[ThSSKrSSKJrJrJr SSKrSSKJrJr \ \S4r \\\ \ 4/\ \S44r /SQrS\S\4SjrS \R"R$S\\\44S jrS \\S\\\44S jrS \S\4Sjrg)N)AnyCallableUnion)tree_flatten_with_pathtree_map.)normalize_source_namemodule_to_nested_dicttrack_dynamism_across_examplesclone_and_convert_to_metanamereturnc2[R"SSU5$)Nz\.([a-zA-Z_][a-zA-Z0-9_]*)z['\1'])resub)r s W/var/www/auris/envauris/lib/python3.13/site-packages/torch/fx/experimental/_dynamism.pyrrs 66/D AAmodulecl0n0US'0US'[U5HnURS5(aM[[X55(aM6[X5n[ U[ R R5(aMl[ U[[[ R45(dM[U5[LdMX1U'M URSS9H upEXQSU'M URSS9H upFXaSU'M UR5HupG[!U5USU'M U$)ziRecursively converts an nn.Module into a nested dictionary with explicit 'parameters' and 'modules' keys. _parameters_modules_F)recurse)dir startswithcallablegetattr isinstancetorchnnModuleintfloatTensortypeboolnamed_parameters named_buffersnamed_childrenr )r self_dict attr_name attr_valuer parambuffer submodules rr r s "I!ImIj[ ##C(('&:T1U1U 3Jz588??;;zC +EFF$D0'1)$!..u.= ).- &>,,U,; )/- &<"002&;I&F *d#3 rexample_inputsc0nUGHnSU;a=[US[RR5(a[ US5US'[ U5up4UGH)upV[U[ [[R45(dM2[U[R5(a[UR5nSnOU4nSnXQ;a2[[U55Vs/sH n[5PM snU4X'OWXupX:wa[U 5[U5:a3U R[55 [U 5[U5:aM3[U5HupXSU R!U 5 M GM, GM 0n UR#5HVunup[SU 55nSSR%SU55-nUSR&nUU ;a0U U'XUU'MX U $s snf) a This function analyzes a list of example inputs to determine the dynamism of their shapes. It tracks whether the dimensions of tensors or non-tensor values change across different examples. The function returns a dictionary where each key represents a path to a value in the input examples, and the corresponding value is a tuple indicating which dimensions are dynamic (i.e., change across examples). This helps in understanding how the structure of data varies across different instances. selfTFrc3># UHn[U5S:v M g7f)N)len).0ss r 1track_dynamism_across_examples..[s7h#a&1*hsLc3:# UHn[U5v M g7f)N)str)r5ks rr7r8\s>X3q6( Xs)rrrr r rr!r"r#tupleshaperanger4setappend enumerateadditemsjoinkey)r/trackingexleaves_with_pathsrkey_pathvaluer? is_tensordim_setsflagidimoutput _is_tensor final_dynkey_strrGs rr r 4s<>H RX>>>qkoo f F3K(s G -= M%'Is$G< example_inputc:S[S[4Sjn[X5$)z This function takes a list of example inputs and for each tensor, clones it and converts it to device=meta. For non-tensor values, it keeps the reference. It uses pytree to handle nested structures recursively. rLr c~[U[R5(aUR5R SS9$U$)Nmeta)device)rrr#cloneto)rLs r transform_fn/clone_and_convert_to_meta..transform_fnjs2 eU\\ * *;;=##6#2 2 r)rr)rVr]s rr r ds" CC L 00r)rtypingrrrrtorch.utils._pytreerrr>KeyPathr!r"NonTensorShapeFn__all__r<rrr dictr listr r rrrgs '' @ S/U3:./sCx@A BBB %((//d38n8-I- #s(^-` 1S 1S 1r