class QAModel(NLPModel, Exportable):
"""
BERT encoder with QA head training.
"""
@property
def input_types(self) -> Optional[Dict[str, NeuralType]]:
return self.bert_model.input_types
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return self.classifier.output_types
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
self.setup_tokenizer(cfg.tokenizer)
super().__init__(cfg=cfg, trainer=trainer)
self.bert_model = get_lm_model(
pretrained_model_name=cfg.language_model.pretrained_model_name,
config_file=cfg.language_model.config_file,
config_dict=OmegaConf.to_container(cfg.language_model.config)
if cfg.language_model.config else None,
checkpoint_file=cfg.language_model.lm_checkpoint,
)
self.classifier = TokenClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_classes=cfg.token_classifier.num_classes,
num_layers=cfg.token_classifier.num_layers,
activation=cfg.token_classifier.activation,
log_softmax=cfg.token_classifier.log_softmax,
dropout=cfg.token_classifier.dropout,
use_transformer_init=cfg.token_classifier.use_transformer_init,
)
self.loss = SpanningLoss()
@typecheck()
def forward(self, input_ids, token_type_ids, attention_mask):
hidden_states = self.bert_model(input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask)
logits = self.classifier(hidden_states=hidden_states)
return logits
def training_step(self, batch, batch_idx):
input_ids, input_type_ids, input_mask, unique_ids, start_positions, end_positions = batch
logits = self.forward(input_ids=input_ids,
token_type_ids=input_type_ids,
attention_mask=input_mask)
loss, _, _ = self.loss(logits=logits,
start_positions=start_positions,
end_positions=end_positions)
lr = self._optimizer.param_groups[0]['lr']
self.log('train_loss', loss)
self.log('lr', lr, prog_bar=True)
return {'loss': loss, 'lr': lr}
def validation_step(self, batch, batch_idx):
if self.testing:
prefix = 'test'
else:
prefix = 'val'
input_ids, input_type_ids, input_mask, unique_ids, start_positions, end_positions = batch
logits = self.forward(input_ids=input_ids,
token_type_ids=input_type_ids,
attention_mask=input_mask)
loss, start_logits, end_logits = self.loss(
logits=logits,
start_positions=start_positions,
end_positions=end_positions)
tensors = {
'unique_ids': unique_ids,
'start_logits': start_logits,
'end_logits': end_logits,
}
self.log(f'{prefix}_loss', loss)
return {f'{prefix}_loss': loss, f'{prefix}_tensors': tensors}
def test_step(self, batch, batch_idx):
return self.validation_step(batch, batch_idx)
def validation_epoch_end(self, outputs):
if self.testing:
prefix = 'test'
else:
prefix = 'val'
avg_loss = torch.stack([x[f'{prefix}_loss'] for x in outputs]).mean()
unique_ids = torch.cat(
[x[f'{prefix}_tensors']['unique_ids'] for x in outputs])
start_logits = torch.cat(
[x[f'{prefix}_tensors']['start_logits'] for x in outputs])
end_logits = torch.cat(
[x[f'{prefix}_tensors']['end_logits'] for x in outputs])
all_unique_ids = []
all_start_logits = []
all_end_logits = []
if torch.distributed.is_initialized():
world_size = torch.distributed.get_world_size()
for ind in range(world_size):
all_unique_ids.append(torch.empty_like(unique_ids))
all_start_logits.append(torch.empty_like(start_logits))
all_end_logits.append(torch.empty_like(end_logits))
torch.distributed.all_gather(all_unique_ids, unique_ids)
torch.distributed.all_gather(all_start_logits, start_logits)
torch.distributed.all_gather(all_end_logits, end_logits)
else:
all_unique_ids.append(unique_ids)
all_start_logits.append(start_logits)
all_end_logits.append(end_logits)
exact_match, f1, all_predictions, all_nbest = -1, -1, [], []
if not torch.distributed.is_initialized(
) or torch.distributed.get_rank() == 0:
unique_ids = []
start_logits = []
end_logits = []
for u in all_unique_ids:
unique_ids.extend(tensor2list(u))
for u in all_start_logits:
start_logits.extend(tensor2list(u))
for u in all_end_logits:
end_logits.extend(tensor2list(u))
eval_dataset = self._test_dl.dataset if self.testing else self._validation_dl.dataset
exact_match, f1, all_predictions, all_nbest = eval_dataset.evaluate(
unique_ids=unique_ids,
start_logits=start_logits,
end_logits=end_logits,
n_best_size=self._cfg.dataset.n_best_size,
max_answer_length=self._cfg.dataset.max_answer_length,
version_2_with_negative=self._cfg.dataset.
version_2_with_negative,
null_score_diff_threshold=self._cfg.dataset.
null_score_diff_threshold,
do_lower_case=self._cfg.dataset.do_lower_case,
)
logging.info(f"{prefix} exact match {exact_match}")
logging.info(f"{prefix} f1 {f1}")
self.log(f'{prefix}_loss', avg_loss)
self.log(f'{prefix}_exact_match', exact_match)
self.log(f'{prefix}_f1', f1)
def test_epoch_end(self, outputs):
return self.validation_epoch_end(outputs)
@torch.no_grad()
def inference(
self,
file: str,
batch_size: int = 1,
num_samples: int = -1,
output_nbest_file: Optional[str] = None,
output_prediction_file: Optional[str] = None,
):
"""
Get prediction for unlabeled inference data
Args:
file: inference data
batch_size: batch size to use during inference
num_samples: number of samples to use of inference data. Default: -1 if all data should be used.
