750 lines
28 KiB
Python
750 lines
28 KiB
Python
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import os
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from functools import partial, reduce
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from typing import TYPE_CHECKING, Callable, Dict, Optional, Tuple, Type, Union
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import transformers
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from .. import PretrainedConfig, is_tf_available, is_torch_available
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from ..utils import TF2_WEIGHTS_NAME, WEIGHTS_NAME, logging
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from .config import OnnxConfig
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, TFPreTrainedModel
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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if is_torch_available():
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from transformers.models.auto import (
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForImageClassification,
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AutoModelForImageSegmentation,
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AutoModelForMaskedImageModeling,
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AutoModelForMaskedLM,
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AutoModelForMultipleChoice,
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AutoModelForObjectDetection,
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AutoModelForQuestionAnswering,
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AutoModelForSemanticSegmentation,
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AutoModelForSeq2SeqLM,
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AutoModelForSequenceClassification,
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AutoModelForSpeechSeq2Seq,
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AutoModelForTokenClassification,
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AutoModelForVision2Seq,
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)
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if is_tf_available():
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from transformers.models.auto import (
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TFAutoModel,
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TFAutoModelForCausalLM,
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TFAutoModelForMaskedLM,
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TFAutoModelForMultipleChoice,
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TFAutoModelForQuestionAnswering,
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TFAutoModelForSemanticSegmentation,
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TFAutoModelForSeq2SeqLM,
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TFAutoModelForSequenceClassification,
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TFAutoModelForTokenClassification,
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)
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if not is_torch_available() and not is_tf_available():
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logger.warning(
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"The ONNX export features are only supported for PyTorch or TensorFlow. You will not be able to export models"
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" without one of these libraries installed."
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)
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def supported_features_mapping(
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*supported_features: str, onnx_config_cls: str = None
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) -> Dict[str, Callable[[PretrainedConfig], OnnxConfig]]:
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"""
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Generate the mapping between supported the features and their corresponding OnnxConfig for a given model.
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Args:
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*supported_features: The names of the supported features.
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onnx_config_cls: The OnnxConfig full name corresponding to the model.
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Returns:
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The dictionary mapping a feature to an OnnxConfig constructor.
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"""
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if onnx_config_cls is None:
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raise ValueError("A OnnxConfig class must be provided")
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config_cls = transformers
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for attr_name in onnx_config_cls.split("."):
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config_cls = getattr(config_cls, attr_name)
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mapping = {}
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for feature in supported_features:
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if "-with-past" in feature:
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task = feature.replace("-with-past", "")
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mapping[feature] = partial(config_cls.with_past, task=task)
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else:
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mapping[feature] = partial(config_cls.from_model_config, task=feature)
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return mapping
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class FeaturesManager:
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_TASKS_TO_AUTOMODELS = {}
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_TASKS_TO_TF_AUTOMODELS = {}
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if is_torch_available():
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_TASKS_TO_AUTOMODELS = {
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"default": AutoModel,
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"masked-lm": AutoModelForMaskedLM,
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"causal-lm": AutoModelForCausalLM,
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"seq2seq-lm": AutoModelForSeq2SeqLM,
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"sequence-classification": AutoModelForSequenceClassification,
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"token-classification": AutoModelForTokenClassification,
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"multiple-choice": AutoModelForMultipleChoice,
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"object-detection": AutoModelForObjectDetection,
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"question-answering": AutoModelForQuestionAnswering,
