~L i  dZddlZddlZddlZddlZddlZddlZddlmZddlm Z ddl m Z m Z m Z mZddlmZddlZddlmZmZmZmZmZmZmZmZmZgdZed Zed Z d.d e eefd ed ede eeffdZ ejBe de"e fdZ#ejBde"e fdZ$ejBe de%e e ffdZ&de fdZ' d/dee de e e ge(fde)e fdZ*de dee de fdZ+eedZ,eedZ-eedZ.ejBde/e%e e)e fe%e efffdZ0e de%e e)e ffdZ1e dZ2ejBde"e fd Z3e d e de4fd!Z5d"Z6Gd#d$Z7d%Z8d&Z9d'Z:d(Z;ejxd)Z=Gd*d+e7Z>ejxd,Z?ejxd-Z@y)0aE Python implementation of ``__torch_function__`` While most of the torch API and handling for ``__torch_function__`` happens at the C++ level, some of the torch API is written in Python so we need python-level handling for ``__torch_function__`` overrides as well. The main developer-facing functionality in this file are handle_torch_function and has_torch_function. See torch/functional.py and test/test_overrides.py for usage examples. Note ---- heavily inspired by NumPy's ``__array_function__`` (see: https://github.com/pytorch/pytorch/issues/24015 and https://www.numpy.org/neps/nep-0018-array-function-protocol.html ) If changing this file in a way that can affect ``__torch_function__`` overhead, please report the benchmarks in ``benchmarks/overrides_benchmark``. See the instructions in the ``README.md`` in that directory. N)Iterable)wraps)AnyCallableOptionalTypeVar) ParamSpec) _add_docstr_get_function_stack_at_has_torch_function_has_torch_function_unary_has_torch_function_variadic_is_torch_function_mode_enabled_len_torch_function_stack_pop_torch_function_stack_push_on_torch_function_stack) get_ignored_functionsget_overridable_functionsget_testing_overrideshandle_torch_functionhas_torch_function resolve_nameis_tensor_likeis_tensor_method_or_propertywrap_torch_functionenable_reentrant_dispatch_P_Rfuncregexmodulereturnctdtjdtjdtffd }|S)a  Decorator that temporarily disables ``UserWarning``s for the given ``module`` if the warning message matches the given ``regex`` pattern. Arguments --------- func : function Function to disable the warnings for. regex : str A regex pattern compilable by ``re.compile``. This is used to match the ``UserWarning`` message. module : str The python module to which the filtering should be restricted. Returns ------- function The wrapped function. argskwargsr"ctj5tjdt|i|cdddS#1swYyxYw)Nignore)categorymessager!)warningscatch_warningsfilterwarnings UserWarning)r$r%rr!r s U/mnt/ssd/data/python-lab/Trading/venv/lib/python3.12/site-packages/torch/overrides.pywrapperz'_disable_user_warnings..wrapperZsJ  $ $ & )  # #;f ((  ) ) )s %AA)rrr$r%r)rr r!r/s``` r._disable_user_warningsr0Bs?0 4[)rww)"))))) Nc#tj}htjtjtjtj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*tj,tj.tj0tj2tj4tj6tj8tj:tj<tj>tj@tjBtjDtjFtjHtjJtjLtjNtjPtjRtjTtjVtjXtjZtj\tj^tj`tjbtjdtjftjhtjjtjltjntjptjrtjttjvtjxtjztj|tj~tjtjtjtjtjtjtjtjtjtjtjtjtjtjtjjtjjtjjtjjtjtjjtjjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjjtjjjtjjjtjjjtjjjtjjjtjjjtjjjtjjjtjjj tjjj tjjjtjjjtjjjtjjjtjjjtjjjtjjjtjjjtjjjtjjjtjjj tjjj"tjjj$tjjj&tjjj(tj*j,tttj.tj0tj2tj4tj6tj8tj:tj<tj>tj@tjBtjDtjFtjHtjJtjLtjNtjPtjRtjTtjVtjXtjZtjjj\tj^tj`tjbtjdtjftjhtjjtjltjntjptjrtjttjvtjxtjztj|tj~tjtjtjtj|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|jj|jj|j|j|j|j|j|j|j|j|j,|jS)a% Return public functions that cannot be overridden by ``__torch_function__``. Returns ------- set[Callable] A tuple of functions that are publicly available in the torch API but cannot be overridden with ``__torch_function__``. Mostly this is because none of the arguments of these functions are tensors or tensor-likes. Examples -------- >>> torch.Tensor.as_subclass in torch.overrides.get_ignored_functions() True >>> torch.add in torch.overrides.get_ignored_functions() False (torchTensortypename is_tensor is_storageset_default_tensor_typeset_default_deviceget_default_device set_rng_state get_rng_state manual_seed