output_nbest_file: optional output file for writing out nbest list
output_prediction_file: optional output file for writing out predictions
Returns:
all_predictions: model predictions
all_nbest: model nbest list
"""
# store predictions for all queries in a single list
all_predictions = []
all_nbest = []
mode = self.training
device = 'cuda' if torch.cuda.is_available() else 'cpu'
try:
# Switch model to evaluation mode
self.eval()
self.to(device)
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
dataloader_cfg = {
"batch_size": batch_size,
"file": file,
"shuffle": False,
"num_samples": num_samples,
'num_workers': 2,
'pin_memory': False,
'drop_last': False,
}
dataloader_cfg = OmegaConf.create(dataloader_cfg)
infer_datalayer = self._setup_dataloader_from_config(
cfg=dataloader_cfg, mode=INFERENCE_MODE)
all_logits = []
all_unique_ids = []
for i, batch in enumerate(infer_datalayer):
input_ids, token_type_ids, attention_mask, unique_ids = batch
logits = self.forward(
input_ids=input_ids.to(device),
token_type_ids=token_type_ids.to(device),
attention_mask=attention_mask.to(device),
)
all_logits.append(logits)
all_unique_ids.append(unique_ids)
logits = torch.cat(all_logits)
unique_ids = tensor2list(torch.cat(all_unique_ids))
s, e = logits.split(dim=-1, split_size=1)
start_logits = tensor2list(s.squeeze())
end_logits = tensor2list(e.squeeze())
(all_predictions, all_nbest,
scores_diff) = infer_datalayer.dataset.get_predictions(
unique_ids=unique_ids,
start_logits=start_logits,
end_logits=end_logits,
n_best_size=self._cfg.dataset.n_best_size,
max_answer_length=self._cfg.dataset.max_answer_length,
version_2_with_negative=self._cfg.dataset.
version_2_with_negative,
null_score_diff_threshold=self._cfg.dataset.
null_score_diff_threshold,
do_lower_case=self._cfg.dataset.do_lower_case,
)
with open(file, 'r') as test_file_fp:
test_data = json.load(test_file_fp)["data"]
id_to_question_mapping = {}
for title in test_data:
for par in title["paragraphs"]:
for question in par["qas"]:
id_to_question_mapping[
question["id"]] = question["question"]
for question_id in all_predictions:
all_predictions[question_id] = (
id_to_question_mapping[question_id],
all_predictions[question_id])
if output_nbest_file is not None:
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest, indent=4) + "\n")
if output_prediction_file is not None:
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return all_predictions, all_nbest
def setup_training_data(self, train_data_config: Optional[DictConfig]):
if not train_data_config or not train_data_config.file:
logging.info(
f"Dataloader config or file_path for the train is missing, so no data loader for test is created!"
)
self._test_dl = None
return
self._train_dl = self._setup_dataloader_from_config(
cfg=train_data_config, mode=TRAINING_MODE)
def setup_validation_data(self, val_data_config: Optional[DictConfig]):
if not val_data_config or not val_data_config.file:
logging.info(
f"Dataloader config or file_path for the validation is missing, so no data loader for test is created!"
)
self._test_dl = None
return
self._validation_dl = self._setup_dataloader_from_config(
cfg=val_data_config, mode=EVALUATION_MODE)
def setup_test_data(self, test_data_config: Optional[DictConfig]):
if not test_data_config or test_data_config.file is None:
logging.info(
f"Dataloader config or file_path for the test is missing, so no data loader for test is created!"
)
self._test_dl = None
return
self._test_dl = self._setup_dataloader_from_config(
cfg=test_data_config, mode=EVALUATION_MODE)
def _setup_dataloader_from_config(self, cfg: DictConfig, mode: str):
dataset = SquadDataset(
tokenizer=self.tokenizer,
data_file=cfg.file,
doc_stride=self._cfg.dataset.doc_stride,
max_query_length=self._cfg.dataset.max_query_length,
max_seq_length=self._cfg.dataset.max_seq_length,
version_2_with_negative=self._cfg.dataset.version_2_with_negative,
num_samples=cfg.num_samples,
mode=mode,
use_cache=self._cfg.dataset.use_cache,
)
dl = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=cfg.batch_size,
collate_fn=dataset.collate_fn,
drop_last=cfg.drop_last,
shuffle=cfg.shuffle,
num_workers=cfg.num_workers,
pin_memory=cfg.pin_memory,
)
return dl
@classmethod
def list_available_models(cls) -> Optional[PretrainedModelInfo]:
"""
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
Returns:
List of available pre-trained models.
"""
result = []
model = PretrainedModelInfo(
pretrained_model_name="BERTBaseUncasedSQuADv1.1",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/BERTBaseUncasedSQuADv1.1.nemo",
description=
"Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 82.43% and an F1 score of 89.59%.",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="BERTBaseUncasedSQuADv2.0",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/BERTBaseUncasedSQuADv2.0.nemo",
description=
"Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 73.35% and an F1 score of 76.44%.",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="BERTLargeUncasedSQuADv1.1",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/BERTLargeUncasedSQuADv1.1.nemo",
description=
"Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 85.47% and an F1 score of 92.10%.",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="BERTLargeUncasedSQuADv2.0",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/BERTLargeUncasedSQuADv2.0.nemo",
description=
"Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 78.8% and an F1 score of 81.85%.",
)
result.append(model)
return result
def _prepare_for_export(self):
return self.bert_model._prepare_for_export()
def export(
self,
output: str,
input_example=None,
output_example=None,
verbose=False,
export_params=True,
do_constant_folding=True,
keep_initializers_as_inputs=False,
onnx_opset_version: int = 12,
try_script: bool = False,
set_eval: bool = True,
check_trace: bool = True,
use_dynamic_axes: bool = True,
):
if input_example is not None or output_example is not None:
logging.warning(
"Passed input and output examples will be ignored and recomputed since"
" QAModel consists of two separate models with different"
" inputs and outputs.")
