sg ddlmZddlmZmZmZmZddlmZddl m Z m Z gdZ dejdeejdefd Zd ejd eeejeejfdejfd Z dd ejdejdedeefdZd eejdedeejfdZd ejdedeeeffdZGddejZy))chain)AnyDictOptionalType)nn)is_parametrizedtype_before_parametrizations)module_contains_param swap_module module_to_fqn fqn_to_moduleget_arg_info_from_tensor_fqn FakeSparsitymoduleparametrizationreturnctt|r,tfd|jjDSy)Nc3LK|]\}}tfd|Dyw)c36K|]}t|ywN) isinstance).0paramrs T/var/www/html/venv/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/utils.py z2module_contains_param...sKu 5/2KsN)any)rkey param_listrs rrz(module_contains_param..s( Z K K K s!$F)r rparametrizationsitems)rrs `rr r s9v #)#:#:#@#@#B    modmappingcJt||vr|t|}|j|}|jjD]}|j ||j jD]}|j |t|j|jDchc]}|j}}t|dks Jd|t|dkDrtt|nd}|r|j||S|Scc}w)a%Swaps the module using from_dense according to the mapping passed in. Args: mod: input module mapping: a dictionary that maps from nn module to sparse nn module Return: The corresponding sparse module of `mod` according to mapping, created using from_dense zOswap_module only works with cpu or single-device CUDA modules, but got devices rN)r from_dense_forward_pre_hooksvaluesregister_forward_pre_hook_forward_hooksregister_forward_hookr parametersbuffersdevicelennextiterto) r#r$ sparse_modnew_mod pre_hook_fnhook_fnpdevicesr/s rr r s$C(G39#>? '',1188: ;K  - -k : ;))002 3G  ) )' 2 3&+3>>+;S[[]%KL188LL LA  g \]d\e f g (+G q(8d7m$d  JJv  Ms8D modelprefixc||ury|jD],\}}t||d}t|ts$||z|zcSy)zS Returns the fqn for a module or None if module not a descendent of model. .N)named_childrenr rstr)r:rr;namechildfqns rr r DsV ++-' eE63/ c3 D=3& &' r"pathcV|dk7r#|jdD]}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:rDrAs rrrSs5  rzJJsO /DE4.E / Lr" tensor_fqncx|jdd}|dt| d|vz }t||}||||dS)zX Uses tensor_fqn to obtain a dict containing module_fqn, module and tensor_name r>N) module_fqnr tensor_namerH)rFr0r)r:rHrLrKrs rrr^s\""3'+KEs;//3*3DEFJ 5* -F!"  r"c.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. cFt||jd|y)Nmask)super__init__register_buffer)selfrO __class__s rrQzFakeSparsity.__init__~s  VT*r"cj|jj|jk(sJ|j|zSr)rOshape)rSxs rforwardzFakeSparsity.forwards*yy!'')))yy1}r"ciSr)rSargskwargss r state_dictzFakeSparsity.state_dicts  r")__name__ __module__ __qualname____doc__rQrXr] __classcell__)rTs@rrrss+r"rN)r=) itertoolsrtypingrrrrtorchrtorch.nn.utils.parametrizer r __all__Moduleboolr r r@r rrrrZr"rrjs,,T "))d299oRV$ $!$ryy/4 ?"BC$YY$P8:  99  ii 14  c] ")),CHRYY