initial_seedseedsaveloadset_printoptionsforkget_default_dtypeget_num_interop_threadsget_num_threadsinit_num_threadsimport_ir_moduleimport_ir_module_from_bufferis_anomaly_enabledis_anomaly_check_nan_enabledis_grad_enabledmerge_type_from_type_commentparse_ir parse_schemaparse_type_commentset_anomaly_enabledset_flush_denormalset_num_interop_threadsset_num_threadswait as_tensor from_numpytensordefault_generatorhas_cuda has_cudnn has_lapackdevicedtypefinfohas_mklhas_mps has_mkldnn has_openmpiinfo memory_formatqschemeset_grad_enabledno_grad enable_gradinference_modeis_inference_mode_enabledlayout align_tensorsarange as_stridedbartlett_windowblackman_windowbroadcast_shapescan_castcompilecudnn_affine_grid_generatorcudnn_batch_normcudnn_convolutioncudnn_convolution_transposecudnn_convolution_relucudnn_convolution_add_relucudnn_grid_samplercudnn_is_acceptableemptyempty_permuted empty_stridedempty_quantizedexportregister_dataclasseyefftfftfreqrfftfreq from_filefullfillhamming_window hann_window kaiser_windowlinspacelogspacemkldnn_adaptive_avg_pool2dmkldnn_convolutionmkldnn_max_pool2dmkldnn_max_pool3dmkldnn_linear_backward_weightsmkldnn_rnn_layernormalones promote_typesrandrandnrandintrandpermrange result_type scalar_tensorsparse_coo_tensorsparse_compressed_tensorsparse_csr_tensorsparse_csc_tensorsparse_bsr_tensorsparse_bsc_tensorsym_constrain_rangesym_constrain_range_for_sizesym_fresh_size tril_indices triu_indicesvanderzeros _jit_internalboolean_dispatchnn functionalassert_int_or_pairupsampleupsample_bilinearupsample_nearestrhas_torch_function_unaryhas_torch_function_variadicrsigmoid hardsigmoidtanh_canonical_mask_none_or_dtypeinitcalculate_gainuniformconstantdiracxavier_uniform xavier_normalkaiming_uniformkaiming_normal orthogonalsparsenestedto_padded_tensorset_autocast_enabledis_autocast_enabledset_autocast_dtypeget_autocast_dtypeclear_autocast_cacheset_autocast_cpu_enabledis_autocast_cpu_enabledset_autocast_xla_enabledis_autocast_xla_enabledset_autocast_ipu_enabledis_autocast_ipu_enabledset_autocast_cpu_dtypeget_autocast_cpu_dtypeset_autocast_ipu_dtypeget_autocast_ipu_dtypeget_autocast_gpu_dtypeset_autocast_gpu_dtypeget_autocast_xla_dtypeset_autocast_xla_dtypeautocast_increment_nestingautocast_decrement_nestingis_autocast_cache_enabledset_autocast_cache_enabled hardswishis_vulkan_available$are_deterministic_algorithms_enableduse_deterministic_algorithms-is_deterministic_algorithms_warn_only_enabledset_deterministic_debug_modeget_device_moduleget_deterministic_debug_modeset_float32_matmul_precisionget_float32_matmul_precisionunify_type_listis_warn_always_enabledset_warn_alwaysvitals_enabled set_vital read_vitalsvmapcond frombufferasarray_functional_sym_constrain_range_make_dep_token __delitem____dir____getattribute____init____iter____init_subclass__ __delattr__ __setattr____torch_function____torch_dispatch____new__ __class____subclasshook____hash__ as_subclasseiglstsq reinforcenew new_tensor new_emptynew_empty_strided new_zerosnew_onesnew_full_make_subclasssolvesymeigstride unflatten to_sparse_coo to_sparse_csr to_sparse_csc to_sparse_bsr to_sparse_bsc _to_sparse_to_sparse_csr_to_sparse_csc_to_sparse_bsr_to_sparse_bsc_typed_storage_reduce_ex_internal _fix_weakref _view_func_view_func_unsafe_rev_view_func_unsafe 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}tjjj}t||r5dd҄|t||<dӄ|t|d|j<i}t}|jD]\}}|j|jdzd|jzdzd|jzdzd|jzdzg}|jj d٫rH|jt d٫d}|j d|zdzd|zdzd|zdzg|D],} t|| d} t| s| |vs#| |vs(||| <.|j||S(YarReturn a dict containing dummy overrides for all overridable functions Returns ------- Dict[Callable, Callable] A dictionary that maps overridable functions in the PyTorch API to lambda functions that have the same signature as the real function and unconditionally return -1. These lambda functions are useful for testing API coverage for a type that defines ``__torch_function__``. 