[retiarii] fix experiment does not exit after done (#4916)

nni/runtime/msg_dispatcher_base.py

@@ -46,8 +46,10 @@ class MsgDispatcherBase(Recoverable):
             self._channel.connect()
         self.default_command_queue = Queue()
         self.assessor_command_queue = Queue()
-        self.default_worker = threading.Thread(target=self.command_queue_worker, args=(self.default_command_queue,))
-        self.assessor_worker = threading.Thread(target=self.command_queue_worker, args=(self.assessor_command_queue,))
+        # here daemon should be True, because their parent thread is configured as daemon to enable smooth exit of NAS experiment.
+        # if daemon is not set, these threads will block the daemon effect of their parent thread.
+        self.default_worker = threading.Thread(target=self.command_queue_worker, args=(self.default_command_queue,), daemon=True)
+        self.assessor_worker = threading.Thread(target=self.command_queue_worker, args=(self.assessor_command_queue,), daemon=True)
         self.worker_exceptions = []
 
     def run(self):

test/ut/retiarii/test_multitrial.py

+import argparse
+import os
+import sys
+import pytorch_lightning as pl
+import pytest
+from subprocess import Popen
+
+from nni.retiarii import strategy
+from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment
+from .test_oneshot import _mnist_net
+
+pytestmark = pytest.mark.skipif(pl.__version__ < '1.0', reason='Incompatible APIs')
+
+def test_multi_trial():
+    evaluator_kwargs = {
+        'max_epochs': 1
+    }
+
+    to_test = [
+        # (model, evaluator)
+        _mnist_net('simple', evaluator_kwargs),
+        _mnist_net('simple_value_choice', evaluator_kwargs),
+        _mnist_net('value_choice', evaluator_kwargs),
+        _mnist_net('repeat', evaluator_kwargs),
+        _mnist_net('custom_op', evaluator_kwargs),
+    ]
+
+    for base_model, evaluator in to_test:
+        search_strategy = strategy.Random()
+        exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
+        exp_config = RetiariiExeConfig('local')
+        exp_config.experiment_name = 'mnist_unittest'
+        exp_config.trial_concurrency = 1
+        exp_config.max_trial_number = 1
+        exp_config.training_service.use_active_gpu = False
+        exp.run(exp_config, 8080)
+        assert isinstance(exp.export_top_models()[0], dict)
+        exp.stop()
+
+python_script = """
+from nni.retiarii import strategy
+from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment
+from test_oneshot import _mnist_net
+
+base_model, evaluator = _mnist_net('simple', {'max_epochs': 1})
+search_strategy = strategy.Random()
+exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
+exp_config = RetiariiExeConfig('local')
+exp_config.experiment_name = 'mnist_unittest'
+exp_config.trial_concurrency = 1
+exp_config.max_trial_number = 1
+exp_config.training_service.use_active_gpu = False
+exp.run(exp_config, 8080)
+assert isinstance(exp.export_top_models()[0], dict)
+"""
+
+@pytest.mark.timeout(600)
+def test_exp_exit_without_stop():
+    script_name = 'tmp_multi_trial.py'
+    with open(script_name, 'w') as f:
+        f.write(python_script)
+    proc = Popen([sys.executable, script_name])
+    proc.wait()
+    os.remove(script_name)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp', type=str, default='all', metavar='E',
+                        help='experiment to run, default = all')
+    args = parser.parse_args()
+
+    if args.exp == 'all':
+        test_multi_trial()
+        test_exp_exit_without_stop()
+    else:
+        globals()[f'test_{args.exp}']()

test/ut/retiarii/test_oneshot.py

@@ -7,6 +7,7 @@ from torchvision import transforms
 from torchvision.datasets import MNIST
 from torch.utils.data import Dataset, RandomSampler
 
+import nni
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import strategy, model_wrapper, basic_unit
 from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment
@@ -216,13 +217,13 @@ def _mnist_net(type_, evaluator_kwargs):
         raise ValueError(f'Unsupported type: {type_}')
     
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
-    train_dataset = MNIST('data/mnist', train=True, download=True, transform=transform)
+    train_dataset = nni.trace(MNIST)('data/mnist', train=True, download=True, transform=transform)
     # Multi-GPU combined dataloader will break this subset sampler. Expected though.
-    train_random_sampler = RandomSampler(train_dataset, True, int(len(train_dataset) / 20))
-    train_loader = DataLoader(train_dataset, 64, sampler=train_random_sampler)
-    valid_dataset = MNIST('data/mnist', train=False, download=True, transform=transform)
-    valid_random_sampler = RandomSampler(valid_dataset, True, int(len(valid_dataset) / 20))
-    valid_loader = DataLoader(valid_dataset, 64, sampler=valid_random_sampler)
+    train_random_sampler = nni.trace(RandomSampler)(train_dataset, True, int(len(train_dataset) / 20))
+    train_loader = nni.trace(DataLoader)(train_dataset, 64, sampler=train_random_sampler)
+    valid_dataset = nni.trace(MNIST)('data/mnist', train=False, download=True, transform=transform)
+    valid_random_sampler = nni.trace(RandomSampler)(valid_dataset, True, int(len(valid_dataset) / 20))
+    valid_loader = nni.trace(DataLoader)(valid_dataset, 64, sampler=valid_random_sampler)
     evaluator = Classification(train_dataloader=train_loader, val_dataloaders=valid_loader, **evaluator_kwargs)
 
     return base_model, evaluator