tj hUdZddlZddlZddlmZmZddlmZddlm Z m Z ddl Z ddl m Z ej eZGdde Zee jee jgefZiZeeedzfed <iZeeefed <d aeezed < d dedzdedzdeedede ffdZejedZejedZdeezdefdZd!deefdZ ej!d"dZ"ej!d"dZ#dede$fdZ%deezdzddfdZ&deezfdZ'dS)#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)CallableSequence) EntryPoint)AnyProtocol)fxcBeZdZdejdeejdffdZdS) CompiledFnargsreturn.cdS)N)selfr s ]/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/_dynamo/backends/registry.py__call__zCompiledFn.__call__KsN)__name__ __module__ __qualname__torchTensortuplerrrrr r Js4LelLuU\35F/GLLLLLLrr _BACKENDS _COMPILER_FNSinductor_default_backendr compiler_fnnametagsr .cH|tjt||St|st dt ||p|j}|tvrt d||tvr dt|<|t|<t||_ |S)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)rrz"compiler_fn must be callable, got zduplicate name: ) functoolspartialregister_backendcallableAssertionErrortyperrrr_tags)rrrs rr#r#Us  !14HHHH K WU${BSBSUUVVV  ';'D }666777)## $%M$d K r)debug)r) experimentalc$t|trz|tvrt|tvrddlm}|||t vr2t|}|#t||t |}|S)z#Expand backend strings to functions)InvalidBackend)rN)rr) isinstancestrr _lazy_importexcr,rr#load)rr, entry_points rlookup_backendr3zs+s## 1 i ' ' NNN i ' ' , , , , , , .k222 2 m + +#K0K& [-=-=-?-?kRRRR#K0 rr(r)ctt|pdfdtD}t|S)za Return valid strings that can be passed to: torch.compile(..., backend="name") rcpg|]2}|tvs%t|j0|3Sr)r intersectionr').0rexclude_tags_sets r z!list_backends..sL  } $ $,,]4-@-FGG % $ $ $r)r/setrsorted) exclude_tagsbackendsr9s @r list_backendsr?sY NNN<-2..H (  rc~ddlm}ddlm}||ddlm}|t dtdS)Nr+)r>)import_submodule)dynamo_minifier_backendz&dynamo_minifier_backend failed to load)r>utilsrArepro.after_dynamorBr%_discover_entrypoint_backends)r>rArBs rr/r/sp((((((X<<<<<<&EFFF!#####rcddlm}d}||fdjD}|D]}||t|<dS)Nr) entry_pointstorch_dynamo_backends)groupc"i|] }|| Srr)r8repss r z1_discover_entrypoint_backends..s666Dc$i666r)importlib.metadatarHnamesr)rH group_nameeps_dict backend_namerLs @rrFrFst0/////(J ,Z ( ( (C6666CI666H 99 "*<"8 ,99rct|tvrdSt|dr|j}|t vs |tvrdSt|dr |jtvSdS)z Check if the given compiler function is a registered backend. Custom backends (user-provided callables not in the registry) return False. T compiler_namerF)r/rvalueshasattrrTrr)rrTs r_is_registered_backendrWs NNNm**,,,,t{O,,#1 I % %-)G)G4{M**A&-*>*>*@*@@@ 5rbackendc|dadSt|ts.t|st dt ||adS)zSet the default backend used by torch.compile when no backend is explicitly specified. Pass None to reset to the default ("inductor"). Nrz*backend must be a string or callable, got )rr-r.r$ TypeErrorr&)rXs rset_default_backendr[s` % gs # #VHW,=,=VTT']]TTUUUrctS)z5Return the current default backend for torch.compile.)rrrrget_default_backendr]s r)NNr)r4)r N)(__doc__r!loggingcollections.abcrrrNrtypingrrrr getLoggerrlogr GraphModulelistr CompilerFnrdictr.__annotations__rrr#r"register_debug_backendregister_experimental_backendr3r?cacher/rFboolrWr[r]rrrrms:::x........)))))) g!!MMMMMMMMr~tEL'9:JF G *, 4Z$&& ',,,') tCO$)))%/# "///&*d" * 3-c3h >+*+;*MMM 1 1,!!! j 0Z&T#Y$  $ $ $ $  9 9 9 9 t4 z!1D!8 T    S:-r