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Model card defaults (#12122)

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* [WIP] Model card defaults

* finetuned_from default value

* Add all mappings to the mapping file

* Be more defensive on finetuned_from arg

* Add default task tag

* Separate tags from tasks

* Edge case for dataset

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sylvain Gugger 2021-06-15 16:01:37 -04:00 committed by GitHub
parent 6e7cc5cc51
commit 7d7ceca396
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GPG key ID: 4AEE18F83AFDEB23
14 changed files with 477 additions and 26 deletions
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2
examples/pytorch/language-modeling/run_clm.py
View file

@ -467,7 +467,7 @@ def main():
trainer.save_metrics("eval", metrics)
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "text-generation"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/language-modeling/run_mlm.py
View file

@ -497,7 +497,7 @@ def main():
trainer.save_metrics("eval", metrics)
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "fill-mask"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/language-modeling/run_plm.py
View file

@ -471,7 +471,7 @@ def main():
trainer.save_metrics("eval", metrics)
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "language-modeling"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "language-modeling"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/multiple-choice/run_swag.py
View file

@ -430,7 +430,7 @@ def main():
if training_args.push_to_hub:
trainer.push_to_hub(
finetuned_from=model_args.model_name_or_path,
tags="multiple-choice",
tasks="multiple-choice",
dataset_tags="swag",
dataset_args="regular",
dataset="SWAG",

2
examples/pytorch/question-answering/run_qa.py
View file

@ -601,7 +601,7 @@ def main():
trainer.save_metrics("predict", metrics)
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "question-answering"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/question-answering/run_qa_beam_search.py
View file

@ -640,7 +640,7 @@ def main():
trainer.save_metrics("predict", metrics)
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "question-answering"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/summarization/run_summarization.py
View file

@ -583,7 +583,7 @@ def main():
writer.write("\n".join(predictions))
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "summarization"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/text-classification/run_glue.py
View file

@ -538,7 +538,7 @@ def main():
writer.write(f"{index}\t{item}\n")
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "text-classification"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
if data_args.task_name is not None:
kwargs["language"] = "en"
kwargs["dataset_tags"] = "glue"

2
examples/pytorch/token-classification/run_ner.py
View file

@ -522,7 +522,7 @@ def main():
writer.write(" ".join(prediction) + "\n")
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "token-classification"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "token-classification"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

2
examples/pytorch/translation/run_translation.py
View file

@ -575,7 +575,7 @@ def main():
writer.write("\n".join(predictions))
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tags": "translation"}
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:

