coreai_opt.quantization.spec.StatefulQParamsCalculatorBase

class coreai_opt.quantization.spec.StatefulQParamsCalculatorBase(**kwargs)

Bases: QParamsCalculatorBase

Stateful base: maintains scale/zero_point/minval as nn.Module buffers across forwards.

Buffer shapes are allocated on first forward and must remain stable (copy_ requires shape compatibility) — use StatelessQParamsCalculatorBase for variable-shape scales.

__init__(**kwargs)

Methods

compute_qparams(tensor, min_val, max_val)	Given the observed min/max range, return (scale, zero_point, minval).
extra_repr()	Return the extra representation of the module.
forward(tensor)	Compute qparams from tensor; cache to buffers; return.
get_class(key)
get_qparams()	Return the computed scale, zero point and minval.
list_registry_keys()
list_registry_values()
register(key)	Register a virtual subclass of an ABC.
resolve(data)	Resolve a string key or class type against this registry.
set_export_mode([enabled])

extra_repr()

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

Return type:

str

forward(tensor)

Compute qparams from tensor; cache to buffers; return.

On the first forward pass, initializes internal buffers using the observed tensor shape and device. Delegates the actual qparams calculation to compute_qparams.

Parameters:

tensor (Tensor)

Return type:

tuple[Tensor, Tensor | None, Tensor | None]

get_qparams()

Return the computed scale, zero point and minval. For FP4/FP8/floating-point quantization, zero_point and minval are None.

Return type:

tuple[Tensor, Tensor | None, Tensor | None]

set_export_mode(enabled=True)

Parameters:

enabled (bool)

Return type:

None

minval: torch.Tensor | None

scale: torch.Tensor

zero_point: torch.Tensor | None