🤗 Transformers は transformers.onnx
パッケージを提供します。
設定オブジェクトを利用することで、モデルのチェックポイントをONNXグラフに変換することができます。
詳細はガイド を参照してください。 を参照してください。
以下の3つの抽象クラスを提供しています。 エクスポートしたいモデルアーキテクチャのタイプに応じて、継承すべき3つの抽象クラスを提供します:
( config: PretrainedConfig task: str = 'default' patching_specs: typing.List[transformers.onnx.config.PatchingSpec] = None )
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
( name: str field: typing.Iterable[typing.Any] ) → (Dict[str, Any])
Flatten any potential nested structure expanding the name of the field with the index of the element within the structure.
( config: PretrainedConfig task: str = 'default' )
Instantiate a OnnxConfig for a specific model
( preprocessor: typing.Union[ForwardRef('PreTrainedTokenizerBase'), ForwardRef('FeatureExtractionMixin'), ForwardRef('ImageProcessingMixin')] batch_size: int = -1 seq_length: int = -1 num_choices: int = -1 is_pair: bool = False framework: typing.Optional[transformers.utils.generic.TensorType] = None num_channels: int = 3 image_width: int = 40 image_height: int = 40 sampling_rate: int = 22050 time_duration: float = 5.0 frequency: int = 220 tokenizer: PreTrainedTokenizerBase = None )
Parameters
int
, optional, defaults to -1) —
The batch size to export the model for (-1 means dynamic axis). int
, optional, defaults to -1) —
The number of candidate answers provided for multiple choice task (-1 means dynamic axis). int
, optional, defaults to -1) —
The sequence length to export the model for (-1 means dynamic axis). bool
, optional, defaults to False
) —
Indicate if the input is a pair (sentence 1, sentence 2) TensorType
, optional, defaults to None
) —
The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for. int
, optional, defaults to 3) —
The number of channels of the generated images. int
, optional, defaults to 40) —
The width of the generated images. int
, optional, defaults to 40) —
The height of the generated images. int
, optional defaults to 22050) —
The sampling rate for audio data generation. float
, optional defaults to 5.0) —
Total seconds of sampling for audio data generation. int
, optional defaults to 220) —
The desired natural frequency of generated audio. Generate inputs to provide to the ONNX exporter for the specific framework
( reference_model_inputs: typing.Mapping[str, typing.Any] ) → Mapping[str, Tensor]
Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq models which have the encoder and decoder exported as separate ONNX files.
( num_parameters: int )
Flag indicating if the model requires using external data format
( config: PretrainedConfig task: str = 'default' patching_specs: typing.List[transformers.onnx.config.PatchingSpec] = None use_past: bool = False )
( inputs_or_outputs: typing.Mapping[str, typing.Mapping[int, str]] direction: str inverted_values_shape: bool = False )
Fill the input_or_outputs mapping with past_key_values dynamic axes considering.
( config: PretrainedConfig task: str = 'default' )
Instantiate a OnnxConfig with use_past
attribute set to True
( config: PretrainedConfig task: str = 'default' patching_specs: typing.List[transformers.onnx.config.PatchingSpec] = None use_past: bool = False )
各 ONNX 構成は、次のことを可能にする一連の 機能 に関連付けられています。 さまざまなタイプのトポロジまたはタスクのモデルをエクスポートします。
( model: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] feature: str = 'default' )
Check whether or not the model has the requested features.
( model: str framework: str = None )
Determines the framework to use for the export.
The priority is in the following order:
framework
.( model_type: str feature: str ) → OnnxConfig
Gets the OnnxConfig for a model_type and feature combination.
( feature: str framework: str = 'pt' )
Attempts to retrieve an AutoModel class from a feature name.
( feature: str model: str framework: str = None cache_dir: str = None )
Attempts to retrieve a model from a model’s name and the feature to be enabled.
( model_type: str model_name: typing.Optional[str] = None )
Tries to retrieve the feature -> OnnxConfig constructor map from the model type.