# quantize_dynamic¶

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

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