Trial.PENDING) bracket, _ = sched._trial_info[trials[1]] self.assertTrue(trials[1] in bracket._live_trials) sched.on_trial_remove(runner, trials[1]) self.assertFalse(trials[1] in bracket._live_trials) for i in range(2): trial = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(None, trial) bracket, _ = sched._trial_info[trial] self.assertTrue(trial in bracket._live_trials) sched.on_trial_remove(runner, trial) # where trial is not running self.assertFalse(trial in bracket._live_trials) def testFilterNoneBracket(self): sched, runner = self.schedulerSetup(100, 20) # "sched" should contains None brackets non_brackets = [ b for hyperband in sched._hyperbands for b in hyperband if b is None ] self.assertTrue(non_brackets) # Make sure "choose_trial_to_run" still works trial = sched.choose_trial_to_run(runner) self.assertIsNotNone(trial) def testSmallMaxTStop(self, stop_last_trials=True): """Assert that trials are stopped after max_t is reached or continued if `stop_last_trials=False`.""" sched, runner = self.schedulerSetup( num_trials=8, max_t=8, reduction_factor=2, stop_last_trials=stop_last_trials ) trials = runner.get_trials() for trial in trials: runner.start_trial(trial) def _result(trial, timestep, reward): action = sched.on_trial_result( runner, trial, {"training_iteration": timestep, "episode_reward_mean": reward}, ) runner.process_action(trial, action) def _execute_delayed_actions(): for hb in sched._hyperbands: for b in hb: for t in b.trials_to_unpause: runner.start_trial(t) # Trials of the first bracket (s=0). # These don't halve. _result(trials[0], timestep=4, reward=10) _result(trials[1], timestep=4, reward=20) _result(trials[2], timestep=4, reward=30) _result(trials[3], timestep=4, reward=40) assert trials[0].status == Trial.RUNNING assert trials[1].status == Trial.RUNNING assert trials[2].status == Trial.RUNNING assert trials[3].status == Trial.RUNNING # Trials of the second bracket (s=1). # These halve after 4 timesteps. _result(trials[4], timestep=4, reward=10) _result(trials[5], timestep=4, reward=20) _result(trials[6], timestep=4, reward=30) _result(trials[7], timestep=4, reward=40) _execute_delayed_actions() assert trials[4].status == Trial.TERMINATED assert trials[5].status == Trial.TERMINATED assert trials[6].status == Trial.RUNNING assert trials[7].status == Trial.RUNNING # First bracket. The trials will be terminated if stop_last_trials=True # and continue otherwise. _result(trials[0], timestep=8, reward=10) _result(trials[1], timestep=8, reward=20) _result(trials[2], timestep=8, reward=30) _result(trials[3], timestep=8, reward=40) _execute_delayed_actions() if stop_last_trials: assert trials[0].status == Trial.TERMINATED assert trials[1].status == Trial.TERMINATED assert trials[2].status == Trial.TERMINATED assert trials[3].status == Trial.TERMINATED else: assert trials[0].status == Trial.RUNNING assert trials[1].status == Trial.RUNNING assert trials[2].status == Trial.RUNNING assert trials[3].status == Trial.RUNNING # Second bracket _result(trials[6], timestep=8, reward=30) _result(trials[7], timestep=8, reward=40) _execute_delayed_actions() if stop_last_trials: assert trials[6].status == Trial.TERMINATED assert trials[7].status == Trial.TERMINATED else: assert trials[6].status == Trial.RUNNING assert trials[7].status == Trial.RUNNING def testSmallMaxTContinue(self): self.testSmallMaxTStop(stop_last_trials=False) def testSmallMaxTOverstepStop(self, stop_last_trials=True): """Test that when trials report timesteps > max_t early, they are stopped correctly. """ sched, runner = self.schedulerSetup( num_trials=8, max_t=8, reduction_factor=2, stop_last_trials=stop_last_trials ) trials = runner.get_trials() for trial in trials: runner.start_trial(trial) def _result(trial, timestep, reward): action = sched.on_trial_result( runner, trial, {"training_iteration": timestep, "episode_reward_mean": reward}, ) runner.process_action(trial, action) def _execute_delayed_actions(): for hb in sched._hyperbands: for b in hb: for t in b.trials_to_unpause: runner.start_trial(t) # Trials of the first bracket (s=0). # These don't halve. _result(trials[0], timestep=4, reward=10) _result(trials[1], timestep=4, reward=20) _result(trials[2], timestep=4, reward=30) _result(trials[3], timestep=4, reward=40) assert trials[0].status == Trial.RUNNING assert trials[1].status == Trial.RUNNING assert trials[2].status == Trial.RUNNING assert trials[3].status == Trial.RUNNING # Trials of the second bracket (s=1). # These halve after 4 timesteps. # ATTN: Here we report timestep=8. This means after the first halving, the # bracket is actually finished, as the trials already progressed very far. # This can e.g. happen if a non-iteration timestep is manually reported _result(trials[4], timestep=8, reward=10) _result(trials[5], timestep=8, reward=20) _result(trials[6], timestep=8, reward=30) _result(trials[7], timestep=8, reward=40) _execute_delayed_actions() assert trials[4].status == Trial.TERMINATED assert trials[5].status == Trial.TERMINATED if stop_last_trials: assert trials[6].status == Trial.TERMINATED assert trials[7].status == Trial.TERMINATED else: assert trials[6].status == Trial.RUNNING assert trials[7].status == Trial.RUNNING def testSmallMaxTOverstepContinue(self, stop_last_trials=True): self.testSmallMaxTOverstepStop(stop_last_trials=False) class BOHBSuite(unittest.TestCase): def setUp(self): ray.init(object_store_memory=int(1e8)) register_mock_trainable() def tearDown(self): ray.shutdown() def testLargestBracketFirst(self): sched = HyperBandForBOHB( metric="episode_reward_mean", mode="max", max_t=3, reduction_factor=3 ) runner = _MockTrialRunner(sched) for i in range(3): t = Trial(MOCK_TRAINABLE_NAME) sched.on_trial_add(runner, t) runner._launch_trial(t) self.assertEqual(sched.state()["num_brackets"], 1) sched.on_trial_add(runner, Trial(MOCK_TRAINABLE_NAME)) self.assertEqual(sched.state()["num_brackets"], 2) def testCheckTrialInfoUpdate(self): def result(score, ts): return {"episode_reward_mean": score, TRAINING_ITERATION: ts} sched = HyperBandForBOHB( metric="episode_reward_mean", mode="max", max_t=3, reduction_factor=3 ) runner = _MockTrialRunner(sched) runner.search_alg = MagicMock() runner.search_alg.searcher = MagicMock() trials = [Trial(MOCK_TRAINABLE_NAME) for i in range(3)] for t in trials: runner.add_trial(t) runner._launch_trial(t) for trial, trial_result in zip(trials, [result(1, 1), result(2, 1)]): decision = sched.on_trial_result(runner, trial, trial_result) self.assertEqual(decision, TrialScheduler.PAUSE) runner.pause_trial(trial) spy_result = result(0, 1) decision = sched.on_trial_result(runner, trials[-1], spy_result) self.assertEqual(decision, TrialScheduler.STOP) sched.choose_trial_to_run(runner) self.assertEqual(runner.search_alg.searcher.on_pause.call_count, 2) self.assertEqual(runner.search_alg.searcher.on_unpause.call_count, 