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Rework some TF tests (#8492)

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* Update some tests

* Small update

* Apply style

* Use max_position_embeddings

* Create a fake attribute

* Create a fake attribute

* Update wrong name

* Wrong TransfoXL model file

* Keep the common tests agnostic
This commit is contained in:
Julien Plu
2020-11-13 23:07:17 +01:00
committed by GitHub
parent f6cdafdec7
commit 24184e73c4
2 changed files with 60 additions and 80 deletions
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58
tests/test_modeling_tf_longformer.py
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@@ -133,23 +133,21 @@ class TFLongformerModelTester:
def create_and_check_longformer_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.return_dict = True
model = TFLongformerModel(config=config)
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 = 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)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
shape_list(result["sequence_output"]), [self.batch_size, self.seq_length, self.hidden_size]
shape_list(result.last_hidden_state), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(shape_list(result["pooled_output"]), [self.batch_size, self.hidden_size])
self.parent.assertListEqual(shape_list(result.pooler_output), [self.batch_size, self.hidden_size])
def create_and_check_longformer_model_with_global_attention_mask(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.return_dict = True
model = TFLongformerModel(config=config)
half_input_mask_length = shape_list(input_mask)[-1] // 2
global_attention_mask = tf.concat(
@@ -160,59 +158,43 @@ class TFLongformerModelTester:
axis=-1,
)
sequence_output, pooled_output = model(
result = model(
input_ids,
attention_mask=input_mask,
global_attention_mask=global_attention_mask,
token_type_ids=token_type_ids,
)
sequence_output, pooled_output = model(
input_ids, token_type_ids=token_type_ids, global_attention_mask=global_attention_mask
)
sequence_output, pooled_output = model(input_ids, global_attention_mask=global_attention_mask)
result = model(input_ids, token_type_ids=token_type_ids, global_attention_mask=global_attention_mask)
result = model(input_ids, global_attention_mask=global_attention_mask)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
shape_list(result["sequence_output"]), [self.batch_size, self.seq_length, self.hidden_size]
shape_list(result.last_hidden_state), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(shape_list(result["pooled_output"]), [self.batch_size, self.hidden_size])
self.parent.assertListEqual(shape_list(result.pooler_output), [self.batch_size, self.hidden_size])
def create_and_check_longformer_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.return_dict = True
model = TFLongformerForMaskedLM(config=config)
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(
shape_list(result["prediction_scores"]), [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(shape_list(result.logits), [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_longformer_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.return_dict = True
model = TFLongformerForQuestionAnswering(config=config)
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(shape_list(result["start_logits"]), [self.batch_size, self.seq_length])
self.parent.assertListEqual(shape_list(result["end_logits"]), [self.batch_size, self.seq_length])
self.parent.assertListEqual(shape_list(result.start_logits), [self.batch_size, self.seq_length])
self.parent.assertListEqual(shape_list(result.end_logits), [self.batch_size, self.seq_length])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()