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542 lines
17 KiB
Python
542 lines
17 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import time
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import unittest
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import ray
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from ray.rllib import _register_all
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from ray.tune import Trainable, TuneError
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from ray.tune import register_env, register_trainable, run_experiments
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from ray.tune.registry import _default_registry, TRAINABLE_CLASS
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from ray.tune.result import DEFAULT_RESULTS_DIR
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from ray.tune.trial import Trial, Resources
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from ray.tune.trial_runner import TrialRunner
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from ray.tune.variant_generator import generate_trials, grid_search, \
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RecursiveDependencyError
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class TrainableFunctionApiTest(unittest.TestCase):
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def tearDown(self):
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ray.worker.cleanup()
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_register_all() # re-register the evicted objects
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def testRegisterEnv(self):
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register_env("foo", lambda: None)
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self.assertRaises(TypeError, lambda: register_env("foo", 2))
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def testRegisterTrainable(self):
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def train(config, reporter):
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pass
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class A(object):
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pass
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class B(Trainable):
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pass
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register_trainable("foo", train)
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register_trainable("foo", B)
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self.assertRaises(TypeError, lambda: register_trainable("foo", B()))
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self.assertRaises(TypeError, lambda: register_trainable("foo", A))
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def testRewriteEnv(self):
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def train(config, reporter):
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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[trial] = run_experiments({"foo": {
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"run": "f1",
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"env": "CartPole-v0",
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}})
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self.assertEqual(trial.config["env"], "CartPole-v0")
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def testConfigPurity(self):
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def train(config, reporter):
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assert config == {"a": "b"}, config
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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run_experiments({"foo": {
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"run": "f1",
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"config": {"a": "b"},
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}})
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def testLogdir(self):
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def train(config, reporter):
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assert "/tmp/logdir/foo" in os.getcwd(), os.getcwd()
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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run_experiments({"foo": {
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"run": "f1",
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"local_dir": "/tmp/logdir",
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"config": {"a": "b"},
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}})
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def testBadParams(self):
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def f():
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run_experiments({"foo": {}})
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self.assertRaises(TuneError, f)
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def testBadParams2(self):
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def f():
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run_experiments({"foo": {
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"bah": "this param is not allowed",
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}})
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self.assertRaises(TuneError, f)
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def testBadParams3(self):
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def f():
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run_experiments({"foo": {
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"run": grid_search("invalid grid search"),
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}})
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self.assertRaises(TuneError, f)
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def testBadParams4(self):
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def f():
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run_experiments({"foo": {
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"run": "asdf",
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}})
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self.assertRaises(TuneError, f)
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def testBadParams5(self):
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def f():
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run_experiments({"foo": {
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"run": "PPO",
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"stop": {"asdf": 1}
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}})
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self.assertRaises(TuneError, f)
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def testBadParams6(self):
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def f():
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run_experiments({"foo": {
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"run": "PPO",
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"resources": {"asdf": 1}
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}})
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self.assertRaises(TuneError, f)
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def testBadReturn(self):
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def train(config, reporter):
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reporter()
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register_trainable("f1", train)
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def f():
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run_experiments({"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}})
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self.assertRaises(TuneError, f)
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def testEarlyReturn(self):
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def train(config, reporter):
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reporter(timesteps_total=100, done=True)
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time.sleep(99999)
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register_trainable("f1", train)
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[trial] = run_experiments({"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result.timesteps_total, 100)
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def testAbruptReturn(self):
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def train(config, reporter):
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reporter(timesteps_total=100)
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register_trainable("f1", train)
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[trial] = run_experiments({"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result.timesteps_total, 100)
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def testErrorReturn(self):
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def train(config, reporter):
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raise Exception("uh oh")
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register_trainable("f1", train)
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def f():
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run_experiments({"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}})
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self.assertRaises(TuneError, f)
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def testSuccess(self):
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def train(config, reporter):
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for i in range(100):
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reporter(timesteps_total=i)
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register_trainable("f1", train)
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[trial] = run_experiments({"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result.timesteps_total, 99)
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class VariantGeneratorTest(unittest.TestCase):
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def testParseToTrials(self):