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"image-classification": AutoModelForImageClassification,
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"image-segmentation": AutoModelForImageSegmentation,
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"masked-im": AutoModelForMaskedImageModeling,
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"semantic-segmentation": AutoModelForSemanticSegmentation,
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"vision2seq-lm": AutoModelForVision2Seq,
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"speech2seq-lm": AutoModelForSpeechSeq2Seq,
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}
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if is_tf_available():
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_TASKS_TO_TF_AUTOMODELS = {
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"default": TFAutoModel,
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"masked-lm": TFAutoModelForMaskedLM,
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"causal-lm": TFAutoModelForCausalLM,
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"seq2seq-lm": TFAutoModelForSeq2SeqLM,
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"sequence-classification": TFAutoModelForSequenceClassification,
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"token-classification": TFAutoModelForTokenClassification,
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"multiple-choice": TFAutoModelForMultipleChoice,
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"question-answering": TFAutoModelForQuestionAnswering,
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"semantic-segmentation": TFAutoModelForSemanticSegmentation,
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}
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# Set of model topologies we support associated to the features supported by each topology and the factory
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_SUPPORTED_MODEL_TYPE = {
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"albert": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.albert.AlbertOnnxConfig",
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),
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"bart": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"seq2seq-lm",
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"seq2seq-lm-with-past",
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"sequence-classification",
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"question-answering",
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onnx_config_cls="models.bart.BartOnnxConfig",
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),
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# BEiT cannot be used with the masked image modeling autoclass, so this feature is excluded here
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"beit": supported_features_mapping(
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"default", "image-classification", onnx_config_cls="models.beit.BeitOnnxConfig"
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),
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"bert": supported_features_mapping(
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"default",
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"masked-lm",
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"causal-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.bert.BertOnnxConfig",
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),
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"big-bird": supported_features_mapping(
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"default",
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"masked-lm",
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"causal-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.big_bird.BigBirdOnnxConfig",
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),
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"bigbird-pegasus": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"seq2seq-lm",
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"seq2seq-lm-with-past",
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"sequence-classification",
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"question-answering",
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onnx_config_cls="models.bigbird_pegasus.BigBirdPegasusOnnxConfig",
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),
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"blenderbot": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"seq2seq-lm",
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"seq2seq-lm-with-past",
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onnx_config_cls="models.blenderbot.BlenderbotOnnxConfig",
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),
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"blenderbot-small": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"seq2seq-lm",
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"seq2seq-lm-with-past",
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onnx_config_cls="models.blenderbot_small.BlenderbotSmallOnnxConfig",
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),
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"bloom": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"sequence-classification",
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"token-classification",
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onnx_config_cls="models.bloom.BloomOnnxConfig",
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),
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"camembert": supported_features_mapping(
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"default",
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"masked-lm",
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"causal-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.camembert.CamembertOnnxConfig",
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),
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"clip": supported_features_mapping(
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"default",
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onnx_config_cls="models.clip.CLIPOnnxConfig",
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),
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"codegen": supported_features_mapping(
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"default",
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"causal-lm",
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onnx_config_cls="models.codegen.CodeGenOnnxConfig",
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),
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"convbert": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.convbert.ConvBertOnnxConfig",
