coreai_opt.quantization.spec.QParamsCalculatorBase

class coreai_opt.quantization.spec.QParamsCalculatorBase(dtype, qscheme, granularity, target_dtype, quant_min, quant_max, range_calculator, float_range, scale_dtype=None, **kwargs)

Bases: ClassRegistryMixin, Module

Base class for implementing logic to calculate quantization parameters (scale, zero_point, minval) given min/max values.

Parameters:

• dtype (torch.dtype)

• qscheme (QuantizationScheme)

• granularity (QuantizationGranularity)

• target_dtype (torch.dtype)

• quant_min (int)

• quant_max (int)

• range_calculator (RangeCalculatorBase)

• float_range (tuple[float | None, float | None])

• scale_dtype (torch.dtype | None)

__init__(dtype, qscheme, granularity, target_dtype, quant_min, quant_max, range_calculator, float_range, scale_dtype=None, **kwargs)

Parameters:

• dtype (dtype)

• qscheme (QuantizationScheme)

• granularity (QuantizationGranularity)

• target_dtype (dtype)

• quant_min (int)

• quant_max (int)

• range_calculator (RangeCalculatorBase)

• float_range (tuple[float | None, float | None])

• scale_dtype (dtype | None)

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 scale, zero point, and minval from the input tensor.
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.
set_export_mode([enabled])

compute_qparams(tensor, min_val, max_val)

Given the observed min/max range, return (scale, zero_point, minval).

The default implementation directly computes qparams from the given range via _compute_scale_zero_point_minval. This is the correct behavior for stateless calculators (e.g. StaticQParamsCalculator).

Stateful calculators override this via RunningRangeMixin to update running-range buffers before computing qparams from the smoothed range.

Parameters:

• tensor (Tensor)

• min_val (Tensor)

• max_val (Tensor)

Return type:

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

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 scale, zero point, and minval from the input tensor.

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

property granularity: QuantizationGranularity

Getter for granularity.

minval: torch.Tensor | None

scale: torch.Tensor

zero_point: torch.Tensor | None