+ "ݳj"^RIt^RIHt^RIHtHt^RIt^RIHtH t ^RI H t ^RI H t ^RIHtHtHt^RIHtHt^R IHtR t]!R R ] 44t]t]!]4R Rl4t!RR] 4t]t]!]4RRl4tRRRlltR#)N) dataclass)CallableOptional)SparseSemiStructuredTensorto_sparse_semi_structured) AOBaseConfig)WeightNormSparsifier) _is_linear_linear_extra_repr)_replace_with_custom_fn_if_matches_filter)_QUANTIZE_CONFIG_HANDLER register_quantize_module_handler)BlockSparseTensorc 2VPR\4p.pVP4F+wrEV!WT4'gKVPRV R2/4K- \ RR^R7pVP W4VP 4VP4R#)zG This function simulates 2:4 sparsity on all linear layers in a model. filter_fn tensor_fqnz.weightg?)sparsity_levelsparse_block_shapezeros_per_blockN))popr named_modulesappendr preparestep squash_mask)modelkwargsr sparse_confignamemod sparsifiers&, \/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchao/sparsity/sparse_api.pyapply_fake_sparsityr%s ; 3IM((*  S    ,4&0@!A B+&vqJu,OOc6a]tRt^1to^@tRtV3RltRtVtR#)BlockSparseWeightConfigcD\PPR4R#)z(torchao.sparsity.BlockSparseWeightConfigNtorch_C_log_api_usage_onceselfs&r$ __post_init__%BlockSparseWeightConfig.__post_init__5s $$%OPr&c&<V^8dQh/S[;R&#) blocksizeint)format __classdict__s"r$ __annotate__$BlockSparseWeightConfig.__annotate__1sr&N) __name__ __module__ __qualname____firstlineno__r4r0__annotate_func____static_attributes____classdictcell__)r8s@r$r(r(1sIQ r&r(cXV^8dQhR\PPR\/#)r3moduleconfig)r+nnModuler()r7s"r$r9r9>s$ HHOO #r&cVPp\P!VPV4p\P P VRR7Vn\P!\V4Vn V#)F requires_grad) r4r from_denseweightr+rF Parametertypes MethodTyper extra_repr)rDrEr4 new_weights&& r$_block_sparse_weight_transformrR=s[   I"--fmmYGJHH&&z&GFM(();VDF Mr&cbaa]tRt^ItoRt]P 3V3RlV3RllltRtRt Vt V;t #)SemiSparseWeightConfigz] Configuration for converting the weight of linear modules to semi-structured (2:4) sparsity c <V^8dQhRS[/#)r3alg_idr5)r7r8s"r$r9#SemiSparseWeightConfig.__annotate__Nssr&c0<\SV`4WnR#N)super__init__rV)r/rV __class__s&&r$r[SemiSparseWeightConfig.__init__Ns  r&cD\PPR4R#)z'torchao.sparsity.SemiSparseWeightConfigNr*r.s&r$r0$SemiSparseWeightConfig.__post_init__Rs $$%NOr&rV) r<r=r>r?__doc__r_DEFAULT_ALG_IDr[r0rArB __classcell__)r\r8s@@r$rTrTIs,&@%O%OPPr&rTcV^8dQhR\PPR\R\PP/#)r3rDrEreturn)r+rFrGrT)r7s"r$r9r9[s6 HHOO " XX__r&cR\P9;'g/R\P9;'gR\P9pV'd#\VPVPR7pM?VP\ P 8wd \R4h\VP4p\PPVRR7Vn\P!\V4Vn V#)devgitfbr`zDSemiSparseWeightConfig.alg_id is only supported in nightly or sourceFrI)r+ __version__rrLrVrrb ValueErrorrFrMrNrOr rP)rDrEis_nightly_or_sourcerQs&& r$_semi_sparse_weight_transformrmZs """ % % E%% % % % 5$$ $ .v}}V]]S ==6FF FV /v}}= HH&&z&GFM(();VDF Mr&c V^8dQhR\PPR\R\\ \PP\ .\3,,R\PP/#)r3rrErre)r+rFrGrrrstrbool)r7s"r$r9r9qs\%% 88??% %%((//3!7!=>?% XX__ %r&c\PPR4\\ V4,p\ TTVf\ MTV3R7R#)aConvert the weight of linear modules in the model with `apply_tensor_subclass`. This function is essentially the same as quantize, put for sparsity subclasses. Currently, we support semi-structured (2:4) sparsity with `semi_sparse_weight`. Args: model (torch.nn.Module): input model config (AOBaseConfig): a workflow configuration object filter_fn (Optional[Callable[[torch.nn.Module, str], bool]]): function that takes a nn.Module instance and fully qualified name of the module, returns True if we want to apply the specified workflow to this module. **Example:** :: import torch import torch.nn as nn from torchao.sparsity import sparsify_ def filter_fn(module: nn.Module, fqn: str) -> bool: return isinstance(module, nn.Linear) m = nn.Sequential(nn.Linear(32, 1024), nn.Linear(1024, 32)) # for 2:4 sparsity from torchao.sparse_api import semi_sparse_weight m = sparsify_(m, semi_sparse_weight(), filter_fn) ztorchao.sparsity.sparsify_N) extra_args)r+r,r-r typer r )rrErhandlers&&& r$ sparsify_ruqsB< HH  !=>&tF|4G- ' Y9 r&rY)rN dataclassesrtypingrrr+ torch.sparserrtorchao.core.configrrs  !% N, ;& QlQ Q.""9:; P\ P,""89:,%%r&