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GG!|%H! '')QI!|%J! OK!|%L! M!|%N! O!|%P! Q!|%R! _S!|%T! 1U!|%V!  +W!|%X!  EUZUjUjnY!|%Z! II[!|%\! G]!|%^! /_!|%`!  a!|%b! /c!|%d! .e!|%f!  =7  +. od  / Fw!|%C~!  ((BBv/0 F GF4 56JYGFc":!;< = E EF D#%G 1 JJ JJ  1::  $ AJJ  % AJJ  %   ::  ,jjZ!23G LL$&$(>RV@VW  D64.D~$c/d'6IT  %.JJt Jr1 dispatchercfd}|S)aWraps a given function with ``__torch_function__`` -related functionality. Parameters ---------- dispatcher: Callable A callable that returns an iterable of Tensor-likes passed into the function. Note ---- This decorator may reduce the performance of your code. Generally, it's enough to express your code as a series of functions that, themselves, support __torch_function__. If you find yourself in the rare situation where this is not the case, e.g. if you're wrapping a low-level library and you also need it to work for Tensor-likes, then this function is available. Examples -------- >>> def dispatcher(a): # Must have the same signature as func ... return (a,) >>> @torch.overrides.wrap_torch_function(dispatcher) >>> def func(a): # This will make func dispatchable by __torch_function__ ... return a + 0 cJtjfdS)NcZ|i|}t|rt|g|i|S|i|Sr)rr)r$r% relevant_argsr~ rwrappeds r.r z3wrap_torch_function..inner..wrapped2sA&77M!-0,WmUdUfUU(( (r1) functoolsr)rr r~ s`@r.rz"wrap_torch_function..inner1s%   )  )r1r;)r~ rs` r.rrs0  Lr1r get_type_fnc|t}tjjsgSt }g}|D]}||}||vst |ds|j tjjk7sE|rX|j|t|}t|D]\}}t|||s|}n|j|||h}|g}|S)a Returns a list of arguments on which to call __torch_function__. Checks arguments in relevant_args for __torch_function__ implementations, storing references to the arguments and their types in overloaded_args and overloaded_types in order of calling precedence. Only distinct types are considered. If a type is a subclass of another type it will have higher precedence, otherwise the precedence order is the same as the order of arguments in relevant_args, that is, from left-to-right in the argument list. The precedence-determining algorithm implemented in this function is described in `NEP-0018`_. See torch::append_overloaded_arg for the equivalent function in the C++ implementation. Parameters ---------- relevant_args : iterable of array-like Iterable of array-like arguments to check for __torch_function__ methods. get_type_fn : callable, optional Function to call on each argument in relevant_args to get its type. Returns ------- overloaded_args : list Arguments from relevant_args on which to call __torch_function__ methods, in the order in which they should be called. .. _NEP-0018: https://numpy.org/neps/nep-0018-array-function-protocol.html r) r r3_C_is_torch_function_enabledsetrq r_disabled_torch_function_implr5rv enumerate issubclassinsert) r r overloaded_typesoverloaded_argsargarg_typeriold_args r._get_overloaded_argsr ?sJ  88 . . 0 "%%!#O(s# , ,"67++uxx/U/UU  $$X.O,"+O"<JAw!(K,@A ! &&uc2$,: #&%9(: r1 public_apic t|}ttt|}t r1t 5}|j ||||}dddtur|S|D]u}|j } t| drD| j|ur6| tjjurtjdt| ||||}|tuss|cS|j d|j"} d| d|D cgc] } t| c} } t r| dt%z } t'| #1swYxYwcc} w)a=Implement a function with checks for ``__torch_function__`` overrides. See torch::autograd::handle_torch_function for the equivalent of this function in the C++ implementation. Arguments --------- public_api : function Function exposed by the public torch API originally called like ``public_api(*args, **kwargs)`` on which arguments are now being checked. relevant_args : iterable Iterable of arguments to check for __torch_function__ methods. args : tuple Arbitrary positional arguments originally passed into ``public_api``. kwargs : tuple Arbitrary keyword arguments originally passed into ``public_api``. Returns ------- object Result from calling ``implementation`` or