`i ddlmZddlmZmZddlmZddlmZm Z gdZ dej de ej de fd Zd ej d ee ej e ej fdej fd Z ddej dej dedeefdZdeej dedeej fdZdej dedeeeffdZGddej ZdS))chain)AnyOptional)nn)is_parametrizedtype_before_parametrizations)module_contains_param swap_module module_to_fqn fqn_to_moduleget_arg_info_from_tensor_fqn FakeSparsitymoduleparametrizationreturnct|r2tfd|jDSdS)Nc3TK|]"\}}tfd|DV#dS)c38K|]}t|VdSN) isinstance).0paramrs u/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/utils.py z2module_contains_param...s-KKu 5/22KKKKKKN)any)rkey param_listrs rrz(module_contains_param..sX  Z KKKK KKK K K      rF)rrparametrizationsitems)rrs `rr r sav     #)#:#@#@#B#B       5rmodmappingct||vr4|t|}||}|jD]}|||jD]}||dt|| D}t|dks Jd|t|dkrtt|nd}|r| ||S|S)a%Swaps the module using from_dense according to the mapping passed in. Args: mod: input module mapping: a dictionary that maps from nn module to sparse nn module Return: The corresponding sparse module of `mod` according to mapping, created using from_dense ch|] }|j S)device)rps r zswap_module..6sLLL18LLLrzOswap_module only works with cpu or single-device CUDA modules, but got devices rN)r from_dense_forward_pre_hooksvaluesregister_forward_pre_hook_forward_hooksregister_forward_hookr parametersbufferslennextiterto)r!r" sparse_modnew_mod pre_hook_fnhook_fndevicesr&s rr r sZ$C((G339#>>? '',,188:: ; ;K  - -k : : : :)0022 3 3G  ) )' 2 2 2 2MLU3>>+;+;S[[]]%K%KLLL7||q   g^e g g!  ),G q(8(8d7mm$$$d   JJv    rmodelprefixc||urdS|D]5\}}t||d}t|tr ||z|zcS6dS)zS Returns the fqn for a module or None if module not a descendent of model. r;.N)named_childrenr rstr)r<rr=namechildfqns rr r Dss r++--'' eE63// c3   'D=3& & & & ' 4rpathcd|dkr)|dD]}t||d}|S)z Given an fqn, returns the corresponding module or tensor or None if the fqn given by `path` doesn't correspond to anything. Similar to model.get_submodule(path) but works for tensors. r;r?N)splitgetattr)r<rErBs rr r Ss>  rzzJJsOO / /DE4..EE Lr tensor_fqnc|ddd}|dt| d|vz }t||}||||dS)zX Uses tensor_fqn to obtain a dict containing module_fqn, module and tensor_name r?r))maxsplitN) module_fqnr tensor_namerI)rsplitr2r )r<rIrNrMrs rr r ^sr##C!#44R8KEs;///3*3DEEFJ 5* - -F!"   rc.eZdZdZfdZdZdZxZS)ra;Parametrization for the weights. Should be attached to the 'weight' or any other parameter that requires a mask applied to it. Note:: Once the mask is passed, the variable should not change the id. The contents of the mask can change, but the mask reference itself should not. ctt|d|dS)Nmask)super__init__register_buffer)selfrR __class__s rrTzFakeSparsity.__init__~s5  VT*****rcD|jj|jksJ|j|zSr)rRshape)rVxs rforwardzFakeSparsity.forwards%y!'))))y1}rciSrr%)rVargskwargss r state_dictzFakeSparsity.state_dicts  r)__name__ __module__ __qualname____doc__rTr[r_ __classcell__)rWs@rrrss`+++++rrN)r;) itertoolsrtypingrrtorchrtorch.nn.utils.parametrizerr__all__Moduletypeboolr dictr rAr r r rr%rrrns TTTTTTTT   ")d29oRV$ $!$ry/4 ?"BC$Y$$$$P8:   9  i 14  c]    "),CHRY