def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
inputs_dict = {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
elif model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values():
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in [
*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
*MODEL_FOR_CAUSAL_LM_MAPPING.values(),
*MODEL_FOR_MASKED_LM_MAPPING.values(),
*MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
]:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
AutoTokenizer,
get_linear_schedule_with_warmup,
)
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
ALL_MODELS = sum(
(tuple(conf.pretrained_config_archive_map.keys())
for conf in MODEL_CONFIG_CLASSES),
(),
)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def register_bert_model(bert_cls):
"""
This function wraps a BertModel inherited cls and automatically:
1. Creates an associated BertConfig
2. Creates an associated BertForMaskedLM
3. Creates an associated BertForSequenceClassification
4. Creates an associated BertForQuestionAnswering
5. Registers these classes with Transformers model mappings
This last step ensures that the resulting config and models may be used by
AutoConfig, AutoModelForMaskedLM, and AutoModelForSequenceClassification.
Assumptions are made to auto-name these classes and the corresponding model type.
For instance, SparseBertModel will have model_type="sparse_bert" and associated
classes like SparseBertConfig.
To customize the the inputs to the model's config, include the dataclass
`bert_cls.ConfigKWargs`. This is, in fact, required. Upon initialization of the
config, the fields of that dataclass will be used to extract extra keyword arguments
and assign them as attributes to the config.
Example
```
@register_bert_model
class SparseBertModel(BertModel):
@dataclass
class ConfigKWargs:
# Keyword arguments to configure sparsity.
sparsity: float = 0.9
# Define __init__, ect.
...
# Model is ready to auto load.
config = AutoConfig.for_model("sparse_bert", sparsity=0.5)
model = AutoModelForMaskedLM.from_config(model)
config.sparsity
>>> 0.5
type(model)
>>> SparseBertModelForMaskedLM
"""
assert bert_cls.__name__.endswith("BertModel")
# Get first part of name e.g. StaticSparseBertModel -> StaticSparse
name_prefix = bert_cls.__name__.replace("BertModel", "")
# Create new bert config and models based off of `bert_cls`.
config_cls = create_config_class(bert_cls, name_prefix)
masked_lm_cls = create_masked_lm_class(bert_cls, name_prefix)
seq_classification_cls = create_sequence_classification_class(bert_cls, name_prefix)
question_answering_cls = create_question_answering_class(bert_cls, name_prefix)
# Specify the correct config class
bert_cls.config_class = config_cls
masked_lm_cls.config_class = config_cls
seq_classification_cls.config_class = config_cls
question_answering_cls.config_class = config_cls
# Update Transformers mappings to auto-load these new models.
CONFIG_MAPPING.update({
config_cls.model_type: config_cls
})
TOKENIZER_MAPPING.update({
config_cls: (BertTokenizer, BertTokenizerFast),
})
MODEL_FOR_MASKED_LM_MAPPING.update({
config_cls: masked_lm_cls,
})
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.update({
config_cls: seq_classification_cls
})
MODEL_FOR_QUESTION_ANSWERING_MAPPING.update({
config_cls: question_answering_cls
})
# Update the `models` modules so that these classes may be imported.
__models_dict__.update({
config_cls.__name__: config_cls,
masked_lm_cls.__name__: masked_lm_cls,
seq_classification_cls.__name__: seq_classification_cls,
question_answering_cls.__name__: question_answering_cls,
})
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
BertConfig,
DistilBertConfig,
)
from .modeling_bert import (
BertForSequenceClassification,
BertForQuestionAnswering,
)
from .modeling_distilbert import (
DistilBertForSequenceClassification,
DistilBertForQuestionAnswering,
)
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.update([
(BertConfig, BertForSequenceClassification),
(DistilBertConfig, DistilBertForSequenceClassification),
])
MODEL_FOR_QUESTION_ANSWERING_MAPPING.update([
(BertConfig, BertForQuestionAnswering),
(DistilBertConfig, DistilBertForQuestionAnswering),
])
from .training_args import TrainingArguments
from .drop_and_restore_utils import LengthDropArguments, SearchArguments
from .trainer import LengthDropTrainer
