def test_generate(self):
input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long()
config = BartConfig(
vocab_size=self.vocab_size,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
output_past=True,
)
lm_model = BartForMaskedLM(config)
new_input_ids = lm_model.generate(input_ids)
self.assertEqual(new_input_ids.shape, (input_ids.shape[0], 20))
def test_generate_beam_search(self):
input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long()
config = BartConfig(
vocab_size=self.vocab_size,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
output_past=True,
)
lm_model = BartForMaskedLM(config)
lm_model.eval()
new_input_ids = lm_model.generate(
input_ids.clone(), num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=5
)
self.assertEqual(new_input_ids.shape, (input_ids.shape[0], 5))
def test_generate_fp16(self):
config, input_ids, batch_size = self._get_config_and_data(
output_past=True)
attention_mask = input_ids.ne(1)
lm_model = BartForMaskedLM(config).eval().to(torch_device).half()
lm_model.generate(input_ids, attention_mask)