o Zh @sUddlZddlZddlmZmZddlmZddlZddlm Z m Z GdddZ de de d efd d Z d d Zeaeed<ejddZGdddZdddZdS)N)CallableOptional) deprecated)KernelRegistrationHandlec@s4eZdZdZdefddZdededefdd Zd S) FakeImplHolderz0A holder where one can register an fake impl to.qualnamecCs||_d|_d|_dSN)rkernellib)selfrr G/var/www/auris/lib/python3.10/site-packages/torch/_library/fake_impl.py__init__s zFakeImplHolder.__init__funcsourcereturncsjdurtdjdjjdtjjdr$tdjdtjjdr5tdjdt||_jdurPj d d }tj |d _t j}j j|dfd d }t|S)z}Register an fake impl. Returns a RegistrationHandle that one can use to de-register this fake impl. Nz!register_fake(...): the operator z( already has an fake impl registered at .ZMetaz already has an DispatchKey::Meta implementation via a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove that registration or don't call register_fake.ZCompositeImplicitAutograda% already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need an fake impl; instead, the operator will decompose into its constituents and those can have fake impls defined on them.z::rZFRAGMENTcs jr jd_d_dSr )r Z_destroyr r r r rderegister_fake_classAs  z6FakeImplHolder.register..deregister_fake_class)r RuntimeErrorrrtorchZ_CZ%_dispatch_has_kernel_for_dispatch_keyrr splitZlibraryLibraryconstruct_meta_kernelimplr)r rrns meta_kernelrr rrregisters0       zFakeImplHolder.registerN) __name__ __module__ __qualname____doc__strrrrrr r r rr srrfake_impl_holderrcs.jdusJtjjfdd}|S)Ncs`jdusJjjfdd}t|j|i|WdS1s)wYdS)Ncstdd)Nz (a): You're trying to run this operator with meta Tensors (as opposed to FakeTensors), but this operator may return an output Tensor with data-dependent shape. Meta Tensors don't support operators with outputs that have data-dependent shapes but FakeTensors do. If your operator does not return an output with data-dependent shape, make sure the FakeTensor and/or meta kernel does not call torch.library.get_ctx(). Otherwise, please use FakeTensors.)rr )rrr r error_on_ctxRsz@construct_meta_kernel..meta_kernel..error_on_ctx)r rset_ctx_getter)argskwargsr%r$r)rrrMs  $z*construct_meta_kernel..meta_kernel)r functoolswrapsr)rr$rr r)rrJs rcCsdSr r r r r rget_nonedsr,global_ctx_getterccs"t}z |adVW|adS|awr )r-)Z ctx_getterprevr r rr&ks r&c@sTeZdZdZddZededdddd ejfd d Z d ddd ejfd dZ dS) FakeImplCtxzO Context object for writing fake implementations for custom operators. cCs||_|j|_||_dSr ) _fake_mode shape_env _shape_env_op)r r0r3r r rr{s zFakeImplCtx.__init__zM`create_unbacked_symint` is deprecated, please use `new_dynamic_size` instead)categoryNminmaxrcCs|j||dSNr6)new_dynamic_sizer r7r8r r rcreate_unbacked_symintsz"FakeImplCtx.create_unbacked_symintrcCsv|jdus |jjstjj|jt|tjst|tjr(t d|d|d|dkr4t d|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) Nzctx.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.) r2Zallow_dynamic_output_shape_opsrZ _subclassesZ fake_tensorZDynamicOutputShapeExceptionr3 isinstanceSymInt ValueError allocate_sizer;r r rr:s 3 zFakeImplCtx.new_dynamic_size) rr r!r"rr FutureWarningrr>r<r:r r r rr/vsr/cCs"|}tjjjj|||d|Sr9)r<rZfxZ experimentalZsymbolic_shapesZ_constrain_range_for_size)r1Zmin_valZmax_valresultr r rr@s  r@)rN) contextlibr*typingrrZtyping_extensionsrrZtorch._library.utilsrrrr#rr,r-__annotations__contextmanagerr&r/r@r r r rs  ?   Z