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cleanup torch unittests (#6196)

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* improve unit tests

this is a sample of one test according to the request in https://github.com/huggingface/transformers/issues/5973
before I apply it to the rest

* batch 1

* batch 2

* batch 3

* batch 4

* batch 5

* style

* non-tf template

* last deletion of check_loss_output
This commit is contained in:
Stas Bekman
2020-08-03 23:42:56 -07:00
committed by GitHub
parent b390a5672a
commit 5deed37f9f
18 changed files with 157 additions and 339 deletions
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48
tests/test_modeling_bert.py
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@@ -152,9 +152,6 @@ class BertModelTester:
encoder_attention_mask,
)
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_bert_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
@@ -164,10 +161,8 @@ class BertModelTester:
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["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(list(result["pooler_output"].size()), [self.batch_size, self.hidden_size])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_bert_model_as_decoder(
self,
@@ -198,10 +193,8 @@ class BertModelTester:
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertListEqual(
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(list(result["pooler_output"].size()), [self.batch_size, self.hidden_size])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_bert_for_causal_lm(
self,
@@ -219,8 +212,7 @@ class BertModelTester:
model.to(torch_device)
model.eval()
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)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_bert_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -229,8 +221,7 @@ class BertModelTester:
model.to(torch_device)
model.eval()
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)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_bert_model_for_causal_lm_as_decoder(
self,
@@ -262,8 +253,7 @@ class BertModelTester:
labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_bert_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -274,8 +264,7 @@ class BertModelTester:
result = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels,
)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, 2])
self.check_loss_output(result)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_bert_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -290,11 +279,8 @@ class BertModelTester:
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertListEqual(
list(result["prediction_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
)
self.parent.assertListEqual(list(result["seq_relationship_logits"].size()), [self.batch_size, 2])
self.check_loss_output(result)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_bert_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -309,9 +295,8 @@ class BertModelTester:
start_positions=sequence_labels,
end_positions=sequence_labels,
)
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)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_bert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -321,8 +306,7 @@ class BertModelTester:
model.to(torch_device)
model.eval()
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)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_bert_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -332,8 +316,7 @@ class BertModelTester:
model.to(torch_device)
model.eval()
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)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_bert_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -351,8 +334,7 @@ class BertModelTester:
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
self.check_loss_output(result)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()