def test_TokenClassifier_module_onnx_export(self):
t_class = nemo_nlp.TokenClassifier(hidden_size=512, num_classes=16, use_transformer_pretrained=False)
self.__test_export_route(
module=t_class,
out_name="t_class.onnx",
mode=nemo.core.DeploymentFormat.ONNX,
input_example=torch.randn(16, 16, 512).cuda(),
)
pretrained_bert_model = nemo_nlp.huggingface.BERT(
pretrained_model_name=args.pretrained_bert_model)
hidden_size = pretrained_bert_model.local_parameters["hidden_size"]
tokenizer = NemoBertTokenizer(args.pretrained_bert_model)
data_layer = nemo_nlp.BertTokenClassificationInferDataLayer(
queries=args.queries,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
batch_size=1,
)
punct_classifier = nemo_nlp.TokenClassifier(
hidden_size=hidden_size,
num_classes=len(punct_labels_dict),
dropout=args.fc_dropout,
num_layers=args.punct_num_fc_layers,
name='Punctuation',
)
capit_classifier = nemo_nlp.TokenClassifier(
hidden_size=hidden_size,
num_classes=len(capit_labels_dict),
dropout=args.fc_dropout,
name='Capitalization',
)
input_ids, input_type_ids, input_mask, loss_mask, subtokens_mask = data_layer()
hidden_states = pretrained_bert_model(
input_ids=input_ids,
def test_squad_v1(self):
version_2_with_negative = False
pretrained_bert_model = 'bert-base-uncased'
batch_size = 3
data_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '../data/nlp/squad/v1.1'))
max_query_length = 64
max_seq_length = 384
doc_stride = 128
max_steps = 100
lr_warmup_proportion = 0
eval_step_freq = 50
lr = 3e-6
do_lower_case = True
n_best_size = 5
max_answer_length = 20
null_score_diff_threshold = 0.0
tokenizer = nemo_nlp.NemoBertTokenizer(pretrained_bert_model)
neural_factory = nemo.core.NeuralModuleFactory(
backend=nemo.core.Backend.PyTorch,
local_rank=None,
create_tb_writer=False,
)
model = nemo_nlp.huggingface.BERT(
pretrained_model_name=pretrained_bert_model)
hidden_size = model.local_parameters["hidden_size"]
qa_head = nemo_nlp.TokenClassifier(
hidden_size=hidden_size,
num_classes=2,
num_layers=1,
log_softmax=False,
)
squad_loss = nemo_nlp.QuestionAnsweringLoss()
data_layer = nemo_nlp.BertQuestionAnsweringDataLayer(
mode='train',
version_2_with_negative=version_2_with_negative,
batch_size=batch_size,
tokenizer=tokenizer,
data_dir=data_dir,
max_query_length=max_query_length,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
)
(
input_ids,
input_type_ids,
input_mask,
start_positions,
end_positions,
_,
) = data_layer()
hidden_states = model(
input_ids=input_ids,
token_type_ids=input_type_ids,
attention_mask=input_mask,
)
qa_output = qa_head(hidden_states=hidden_states)
loss, _, _ = squad_loss(
logits=qa_output,
start_positions=start_positions,
end_positions=end_positions,
)
data_layer_eval = nemo_nlp.BertQuestionAnsweringDataLayer(
mode='dev',
version_2_with_negative=version_2_with_negative,
batch_size=batch_size,
tokenizer=tokenizer,
data_dir=data_dir,
max_query_length=max_query_length,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
)
(
input_ids_eval,
input_type_ids_eval,
input_mask_eval,
start_positions_eval,
end_positions_eval,
unique_ids_eval,
) = data_layer_eval()
hidden_states_eval = model(
input_ids=input_ids_eval,
token_type_ids=input_type_ids_eval,
attention_mask=input_mask_eval,
)
qa_output_eval = qa_head(hidden_states=hidden_states_eval)
_, start_logits_eval, end_logits_eval = squad_loss(
logits=qa_output_eval,
start_positions=start_positions_eval,
end_positions=end_positions_eval,
)
eval_output = [start_logits_eval, end_logits_eval, unique_ids_eval]
callback_train = nemo.core.SimpleLossLoggerCallback(
tensors=[loss],
print_func=lambda x: print("Loss: {:.3f}".format(x[0].item())),
get_tb_values=lambda x: [["loss", x[0]]],
step_freq=10,
tb_writer=neural_factory.tb_writer,
)
callbacks_eval = nemo.core.EvaluatorCallback(
eval_tensors=eval_output,
user_iter_callback=lambda x, y: eval_iter_callback(x, y),