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trials = generate_trials({
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"run": "PPO",
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"repeat": 2,
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"config": {
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"env": "Pong-v0",
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"foo": "bar"
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},
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}, "tune-pong")
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trials = list(trials)
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self.assertEqual(len(trials), 2)
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self.assertEqual(str(trials[0]), "PPO_Pong-v0_0")
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self.assertEqual(trials[0].config, {"foo": "bar", "env": "Pong-v0"})
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self.assertEqual(trials[0].trainable_name, "PPO")
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self.assertEqual(trials[0].experiment_tag, "0")
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self.assertEqual(
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trials[0].local_dir,
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os.path.join(DEFAULT_RESULTS_DIR, "tune-pong"))
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self.assertEqual(trials[1].experiment_tag, "1")
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def testEval(self):
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trials = generate_trials({
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"run": "PPO",
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"config": {
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"foo": {
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"eval": "2 + 2"
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},
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},
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})
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trials = list(trials)
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self.assertEqual(len(trials), 1)
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self.assertEqual(trials[0].config, {"foo": 4})
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self.assertEqual(trials[0].experiment_tag, "0_foo=4")
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def testGridSearch(self):
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trials = generate_trials({
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"run": "PPO",
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"config": {
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"bar": {
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"grid_search": [True, False]
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},
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"foo": {
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"grid_search": [1, 2, 3]
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},
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},
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})
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trials = list(trials)
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self.assertEqual(len(trials), 6)
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self.assertEqual(trials[0].config, {"bar": True, "foo": 1})
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self.assertEqual(trials[0].experiment_tag, "0_bar=True,foo=1")
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self.assertEqual(trials[1].config, {"bar": False, "foo": 1})
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self.assertEqual(trials[1].experiment_tag, "1_bar=False,foo=1")
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self.assertEqual(trials[2].config, {"bar": True, "foo": 2})
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self.assertEqual(trials[3].config, {"bar": False, "foo": 2})
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self.assertEqual(trials[4].config, {"bar": True, "foo": 3})
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self.assertEqual(trials[5].config, {"bar": False, "foo": 3})
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def testGridSearchAndEval(self):
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trials = generate_trials({
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"run": "PPO",
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"config": {
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"qux": lambda spec: 2 + 2,
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"bar": grid_search([True, False]),
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"foo": grid_search([1, 2, 3]),
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},
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})
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trials = list(trials)
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self.assertEqual(len(trials), 6)
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self.assertEqual(trials[0].config, {"bar": True, "foo": 1, "qux": 4})
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self.assertEqual(trials[0].experiment_tag, "0_bar=True,foo=1,qux=4")
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def testConditionResolution(self):
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trials = generate_trials({
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"run": "PPO",
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"config": {
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"x": 1,
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"y": lambda spec: spec.config.x + 1,
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"z": lambda spec: spec.config.y + 1,
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},
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})
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trials = list(trials)
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self.assertEqual(len(trials), 1)
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self.assertEqual(trials[0].config, {"x": 1, "y": 2, "z": 3})
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def testDependentLambda(self):
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trials = generate_trials({
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"run": "PPO",
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"config": {
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"x": grid_search([1, 2]),
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"y": lambda spec: spec.config.x * 100,
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},
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})
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trials = list(trials)
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self.assertEqual(len(trials), 2)
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self.assertEqual(trials[0].config, {"x": 1, "y": 100})
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self.assertEqual(trials[1].config, {"x": 2, "y": 200})
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def testDependentGridSearch(self):
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trials = generate_trials({
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"run": "PPO",
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"config": {
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"x": grid_search([
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lambda spec: spec.config.y * 100,
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lambda spec: spec.config.y * 200
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]),
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"y": lambda spec: 1,
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},
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})
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trials = list(trials)
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self.assertEqual(len(trials), 2)
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self.assertEqual(trials[0].config, {"x": 100, "y": 1})
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self.assertEqual(trials[1].config, {"x": 200, "y": 1})
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def testRecursiveDep(self):
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try:
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list(generate_trials({
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"run": "PPO",
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"config": {
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"foo": lambda spec: spec.config.foo,
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},
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}))
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except RecursiveDependencyError as e:
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assert "`foo` recursively depends on" in str(e), e
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else:
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assert False
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class TrialRunnerTest(unittest.TestCase):
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def tearDown(self):
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ray.worker.cleanup()
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_register_all() # re-register the evicted objects
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def testTrialStatus(self):
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ray.init()
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trial = Trial("__fake")
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self.assertEqual(trial.status, Trial.PENDING)
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trial.start()
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self.assertEqual(trial.status, Trial.RUNNING)
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trial.stop()
