h &SSKJr \"SS9"SS55r\"SS9"SS\55r\"SS9"SS \55r\"SS9"S S \55r\"SS9"S S \55r\"SS9"SS\55r\"SS9"SS\55rg)) dataclassT)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__rZ/home/james-whalen/.local/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$\rSrSr%Sr\\S'Srg)PerAxis"a Represents per-axis granularity in quantization. This granularity type calculates different quantization parameters along a specified axis of the tensor. For example if the input tensor is shape [8, 16] and axis=0, then the quantization parameters are calculated for each row of the tensor. Giving a total of 8 quantization parameters. Attributes: axis (int): The axis along which reduction is performed. axisrNr r r r rint__annotations__rrrrrr"s  Irrc$\rSrSr%Sr\\S'Srg)PerGroup5a 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_sizerNrrrrrr5s Orrc\rSrSrSrSrg)PerRowJa Represents row-wise granularity in quantization. This is a special case of per-axis quantization and is unique to Float8 matmuls where the input is quantized with a block_size of (1, ..., input.shape[-1]). And the weight is quantized with a block_size of (1, weight.shape[1]). rNr rrrr"r"Js  rr"c\rSrSrSrSrg)PerTokenWa 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%Ws   rr%c.\rSrSr%Sr\\S4\S'Srg)PerBlockjz Represents per-block granularity in quantization. See :func:`~torchao.quantization.quant_primitives.quantize_affine` for docs for `block_size` Attributes: block_size (tuple[int, ...]): The size of each quantization group . block_sizerN) r r r r rtuplerrrrrrr(r(jsc3hrr(N) dataclassesrrrrrr"r%r(rrrr-s" $    $    $k$ ${( $  [     $ {  $ $  {    r