qual_name = self.__module__ + '.' + self.__class__.__qualname__
output1 = os.path.join(os.path.dirname(output),
'bert_' + os.path.basename(output))
output1_descr = qual_name + ' BERT exported to ONNX'
bert_model_onnx = self.bert_model.export(
output1,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
output2 = os.path.join(os.path.dirname(output),
'classifier_' + os.path.basename(output))
output2_descr = qual_name + ' Classifier exported to ONNX'
classifier_onnx = self.classifier.export(
output2,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
output_model = attach_onnx_to_onnx(bert_model_onnx, classifier_onnx,
"QA")
output_descr = qual_name + ' BERT+Classifier exported to ONNX'
onnx.save(output_model, output)
return ([output, output1,
output2], [output_descr, output1_descr, output2_descr])
class PunctuationCapitalizationModel(NLPModel, Exportable):
@property
def input_types(self) -> Optional[Dict[str, NeuralType]]:
return self.bert_model.input_types
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {
"punct_logits": NeuralType(('B', 'T', 'C'), LogitsType()),
"capit_logits": NeuralType(('B', 'T', 'C'), LogitsType()),
}
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
"""
Initializes BERT Punctuation and Capitalization model.
"""
self.setup_tokenizer(cfg.tokenizer)
super().__init__(cfg=cfg, trainer=trainer)
self.bert_model = get_lm_model(
pretrained_model_name=cfg.language_model.pretrained_model_name,
config_file=cfg.language_model.config_file,
config_dict=OmegaConf.to_container(cfg.language_model.config) if cfg.language_model.config else None,
checkpoint_file=cfg.language_model.lm_checkpoint,
)
self.punct_classifier = TokenClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_classes=len(self._cfg.punct_label_ids),
activation=cfg.punct_head.activation,
log_softmax=False,
dropout=cfg.punct_head.fc_dropout,
num_layers=cfg.punct_head.punct_num_fc_layers,
use_transformer_init=cfg.punct_head.use_transformer_init,
)
self.capit_classifier = TokenClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_classes=len(self._cfg.capit_label_ids),
activation=cfg.capit_head.activation,
log_softmax=False,
dropout=cfg.capit_head.fc_dropout,
num_layers=cfg.capit_head.capit_num_fc_layers,
use_transformer_init=cfg.capit_head.use_transformer_init,
)
self.loss = CrossEntropyLoss(logits_ndim=3)
self.agg_loss = AggregatorLoss(num_inputs=2)
# setup to track metrics
self.punct_class_report = ClassificationReport(
num_classes=len(self._cfg.punct_label_ids),
label_ids=self._cfg.punct_label_ids,
mode='macro',
dist_sync_on_step=True,
)
self.capit_class_report = ClassificationReport(
num_classes=len(self._cfg.capit_label_ids),
label_ids=self._cfg.capit_label_ids,
mode='macro',
dist_sync_on_step=True,
)
@typecheck()
def forward(self, input_ids, attention_mask, token_type_ids=None):
"""
No special modification required for Lightning, define it as you normally would
in the `nn.Module` in vanilla PyTorch.
"""
hidden_states = self.bert_model(
input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask
)
punct_logits = self.punct_classifier(hidden_states=hidden_states)
capit_logits = self.capit_classifier(hidden_states=hidden_states)
return punct_logits, capit_logits
def _make_step(self, batch):
input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, punct_labels, capit_labels = batch
punct_logits, capit_logits = self(
input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask
)
punct_loss = self.loss(logits=punct_logits, labels=punct_labels, loss_mask=loss_mask)
capit_loss = self.loss(logits=capit_logits, labels=capit_labels, loss_mask=loss_mask)
loss = self.agg_loss(loss_1=punct_loss, loss_2=capit_loss)
return loss, punct_logits, capit_logits
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop with the data from the training dataloader
passed in as `batch`.
"""
loss, _, _ = self._make_step(batch)
lr = self._optimizer.param_groups[0]['lr']
self.log('lr', lr, prog_bar=True)
self.log('train_loss', loss)
return {'loss': loss, 'lr': lr}
def validation_step(self, batch, batch_idx, dataloader_idx=0):
"""
Lightning calls this inside the validation loop with the data from the validation dataloader
passed in as `batch`.
"""
_, _, _, subtokens_mask, _, punct_labels, capit_labels = batch
val_loss, punct_logits, capit_logits = self._make_step(batch)
subtokens_mask = subtokens_mask > 0.5
punct_preds = torch.argmax(punct_logits, axis=-1)[subtokens_mask]
punct_labels = punct_labels[subtokens_mask]
self.punct_class_report.update(punct_preds, punct_labels)
capit_preds = torch.argmax(capit_logits, axis=-1)[subtokens_mask]
capit_labels = capit_labels[subtokens_mask]
self.capit_class_report.update(capit_preds, capit_labels)
return {
'val_loss': val_loss,
'punct_tp': self.punct_class_report.tp,
'punct_fn': self.punct_class_report.fn,
'punct_fp': self.punct_class_report.fp,
'capit_tp': self.capit_class_report.tp,
'capit_fn': self.capit_class_report.fn,
'capit_fp': self.capit_class_report.fp,
}
def test_step(self, batch, batch_idx, dataloader_idx=0):
"""
Lightning calls this inside the validation loop with the data from the validation dataloader
passed in as `batch`.