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self.assertEqual(trial.status, Trial.TERMINATED)
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trial.stop(error=True)
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self.assertEqual(trial.status, Trial.ERROR)
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def testTrialErrorOnStart(self):
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ray.init()
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_default_registry.register(TRAINABLE_CLASS, "asdf", None)
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trial = Trial("asdf")
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try:
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trial.start()
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except Exception as e:
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self.assertIn("a class", str(e))
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def testResourceScheduler(self):
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ray.init(num_cpus=4, num_gpus=1)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 1},
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"resources": Resources(cpu=1, gpu=1),
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}
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trials = [
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Trial("__fake", **kwargs),
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Trial("__fake", **kwargs)]
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for t in trials:
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runner.add_trial(t)
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(trials[1].status, Trial.PENDING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.TERMINATED)
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self.assertEqual(trials[1].status, Trial.PENDING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.TERMINATED)
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self.assertEqual(trials[1].status, Trial.RUNNING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.TERMINATED)
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self.assertEqual(trials[1].status, Trial.TERMINATED)
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def testMultiStepRun(self):
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ray.init(num_cpus=4, num_gpus=2)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 5},
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"resources": Resources(cpu=1, gpu=1),
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}
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trials = [
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Trial("__fake", **kwargs),
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Trial("__fake", **kwargs)]
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for t in trials:
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runner.add_trial(t)
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(trials[1].status, Trial.PENDING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(trials[1].status, Trial.RUNNING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(trials[1].status, Trial.RUNNING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(trials[1].status, Trial.RUNNING)
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def testErrorHandling(self):
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ray.init(num_cpus=4, num_gpus=2)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 1},
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"resources": Resources(cpu=1, gpu=1),
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}
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_default_registry.register(TRAINABLE_CLASS, "asdf", None)
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trials = [
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Trial("asdf", **kwargs),
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Trial("__fake", **kwargs)]
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for t in trials:
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runner.add_trial(t)
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runner.step()
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self.assertEqual(trials[0].status, Trial.ERROR)
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self.assertEqual(trials[1].status, Trial.PENDING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.ERROR)
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self.assertEqual(trials[1].status, Trial.RUNNING)
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def testCheckpointing(self):
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ray.init(num_cpus=1, num_gpus=1)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 1},
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"resources": Resources(cpu=1, gpu=1),
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}
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runner.add_trial(Trial("__fake", **kwargs))
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trials = runner.get_trials()
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(ray.get(trials[0].runner.set_info.remote(1)), 1)
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path = trials[0].checkpoint()
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kwargs["restore_path"] = path
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runner.add_trial(Trial("__fake", **kwargs))
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trials = runner.get_trials()
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runner.step()
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self.assertEqual(trials[0].status, Trial.TERMINATED)
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self.assertEqual(trials[1].status, Trial.PENDING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.TERMINATED)
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self.assertEqual(trials[1].status, Trial.RUNNING)
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self.assertEqual(ray.get(trials[1].runner.get_info.remote()), 1)
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self.addCleanup(os.remove, path)
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def testResultDone(self):
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"""Tests that last_result is marked `done` after trial is complete."""
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ray.init(num_cpus=1, num_gpus=1)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 2},
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"resources": Resources(cpu=1, gpu=1),
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}
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runner.add_trial(Trial("__fake", **kwargs))
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trials = runner.get_trials()
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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runner.step()
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self.assertNotEqual(trials[0].last_result.done, True)
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runner.step()
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self.assertEqual(trials[0].last_result.done, True)
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def testPauseThenResume(self):
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ray.init(num_cpus=1, num_gpus=1)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 2},
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"resources": Resources(cpu=1, gpu=1),
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}
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runner.add_trial(Trial("__fake", **kwargs))
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trials = runner.get_trials()
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(ray.get(trials[0].runner.get_info.remote()), None)
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self.assertEqual(ray.get(trials[0].runner.set_info.remote(1)), 1)
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trials[0].pause()
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self.assertEqual(trials[0].status, Trial.PAUSED)
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trials[0].resume()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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runner.step()
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self.assertEqual(trials[0].status, Trial.RUNNING)
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self.assertEqual(ray.get(trials[0].runner.get_info.remote()), 1)
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runner.step()
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self.assertEqual(trials[0].status, Trial.TERMINATED)
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def testStopTrial(self):
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ray.init(num_cpus=4, num_gpus=2)
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runner = TrialRunner()
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kwargs = {
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"stopping_criterion": {"training_iteration": 5},
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"resources": Resources(cpu=1, gpu=1),
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}
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trials = [
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs)]
|
|
for t in trials:
|
|
runner.add_trial(t)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(trials[1].status, Trial.PENDING)
|
|
|
|
# Stop trial while running
|
|
runner.stop_trial(trials[0])
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.PENDING)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(trials[-1].status, Trial.PENDING)
|
|
|
|
# Stop trial while pending
|
|
runner.stop_trial(trials[-1])
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(trials[-1].status, Trial.TERMINATED)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(trials[2].status, Trial.RUNNING)
|
|
self.assertEqual(trials[-1].status, Trial.TERMINATED)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main(verbosity=2)
|