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fixed imports in tests and gpt2 config test

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This commit is contained in:
thomwolf
2019-09-09 11:04:03 +02:00
parent 728637356c
commit b7175a2701
20 changed files with 53 additions and 40 deletions
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2
pytorch_transformers/__init__.py
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@@ -89,7 +89,7 @@ try:
import tensorflow as tf
assert int(tf.__version__[0]) >= 2
_tf_available = True # pylint: disable=invalid-name
except ImportError:
except (ImportError, AssertionError):
_tf_available = False # pylint: disable=invalid-name
if _tf_available:

1
pytorch_transformers/modeling_tf_gpt2.py
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@@ -699,6 +699,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
head_mask = inputs.get('head_mask', None)
assert len(inputs) <= 5, "Too many inputs."
assert len(shape_list(input_ids)) == 3, "Inputs should have 3 dimensions: batch, choices, sequence length"
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]

3
pytorch_transformers/modeling_tf_utils.py
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@@ -313,7 +313,7 @@ class TFSequenceSummary(tf.keras.layers.Layer):
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
raise NotImplementedError
self.summary = tf.keras.layers.Identity(name='summary')
self.summary = None
if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
num_classes = config.num_labels
@@ -372,6 +372,7 @@ class TFSequenceSummary(tf.keras.layers.Layer):
if training and self.first_dropout is not None:
output = self.first_dropout(output)
if self.summary is not None:
output = self.summary(output)
if self.activation is not None:

6
pytorch_transformers/modeling_xlnet.py
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@@ -525,8 +525,10 @@ XLNET_INPUTS_DOCSTRING = r"""
Only used during pretraining for partial prediction or for sequential decoding (generation).
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and
the important thing is that they should be different for tokens which belong to different segments.
The model will compute relative segment differences from the given type indices:
0 if the segment id of two tokens are the same, 1 if not.
**input_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding.

6
pytorch_transformers/tests/modeling_auto_test.py
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@@ -21,7 +21,9 @@ import shutil
import pytest
import logging
try:
from pytorch_transformers import is_torch_available
if is_torch_available():
from pytorch_transformers import (AutoConfig, BertConfig,
AutoModel, BertModel,
AutoModelWithLMHead, BertForMaskedLM,
@@ -31,7 +33,7 @@ try:
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")

4
pytorch_transformers/tests/modeling_bert_test.py
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@@ -25,13 +25,13 @@ from pytorch_transformers import is_torch_available
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
try:
if is_torch_available():
from pytorch_transformers import (BertConfig, BertModel, BertForMaskedLM,
BertForNextSentencePrediction, BertForPreTraining,
BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification, BertForMultipleChoice)
from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")

6
pytorch_transformers/tests/modeling_common_test.py
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@@ -27,13 +27,15 @@ import unittest
import logging
import pytest
try:
from pytorch_transformers import is_torch_available
if is_torch_available():
import torch
from pytorch_transformers import (PretrainedConfig, PreTrainedModel,
BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")

4
pytorch_transformers/tests/modeling_distilbert_test.py
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@@ -21,10 +21,10 @@ import pytest
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
from pytorch_transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

4
pytorch_transformers/tests/modeling_gpt2_test.py
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@@ -22,10 +22,10 @@ import shutil
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
from pytorch_transformers import (GPT2Config, GPT2Model, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2DoubleHeadsModel)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

4
pytorch_transformers/tests/modeling_openai_test.py
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@@ -22,10 +22,10 @@ import shutil
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
from pytorch_transformers import (OpenAIGPTConfig, OpenAIGPTModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

4
pytorch_transformers/tests/modeling_roberta_test.py
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@@ -22,11 +22,11 @@ import pytest
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
import torch
from pytorch_transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification)
from pytorch_transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

6
pytorch_transformers/tests/modeling_tf_auto_test.py
View File

@@ -21,7 +21,9 @@ import shutil
import pytest
import logging
try:
from pytorch_transformers import is_tf_available
if is_tf_available():
from pytorch_transformers import (AutoConfig, BertConfig,
TFAutoModel, TFBertModel,
TFAutoModelWithLMHead, TFBertForMaskedLM,
@@ -31,7 +33,7 @@ try:
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
except ImportError:
else:
pytestmark = pytest.mark.skip("Require TensorFlow")

4
pytorch_transformers/tests/modeling_tf_bert_test.py
View File

@@ -26,7 +26,7 @@ from .configuration_common_test import ConfigTester
from pytorch_transformers import BertConfig, is_tf_available
try:
if is_tf_available():
import tensorflow as tf
from pytorch_transformers.modeling_tf_bert import (TFBertModel, TFBertForMaskedLM,
TFBertForNextSentencePrediction,
@@ -36,7 +36,7 @@ try:
TFBertForTokenClassification,
TFBertForQuestionAnswering,
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require TensorFlow")

6
pytorch_transformers/tests/modeling_tf_common_test.py
View File

@@ -25,11 +25,13 @@ import uuid
import pytest
import sys
try:
from pytorch_transformers import is_tf_available
if is_tf_available():
import tensorflow as tf
from pytorch_transformers import TFPreTrainedModel
# from pytorch_transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
except ImportError:
else:
pytestmark = pytest.mark.skip("Require TensorFlow")

11
pytorch_transformers/tests/modeling_tf_gpt2_test.py
View File

@@ -26,19 +26,20 @@ from .configuration_common_test import ConfigTester
from pytorch_transformers import GPT2Config, is_tf_available
try:
if is_tf_available():
import tensorflow as tf
from pytorch_transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel) if is_tf_available() else ()
# all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel,
# TFGPT2DoubleHeadsModel) if is_tf_available() else ()
all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel) if is_tf_available() else ()
class TFGPT2ModelTester(object):
@@ -186,7 +187,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
def setUp(self):
self.model_tester = TFGPT2ModelTest.TFGPT2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPT2Config, hidden_size=37)
self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()

4
pytorch_transformers/tests/modeling_transfo_xl_test.py
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@@ -23,11 +23,11 @@ import pytest
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
import torch
from pytorch_transformers import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel)
from pytorch_transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

4
pytorch_transformers/tests/modeling_xlm_test.py
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@@ -22,11 +22,11 @@ import pytest
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
from pytorch_transformers import (XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
XLMForSequenceClassification)
from pytorch_transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

4
pytorch_transformers/tests/modeling_xlnet_test.py
View File

@@ -25,12 +25,12 @@ import pytest
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
import torch
from pytorch_transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering)
from pytorch_transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)

4
pytorch_transformers/tests/optimization_test.py
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@@ -22,12 +22,12 @@ import pytest
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
import torch
from pytorch_transformers import (AdamW, ConstantLRSchedule, WarmupConstantSchedule,
WarmupCosineSchedule, WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch")
from .tokenization_tests_commons import TemporaryDirectory

4
pytorch_transformers/tests/tokenization_transfo_xl_test.py
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@@ -21,10 +21,10 @@ from io import open
from pytorch_transformers import is_torch_available
try:
if is_torch_available():
import torch
from pytorch_transformers.tokenization_transfo_xl import TransfoXLTokenizer, VOCAB_FILES_NAMES
except ImportError:
else:
pytestmark = pytest.mark.skip("Require Torch") # TODO: untangle Transfo-XL tokenizer from torch.load and torch.save
from .tokenization_tests_commons import CommonTestCases