🤗 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: List = 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: Iterable ) → (Dict[str, Any])
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.
Instantiate a OnnxConfig for a specific model
generate_dummy_inputs
< source >( preprocessor: Union batch_size: int = -1 seq_length: int = -1 num_choices: int = -1 is_pair: bool = False framework: Optional = 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
- 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 toFalse) — Indicate if the input is a pair (sentence 1, sentence 2) - framework (
TensorType, optional, defaults toNone) — 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: Mapping ) → 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.
Flag indicating if the model requires using external data format
OnnxConfigWithPast
class transformers.onnx.OnnxConfigWithPast
< source >( config: PretrainedConfig task: str = 'default' patching_specs: List = None use_past: bool = False )
fill_with_past_key_values_
< source >( inputs_or_outputs: Mapping direction: str inverted_values_shape: bool = False )
Fill the input_or_outputs mapping with past_key_values dynamic axes considering.
Instantiate a OnnxConfig with use_past attribute set to True
OnnxSeq2SeqConfigWithPast
class transformers.onnx.OnnxSeq2SeqConfigWithPast
< source >( config: PretrainedConfig task: str = 'default' patching_specs: List = 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
Check whether or not the model has the requested features.
determine_framework
< source >( model: str framework: str = None )
Determines the framework to use for the export.
The priority is in the following order:
- User input via
framework. - If local checkpoint is provided, use the same framework as the checkpoint.
- Available framework in environment, with priority given to PyTorch
get_config
< source >( model_type: str feature: str ) → OnnxConfig
Gets the OnnxConfig for a model_type and feature combination.
get_model_class_for_feature
< source >( feature: str framework: str = 'pt' )
Attempts to retrieve an AutoModel class from a feature name.
get_model_from_feature
< source >( 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.
get_supported_features_for_model_type
< source >( model_type: str model_name: Optional = None )
Tries to retrieve the feature -> OnnxConfig constructor map from the model type.