neural_compressor.common.tuning_param
=====================================

.. py:module:: neural_compressor.common.tuning_param

.. autoapi-nested-parse::

   The tunable parameters module.



Classes
-------

.. autoapisummary::

   neural_compressor.common.tuning_param.ParamLevel
   neural_compressor.common.tuning_param.TuningParam


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

.. py:class:: ParamLevel



   Enumeration representing the different levels of tuning parameters.

   .. attribute:: OP_LEVEL

      Represents the level of tuning parameters for operations.

   .. attribute:: OP_TYPE_LEVEL

      Represents the level of tuning parameters for operation types.

   .. attribute:: MODEL_LEVEL

      Represents the level of tuning parameters for models.


.. py:class:: TuningParam(name: str, default_val: Any = None, tunable_type=None, options=None, level: ParamLevel = ParamLevel.OP_LEVEL)

   Define the tunable parameter for the algorithm.

   .. rubric:: Example

   Class FakeAlgoConfig(BaseConfig):
       '''Fake algo config.'''.

       params_list = [
           ...
           # For simple tunable types, like a list of int, giving
           # the param name is enough. `BaseConfig` class will
           # create the `TuningParam` implicitly.
           "simple_attr"

           # For complex tunable types, like a list of lists,
           # developers need to create the `TuningParam` explicitly.
           TuningParam("complex_attr", tunable_type=List[List[str]])

           # The default parameter level is `ParamLevel.OP_LEVEL`.
           # If the parameter is at a different level, developers need
           # to specify it explicitly.
           TuningParam("model_attr", level=ParamLevel.MODEL_LEVEL)

       ...

   # TODO: more examples to explain the usage of `TuningParam`.