def test_fast_integration(self):
torch.manual_seed(0)
input_ids = torch.randint(
self.model_tester.vocab_size,
(self.model_tester.batch_size, self.model_tester.seq_length),
device=torch_device,
)
attention_mask = torch.ones(
(self.model_tester.batch_size, self.model_tester.seq_length),
device=torch_device)
attention_mask[:, :-10] = 0
token_type_ids = torch.randint(
self.model_tester.type_vocab_size,
(self.model_tester.batch_size, self.model_tester.seq_length),
device=torch_device,
)
config, _, _, _, _, _, _ = self.model_tester.prepare_config_and_inputs(
)
model = BigBirdModel(config).to(torch_device).eval()
with torch.no_grad():
hidden_states = model(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask).last_hidden_state
self.assertTrue(
torch.allclose(
hidden_states[0, 0, :5],
torch.tensor([-0.6326, 0.6124, -0.0844, 0.6698, -1.7155],
device=torch_device),
atol=1e-3,
))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = BigBirdModel(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))
def test_fast_integration(self):
# fmt: off
input_ids = torch.tensor(
[[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 122, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 44, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 98, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73],[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 12, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 28, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 18, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73]], # noqa: E231
dtype=torch.long,
device=torch_device,
)
# fmt: on
input_ids = input_ids % self.model_tester.vocab_size
input_ids[1] = input_ids[1] - 1
attention_mask = torch.ones((input_ids.shape), device=torch_device)
attention_mask[:, :-10] = 0
config, _, _, _, _, _, _ = self.model_tester.prepare_config_and_inputs()
torch.manual_seed(0)
model = BigBirdModel(config).eval().to(torch_device)
with torch.no_grad():
hidden_states = model(input_ids, attention_mask=attention_mask).last_hidden_state
self.assertTrue(
torch.allclose(
hidden_states[0, 0, :5],
torch.tensor([1.4943, 0.0928, 0.8254, -0.2816, -0.9788], device=torch_device),
atol=1e-3,
)
)
def create_and_check_for_auto_padding(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = BigBirdModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_change_to_full_attn(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = BigBirdModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
# the config should not be changed
self.parent.assertTrue(model.config.attention_type == "block_sparse")
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False)
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False
)
config = BigBirdConfig(attention_type="original_full")
model = BigBirdModel(config)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)