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),
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"convnext": supported_features_mapping(
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"default",
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"image-classification",
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onnx_config_cls="models.convnext.ConvNextOnnxConfig",
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),
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"data2vec-text": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.data2vec.Data2VecTextOnnxConfig",
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),
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"data2vec-vision": supported_features_mapping(
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"default",
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"image-classification",
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# ONNX doesn't support `adaptive_avg_pool2d` yet
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# "semantic-segmentation",
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onnx_config_cls="models.data2vec.Data2VecVisionOnnxConfig",
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),
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"deberta": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.deberta.DebertaOnnxConfig",
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),
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"deberta-v2": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.deberta_v2.DebertaV2OnnxConfig",
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),
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"deit": supported_features_mapping(
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"default", "image-classification", onnx_config_cls="models.deit.DeiTOnnxConfig"
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),
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"detr": supported_features_mapping(
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"default",
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"object-detection",
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"image-segmentation",
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onnx_config_cls="models.detr.DetrOnnxConfig",
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),
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"distilbert": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.distilbert.DistilBertOnnxConfig",
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),
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"electra": supported_features_mapping(
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"default",
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"masked-lm",
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"causal-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.electra.ElectraOnnxConfig",
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),
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"flaubert": supported_features_mapping(
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"default",
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"masked-lm",
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"causal-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.flaubert.FlaubertOnnxConfig",
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),
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"gpt2": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"sequence-classification",
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"token-classification",
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onnx_config_cls="models.gpt2.GPT2OnnxConfig",
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),
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"gptj": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"question-answering",
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"sequence-classification",
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onnx_config_cls="models.gptj.GPTJOnnxConfig",
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),
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"gpt-neo": supported_features_mapping(
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"default",
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"default-with-past",
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"causal-lm",
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"causal-lm-with-past",
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"sequence-classification",
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onnx_config_cls="models.gpt_neo.GPTNeoOnnxConfig",
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),
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"groupvit": supported_features_mapping(
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"default",
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onnx_config_cls="models.groupvit.GroupViTOnnxConfig",
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),
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"ibert": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"multiple-choice",
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"token-classification",
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"question-answering",
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onnx_config_cls="models.ibert.IBertOnnxConfig",
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),
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"imagegpt": supported_features_mapping(
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"default", "image-classification", onnx_config_cls="models.imagegpt.ImageGPTOnnxConfig"
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),
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"layoutlm": supported_features_mapping(
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"default",
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"masked-lm",
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"sequence-classification",
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"token-classification",
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onnx_config_cls="models.layoutlm.LayoutLMOnnxConfig",
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),
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"layoutlmv3": supported_features_mapping(
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"default",
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"question-answering",
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"sequence-classification",
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"token-classification",
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onnx_config_cls="models.layoutlmv3.LayoutLMv3OnnxConfig",
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),
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"levit": supported_features_mapping(
