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Add GPT2ForSequenceClassification based on DialogRPT (#7501)

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* Add GPT2ForSequenceClassification based on DialogRPT

* Better documentation

* Code quality
This commit is contained in:
Lysandre Debut
2020-10-06 23:31:21 +02:00
committed by GitHub
parent 500be01c5d
commit 5982431814
5 changed files with 188 additions and 6 deletions
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28
tests/test_modeling_gpt2.py
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@@ -30,6 +30,7 @@ if is_torch_available():
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2Config,
GPT2DoubleHeadsModel,
GPT2ForSequenceClassification,
GPT2LMHeadModel,
GPT2Model,
)
@@ -87,6 +88,7 @@ class GPT2ModelTester:
self.scope = None
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
def prepare_config_and_inputs(self, gradient_checkpointing=False):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -126,6 +128,7 @@ class GPT2ModelTester:
# initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
return_dict=True,
gradient_checkpointing=gradient_checkpointing,
)
@@ -337,6 +340,17 @@ class GPT2ModelTester:
)
self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
def create_and_check_gpt2_for_sequence_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = GPT2ForSequenceClassification(config)
model.to(torch_device)
model.eval()
print(config.num_labels, sequence_labels.size())
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
@@ -364,10 +378,12 @@ class GPT2ModelTester:
@require_torch
class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
all_generative_model_classes = (
(GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
all_model_classes = (
(GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForSequenceClassification)
if is_torch_available()
else ()
)
all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
test_missing_keys = False
def setUp(self):
@@ -401,6 +417,10 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
def test_gpt2_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs)
def test_gpt2_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)