89
src/transformers/modelcard.py
View file

@ -42,8 +42,31 @@ from .file_utils import (
)
from .training_args import ParallelMode
from .utils import logging
from .utils.modeling_auto_mapping import (
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
TASK_MAPPING = {
"text-generation": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"fill-mask": MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"object-detection": MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"text2text-generation": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"text-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"table-question-answering": MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
"token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
}
logger = logging.get_logger(__name__)
@ -246,9 +269,12 @@ should probably proofread and complete it, then remove this comment. -->
TASK_TAG_TO_NAME_MAPPING = {
"fill-mask": "Masked Language Modeling",
"image-classification": "Image Classification",
"multiple-choice": "Multiple Choice",
"object-detection": "Object Detection",
"question-answering": "Question Answering",
"summarization": "Summarization",
"table-question-answering": "Table Question Answering",
"text-classification": "Text Classification",
"text-generation": "Causal Language Modeling",
"text2text-generation": "Sequence-to-sequence Language Modeling",
@ -304,6 +330,25 @@ def infer_metric_tags_from_eval_results(eval_results):
return result
def is_hf_dataset(dataset):
if not is_datasets_available():
return False
from datasets import Dataset
return isinstance(dataset, Dataset)
def _get_mapping_values(mapping):
result = []
for v in mapping.values():
if isinstance(v, (tuple, list)):
result += list(v)
else:
result.append(v)
return result
@dataclass
class TrainingSummary:
model_name: str
@ -311,6 +356,7 @@ class TrainingSummary:
license: Optional[str] = None
tags: Optional[Union[str, List[str]]] = None
finetuned_from: Optional[str] = None
tasks: Optional[Union[str, List[str]]] = None
dataset: Optional[Union[str, List[str]]] = None
dataset_tags: Optional[Union[str, List[str]]] = None
dataset_args: Optional[Union[str, List[str]]] = None
@ -320,7 +366,12 @@ class TrainingSummary:
def __post_init__(self):
# Infer default license from the checkpoint used, if possible.
if self.license is None and not is_offline_mode() and self.finetuned_from is not None:
if (
self.license is None
and not is_offline_mode()
and self.finetuned_from is not None
and len(self.finetuned_from) > 0
):
try:
model_info = HfApi().model_info(self.finetuned_from)
for tag in model_info.tags:
@ -342,7 +393,7 @@ class TrainingSummary:
dataset_arg_mapping = {tag: arg for tag, arg in zip(dataset_tags, dataset_args)}
task_mapping = {
tag: TASK_TAG_TO_NAME_MAPPING[tag] for tag in _listify(self.tags) if tag in TASK_TAG_TO_NAME_MAPPING
task: TASK_TAG_TO_NAME_MAPPING[task] for task in _listify(self.tasks) if task in TASK_TAG_TO_NAME_MAPPING
}
if len(task_mapping) == 0 and len(dataset_mapping) == 0:
@ -405,6 +456,8 @@ class TrainingSummary:
else:
if isinstance(self.dataset, str):
model_card += f"the {self.dataset} dataset."
elif isinstance(self.dataset, (tuple, list)) and len(self.dataset) == 1:
model_card += f"the {self.dataset[0]} dataset."
else:
model_card += (
", ".join([f"the {ds}" for ds in self.dataset[:-1]]) + f" and the {self.dataset[-1]} datasets."
@ -459,11 +512,40 @@ class TrainingSummary:
tags=None,
model_name=None,
finetuned_from=None,
tasks=None,
dataset_tags=None,
dataset=None,
dataset_args=None,
):
# TODO (Sylvain) Add a default for `pipeline-tag` inferred from the model.
# Infer default from dataset
one_dataset = trainer.train_dataset if trainer.train_dataset is not None else trainer.eval_dataset
if is_hf_dataset(one_dataset) and (dataset_tags is None or dataset_args is None):
default_tag = one_dataset.builder_name
# Those are not real datasets from the Hub so we exclude them.
if default_tag not in ["csv", "json", "pandas", "parquet", "text"]:
if dataset_tags is None:
dataset_tags = [default_tag]
if dataset_args is None:
dataset_args = [one_dataset.config_name]
if dataset is None and dataset_tags is not None:
dataset = dataset_tags
# Infer default finetuned_from
if (
finetuned_from is None
and hasattr(trainer.model.config, "_name_or_path")
and not os.path.isdir(trainer.model.config._name_or_path)
):
finetuned_from = trainer.model.config._name_or_path
# Infer default task tag:
if tasks is None:
model_class_name = trainer.model.__class__.__name__
for task, mapping in TASK_MAPPING.items():
if model_class_name in _get_mapping_values(mapping):
tasks = task
if model_name is None:
model_name = Path(trainer.args.output_dir).name
@ -476,6 +558,7 @@ class TrainingSummary:
tags=tags,
model_name=model_name,
finetuned_from=finetuned_from,
tasks=tasks,
dataset_tags=dataset_tags,
dataset=dataset,
dataset_args=dataset_args,

2
src/transformers/trainer.py
View file

@ -2433,6 +2433,7 @@ class Trainer:
tags: Optional[str] = None,
model_name: Optional[str] = None,
finetuned_from: Optional[str] = None,
tasks: Optional[str] = None,
dataset_tags: Optional[Union[str, List[str]]] = None,
dataset: Optional[Union[str, List[str]]] = None,
dataset_args: Optional[Union[str, List[str]]] = None,
@ -2444,6 +2445,7 @@ class Trainer:
tags=tags,
model_name=model_name,
finetuned_from=finetuned_from,
tasks=tasks,
dataset_tags=dataset_tags,
dataset=dataset,
dataset_args=dataset_args,

