ijSSKJr SSKr\"SS9"SS55r\"SS9"SS\55r\"SS9"S S \55r\"SS9"S S \55r\"SS9"S S\55r\"SS9"SS\55r\"SS9"SS\55r \RR\ \\/5 g)) dataclassNT)frozenc\rSrSrSrSrg) Granularity z Base class for representing the granularity of quantization. This class serves as a parent for specific granularity types used in quantization operations, such as per-tensor or per-axis quantization. N__name__ __module__ __qualname____firstlineno____doc____static_attributes__r_/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torchao/quantization/granularity.pyrr  rrc\rSrSrSrSrg) PerTensorz Represents per-tensor granularity in quantization. This granularity type calculates the quantization parameters based off the entire tensor. rNr rrrrrrrrc$\rSrSr%Sr\\S'Srg)PerAxis$a Represents per-axis granularity in quantization. This granularity type calculates different quantization parameters along a specified axis of the tensor. Examples: * input_tensor shape [A, B], axis 0 -> scale_shape [A, 1] * input_tensor shape [A, B], axis 1 -> scale_shape [1, B] * input_tensor shape [A, B, C], axis 1 -> scale_shape [1, B, 1] Attributes: axis (int): The axis which is kept, reduction is performed across all the other axes axisrNr r r r rint__annotations__rrrrrr$s Irrc$\rSrSr%Sr\\S'Srg)PerGroup9a Represents per-channel group granularity in quantization. This granularity type calculates different quantization parameters for each group of elements. For example if the input tensor is shape [8, 16], and the group size is 4, then the input tensor is reshaped to [64, 4] quantization parameters are calculated for each group of 4 elements, giving a total of 64 quantization parameters. Attributes: group_size (int): The size of each quantization group group_sizerNrrrrrr9s Orrc(\rSrSr%SrSr\\S'Srg)PerRowNa} Represents row-wise granularity in quantization. Examples: * input_tensor shape [A, B], dim 0 -> scale_shape [1, B] * input_tensor shape [A, B], dim 1 -> scale_shape [A, 1] * input_tensor shape [A, B], dim -1 -> scale_shape [A, 1] * input_tensor shape [A, B, C], dim 1 -> scale_shape [A, 1, C] Attributes: dim (int): The dim which is reduced across, all other dims are kept dimrN) r r r r rr%rrrrrrr"r"Ns CMrr"c\rSrSrSrSrg)PerToken`a Represents per-token granularity in quantization. This granularity type calculates a different set of quantization parameters for each token, which is represented as the last dimension of the tensor. For example, if the input tensor has shape [2, 3, 4], then there are 6 tokens with 4 elements each, and we will calculate 6 sets of quantization parameters, one for each token. If the input tensor has only two dimensions, e.g. [8, 16], then this is equivalent to `PerAxis(axis=0)`, which yields 8 sets of quantization parameters. rNr rrrr'r'`s   rr'c.\rSrSr%Sr\\S4\S'Srg)PerBlocksa Represents multidimensional per-block granularity in quantization. Example: * block_size has shape [X, Y] * input_tensor shape [A] -> scaling undefined * input_tensor shape [A, B] -> scale shape [A // X, B // Y] * input_tensor shape [A, B, C] -> scale shape [A, B // X, C // Y] * input_tensor shape [A, B, C, D] -> scale shape [A, B, C // X, D // Y], and so on Note that `PerBlock((1, Y))` is equivalent to `PerGroup(Y)` Attributes: block_size (tuple[int, ...]): The size of each quantization group . block_sizerN) r r r r rtuplerrrrrrr*r*ss2c3hrr*) dataclassesrtorchrrrrr"r'r* serializationadd_safe_globalsrrrr2s"  $    $    $k( ${( $[" $ {  $ $ {  :$$h %BCr