1) self.assertTrue("hyperband_info" in spy_result) self.assertEqual(spy_result["hyperband_info"]["budget"], 1) def testCheckTrialInfoUpdateMin(self): def result(score, ts): return {"episode_reward_mean": score, TRAINING_ITERATION: ts} sched = HyperBandForBOHB( metric="episode_reward_mean", mode="min", max_t=3, reduction_factor=3 ) runner = _MockTrialRunner(sched) runner.search_alg = MagicMock() runner.search_alg.searcher = MagicMock() trials = [Trial(MOCK_TRAINABLE_NAME) for i in range(3)] for t in trials: runner.add_trial(t) runner._launch_trial(t) for trial, trial_result in zip(trials, [result(1, 1), result(2, 1)]): decision = sched.on_trial_result(runner, trial, trial_result) self.assertEqual(decision, TrialScheduler.PAUSE) runner.pause_trial(trial) spy_result = result(0, 1) decision = sched.on_trial_result(runner, trials[-1], spy_result) self.assertEqual(decision, TrialScheduler.CONTINUE) sched.choose_trial_to_run(runner) self.assertEqual(runner.search_alg.searcher.on_pause.call_count, 2) self.assertTrue("hyperband_info" in spy_result) self.assertEqual(spy_result["hyperband_info"]["budget"], 1) def testPauseResumeChooseTrial(self): def result(score, ts): return {"episode_reward_mean": score, TRAINING_ITERATION: ts} sched = HyperBandForBOHB( metric="episode_reward_mean", mode="min", max_t=10, reduction_factor=3 ) runner = _MockTrialRunner(sched) runner.search_alg = MagicMock() runner.search_alg.searcher = MagicMock() trials = [Trial(MOCK_TRAINABLE_NAME) for i in range(3)] for t in trials: runner.add_trial(t) runner._launch_trial(t) all_results = [result(1, 5), result(2, 1), result(3, 5)] for trial, trial_result in zip(trials, all_results): decision = sched.on_trial_result(runner, trial, trial_result) self.assertEqual(decision, TrialScheduler.PAUSE) runner.pause_trial(trial) run_trial = sched.choose_trial_to_run(runner) self.assertEqual(run_trial, trials[1]) self.assertSequenceEqual( [t.status for t in trials], [Trial.PAUSED, Trial.PAUSED, Trial.PAUSED] ) @pytest.mark.skipif( sys.version_info >= (3, 12), reason="BOHB doesn't support py312" ) def testNonstopBOHB(self): from ray.tune.search.bohb import TuneBOHB def train_fn(cfg): start = 0 if tune.get_checkpoint(): with tune.get_checkpoint().as_directory() as checkpoint_dir: with open(os.path.join(checkpoint_dir, "checkpoint")) as f: start = int(f.read()) for i in range(start, 200): time.sleep(0.1) with tempfile.TemporaryDirectory() as checkpoint_dir: with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as f: f.write(str(i)) tune.report( dict(episode_reward_mean=i), checkpoint=Checkpoint.from_directory(checkpoint_dir), ) config = {"test_variable": tune.uniform(0, 20)} sched = HyperBandForBOHB(max_t=10, reduction_factor=3, stop_last_trials=False) alg = ConcurrencyLimiter(TuneBOHB(), 4) analysis = tune.run( train_fn, scheduler=sched, search_alg=alg, stop={"training_iteration": 32}, num_samples=20, config=config, metric="episode_reward_mean", mode="min", verbose=1, fail_fast="raise", ) counter = Counter( t.run_metadata.last_result.get("training_iteration") for t in analysis.trials ) assert 32 in counter assert counter[32] > 1 def testBOHBProcessing(self): trials = [Trial("foo", stub=True) for i in range(5)] bohb = HyperBandForBOHB(max_t=10, metric="metric", mode="max") for trial in trials: bohb.on_trial_add(None, trial) trial.status = Trial.RUNNING mock = MagicMock() bohb.on_trial_result(mock, trials[0], {"training_iteration": 10, "metric": 40}) trials[0].status = Trial.PAUSED bohb.on_trial_result(mock, trials[1], {"training_iteration": 10, "metric": 30}) trials[1].status = Trial.PAUSED bohb.on_trial_result(mock, trials[2], {"training_iteration": 10, "metric": 20}) trials[2].status = Trial.PAUSED bohb.on_trial_result(mock, trials[3], {"training_iteration": 10, "metric": 10}) trials[3].status = Trial.PAUSED bohb.on_trial_result(mock, trials[4], {"training_iteration": 10, "metric": 0}) trials[4].status = Trial.PAUSED def set_status(trial, status): trial.status = status return None def stop_trial(trial): # See TrialRunner.stop_trial() if trial.status in [Trial.PENDING, Trial.PAUSED]: bohb.on_trial_remove(mock, trial) trial.status = Trial.TERMINATED return None mock._set_trial_status.side_effect = set_status mock.stop_trial.side_effect = stop_trial assert not bohb._hyperbands[0][0].is_being_processed bohb.choose_trial_to_run(mock, allow_recurse=False) assert bohb._hyperbands[0][0].is_being_processed class _MockTrial(Trial): def __init__(self, i, config, storage): self.trainable_name = "trial_{}".format(i) self.trial_id = str(i) self.config = config self.experiment_tag = "{}tag".format(i) self.trial_name_creator = None self.logger_running = False self._restored_checkpoint = None self._restore_checkpoint_result = None self.placement_group_factory = PlacementGroupFactory([{"CPU": 1}]) self.custom_trial_name = None self.custom_dirname = None self._legacy_local_experiment_path = None self.relative_logdir = None self._default_result_or_future = None self.run_metadata = _TrainingRunMetadata() self.run_metadata.checkpoint_manager = _CheckpointManager( checkpoint_config=CheckpointConfig( num_to_keep=2, checkpoint_score_attribute="episode_reward_mean", ), ) self.temporary_state = _TemporaryTrialState() self.storage = storage @property def restored_checkpoint(self): if hasattr(self.run_metadata.checkpoint_manager, "_latest_checkpoint_result"): result = self.run_metadata.checkpoint_manager._latest_checkpoint_result return result.checkpoint.path return self._restored_checkpoint class PopulationBasedTestingSuite(unittest.TestCase): def setUp(self): ray.init(num_cpus=2) register_mock_trainable() def tearDown(self): ray.shutdown() # Helper function to call pbt.on_trial_result and assert decision, # or trial status upon existing. # Need to have the `trial` in `RUNNING` status first. def on_trial_result(self, pbt, runner, trial, result, expected_decision=None): trial.status = Trial.RUNNING decision = pbt.on_trial_result(runner, trial, result) if expected_decision is None: pass elif expected_decision == TrialScheduler.PAUSE: self.assertTrue( trial.status == Trial.PAUSED or decision == expected_decision ) elif expected_decision == TrialScheduler.CONTINUE: self.assertEqual(decision, expected_decision) return decision def basicSetup( self, num_trials=5, resample_prob=0.0, explore=None, perturbation_interval=10, log_config=False, require_attrs=True, hyperparams=None, hyperparam_mutations=None, step_once=True, synch=False, ): hyperparam_mutations = hyperparam_mutations or { "float_factor": lambda: 100.0, "int_factor": lambda: 10, "id_factor": [100], } pbt = PopulationBasedTraining( time_attr="training_iteration", metric="episode_reward_mean", mode="max", perturbation_interval=perturbation_interval, resample_probability=resample_prob, quantile_fraction=0.25, hyperparam_mutations=hyperparam_mutations, custom_explore_fn=explore, log_config=log_config, synch=synch, require_attrs=require_attrs, ) tmpdir = tempfile.mkdtemp() self.storage = StorageContext( storage_path=tmpdir, experiment_dir_name="test_trial_scheduler" ) runner = _MockTrialRunner(pbt) for i in range(num_trials): trial_hyperparams = hyperparams or { "float_factor": 2.0, "const_factor": 3, "int_factor": 10, "id_factor": i, } trial = _MockTrial(i, trial_hyperparams, self.storage) trial.init_local_path() runner.add_trial(trial) trial.status = Trial.RUNNING for i in range(num_trials): trial = runner.trials[i] if step_once: if synch: self.on_trial_result( pbt, runner, trial, result(10, 50 * i), expected_decision=TrialScheduler.PAUSE, ) else: self.on_trial_result( pbt, runner, trial, result(10, 50 * i), expected_decision=TrialScheduler.CONTINUE, ) pbt.reset_stats() return pbt, runner def testSearchError(self): pbt, runner = self.basicSetup(num_trials=0) def mock_train(config): return 1 with self.assertRaises(ValueError): tune.run( mock_train, config={"x": 1}, scheduler=pbt, search_alg=_MockSearcher() ) def testMetricError(self): pbt, runner = self.basicSetup() trials = runner.get_trials() # Should error if training_iteration not in result dict. with self.assertRaises(RuntimeError): self.on_trial_result( pbt, runner, trials[0], result={"episode_reward_mean": 4} ) # Should error if episode_reward_mean not in result dict. with self.assertRaises(RuntimeError): self.on_trial_result( pbt, runner, trials[0], result={"random_metric": 10, "training_iteration": 20}, ) def testMetricLog(self): pbt, runner = self.basicSetup(require_attrs=False) trials = runner.get_trials() # Should not error if training_iteration not in result dict with self.assertLogs("ray.tune.schedulers.pbt", level="WARN"): self.on_trial_result( pbt, runner, trials[0], result={"episode_reward_mean": 4} ) # Should not error if episode_reward_mean not in result dict. with self.assertLogs("ray.tune.schedulers.pbt", level="WARN"): self.on_trial_result( pbt, runner, trials[0], result={"random_metric": 10, "training_iteration": 20}, ) def testCheckpointsMostPromisingTrials(self): pbt, runner = self.basicSetup() trials = runner.get_trials() # no checkpoint: haven't hit next perturbation interval yet self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, 200]) self.on_trial_result( pbt, runner, trials[0], result(15, 200), TrialScheduler.CONTINUE ) self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, 200]) self.assertEqual(pbt._num_checkpoints, 0) # checkpoint: both past interval and upper quantile self.on_trial_result( pbt, runner, trials[0], result(20, 200), TrialScheduler.CONTINUE ) self.assertEqual(pbt.last_scores(trials), [200, 50, 100, 150, 200]) self.assertEqual(pbt._num_checkpoints, 1) self.on_trial_result( pbt, runner, trials[1], result(30, 201), TrialScheduler.CONTINUE ) self.assertEqual(pbt.last_scores(trials), [200, 201, 100, 150, 200]) self.assertEqual(pbt._num_checkpoints, 2) # not upper quantile any more self.on_trial_result( pbt, runner, trials[4], result(30, 199), TrialScheduler.CONTINUE ) self.assertEqual(pbt._num_checkpoints, 2) self.assertEqual(pbt._num_perturbations, 0) def testCheckpointMostPromisingTrialsSynch(self): pbt, runner = self.basicSetup(synch=True) trials = runner.get_trials() # no checkpoint: haven't hit next perturbation interval yet self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, 200]) self.on_trial_result( pbt, runner, trials[0], result(15, 200), TrialScheduler.CONTINUE ) self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, 200]) self.assertEqual(pbt._num_checkpoints, 0) # trials should be paused until all trials are synced. for i in range(len(trials) - 1): self.on_trial_result( pbt, runner, trials[i], result(20, 200 + i), TrialScheduler.PAUSE ) self.assertEqual(pbt.last_scores(trials), [200, 201, 202, 203, 200]) self.assertEqual(pbt._num_checkpoints, 0) self.on_trial_result( pbt, runner, trials[-1], result(20, 204), TrialScheduler.PAUSE ) self.assertEqual(pbt._num_checkpoints, 2) def testPerturbsLowPerformingTrials(self): pbt, runner = self.basicSetup() trials = runner.get_trials() # no perturbation: haven't hit next perturbation interval self.on_trial_result( pbt, runner, trials[0], result(15, -100), TrialScheduler.CONTINUE ) self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, 200]) self.assertTrue("@perturbed" not in trials[0].experiment_tag) self.assertEqual(pbt._num_perturbations, 0) # perturb since it's lower quantile self.on_trial_result( pbt, runner, trials[0], result(20, -100), TrialScheduler.PAUSE ) self.assertEqual(pbt.last_scores(trials), [-100, 50, 100, 150, 200]) self.assertTrue("@perturbed" in trials[0].experiment_tag) self.assertIn(trials[0].restored_checkpoint, ["trial_3", "trial_4"]) self.assertEqual(pbt._num_perturbations, 1) # also perturbed self.on_trial_result( pbt, runner, trials[2], result(20, 40), TrialScheduler.PAUSE ) self.assertEqual(pbt.last_scores(trials), [-100, 50, 40, 150, 200]) self.assertEqual(pbt._num_perturbations, 2) self.assertIn(trials[0].restored_checkpoint, ["trial_3", "trial_4"]) self.assertTrue("@perturbed" in trials[2].experiment_tag) def testPerturbsLowPerformingTrialsSynch(self): pbt, runner = self.basicSetup(synch=True) trials = runner.get_trials() # no perturbation: haven't hit next perturbation interval self.on_trial_result( pbt, runner, trials[-1], result(15, -100), TrialScheduler.CONTINUE ) self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, 200]) self.assertTrue("@perturbed" not in trials[-1].experiment_tag) self.assertEqual(pbt._num_perturbations, 0) # Don't perturb until all trials are synched. self.on_trial_result( pbt, runner, trials[-1], result(20, -100), TrialScheduler.PAUSE ) self.assertEqual(pbt.last_scores(trials), [0, 50, 100, 150, -100]) self.assertTrue("@perturbed" not in trials[-1].experiment_tag) # Synch all trials. for i in range(len(trials) - 1): self.on_trial_result( pbt, runner, trials[i], result(20, -10 * i), TrialScheduler.PAUSE ) self.assertEqual(pbt.last_scores(trials), [0, -10, -20, -30, -100]) self.assertIn(trials[-1].restored_checkpoint, ["trial_0", "trial_1"]) self.assertIn(trials[-2].restored_checkpoint, ["trial_0", "trial_1"]) self.assertEqual(pbt._num_perturbations, 2) def testPerturbWithoutResample(self): pbt, runner = self.basicSetup(resample_prob=0.0) trials = runner.get_trials() self.on_trial_result( pbt, runner, trials[0], result(20, -100), TrialScheduler.PAUSE ) self.assertIn(trials[0].restored_checkpoint, ["trial_3", "trial_4"]) self.assertIn(trials[0].config["id_factor"], [100]) self.assertIn(trials[0].config["float_factor"], [2.4, 1.6]) self.assertEqual(type(trials[0].config["float_factor"]), float) self.assertIn(trials[0].config["int_factor"], [8, 