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, 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 __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 test_model_from_pretrained(self):
for model_name in MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = MobileBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
