class Config(ConfigBase):
data: Data.Config = Data.Config()
model: Model.Config
trainer: NewTaskTrainer.Config = NewTaskTrainer.Config()
optimizer: Optimizer.Config = Adam.Config()
scheduler: Scheduler.Config = Scheduler.Config()
exporter: Optional[ModelExporter.Config] = None
class Config(ConfigBase):
features: FeatureConfig = FeatureConfig()
featurizer: Featurizer.Config = SimpleFeaturizer.Config()
data_handler: DataHandler.Config
trainer: Trainer.Config = Trainer.Config()
optimizer: Optimizer.Config = Adam.Config()
scheduler: Optional[Scheduler.Config] = Scheduler.Config()
exporter: Optional[ModelExporter.Config] = None
def test_load_checkpoint(self):
with tempfile.NamedTemporaryFile() as checkpoint_file:
train_data = tests_module.test_file("train_data_tiny.tsv")
eval_data = tests_module.test_file("test_data_tiny.tsv")
config = PyTextConfig(
task=DocumentClassificationTask.Config(data=Data.Config(
source=TSVDataSource.Config(
train_filename=train_data,
eval_filename=eval_data,
field_names=["label", "slots", "text"],
))),
version=LATEST_VERSION,
save_snapshot_path=checkpoint_file.name,
)
task = create_task(config.task)
model = task.model
# test checkpoint saving and loading
optimizer = create_optimizer(Adam.Config(), model)
scheduler = create_scheduler(Scheduler.Config(), optimizer)
training_state = TrainingState(
model=model,
optimizer=optimizer,
scheduler=scheduler,
start_time=0,
epoch=0,
rank=0,
stage=Stage.TRAIN,
epochs_since_last_improvement=0,
best_model_state=None,
best_model_metric=None,
tensorizers=None,
)
checkpoint_path = checkpoint_file.name
save(
config,
model,
None,
task.data.tensorizers,
training_state,
checkpoint_file,
)
task_restored, config_restored, training_state_restored = load(
checkpoint_path)
optimizer_restored = training_state_restored.optimizer
scheduler_restored = training_state_restored.scheduler
self.assertOptimizerEqual(optimizer, optimizer_restored)
self.assertNotNone(scheduler_restored)
self.assertEqual(config, config_restored)
self.assertModulesEqual(model, task_restored.model)
model.eval()
task_restored.model.eval()
inputs = torch.LongTensor([[1, 2, 3]]), torch.LongTensor([3])
self.assertEqual(
model(*inputs).tolist(),
task_restored.model(*inputs).tolist())
def test_load_checkpoint_in_dist_training(self):
with tempfile.NamedTemporaryFile() as checkpoint_file:
train_data = tests_module.test_file("train_data_tiny.tsv")
eval_data = tests_module.test_file("test_data_tiny.tsv")
config = PyTextConfig(
task=DocumentClassificationTask.Config(data=Data.Config(
source=BlockShardedTSVDataSource.Config(
train_filename=train_data,
eval_filename=eval_data,
field_names=["label", "slots", "text"],
))),
version=LATEST_VERSION,
save_snapshot_path=checkpoint_file.name,
)
task = create_task(config.task)
model = task.model
# test checkpoint saving and loading
optimizer = create_optimizer(Adam.Config(), model)
scheduler = create_scheduler(Scheduler.Config(), optimizer)
training_state = TrainingState(
model=model,
optimizer=optimizer,
scheduler=scheduler,
start_time=0,
epoch=0,
rank=0,
stage=Stage.TRAIN,
epochs_since_last_improvement=0,
best_model_state=None,
best_model_metric=None,
tensorizers=task.data.tensorizers,
)
id = "epoch-1"
saved_path = save(config, model, None, task.data.tensorizers,
training_state, id)
new_rank = 2
new_world_size = 4
task_restored, config_restored, training_state_restored = load(
saved_path, rank=new_rank, world_size=new_world_size)
self.assertCheckpointEqual(
model,
config,
training_state,
task_restored.model,
config_restored,
training_state_restored,
)
self.assertEqual(task_restored.data.data_source.rank, new_rank)
self.assertEqual(task_restored.data.data_source.world_size,
new_world_size)
