o eiBk@sUddlZddlZddlZddlZddlZddlZddlZddlZddlm Z ddl Zddl m Z ddl m Z ddl mZddlmZmZmZmZmZmZddlmZmZ gdZdd d Zhd Zd d Z dd:d;Z7dS)?N) infer_schema)get_ctx)BaseTyBaseTypeFunctionSchemaListType OperatorName SchemaKind)autograd_kernel_indirectionconstruct_autograd_kernel) custom_opCustomOprCPUCUDA)cpucuda>atatenprimprimstorchpytorchcCstjdtdddS)Nzwtorch._custom_op is deprecated and will be removed in PyTorch 2.6, please use the equivalent torch.library API instead. stacklevel)warningswarnDeprecationWarningrr_/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/site-packages/torch/_custom_op/impl.pywarn_deprecated5s  r!qualname manual_schemareturncstfdd}|S)L This API is deprecated, please use torch.library.custom_op instead c s,t|stdt|t\}}t||j|kr,tdd|d|jddur6t|ddn}||}t |}t |durPt ||t |d}||t||j}t|||||d d }|j|_|j|_|j|_t ||jd tt|tj|ttt||S) NzDcustom_op(...)(func): Expected `func` to be a Python function, got: zcustom_op(qualname='z-', ...)(func): expected `func` to have name 'z ' but got 'zX'. Please either change the name of `func` or the qualname that is passed to `custom_op`r) mutates_argsFRAGMENTT_private_accessAutograd)inspect isfunction ValueErrortypeparse_qualnamevalidate_namespace__name__rrparsevalidate_schema validate_function_matches_schemalibraryLibrarydefinefind_ophandle_or_thrownamer __module____doc__impl_opnamer weakrefproxyr_C#_dispatch_set_report_error_callback functoolspartialreport_error_callback) funcnsr9schema schema_strfunction_schemalibophandleresultr#r"rr innerFsR            zcustom_op..inner)r!)r"r#rNrrMr r >s1r rglobal_registrycseZdZdZddfdd ZddZd,d d Zd d Zd dZddZ ddZ ddZ d,de j ee jefde jfddZddZde jfddZd,de jfddZdd Zd!d"Zd#d$Zd%d&Zd,d'd(Zd-d*d+ZZS).rr%Fr(csntt|std|d|}||_||_||_||_||_||_ d|_ i|_ d|_ |t |j <dS)Nz|The CustomOp constructor is private and we do not guarantee BC for it. Please use custom_op(...) to create a CustomOp object::F)super__init__r! RuntimeError_schema_cpp_ns_lib _ophandler= _qualnamer1_impls'_registered_autograd_kernel_indirectionrO)selfrJcpp_nsrG operator_namerKr)r9 __class__rr rRs" zCustomOp.__init__cCs4|jrtd|j|jtt|dd|_dS)Nz.autograd kernel indirection already registeredr*T)rZAssertionErrorrVr<r=r r>r?r[rrr %_register_autograd_kernel_indirections  z.CustomOp._register_autograd_kernel_indirectionrc Cs||r'|j|}|durtd|j}td|d|jd|d|d tt |}|j d|j }t |||j|<dS)Nz&func_and_location is unexpectedly NonezAttempting to register a z impl for operator z that already has a z impl registered from Python at z. This is not supported.:) _has_implrYr`locationrSrXr+ getframeinfosys _getframefilenamelinenoFuncAndLocation)r[kindrErfunc_and_locationreframerrr _register_impls  zCustomOp._register_implcCs |j|SNrYr[rlrrr _get_impl zCustomOp._get_implcCs ||jvSrprqrrrrr rdrtzCustomOp._has_implcCs6|`ttj|j}t||jrt||jt|j =dSrp) rVgetattrropsrUhasattrr=delattrrOrX)r[ opnamespacerrr _destroys    zCustomOp._destroycCsd|jdS)Nz)rXrarrr __repr__szCustomOp.__repr__cOstj|jg|Ri|}|Srp)r@_dispatch_call_boxedrW)r[argskwargsrLrrr __call__szCustomOp.__call__ device_typesr$cs6ttrgD]}t|q fdd}|S)T This API is deprecated, please use torch.library.custom_op instead csJtD]}|j||dt|}tjj||q|S)Nr)set_check_doesnt_have_library_implroSUPPORTED_DEVICE_TYPE_TO_KEYr5r<rVr=)f device_type dispatch_key _stacklevelrr[rr rNs  zCustomOp.impl..inner) isinstancestrvalidate_device_type)r[rrrrNrrr r<s  z CustomOp.implcCs@||rdSt|}t|j|rtd|d|jddS)Nzimpl(..., device_types=z): the operator zs already has an implementation for this device type via a pre-existing torch.library or TORCH_LIBRARY registration.)rdrr@._dispatch_has_computed_kernel_for_dispatch_keyrXrS)r[rkeyrrr rs z(CustomOp._check_doesnt_have_library_implcsfdd}|S)z2Register an implementation for a factory function.cs&d|tjjd||S)Nfactory BackendSelect)ror5r<rVr=rrarr rNs z$CustomOp.impl_factory..innerr)r[rNrrar impl_factorys zCustomOp.impl_factorycfdd}|S)rcsZjdddjjtfdd}jj |dS)NabstractrcsNfdd}tjj||i|WdS1s wYdS)Nctdd)Nz.inner..f_with_ctx..error_on_ctx)r_library