Quantization

Quantization

FuriosaAIQuantizer

class optimum.furiosa.FuriosaAIQuantizer

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( model_path: Path config: Optional = None )

Handles the FuriosaAI quantization process for models shared on huggingface.co/models.

compute_ranges

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( )

Computes the quantization ranges.

fit

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( dataset: Dataset calibration_config: CalibrationConfig batch_size: int = 1 )

Parameters

• dataset (Dataset) — The dataset to use when performing the calibration step.

• calibration_config (~CalibrationConfig) — The configuration containing the parameters related to the calibration step.

• batch_size (int, optional, defaults to 1) — The batch size to use when collecting the quantization ranges values.

Performs the calibration step and computes the quantization ranges.

from_pretrained

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( model_or_path: Union file_name: Optional = None )

Parameters

• model_or_path (Union[FuriosaAIModel, str, Path]) — Can be either:

  • A path to a saved exported ONNX Intermediate Representation (IR) model, e.g., `./my_model_directory/.

  • Or an FuriosaAIModelModelForXX class, e.g., FuriosaAIModelModelForImageClassification.

• file_name(Optional[str], optional) — Overwrites the default model file name from "model.onnx" to file_name. This allows you to load different model files from the same repository or directory.

Instantiates a FuriosaAIQuantizer from a model path.

get_calibration_dataset

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( dataset_name: str num_samples: int = 100 dataset_config_name: Optional = None dataset_split: Optional = None preprocess_function: Optional = None preprocess_batch: bool = True seed: int = 2016 use_auth_token: bool = False )

Parameters

• dataset_name (str) — The dataset repository name on the Hugging Face Hub or path to a local directory containing data files to load to use for the calibration step.

• num_samples (int, optional, defaults to 100) — The maximum number of samples composing the calibration dataset.

• dataset_config_name (Optional[str], optional) — The name of the dataset configuration.

• dataset_split (Optional[str], optional) — Which split of the dataset to use to perform the calibration step.

• preprocess_function (Optional[Callable], optional) — Processing function to apply to each example after loading dataset.

• preprocess_batch (bool, optional, defaults to True) — Whether the preprocess_function should be batched.

• seed (int, optional, defaults to 2016) — The random seed to use when shuffling the calibration dataset.

• use_auth_token (bool, optional, defaults to False) — Whether to use the token generated when running transformers-cli login (necessary for some datasets like ImageNet).

Creates the calibration datasets.Dataset to use for the post-training static quantization calibration step.

partial_fit

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( dataset: Dataset calibration_config: CalibrationConfig batch_size: int = 1 )

Parameters

• dataset (Dataset) — The dataset to use when performing the calibration step.

• calibration_config (CalibrationConfig) — The configuration containing the parameters related to the calibration step.

• batch_size (int, optional, defaults to 1) — The batch size to use when collecting the quantization ranges values.

Performs the calibration step and collects the quantization ranges without computing them.

quantize

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( quantization_config: QuantizationConfig save_dir: Union file_suffix: Optional = 'quantized' calibration_tensors_range: Optional = None )

Parameters

• quantization_config (QuantizationConfig) — The configuration containing the parameters related to quantization.

• save_dir (Union[str, Path]) — The directory where the quantized model should be saved.

• file_suffix (Optional[str], optional, defaults to "quantized") — The file_suffix used to save the quantized model.

• calibration_tensors_range (Optional[Dict[NodeName, Tuple[float, float]]], optional) — The dictionary mapping the nodes name to their quantization ranges, used and required only when applying static quantization.

Quantizes a model given the optimization specifications defined in quantization_config.