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Model output test (#6155)

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* Use return_dict=True in all tests

* Formatting
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Sylvain Gugger
2020-07-31 09:44:37 -04:00
committed by GitHub
parent 86caab1e0b
commit d951c14ae4
26 changed files with 320 additions and 765 deletions
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113
tests/test_modeling_bert.py
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@@ -120,6 +120,7 @@ class BertModelTester:
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -160,18 +161,13 @@ class BertModelTester:
model = BertModel(config=config)
model.to(torch_device)
model.eval()
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
self.parent.assertListEqual(list(result["pooler_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_bert_model_as_decoder(
self,
@@ -188,29 +184,24 @@ class BertModelTester:
model = BertModel(config)
model.to(torch_device)
model.eval()
sequence_output, pooled_output = model(
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
sequence_output, pooled_output = model(
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
self.parent.assertListEqual(list(result["pooler_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_bert_for_causal_lm(
self,
@@ -227,16 +218,8 @@ class BertModelTester:
model = BertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_bert_for_masked_lm(
@@ -245,16 +228,8 @@ class BertModelTester:
model = BertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_bert_model_for_causal_lm_as_decoder(
@@ -272,7 +247,7 @@ class BertModelTester:
model = BertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores = model(
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
@@ -280,20 +255,14 @@ class BertModelTester:
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
loss, prediction_scores = model(
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_bert_for_next_sequence_prediction(
@@ -302,14 +271,10 @@ class BertModelTester:
model = BertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
loss, seq_relationship_score = model(
result = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels,
)
result = {
"loss": loss,
"seq_relationship_score": seq_relationship_score,
}
self.parent.assertListEqual(list(result["seq_relationship_score"].size()), [self.batch_size, 2])
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, 2])
self.check_loss_output(result)
def create_and_check_bert_for_pretraining(
@@ -318,22 +283,17 @@ class BertModelTester:
model = BertForPreTraining(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores, seq_relationship_score = model(
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
"seq_relationship_score": seq_relationship_score,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
list(result["prediction_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
)
self.parent.assertListEqual(list(result["seq_relationship_score"].size()), [self.batch_size, 2])
self.parent.assertListEqual(list(result["seq_relationship_logits"].size()), [self.batch_size, 2])
self.check_loss_output(result)
def create_and_check_bert_for_question_answering(
@@ -342,18 +302,13 @@ class BertModelTester:
model = BertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
loss, start_logits, end_logits = model(
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
self.check_loss_output(result)
@@ -365,13 +320,7 @@ class BertModelTester:
model = BertForSequenceClassification(config)
model.to(torch_device)
model.eval()
loss, logits = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
)
result = {
"loss": loss,
"logits": logits,
}
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
self.check_loss_output(result)
@@ -382,11 +331,7 @@ class BertModelTester:
model = BertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
@@ -400,16 +345,12 @@ class BertModelTester:
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
loss, logits = model(
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
self.check_loss_output(result)