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import copy
from unittest.mock import patch
import torch
import torchtext
from torch.nn import functional as torch_F
from ..common.torchtext_test_case import TorchtextTestCase
class TestModels(TorchtextTestCase):
def test_roberta_bundler_build_model(self) -> None:
from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaModel, RobertaBundle
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
)
# case: user provide encoder checkpoint state dict
dummy_encoder = RobertaModel(dummy_encoder_conf)
model = RobertaBundle.build_model(encoder_conf=dummy_encoder_conf, checkpoint=dummy_encoder.state_dict())
self.assertEqual(model.state_dict(), dummy_encoder.state_dict())
# case: user provide classifier checkpoint state dict when head is given and override_head is False (by default)
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
another_dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
model = RobertaBundle.build_model(
encoder_conf=dummy_encoder_conf,
head=another_dummy_classifier_head,
checkpoint=dummy_classifier.state_dict(),
)
self.assertEqual(model.state_dict(), dummy_classifier.state_dict())
# case: user provide classifier checkpoint state dict when head is given and override_head is set True
another_dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
model = RobertaBundle.build_model(
encoder_conf=dummy_encoder_conf,
head=another_dummy_classifier_head,
checkpoint=dummy_classifier.state_dict(),
override_checkpoint_head=True,
)
self.assertEqual(model.head.state_dict(), another_dummy_classifier_head.state_dict())
# case: user provide only encoder checkpoint state dict when head is given
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
encoder_state_dict = {}
for k, v in dummy_classifier.encoder.state_dict().items():
encoder_state_dict["encoder." + k] = v
model = torchtext.models.RobertaBundle.build_model(
encoder_conf=dummy_encoder_conf, head=dummy_classifier_head, checkpoint=encoder_state_dict
)
self.assertEqual(model.state_dict(), dummy_classifier.state_dict())
def test_roberta_bundler_train(self) -> None:
from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaModel, RobertaBundle
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
)
from torch.optim import SGD
def _train(model):
optim = SGD(model.parameters(), lr=1)
model_input = torch.tensor([[0, 1, 2, 3, 4, 5]])
target = torch.tensor([0])
logits = model(model_input)
loss = torch_F.cross_entropy(logits, target)
loss.backward()
optim.step()
# does not freeze encoder
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
model = RobertaBundle.build_model(
encoder_conf=dummy_encoder_conf,
head=dummy_classifier_head,
freeze_encoder=False,
checkpoint=dummy_classifier.state_dict(),
)
encoder_current_state_dict = copy.deepcopy(model.encoder.state_dict())
head_current_state_dict = copy.deepcopy(model.head.state_dict())
_train(model)
self.assertNotEqual(model.encoder.state_dict(), encoder_current_state_dict)
self.assertNotEqual(model.head.state_dict(), head_current_state_dict)
# freeze encoder
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
model = RobertaBundle.build_model(
encoder_conf=dummy_encoder_conf,
head=dummy_classifier_head,
freeze_encoder=True,
checkpoint=dummy_classifier.state_dict(),
)
encoder_current_state_dict = copy.deepcopy(model.encoder.state_dict())
head_current_state_dict = copy.deepcopy(model.head.state_dict())
_train(model)
self.assertEqual(model.encoder.state_dict(), encoder_current_state_dict)
self.assertNotEqual(model.head.state_dict(), head_current_state_dict)
@patch("logging.Logger.warning")
def test_roberta_bundler_get_model(self, mock):
from torchtext.models import RobertaEncoderConf, RobertaBundle
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
)
model_bundle = RobertaBundle(dummy_encoder_conf)
model_bundle.get_model(load_weights=False, freeze_encoder=True)
mock.assert_called_with(
"The encoder is not loaded with pre-trained weights. Setting freeze_encoder to True will hinder encoder from learning appropriate weights."
)
def test_roberta_bundler_raise_checkpoint(self) -> None:
from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaBundle
with self.assertRaises(TypeError):
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
)
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
RobertaBundle.build_model(
encoder_conf=dummy_encoder_conf,
head=dummy_classifier_head,
freeze_encoder=True,
checkpoint=1,
)
def test_roberta_bundler_encode_conf_property(self) -> None:
from torchtext.models import RobertaEncoderConf, RobertaBundle
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
)
model_bundle = RobertaBundle(dummy_encoder_conf)
self.assertTrue(isinstance(model_bundle.encoderConf, RobertaEncoderConf))
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