^j ^UdZddlZddlZddlmZmZddlmZddlm Z m Z ddl Z ddl m Z ejeZGdde Zee j"ee j&gefZiZeeedzfed <iZeeefed <d aeezed < dd edzdedzdeedede ffdZej8edZej8edZd eezdefdZddeefdZ ejBd dZ"ejBd dZ#d ede$fdZ%deezdzddfdZ&deezfdZ'y)!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)fxcNeZdZdejdeejdffdZy) CompiledFnargsreturn.cy)N)selfr s j/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/torch/_dynamo/backends/registry.py__call__zCompiledFn.__call__KsN)__name__ __module__ __qualname__torchTensortuplerrrrr r Js!LellLuU\\35F/GLrr _BACKENDS _COMPILER_FNSinductor_default_backend compiler_fnnametagsr .c&|tjt||St|st dt ||xs |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   !14HH K A${BSATUVV  ';''D }/v677)# $%M$d K r)debug)r) experimentalct|tr`|tvr t|tvrddlm}|||t vr&t|}|t|j|t |}|S)z#Expand backend strings to functions)InvalidBackend)r)rr) isinstancestrr _lazy_importexcr,rr#load)rr, entry_points rlookup_backendr3zsj+s# i ' N i ' , k2 2 m +#K0K& [-=-=-?kR#K0 rctt|xsd}tDcgc].}|tvs"|j t|j s|0}}t |Scc}w)za Return valid strings that can be passed to: torch.compile(..., backend="name") r)r/setrr intersectionr'sorted) exclude_tagsexclude_tags_setrbackendss r list_backendsr;sk N<-2.  } $,,]4-@-F-FG H (  s3A"cfddlm}ddlm}||ddlm}| t dty)Nr+)r:)import_submodule)dynamo_minifier_backendz&dynamo_minifier_backend failed to load)r:utilsr=repro.after_dynamor>r%_discover_entrypoint_backends)r:r=r>s rr/r/s+(X<&EFF!#rcddlm}d}||}|jDcic]}||| }}|D]}||t|<ycc}w)Nr) entry_pointstorch_dynamo_backends)group)importlib.metadatarDnamesr)rD group_nameepsreps_dict backend_names rrBrBsX0(J Z (C,/II6Dc$i6H6 9 "*<"8 ,97s Act|tjvryt|dr|j}|t vs|tvryt|dr |j tjvSy)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/rvalueshasattrrNrr)rrNs r_is_registered_backendrQsq Nm**,,{O,#11 I %-)G{M*&&-*>*>*@@@ rbackendcv|dayt|ts"t|st dt ||ay)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&)rRs rset_default_backendrUs@ % gs #HW,=DT']OTUUrctS)z5Return the current default backend for torch.compile.)rrrrget_default_backendrWs r)NNr))r(r))r N)(__doc__r!loggingcollections.abcrrrGrtypingrrrr getLoggerrlogr GraphModulelistr CompilerFnrdictr.__annotations__rrr#r"register_debug_backendregister_experimental_backendr3r;cacher/rBboolrQrUrWrrrrgs:x.) g!MMr~~tELL'9:JF G *, 4Z$&& ',') tCO$)%/# "/&*d" * 3-c3h >+**+;*M 1 1 1,! j 0Z&T#Y$  $ $  9 9 t4 z!1D!8 T S:-r