U yhZ*@s(ddlZddlmZmZmZmZmZddlZddlm Z e e Z GdddZ GdddZGdd d eZGd d d ZeZd d Zeje dddZd&eedddZddZedddZeedddZeedddZeeeefddd Zejedd!d"Zd#Zedd$d%ZdS)'N)AnyDictOptionalProtocolTupleparse_namespacec@seZdZeedddZdS)FakeScriptObject wrapped_objscript_class_namecCs||_||_dSNr )selfr r rT/var/www/html/venv/lib/python3.8/site-packages/torch/_library/fake_class_registry.py__init__ szFakeScriptObject.__init__N)__name__ __module__ __qualname__rstrrrrrrr sr c@s,eZdZeeeejdddZddZ dS)FakeScriptMethod self_fake_obj method_nameschemacCs||_||_||_dSr r)rrrrrrrrszFakeScriptMethod.__init__cOs"ddlm}||j|jf||S)Nr)call_torchbind)Z!torch._higher_order_ops.torchbindrrr)rargskwargsrrrr__call__s zFakeScriptMethod.__call__N) rrrr rrtorchZFunctionSchemarrrrrrrs  rc@s eZdZeejdddZdS)HasStaticMethodFromReal)real_objcCsdSr r)clsr!rrr from_real&sz!HasStaticMethodFromReal.from_realN)rrr classmethodr ScriptObjectr#rrrrr %sr c@steZdZddZeedddZeedddZdeddd d Z eedd d Z dd ddZ eddddZ dS)FakeClassRegistrycCs i|_dSr _registered_classrrrrr,szFakeClassRegistry.__init__ full_qualnamereturncCs ||jkSr r'rr+rrrhas_impl/szFakeClassRegistry.has_implcCs|||j|Sr )_check_registeredr(r-rrrget_impl2s zFakeClassRegistry.get_implNcCs&||rtd||||j|<dS)NzD%s is already registered. 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Please use register_fake_class to register it first.)r( RuntimeErrorr-rrrr/Ls z#FakeClassRegistry._check_registered)N) rrrrrboolr.rr0r4r6r8r/rrrrr&+s  r&cCsTt|tstd|D]8}t|ts,tdt|dkrFt|dtstdqdS)NzExpect flat x to be a tuple.z&Expect flat x to be a tuple of tuples.rzXExpect element of flat x to be a tuple of two elements with first element being a string) isinstancetupler9lenr)flat_xtprrr_check_valid_flat_script_objWs  rA)xr,c sddlmm}|}t||tjfdd|}t| |}t || }| D]v}t||d}|rt|std|d|dt||} d} t| tjr| j} t||t||| qbtd||qb|S)Nrcs |Sr )Z from_tensor)t fake_moderrnzto_fake_obj..zExpect z to be a callable but got .z.fake object of %s doesn't implement method %s.)Ztorch.utils._pytreeutilsZ_pytreeZ__obj_flatten__rAZ tree_map_onlyrZTensor"_find_fake_class_for_script_object__obj_unflatten__r _typequalified_nameZ _method_namesgetattrcallabler9r<Z ScriptMethodrsetattrrr1r2) rErBZpytreer?Zfake_flattenedZfake_xZfake_x_wrappednameattrZ real_attrZ method_schemarrDr to_fake_objes4      rSr3cs&tdfdd }|dkr|S||S)a Register a fake implementation for this class. It's in the same spirit of registering a fake implementation for an operator but with the difference that it associates a fake class with the original torch bind class (registered with torch::class_). In this way, torch.compile can handle them properly in components such as Dynamo and AOTAutograd. This API may be used as a decorator (see example). For the fake class, users are required to provide a from_real classmethod that takes a real object and returns an instance of the fake class. All tensors in the fake object should also be properly fakified with to_fake_tensor() in from_real. Examples: # For a custom class Foo defined in test_custom_class_registration.cpp: TORCH_LIBRARY(_TorchScriptTesting, m) { m.class_("_TensorQueue") .def(torch::init()) .def("push", &TensorQueue::push) .def("pop", &TensorQueue::pop) .def("top", &TensorQueue::top) .def("size", &TensorQueue::size) .def("clone_queue", &TensorQueue::clone_queue) .def("__obj_flatten__", &TensorQueue::__obj_flatten__) .def_pickle( // __getstate__ [](const c10::intrusive_ptr& self) -> c10::Dict { return self->serialize(); }, // __setstate__ [](c10::Dict data) -> c10::intrusive_ptr { return c10::make_intrusive(std::move(data)); }); }; # We could register a fake class FakeTensorQueue in Python as follows: import torch @torch._library.register_fake_class("_TorchScriptTesting::_TensorQueue") class FakeTensorQueue: def __init__(self, queue): self.queue = queue @classmethod def __obj_unflatten__(cls, flattened_ctx): return cls(**dict(ctx)) def push(self, x): self.queue.append(x) def pop(self): return self.queue.pop(0) def size(self): return len(self.queue) In this example, the original TensorQeue need to addd a __obj_flatten__ method to the class TensorQueue and the flattend result is passed into FakeTensorQueue's __obj_unflatten__ as inputs to create a fake class. This protocol allows pytorch to look at the contents of the script object and properly handle them in the subsystems like dynamo, aot_aotugrad or more. rTcspt\}}tj||}t|td}|s>t|dtdt|jtt s\ttdt t ||S)Nz doesn't define a classmethod rHz method is not a classmethod.) rrZ_CZ _get_custom_class_python_wrapperrN_CONVERT_FROM_REAL_NAMEr9r<__dict__r$global_fake_class_registryr4_full_qual_class_name)r3nsrQZtorchbind_classZ from_methodqualnamerrinners  z"register_fake_class..innerN)r )r[r3r\rrZrregister_fake_classsCr]cCstt|Sr )rWr6rXrZrrrderegister_fake_classsr^r7cCs t|Sr )rWr.r+rrrhas_fake_classsr`cCst|s dSt|Sr )r`rWr0r_rrrfind_fake_classsra)r[r,cCst|\}}d|d|S)Nz__torch__.torch.classes.rHr)r[rYrQrrrrXs rXr*cCs0|d}t|dkst|\}}}}}||fS)NrH)splitr>AssertionError)r+ZsplitsZ_torchZtorch_nsclassesrY class_namerrr_ns_and_class_names rgc CsN|}t|\}}t|}|dkrJtd|d|d|dtd |S)Nz ScriptObject's zA haven't registered a fake class. Please use register_fake_class(z::a) to annotate a fake class for the script obj. Specifically, create a python class that implements a fake version for all the methods that're used in the program and put annotated class in the program e.g. after loading the library. The fake methods can be written in the same way as a meta kernel for an operator but need to additionally simulate the object's states. Be sure to add a z; classmethod to enable creating a fake obj from a real one.)rLrMrgrar9rU)rBr+rYrfr3rrrrJs   rJrKc stt|}t|td}|s,t|dtdtjj|dtjjfdd| |W5QRSQRXdS)Nz must define a classmethod z2 that converts the real object to the fake object.csSr rrctxrrrF$rGz%_fake_obj_from_real..) rJrNrUr9rZ_libraryZ abstract_implZAbstractImplCtxZset_ctx_getterr#)rErBr3Zfrom_real_methodrrhr_fake_obj_from_reals rj)N) loggingtypingrrrrrrZtorch._library.utilsr getLoggerrr1r rr r&rWrAr%rSr]r^r:r`rarrXrgrJrUrjrrrrs(  ((\