o Zh0@s,ddlZddlZddlZddlmZddlmZmZddlZddlmZm Z m Z mZddl m Z ej Z e ddddejd eed efd d Zd eeejfdedededef ddZddZe deje dee dedededejdiZddZeZdejd efdd Zd!ee d eefd"d#Z!dS)$N) GenericAlias)OptionalUnion)devicedtypeTensortypes) exposed_inz torch.library)op_nameprototype_functionr returnc sd}|jdt|fdddtffdd dtttttfd fd tttj d ft fffd d d ttttfd ttj t fffdd g}t }d}t j D]9\}\}} t| sod| jtjjkr|s|dd}| jtjjurd|d| j\} } | tvr| jturt| } d} | tvrd| d} d|d| jd| dtd nd|d| jdtdt| }t|tkr||krtd|drd |d!|tdd}n!||vr|dsd|d"d |d!|tdd}||| jtjjur5||d#|q`d}| jdusGt| jt t!t frMt| j}n@t| jtt"j#fr_d$| jd$}n.t| jt"j$rt| j}d%}||svJ|t|d}nd|d&t| jd'||d#|d(|q`||krt ||}t|d)kr|d*j%\}} t&|}|dur|d+d,'|d-|Sd+d,'|d-|S).aJParses the schema of a given function with type hints. The schema is inferred from the function's type hints, and can be used to define a new operator. We make the following assumptions: * None of the outputs alias any of the inputs or each other. * | String type annotations "device, dtype, Tensor, types" without library specification are | assumed to be torch.*. Similarly, string type annotations "Optional, List, Sequence, Union" | without library specification are assumed to be typing.*. * | Only the args listed in ``mutates_args`` are being mutated. If ``mutates_args`` is "unknown", | it assumes that all inputs to the operator are being mutates. Callers (e.g. the custom ops API) are responsible for checking these assumptions. Args: prototype_function: The function from which to infer a schema for from its type annotations. op_name (Optional[str]): The name of the operator in the schema. If ``name`` is None, then the name is not included in the inferred schema. Note that the input schema to ``torch.library.Library.define`` requires a operator name. mutates_args ("unknown" | Iterable[str]): The arguments that are mutated in the function. Returns: The inferred schema. Example: >>> def foo_impl(x: torch.Tensor) -> torch.Tensor: >>> return x.sin() >>> >>> infer_schema(foo_impl, op_name="foo", mutates_args={}) foo(Tensor x) -> Tensor >>> >>> infer_schema(foo_impl, mutates_args={}) (Tensor x) -> Tensor unknownNcstd|dd)Nzinfer_schema(func): z Got func with signature )) ValueError)what)sigJ/var/www/auris/lib/python3.10/site-packages/torch/_library/infer_schema.pyerror_fnDszinfer_schema..error_fnannotation_typecs4zt|WStyd|dYdSw)NzUnsupported type annotation z. It is not a type.)eval Exception)r)r pf_globals pf_localsrrconvert_type_stringGs   z)infer_schema..convert_type_stringtys.r csHg}d}|D]}|\}}||||O}q|r t|dfS|dfS)NFT)appendtuple)rreschangedtyZ ty_changed)unstringify_typerrunstringify_typesOs    z'infer_schema..unstringify_typesr csPt|tr |dfSt|}r$t|\}}|r$t||dfS|dfS)NTF) isinstancestrtyping get_originget_argsr)r originargsZ args_changed)rr"rrr!]s  z&infer_schema..unstringify_typeFz>We do not support positional-only args, varargs, or varkwargs.*Tz Parameter z must have a type annotation.z z For example, z. z has unsupported type z]. We do not support Tuple inputs in schema. As a workaround, please try to use List instead. zThe valid types are: .. The valid types are: zomutates_args must either be a sequence of the names of the arguments that are mutated or the string 'unknown'. rzTensor(az!)zM is in mutable_args but only Tensors or collections of Tensors can be mutated "ztorch.z' has an unsupported default value type z;. Please file an issue on GitHub so we can prioritize this.=rz in mutates_args were not found in the custom op's signature. mutates_args should contain the names of all args that the custom op mutates, or just the string 'unknown' if you don't know.(, z) -> )( __globals__inspect signaturer$rrtypeobjectr%Anyboolset enumerate parametersitemssupported_paramkind Parameter KEYWORD_ONLYr annotationemptySUPPORTED_PARAM_TYPES __origin__ tuple_to_listkeysr startswithlenadddefaultr#intfloattorchrrreturn_annotation parse_returnjoin)r Z mutates_argsr ZUNKNOWN_MUTATESparamsZ seen_argsZsaw_kwarg_only_argidxnameparamr_Z list_typeZexample_type_strZ schema_typeZ default_reprZ dtype_reprZ torch_dotZmutates_args_not_seenrNretr)rrrrrr!r"r infer_schemas*  .                  rW base_typecpp_type list_baseoptional_base_listoptional_list_basecs|ftj|dfg}dtttjffdd}|r*|fdd||D|r=|fdd|tj|D|rM|fdd||D|S) N?typcSs,tj|tj|ttjj|ftt|ffSN)r%SequenceListr collectionsabclist)r^rrrderived_seq_typess  z(derived_types..derived_seq_typesc3|] }|dfVqdS)z[]Nr.0Zseq_typrYrr s z derived_types..c3rf)z?[]Nrrgrirrrjs  c3s$|] }tj|dfVqdS)z[]?N)r%rrgrirrrjs  )r%rrr5 _SpecialFormextend)rXrYrZr[r\resultrerrir derived_typess" rnc Cstddddftddddftddddftddddftddddftjddddftd dddftd dddfg}g}|D] }| t |q7t |S) NrTFSymIntrLr8r$ScalarZ ScalarTypeZDevice) rrKrLr8r$rNumberrrrlrndict)datarmlinerrrget_supported_param_typess        rurzTensor[]rorLr8rpcCs|durdS|tjjur|dt|}|tur.|tvr*|d|dtdt|St|}|D]}|tvrF|d|dtdq5dd dd |Dd S) Nz()z7No return type annotation was provided. Please add one.zReturn has unsupported type r,r+r0r1cSsg|]}t|qSr)SUPPORTED_RETURN_TYPES)rhargrrr sz parse_return..r) r3r?rBr%r&rrvrFr'rP)rArr(r)rwrrrrOs.    rOrTcCs|jtjjtjjfvSr_)r>r3r?POSITIONAL_OR_KEYWORDr@)rTrrrr=$sr= tuple_typecCst|dd}|tjus|tus|dks|durtSt|dkr%t|dSt|dkr7|dtur7t|dSttjt|S)z} Convert `tuple_type` into a list type with the same type arguments. Assumes that `tuple_type` is typing.Tuple type. __args__Nrr)getattrr%TuplerrdrHEllipsisr)rzZ type_argsrrrrE+s     rE)"rbr3r%rrrrrMrrrZtorch.utils._exposed_inr Z _TestTensorCallabler$rWr5rkr8rnrurardrKrLrqrvrOrCr?r=rrErrrrsV   2  %