torch.quantization.rst

torch.quantization

.. automodule:: torch.quantization

This module implements the functions you call directly to convert your model from FP32 to quantized form. For example the :func:`~torch.quantization.prepare` is used in post training quantization to prepares your model for the calibration step and :func:`~torch.quantization.convert` actually converts the weights to int8 and replaces the operations with their quantized counterparts. There are other helper functions for things like quantizing the input to your model and performing critical fusions like conv+relu.

Top-level quantization APIs

.. autofunction:: quantize

.. autofunction:: quantize_dynamic

.. autofunction:: quantize_qat

.. autofunction:: prepare

.. autofunction:: prepare_qat

.. autofunction:: convert

.. autoclass:: QConfig

.. autoclass:: QConfigDynamic

Preparing model for quantization

.. autofunction:: fuse_modules

.. autoclass:: QuantStub

.. autoclass:: DeQuantStub

.. autoclass:: QuantWrapper

.. autofunction:: add_quant_dequant

Utility functions

.. autofunction:: add_observer_

.. autofunction:: swap_module

.. autofunction:: propagate_qconfig_

.. autofunction:: default_eval_fn

Observers

.. autoclass:: ObserverBase
    :members:

.. autoclass:: MinMaxObserver

.. autoclass:: MovingAverageMinMaxObserver

.. autoclass:: PerChannelMinMaxObserver

.. autoclass:: MovingAveragePerChannelMinMaxObserver

.. autoclass:: HistogramObserver

.. autoclass:: FakeQuantize

.. autoclass:: NoopObserver

Debugging utilities

.. autofunction:: get_observer_dict

.. autoclass:: RecordingObserver

.. currentmodule:: torch

.. autosummary::
    :nosignatures:

    nn.intrinsic