fake_implset_ctx_getter)r}r~rrrer"rr f_with_ctxs $z9CustomOp.impl_abstract..inner..f_with_ctxMeta) $_check_doesnt_have_library_meta_implrorsrerXrBwrapsrVr<r=)rrrr[rr rNs z%CustomOp.impl_abstract..innerrr[rrNrrr impl_abstract szCustomOp.impl_abstractc sfdd}jtjkr|dj}js|dt|dkr.tdt|tdd|D}|r=|d tt j d tt j d tt j d tt j d tt jdttt jddi}jD]}|j|vrkqc|dt|d|jdqcdS)Ncstd|djd)NzCCannot use torch._custom_ops APIs to register backward formula for z. Got operator z with schema: )rSrX)detailrGr[rr error-sz4CustomOp._check_can_register_backward..errorznon-functional operatorzoperator with no returnsrz"expected at least one return, got css$|] }|jduo |jj VqdSrp) annotationis_write).0rrrr >s z8CustomOp._check_can_register_backward..zoperator that returns viewsintSymIntboolfloatTensorz List[Tensor]zoperator with return not in z (got ))rTrlr functionalreturnslenr`anyrrrrrrrrr.listvalues)r[rretsis_non_mutating_viewallowed_return_typesretrrr _check_can_register_backward,s8        z%CustomOp._check_can_register_backwardcCs^|jrdSt|jdrtd|jddD]}t|j|r,td|jd|dqdS)NCompositeImplicitAutogradz3impl_backward/impl_save_for_backward: the operator a3 already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need an autograd formula; instead, the operator will decompose into its constituents and those can have autograd formulas defined on them.)r* AutogradCPU AutogradCUDAz; already has an Autograd kernel registered to DispatchKey::z vi a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove those registrations or don't use the torch._custom_ops APIs)rZr@%_dispatch_has_kernel_for_dispatch_keyrXrS)r[rrrr (_check_doesnt_have_library_autograd_implTs&  z1CustomOp._check_doesnt_have_library_autograd_implcCsr|drdSt|jdrt|jdsdSt|jdr'td|jdt|jdr7td|jddS)NrCompositeExplicitAutogradrrz!impl_abstract(...): the operator a- already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need an abstract impl; instead, the operator will decompose into its constituents and those can have abstract impls defined on them.z already has an DispatchKey::Meta implementation via a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove that registration or don't call impl_abstract.)rdr@rrXrSrarrr rps&     z-CustomOp._check_doesnt_have_library_meta_implc Cs`|ds td|dstdt|j|j|t|j|dj|dj}| d|dS)Nbackwardz&backward impl must be registered firstsave_for_backwardz/save_for_backward impl must be registered firstautograd) rdr`r rT_output_differentiabilityget_oprXrsrEro)r[kernelrrr _register_autograd_kernels    z"CustomOp._register_autograd_kernelcr)zyRegister a function that tells us what to save for backward. Please see impl_backward for more details. csHjsjd|ddr"dSdS)Nrrr)rrrZrbrordrrrrr rNs  z.CustomOp.impl_save_for_backward..innerrrrrr impl_save_for_backwards zCustomOp.impl_save_for_backwardNcsldur,fdd}tts|D] }t|ts|qtjjtkr,|fdd}|S)rNcstd)Nzimpl_backward(output_differentiability): expected output_differentiability to be a list of bools with length equal to the number of outputs of this CustomOp got: rr)output_differentiabilityrr yells z$CustomOp.impl_backward..yellcsNjsjd|d_dr%dSdS)Nrrr)rrrZrbrorrdrrrrr[rr rNs  z%CustomOp.impl_backward..inner)rrrrrTr)r[rrrdiffrNrrr impl_backwards    zCustomOp.impl_backward)r)Nr)r1r: __qualname__r;rRrbrorsrdrzr{rtypingUnionrIterableCallabler<rrrrrrrrr __classcell__rrr^r rs4     #(, c@s eZdZUejed<eed<dS)rkrEreN)r1r:rrr__annotations__rrrrr rks  rkr\r]cCs0|jdurdn|j}t|dt|j|S)NrP) overload_namer@_dispatch_find_schema_or_throwrr9)r\r]rrrr r8s r8rFcCs:d|vr td|d|tvrtd|d|ddS)N.zcustom_op(..., ns="zC"): expected ns to not contain any . (and be a valid variable name)zcustom_op(..., ns='z'): 'z9' is a reserved namespace, please choose something else. )r- RESERVED_NS)rFrrr r0s r0rGcCs:tjj|std||jjdurtd|dS)Nzcustom_op only supports functional operators (ops that do not mutate any inputs, do not return views of the inputs, and has at least one return). Got the following non-functional schema: zUcustom_op does not support arguments named 