320
src/transformers/utils/modeling_auto_mapping.py
View file

@ -36,3 +36,323 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
("IBertConfig", "IBertForQuestionAnswering"),
]
)
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
[
("RoFormerConfig", "RoFormerForCausalLM"),
("BigBirdPegasusConfig", "BigBirdPegasusForCausalLM"),
("GPTNeoConfig", "GPTNeoForCausalLM"),
("BigBirdConfig", "BigBirdForCausalLM"),
("CamembertConfig", "CamembertForCausalLM"),
("XLMRobertaConfig", "XLMRobertaForCausalLM"),
("RobertaConfig", "RobertaForCausalLM"),
("BertConfig", "BertLMHeadModel"),
("OpenAIGPTConfig", "OpenAIGPTLMHeadModel"),
("GPT2Config", "GPT2LMHeadModel"),
("TransfoXLConfig", "TransfoXLLMHeadModel"),
("XLNetConfig", "XLNetLMHeadModel"),
("XLMConfig", "XLMWithLMHeadModel"),
("CTRLConfig", "CTRLLMHeadModel"),
("ReformerConfig", "ReformerModelWithLMHead"),
("BertGenerationConfig", "BertGenerationDecoder"),
("XLMProphetNetConfig", "XLMProphetNetForCausalLM"),
("ProphetNetConfig", "ProphetNetForCausalLM"),
("BartConfig", "BartForCausalLM"),
("MBartConfig", "MBartForCausalLM"),
("PegasusConfig", "PegasusForCausalLM"),
("MarianConfig", "MarianForCausalLM"),
("BlenderbotConfig", "BlenderbotForCausalLM"),
("BlenderbotSmallConfig", "BlenderbotSmallForCausalLM"),
("MegatronBertConfig", "MegatronBertForCausalLM"),
]
)
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
("ViTConfig", "ViTForImageClassification"),
("DeiTConfig", "('DeiTForImageClassification', 'DeiTForImageClassificationWithTeacher')"),
]
)
MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
[
("RoFormerConfig", "RoFormerForMaskedLM"),
("BigBirdConfig", "BigBirdForMaskedLM"),
("Wav2Vec2Config", "Wav2Vec2ForMaskedLM"),
("ConvBertConfig", "ConvBertForMaskedLM"),
("LayoutLMConfig", "LayoutLMForMaskedLM"),
("DistilBertConfig", "DistilBertForMaskedLM"),
("AlbertConfig", "AlbertForMaskedLM"),
("BartConfig", "BartForConditionalGeneration"),
("MBartConfig", "MBartForConditionalGeneration"),
("CamembertConfig", "CamembertForMaskedLM"),
("XLMRobertaConfig", "XLMRobertaForMaskedLM"),
("LongformerConfig", "LongformerForMaskedLM"),
("RobertaConfig", "RobertaForMaskedLM"),
("SqueezeBertConfig", "SqueezeBertForMaskedLM"),
("BertConfig", "BertForMaskedLM"),
("MegatronBertConfig", "MegatronBertForMaskedLM"),
("MobileBertConfig", "MobileBertForMaskedLM"),
("FlaubertConfig", "FlaubertWithLMHeadModel"),
("XLMConfig", "XLMWithLMHeadModel"),
("ElectraConfig", "ElectraForMaskedLM"),
("ReformerConfig", "ReformerForMaskedLM"),
("FunnelConfig", "FunnelForMaskedLM"),
("MPNetConfig", "MPNetForMaskedLM"),
("TapasConfig", "TapasForMaskedLM"),
("DebertaConfig", "DebertaForMaskedLM"),
("DebertaV2Config", "DebertaV2ForMaskedLM"),
("IBertConfig", "IBertForMaskedLM"),
]
)
MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
[
("RoFormerConfig", "RoFormerForMultipleChoice"),
("BigBirdConfig", "BigBirdForMultipleChoice"),
("ConvBertConfig", "ConvBertForMultipleChoice"),
("CamembertConfig", "CamembertForMultipleChoice"),
("ElectraConfig", "ElectraForMultipleChoice"),
("XLMRobertaConfig", "XLMRobertaForMultipleChoice"),
("LongformerConfig", "LongformerForMultipleChoice"),
("RobertaConfig", "RobertaForMultipleChoice"),
("SqueezeBertConfig", "SqueezeBertForMultipleChoice"),
("BertConfig", "BertForMultipleChoice"),
("DistilBertConfig", "DistilBertForMultipleChoice"),
("MegatronBertConfig", "MegatronBertForMultipleChoice"),
("MobileBertConfig", "MobileBertForMultipleChoice"),
("XLNetConfig", "XLNetForMultipleChoice"),
("AlbertConfig", "AlbertForMultipleChoice"),
("XLMConfig", "XLMForMultipleChoice"),
("FlaubertConfig", "FlaubertForMultipleChoice"),
("FunnelConfig", "FunnelForMultipleChoice"),
("MPNetConfig", "MPNetForMultipleChoice"),
("IBertConfig", "IBertForMultipleChoice"),
]
)
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
[
("BertConfig", "BertForNextSentencePrediction"),
("MegatronBertConfig", "MegatronBertForNextSentencePrediction"),
("MobileBertConfig", "MobileBertForNextSentencePrediction"),
]
)
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
[