"""
_, _, _, subtokens_mask, _, punct_labels, capit_labels = batch
test_loss, punct_logits, capit_logits = self._make_step(batch)
subtokens_mask = subtokens_mask > 0.5
punct_preds = torch.argmax(punct_logits, axis=-1)[subtokens_mask]
punct_labels = punct_labels[subtokens_mask]
self.punct_class_report.update(punct_preds, punct_labels)
capit_preds = torch.argmax(capit_logits, axis=-1)[subtokens_mask]
capit_labels = capit_labels[subtokens_mask]
self.capit_class_report.update(capit_preds, capit_labels)
return {
'test_loss': test_loss,
'punct_tp': self.punct_class_report.tp,
'punct_fn': self.punct_class_report.fn,
'punct_fp': self.punct_class_report.fp,
'capit_tp': self.capit_class_report.tp,
'capit_fn': self.capit_class_report.fn,
'capit_fp': self.capit_class_report.fp,
}
def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0):
"""
Called at the end of validation to aggregate outputs.
outputs: list of individual outputs of each validation step.
"""
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
# calculate metrics and log classification report for Punctuation task
punct_precision, punct_recall, punct_f1, punct_report = self.punct_class_report.compute()
logging.info(f'Punctuation report: {punct_report}')
# calculate metrics and log classification report for Capitalization task
capit_precision, capit_recall, capit_f1, capit_report = self.capit_class_report.compute()
logging.info(f'Capitalization report: {capit_report}')
self.log('val_loss', avg_loss, prog_bar=True)
self.log('punct_precision', punct_precision)
self.log('punct_f1', punct_f1)
self.log('punct_recall', punct_recall)
self.log('capit_precision', capit_precision)
self.log('capit_f1', capit_f1)
self.log('capit_recall', capit_recall)
def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
"""
Called at the end of test to aggregate outputs.
outputs: list of individual outputs of each validation step.
"""
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
# calculate metrics and log classification report for Punctuation task
punct_precision, punct_recall, punct_f1, punct_report = self.punct_class_report.compute()
logging.info(f'Punctuation report: {punct_report}')
# calculate metrics and log classification report for Capitalization task
capit_precision, capit_recall, capit_f1, capit_report = self.capit_class_report.compute()
logging.info(f'Capitalization report: {capit_report}')
self.log('test_loss', avg_loss, prog_bar=True)
self.log('punct_precision', punct_precision)
self.log('punct_f1', punct_f1)
self.log('punct_recall', punct_recall)
self.log('capit_precision', capit_precision)
self.log('capit_f1', capit_f1)
self.log('capit_recall', capit_recall)
def update_data_dir(self, data_dir: str) -> None:
"""
Update data directory
Args:
data_dir: path to data directory
"""
if os.path.exists(data_dir):
logging.info(f'Setting model.dataset.data_dir to {data_dir}.')
self._cfg.dataset.data_dir = data_dir
else:
raise ValueError(f'{data_dir} not found')
def setup_training_data(self, train_data_config: Optional[DictConfig] = None):
"""Setup training data"""
if train_data_config is None:
train_data_config = self._cfg.train_ds
# for older(pre - 1.0.0.b3) configs compatibility
if not hasattr(self._cfg, "class_labels") or self._cfg.class_labels is None:
OmegaConf.set_struct(self._cfg, False)
self._cfg.class_labels = {}
self._cfg.class_labels = OmegaConf.create(
{'punct_labels_file': 'punct_label_ids.csv', 'capit_labels_file': 'capit_label_ids.csv'}
)
self._train_dl = self._setup_dataloader_from_config(cfg=train_data_config)
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
self.register_artifact(
self._cfg.class_labels.punct_labels_file, self._train_dl.dataset.punct_label_ids_file
)
self.register_artifact(
self._cfg.class_labels.capit_labels_file, self._train_dl.dataset.capit_label_ids_file
)
# save label maps to the config
self._cfg.punct_label_ids = OmegaConf.create(self._train_dl.dataset.punct_label_ids)
self._cfg.capit_label_ids = OmegaConf.create(self._train_dl.dataset.capit_label_ids)
def setup_validation_data(self, val_data_config: Optional[Dict] = None):
"""
Setup validaton data
val_data_config: validation data config
"""
if val_data_config is None:
val_data_config = self._cfg.validation_ds
self._validation_dl = self._setup_dataloader_from_config(cfg=val_data_config)
def setup_test_data(self, test_data_config: Optional[Dict] = None):
if test_data_config is None:
test_data_config = self._cfg.test_ds
self._test_dl = self._setup_dataloader_from_config(cfg=test_data_config)
def _setup_dataloader_from_config(self, cfg: DictConfig):
# use data_dir specified in the ds_item to run evaluation on multiple datasets
if 'ds_item' in cfg and cfg.ds_item is not None:
data_dir = cfg.ds_item
else:
data_dir = self._cfg.dataset.data_dir
text_file = os.path.join(data_dir, cfg.text_file)
label_file = os.path.join(data_dir, cfg.labels_file)
dataset = BertPunctuationCapitalizationDataset(
tokenizer=self.tokenizer,
text_file=text_file,
label_file=label_file,
pad_label=self._cfg.dataset.pad_label,
punct_label_ids=self._cfg.punct_label_ids,
capit_label_ids=self._cfg.capit_label_ids,
max_seq_length=self._cfg.dataset.max_seq_length,
ignore_extra_tokens=self._cfg.dataset.ignore_extra_tokens,
ignore_start_end=self._cfg.dataset.ignore_start_end,
use_cache=self._cfg.dataset.use_cache,
num_samples=cfg.num_samples,
punct_label_ids_file=self._cfg.class_labels.punct_labels_file
if 'class_labels' in self._cfg
else 'punct_label_ids.csv',
capit_label_ids_file=self._cfg.class_labels.capit_labels_file
if 'class_labels' in self._cfg
else 'capit_label_ids.csv',
)
return torch.utils.data.DataLoader(
dataset=dataset,
collate_fn=dataset.collate_fn,
batch_size=cfg.batch_size,
shuffle=cfg.shuffle,
num_workers=self._cfg.dataset.num_workers,
pin_memory=self._cfg.dataset.pin_memory,
drop_last=self._cfg.dataset.drop_last,
)
def _setup_infer_dataloader(self, queries: List[str], batch_size: int) -> 'torch.utils.data.DataLoader':
"""
Setup function for a infer data loader.