'self'. Please rename your argument. Got: )rrutilsis_functional_schemar- argumentsself_arg)rGrrr r3s r3cCsR|dd}t|dkrtd|dd|dvr!td||d|dfS) NrPr rz$Expected there to be a namespace in z;, i.e. The operator name should look something like ns::foorzThe torch.custom_ops APIs do not handle overloads, i.e. operator names with '.' in them. Please name your operator something like ns::foo. Got: r)splitrr-)r"namesrrr r/ s    r/rcCs&|tvrtd|dtddS)NzCustomOp.impl(device_types=[z(, ...]): we only support device_type in r)rr-keys)rrrr rsrparamcCs|jtjjtjjfvSrp)rlr+ ParameterPOSITIONAL_OR_KEYWORD KEYWORD_ONLY)rrrr supported_param%srrEcst|tddjDstdtddjDs+jtjj ur2tdddjD}ddjD}fd d fd d fd d}||j j ||j j dS)Ncss|] \}}t|VqdSrp)rr_prrr r1sz3validate_function_matches_schema..zcustom_op(..., manual_schema)(func): positional-only args, varargs, and kwargs are not supported. Please rewrite `func` to not have them. Got `func` with signature: css"|] \}}|jtjjuVqdSrp)rr+remptyrrrr r9s  zcustom_op(..., manual_schema)(func): When passing in a manual schema, we expect `func` to have no type annotations to avoid ambiguity. Got `func` with signature: cS&g|]\}}|jtjjkr||fqSr)rlr+rrrr9rrrr E z4validate_function_matches_schema..cSrr)rlr+rrrrrr rJrcr)Nzcustom_op(..., manual_schema)(func): When passing in a manual schema, we expect `func`'s signature to match `manual_schema` (aside from type annotations). func's signature: , manual_schema: r-rrGsigrr rPz/validate_function_matches_schema..errorcr)Nzycustom_op(..., manual_schema)(func): neither func nor manual_schema should have default arguments. Got func's signature: rrrrrr error_default_argsXrz.error_default_argscs`t|t|kr t||D]\\}}}||jkr|jtjjus*|jdur-qdSrp)rzipr9defaultr+rr)sig_args schema_argsr9rarg)rrrr compare`s z1validate_function_matches_schema..compare) r+ signatureall parametersitemsr-rreturn_annotation Signaturerrflat_positionalflat_kwarg_only)rGrE positional kwargonlyrr)rrrGrr r4,s8  r4rc Csp|dkr t|d|dkrt|d|dvr.|}t|d|d|d|d t|d |d ) N Undefineda: There were no Tensor inputs to this operator (e.g. you passed an empty list of Tensors). If your operator is a factory function (that is, it takes no Tensors and constructs a new one), then please use CustomOp.impl_factory to register an implementation for itrz: when running with device='Meta' tensors: there is no abstract impl registered for this CustomOp. Please register one via CustomOp.impl_abstract to get this CustomOp to work with Meta tensors)rrz: when running with device='z' tensors: there is no zW impl registered for this CustomOp. Please register one via CustomOp.impl(device_type='z')z%: No implementation for dispatch key z. It is likely that we have not added this functionality yet, please either open an issue or if you're feeling adventurous, use the low-level torch.library API)NotImplementedErrorlower)r rdevicerrr rDms( rDcCs`|j}tj|d}|dd}t|j}|jdddd}t |}t |||||ddS)Nr'rPr )maxsplitTr() namespacerr5r6r9rrrTrsplitrr2r)oprFrJr9rHrGrrr custom_op_from_existings  rcsffdd}t\}}ttj|s|ttj|}t||s#|t||}t|ds0||jS)Ncstdd)NzCould not find the operator z~. Please make sure you have already registered the operator and (if registered from C++) loaded it via torch.ops.load_library.rrr"rr error_not_founds zget_op..error_not_foundr)r/rwrrvrur)r"rrFr9rypacketrrr rs       rFcCs8|tvrt|S|std|dt|}t|}|S)NzCould not find custom op "z5". Did you register it via the torch._custom_ops API?)rOrSrr)r"also_check_torch_libraryoverloadrLrrr _find_custom_ops rcCsF|tjjjvr dStjjj|}|durdS|dsdS|djS)Nr)r _custom_opr<rOrdrsrE)r"r rrr get_abstract_impls  rTc Cs|d\}}||}t|}t||rtjjjgng}t |d}|j ||dt ||j } t ||||| dd} | tj| ttt| t|S)NrPr')tagsTr()rrr2r3rr@Tagneeds_fixed_stride_orderr5r6r7r8r9rrbrArBrCrDr>r?r) r"rGrrFr9rHrIrrJrKrLrrr _custom_op_with_schemas    rrp)F)T)8 dataclassesrBr+rgrrr>rtorch._Cr@torch._library.infer_schema torch.libraryr5rrtorchgen.modelrrrrrr rr r __all__rrr!rOptionalrr rOdictrr dataclassrkr8r0r3tupler/rrrrr4AnyrDrrrrrrrrr sn          B^   A