user_epochs_done_callback=lambda x: eval_epochs_done_callback(
x,
eval_data_layer=data_layer_eval,
do_lower_case=do_lower_case,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
version_2_with_negative=version_2_with_negative,
null_score_diff_threshold=null_score_diff_threshold,
),
tb_writer=neural_factory.tb_writer,
eval_step=eval_step_freq,
)
lr_policy_fn = get_lr_policy(
'WarmupAnnealing',
total_steps=max_steps,
warmup_ratio=lr_warmup_proportion,
)
neural_factory.train(
tensors_to_optimize=[loss],
callbacks=[callback_train, callbacks_eval],
lr_policy=lr_policy_fn,
optimizer='adam_w',
optimization_params={
"max_steps": max_steps,
"lr": lr
},
)
labels_dict = get_vocab(args.labels_dict)
""" Load the pretrained BERT parameters
See the list of pretrained models, call:
nemo_nlp.huggingface.BERT.list_pretrained_models()
"""
pretrained_bert_model = nemo_nlp.huggingface.BERT(pretrained_model_name=args.pretrained_bert_model)
hidden_size = pretrained_bert_model.local_parameters["hidden_size"]
tokenizer = NemoBertTokenizer(args.pretrained_bert_model)
data_layer = nemo_nlp.BertTokenClassificationInferDataLayer(
queries=args.queries, tokenizer=tokenizer, max_seq_length=args.max_seq_length, batch_size=1,
)
classifier = nemo_nlp.TokenClassifier(hidden_size=hidden_size, num_classes=len(labels_dict), dropout=args.fc_dropout,)
input_ids, input_type_ids, input_mask, _, subtokens_mask = data_layer()
hidden_states = pretrained_bert_model(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask,)
logits = classifier(hidden_states=hidden_states)
###########################################################################
# Instantiate an optimizer to perform `infer` action
evaluated_tensors = nf.infer(tensors=[logits, subtokens_mask], checkpoint_dir=args.work_dir,)
def concatenate(lists):
return np.concatenate([t.cpu() for t in lists])
encoder = nemo_nlp.TransformerEncoderNM(
d_model=args.d_model,
d_inner=args.d_inner,
num_layers=args.num_layers,
embedding_dropout=args.embedding_dropout,
num_attn_heads=args.num_attn_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=vocab_size,
mask_future=True,
attn_score_dropout=args.attn_score_dropout,
attn_layer_dropout=args.attn_layer_dropout,
max_seq_length=args.max_seq_length,
)
log_softmax = nemo_nlp.TokenClassifier(args.d_model, num_classes=vocab_size, num_layers=1, log_softmax=True)
loss = nemo_nlp.PaddedSmoothedCrossEntropyLossNM(pad_id=tokenizer.pad_id(), label_smoothing=args.label_smoothing)
# tie weight of embedding and log_softmax layers
log_softmax.mlp.last_linear_layer.weight = encoder.embedding_layer.token_embedding.weight
def create_pipeline(
dataset, max_seq_length=args.max_seq_length, batch_step=args.max_seq_length, batch_size=args.batch_size,
):
data_layer = nemo_nlp.LanguageModelingDataLayer(
dataset, tokenizer, max_seq_length, batch_step, batch_size=batch_size
)
src, src_mask, labels = data_layer()
src_hiddens = encoder(input_ids=src, input_mask_src=src_mask)
if args.bert_config is not None:
with open(args.bert_config) as json_file:
config = json.load(json_file)
model = nemo_nlp.huggingface.BERT(**config)
else:
""" Use this if you're using a standard BERT model.
To see the list of pretrained models, call:
nemo_nlp.huggingface.BERT.list_pretrained_models()
"""
model = nemo_nlp.huggingface.BERT(
pretrained_model_name=args.pretrained_bert_model)
hidden_size = model.local_parameters["hidden_size"]
qa_head = nemo_nlp.TokenClassifier(hidden_size=hidden_size,
num_classes=2,
num_layers=1,
log_softmax=False)
squad_loss = nemo_nlp.QuestionAnsweringLoss()
if args.bert_checkpoint is not None:
model.restore_from(args.bert_checkpoint)
if not args.evaluation_only:
train_loss, train_steps_per_epoch, _, _ = create_pipeline(
data_dir=args.data_dir,
model=model,
head=qa_head,
loss_fn=squad_loss,
max_query_length=args.max_query_length,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
batch_size=args.batch_size,
