Ei"%SSKrSSKrSSKJr SSKJr SSKrSSKJrJ r "SS5r S\ S\ S \4S jr S r \ q\\S '\R S 5r"SS5rSSjrg)N)Callable) deprecated)KernelRegistrationHandlecx\rSrSrSrS\4Sjr\S5r\RS5rSS.S \ S \S \ 4S jjr S r g)FakeImplHolder z0A holder where one can register an fake impl to.qualnamecXl/UlgN)r kernels)selfr s U/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/_library/fake_impl.py__init__FakeImplHolder.__init__s% &( cT[UR5S:XagURS$)Nr)lenr )rs rkernelFakeImplHolder.kernels% t||  !||Brc[S5e)NzUnable to directly set kernel. RuntimeError)rvalues rrrs;<TRRT5 gr )r remove)rrsrderegister_fake_kernel7FakeImplHolder.register..deregister_fake_kernelNs LL   'rr) rrr rtorch_C%_dispatch_has_kernel_for_dispatch_keyrr appendconstruct_meta_kernelimplr) rrrlibrr' meta_kernelhandlers ` @rregisterFakeImplHolder.register"s,{{&"7 G>{{))*!- xx==dmmVTT"7 G%&xx== :#7 G;<  % F# (,DMM4@   VNS#$:; r)r r N)__name__ __module__ __qualname____firstlineno____doc__strrpropertyrsetterrrr2__static_attributes__rrrr sf:((   ]]==CH33&)3 33rrr fake_impl_holderr c^^TRc [S5e[R"TRR5UU4Sj5nU$)N(fake_impl_holder.kernel must not be Nonec>^TRc [S5eTRRmUU4Sjn[U5 TR"U0UD6sSSS5 $!,(df  g=f)Nr@c&>[TSTS35e)Nz (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 rsr error_on_ctx@construct_meta_kernel..meta_kernel..error_on_ctxbs'*Bvh'NO  r)rAssertionErrorrset_ctx_getter)argskwargsrCrr>r s @rr0*construct_meta_kernel..meta_kernel\sZ  " " * !KL L!((// L )#**D;F;* ) )s A A-)rrE functoolswrapsr)r r>r0s`` rr-r-XsK&GHH__%,,112<3<( rcgr r=r=rrget_nonerMts rglobal_ctx_getterc#8# [nUqSv Uqg!Uqf=f7fr )rN) ctx_getterprevs rrFrF{s& D!&  Dsc\rSrSrSrSr\"S\S9SSS.S \R4S jj5r S SS.S \R4S jjr S r g) FakeImplCtxzG Context object for writing fake implementations for custom operators. c>XlURUlX lgr ) _fake_mode shape_env _shape_env_op)rrVrYs rrFakeImplCtx.__init__s$$..rzM`create_unbacked_symint` is deprecated, please use `new_dynamic_size` instead)categoryNminmaxr c URXS9$Nr])new_dynamic_sizerr^r_s rcreate_unbacked_symint"FakeImplCtx.create_unbacked_symints $$$66rrcURbURR(d3[RRR UR 5e[U[R5(d[U[R5(a[SUSUS35eUS:a[SUS35e[URX5$)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 an 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.rzc, ...): expected min to be greater than or equal to 0: this API can only create non-negative sizes.) rXallow_dynamic_output_shape_opsr) _subclasses fake_tensorDynamicOutputShapeExceptionrY isinstanceSymInt ValueError allocate_sizercs rrbFakeImplCtx.new_dynamic_sizesf OO #??AA##//KKDHHU U c5<< ( (JsELL,I,I+C5se<)*  7+C51&'  T__c77r)rVrYrX) r4r5r6r7r8rr FutureWarningr)rlrdrbr<r=rrrSrSs\ W-.47ELL7 7'(TF8ellF8F8rrScUR5n[RRRR X1US9 U$ra)rdr)fx experimentalsymbolic_shapes_constrain_range_for_size)rWmin_valmax_valresults rrnrns@  - - /F HH))CCD Mr)rN) contextlibrJcollections.abcrtyping_extensionsrr)torch._library.utilsrrrr9r-rMrN__annotations__contextmanagerrFrSrnr=rrrs|$( ;JJZC>h8'8& !!W8W8tr