12]) self.assertEqual(type(trials[0].config["int_factor"]), int) self.assertEqual(trials[0].config["const_factor"], 3) def testPerturbWithResample(self): pbt, runner = self.basicSetup(resample_prob=1.0) trials = runner.get_trials() self.on_trial_result( pbt, runner, trials[0], result(20, -100), TrialScheduler.PAUSE ) self.assertEqual(trials[0].status, Trial.PAUSED) self.assertIn(trials[0].restored_checkpoint, ["trial_3", "trial_4"]) self.assertEqual(trials[0].config["id_factor"], 100) self.assertEqual(trials[0].config["float_factor"], 100.0) self.assertEqual(type(trials[0].config["float_factor"]), float) self.assertEqual(trials[0].config["int_factor"], 10) self.assertEqual(type(trials[0].config["int_factor"]), int) self.assertEqual(trials[0].config["const_factor"], 3) def testTuneSamplePrimitives(self): pbt, runner = self.basicSetup( resample_prob=1.0, hyperparam_mutations={ "float_factor": lambda: 100.0, "int_factor": lambda: 10, "id_factor": tune.choice([100]), }, ) trials = runner.get_trials() self.on_trial_result( pbt, runner, trials[0], result(20, -100), TrialScheduler.PAUSE ) self.assertIn(trials[0].restored_checkpoint, ["trial_3", "trial_4"]) self.assertEqual(trials[0].config["id_factor"], 100) self.assertEqual(trials[0].config["float_factor"], 100.0) self.assertEqual(type(trials[0].config["float_factor"]), float) self.assertEqual(trials[0].config["int_factor"], 10) self.assertEqual(type(trials[0].config["int_factor"]), int) self.assertEqual(trials[0].config["const_factor"], 3) def testTuneSampleFromError(self): with self.assertRaises(ValueError): pbt, runner = self.basicSetup( hyperparam_mutations={"float_factor": tune.sample_from(lambda: 100.0)} ) def testPerturbationValues(self): def assertProduces(fn, values): random.seed(0) seen = set() for _ in range(100): seen.add(fn()["v"]) self.assertEqual(seen, values) def explore_fn(config, mutations, resample_probability, custom_explore_fn=None): if custom_explore_fn is None: custom_explore_fn = lambda x: x # noqa: E731 new_config, _ = _explore( config, mutations, resample_probability, perturbation_factors=(1.2, 0.8), custom_explore_fn=custom_explore_fn, ) return new_config # Categorical case assertProduces(lambda: explore_fn({"v": 4}, {"v": [3, 4, 8, 10]}, 0.0), {3, 8}) assertProduces(lambda: explore_fn({"v": 3}, {"v": [3, 4, 8, 10]}, 0.0), {3, 4}) assertProduces( lambda: explore_fn({"v": 10}, {"v": [3, 4, 8, 10]}, 0.0), {8, 10} ) assertProduces( lambda: explore_fn({"v": 7}, {"v": [3, 4, 8, 10]}, 0.0), {3, 4, 8, 10}, ) assertProduces( lambda: explore_fn({"v": 4}, {"v": [3, 4, 8, 10]}, 1.0), {3, 4, 8, 10}, ) # Check that tuple also works assertProduces(lambda: explore_fn({"v": 4}, {"v": (3, 4, 8, 10)}, 0.0), {3, 8}) assertProduces(lambda: explore_fn({"v": 3}, {"v": (3, 4, 8, 10)}, 0.0), {3, 4}) # Passing in an invalid types should raise an error with self.assertRaises(ValueError): explore_fn({"v": 4}, {"v": {3, 4, 8, 10}}, 0.0) with self.assertRaises(ValueError): explore_fn({"v": 4}, {"v": "invalid"}, 0.0) # Continuous case assertProduces( lambda: explore_fn( {"v": 100}, {"v": lambda: random.choice([10, 100])}, 0.0 ), {80, 120}, ) assertProduces( lambda: explore_fn( {"v": 100.0}, {"v": lambda: random.choice([10, 100])}, 0.0 ), {80.0, 120.0}, ) assertProduces( lambda: explore_fn( {"v": 100.0}, {"v": lambda: random.choice([10, 100])}, 1.0 ), {10.0, 100.0}, ) def deep_add(seen, new_values): for k, new_value in new_values.items(): if isinstance(new_value, dict): if k not in seen: seen[k] = {} seen[k].update(deep_add(seen[k], new_value)) else: if k not in seen: seen[k] = set() seen[k].add(new_value) return seen def assertNestedProduces(fn, values): random.seed(0) seen = {} for _ in range(100): new_config = fn() seen = deep_add(seen, new_config) self.assertEqual(seen, values) # Nested mutation and spec assertNestedProduces( lambda: explore_fn( { "a": {"b": 4}, "1": {"2": {"3": 100}}, }, { "a": {"b": [3, 4, 8, 10]}, "1": {"2": {"3": lambda: random.choice([10, 100])}}, }, 0.0, ), { "a": {"b": {3, 8}}, "1": {"2": {"3": {80, 120}}}, }, ) custom_explore_fn = MagicMock(side_effect=lambda x: x) # Nested mutation and spec assertNestedProduces( lambda: explore_fn( { "a": {"b": 4}, "1": {"2": {"3": 100}}, }, { "a": {"b": [3, 4, 8, 10]}, "1": {"2": {"3": lambda: random.choice([10, 100])}}, }, 0.0, custom_explore_fn=custom_explore_fn, ), { "a": {"b": {3, 8}}, "1": {"2": {"3": {80, 120}}}, }, ) # Expect call count to be 100 because we call explore 100 times self.assertEqual(custom_explore_fn.call_count, 100) def testDictPerturbation(self): pbt, runner = self.basicSetup( resample_prob=1.0, hyperparams={ "float_factor": 2.0, "nest": {"nest_float": 3.0}, "int_factor": 10, "const_factor": 3, }, hyperparam_mutations={ "float_factor": lambda: 100.0, "nest": {"nest_float": lambda: 101.0}, "int_factor": lambda: 10, }, ) trials = runner.get_trials() self.on_trial_result( pbt, runner, trials[0], result(20, -100), TrialScheduler.PAUSE ) self.assertIn(trials[0].restored_checkpoint, ["trial_3", "trial_4"]) self.assertEqual(trials[0].config["float_factor"], 100.0) self.assertIsInstance(trials[0].config["float_factor"], float) self.assertEqual(trials[0].config["int_factor"], 10) self.assertIsInstance(trials[0].config["int_factor"], int) self.assertEqual(trials[0].config["const_factor"], 3) self.assertEqual(trials[0].config["nest"]["nest_float"], 101.0) self.assertIsInstance(trials[0].config["nest"]["nest_float"], float) def testYieldsTimeToOtherTrials(self): pbt, runner = self.basicSetup() trials = runner.get_trials() trials[0].status = Trial.PENDING # simulate not enough resources self.on_trial_result( pbt, runner, trials[1], result(20, 1000), TrialScheduler.PAUSE ) self.assertEqual(pbt.last_scores(trials), [0, 1000, 100, 150, 200]) self.assertEqual(pbt.choose_trial_to_run(runner), trials[0]) def testSchedulesMostBehindTrialToRun(self): pbt, runner = self.basicSetup() trials = runner.get_trials() self.on_trial_result(pbt, runner, trials[0], result(800, 1000)) self.on_trial_result(pbt, runner, trials[1], result(700, 1001)) self.on_trial_result(pbt, runner, trials[2], result(600, 1002)) self.on_trial_result(pbt, runner, trials[3], result(500, 1003)) self.on_trial_result(pbt, runner, trials[4], result(700, 1004)) self.assertEqual(pbt.choose_trial_to_run(runner), None) for i in range(5): trials[i].status = Trial.PENDING self.assertEqual(pbt.choose_trial_to_run(runner), trials[3]) def testSchedulesMostBehindTrialToRunSynch(self): pbt, runner = self.basicSetup(synch=True) trials = runner.get_trials() runner.process_action( trials[0], self.on_trial_result(pbt, runner, trials[0], result(800, 1000)) ) runner.process_action( trials[1], self.on_trial_result(pbt, runner, trials[1], result(700, 1001)) ) runner.process_action( trials[2], self.on_trial_result(pbt, runner, trials[2], result(600, 1002)) ) runner.process_action( trials[3], self.on_trial_result(pbt, runner, trials[3], result(500, 