+ j#aRt0t^RIt^RIt^RIt^RIHt^RIHt^RIt^RI H t H t ]P!] 4t!RR4tRRltR t]s]^k]P&R 4t!R R 4tRR ltR#)N)Callable) deprecated)KernelRegistrationHandleca]tRt^toRtV3RlRlt]R4t]PR4tRR/V3RlR llt R t Vt R #) FakeImplHolderz/A holder where one can register a fake impl to.c <V^8dQhRS[/#)qualname)str)format __classdict__s"Y/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/_library/fake_impl.py __annotate__FakeImplHolder.__annotate__s(((c Wn.VnR#N)r kernels)selfr s&&r__init__FakeImplHolder.__init__s% &( rc`\VP4^8XdR#VPR,#)rN)lenr)rs&rkernelFakeImplHolder.kernels% t||  !||Brc\R4h)zUnable to directly set kernel. RuntimeError)rvalues&&rrr"s;<\PR SP4SPPS4hi;i) zdRegister a fake impl. Returns a RegistrationHandle that one can use to de-register this fake impl. z!register_fake(...): the operator z' already has a fake impl registered at .Metaz already has a DispatchKey::Meta implementation via a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove that registration or don't call register_fake.CompositeImplicitAutograda$ already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need a fake impl; instead, the operator will decompose into its constituents and those can have fake impls defined on them.c><SPPS4R#r)rremove)rrsrderegister_fake_kernel7FakeImplHolder.register..deregister_fake_kernelRs LL   'r)r"z"Failed to register fake_impl '%s':)rr r r%torch_C%_dispatch_has_kernel_for_dispatch_keyrrappendconstruct_meta_kernelimpl Exceptionloginfor,r) rr$r%libr"r- meta_kernelhandlers f&&&$ @rregisterFakeImplHolder.register&sq{{&"7 G={{))*!- xx==dmmVTT"7 G%&xx== :#7 G;<  % F# (,DMM4@   HHT]]KH W$$:;   HH4   LL   '   s E AF)rr N) __name__ __module__ __qualname____firstlineno____doc__rpropertyrsetterr;__static_attributes____classdictcell__rs@rrrsU9((   ]]==;BF;;;rrc<V^8dQhR\R\R\/#)r r fake_impl_holderr&)r rr)r s"rrrds!C>hrcaaSPf \R4h\P!SPP4VV3Rl4pV#)N(fake_impl_holder.kernel must not be Nonec<aSPf \R4hSPPoVV3Rlp\V4;_uu_4SP!V/VBuuRRR4# +'giR#;i)NrJc&<\S RS R24h)z (a): You're trying to run this operator with meta Tensors (as opposed to FakeTensors), but this operator may return an output Tensor with data-dependent shape. Meta Tensors don't support operators with outputs that have data-dependent shapes but FakeTensors do. If your operator does not return an output with data-dependent shape, make sure the FakeTensor and/or meta kernel does not call torch.library.get_ctx(). Otherwise, please use FakeTensors.r)r r%sr error_on_ctx@construct_meta_kernel..meta_kernel..error_on_ctxns'*Bvh'NO  r)rAssertionErrorr%set_ctx_getter)argskwargsrMr%rHr s*, @rr9*construct_meta_kernel..meta_kernelhs`  " " * !KL L!((// L ) )#**D;F;* ) ) )s A)) A: )rrO functoolswrapsr$)r rHr9sff rr3r3dsK&GHH__%,,112<3<( rcR#rrWrrget_nonerXs rc#:"\pVsRxVsR# Tsi;i5ir)global_ctx_getter) ctx_getterprevs& rrPrPs& D!&  Dsca]tRt^toRtRt]!R]R7R^RR/V3RlR ll4tR^RR/V3R lR llt R t Vt R#) FakeImplCtxzG Context object for writing fake implementations for custom operators. c@WnVPVnW nR#r) _fake_mode shape_env _shape_env_op)rr`rcs&&&rrFakeImplCtx.__init__s$$..rzM`create_unbacked_symint` is deprecated, please use `new_dynamic_size` instead)categoryminmaxNc4<V^8dQhRS[P/#r r&r/SymInt)r rs"rrFakeImplCtx.__annotate__s77ELL7rc&VPWR7#)rfrg)new_dynamic_sizerrfrgs&$$rcreate_unbacked_symint"FakeImplCtx.create_unbacked_symints $$$66rc4<V^8dQhRS[P/#rirj)r rs"rrrlsF8F8ellF8rc VPeVPP'g4\PPP VP 4h\V\P4'g!\V\P4'd\RV RV R24hV^8d\RV R24h\VPW4#)aConstructs a new symint (symbolic int) representing a data-dependent value. This is useful for writing the fake implementation (which is necessary for torch.compile) for a CustomOp where an output Tensor has a size that depends on the data of the input Tensors. Args: min (int): A statically known inclusive lower bound for this symint. Default: 0 max (Optional[int]): A statically known inclusive upper bound for this symint. Default: None .. warning: It is important that the ``min`` and ``max`` (if not None) values are set correctly, otherwise, there will be undefined behavior under torch.compile. The default value of ``min`` is 2 due to torch.compile specializing on 0/1 sizes. You must also verify that your implementation on concrete Tensors (e.g. CPU/CUDA) only returns Tensors where the size that corresponds to the symint also has respects these constraint. The easiest way to do this is to add an assertion in the CPU/CUDA/etc implementation that the size follows these bounds. Example:: >>> # An operator with data-dependent output shape >>> lib = torch.library.Library("mymodule", "FRAGMENT") >>> lib.define("mymodule::custom_nonzero(Tensor x) -> Tensor") >>> >>> @torch.library.register_fake("mymodule::custom_nonzero") >>> def _(x): >>> # Number of nonzero-elements is data-dependent. >>> # Since we cannot peek at the data in a fake impl, >>> # we use the ctx object to construct a new symint that >>> # represents the data-dependent size. >>> ctx = torch.library.get_ctx() >>> nnz = ctx.new_dynamic_size() >>> shape = [nnz, x.dim()] >>> result = x.new_empty(shape, dtype=torch.int64) >>> return result >>> >>> @torch.library.impl(lib, "custom_nonzero", "CPU") >>> def _(x): >>> x_np = x.numpy() >>> res = np.stack(np.nonzero(x_np), axis=1) >>> return torch.tensor(res, device=x.device) zctx.new_dynamic_size(min=z, max=zZ): expected min and max to be statically known ints but got SymInt. This is not supported.zc, ...): expected min to be greater than or equal to 0: this API can only create non-negative sizes.) rballow_dynamic_output_shape_opsr/ _subclasses fake_tensorDynamicOutputShapeExceptionrc isinstancerk ValueError allocate_sizerps&$$rroFakeImplCtx.new_dynamic_sizesf OO #??AAA##//KKDHHU U c5<< ( (JsELL,I,I+C5se<)*  7+C51&'  T__c77r)r`rcrb) r=r>r?r@rArr FutureWarningrqrorDrErFs@rr^r^sc W7A7477 7F8aF8TF8F8F8rr^cVP4p\PPPP W1VR7V#rn)rqr/fx experimentalsymbolic_shapes_constrain_range_for_size)ramin_valmax_valresults&&& rr{r{s@  - - /F HH))CCD Mrc@V^8dQh/^\9d \;R&#)r rZ)__conditional_annotations__r)r s"rrrsD'&8&Er)rN)r contextlibrTloggingcollections.abcrtyping_extensionsrr/torch._library.utilsrr getLoggerr=r6rr3rXrZcontextmanagerrPr^r{r)rs@rrs~$( ;!RRj8'& !!W8W8tr