an ``__torch_function__`` method, as appropriate. Raises ------ TypeError : if no implementation is found. Example ------- >>> def func(a): ... if has_torch_function_unary(a): ... return handle_torch_function(func, (a,), a) ... return a + 0 N__self__zDefining your `__torch_function__ as a plain method is deprecated and will be an error in future, please define it as a classmethod..zno implementation found for 'z.' on types that implement __torch_function__: z nor in mode )r tuplemapr r_pop_mode_temporarilyrNotImplementedrq r r3r r r*warnDeprecationWarning __module__rt _get_current_function_mode TypeError) r r r$r%r typesrresultoverloaded_argtorch_func_method func_namer rs r.rrsfT+=9O #dO, -E'(# $ N,,ZfMF N  'M*+== %z 2!**n<!)O)OO MMQ" #:udFC  'M),(():+>+>*?@I ' {35DEcS EF H'( 9;<== C.G N N> FsD9=E 9EaCheck for __torch_function__ implementations in the elements of an iterable or if a __torch_function__ mode is enabled. Considers exact ``Tensor`` s and ``Parameter`` s non-dispatchable. Use this to guard a call to :func:`handle_torch_function`; don't use it to test if something is Tensor-like, use :func:`is_tensor_like` instead. Arguments --------- relevant_args : iterable Iterable or arguments to check for __torch_function__ methods. Returns ------- bool True if any of the elements of relevant_args have __torch_function__ implementations, False otherwise. See Also ________ torch.is_tensor_like Checks if something is a Tensor-like, including an exact ``Tensor``. zSpecial case of `has_torch_function` for single inputs. Instead of: `has_torch_function((t,))` call: `has_torch_function_unary(t)` which skips unnecessary packing and unpacking work. a'Special case of `has_torch_function` that skips tuple creation. This uses the METH_FASTCALL protocol introduced in Python 3.7 Instead of: `has_torch_function((a, b))` call: `has_torch_function_variadic(a, b)` which skips unnecessary packing and unpacking work. c dtjt}i}dttjfdtj tj jfdtj j ttj j fdtj jttj jfdtjttjfdtjttjfdtjttjfdtjttjfg}|D],\}}}|D]}d }|tjurV|jd r*|jd rd }nW|jd rd }nC|d jsd }n-|dk(r(nt!||}t!t"|d|k(r|dk(rt!||}|tjurt!t"|d|k(rt%|t&j(rt%|t*j,rt/|st1|dr|d|d||j2<|d|d||j4<|rC|j2t7vr;d} |j2t9vs!J| j;||j<||j?|j2t/|s|d|||<|r|t7vr1d} |t9vs!J| j;||j< ||j?|"/||fS)Nr3ztorch.functionalztorch.nn.functionalz torch.nn.initz torch.Tensorz torch.linalgz torch.fftz torch.specialFrr Tr unique_dim __weakref__r&r z.__get__z.__set__zk{}.{} is in the tuple returned by torch._overrides.get_ignored_functions but still has an explicit override) collections defaultdictlistr3__all__rrdirrr4ryrrM ru endswithislowerrr object isinstancer ModuleType __future___Featurer|rq r&__set__rrformatrt rU) overridable_funcsrtested_namespaces namespace_str namespacens_funcsr r'rrs r._get_overridable_functionsr sE$//5 E %' U--u/?/?/G/GH  3 3S9L9L5MN %((--UXX]]);< s5<<'89 s5<<'89 eiiUYY0 %--U]]);< /@C6* y(!B 6IF ,''-))#.!F'',!F"1--/!F,.y)469d3t; -9i0DELL(WVY-MQU-U$ 0 01$ 3 34D>gdI&>)6q 8&Ldll#)6q 8&Ldll#<<#8#::= <*O1YK8E$K,..9#8#::CJJt}}=: i ( / / 5EB 6C6H e ##r1ctdS)aList functions that are overridable via __torch_function__ Returns ------- Dict[Any, List[Callable]] A dictionary that maps namespaces that contain overridable functions to functions in that namespace that can be overridden. r)r r;r1r.rrms & ' **r1ct|tjjtjjfr t |St dj|S)a&Get a human readable string name for a function passed to __torch_function__ Arguments --------- f : Callable Function to resolve the name of. Returns ------- str Name of the function; if eval'ed it should give back the input function. r)r r3_ops OpOverloadOpOverloadPacketstrr get)fs r.rrzsI !ejj++UZZ-H-HIJ1v % ' * . .q 11r1cRt}t|tj}|S)z>> is_tensor_method_or_property(torch.Tensor.add) True >>> is_tensor_method_or_property(torch.add) False r&)r rt )rs r.rrs!. &( ( FDMMY,FFr1cTt|tjuxs