Table of Contents
Shortcuts

# quantize_dynamic

class torch.ao.quantization.quantize_dynamic(model, qconfig_spec=None, dtype=torch.qint8, mapping=None, inplace=False)

  Converts a float model to dynamic (i.e. weights-only) quantized model.

  Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.

  For simplest usage provide dtype argument that can be float16 or qint8. Weight-only quantization by default is performed for layers with large weights size - i.e. Linear and RNN variants.

  Fine grained control is possible with qconfig and mapping that act similarly to quantize(). If qconfig is provided, the dtype argument is ignored.

  Parameters:

    - model – input model

    - qconfig_spec –

      Either:

      - A dictionary that maps from name or type of submodule to quantization configuration, qconfig applies to all submodules of a given module unless qconfig for the submodules are specified (when the submodule already has qconfig attribute). Entries in the dictionary need to be QConfig instances.

      - A set of types and/or submodule names to apply dynamic quantization to, in which case the dtype argument is used to specify the bit-width

    - inplace – carry out model transformations in-place, the original module is mutated

    - mapping – maps type of a submodule to a type of corresponding dynamically quantized version with which the submodule needs to be replaced

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.