def test_inference_no_head(self):
model = MobileBertModel.from_pretrained(
"google/mobilebert-uncased").to(torch_device)
input_ids = _long_tensor(
[[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 9, 512))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[
[-2.4736526e07, 8.2691656e04, 1.6521838e05],
[-5.7541704e-01, 3.9056022e00, 4.4011507e00],
[2.6047359e00, 1.5677652e00, -1.7324188e-01],
]],
device=torch_device,
)
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
lower_bound = torch.all(
(expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE)
upper_bound = torch.all(
(expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE)
self.assertTrue(lower_bound and upper_bound)
def __init__(self,
config,
project_dim: int = 0,
ctx_bottleneck: bool = False):
MobileBertModel.__init__(self, config)
assert config.hidden_size > 0, 'Encoder hidden_size can\'t be zero'
self.encode_proj = nn.Linear(
config.hidden_size, project_dim) if project_dim != 0 else None
self.decode_proj = nn.Sequential(
nn.Tanh(),
nn.Linear(project_dim,
(config.hidden_size + project_dim) // 2),
nn.Tanh(),
nn.Linear((config.hidden_size + project_dim) //
2, config.hidden_size),
) if ctx_bottleneck else None
self.init_weights()
def __init__(self, num_labels=17):
self.num_labels = num_labels
super(MobileBertForMultiLabelSequenceClassification, self).__init__()
self.bert = MobileBertModel.from_pretrained(
'google/mobilebert-uncased',
hidden_act="gelu",
num_labels=num_labels)
self.dropout = torch.nn.Dropout(0.1)
self.classifier = torch.nn.Linear(512, num_labels)
def create_and_check_mobilebert_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = MobileBertModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def __init__(self, config, num_labels=17, mobilebert = True):
self.mobilebert = mobilebert
if not mobilebert:
super(BertForMultiLabelSequenceClassification, self).__init__(config)
else:
super(BertForMultiLabelSequenceClassification, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config) if not mobilebert else MobileBertModel.from_pretrained(
'google/mobilebert-uncased',
num_labels=num_labels,)
self.dropout = torch.nn.Dropout( config.hidden_dropout_prob)
self.classifier = torch.nn.Linear( config.hidden_size, num_labels)
if not mobilebert:
self.apply(self.init_bert_weights)
def Net():
bert = MobileBertModel.from_pretrained('google/mobilebert-uncased')
HIDDEN_DIM = 256
OUTPUT_DIM = 1
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.25
model = BERTGRUSentiment(bert, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS,
BIDIRECTIONAL, DROPOUT)
for name, param in model.named_parameters():
if name.startswith('bert'):
param.requires_grad = False
return model
def create_and_check_mobilebert_model(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = MobileBertModel(config=config)
model.to(torch_device)
model.eval()
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)
self.parent.assertListEqual(
list(result["last_hidden_state"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooler_output"].size()),
[self.batch_size, self.hidden_size])
def create_and_check_mobilebert_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = MobileBertModel(config)
model.to(torch_device)
model.eval()
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
sequence_output, pooled_output = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()),
[self.batch_size, self.hidden_size])
def test_model_from_pretrained(self):
for model_name in MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = MobileBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