Args:
queries: lower cased text without punctuation
batch_size: batch size to use during inference
Returns:
A pytorch DataLoader.
"""
dataset = BertPunctuationCapitalizationInferDataset(
tokenizer=self.tokenizer, queries=queries, max_seq_length=self._cfg.dataset.max_seq_length
)
return torch.utils.data.DataLoader(
dataset=dataset,
collate_fn=dataset.collate_fn,
batch_size=batch_size,
shuffle=False,
num_workers=self._cfg.dataset.num_workers,
pin_memory=self._cfg.dataset.pin_memory,
drop_last=False,
)
def add_punctuation_capitalization(self, queries: List[str], batch_size: int = None) -> List[str]:
"""
Adds punctuation and capitalization to the queries. Use this method for debugging and prototyping.
Args:
queries: lower cased text without punctuation
batch_size: batch size to use during inference
Returns:
result: text with added capitalization and punctuation
"""
if queries is None or len(queries) == 0:
return []
if batch_size is None:
batch_size = len(queries)
logging.info(f'Using batch size {batch_size} for inference')
# We will store the output here
result = []
# Model's mode and device
mode = self.training
device = 'cuda' if torch.cuda.is_available() else 'cpu'
try:
# Switch model to evaluation mode
self.eval()
self = self.to(device)
infer_datalayer = self._setup_infer_dataloader(queries, batch_size)
# store predictions for all queries in a single list
all_punct_preds = []
all_capit_preds = []
for batch in infer_datalayer:
input_ids, input_type_ids, input_mask, subtokens_mask = batch
punct_logits, capit_logits = self.forward(
input_ids=input_ids.to(device),
token_type_ids=input_type_ids.to(device),
attention_mask=input_mask.to(device),
)
subtokens_mask = subtokens_mask > 0.5
punct_preds = tensor2list(torch.argmax(punct_logits, axis=-1)[subtokens_mask])
capit_preds = tensor2list(torch.argmax(capit_logits, axis=-1)[subtokens_mask])
all_punct_preds.extend(punct_preds)
all_capit_preds.extend(capit_preds)
queries = [q.strip().split() for q in queries]
queries_len = [len(q) for q in queries]
if sum(queries_len) != len(all_punct_preds) or sum(queries_len) != len(all_capit_preds):
raise ValueError('Pred and words must have the same length')
punct_ids_to_labels = {v: k for k, v in self._cfg.punct_label_ids.items()}
capit_ids_to_labels = {v: k for k, v in self._cfg.capit_label_ids.items()}
start_idx = 0
end_idx = 0
for query in queries:
end_idx += len(query)
# extract predictions for the current query from the list of all predictions
punct_preds = all_punct_preds[start_idx:end_idx]
capit_preds = all_capit_preds[start_idx:end_idx]
start_idx = end_idx
query_with_punct_and_capit = ''
for j, word in enumerate(query):
punct_label = punct_ids_to_labels[punct_preds[j]]
capit_label = capit_ids_to_labels[capit_preds[j]]
if capit_label != self._cfg.dataset.pad_label:
word = word.capitalize()
query_with_punct_and_capit += word
if punct_label != self._cfg.dataset.pad_label:
query_with_punct_and_capit += punct_label
query_with_punct_and_capit += ' '
result.append(query_with_punct_and_capit.strip())
finally:
# set mode back to its original value
self.train(mode=mode)
return result
@classmethod
def list_available_models(cls) -> Optional[Dict[str, str]]:
"""
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
Returns:
List of available pre-trained models.
"""
result = []
result.append(
PretrainedModelInfo(
pretrained_model_name="Punctuation_Capitalization_with_BERT",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/Punctuation_Capitalization_with_BERT.nemo",
description="The model was trained with NeMo BERT base uncased checkpoint on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.",
)
)
result.append(
PretrainedModelInfo(
pretrained_model_name="Punctuation_Capitalization_with_DistilBERT",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/Punctuation_Capitalization_with_DistilBERT.nemo",
description="The model was trained with DiltilBERT base uncased checkpoint from HuggingFace on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.",
)
)
return result
def _prepare_for_export(self):
return self.bert_model._prepare_for_export()
def export(
self,
output: str,
input_example=None,
output_example=None,
verbose=False,
export_params=True,
do_constant_folding=True,
keep_initializers_as_inputs=False,
onnx_opset_version: int = 12,
try_script: bool = False,
set_eval: bool = True,
check_trace: bool = True,
use_dynamic_axes: bool = True,
):
"""
Unlike other models' export() this one creates 5 output files, not 3:
punct_<output> - fused punctuation model (BERT+PunctuationClassifier)
capit_<output> - fused capitalization model (BERT+CapitalizationClassifier)
bert_<output> - common BERT neural net
punct_classifier_<output> - Punctuation Classifier neural net
capt_classifier_<output> - Capitalization Classifier neural net
"""
if input_example is not None or output_example is not None:
logging.warning(
"Passed input and output examples will be ignored and recomputed since"
" PunctuationCapitalizationModel consists of three separate models with different"
" inputs and outputs."
