i UdZddlZddlZddlmZmZddlmZddlm Z m Z m Z m Z ddl Z ddl mZejeZGdde Zeej&ee j*gefZiZeee efed <iZeeefed < dd e ed e ed eedede ffdZej:edZej:edZd e eefdefdZ ddeefdZ!ejDddZ#ejDddZ$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)AnyOptionalProtocolUnion)fxcNeZdZdejdeejdffdZy) CompiledFnargsreturn.cy)N)selfr s Y/var/www/html/engine/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_FNS compiler_fnnametagsr.c|tjt||St|sJ|xs |j}|t vs Jd||t vr 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)rrzduplicate name: ) functoolspartialregister_backendcallablerrrr_tags)rrrs rr#r#Ts   !14HH K   ';''D } $?(8&?? $)# $%M$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_backendr1wsj+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_backendsr9sk N<-2.  } $,,]4-@-F-FG H (  s3A"cTddlm}ddlm}||ddlm}|Jt y)Nr))r8)import_submodule)dynamo_minifier_backend)r8utilsr;repro.after_dynamor<_discover_entrypoint_backends)r8r;r<s rr-r-s%(X< " .. .!#rcddlm}d}||}|jDcic]}||| }}|D]}||t|<ycc}w)Nr) entry_pointstorch_dynamo_backends)group)importlib.metadatarBnamesr)rB group_nameepsreps_dict backend_names rr@r@sX0(J Z (C,/II6Dc$i6H6 9 "*<"8 ,97s A)NNr))r&r')rN)%__doc__r!loggingcollections.abcrrrErtypingrrrr rr getLoggerrlogr GraphModulelistr CompilerFnrdictr,__annotations__rr#r"register_debug_backendregister_experimental_backendr1r9cacher-r@rrrrYsm:x.)11 g!MMr~~tELL'9:JF G -/ 4Xj)) */') tCO$))-*% 3- 3-c3h :+**+;*M 1 1 1,! c:o 6:&T#Y$  $ $ 99r