Quantization

IncQuantizer

class optimum.intel.IncQuantizer

< >

( model: typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] config_path_or_obj: typing.Union[str, optimum.intel.neural_compressor.config.IncQuantizationConfig] tokenizer: typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None eval_func: typing.Optional[typing.Callable] = None train_func: typing.Optional[typing.Callable] = None calib_dataloader: typing.Optional[torch.utils.data.dataloader.DataLoader] = None )

from_config

< >

( model_name_or_path: str inc_config: typing.Union[optimum.intel.neural_compressor.config.IncQuantizationConfig, str, NoneType] = None config_name: str = None **kwargs ) → quantizer

Parameters

  • model_name_or_path (str) — Repository name in the Hugging Face Hub or path to a local directory hosting the model.
  • inc_config (Union[IncQuantizationConfig, str], optional) — Configuration file containing all the information related to the model quantization. Can be either:
    • an instance of the class IncQuantizationConfig,
    • a string valid as input to IncQuantizationConfig.from_pretrained.
  • config_name (str, optional) — Name of the configuration file.
  • cache_dir (str, optional) — Path to a directory in which a downloaded configuration should be cached if the standard cache should not be used.
  • force_download (bool, optional, defaults to False) — Whether or not to force to (re-)download the configuration files and override the cached versions if they exist.
  • resume_download (bool, optional, defaults to False) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
  • revision(str, optional) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  • calib_dataloader (DataLoader, optional) — DataLoader for post-training quantization calibration.
  • eval_func (Callable, optional) — Evaluation function to evaluate the tuning objective.
  • train_func (Callable, optional) — Training function for quantization aware training approach.

Returns

quantizer

IncQuantizer object.

Instantiate a IncQuantizer object from a configuration file which can either be hosted on huggingface.co or from a local directory path.

IncQuantizedModel

class optimum.intel.neural_compressor.quantization.IncQuantizedModel

< >

( *args **kwargs )

from_pretrained

< >

( model_name_or_path: str inc_config: typing.Union[optimum.intel.neural_compressor.config.IncOptimizedConfig, str] = None q_model_name: typing.Optional[str] = None input_names: typing.Optional[typing.List[str]] = None batch_size: typing.Optional[int] = None sequence_length: typing.Union[int, typing.List[int], typing.Tuple[int], NoneType] = None num_choices: typing.Optional[int] = -1 **kwargs ) → q_model

Parameters

  • model_name_or_path (str) — Repository name in the Hugging Face Hub or path to a local directory hosting the model.
  • inc_config (Union[IncOptimizedConfig, str], optional) — Configuration file containing all the information related to the model quantization. Can be either:
    • an instance of the class IncOptimizedConfig,
    • a string valid as input to IncOptimizedConfig.from_pretrained.
  • q_model_name (str, optional) — Name of the state dictionary located in model_name_or_path used to load the quantized model. If state_dict is specified, the latter will not be used.
  • input_names (List[str], optional) — List of names of the inputs used when tracing the model. If unset, model.dummy_inputs().keys() are used instead.
  • batch_size (int, optional) — Batch size of the traced model inputs.
  • sequence_length (Union[int, List[int], Tuple[int]], optional) — Sequence length of the traced model inputs. For sequence-to-sequence models with different sequence lengths between the encoder and the decoder inputs, this must be [encoder_sequence_length, decoder_sequence_length].
  • num_choices (int, optional, defaults to -1) — The number of possible choices for a multiple choice task.
  • cache_dir (str, optional) — Path to a directory in which a downloaded configuration should be cached if the standard cache should not be used.
  • force_download (bool, optional, defaults to False) — Whether or not to force to (re-)download the configuration files and override the cached versions if they exist.
  • resume_download (bool, optional, defaults to False) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
  • revision(str, optional) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  • state_dict (Dict[str, torch.Tensor], optional) — State dictionary of the quantized model, if not specified q_model_name will be used to load the state dictionary.

Returns

q_model

Quantized model.

Instantiate a quantized pytorch model from a given Intel Neural Compressor (INC) configuration file.