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"default", "image-classification", onnx_config_cls="models.levit.LevitOnnxConfig"
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),
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"longt5": supported_features_mapping(
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"default",
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"default-with-past",
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"seq2seq-lm",
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"seq2seq-lm-with-past",
|
||
|
|
onnx_config_cls="models.longt5.LongT5OnnxConfig",
|
||
|
|
),
|
||
|
|
"longformer": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"multiple-choice",
|
||
|
|
"question-answering",
|
||
|
|
"sequence-classification",
|
||
|
|
"token-classification",
|
||
|
|
onnx_config_cls="models.longformer.LongformerOnnxConfig",
|
||
|
|
),
|
||
|
|
"marian": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"default-with-past",
|
||
|
|
"seq2seq-lm",
|
||
|
|
"seq2seq-lm-with-past",
|
||
|
|
"causal-lm",
|
||
|
|
"causal-lm-with-past",
|
||
|
|
onnx_config_cls="models.marian.MarianOnnxConfig",
|
||
|
|
),
|
||
|
|
"mbart": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"default-with-past",
|
||
|
|
"causal-lm",
|
||
|
|
"causal-lm-with-past",
|
||
|
|
"seq2seq-lm",
|
||
|
|
"seq2seq-lm-with-past",
|
||
|
|
"sequence-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.mbart.MBartOnnxConfig",
|
||
|
|
),
|
||
|
|
"mobilebert": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"token-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.mobilebert.MobileBertOnnxConfig",
|
||
|
|
),
|
||
|
|
"mobilenet-v1": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"image-classification",
|
||
|
|
onnx_config_cls="models.mobilenet_v1.MobileNetV1OnnxConfig",
|
||
|
|
),
|
||
|
|
"mobilenet-v2": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"image-classification",
|
||
|
|
onnx_config_cls="models.mobilenet_v2.MobileNetV2OnnxConfig",
|
||
|
|
),
|
||
|
|
"mobilevit": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"image-classification",
|
||
|
|
onnx_config_cls="models.mobilevit.MobileViTOnnxConfig",
|
||
|
|
),
|
||
|
|
"mt5": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"default-with-past",
|
||
|
|
"seq2seq-lm",
|
||
|
|
"seq2seq-lm-with-past",
|
||
|
|
onnx_config_cls="models.mt5.MT5OnnxConfig",
|
||
|
|
),
|
||
|
|
"m2m-100": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"default-with-past",
|
||
|
|
"seq2seq-lm",
|
||
|
|
"seq2seq-lm-with-past",
|
||
|
|
onnx_config_cls="models.m2m_100.M2M100OnnxConfig",
|
||
|
|
),
|
||
|
|
"owlvit": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
onnx_config_cls="models.owlvit.OwlViTOnnxConfig",
|
||
|
|
),
|
||
|
|
"perceiver": supported_features_mapping(
|
||
|
|
"image-classification",
|
||
|
|
"masked-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
onnx_config_cls="models.perceiver.PerceiverOnnxConfig",
|
||
|
|
),
|
||
|
|
"poolformer": supported_features_mapping(
|
||
|
|
"default", "image-classification", onnx_config_cls="models.poolformer.PoolFormerOnnxConfig"
|
||
|
|
),
|
||
|
|
"rembert": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"causal-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"token-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.rembert.RemBertOnnxConfig",
|
||
|
|
),
|
||
|
|
"resnet": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"image-classification",
|
||
|
|
onnx_config_cls="models.resnet.ResNetOnnxConfig",
|
||
|
|
),
|
||
|
|
"roberta": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"causal-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"token-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.roberta.RobertaOnnxConfig",
|
||
|
|
),
|
||
|
|
"roformer": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"causal-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"token-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"question-answering",
|
||
|
|
"token-classification",
|
||
|
|
onnx_config_cls="models.roformer.RoFormerOnnxConfig",
|
||
|
|
),
|
||
|
|
"segformer": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"image-classification",
|
||
|
|
"semantic-segmentation",
|
||
|
|
onnx_config_cls="models.segformer.SegformerOnnxConfig",
|
||
|
|
),
|
||
|
|
"squeezebert": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"token-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.squeezebert.SqueezeBertOnnxConfig",
|
||
|
|
),
|
||
|
|
"swin": supported_features_mapping(
|
||
|
|
"default", "image-classification", onnx_config_cls="models.swin.SwinOnnxConfig"
|
||
|
|
),
|
||
|
|
"t5": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"default-with-past",
|
||
|
|
"seq2seq-lm",
|
||
|
|
"seq2seq-lm-with-past",
|
||
|
|
onnx_config_cls="models.t5.T5OnnxConfig",
|
||
|
|
),
|
||
|
|
"vision-encoder-decoder": supported_features_mapping(
|
||
|
|
"vision2seq-lm", onnx_config_cls="models.vision_encoder_decoder.VisionEncoderDecoderOnnxConfig"
|
||
|
|
),
|
||
|
|
"vit": supported_features_mapping(
|
||
|
|
"default", "image-classification", onnx_config_cls="models.vit.ViTOnnxConfig"
|
||
|
|
),
|
||
|
|
"whisper": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"default-with-past",
|
||
|
|
"speech2seq-lm",
|
||
|
|
"speech2seq-lm-with-past",
|
||
|
|
onnx_config_cls="models.whisper.WhisperOnnxConfig",
|
||
|
|
),
|
||
|
|
"xlm": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"causal-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"token-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.xlm.XLMOnnxConfig",
|
||
|
|
),
|
||
|
|
"xlm-roberta": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"masked-lm",
|
||
|
|
"causal-lm",
|
||
|
|
"sequence-classification",
|
||
|
|
"multiple-choice",
|
||
|
|
"token-classification",
|
||
|
|
"question-answering",
|
||
|
|
onnx_config_cls="models.xlm_roberta.XLMRobertaOnnxConfig",
|
||
|
|
),
|
||
|
|
"yolos": supported_features_mapping(
|
||
|
|
"default",
|
||
|
|
"object-detection",
|
||
|
|
onnx_config_cls="models.yolos.YolosOnnxConfig",
|
||
|
|
),
|
||
|
|
}
|
||
|
|
|
||
|
|
AVAILABLE_FEATURES = sorted(reduce(lambda s1, s2: s1 | s2, (v.keys() for v in _SUPPORTED_MODEL_TYPE.values())))
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def get_supported_features_for_model_type(
|
||
|
|
model_type: str, model_name: Optional[str] = None
|
||
|
|
) -> Dict[str, Callable[[PretrainedConfig], OnnxConfig]]:
|
||
|
|
"""
|
||
|
|
Tries to retrieve the feature -> OnnxConfig constructor map from the model type.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
model_type (`str`):
|
||
|
|
The model type to retrieve the supported features for.
|
||
|
|
model_name (`str`, *optional*):
|
||
|
|
The name attribute of the model object, only used for the exception message.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The dictionary mapping each feature to a corresponding OnnxConfig constructor.