1003)) ) runner.process_action( trials[4], self.on_trial_result(pbt, runner, trials[4], result(700, 1004)) ) self.assertIn( pbt.choose_trial_to_run(runner), [trials[0], trials[1], trials[3]] ) def testPerturbationResetsLastPerturbTime(self): pbt, runner = self.basicSetup() trials = runner.get_trials() self.on_trial_result(pbt, runner, trials[0], result(10000, 1005)) self.on_trial_result(pbt, runner, trials[1], result(10000, 1004)) self.on_trial_result(pbt, runner, trials[2], result(600, 1003)) self.assertEqual(pbt._num_perturbations, 0) self.on_trial_result(pbt, runner, trials[3], result(500, 1002)) self.assertEqual(pbt._num_perturbations, 1) self.on_trial_result(pbt, runner, trials[3], result(600, 100)) self.assertEqual(pbt._num_perturbations, 1) self.on_trial_result(pbt, runner, trials[3], result(11000, 100)) self.assertEqual(pbt._num_perturbations, 2) def testLogConfig(self): def check_policy(policy): self.assertIsInstance(policy[2], int) self.assertIsInstance(policy[3], int) self.assertIn(policy[0], ["0tag", "2tag", "3tag", "4tag"]) self.assertIn(policy[1], ["0tag", "2tag", "3tag", "4tag"]) self.assertIn(policy[2], [0, 2, 3, 4]) self.assertIn(policy[3], [0, 2, 3, 4]) for i in [4, 5]: self.assertIsInstance(policy[i], dict) for key in ["const_factor", "int_factor", "float_factor", "id_factor"]: self.assertIn(key, policy[i]) self.assertIsInstance(policy[i]["float_factor"], float) self.assertIsInstance(policy[i]["int_factor"], int) self.assertIn(policy[i]["const_factor"], [3]) self.assertIn(policy[i]["int_factor"], [8, 10, 12]) self.assertIn(policy[i]["float_factor"], [2.4, 2, 1.6]) self.assertIn(policy[i]["id_factor"], [3, 4, 100]) pbt, runner = self.basicSetup(log_config=True) trials = runner.get_trials() for i, trial in enumerate(trials): trial.run_metadata.last_result = {TRAINING_ITERATION: i} self.on_trial_result(pbt, runner, trials[0], result(15, -100)) self.on_trial_result(pbt, runner, trials[0], result(20, -100)) self.on_trial_result(pbt, runner, trials[2], result(20, 40)) log_files = ["pbt_global.txt", "pbt_policy_0.txt", "pbt_policy_2.txt"] for log_file in log_files: self.assertTrue( os.path.exists( os.path.join(self.storage.experiment_driver_staging_path, log_file) ) ) raw_policy = open( os.path.join(self.storage.experiment_driver_staging_path, log_file), "r" ).readlines() for line in raw_policy: check_policy(json.loads(line)) def testLogConfigSynch(self): def check_policy(policy): self.assertIsInstance(policy[2], int) self.assertIsInstance(policy[3], int) self.assertIn(policy[0], ["0tag", "1tag"]) self.assertIn(policy[1], ["3tag", "4tag"]) self.assertIn(policy[2], [0, 1]) self.assertIn(policy[3], [3, 4]) for i in [4, 5]: self.assertIsInstance(policy[i], dict) for key in ["const_factor", "int_factor", "float_factor", "id_factor"]: self.assertIn(key, policy[i]) self.assertIsInstance(policy[i]["float_factor"], float) self.assertIsInstance(policy[i]["int_factor"], int) self.assertIn(policy[i]["const_factor"], [3]) self.assertIn(policy[i]["int_factor"], [8, 10, 12]) self.assertIn(policy[i]["float_factor"], [2.4, 2, 1.6]) self.assertIn(policy[i]["id_factor"], [3, 4, 100]) pbt, runner = self.basicSetup(log_config=True, synch=True, step_once=False) trials = runner.get_trials() for i, trial in enumerate(trials): trial.run_metadata.last_result = {TRAINING_ITERATION: i} self.on_trial_result(pbt, runner, trials[i], result(10, i)) log_files = ["pbt_global.txt", "pbt_policy_0.txt", "pbt_policy_1.txt"] for log_file in log_files: self.assertTrue( os.path.exists( os.path.join(self.storage.experiment_driver_staging_path, log_file) ) ) raw_policy = open( os.path.join(self.storage.experiment_driver_staging_path, log_file), "r" ).readlines() for line in raw_policy: check_policy(json.loads(line)) def testReplay(self): # Returns unique increasing parameter mutations class _Counter: def __init__(self, start=0): self.count = start - 1 def __call__(self, *args, **kwargs): self.count += 1 return self.count pbt, runner = self.basicSetup( num_trials=4, perturbation_interval=5, log_config=True, step_once=False, synch=False, hyperparam_mutations={ "float_factor": lambda: 100.0, "int_factor": _Counter(1000), }, ) trials = runner.get_trials() # Internal trial state to collect the real PBT history class _TrialState: def __init__(self, config): self.step = 0 self.config = config self.history = [] def forward(self, t): while self.step < t: self.history.append(self.config) self.step += 1 trial_state = [] for i, trial in enumerate(trials): trial.run_metadata.last_result = {TRAINING_ITERATION: 0} trial_state.append(_TrialState(trial.config)) # Helper function to simulate stepping trial k a number of steps, # and reporting a score at the end def trial_step(k, steps, score): res = result(trial_state[k].step + steps, score) trials[k].run_metadata.last_result = res trial_state[k].forward(res[TRAINING_ITERATION]) old_config = trials[k].config self.on_trial_result(pbt, runner, trials[k], res) new_config = trials[k].config trial_state[k].config = new_config.copy() if old_config != new_config: # Copy history from source trial source = -1 for m, cand in enumerate(trials): if cand.trainable_name == trials[k].restored_checkpoint: source = m break assert source >= 0 trial_state[k].history = trial_state[source].history.copy() trial_state[k].step = trial_state[source].step # Initial steps trial_step(0, 10, 0) trial_step(1, 11, 10) trial_step(2, 12, 0) trial_step(3, 13, 0) # Next block trial_step(0, 10, -10) # 0 <-- 1, new_t=11 trial_step(2, 8, -20) # 2 <-- 1, new_t=11 trial_step(3, 9, 0) trial_step(1, 7, 0) # Next block trial_step(1, 12, 0) trial_step(2, 13, 0) trial_step(3, 14, 10) trial_step(0, 11, 0) # 0 <-- 3, new_t=13+9+14=36 # Next block trial_step(0, 6, 20) trial_step(3, 9, -40) # 3 <-- 0, new_t=42 trial_step(2, 8, -50) # 2 <-- 0, new_t=42 trial_step(1, 7, 30) trial_step(2, 8, -60) # 2 <-- 1, new_t=37 # Next block trial_step(0, 10, 0) trial_step(1, 10, 0) trial_step(2, 10, 0) trial_step(3, 10, 0) # Playback trainable to collect configs at each step class Playback(Trainable): def setup(self, config): self.config = config self.replayed = [] self.iter = 0 def step(self): self.iter += 1 self.replayed.append(self.config) return { "reward": 0, "done": False, "replayed": self.replayed, TRAINING_ITERATION: self.iter, } def save_checkpoint(self, checkpoint_dir): path = os.path.join(checkpoint_dir, "checkpoint") with open(path, "w") as f: f.write(json.dumps({"iter": self.iter, "replayed": self.replayed})) def load_checkpoint(self, checkpoint_dir): path = os.path.join(checkpoint_dir, "checkpoint") with open(path, "r") as f: checkpoint_json = json.loads(f.read()) self.iter = checkpoint_json["iter"] self.replayed = checkpoint_json["replayed"] # Loop through all trials and check if PBT history is the # same as