)
qual_name = self.__module__ + '.' + self.__class__.__qualname__
output1 = os.path.join(os.path.dirname(output), 'bert_' + os.path.basename(output))
output1_descr = qual_name + ' BERT exported to ONNX'
bert_model_onnx = self.bert_model.export(
output1,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
output2 = os.path.join(os.path.dirname(output), 'punct_classifier_' + os.path.basename(output))
output2_descr = qual_name + ' Punctuation Classifier exported to ONNX'
punct_classifier_onnx = self.punct_classifier.export(
output2,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
output3 = os.path.join(os.path.dirname(output), 'capit_classifier_' + os.path.basename(output))
output3_descr = qual_name + ' Capitalization Classifier exported to ONNX'
capit_classifier_onnx = self.capit_classifier.export(
output3,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
punct_output_model = attach_onnx_to_onnx(bert_model_onnx, punct_classifier_onnx, "PTCL")
output4 = os.path.join(os.path.dirname(output), 'punct_' + os.path.basename(output))
output4_descr = qual_name + ' Punctuation BERT+Classifier exported to ONNX'
onnx.save(punct_output_model, output4)
capit_output_model = attach_onnx_to_onnx(bert_model_onnx, capit_classifier_onnx, "CPCL")
output5 = os.path.join(os.path.dirname(output), 'capit_' + os.path.basename(output))
output5_descr = qual_name + ' Capitalization BERT+Classifier exported to ONNX'
onnx.save(capit_output_model, output5)
return (
[output1, output2, output3, output4, output5],
[output1_descr, output2_descr, output3_descr, output4_descr, output5_descr],
)
class TokenClassificationModel(NLPModel, Exportable):
"""Token Classification Model with BERT, applicable for tasks such as Named Entity Recognition"""
@property
def input_types(self) -> Optional[Dict[str, NeuralType]]:
return self.bert_model.input_types
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return self.classifier.output_types
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
"""Initializes Token Classification Model."""
# extract str to int labels mapping if a mapping file provided
if isinstance(cfg.label_ids, str):
if os.path.exists(cfg.label_ids):
logging.info(
f'Reusing label_ids file found at {cfg.label_ids}.')
label_ids = get_labels_to_labels_id_mapping(cfg.label_ids)
# update the config to store name to id mapping
cfg.label_ids = OmegaConf.create(label_ids)
else:
raise ValueError(f'{cfg.label_ids} not found.')
self._setup_tokenizer(cfg.tokenizer)
super().__init__(cfg=cfg, trainer=trainer)
self.bert_model = get_lm_model(
pretrained_model_name=cfg.language_model.pretrained_model_name,
config_file=cfg.language_model.config_file,
config_dict=OmegaConf.to_container(cfg.language_model.config)
if cfg.language_model.config else None,
checkpoint_file=cfg.language_model.lm_checkpoint,
)
self.classifier = TokenClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_classes=len(self._cfg.label_ids),
num_layers=self._cfg.head.num_fc_layers,
activation=self._cfg.head.activation,
log_softmax=False,
dropout=self._cfg.head.fc_dropout,
use_transformer_init=self._cfg.head.use_transformer_init,
)
self.class_weights = None
self.loss = self.setup_loss(
class_balancing=self._cfg.dataset.class_balancing)
# setup to track metrics
self.classification_report = ClassificationReport(
len(self._cfg.label_ids),
label_ids=self._cfg.label_ids,
dist_sync_on_step=True)
def update_data_dir(self, data_dir: str) -> None:
"""
Update data directory and get data stats with Data Descriptor
Weights are later used to setup loss
Args:
data_dir: path to data directory
"""
self._cfg.dataset.data_dir = data_dir
logging.info(f'Setting model.dataset.data_dir to {data_dir}.')
def setup_loss(self, class_balancing: str = None):
"""Setup loss
Setup or update loss.
Args:
class_balancing: whether to use class weights during training
"""
if class_balancing == 'weighted_loss' and self.class_weights:
# you may need to increase the number of epochs for convergence when using weighted_loss
loss = CrossEntropyLoss(logits_ndim=3, weight=self.class_weights)
else:
loss = CrossEntropyLoss(logits_ndim=3)
return loss
@typecheck()
def forward(self, input_ids, token_type_ids, attention_mask):
hidden_states = self.bert_model(input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask)
logits = self.classifier(hidden_states=hidden_states)
return logits
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop with the data from the training dataloader
passed in as `batch`.
"""
input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, labels = batch
logits = self(input_ids=input_ids,
token_type_ids=input_type_ids,
attention_mask=input_mask)
loss = self.loss(logits=logits, labels=labels, loss_mask=loss_mask)
lr = self._optimizer.param_groups[0]['lr']
self.log('train_loss', loss)
self.log('lr', lr, prog_bar=True)
return {
'loss': loss,
'lr': lr,
}
def validation_step(self, batch, batch_idx):
"""
Lightning calls this inside the validation loop with the data from the validation dataloader
passed in as `batch`.
"""
input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, labels = batch
logits = self(input_ids=input_ids,
token_type_ids=input_type_ids,
attention_mask=input_mask)
val_loss = self.loss(logits=logits, labels=labels, loss_mask=loss_mask)
subtokens_mask = subtokens_mask > 0.5
preds = torch.argmax(logits, axis=-1)[subtokens_mask]
labels = labels[subtokens_mask]
tp, fn, fp, _ = self.classification_report(preds, labels)
return {'val_loss': val_loss, 'tp': tp, 'fn': fn, 'fp': fp}
def validation_epoch_end(self, outputs):
"""
Called at the end of validation to aggregate outputs.
outputs: list of individual outputs of each validation step.
"""
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
# calculate metrics and classification report
precision, recall, f1, report = self.classification_report.compute()
logging.info(report)
self.log('val_loss', avg_loss, prog_bar=True)
self.log('precision', precision)
self.log('f1', f1)
self.log('recall', recall)
def test_step(self, batch, batch_idx):
"""
Lightning calls this inside the test loop with the data from the test dataloader
passed in as `batch`.
"""
return self.validation_step(batch, batch_idx)
def test_epoch_end(self, outputs):
"""
Called at the end of test to aggregate outputs.
