U zhe?@s|UddlZddlmZddlmZddlmZddlmZm Z m Z m Z m Z ddl Z ddlZ ddlmZmZmZddlmZddlmZd d d d d dgZeeZe ee e jjeffed<edZedZ edZ!ddZ"ddZ#d!ddddd Z$d"e e e jjefee e jjefddd Z%e e jjefe e eefddddZ&ddl'Z ddl(Z e e jjefdd dZ)dS)#N) defaultdict)wraps)chain)CallableDictListSequenceUnion)HigherOrderOperator OpOverloadOpOverloadPacket)CustomOutParamAnnotation)_pytreedecomposition_table pre_autograd_decomposition_table meta_tableregister_decompositionget_decompositionscore_aten_decompositionsglobal_decomposition_table post_autograd pre_autogradmetacCsg}t|tr|||<dSt|tr0||n,t|ts>t|D]}|t||qF|D]2}||krztd|t j | r`|||<q`dS)z This is an internal API for adding an op to the decomposition table. If op is OpOverload, it will be added to the registry directly. If op is OpOverloadPacket, all the valid op_overloads in the packet will be added to the registry. Nzduplicate registrations for ) isinstancer r appendr AssertionError overloadsgetattr RuntimeErrortorchZ_CZ_dispatch_has_kernelname)registryopfnrol op_overloadr&H/var/www/html/venv/lib/python3.8/site-packages/torch/_decomp/__init__.py_add_op_to_registry#s    r(csljd}|sSt|ddtkrt}|jjtfdd}ddt |j D}t dd|j D|}tj||jd |_d d j D|_|D]}|j|j|j<qj|_|Sjtdrhtfd d}tjtjjd|d }t}t dd|j D|f}tj||jd |_dd j D|_|j|j|j<|SS)Nout __origin__csVtfddD}|ddktfdd|DsPsz3_convert_out_params.._fn..rc3s|]}|dkkVqdSr+r&r.)is_noner&r'r2Ssr))tupleallr)argsr1Z out_kwargs)f out_names)r3r1r'_fnNs z _convert_out_params.._fncSs&g|]\}}tj|tjjd|dqS)Nkinddefault annotation)inspect Parameter KEYWORD_ONLY)r/r0tr&r&r' Vsz'_convert_out_params..css|]\}}|dkr|VqdSr)Nr&r/kvr&r&r'r2`sz&_convert_out_params..) parametersreturn_annotationcSsi|]\}}|dkr||qSr)r&rDr&r&r' esz'_convert_out_params..cs|d}||d|iS)Nr)r,)r6r1Z out_kwarg)custom_out_param_namer7r&r'r9ts r:css|]\}}|dkr|VqdSrCr&rDr&r&r'r2scSsi|]\}}|dkr||qSrIr&rDr&r&r'rJs)__annotations__getrr4r> signaturerH_fieldsrzip__args__rrGitems Signature __signature__r=r Z!_torch_decompositions_out_wrapperr-r r?r@)r7Zout_annotationsigr9Z out_paramsparamsr0Z out_paramr&)rKr7r8r'_convert_out_params@sX     rWF)typeunsafecs*dks tttdfdd }|S)az A decorator to register a function as a decomposition to the Python decomposition table. Use it like this:: @register_decomposition(torch.ops.aten.clamp_min) def clamp_min(x): return torch.clamp(self, min=min) If you are writing a new decomposition, consider contributing it directly to PyTorch in torch._decomp.decompositions. This API is experimental; we are almost certainly going to extend the API when we make decompositions eligible for use in transforms (e.g., autograd) and not just backend tracing, where we then need to know if a decomposition can be used to simulate a transform. By default, we also will register it to the Meta key of dispatcher, and replace the c++ Meta implementation if there is already one. unsafe kwarg is for reuse of this function for registering non-function things >rrr)r#returncs>}stdkr tfdd}t||S)Ncst|dSr+)r()r")r#r!r&r'registerszIregister_decomposition..decomposition_decorator..register)rWrpytreeZ tree_map_)r#Zorig_fnr[aten_opr!rXrY)r#r'decomposition_decorators z7register_decomposition..decomposition_decorator)rr)r^r!rXrYr_r&r]r'rs )aten_opsrXrZcCs|dks tt|}tt}|D]"}t|ttfr ||j|q i}|D]T}t|tr~||kr~||D]}||||<qjqLt|t j j rL||krL||||<qL|S)a, Retrieve a dictionary of decompositions corresponding to the list of operator overloads and overload packets passed as input. Overload packets will include all decomposed overloads in the packet. If there is no decomposition for a requested operator, it is silently ignored. 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