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
