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🤗 Transformers 모델을 ONNX로 내보내기

🤗 트랜스포머는 `transformers.onnx` 패키지를 제공하며, 이 패키지는 설정 객체를 활용하여 모델 체크포인트를 ONNX 그래프로 변환할 수 있게 합니다.

🤗 Transformers에 대한 자세한 내용은 이 가이드를 참조하세요.

##
ONNX 설정

내보내려는(export) 모델 아키텍처의 유형에 따라 상속받아야 할 세 가지 추상 클래스를 제공합니다:

- 인코더 기반 모델은 OnnxConfig을 상속받습니다.
- 디코더 기반 모델은 OnnxConfigWithPast을 상속받습니다.
- 인코더-디코더 기반 모델은 OnnxSeq2SeqConfigWithPast을 상속받습니다.

###
OnnxConfig

###
class transformers.onnx.OnnxConfig

< source >

( 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.

####
flatten_output_collection_property

< source >

( name: str field: typing.Iterable[typing.Any] ) →
(Dict[str, Any])

Parameters

- name — The name of the nested structure
- field — The structure to, potentially, be flattened

Returns

(Dict[str, Any])

Outputs with flattened structure and key mapping this new structure.

Flatten any potential nested structure expanding the name of the field with the index of the element within the structure.

####
from_model_config

< source >

( config: PretrainedConfig task: str = 'default' )

Parameters

- config — The model’s configuration to use when exporting to ONNX

Instantiate a OnnxConfig for a specific model

####
generate_dummy_inputs

< source >

( 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

- preprocessor — (PreTrainedTokenizerBase, FeatureExtractionMixin, or ImageProcessingMixin): The preprocessor associated with this model configuration.
- batch_size (`int`, optional, defaults to -1) — The batch size to export the model for (-1 means dynamic axis).
- num_choices (`int`, optional, defaults to -1) — The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
- seq_length (`int`, optional, defaults to -1) — The sequence length to export the model for (-1 means dynamic axis).
- is_pair (`bool`, optional, defaults to `False`) — Indicate if the input is a pair (sentence 1, sentence 2)
- framework (`TensorType`, optional, defaults to `None`) — The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
- num_channels (`int`, optional, defaults to 3) — The number of channels of the generated images.
- image_width (`int`, optional, defaults to 40) — The width of the generated images.
- image_height (`int`, optional, defaults to 40) — The height of the generated images.
- sampling_rate (`int`, optional defaults to 22050) — The sampling rate for audio data generation.
- time_duration (`float`, optional defaults to 5.0) — Total seconds of sampling for audio data generation.
- frequency (`int`, optional defaults to 220) — The desired natural frequency of generated audio.

Generate inputs to provide to the ONNX exporter for the specific framework

####
generate_dummy_inputs_onnxruntime

< source >

( reference_model_inputs: typing.Mapping[str, typing.Any] ) →
`Mapping[str, Tensor]`

Parameters

- reference_model_inputs ([`Mapping[str, Tensor]`) — Reference inputs for the model.

Returns

`Mapping[str, Tensor]`

The mapping holding the kwargs to provide to the model’s forward function

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.

####
use_external_data_format

< source >

( num_parameters: int )

Parameters

- num_parameters — Number of parameter on the model

Flag indicating if the model requires using external data format

###
OnnxConfigWithPast

###
class transformers.onnx.OnnxConfigWithPast

< source >

( config: PretrainedConfig task: str = 'default' patching_specs: typing.List[transformers.onnx.config.PatchingSpec] = None use_past: bool = False )

####
fill_with_past_key_values_

< source >

( inputs_or_outputs: typing.Mapping[str, typing.Mapping[int, str]] direction: str inverted_values_shape: bool = False )

Parameters

- inputs_or_outputs — The mapping to fill.
- direction — either “inputs” or “outputs”, it specifies whether input_or_outputs is the input mapping or the output mapping, this is important for axes naming.
- inverted_values_shape — If `True`, store values on dynamic axis 1, else on axis 2.

Fill the input_or_outputs mapping with past_key_values dynamic axes considering.

####
with_past

< source >

( config: PretrainedConfig task: str = 'default' )

Parameters

- config — The underlying model’s config to use when exporting to ONNX

Instantiate a OnnxConfig with `use_past` attribute set to True

###
OnnxSeq2SeqConfigWithPast

###
class transformers.onnx.OnnxSeq2SeqConfigWithPast

< source >

( config: PretrainedConfig task: str = 'default' patching_specs: typing.List[transformers.onnx.config.PatchingSpec] = None use_past: bool = False )

##
ONNX 특징

각 ONNX 설정은 다양한 유형의 토폴로지나 작업에 대해 모델을 내보낼 수 있게(exporting) 해주는 features 세트와 연관되어 있습니다.

###
FeaturesManager

###
class transformers.onnx.FeaturesManager

< source >

( )

####
check_supported_model_or_raise

< source >

( model: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] feature: str = 'default' )

Parameters

- model — The model to export.
- feature — The name of the feature to check if it is available.

Check whether or not the model has the requested features.

####
determine_framework

< source >

( model: str framework: typing.Optional[str] = None )

Parameters

- 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.

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

####
get_config

< source >

( model_type: str feature: str ) →
`OnnxConfig`

Parameters

- model_type (`str`) — The model type to retrieve the config for.
- feature (`str`) — The feature to retrieve the config for.

Returns

`OnnxConfig`

config for the combination

Gets the OnnxConfig for a model_type and feature combination.

####
get_model_class_for_feature

< source >

( feature: str framework: str = 'pt' )

Parameters

- feature (`str`) — The feature required.
- framework (`str`, optional, defaults to `"pt"`) — The framework to use for the export.

Attempts to retrieve an AutoModel class from a feature name.

####
get_model_from_feature

< source >

( feature: str model: str framework: typing.Optional[str] = None cache_dir: typing.Optional[str] = None )

Parameters

- 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.

Attempts to retrieve a model from a model’s name and the feature to be enabled.

####
get_supported_features_for_model_type

< source >

( model_type: str model_name: typing.Optional[str] = None )

Parameters

- 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.

Tries to retrieve the feature -> OnnxConfig constructor map from the model type.

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