[ThF%SrSSKrSSKrSSKrSSKJr SSKJr SSKJ r J r J r SSK r SSK J r \R"\5r"SS\ 5r\ \ R$\\ R(/\4r0r\\\ \4\S '0r\\\4\S 'SS \ \S \ \S \\4Sjjr\R8"\SS9r\R8"\SS9rSrSS\\4Sjjr \RB"S5S5r"\RB"S5S5r#g)a This module implements TorchDynamo's backend registry system for managing compiler backends. The registry provides a centralized way to register, discover and manage different compiler backends that can be used with torch.compile(). It handles: - Backend registration and discovery through decorators and entry points - Lazy loading of backend implementations - Lookup and validation of backend names - Categorization of backends using tags (debug, experimental, etc.) Key components: - CompilerFn: Type for backend compiler functions that transform FX graphs - _BACKENDS: Registry mapping backend names to entry points - _COMPILER_FNS: Registry mapping backend names to loaded compiler functions Example usage: @register_backend def my_compiler(fx_graph, example_inputs): # Transform FX graph into optimized implementation return compiled_fn # Use registered backend torch.compile(model, backend="my_compiler") The registry also supports discovering backends through setuptools entry points in the "torch_dynamo_backends" group. Example: ``` setup.py --- from setuptools import setup setup( name='my_torch_backend', version='0.1', packages=['my_torch_backend'], entry_points={ 'torch_dynamo_backends': [ # name = path to entry point of backend implementation 'my_compiler = my_torch_backend.compiler:my_compiler_function', ], }, ) ``` ``` my_torch_backend/compiler.py --- def my_compiler_function(fx_graph, example_inputs): # Transform FX graph into optimized implementation return compiled_fn ``` Using `my_compiler` backend: ``` import torch model = ... # Your PyTorch model optimized_model = torch.compile(model, backend="my_compiler") ``` N)Sequence) EntryPoint)CallableOptionalProtocol)fxcX\rSrSrS\R S\\R S44SjrSrg) CompiledFnMargsreturn.cg)N)selfr s W/var/www/auris/envauris/lib/python3.13/site-packages/torch/_dynamo/backends/registry.py__call__CompiledFn.__call__NsrN) __name__ __module__ __qualname____firstlineno__torchTensortupler__static_attributes__rrrr r Ms!LellLuU\\35F/GLrr _BACKENDS _COMPILER_FNS compiler_fnnametagsc Uc[R"[XS9$[U5(deU=(d URnU[ ;d SU35eU[ ;a S[ U'U[ U'[U5UlU$)a Decorator to add a given compiler to the registry to allow calling `torch.compile` with string shorthand. Note: for projects not imported by default, it might be easier to pass a function directly as a backend and not use a string. Args: compiler_fn: Callable taking a FX graph and fake tensor inputs name: Optional name, defaults to `compiler_fn.__name__` tags: Optional set of string tags to categorize backend with N)r r!zduplicate name: ) functoolspartialregister_backendcallablerrrr_tags)rr r!s rr%r%Ws   !1HH K   ';''D } $?(8&?? $)# $%M$d K r)debug)r!) experimentalc[U[5(a^U[;a [5 U[;a SSKJn U"US9eU[ ;a![Un[UR5US9 [ UnU$)z#Expand backend strings to functions)InvalidBackend)r )rr ) isinstancestrr _lazy_importexcr,rr%load)rr, entry_points rlookup_backendr3zsg+s## i ' N i ' , k2 2 m +#K0K )9)9);+ N#K0 rr c [5 [U=(d S5n[R5Vs/sH8nU[;d)UR [UR 5(aM6UPM: nn[U5$s snf)zU Return valid strings that can be passed to: torch.compile(..., backend="name") r)r/setrkeysr intersectionr'sorted) exclude_tagsr backendss r list_backendsr;st N|)r*LNN$$D } $((t)<)B)BC $  (  s 5B.BcVSSKJn SSKJn U"U5 SSKJn Uce[ 5 g)Nr+)r:)import_submodule)dynamo_minifier_backend)r:utilsr=repro.after_dynamor>_discover_entrypoint_backends)r:r=r>s rr/r/s%(X< " .. .!#rcSSKJn Sn[RS:a/U"5nX;aX!O/nUVs0sHo3RU_M nnO$U"US9nUR Vs0sHoDX$_M nnUHnX%[ U'M gs snfs snf)Nr) entry_pointstorch_dynamo_backends) )group)importlib.metadatarDsys version_infor namesr)rD group_nameepsepr backend_names rrBrBs0(J '!n!+!2co%()Srww{S),+.99594SY95 "%"3 , *6s B B )NNr))r(r))$__doc__r#loggingrJcollections.abcrrIrtypingrrrrr getLoggerrlogr GraphModulelistr CompilerFnrdictr.__annotations__rr%r$register_debug_backendregister_experimental_backendr3r; lru_cacher/rBrrrr_sN:x $)// !MMr~~tELL'9:JF G -/ 4Xj)) */') tCO$))-*% 3- 3-:#**+;*M ) 1 1,! "T#Y$ T $ $ T 4 4r