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[Longformer] Major Refactor (#5219)

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* refactor naming

* add small slow test

* refactor

* refactor naming

* rename selected to extra

* big global attention refactor

* make style

* refactor naming

* save intermed

* refactor functions

* finish function refactor

* fix tests

* fix longformer

* fix longformer

* fix longformer

* fix all tests but one

* finish longformer

* address sams and izs comments

* fix transpose
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Patrick von Platen 2020-07-01 17:43:32 +02:00 committed by GitHub
parent e0d58ddb65
commit d697b6ca75
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3 changed files with 697 additions and 293 deletions
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893
src/transformers/modeling_longformer.py
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2
tests/test_modeling_common.py
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@ -811,7 +811,7 @@ class ModelTesterMixin:
# Wrap model in nn.DataParallel
model = torch.nn.DataParallel(model)
with torch.no_grad():
_ = model(**inputs_dict)
_ = model(**self._prepare_for_class(inputs_dict, model_class))
global_rng = random.Random()

95
tests/test_modeling_longformer.py
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@ -115,6 +115,18 @@ class LongformerModelTester:
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_attention_mask_determinism(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerModel(config=config)
model.to(torch_device)
model.eval()
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
output_with_mask = model(input_ids, attention_mask=attention_mask)[0]
output_without_mask = model(input_ids)[0]
self.parent.assertTrue(torch.allclose(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], atol=1e-4))
def create_and_check_longformer_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
@ -134,6 +146,36 @@ class LongformerModelTester:
)
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_longformer_model_with_global_attention_mask(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerModel(config=config)
model.to(torch_device)
model.eval()
global_attention_mask = input_mask.clone()
global_attention_mask[:, input_mask.shape[-1] // 2] = 0
global_attention_mask = global_attention_mask.to(torch_device)
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
global_attention_mask=global_attention_mask,
token_type_ids=token_type_ids,
)
sequence_output, pooled_output = model(
input_ids, token_type_ids=token_type_ids, global_attention_mask=global_attention_mask
)
sequence_output, pooled_output = model(input_ids, global_attention_mask=global_attention_mask)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_longformer_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
@ -243,7 +285,13 @@ class LongformerModelTester:
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
global_attention_mask = torch.zeros_like(input_ids)
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
"global_attention_mask": global_attention_mask,
}
return config, inputs_dict
def prepare_config_and_inputs_for_question_answering(self):
@ -277,11 +325,10 @@ class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
(
LongformerModel,
LongformerForMaskedLM,
# TODO: make tests pass for those models
# LongformerForSequenceClassification,
# LongformerForQuestionAnswering,
# LongformerForTokenClassification,
# LongformerForMultipleChoice,
LongformerForSequenceClassification,
LongformerForQuestionAnswering,
LongformerForTokenClassification,
LongformerForMultipleChoice,
)
if is_torch_available()
else ()
@ -298,6 +345,14 @@ class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_longformer_model(*config_and_inputs)
def test_longformer_model_attention_mask_determinism(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_attention_mask_determinism(*config_and_inputs)
def test_longformer_model_global_attention_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_longformer_model_with_global_attention_mask(*config_and_inputs)
def test_longformer_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_longformer_for_masked_lm(*config_and_inputs)
@ -325,15 +380,31 @@ class LongformerModelIntegrationTest(unittest.TestCase):
model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
model.to(torch_device)
# 'Hello world!'
input_ids = torch.tensor([[0, 20920, 232, 328, 1437, 2]], dtype=torch.long, device=torch_device)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
output = model(input_ids, attention_mask=attention_mask)[0]
output_without_mask = model(input_ids)[0]
expected_output_slice = torch.tensor([0.0549, 0.1087, -0.1119, -0.0368, 0.0250], device=torch_device)
self.assertTrue(torch.allclose(output[0, 0, -5:], expected_output_slice, atol=1e-4))
self.assertTrue(torch.allclose(output_without_mask[0, 0, -5:], expected_output_slice, atol=1e-4))
@slow
def test_inference_no_head_long(self):
model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
model.to(torch_device)
# 'Hello world! ' repeated 1000 times
input_ids = torch.tensor(
[[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device
) # long input
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
attention_mask[:, [1, 4, 21]] = 2 # Set global attention on a few random positions
global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device)
global_attention_mask[:, [1, 4, 21]] = 1 # Set global attention on a few random positions
output = model(input_ids, attention_mask=attention_mask)[0]
output = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)[0]
expected_output_sum = torch.tensor(74585.8594, device=torch_device)
expected_output_mean = torch.tensor(0.0243, device=torch_device)
@ -341,7 +412,7 @@ class LongformerModelIntegrationTest(unittest.TestCase):
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_masked_lm(self):
def test_inference_masked_lm_long(self):
model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
model.to(torch_device)
@ -352,9 +423,9 @@ class LongformerModelIntegrationTest(unittest.TestCase):
loss, prediction_scores = model(input_ids, labels=input_ids)
expected_loss = torch.tensor(0.0620, device=torch_device)
expected_prediction_scores_sum = torch.tensor(-6.1599e08, device=torch_device)
expected_prediction_scores_mean = torch.tensor(-3.0622, device=torch_device)
expected_loss = torch.tensor(0.0074, device=torch_device)
expected_prediction_scores_sum = torch.tensor(-6.1048e08, device=torch_device)
expected_prediction_scores_mean = torch.tensor(-3.0348, device=torch_device)
input_ids = input_ids.to(torch_device)
self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-4))