|
||
|
|
"""
|
||
|
|
model_type = model_type.lower()
|
||
|
|
if model_type not in FeaturesManager._SUPPORTED_MODEL_TYPE:
|
||
|
|
model_type_and_model_name = f"{model_type} ({model_name})" if model_name else model_type
|
||
|
|
raise KeyError(
|
||
|
|
f"{model_type_and_model_name} is not supported yet. "
|
||
|
|
f"Only {list(FeaturesManager._SUPPORTED_MODEL_TYPE.keys())} are supported. "
|
||
|
|
f"If you want to support {model_type} please propose a PR or open up an issue."
|
||
|
|
)
|
||
|
|
return FeaturesManager._SUPPORTED_MODEL_TYPE[model_type]
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def feature_to_task(feature: str) -> str:
|
||
|
|
return feature.replace("-with-past", "")
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _validate_framework_choice(framework: str):
|
||
|
|
"""
|
||
|
|
Validates if the framework requested for the export is both correct and available, otherwise throws an
|
||
|
|
exception.
|
||
|
|
"""
|
||
|
|
if framework not in ["pt", "tf"]:
|
||
|
|
raise ValueError(
|
||
|
|
f"Only two frameworks are supported for ONNX export: pt or tf, but {framework} was provided."
|
||
|
|
)
|
||
|
|
elif framework == "pt" and not is_torch_available():
|
||
|
|
raise RuntimeError("Cannot export model to ONNX using PyTorch because no PyTorch package was found.")
|
||
|
|
elif framework == "tf" and not is_tf_available():
|
||
|
|
raise RuntimeError("Cannot export model to ONNX using TensorFlow because no TensorFlow package was found.")
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def get_model_class_for_feature(feature: str, framework: str = "pt") -> Type:
|
||
|
|
"""
|
||
|
|
Attempts to retrieve an AutoModel class from a feature name.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
feature (`str`):
|
||
|
|
The feature required.
|
||
|
|
framework (`str`, *optional*, defaults to `"pt"`):
|
||
|
|
The framework to use for the export.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The AutoModel class corresponding to the feature.
|
||
|
|
"""
|
||
|
|
task = FeaturesManager.feature_to_task(feature)
|
||
|
|
FeaturesManager._validate_framework_choice(framework)
|
||
|
|
if framework == "pt":
|
||
|
|
task_to_automodel = FeaturesManager._TASKS_TO_AUTOMODELS
|
||
|
|
else:
|
||
|
|
task_to_automodel = FeaturesManager._TASKS_TO_TF_AUTOMODELS
|
||
|
|
if task not in task_to_automodel:
|
||
|
|
raise KeyError(
|
||
|
|
f"Unknown task: {feature}. Possible values are {list(FeaturesManager._TASKS_TO_AUTOMODELS.values())}"
|
||
|
|
)
|
||
|
|
|
||
|
|
return task_to_automodel[task]
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def determine_framework(model: str, framework: str = None) -> str:
|
||
|
|
"""
|
||
|
|
Determines the framework to use for the export.
|
||
|
|
|
||
|
|
The priority is in the following order:
|
||
|
|
1. User input via `framework`.
|
||
|
|
2. If local checkpoint is provided, use the same framework as the checkpoint.
|
||
|
|
3. Available framework in environment, with priority given to PyTorch
|
||
|
|
|
||
|
|
Args:
|
||
|
|
model (`str`):
|
||
|
|
The name of the model to export.
|
||
|
|
framework (`str`, *optional*, defaults to `None`):
|
||
|
|
The framework to use for the export. See above for priority if none provided.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The framework to use for the export.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if framework is not None:
|
||
|
|
return framework
|
||
|
|
|
||
|
|
framework_map = {"pt": "PyTorch", "tf": "TensorFlow"}
|
||
|
|
exporter_map = {"pt": "torch", "tf": "tf2onnx"}
|
||
|
|
|
||
|
|
if os.path.isdir(model):
|
||
|
|
if os.path.isfile(os.path.join(model, WEIGHTS_NAME)):
|
||
|
|
framework = "pt"
|
||
|
|
elif os.path.isfile(os.path.join(model, TF2_WEIGHTS_NAME)):
|
||
|
|
framework = "tf"
|
||
|
|
else:
|
||
|
|
raise FileNotFoundError(
|
||
|
|
"Cannot determine framework from given checkpoint location."