the playback history for i, trial in enumerate(trials): if trial.trial_id == "1": # Did not exploit anything continue replay = PopulationBasedTrainingReplay( os.path.join( self.storage.experiment_driver_staging_path, "pbt_policy_{}.txt".format(trial.trial_id), ) ) analysis = tune.run( Playback, scheduler=replay, stop={TRAINING_ITERATION: trial_state[i].step}, ) replayed = analysis.trials[0].run_metadata.last_result["replayed"] self.assertSequenceEqual(trial_state[i].history, replayed) # Trial 1 did not exploit anything and should raise an error with self.assertRaises(ValueError): replay = PopulationBasedTrainingReplay( os.path.join( self.storage.experiment_driver_staging_path, "pbt_policy_{}.txt".format(trials[1].trial_id), ) ) tune.run( Playback, scheduler=replay, stop={TRAINING_ITERATION: trial_state[1].step}, ) @unittest.skip("Pausing is now a multi-step action. This test needs refactoring.") def testReplaySynch(self): # Returns unique increasing parameter mutations class _Counter: def __init__(self, start=0): self.count = start - 1 def __call__(self, *args, **kwargs): self.count += 1 return self.count pbt, runner = self.basicSetup( num_trials=4, perturbation_interval=5, log_config=True, step_once=False, synch=True, hyperparam_mutations={ "float_factor": lambda: 100.0, "int_factor": _Counter(1000), }, ) trials = runner.get_trials() tmpdir = tempfile.mkdtemp() # Internal trial state to collect the real PBT history class _TrialState: def __init__(self, config): self.step = 0 self.config = config self.history = [] def forward(self, t): while self.step < t: self.history.append(self.config) self.step += 1 trial_state = [] for i, trial in enumerate(trials): trial.run_metadata.last_result = {TRAINING_ITERATION: 0} trial_state.append(_TrialState(trial.config)) # Helper function to simulate stepping trial k a number of steps, # and reporting a score at the end def trial_step(k, steps, score, synced=False): res = result(trial_state[k].step + steps, score) trials[k].run_metadata.last_result = res trial_state[k].forward(res[TRAINING_ITERATION]) if not synced: action = self.on_trial_result(pbt, runner, trials[k], res) runner.process_action(trials[k], action) return else: # Reached synchronization point old_configs = [trial.config for trial in trials] action = self.on_trial_result(pbt, runner, trials[k], res) runner.process_action(trials[k], action) new_configs = [trial.config for trial in trials] for i in range(len(trials)): old_config = old_configs[i] new_config = new_configs[i] if old_config != new_config: # Copy history from source trial source = -1 for m, cand in enumerate(trials): if cand.trainable_name == trials[i].restored_checkpoint: source = m break assert source >= 0 trial_state[i].history = trial_state[source].history.copy() trial_state[i].step = trial_state[source].step trial_state[i].config = new_config.copy() # Initial steps trial_step(0, 10, 0) trial_step(1, 11, 10) trial_step(2, 12, 0) trial_step(3, 13, -1, synced=True) # 3 <-- 1, new_t 11 # next_perturb_sync = 13 # Next block trial_step(0, 17, -10) # 20 trial_step(2, 15, -20) # 20 trial_step(3, 16, 0) # 20 trial_step(1, 7, 1, synced=True) # 18 # 2 <-- 1, new_t=11+7=18 # next_perturb_sync = 20 # Next block trial_step(2, 13, 0) # 31 trial_step(3, 14, 10) # 34 trial_step(0, 11, -1) # 31 trial_step(1, 12, 0, synced=True) # 30 # 0 <-- 3, new_t=11+9+14=34 # next_perturb_sync = 34 # Next block trial_step(0, 6, 20) # 40 trial_step(3, 9, -40) # 43 trial_step(2, 8, -50) # 39 trial_step(1, 7, 30, synced=True) # 37 # 2 <-- 1, new_t=18+13+8=37 # next_perturb_sync = 43 # Playback trainable to collect configs at each step class Playback(Trainable): def setup(self, config): self.config = config self.replayed = [] self.iter = 0 def step(self): self.iter += 1 self.replayed.append(self.config) return { "reward": 0, "done": False, "replayed": self.replayed, TRAINING_ITERATION: self.iter, } def save_checkpoint(self, checkpoint_dir): path = os.path.join(checkpoint_dir, "checkpoint") with open(path, "w") as f: f.write(json.dumps({"iter": self.iter, "replayed": self.replayed})) def load_checkpoint(self, checkpoint_dir): path = os.path.join(checkpoint_dir, "checkpoint") with open(path, "r") as f: checkpoint_json = json.loads(f.read()) self.iter = checkpoint_json["iter"] self.replayed = checkpoint_json["replayed"] # Loop through all trials and check if PBT history is the # same as the playback history for i, trial in enumerate(trials): if trial.trial_id in ["1"]: # Did not exploit anything continue replay = PopulationBasedTrainingReplay( os.path.join(tmpdir, "pbt_policy_{}.txt".format(trial.trial_id)) ) analysis = tune.run( Playback, scheduler=replay, stop={TRAINING_ITERATION: trial_state[i].step}, ) replayed = analysis.trials[0].run_metadata.last_result["replayed"] self.assertSequenceEqual(trial_state[i].history, replayed) # Trial 1 did not exploit anything and should raise an error with self.assertRaises(ValueError): replay = PopulationBasedTrainingReplay( os.path.join(tmpdir, "pbt_policy_{}.txt".format(trials[1].trial_id)) ) tune.run( Playback, scheduler=replay, stop={TRAINING_ITERATION: trial_state[1].step}, ) shutil.rmtree(tmpdir) def testPostprocessingHook(self): def explore(new_config): new_config["id_factor"] = 42 new_config["float_factor"] = 43 return new_config pbt, runner = self.basicSetup(resample_prob=0.0, explore=explore) trials = runner.get_trials() self.on_trial_result( pbt, runner, trials[0], result(20, -100), TrialScheduler.PAUSE ) self.assertEqual(trials[0].config["id_factor"], 42) self.assertEqual(trials[0].config["float_factor"], 43) def testFastPerturb(self): pbt, runner = self.basicSetup( perturbation_interval=1, step_once=False, log_config=True ) trials = runner.get_trials() tmpdir = tempfile.mkdtemp() for i, trial in enumerate(trials): trial.run_metadata.last_result = {} self.on_trial_result( pbt, runner, trials[1], result(1, 10), TrialScheduler.CONTINUE ) self.on_trial_result( pbt, runner, trials[2], result(1, 200), TrialScheduler.CONTINUE ) self.assertEqual(pbt._num_checkpoints, 1) pbt._exploit(runner, trials[1], trials[2]) shutil.rmtree(tmpdir) @pytest.mark.skip( reason=( "This test is generally flaky: The print after writing `Cleanup` " "to the file is printed, but the data is not always written. " "For some reason, this only persistently (though flaky) comes up " "in the new execution backend - presumably because less time " "passes between actor re-use. Skipping test for now." ), ) def testContextExit(self): vals = [5, 1] class MockContext: def __init__(self, config): self.config = config self.active = False def __enter__(self): print("Set up resource.", self.config) with open("status.txt", "wt") as fp: fp.write(f"Activate {self.config['x']}\n") print("Cleaned up.", self.config) self.active = True return self def __exit__(self, type, value, traceback): print("Clean