("DetrConfig", "DetrForObjectDetection"),
]
)
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
[
("BigBirdPegasusConfig", "BigBirdPegasusForConditionalGeneration"),
("M2M100Config", "M2M100ForConditionalGeneration"),
("LEDConfig", "LEDForConditionalGeneration"),
("BlenderbotSmallConfig", "BlenderbotSmallForConditionalGeneration"),
("MT5Config", "MT5ForConditionalGeneration"),
("T5Config", "T5ForConditionalGeneration"),
("PegasusConfig", "PegasusForConditionalGeneration"),
("MarianConfig", "MarianMTModel"),
("MBartConfig", "MBartForConditionalGeneration"),
("BlenderbotConfig", "BlenderbotForConditionalGeneration"),
("BartConfig", "BartForConditionalGeneration"),
("FSMTConfig", "FSMTForConditionalGeneration"),
("EncoderDecoderConfig", "EncoderDecoderModel"),
("XLMProphetNetConfig", "XLMProphetNetForConditionalGeneration"),
("ProphetNetConfig", "ProphetNetForConditionalGeneration"),
]
)
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
("RoFormerConfig", "RoFormerForSequenceClassification"),
("BigBirdPegasusConfig", "BigBirdPegasusForSequenceClassification"),
("BigBirdConfig", "BigBirdForSequenceClassification"),
("ConvBertConfig", "ConvBertForSequenceClassification"),
("LEDConfig", "LEDForSequenceClassification"),
("DistilBertConfig", "DistilBertForSequenceClassification"),
("AlbertConfig", "AlbertForSequenceClassification"),
("CamembertConfig", "CamembertForSequenceClassification"),
("XLMRobertaConfig", "XLMRobertaForSequenceClassification"),
("MBartConfig", "MBartForSequenceClassification"),
("BartConfig", "BartForSequenceClassification"),
("LongformerConfig", "LongformerForSequenceClassification"),
("RobertaConfig", "RobertaForSequenceClassification"),
("SqueezeBertConfig", "SqueezeBertForSequenceClassification"),
("LayoutLMConfig", "LayoutLMForSequenceClassification"),
("BertConfig", "BertForSequenceClassification"),
("XLNetConfig", "XLNetForSequenceClassification"),
("MegatronBertConfig", "MegatronBertForSequenceClassification"),
("MobileBertConfig", "MobileBertForSequenceClassification"),
("FlaubertConfig", "FlaubertForSequenceClassification"),
("XLMConfig", "XLMForSequenceClassification"),
("ElectraConfig", "ElectraForSequenceClassification"),
("FunnelConfig", "FunnelForSequenceClassification"),
("DebertaConfig", "DebertaForSequenceClassification"),
("DebertaV2Config", "DebertaV2ForSequenceClassification"),
("GPT2Config", "GPT2ForSequenceClassification"),
("GPTNeoConfig", "GPTNeoForSequenceClassification"),
("OpenAIGPTConfig", "OpenAIGPTForSequenceClassification"),
("ReformerConfig", "ReformerForSequenceClassification"),
("CTRLConfig", "CTRLForSequenceClassification"),
("TransfoXLConfig", "TransfoXLForSequenceClassification"),
("MPNetConfig", "MPNetForSequenceClassification"),
("TapasConfig", "TapasForSequenceClassification"),
("IBertConfig", "IBertForSequenceClassification"),
]
)
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
("TapasConfig", "TapasForQuestionAnswering"),
]
)
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
("RoFormerConfig", "RoFormerForTokenClassification"),
("BigBirdConfig", "BigBirdForTokenClassification"),
("ConvBertConfig", "ConvBertForTokenClassification"),
("LayoutLMConfig", "LayoutLMForTokenClassification"),
("DistilBertConfig", "DistilBertForTokenClassification"),
("CamembertConfig", "CamembertForTokenClassification"),
("FlaubertConfig", "FlaubertForTokenClassification"),
("XLMConfig", "XLMForTokenClassification"),
("XLMRobertaConfig", "XLMRobertaForTokenClassification"),
("LongformerConfig", "LongformerForTokenClassification"),
("RobertaConfig", "RobertaForTokenClassification"),
("SqueezeBertConfig", "SqueezeBertForTokenClassification"),
("BertConfig", "BertForTokenClassification"),
("MegatronBertConfig", "MegatronBertForTokenClassification"),
("MobileBertConfig", "MobileBertForTokenClassification"),
("XLNetConfig", "XLNetForTokenClassification"),
("AlbertConfig", "AlbertForTokenClassification"),
("ElectraConfig", "ElectraForTokenClassification"),
("FunnelConfig", "FunnelForTokenClassification"),
("MPNetConfig", "MPNetForTokenClassification"),
("DebertaConfig", "DebertaForTokenClassification"),