"""
return self.validation_epoch_end(outputs)
def _setup_tokenizer(self, cfg: DictConfig):
tokenizer = get_tokenizer(
tokenizer_name=cfg.tokenizer_name,
vocab_file=self.register_artifact(
config_path='tokenizer.vocab_file', src=cfg.vocab_file),
special_tokens=OmegaConf.to_container(cfg.special_tokens)
if cfg.special_tokens else None,
tokenizer_model=self.register_artifact(
config_path='tokenizer.tokenizer_model',
src=cfg.tokenizer_model),
)
self.tokenizer = tokenizer
def setup_training_data(self,
train_data_config: Optional[DictConfig] = None):
if train_data_config is None:
train_data_config = self._cfg.train_ds
labels_file = os.path.join(self._cfg.dataset.data_dir,
train_data_config.labels_file)
label_ids, label_ids_filename, self.class_weights = get_label_ids(
label_file=labels_file,
is_training=True,
pad_label=self._cfg.dataset.pad_label,
label_ids_dict=self._cfg.label_ids,
get_weights=self._cfg.dataset.class_balancing == 'weighted_loss',
)
# save label maps to the config
self._cfg.label_ids = OmegaConf.create(label_ids)
self.register_artifact('label_ids.csv', label_ids_filename)
self._train_dl = self._setup_dataloader_from_config(
cfg=train_data_config)
def setup_validation_data(self,
val_data_config: Optional[DictConfig] = None):
if val_data_config is None:
val_data_config = self._cfg.validation_ds
labels_file = os.path.join(self._cfg.dataset.data_dir,
val_data_config.labels_file)
get_label_ids(
label_file=labels_file,
is_training=False,
pad_label=self._cfg.dataset.pad_label,
label_ids_dict=self._cfg.label_ids,
get_weights=False,
)
self._validation_dl = self._setup_dataloader_from_config(
cfg=val_data_config)
def setup_test_data(self, test_data_config: Optional[DictConfig] = None):
if test_data_config is None:
test_data_config = self._cfg.test_ds
labels_file = os.path.join(self._cfg.dataset.data_dir,
test_data_config.labels_file)
get_label_ids(
label_file=labels_file,
is_training=False,
pad_label=self._cfg.dataset.pad_label,
label_ids_dict=self._cfg.label_ids,
get_weights=False,
)
self._test_dl = self._setup_dataloader_from_config(
cfg=test_data_config)
def _setup_dataloader_from_config(self, cfg: DictConfig) -> DataLoader:
"""
Setup dataloader from config
Args:
cfg: config for the dataloader
Return:
Pytorch Dataloader
"""
dataset_cfg = self._cfg.dataset
data_dir = dataset_cfg.data_dir
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"Data directory is not found at: {data_dir}.")
text_file = os.path.join(data_dir, cfg.text_file)
labels_file = os.path.join(data_dir, cfg.labels_file)
if not (os.path.exists(text_file) and os.path.exists(labels_file)):
raise FileNotFoundError(
f'{text_file} or {labels_file} not found. The data should be split into 2 files: text.txt and \
labels.txt. Each line of the text.txt file contains text sequences, where words are separated with \
spaces. The labels.txt file contains corresponding labels for each word in text.txt, the labels are \
separated with spaces. Each line of the files should follow the format: \
[WORD] [SPACE] [WORD] [SPACE] [WORD] (for text.txt) and \
[LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt).')
dataset = BertTokenClassificationDataset(
text_file=text_file,
label_file=labels_file,
max_seq_length=dataset_cfg.max_seq_length,
tokenizer=self.tokenizer,
num_samples=cfg.num_samples,
pad_label=dataset_cfg.pad_label,
label_ids=self._cfg.label_ids,
ignore_extra_tokens=dataset_cfg.ignore_extra_tokens,
ignore_start_end=dataset_cfg.ignore_start_end,
use_cache=dataset_cfg.use_cache,
)
return DataLoader(
dataset=dataset,
collate_fn=dataset.collate_fn,
batch_size=cfg.batch_size,
shuffle=cfg.shuffle,
num_workers=dataset_cfg.num_workers,
pin_memory=dataset_cfg.pin_memory,
drop_last=dataset_cfg.drop_last,
)
def _setup_infer_dataloader(
self, queries: List[str],
batch_size: int) -> 'torch.utils.data.DataLoader':
"""
Setup function for a infer data loader.
Args:
queries: text
batch_size: batch size to use during inference
Returns:
A pytorch DataLoader.
"""
dataset = BertTokenClassificationInferDataset(tokenizer=self.tokenizer,
queries=queries,
max_seq_length=-1)
return torch.utils.data.DataLoader(
dataset=dataset,
collate_fn=dataset.collate_fn,
batch_size=batch_size,
shuffle=False,
num_workers=self._cfg.dataset.num_workers,
pin_memory=self._cfg.dataset.pin_memory,
drop_last=False,
)
@torch.no_grad()
def _infer(self, queries: List[str], batch_size: int = None) -> List[int]:
"""
Get prediction for the queries
Args:
queries: text sequences
batch_size: batch size to use during inference.
Returns:
all_preds: model predictions
"""
# store predictions for all queries in a single list
all_preds = []
mode = self.training
try:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Switch model to evaluation mode
self.eval()
self.to(device)
infer_datalayer = self._setup_infer_dataloader(queries, batch_size)
for batch in infer_datalayer:
input_ids, input_type_ids, input_mask, subtokens_mask = batch
logits = self.forward(
input_ids=input_ids.to(device),
token_type_ids=input_type_ids.to(device),
attention_mask=input_mask.to(device),
)
subtokens_mask = subtokens_mask > 0.5
preds = tensor2list(
torch.argmax(logits, axis=-1)[subtokens_mask])
all_preds.extend(preds)
finally:
# set mode back to its original value
self.train(mode=mode)
return all_preds
def add_predictions(self,
queries: Union[List[str], str],
batch_size: int = 32) -> List[str]:
"""
Add predicted token labels to the queries. Use this method for debugging and prototyping.
