a h,1@sddlZddlZddlZddlmZddlmZmZddlZddlmZm Z m Z mZddl m Z ej Z e dddejeeedd d Zeeejfeeeed d d ZddZe deje dee dedededejdiZddZeZejedddZee eedddZ!dS)N) GenericAlias)OptionalUnion)devicedtypeTensortypes) exposed_inz torch.library)op_name)prototype_functionr returnc sd}|jdt|fddtdfdd tttttfdftttj dft fd fd d ttttfttj t fd fd d g}t }d}t j D]\}\}} t| sڈd| jtjjkr|s|dd}| jtjjurd|d| j\} } | tvr| jturt| } d} | tvrdd| d} d|d| jd| dtd n"d|d| jdtdt| }t|tkr||krtd|drDd|d |tdd}nB||vrD|ds(d|d!d|d |tdd}||| jtjjurt||d"|qd}| jdust| jt t!t frt| j}nt| jtt"j#frd#| jd#}n\t| jt"j$rt| j}d$}||sJ|t|d}nd|d%t| jd&||d"|d'|q||krrt ||}t|d(krr|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|dddS)Nzinfer_schema(func): z Got func with signature )) ValueError)what)sigI/var/www/auris/lib/python3.9/site-packages/torch/_library/infer_schema.pyerror_fnDszinfer_schema..error_fnannotation_typecs6zt|WSty0d|dYn0dS)NzUnsupported type annotation z. It is not a type.)eval Exceptionr)r pf_globals pf_localsrrconvert_type_stringGs   z)infer_schema..convert_type_string.)tysr csLg}d}|D]"}|\}}||||O}q |r@t|dfS|dfSdS)NFT)appendtuple)rreschangedtyZ ty_changed)unstringify_typerrunstringify_typesOs    z'infer_schema..unstringify_types)r!r csPt|tr|dfSt|}rHt|\}}|rHt||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;. 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Please add one.zReturn has unsupported type r-r,r2cSsg|] }t|qSr)SUPPORTED_RETURN_TYPES)riargrrr z parse_return..r1r) r4r@rCr&r'rrwrGr(rQrI)rBrr)r*rxZ output_tyrrrrPs2      rP)rUr cCs|jtjjtjjfvSra)r?r4r@POSITIONAL_OR_KEYWORDrA)rUrrrr>(sr>) tuple_typer cCst|dd}|tjus.|tus.|dks.|dur2tSt|dkrJt|dSt|dkrn|dturnt|dSttjt|SdS)z} Convert `tuple_type` into a list type with the same type arguments. Assumes that `tuple_type` is typing.Tuple type. __args__Nrr{r)getattrr&TuplerrfrIEllipsisr)r}Z type_argsrrrrF/s    rF)"rdr4r&rrrrrNrrrZtorch.utils._exposed_inr Z _TestTensorCallabler%rXr6rlr9rorvrcrfrLrMrrrwrPrDr@r>rrFrrrrsF  3  %