("DebertaV2Config", "DebertaV2ForTokenClassification"),
("IBertConfig", "IBertForTokenClassification"),
]
)
MODEL_MAPPING_NAMES = OrderedDict(
[
("VisualBertConfig", "VisualBertModel"),
("RoFormerConfig", "RoFormerModel"),
("CLIPConfig", "CLIPModel"),
("BigBirdPegasusConfig", "BigBirdPegasusModel"),
("DeiTConfig", "DeiTModel"),
("LukeConfig", "LukeModel"),
("DetrConfig", "DetrModel"),
("GPTNeoConfig", "GPTNeoModel"),
("BigBirdConfig", "BigBirdModel"),
("Speech2TextConfig", "Speech2TextModel"),
("ViTConfig", "ViTModel"),
("Wav2Vec2Config", "Wav2Vec2Model"),
("M2M100Config", "M2M100Model"),
("ConvBertConfig", "ConvBertModel"),
("LEDConfig", "LEDModel"),
("BlenderbotSmallConfig", "BlenderbotSmallModel"),
("RetriBertConfig", "RetriBertModel"),
("MT5Config", "MT5Model"),
("T5Config", "T5Model"),
("PegasusConfig", "PegasusModel"),
("MarianConfig", "MarianModel"),
("MBartConfig", "MBartModel"),
("BlenderbotConfig", "BlenderbotModel"),
("DistilBertConfig", "DistilBertModel"),
("AlbertConfig", "AlbertModel"),
("CamembertConfig", "CamembertModel"),
("XLMRobertaConfig", "XLMRobertaModel"),
("BartConfig", "BartModel"),
("LongformerConfig", "LongformerModel"),
("RobertaConfig", "RobertaModel"),
("LayoutLMConfig", "LayoutLMModel"),
("SqueezeBertConfig", "SqueezeBertModel"),
("BertConfig", "BertModel"),
("OpenAIGPTConfig", "OpenAIGPTModel"),
("GPT2Config", "GPT2Model"),
("MegatronBertConfig", "MegatronBertModel"),
("MobileBertConfig", "MobileBertModel"),
("TransfoXLConfig", "TransfoXLModel"),
("XLNetConfig", "XLNetModel"),
("FlaubertConfig", "FlaubertModel"),
("FSMTConfig", "FSMTModel"),
("XLMConfig", "XLMModel"),
("CTRLConfig", "CTRLModel"),
("ElectraConfig", "ElectraModel"),
("ReformerConfig", "ReformerModel"),
("FunnelConfig", "('FunnelModel', 'FunnelBaseModel')"),
("LxmertConfig", "LxmertModel"),
("BertGenerationConfig", "BertGenerationEncoder"),
("DebertaConfig", "DebertaModel"),
("DebertaV2Config", "DebertaV2Model"),
("DPRConfig", "DPRQuestionEncoder"),
("XLMProphetNetConfig", "XLMProphetNetModel"),
("ProphetNetConfig", "ProphetNetModel"),
("MPNetConfig", "MPNetModel"),
("TapasConfig", "TapasModel"),
("IBertConfig", "IBertModel"),
]
)
MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
[
("RoFormerConfig", "RoFormerForMaskedLM"),
("BigBirdPegasusConfig", "BigBirdPegasusForConditionalGeneration"),
("GPTNeoConfig", "GPTNeoForCausalLM"),
("BigBirdConfig", "BigBirdForMaskedLM"),
("Speech2TextConfig", "Speech2TextForConditionalGeneration"),
("Wav2Vec2Config", "Wav2Vec2ForMaskedLM"),
("M2M100Config", "M2M100ForConditionalGeneration"),
("ConvBertConfig", "ConvBertForMaskedLM"),
("LEDConfig", "LEDForConditionalGeneration"),
("BlenderbotSmallConfig", "BlenderbotSmallForConditionalGeneration"),
("LayoutLMConfig", "LayoutLMForMaskedLM"),
("T5Config", "T5ForConditionalGeneration"),
("DistilBertConfig", "DistilBertForMaskedLM"),
("AlbertConfig", "AlbertForMaskedLM"),
("CamembertConfig", "CamembertForMaskedLM"),
("XLMRobertaConfig", "XLMRobertaForMaskedLM"),
("MarianConfig", "MarianMTModel"),
("FSMTConfig", "FSMTForConditionalGeneration"),
("BartConfig", "BartForConditionalGeneration"),
("LongformerConfig", "LongformerForMaskedLM"),
("RobertaConfig", "RobertaForMaskedLM"),
("SqueezeBertConfig", "SqueezeBertForMaskedLM"),
("BertConfig", "BertForMaskedLM"),
("OpenAIGPTConfig", "OpenAIGPTLMHeadModel"),
("GPT2Config", "GPT2LMHeadModel"),
("MegatronBertConfig", "MegatronBertForCausalLM"),
("MobileBertConfig", "MobileBertForMaskedLM"),
("TransfoXLConfig", "TransfoXLLMHeadModel"),
("XLNetConfig", "XLNetLMHeadModel"),
("FlaubertConfig", "FlaubertWithLMHeadModel"),
("XLMConfig", "XLMWithLMHeadModel"),
("CTRLConfig", "CTRLLMHeadModel"),
("ElectraConfig", "ElectraForMaskedLM"),
("EncoderDecoderConfig", "EncoderDecoderModel"),
("ReformerConfig", "ReformerModelWithLMHead"),
("FunnelConfig", "FunnelForMaskedLM"),
("MPNetConfig", "MPNetForMaskedLM"),
("TapasConfig", "TapasForMaskedLM"),
("DebertaConfig", "DebertaForMaskedLM"),
("DebertaV2Config", "DebertaV2ForMaskedLM"),
("IBertConfig", "IBertForMaskedLM"),
]
)

