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huggingface-transformers
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Fix integration slow tests (#10670) 

* PoC

* Fix slow tests for the PT1.8 Embedding problem
This commit is contained in:
Sylvain Gugger 2021-03-11 13:43:53 -05:00 коммит произвёл GitHub
Родитель 3ab6820370
Коммит fda703a553
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9 изменённых файлов: 47 добавлений и 55 удалений
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11
tests/test_modeling_albert.py
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@ -291,13 +291,14 @@ class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
class AlbertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = AlbertForPreTraining.from_pretrained("albert-base-v2")
model = AlbertModel.from_pretrained("albert-base-v2")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 30000))
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[4.6061, 0.7321, -1.7725], [4.6061, 0.7323, -1.7727], [4.6061, 0.7323, -1.7727]]]
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

25
tests/test_modeling_bert.py
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@ -555,35 +555,38 @@ class BertModelIntegrationTest(unittest.TestCase):
def test_inference_no_head_absolute_embedding(self):
model = BertModel.from_pretrained("bert-base-uncased")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0]
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-0.0483, 0.1188, -0.0313], [-0.0606, 0.1435, 0.0199], [-0.0235, 0.1519, 0.0175]]]
)
expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]])
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
@slow
def test_inference_no_head_relative_embedding_key(self):
model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0]
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[0.3492, 0.4126, -0.1484], [0.2274, -0.0549, 0.1623], [0.5889, 0.6797, -0.0189]]]
[[[0.0756, 0.3142, -0.5128], [0.3761, 0.3462, -0.5477], [0.2052, 0.3760, -0.1240]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
@slow
def test_inference_no_head_relative_embedding_key_query(self):
model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key-query")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0]
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[[1.1677, 0.5129, 0.9524], [0.6659, 0.5958, 0.6688], [1.1714, 0.1764, 0.6266]]])
expected_slice = torch.tensor(
[[[0.6496, 0.3784, 0.8203], [0.8148, 0.5656, 0.2636], [-0.0681, 0.5597, 0.7045]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

8
tests/test_modeling_convbert.py
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@ -416,18 +416,16 @@ class ConvBertModelTest(ModelTesterMixin, unittest.TestCase):
@require_torch
class ConvBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
def test_inference_no_head(self):
model = ConvBertModel.from_pretrained("YituTech/conv-bert-base")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6]])
output = model(input_ids)[0]
print(output[:, :3, :3])
expected_shape = torch.Size((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
# TODO Replace values below with what was printed above.
expected_slice = torch.tensor(
[[[-0.0348, -0.4686, -0.3064], [0.2264, -0.2699, -0.7423], [0.1032, -0.4501, -0.5828]]]
[[[-0.0864, -0.4898, -0.3677], [0.1434, -0.2952, -0.7640], [-0.0112, -0.4432, -0.5432]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

15
tests/test_modeling_deberta.py
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@ -13,12 +13,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
@ -275,16 +271,13 @@ class DebertaModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
model = DebertaModel.from_pretrained("microsoft/deberta-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-0.0218, -0.6641, -0.3665], [-0.3907, -0.4716, -0.6640], [0.7461, 1.2570, -0.9063]]]
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4), f"{output[:, :3, :3]}")
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4), f"{output[:, 1:4, 1:4]}")

15
tests/test_modeling_deberta_v2.py
Просмотреть файл

@ -13,12 +13,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
@ -275,16 +271,13 @@ class DebertaV2ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
model = DebertaV2Model.from_pretrained("microsoft/deberta-v2-xlarge")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-0.2913, 0.2647, 0.5627], [-0.4318, 0.1389, 0.3881], [-0.2929, -0.2489, 0.3452]]]
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4), f"{output[:, :3, :3]}")
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4), f"{output[:, 1:4, 1:4]}")

7
tests/test_modeling_distilbert.py
Просмотреть файл

@ -256,11 +256,12 @@ class DistilBertModelIntergrationTest(unittest.TestCase):
def test_inference_no_head_absolute_embedding(self):
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0]
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[0.4026, -0.2919, 0.3902], [0.3828, -0.2129, 0.3563], [0.3919, -0.2287, 0.3438]]]
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

11
tests/test_modeling_electra.py
Просмотреть файл

@ -350,13 +350,14 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
class ElectraModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
model = ElectraModel.from_pretrained("google/electra-small-discriminator")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11))
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 256))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[-8.9253, -4.0305, -3.9306, -3.8774, -4.1873, -4.1280, 0.9429, -4.1672, 0.9281, 0.0410, -3.4823]]
[[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]
)
self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

6
tests/test_modeling_mbart.py
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@ -343,7 +343,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor face decât să înrăutăţească violenţa şi mizeria pentru milioane de oameni.',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor face decât să înrăutăţească violenţa şi mizeria a milioane de oameni.',
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, 250004]
@ -359,7 +359,9 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
@slow
def test_enro_generate_batch(self):
batch: BatchEncoding = self.tokenizer(self.src_text, return_tensors="pt").to(torch_device)
batch: BatchEncoding = self.tokenizer(self.src_text, return_tensors="pt", padding=True, truncation=True).to(
torch_device
)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
assert self.tgt_text == decoded

4
tests/test_modeling_squeezebert.py
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@ -278,9 +278,9 @@ class SqueezeBertModelIntegrationTest(unittest.TestCase):
def test_inference_classification_head(self):
model = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")
input_ids = torch.tensor([[0, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]])
input_ids = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 3))
self.assertEqual(output.shape, expected_shape)
expected_tensor = torch.tensor([[0.5075, 0.0682, -0.5881]])
expected_tensor = torch.tensor([[0.6401, -0.0349, -0.6041]])
self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))