|
||
|
|
f" There should be a {WEIGHTS_NAME} for PyTorch"
|
||
|
|
f" or {TF2_WEIGHTS_NAME} for TensorFlow."
|
||
|
|
)
|
||
|
|
logger.info(f"Local {framework_map[framework]} model found.")
|
||
|
|
else:
|
||
|
|
if is_torch_available():
|
||
|
|
framework = "pt"
|
||
|
|
elif is_tf_available():
|
||
|
|
framework = "tf"
|
||
|
|
else:
|
||
|
|
raise EnvironmentError("Neither PyTorch nor TensorFlow found in environment. Cannot export to ONNX.")
|
||
|
|
|
||
|
|
logger.info(f"Framework not requested. Using {exporter_map[framework]} to export to ONNX.")
|
||
|
|
|
||
|
|
return framework
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def get_model_from_feature(
|
||
|
|
feature: str, model: str, framework: str = None, cache_dir: str = None
|
||
|
|
) -> Union["PreTrainedModel", "TFPreTrainedModel"]:
|
||
|
|
"""
|
||
|
|
Attempts to retrieve a model from a model's name and the feature to be enabled.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
feature (`str`):
|
||
|
|
The feature required.
|
||
|
|
model (`str`):
|
||
|
|
The name of the model to export.
|
||
|
|
framework (`str`, *optional*, defaults to `None`):
|
||
|
|
The framework to use for the export. See `FeaturesManager.determine_framework` for the priority should
|
||
|
|
none be provided.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The instance of the model.
|
||
|
|
|
||
|
|
"""
|
||
|
|
framework = FeaturesManager.determine_framework(model, framework)
|
||
|
|
model_class = FeaturesManager.get_model_class_for_feature(feature, framework)
|
||
|
|
try:
|
||
|
|
model = model_class.from_pretrained(model, cache_dir=cache_dir)
|
||
|
|
except OSError:
|
||
|
|
if framework == "pt":
|
||
|
|
logger.info("Loading TensorFlow model in PyTorch before exporting to ONNX.")
|
||
|
|
model = model_class.from_pretrained(model, from_tf=True, cache_dir=cache_dir)
|
||
|
|
else:
|
||
|
|
logger.info("Loading PyTorch model in TensorFlow before exporting to ONNX.")
|
||
|
|
model = model_class.from_pretrained(model, from_pt=True, cache_dir=cache_dir)
|
||
|
|
return model
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def check_supported_model_or_raise(
|
||
|
|
model: Union["PreTrainedModel", "TFPreTrainedModel"], feature: str = "default"
|
||
|
|
) -> Tuple[str, Callable]:
|
||
|
|
"""
|
||
|
|
Check whether or not the model has the requested features.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
model: The model to export.
|
||
|
|
feature: The name of the feature to check if it is available.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
(str) The type of the model (OnnxConfig) The OnnxConfig instance holding the model export properties.
|
||
|
|
|
||
|
|
"""
|
||
|
|
model_type = model.config.model_type.replace("_", "-")
|
||
|
|
model_name = getattr(model, "name", "")
|
||
|
|
model_features = FeaturesManager.get_supported_features_for_model_type(model_type, model_name=model_name)
|
||
|
|
if feature not in model_features:
|
||
|
|
raise ValueError(
|
||
|
|
f"{model.config.model_type} doesn't support feature {feature}. Supported values are: {model_features}"
|
||
|
|
)
|
||
|
|
|
||
|
|
return model.config.model_type, FeaturesManager._SUPPORTED_MODEL_TYPE[model_type][feature]
|
||
|
|
|
||
|
|
def get_config(model_type: str, feature: str) -> OnnxConfig:
|
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"""
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Gets the OnnxConfig for a model_type and feature combination.
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Args:
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model_type (`str`):
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The model type to retrieve the config for.
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feature (`str`):
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The feature to retrieve the config for.
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Returns:
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`OnnxConfig`: config for the combination
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"""
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return FeaturesManager._SUPPORTED_MODEL_TYPE[model_type][feature]
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