72
utils/class_mapping_update.py
View file

@ -30,31 +30,77 @@ sys.path.insert(1, git_repo_path)
src = "src/transformers/models/auto/modeling_auto.py"
dst = "src/transformers/utils/modeling_auto_mapping.py"
if os.path.exists(dst) and os.path.getmtime(src) < os.path.getmtime(dst):
# speed things up by only running this script if the src is newer than dst
sys.exit(0)
# only load if needed
from transformers.models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING # noqa
entries = "\n".join(
[f' ("{k.__name__}", "{v.__name__}"),' for k, v in MODEL_FOR_QUESTION_ANSWERING_MAPPING.items()]
from transformers.models.auto.modeling_auto import ( # noqa
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
MODEL_FOR_OBJECT_DETECTION_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
)
# Those constants don't have a name attribute, so we need to define it manually
mappings = {
"MODEL_FOR_QUESTION_ANSWERING_MAPPING": MODEL_FOR_QUESTION_ANSWERING_MAPPING,
"MODEL_FOR_CAUSAL_LM_MAPPING": MODEL_FOR_CAUSAL_LM_MAPPING,
"MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
"MODEL_FOR_MASKED_LM_MAPPING": MODEL_FOR_MASKED_LM_MAPPING,
"MODEL_FOR_MULTIPLE_CHOICE_MAPPING": MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
"MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
"MODEL_FOR_OBJECT_DETECTION_MAPPING": MODEL_FOR_OBJECT_DETECTION_MAPPING,
"MODEL_FOR_OBJECT_DETECTION_MAPPING": MODEL_FOR_OBJECT_DETECTION_MAPPING,
"MODEL_FOR_QUESTION_ANSWERING_MAPPING": MODEL_FOR_QUESTION_ANSWERING_MAPPING,
"MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
"MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
"MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING": MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
"MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
"MODEL_MAPPING": MODEL_MAPPING,
"MODEL_WITH_LM_HEAD_MAPPING": MODEL_WITH_LM_HEAD_MAPPING,
}
def get_name(value):
if isinstance(value, tuple):
return tuple(get_name(o) for o in value)
return value.__name__
content = [
"# THIS FILE HAS BEEN AUTOGENERATED. To update:",
"# 1. modify: models/auto/modeling_auto.py",
"# 2. run: python utils/class_mapping_update.py",
"from collections import OrderedDict",
"",
"",
"MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(",
" [",
entries,
" ]",
")",
"",
]
print(f"updating {dst}")
for name, mapping in mappings.items():
entries = "\n".join([f' ("{k.__name__}", "{get_name(v)}"),' for k, v in mapping.items()])
content += [
"",
f"{name}_NAMES = OrderedDict(",
" [",
entries,
" ]",
")",
"",
]
print(f"Updating {dst}")
with open(dst, "w", encoding="utf-8", newline="\n") as f:
f.write("\n".join(content))