def _construct_model(self, model):
"""
Construct the inference model for the predictor.
"""
self._model = BertModel.from_pretrained(self._task_path,
pool_act='linear')
self._model.eval()
def get_bert(params):
model_bert = BertModel.from_pretrained("bert-base-uncased")
bert_config = BertPretrainedModel.pretrained_init_configuration[
"bert-base-uncased"]
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
return model_bert, tokenizer, bert_config
def test_bert_base_uncased(self):
model = BertModel.from_pretrained('bert-base-uncased',
attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
self.config = copy.deepcopy(model.config)
self.config['seq_len'] = 32
self.config['batch_size'] = 3
input_ids, _ = create_input_data(self.config, 102)
input_ids = paddle.to_tensor(input_ids)
output = model(input_ids)
expected_seq_shape = (self.config['batch_size'],
self.config['seq_len'],
self.config['hidden_size'])
expected_pooled_shape = (self.config['batch_size'],
self.config['hidden_size'])
self.check_output_equal(output[0].numpy().shape, expected_seq_shape)
self.check_output_equal(output[1].numpy().shape, expected_pooled_shape)
expected_seq_slice = np.array([[0.17383946, 0.09206937, 0.45788339],
[-0.28287640, 0.06244858, 0.54864359],
[-0.54589444, 0.04811822, 0.50559914]])
# There's output diff about 1e-6 between cpu and gpu
self.check_output_equal(output[0].numpy()[0, 0:3, 0:3],
expected_seq_slice,
atol=1e-6)
expected_pooled_slice = np.array(
[[-0.67418981, -0.07148759, 0.85799801],
[-0.62072051, -0.08452632, 0.96691507],
[-0.74019802, -0.10187808, 0.95353240]])
self.check_output_equal(output[1].numpy()[0:3, 0:3],
expected_pooled_slice,
atol=1e-6)