Lj"UddlZddlZddlmZddlmZddlZddlmZm Z GddZ de de d efd Z d Z e aeed <ej d ZGddZddZy)N)Callable) deprecated)KernelRegistrationHandlecjeZdZdZdefdZedZejdZddde d ed e fd Z y ) FakeImplHolderz0A holder where one can register an fake impl to.qualnamec ||_g|_yN)r kernels)selfr s W/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/_library/fake_impl.py__init__zFakeImplHolder.__init__s% &( cRt|jdk(ry|jdS)Nr)lenr )r s rkernelzFakeImplHolder.kernels% t||  !||Brctd)NzUnable to directly set kernel. RuntimeError)r values rrzFakeImplHolder.kernels;<.deregister_fake_kernelNs LL   'rr) rrr rtorch_C%_dispatch_has_kernel_for_dispatch_keyrr appendconstruct_meta_kernelimplr) r rrlibrr$ meta_kernelhandlers ` @rregisterzFakeImplHolder.register"s&{{&"7 G>{{))*!- xx==dmmVT"7 G%&xx== :#7 G;<  f% F# (,DMM4@   VNS#$:; rN) __name__ __module__ __qualname____doc__strrpropertyrsetterrrr.rrrr sa:((   ]]==CH33&)3 3rrr fake_impl_holderrcj tdtjjjfd}|S)N(fake_impl_holder.kernel must not be Nonecj tdjjfd}t|5j|i|cdddS#1swYyxYw)Nr9c&tdd)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_ctxz@construct_meta_kernel..meta_kernel..error_on_ctxbs'*Bvh'NO  r)rAssertionErrorrset_ctx_getter)argskwargsr<rr7r s @rr,z*construct_meta_kernel..meta_kernel\sf  " " * !KL L!((// L ) <*#**D;F; < < r>{s' D!&  DsceZdZdZdZededdddejfd Z d dddejfd Z y) FakeImplCtxzO Context object for writing fake implementations for custom operators. cB||_|j|_||_yr ) _fake_mode shape_env _shape_env_op)r rLrOs rrzFakeImplCtx.__init__s$$..rzM`create_unbacked_symint` is deprecated, please use `new_dynamic_size` instead)categoryNminmaxrc(|j||SNrR)new_dynamic_sizer rSrTs rcreate_unbacked_symintz"FakeImplCtx.create_unbacked_symints $$#$66rrc|j|jjs3tjjj |j t|tjst|tjrtd|d|d|dkrtd|dt|j||S)a Constructs 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.) rNallow_dynamic_output_shape_opsr% _subclasses fake_tensorDynamicOutputShapeExceptionrO isinstanceSymInt ValueError allocate_sizerXs rrWzFakeImplCtx.new_dynamic_sizesf OO #??AA##//KKDHHU U c5<< (JsELL,I+C5se<)*  7+C51&'  T__c377r) r/r0r1r2rr FutureWarningr%r`rYrWr6rrrJrJsV W-.47ELL7 7'(TF8ellF8rrJc|j}tjjjj ||||SrV)rYr%fx experimentalsymbolic_shapes_constrain_range_for_size)rMmin_valmax_valresults rrbrbsB  - - /F HH))CCGD Mr)rN) contextlibrAcollections.abcrtyping_extensionsrr%torch._library.utilsrrrr3r)rDrE__annotations__contextmanagerr>rJrbr6rrrrs|$( ;JJZC>h8'8& !!W8W8tr