up resource.", self.config) with open("status.txt", "at") as fp: fp.write(f"Cleanup {self.config['x']}\n") print("Cleaned up.", self.config) self.active = False def train_fn(config): with MockContext(config): for i in range(10): tune.report(metric=i + config["x"]) class MockScheduler(FIFOScheduler): def on_trial_result(self, tune_controller, trial, result): return TrialScheduler.STOP scheduler = MockScheduler() out = tune.run( train_fn, config={"x": tune.grid_search(vals)}, scheduler=scheduler ) ever_active = set() active = set() for trial in out.trials: with open(os.path.join(trial.local_path, "status.txt"), "rt") as fp: status = fp.read() print(f"Status for trial {trial}: {status}") if "Activate" in status: ever_active.add(trial) active.add(trial) if "Cleanup" in status: active.remove(trial) print(f"Ever active: {ever_active}") print(f"Still active: {active}") self.assertEqual(len(ever_active), len(vals)) self.assertEqual(len(active), 0) class E2EPopulationBasedTestingSuite(unittest.TestCase): def setUp(self): ray.init(num_cpus=4) register_mock_trainable() def tearDown(self): ray.shutdown() def basicSetup( self, resample_prob=0.0, explore=None, perturbation_interval=10, log_config=False, hyperparams=None, hyperparam_mutations=None, step_once=True, ): hyperparam_mutations = hyperparam_mutations or { "float_factor": lambda: 100.0, "int_factor": lambda: 10, "id_factor": [100], } pbt = PopulationBasedTraining( metric="mean_accuracy", mode="max", time_attr="training_iteration", perturbation_interval=perturbation_interval, resample_probability=resample_prob, quantile_fraction=0.25, hyperparam_mutations=hyperparam_mutations, custom_explore_fn=explore, log_config=log_config, ) return pbt def testCheckpointing(self): pbt = self.basicSetup(perturbation_interval=10) class train(tune.Trainable): def step(self): return {"mean_accuracy": self.training_iteration} def save_checkpoint(self, path): checkpoint = os.path.join(path, "checkpoint") with open(checkpoint, "w") as f: f.write("OK") def reset_config(self, config): return True def load_checkpoint(self, checkpoint): pass trial_hyperparams = { "float_factor": 2.0, "const_factor": 3, "int_factor": 10, "id_factor": 0, } analysis = tune.run( train, num_samples=3, scheduler=pbt, checkpoint_config=CheckpointConfig(checkpoint_frequency=3), config=trial_hyperparams, stop={"training_iteration": 30}, ) for trial in analysis.trials: self.assertEqual(trial.status, Trial.TERMINATED) self.assertTrue(trial.has_checkpoint()) def testCheckpointDict(self): pbt = self.basicSetup(perturbation_interval=10) class train_dict(tune.Trainable): def setup(self, config): self.state = {"hi": 1} def step(self): return {"mean_accuracy": self.training_iteration} def save_checkpoint(self, path): return self.state def load_checkpoint(self, state): self.state = state def reset_config(self, config): return True trial_hyperparams = { "float_factor": 2.0, "const_factor": 3, "int_factor": 10, "id_factor": 0, } analysis = tune.run( train_dict, num_samples=3, scheduler=pbt, checkpoint_config=CheckpointConfig(checkpoint_frequency=3), config=trial_hyperparams, stop={"training_iteration": 30}, ) for trial in analysis.trials: self.assertEqual(trial.status, Trial.TERMINATED) self.assertTrue(trial.has_checkpoint()) class AsyncHyperBandSuite(unittest.TestCase): def setUp(self): ray.init(num_cpus=2) register_mock_trainable() def tearDown(self): ray.shutdown() def basicSetup(self, scheduler): t1 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 900, t_max=10 t2 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 450, t_max=5 scheduler.on_trial_add(None, t1) scheduler.on_trial_add(None, t2) for i in range(10): self.assertEqual( scheduler.on_trial_result(None, t1, result(i, i * 100)), TrialScheduler.CONTINUE, ) for i in range(5): self.assertEqual( scheduler.on_trial_result(None, t2, result(i, 450)), TrialScheduler.CONTINUE, ) return t1, t2 def nanSetup(self, scheduler): t1 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 450, t_max=10 t2 = Trial(MOCK_TRAINABLE_NAME) # mean is nan, max nan, t_max=10 scheduler.on_trial_add(None, t1) scheduler.on_trial_add(None, t2) for i in range(10): self.assertEqual( scheduler.on_trial_result(None, t1, result(i, 450)), TrialScheduler.CONTINUE, ) for i in range(10): self.assertEqual( scheduler.on_trial_result(None, t2, result(i, np.nan)), TrialScheduler.CONTINUE, ) return t1, t2 def nanInfSetup(self, scheduler, runner=None): t1 = Trial(MOCK_TRAINABLE_NAME) t2 = Trial(MOCK_TRAINABLE_NAME) t3 = Trial(MOCK_TRAINABLE_NAME) scheduler.on_trial_add(runner, t1) scheduler.on_trial_add(runner, t2) scheduler.on_trial_add(runner, t3) for i in range(10): scheduler.on_trial_result(runner, t1, result(i, np.nan)) for i in range(10): scheduler.on_trial_result(runner, t2, result(i, float("inf"))) for i in range(10): scheduler.on_trial_result(runner, t3, result(i, float("-inf"))) return t1, t2, t3 def testAsyncHBOnComplete(self): scheduler = AsyncHyperBandScheduler( metric="episode_reward_mean", mode="max", max_t=10, brackets=1 ) t1, t2 = self.basicSetup(scheduler) t3 = Trial(MOCK_TRAINABLE_NAME) scheduler.on_trial_add(None, t3) scheduler.on_trial_complete(None, t3, result(10, 1000)) self.assertEqual( scheduler.on_trial_result(None, t2, result(101, 0)), TrialScheduler.STOP ) def testAsyncHBGracePeriod(self): scheduler = AsyncHyperBandScheduler( metric="episode_reward_mean", mode="max", grace_period=2.5, reduction_factor=3, brackets=1, ) t1, t2 = self.basicSetup(scheduler) scheduler.on_trial_complete(None, t1, result(10, 1000)) scheduler.on_trial_complete(None, t2, result(10, 1000)) t3 = Trial(MOCK_TRAINABLE_NAME) scheduler.on_trial_add(None, t3) self.assertEqual( scheduler.on_trial_result(None, t3, result(1, 10)), TrialScheduler.CONTINUE ) self.assertEqual( scheduler.on_trial_result(None, t3, result(2, 10)), TrialScheduler.CONTINUE ) self.assertEqual( scheduler.on_trial_result(None, t3, result(3, 10)), TrialScheduler.STOP ) def testAsyncHBAllCompletes(self): scheduler = AsyncHyperBandScheduler( metric="episode_reward_mean", mode="max", max_t=10, brackets=10 ) trials = [Trial(MOCK_TRAINABLE_NAME) for i in range(10)] for t in trials: scheduler.on_trial_add(None, t) for t in trials: self.assertEqual( scheduler.on_trial_result(None, t, result(10, -2)), TrialScheduler.STOP ) def testAsyncHBUsesPercentile(self): scheduler = AsyncHyperBandScheduler( metric="episode_reward_mean", mode="max", grace_period=1, max_t=10, reduction_factor=2, brackets=1, ) t1, t2 = self.basicSetup(scheduler) scheduler.on_trial_complete(None, t1, result(10, 1000)) scheduler.on_trial_complete(None, t2, result(10, 1000)) t3 = Trial(MOCK_TRAINABLE_NAME) scheduler.on_trial_add(None, t3) self.assertEqual( scheduler.on_trial_result(None, t3, result(1, 260)), TrialScheduler.STOP ) self.assertEqual( scheduler.on_trial_result(None, t3, result(2, 