def test_training_from_pretrained_with_head_replace(pipeline: Pipeline,
dataset: Dataset,
tmp_path: str):
configuration = TrainerConfiguration(
data_bucketing=True,
batch_size=2,
num_epochs=5,
cuda_device=-1,
)
output_dir = os.path.join(tmp_path, "output")
pipeline.train(output=output_dir,
trainer=configuration,
training=dataset,
quiet=True)
pipeline.set_head(TestHead)
pipeline.config.tokenizer_config.max_nr_of_sentences = 3
copied = pipeline.copy()
assert isinstance(copied.head, TestHead)
assert copied.num_parameters == pipeline.num_parameters
assert copied.num_trainable_parameters == pipeline.num_trainable_parameters
copied_model_state = copied._model.state_dict()
original_model_state = pipeline._model.state_dict()
for key, value in copied_model_state.items():
if "backbone" in key:
assert torch.all(torch.eq(value, original_model_state[key]))
assert copied.backbone.featurizer.tokenizer.config.max_nr_of_sentences == 3
def test_training_from_pretrained_with_head_replace(
pipeline_test: Pipeline, datasource_test: DataSource, tmp_path: str
):
training = pipeline_test.create_dataset(datasource_test)
pipeline_test.create_vocabulary(VocabularyConfiguration(sources=[training]))
configuration = TrainerConfiguration(
data_bucketing=True, batch_size=2, num_epochs=5
)
output_dir = os.path.join(tmp_path, "output")
results = pipeline_test.train(
output=output_dir, trainer=configuration, training=training, quiet=True
)
trained = Pipeline.from_pretrained(results.model_path)
trained.set_head(TestHead)
trained.config.tokenizer.max_nr_of_sentences = 3
copied = trained._make_copy()
assert isinstance(copied.head, TestHead)
assert copied.num_parameters == trained.num_parameters
assert copied.num_trainable_parameters == trained.num_trainable_parameters
copied_model_state = copied._model.state_dict()
original_model_state = trained._model.state_dict()
for key, value in copied_model_state.items():
if "backbone" in key:
assert torch.all(torch.eq(value, original_model_state[key]))
assert copied.backbone.featurizer.tokenizer.max_nr_of_sentences == 3
def test_training_with_logging(pipeline: Pipeline, dataset: Dataset,
tmp_path: str):
configuration = TrainerConfiguration(data_bucketing=True,
batch_size=2,
num_epochs=5)
output_dir = os.path.join(tmp_path, "output")
pipeline.train(output=output_dir,
trainer=configuration,
training=dataset,
quiet=True)
assert os.path.exists(os.path.join(output_dir, "train.log"))
with open(os.path.join(output_dir, "train.log")) as train_log:
for line in train_log.readlines()[3:]:
assert "allennlp" in line
assert logging.getLogger("allennlp").level == logging.ERROR
assert logging.getLogger("biome").level == logging.INFO
def test_training_with_logging(
pipeline_test: Pipeline, datasource_test: DataSource, tmp_path: str
):
training = pipeline_test.create_dataset(datasource_test)
pipeline_test.create_vocabulary(VocabularyConfiguration(sources=[training]))
configuration = TrainerConfiguration(
data_bucketing=True, batch_size=2, num_epochs=5
)
output_dir = os.path.join(tmp_path, "output")
pipeline_test.train(
output=output_dir, trainer=configuration, training=training, quiet=True
)
assert os.path.exists(os.path.join(output_dir, "train.log"))
with open(os.path.join(output_dir, "train.log")) as train_log:
for line in train_log.readlines():
assert "allennlp" in line
assert logging.getLogger("allennlp").level == logging.ERROR
assert logging.getLogger("biome").level == logging.INFO
def test_training_with_data_bucketing(
pipeline_test: Pipeline, datasource_test: DataSource, tmp_path: str
):
lazy_ds = pipeline_test.create_dataset(datasource_test, lazy=True)
non_lazy_ds = pipeline_test.create_dataset(datasource_test)
pipeline_test.create_vocabulary(VocabularyConfiguration(sources=[lazy_ds]))
configuration = TrainerConfiguration(
data_bucketing=True, batch_size=2, num_epochs=5
)
pipeline_test.train(
output=os.path.join(tmp_path, "output"),
trainer=configuration,
training=lazy_ds,
validation=non_lazy_ds,
)
pipeline_test.train(
output=os.path.join(tmp_path, "output"),
trainer=configuration,
training=non_lazy_ds,
validation=lazy_ds,
)