def test_advanced_inputs(self):
# (config, input_ids, token_type_ids, input_mask, *unused) = \
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(
config, inputs_dict["input_ids"])
model = BartModel(config)
model.to(torch_device)
model.eval()
# test init
self.assertTrue(
(model.encoder.embed_tokens.weight == model.shared.weight
).all().item())
def _check_var(module):
"""Check that we initialized various parameters from N(0, config.init_std)."""
self.assertAlmostEqual(
torch.std(module.weight).item(), config.init_std, 2)
_check_var(model.encoder.embed_tokens)
_check_var(model.encoder.layers[0].self_attn.k_proj)
_check_var(model.encoder.layers[0].fc1)
_check_var(model.encoder.embed_positions)
decoder_features_with_created_mask = model.forward(**inputs_dict)[0]
decoder_features_with_passed_mask = model.forward(
decoder_attention_mask=decoder_attn_mask,
decoder_input_ids=decoder_input_ids,
**inputs_dict)[0]
_assert_tensors_equal(decoder_features_with_passed_mask,
decoder_features_with_created_mask)
useless_mask = torch.zeros_like(decoder_attn_mask)
decoder_features = model.forward(decoder_attention_mask=useless_mask,
**inputs_dict)[0]
self.assertTrue(isinstance(
decoder_features, torch.Tensor)) # no hidden states or attentions
self.assertEqual(decoder_features.size(),
(self.model_tester.batch_size,
self.model_tester.seq_length, config.d_model))
if decoder_attn_mask.min().item() < -1e3: # some tokens were masked
self.assertFalse(
(decoder_features_with_created_mask == decoder_features
).all().item())
# Test different encoder attention masks
decoder_features_with_long_encoder_mask = model.forward(
inputs_dict["input_ids"],
attention_mask=inputs_dict["attention_mask"].long())[0]
_assert_tensors_equal(decoder_features_with_long_encoder_mask,
decoder_features_with_created_mask)
def test_initialization_more(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = BartModel(config)
model.to(torch_device)
model.eval()
# test init
self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item())
def _check_var(module):
"""Check that we initialized various parameters from N(0, config.init_std)."""
self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2)
_check_var(model.encoder.embed_tokens)
_check_var(model.encoder.layers[0].self_attn.k_proj)
_check_var(model.encoder.layers[0].fc1)
_check_var(model.encoder.embed_positions)