Args:
queries: text
batch_size: batch size to use during inference.
Returns:
result: text with added entities
"""
if queries is None or len(queries) == 0:
return []
result = []
all_preds = self._infer(queries, batch_size)
queries = [q.strip().split() for q in queries]
num_words = [len(q) for q in queries]
if sum(num_words) != len(all_preds):
raise ValueError('Pred and words must have the same length')
ids_to_labels = {v: k for k, v in self._cfg.label_ids.items()}
start_idx = 0
end_idx = 0
for query in queries:
end_idx += len(query)
# extract predictions for the current query from the list of all predictions
preds = all_preds[start_idx:end_idx]
start_idx = end_idx
query_with_entities = ''
for j, word in enumerate(query):
# strip out the punctuation to attach the entity tag to the word not to a punctuation mark
# that follows the word
if word[-1].isalpha():
punct = ''
else:
punct = word[-1]
word = word[:-1]
query_with_entities += word
label = ids_to_labels[preds[j]]
if label != self._cfg.dataset.pad_label:
query_with_entities += '[' + label + ']'
query_with_entities += punct + ' '
result.append(query_with_entities.strip())
return result
def evaluate_from_file(
self,
output_dir: str,
text_file: str,
labels_file: Optional[str] = None,
add_confusion_matrix: Optional[bool] = False,
normalize_confusion_matrix: Optional[bool] = True,
batch_size: int = 1,
) -> None:
"""
Run inference on data from a file, plot confusion matrix and calculate classification report.
Use this method for final evaluation.
Args:
output_dir: path to output directory to store model predictions, confusion matrix plot (if set to True)
text_file: path to file with text. Each line of the text.txt file contains text sequences, where words
are separated with spaces: [WORD] [SPACE] [WORD] [SPACE] [WORD]
labels_file (Optional): path to file with labels. Each line of the labels_file should contain
labels corresponding to each word in the text_file, the labels are separated with spaces:
[LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt).'
add_confusion_matrix: whether to generate confusion matrix
normalize_confusion_matrix: whether to normalize confusion matrix
batch_size: batch size to use during inference.
"""
output_dir = os.path.abspath(output_dir)
with open(text_file, 'r') as f:
queries = f.readlines()
all_preds = self._infer(queries, batch_size)
with_labels = labels_file is not None
if with_labels:
with open(labels_file, 'r') as f:
all_labels_str = f.readlines()
all_labels_str = ' '.join(
[labels.strip() for labels in all_labels_str])
# writing labels and predictions to a file in output_dir is specified in the config
os.makedirs(output_dir, exist_ok=True)
filename = os.path.join(output_dir,
'infer_' + os.path.basename(text_file))
try:
with open(filename, 'w') as f:
if with_labels:
f.write('labels\t' + all_labels_str + '\n')
logging.info(f'Labels save to {filename}')
# convert labels from string label to ids
ids_to_labels = {v: k for k, v in self._cfg.label_ids.items()}
all_preds_str = [ids_to_labels[pred] for pred in all_preds]
f.write('preds\t' + ' '.join(all_preds_str) + '\n')
logging.info(f'Predictions saved to {filename}')
if with_labels and add_confusion_matrix:
all_labels = all_labels_str.split()
# convert labels from string label to ids
label_ids = self._cfg.label_ids
all_labels = [label_ids[label] for label in all_labels]
plot_confusion_matrix(all_labels,
all_preds,
output_dir,
label_ids=label_ids,
normalize=normalize_confusion_matrix)
logging.info(
get_classification_report(all_labels, all_preds,
label_ids))
except Exception:
logging.error(
f'When providing a file with labels, check that all labels in {labels_file} were'
f'seen during training.')
raise
@classmethod
def list_available_models(cls) -> Optional[PretrainedModelInfo]:
"""
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
Returns:
List of available pre-trained models.
"""
result = []
model = PretrainedModelInfo(
pretrained_model_name="NERModel",
location=
"https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/NERModel.nemo",
description=
"The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition and achieves 74.61 F1 Macro score.",
)
result.append(model)
return result
def _prepare_for_export(self):
return self.bert_model._prepare_for_export()
def export(
self,
output: str,
input_example=None,
output_example=None,
verbose=False,
export_params=True,
do_constant_folding=True,
keep_initializers_as_inputs=False,
onnx_opset_version: int = 12,
try_script: bool = False,
set_eval: bool = True,
check_trace: bool = True,
use_dynamic_axes: bool = True,
):
if input_example is not None or output_example is not None:
logging.warning(
"Passed input and output examples will be ignored and recomputed since"
" TokenClassificationModel consists of two separate models with different"
" inputs and outputs.")
qual_name = self.__module__ + '.' + self.__class__.__qualname__
output1 = os.path.join(os.path.dirname(output),
'bert_' + os.path.basename(output))
output1_descr = qual_name + ' BERT exported to ONNX'
bert_model_onnx = self.bert_model.export(
output1,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
output2 = os.path.join(os.path.dirname(output),
'classifier_' + os.path.basename(output))
output2_descr = qual_name + ' Classifier exported to ONNX'
classifier_onnx = self.classifier.export(
output2,
None, # computed by input_example()
None,
verbose,
export_params,
do_constant_folding,
keep_initializers_as_inputs,
onnx_opset_version,
try_script,
set_eval,
check_trace,
use_dynamic_axes,
)
output_model = attach_onnx_to_onnx(bert_model_onnx, classifier_onnx,
"TKCL")
output_descr = qual_name + ' BERT+Classifier exported to ONNX'
onnx.save(output_model, output)
return ([output, output1,
output2], [output_descr, output1_descr, output2_descr])