260)), TrialScheduler.STOP ) def testAsyncHBNanPercentile(self): scheduler = AsyncHyperBandScheduler( metric="episode_reward_mean", mode="max", grace_period=1, max_t=10, reduction_factor=2, brackets=1, ) t1, t2 = self.nanSetup(scheduler) scheduler.on_trial_complete(None, t1, result(10, 450)) scheduler.on_trial_complete(None, t2, result(10, np.nan)) t3 = Trial(MOCK_TRAINABLE_NAME) scheduler.on_trial_add(None, t3) self.assertEqual( scheduler.on_trial_result(None, t3, result(1, 260)), TrialScheduler.STOP ) self.assertEqual( scheduler.on_trial_result(None, t3, result(2, 260)), TrialScheduler.STOP ) def testAsyncHBSaveRestore(self): _, tmpfile = tempfile.mkstemp() scheduler = AsyncHyperBandScheduler( metric="episode_reward_mean", mode="max", grace_period=1, max_t=10, reduction_factor=2, brackets=1, ) # Add some trials trials = [Trial(MOCK_TRAINABLE_NAME) for i in range(10)] for t in trials: scheduler.on_trial_add(None, t) # Report some results for t in trials[0:5]: self.assertNotEqual( scheduler.on_trial_result(None, t, result(1, 10)), TrialScheduler.STOP ) # Report worse result: Trial should stop self.assertEqual( scheduler.on_trial_result(None, trials[5], result(1, 5)), TrialScheduler.STOP, ) scheduler.save(tmpfile) scheduler2 = AsyncHyperBandScheduler() scheduler2.restore(tmpfile) # Report a new bad result: Trial should stop self.assertEqual( scheduler2.on_trial_result(None, trials[6], result(1, 4)), TrialScheduler.STOP, ) # Create a new trial and report bad result: Trial should stop # Report a new bad result: Trial should stop new_trial = Trial(MOCK_TRAINABLE_NAME) scheduler2.on_trial_add(None, new_trial) self.assertEqual( scheduler2.on_trial_result(None, new_trial, result(1, 2)), TrialScheduler.STOP, ) def testAsyncHBNonStopTrials(self): trials = [Trial(MOCK_TRAINABLE_NAME) for i in range(4)] scheduler = AsyncHyperBandScheduler( metric="metric", mode="max", grace_period=1, max_t=3, reduction_factor=2, brackets=1, stop_last_trials=False, ) scheduler.on_trial_add(None, trials[0]) scheduler.on_trial_add(None, trials[1]) scheduler.on_trial_add(None, trials[2]) scheduler.on_trial_add(None, trials[3]) # Report one result action = scheduler.on_trial_result( None, trials[0], {"training_iteration": 2, "metric": 10} ) assert action == TrialScheduler.CONTINUE action = scheduler.on_trial_result( None, trials[1], {"training_iteration": 2, "metric": 8} ) assert action == TrialScheduler.STOP action = scheduler.on_trial_result( None, trials[2], {"training_iteration": 2, "metric": 6} ) assert action == TrialScheduler.STOP action = scheduler.on_trial_result( None, trials[3], {"training_iteration": 2, "metric": 4} ) assert action == TrialScheduler.STOP # Report more. This will fail if `stop_last_trials=True` action = scheduler.on_trial_result( None, trials[0], {"training_iteration": 4, "metric": 10} ) assert action == TrialScheduler.CONTINUE action = scheduler.on_trial_result( None, trials[0], {"training_iteration": 8, "metric": 10} ) assert action == TrialScheduler.CONTINUE # Also continue if we fall below the cutoff eventually action = scheduler.on_trial_result( None, trials[0], {"training_iteration": 14, "metric": 1} ) assert action == TrialScheduler.CONTINUE def testMedianStoppingNanInf(self): scheduler = MedianStoppingRule(metric="episode_reward_mean", mode="max") t1, t2, t3 = self.nanInfSetup(scheduler) scheduler.on_trial_complete(None, t1, result(10, np.nan)) scheduler.on_trial_complete(None, t2, result(10, float("inf"))) scheduler.on_trial_complete(None, t3, result(10, float("-inf"))) def testHyperbandNanInf(self): scheduler = HyperBandScheduler(metric="episode_reward_mean", mode="max") t1, t2, t3 = self.nanInfSetup(scheduler) scheduler.on_trial_complete(None, t1, result(10, np.nan)) scheduler.on_trial_complete(None, t2, result(10, float("inf"))) scheduler.on_trial_complete(None, t3, result(10, float("-inf"))) def testBOHBNanInf(self): scheduler = HyperBandForBOHB(metric="episode_reward_mean", mode="max") runner = _MockTrialRunner(scheduler) runner.search_alg = MagicMock() runner.search_alg.searcher = MagicMock() t1, t2, t3 = self.nanInfSetup(scheduler, runner) # skip trial complete in this mock setting def testPBTNanInf(self): scheduler = PopulationBasedTraining( metric="episode_reward_mean", mode="max", hyperparam_mutations={"ignored": [1]}, ) t1, t2, t3 = self.nanInfSetup(scheduler, runner=MagicMock()) scheduler.on_trial_complete(None, t1, result(10, np.nan)) scheduler.on_trial_complete(None, t2, result(10, float("inf"))) scheduler.on_trial_complete(None, t3, result(10, float("-inf"))) def _test_metrics(self, result_func, metric, mode): scheduler = AsyncHyperBandScheduler( grace_period=1, time_attr="training_iteration", metric=metric, mode=mode, brackets=1, ) t1 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 900, t_max=10 t2 = Trial(MOCK_TRAINABLE_NAME) # mean is 450, max 450, t_max=5 scheduler.on_trial_add(None, t1) scheduler.on_trial_add(None, t2) for i in range(10): self.assertEqual( scheduler.on_trial_result(None, t1, result_func(i, i * 100)), TrialScheduler.CONTINUE, ) for i in range(5): self.assertEqual( scheduler.on_trial_result(None, t2, result_func(i, 450)), TrialScheduler.CONTINUE, ) scheduler.on_trial_complete(None, t1, result_func(10, 1000)) self.assertEqual( scheduler.on_trial_result(None, t2, result_func(5, 450)), TrialScheduler.CONTINUE, ) self.assertEqual( scheduler.on_trial_result(None, t2, result_func(6, 0)), TrialScheduler.CONTINUE, ) def testAlternateMetrics(self): def result2(t, rew): return dict(training_iteration=t, neg_mean_loss=rew) self._test_metrics(result2, "neg_mean_loss", "max") def testAlternateMetricsMin(self): def result2(t, rew): return dict(training_iteration=t, mean_loss=-rew) self._test_metrics(result2, "mean_loss", "min") def _testAnonymousMetricEndToEnd(self, scheduler_cls, searcher=None): def train_fn(config): return config["value"] out = tune.run( train_fn, mode="max", num_samples=1, config={"value": tune.uniform(-2.0, 2.0)}, scheduler=scheduler_cls(), search_alg=searcher, ) self.assertTrue(bool(out.best_trial)) def testAnonymousMetricEndToEndFIFO(self): self._testAnonymousMetricEndToEnd(FIFOScheduler) def testAnonymousMetricEndToEndASHA(self): self._testAnonymousMetricEndToEnd(AsyncHyperBandScheduler) @pytest.mark.skipif( sys.version_info >= (3, 12), reason="BOHB doesn't support py312" ) def testAnonymousMetricEndToEndBOHB(self): from ray.tune.search.bohb import TuneBOHB self._testAnonymousMetricEndToEnd(HyperBandForBOHB, TuneBOHB()) def testAnonymousMetricEndToEndMedian(self): self._testAnonymousMetricEndToEnd(MedianStoppingRule) def testAnonymousMetricEndToEndPBT(self): self._testAnonymousMetricEndToEnd( lambda: PopulationBasedTraining(hyperparam_mutations={"ignored": [1]}) ) if __name__ == "__main__": sys.exit(pytest.main(["-v", __file__]))