:orphan:

:py:mod:`neural_compressor.torch.quantization.config`
=====================================================

.. py:module:: neural_compressor.torch.quantization.config


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.torch.quantization.config.RTNConfig
   neural_compressor.torch.quantization.config.GPTQConfig
   neural_compressor.torch.quantization.config.HQQConfig



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.torch.quantization.config.get_default_rtn_config
   neural_compressor.torch.quantization.config.get_default_gptq_config
   neural_compressor.torch.quantization.config.get_default_hqq_config
   neural_compressor.torch.quantization.config.get_woq_tuning_config



.. py:class:: RTNConfig(dtype: str = 'int', bits: int = 4, use_sym: bool = True, group_size: int = 32, group_dim: int = 1, use_full_range: bool = False, use_mse_search: bool = False, use_layer_wise: bool = False, model_path: str = '', use_double_quant: bool = False, double_quant_dtype: str = 'int', double_quant_bits: int = 8, double_quant_use_sym: bool = False, double_quant_group_size: int = 256, white_list: Optional[List[neural_compressor.common.utils.OP_NAME_OR_MODULE_TYPE]] = DEFAULT_WHITE_LIST)




   Config class for round-to-nearest weight-only quantization.


.. py:function:: get_default_rtn_config() -> RTNConfig

   Generate the default rtn config.

   :returns: the default rtn config.


.. py:class:: GPTQConfig(dtype: str = 'int', bits: int = 4, use_sym: bool = True, group_size: int = 32, use_mse_search: bool = False, use_layer_wise: bool = False, model_path: str = '', use_double_quant: bool = False, double_quant_dtype: str = 'int', double_quant_bits: int = 8, double_quant_use_sym: bool = False, double_quant_group_size: int = 256, act_order: bool = False, percdamp: float = 0.01, block_size: int = 2048, static_groups: bool = False, white_list: Optional[List[neural_compressor.common.utils.OP_NAME_OR_MODULE_TYPE]] = DEFAULT_WHITE_LIST)




   Config class for GPTQ.

   GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.
   https://arxiv.org/abs/2210.17323


.. py:function:: get_default_gptq_config() -> GPTQConfig

   Generate the default gptq config.

   :returns: the default gptq config.


.. py:class:: HQQConfig(bits: int = 4, group_size: int = 64, quant_zero: bool = True, quant_scale: bool = False, scale_quant_group_size: int = 128, skip_lm_head: bool = True, white_list: Optional[List[neural_compressor.common.utils.OP_NAME_OR_MODULE_TYPE]] = DEFAULT_WHITE_LIST)




   The base config for all algorithm configs.


.. py:function:: get_default_hqq_config() -> HQQConfig

   Generate the default HQQ config.

   :returns: the default HQQ config.


.. py:function:: get_woq_tuning_config() -> list

   Generate the config set for WOQ tuning.

   :returns: the list of WOQ quant config.