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
def test_reset_incremental_states(self):
"""
This test might seem trivial. However, interacting with the scripted
sequence generator crosses the Torchscript boundary, which can lead
to weird behavior. If the incremental states don't get properly
reset, the model will produce garbage _after_ the first call, which
is a pain to debug when you only catch it after training.
"""
tensorizers = get_tensorizers()
# Avoid numeric issues with quantization by setting a known seed.
torch.manual_seed(42)
model = Seq2SeqModel.from_config(
Seq2SeqModel.Config(
source_embedding=WordEmbedding.Config(embed_dim=512),
target_embedding=WordEmbedding.Config(embed_dim=512),
),
tensorizers,
)
# Get sample inputs using a data source.
schema = {
"source_sequence": str,
"dict_feat": Gazetteer,
"target_sequence": str,
}
data = Data.from_config(
Data.Config(source=TSVDataSource.Config(
train_filename=TEST_FILE_NAME,
field_names=[
"source_sequence", "dict_feat", "target_sequence"
],
)),
schema,
tensorizers,
)
data.batcher = Batcher(1, 1, 1)
raw_batch, batch = next(
iter(data.batches(Stage.TRAIN, load_early=True)))
inputs = model.arrange_model_inputs(batch)
model.eval()
outputs = model(*inputs)
pred, scores = model.get_pred(outputs, {"stage": Stage.TEST})
# Verify that the incremental states reset correctly.
decoder = model.sequence_generator.beam_search.decoder_ens
decoder.reset_incremental_states()
self.assertDictEqual(decoder.incremental_states, {"0": {}})
# Verify that the model returns the same predictions.
new_pred, new_scores = model.get_pred(outputs, {"stage": Stage.TEST})
self.assertEqual(new_scores, scores)
def test_force_predictions_on_eval(self):
tensorizers = get_tensorizers()
model = Seq2SeqModel.from_config(
Seq2SeqModel.Config(
source_embedding=WordEmbedding.Config(embed_dim=512),
target_embedding=WordEmbedding.Config(embed_dim=512),
),
tensorizers,
)
# Get sample inputs using a data source.
schema = {
"source_sequence": str,
"dict_feat": Gazetteer,
"target_sequence": str,
}
data = Data.from_config(
Data.Config(source=TSVDataSource.Config(
train_filename=TEST_FILE_NAME,
field_names=[
"source_sequence", "dict_feat", "target_sequence"
],
)),
schema,
tensorizers,
)
data.batcher = Batcher(1, 1, 1)
raw_batch, batch = next(
iter(data.batches(Stage.TRAIN, load_early=True)))
inputs = model.arrange_model_inputs(batch)
# Verify that model does not run sequence generation on prediction.
outputs = model(*inputs)
pred = model.get_pred(outputs, {"stage": Stage.EVAL})
self.assertEqual(pred, (None, None))
# Verify that attempting to set force_eval_predictions is correctly
# accounted for.
model.force_eval_predictions = True
with self.assertRaises(AssertionError):
_ = model.get_pred(outputs, {"stage": Stage.EVAL})
class Config(ConfigBase):
data: Data.Config = Data.Config()
trainer: NewTaskTrainer.Config = NewTaskTrainer.Config()
class Config(ConfigBase):
data: Data.Config = Data.Config()
trainer: TaskTrainer.Config = TaskTrainer.Config()
# TODO: deprecate this
use_elastic: Optional[bool] = None
class Config(ConfigBase):
data: Data.Config = Data.Config()
model: Model.Config
trainer: NewTaskTrainer.Config = NewTaskTrainer.Config()
exporter: Optional[ModelExporter.Config] = None
class Config(ConfigBase):
data: Data.Config = Data.Config()
model: Model.Config
trainer: NewTaskTrainer.Config = NewTaskTrainer.Config()
class Config(ConfigBase):
data: Data.Config = Data.Config()
trainer: TaskTrainer.Config = TaskTrainer.Config()
use_elastic: Optional[bool] = None