t|dS)a9 Returns ``True`` if the passed-in input is a Tensor-like. Currently, this occurs whenever there's a ``__torch_function__`` attribute on the type of the input. Examples -------- A subclass of tensor is generally a Tensor-like. >>> class SubTensor(torch.Tensor): ... >>> is_tensor_like(SubTensor([0])) True Built-in or user types aren't usually Tensor-like. >>> is_tensor_like(6) False >>> is_tensor_like(None) False >>> class NotATensor: ... >>> is_tensor_like(NotATensor()) False But, they can be made Tensor-like by implementing __torch_function__. >>> class TensorLike: ... @classmethod ... def __torch_function__(cls, func, types, args, kwargs): ... return -1 >>> is_tensor_like(TensorLike()) True r)r r3r4rq )inps r.rrs%D 9 $ J5I(JJr1cHeZdZUdZded<d dZd dZdZdZe dZ y) TorchFunctionModea A ``TorchFunctionMode`` allows you to override the meaning of all ``__torch_function__`` overridable functions within a dynamic scope, without having to actually create a tensor subclass or manually monkey-patch functions in the PyTorch API. Some common situations where you should use a mode: * You want to override the meaning of factory functions, or other functions that do not otherwise take a tensor as an argument (these cannot be overridden with tensor subclasses). * You want to override the behavior of all functions without needing to wrap your inputs in tensor subclasses; e.g., if you are just interested in logging intermediate computations. * You want to control the order of execution of various tensor subclasses explicitly, rather than implicitly via the return of ``NotImplemented``. Independent subclasses of :class:`TorchFunctionMode` are compositional: modes can be pushed onto a stack using ``with MyMode():``. When you call functions in the PyTorch API inside your ``__torch_function__`` implementation, by default, they will forward on to the next mode on the mode stack. If you want recursively call back into your current ``__torch_function__`` implementation, either explicitly invoke ``self.__torch_function__(...)``, or use the context manager ``enable_torch_function_mode(self, replace=self.inner)`` to make PyTorch API self-referential (beware of infinite loops, in this case!) rNcyrr;rs r.rzTorchFunctionMode.__init__s r1ctr)NotImplementedErrorrrr r$r%s r.rz$TorchFunctionMode.__torch_function__s!!r1ct||Sr) _push_moders r. __enter__zTorchFunctionMode.__enter__s4 r1ctyr) _pop_mode)rexc_typeexc_valexc_tbs r.__exit__zTorchFunctionMode.__exit__s r1c@tjd||i|}|S)NzP`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`)r*r )clsr$r%instances r.pushzTorchFunctionMode.pushs' ^ ''r1)r"Nr;N) rt r __qualname____doc____annotations__rrr r classmethodr r;r1r.r r s7<  "r1r c@t}|dkDrt|dz SdS)Nrr)rr ) stack_lens r.r r s%)+I4=M !)a- 0KtKr1cdt}t|Dcgc] }t|c}Scc}wr)rrr )r r s r. _get_current_function_mode_stackr s))+I/4Y/? @! "1 % @@ @s-ct|yr)r)rs r.r r s !$'r1ct}|Sr)rolds r.r r s # %C Jr1c#`Kt} |t|y#t|wxYwwr)r r r s r.r r !s% +C 3 3s . . +.ceZdZddZy)BaseTorchFunctionModeNc|i}||i|Srr;r s r.rz(BaseTorchFunctionMode.__torch_function__+s >FT$V$$r1r )rt r r rr;r1r.r r *s%r1r c#ZKtjj} tjjtjjj dtjj|y#tjj|wxYwwr)r3r _get_torch_function_state_set_torch_function_state_TorchFunctionStateENABLED) old_states r._enable_torch_functionr 1sf224I6 **588+G+G+O+OP  **95**95sB+AB' B+!B((B+c#Ktjj5 d dddy#wxYw#1swYyxYwwr)r3r _RestorePythonTLSSnapshotr;r1r.rr;sC  + + -      s#A61 A36?A)z.*is deprecated, please use.*r3r)Ar r r contextlibr r r*collections.abcrrtypingrrrrtyping_extensionsr r3torch._Cr r r r rrrrrr rrr r0cacher rr6dictrrr r r rrrrr r rrr r rrr r r r r contextmanagerr r r rr;r1r.r s*, $33'     t_ T] 1  2r6      b"f F Ts8}TTn c(m2 |tHh$67||~##H#P48KC=K(C5$;/0K #YK\UUC=U  Up!.' *    S$Ed8n tHcM22%S$S$l +4T(^(;#< + +22( S]GxGDGG2"KJ55pL A (  %-% 66   r1