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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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28
tests/test_modeling_distilbert.py
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@@ -115,9 +115,6 @@ if is_torch_available():
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_distilbert_model(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
@@ -126,8 +123,8 @@ if is_torch_available():
model.eval()
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertListEqual(
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size]
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)
)
def create_and_check_distilbert_for_masked_lm(
@@ -137,10 +134,7 @@ if is_torch_available():
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, 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_distilbert_for_question_answering(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -151,9 +145,8 @@ if is_torch_available():
result = model(
input_ids, attention_mask=input_mask, 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_distilbert_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -163,8 +156,7 @@ if is_torch_available():
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, 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_distilbert_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -175,10 +167,7 @@ if is_torch_available():
model.eval()
result = model(input_ids, attention_mask=input_mask, 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_distilbert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -192,8 +181,7 @@ if is_torch_available():
result = model(
multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, 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()