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Add TFGPT2ForSequenceClassification based on DialogRPT (#8714)

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* Add TFGPT2ForSequenceClassification based on DialogRPT

* Add TFGPT2ForSequenceClassification based on DialogRPT

* TFGPT2ForSequenceClassification based on DialogRPT-refactored code, implemented review comments and added input processing

* Add TFGPT2ForSequenceClassification based on DialogRPT

* TFGPT2ForSequenceClassification based on DialogRPT-refactored code, implemented review comments and added input processing

* code refactor for latest other TF PR

* code refactor

* code refactor

* Update modeling_tf_gpt2.py
This commit is contained in:
sandip
2020-12-07 21:28:37 +05:30
committed by GitHub
parent 28c77ddf3b
commit 483e13273f
8 changed files with 250 additions and 3 deletions
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29
tests/test_modeling_tf_gpt2.py
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@@ -29,6 +29,7 @@ if is_tf_available():
from transformers.models.gpt2.modeling_tf_gpt2 import (
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGPT2DoubleHeadsModel,
TFGPT2ForSequenceClassification,
TFGPT2LMHeadModel,
TFGPT2Model,
shape_list,
@@ -65,6 +66,7 @@ class TFGPT2ModelTester:
self.scope = None
self.bos_token_id = self.vocab_size - 1
self.eos_token_id = self.vocab_size - 1
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -104,6 +106,8 @@ class TFGPT2ModelTester:
# 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,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
@@ -271,6 +275,21 @@ class TFGPT2ModelTester:
)
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
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"labels": sequence_labels,
}
model = TFGPT2ForSequenceClassification(config)
result = model(inputs)
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()
@@ -297,7 +316,11 @@ class TFGPT2ModelTester:
@require_tf
class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel) if is_tf_available() else ()
all_model_classes = (
(TFGPT2Model, TFGPT2LMHeadModel, TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel)
if is_tf_available()
else ()
)
all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else ()
def setUp(self):
@@ -331,6 +354,10 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_double_head(*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)
@slow
def test_model_from_pretrained(self):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: