SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Tf model outputs (#6247)

Browse Source 
* TF outputs and test on BERT

* Albert to DistilBert

* All remaining TF models except T5

* Documentation

* One file forgotten

* TF outputs and test on BERT

* Albert to DistilBert

* All remaining TF models except T5

* Documentation

* One file forgotten

* Add new models and fix issues

* Quality improvements

* Add T5

* A bit of cleanup

* Fix for slow tests

* Style
This commit is contained in:
Sylvain Gugger
2020-08-05 11:34:39 -04:00
committed by GitHub
parent bd0eab351a
commit c67d1a0259
51 changed files with 3253 additions and 2430 deletions
Download Patch File Download Diff File Expand all files Collapse all files

60
tests/test_modeling_tf_mobilebert.py
View File

@@ -138,6 +138,7 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
embedding_size=self.embedding_size,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -147,33 +148,26 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
):
model = TFMobileBertModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
sequence_output, pooled_output = model(inputs)
result = model(inputs)
inputs = [input_ids, input_mask]
sequence_output, pooled_output = model(inputs)
result = model(inputs)
sequence_output, pooled_output = model(input_ids)
result = model(input_ids)
result = {
"sequence_output": sequence_output.numpy(),
"pooled_output": pooled_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
list(result["last_hidden_state"].shape), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(list(result["pooled_output"].shape), [self.batch_size, self.hidden_size])
self.parent.assertListEqual(list(result["pooler_output"].shape), [self.batch_size, self.hidden_size])
def create_and_check_mobilebert_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFMobileBertForMaskedLM(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(prediction_scores,) = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
result = model(inputs)
self.parent.assertListEqual(
list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size]
list(result["logits"].shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_mobilebert_for_next_sequence_prediction(
@@ -181,26 +175,19 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
):
model = TFMobileBertForNextSentencePrediction(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(seq_relationship_score,) = model(inputs)
result = {
"seq_relationship_score": seq_relationship_score.numpy(),
}
self.parent.assertListEqual(list(result["seq_relationship_score"].shape), [self.batch_size, 2])
result = model(inputs)
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, 2])
def create_and_check_mobilebert_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFMobileBertForPreTraining(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
prediction_scores, seq_relationship_score = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
"seq_relationship_score": seq_relationship_score.numpy(),
}
result = model(inputs)
self.parent.assertListEqual(
list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size]
list(result["prediction_logits"].shape), [self.batch_size, self.seq_length, self.vocab_size]
)
self.parent.assertListEqual(list(result["seq_relationship_score"].shape), [self.batch_size, 2])
self.parent.assertListEqual(list(result["seq_relationship_logits"].shape), [self.batch_size, 2])
def create_and_check_mobilebert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -208,10 +195,7 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
config.num_labels = self.num_labels
model = TFMobileBertForSequenceClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
result = model(inputs)
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_labels])
def create_and_check_mobilebert_for_multiple_choice(
@@ -227,10 +211,7 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
result = model(inputs)
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
def create_and_check_mobilebert_for_token_classification(
@@ -239,10 +220,7 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
config.num_labels = self.num_labels
model = TFMobileBertForTokenClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
result = model(inputs)
self.parent.assertListEqual(
list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels]
)
@@ -252,11 +230,7 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
):
model = TFMobileBertForQuestionAnswering(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
start_logits, end_logits = model(inputs)
result = {
"start_logits": start_logits.numpy(),
"end_logits": end_logits.numpy(),
}
result = model(inputs)
self.parent.assertListEqual(list(result["start_logits"].shape), [self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].shape), [self.batch_size, self.seq_length])