o H&iz@sUddlZddlZddlZddlmZmZddlZddlmZm Z GdddZ de de defd d Z d d Z e aeed <ejddZGdddZdS)N)CallableOptional)KernelRegistrationHandlec@s4eZdZdZdefddZdededefdd Zd S) AbstractImplHolderz4A holder where one can register an abstract impl to.qualnamecCs||_d|_d|_dSN)rkernellib)selfrr GC:\wamp64\www\opt\env\Lib\site-packages\torch/_library/abstract_impl.py__init__ s zAbstractImplHolder.__init__funcsourcereturncsjdurtdjdjjdtjjdr$tdjdtjjdr5tdjdt||_jdurPj d d }tj |d _t j}j j|dfd d }t|S)zRegister an abstract impl. Returns a RegistrationHandle that one can use to de-register this abstract impl. Nz!impl_abstract(...): the operator z, already has an abstract impl registered at .ZMetaz already has an DispatchKey::Meta implementation via a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove that registration or don't call impl_abstract.ZCompositeImplicitAutograda- already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need an abstract impl; instead, the operator will decompose into its constituents and those can have abstract impls defined on them.z::rZFRAGMENTcs jr jd_d_dSr)r Z_destroyr r r r r deregister_abstract_impl@s  z=AbstractImplHolder.register..deregister_abstract_impl)r RuntimeErrorrrtorchZ_CZ%_dispatch_has_kernel_for_dispatch_keyrr splitZlibraryLibraryconstruct_meta_kernelimplr)r rrns meta_kernelrr rr registers0       zAbstractImplHolder.registerN) __name__ __module__ __qualname____doc__strrrrrr r r r r srrabstract_impl_holderrcs.jdusJtjjfdd}|S)Ncs`jdusJjjfdd}t|j|i|WdS1s)wYdS)Ncstddd)Nz.meta_kernel..error_on_ctx)r rset_ctx_getter)argskwargsr$r#r)rr rNs  $z*construct_meta_kernel..meta_kernel)r functoolswrapsr)rr#rr r(r rIs rcCsdSrr r r r r get_nonecsr+global_ctx_getterccs"t}z |adVW|adS|awr)r,)Z ctx_getterprevr r r r%js r%c@sHeZdZdZddZddddejfdd Zd dddejfd d ZdS) AbstractImplCtxzS Context object for writing abstract implementations for custom operators. cCs||_||_dSr) _shape_env_op)r r/r0r r r rzs zAbstractImplCtx.__init__NminmaxrcCstd|j||dS)NzIcreate_unbacked_symint is deprecated, please use new_dynamic_size insteadr2)warningswarnnew_dynamic_size)r r3r4r r r create_unbacked_symint~sz&AbstractImplCtx.create_unbacked_symintrcCs|jdus |jjstjj|jt|tjst|tjr(t d|d|d|dkr4t d|d|j }tj j j j|||d|S)a= Constructs a new symint (symbolic int) representing a data-dependent value. This is useful for writing the abstract 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.impl_abstract("mymodule::custom_nonzero") >>> def custom_nonzero_abstract(x): >>> # Number of nonzero-elements is data-dependent. >>> # Since we cannot peek at the data in an abstract 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 custom_nonzero_cpu(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.r2)r/Zallow_dynamic_output_shape_opsrZ _subclassesZ fake_tensorZDynamicOutputShapeExceptionr0 isinstanceSymInt ValueErrorr8ZfxZ experimentalZsymbolic_shapesZ_constrain_range_for_size)r r3r4resultr r r r7s" 3   z AbstractImplCtx.new_dynamic_size) rrr r!rrr:r8r7r r r r r.us r.) contextlibr)r5typingrrrZtorch._library.utilsrrrr"rr+r,__annotations__contextmanagerr%r.r r r r s& ?