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Fix integration tests #11

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Merged
NielsRogge merged 1 commit into from
Feb 25, 2021
Merged

Fix integration tests #11

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19 changes: 9 additions & 10 deletions tests/test_modeling_luke.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@
LukeConfig,
LukeModel,
LukeEntityAwareAttentionModel,
LukeTokenizer
)
from transformers.models.luke.modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
Expand Down Expand Up @@ -184,12 +185,12 @@ def test_model_from_pretrained(self):
self.assertIsNotNone(model)


def prepare_luke_batch_inputs():
def prepare_luke_batch_inputs(tokenizer):
# Taken from Open Entity dev set
text = """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the new world number one avoid a humiliating second- round exit at Wimbledon ."""
span = (39,42)

ENTITY_TOKEN = '[ENT]'
ENTITY_TOKEN = '<ent>'
max_mention_length = 30

conv_tables = (
Expand All @@ -206,10 +207,7 @@ def preprocess_and_tokenize(text, start, end=None):
for a, b in conv_tables:
target_text = target_text.replace(a, b)

if isinstance(tokenizer, RobertaTokenizer):
return tokenizer.tokenize(target_text, add_prefix_space=True)
else:
return tokenizer.tokenize(target_text)
return tokenizer.tokenize(target_text.strip(), add_prefix_space=True)

tokens = [tokenizer.cls_token]
tokens += preprocess_and_tokenize(text, 0, span[0])
Expand Down Expand Up @@ -244,7 +242,8 @@ class LukeModelIntegrationTests(unittest.TestCase):
def test_inference_no_head(self):
model = LukeEntityAwareAttentionModel.from_pretrained("nielsr/luke-large").to(torch_device)

encoding = prepare_luke_batch_inputs()
tokenizer = LukeTokenizer.from_pretrained("nielsr/luke-large")
encoding = prepare_luke_batch_inputs(tokenizer)
# convert all values to PyTorch tensors
for key, value in encoding.items():
encoding[key] = torch.as_tensor(encoding[key]).unsqueeze(0).to(torch_device)
Expand All @@ -263,9 +262,9 @@ def test_inference_no_head(self):

# Verify entity hidden states
expected_shape = torch.Size((1, 2, 1024))
self.assertEqual(outputs.entity_last_hidden_state.shape == expected_shape)

self.assertTrue(outputs.entity_last_hidden_state.shape == expected_shape)
expected_slice = torch.tensor([[ 0.3251, 0.3981, -0.0689],
[-0.0098, 0.1215, 0.3544]])
self.assertTrue(torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))

self.assertTrue(torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))