sh+pSSKJr SSKrSSKJr SSKJrJrJrJ r J r SSK r SSK J r Jr SSKJr SSKJr \ (aSSKJr \ R*R-\S 5r\ R*R-\S 5r\\\ \\ R84r\\\/\4r\R>"S S 9"S S55r \R>"S S 9"SS55r!\R>"S S 9"SS55r"S(Sjr#S)Sjr$S*Sjr%S+Sjr&S,Sjr'S-Sjr(S.Sjr)S/Sjr*S0Sjr+S0Sjr,S1Sjr-\R>"SS55r.S2S jr/S3S!jr0"S"S#\5r1S4S$jr2S5S%jr3\R>"S S 9"S&S'55r4g)6) annotationsN)Enum)AnyCallableOptional TYPE_CHECKINGUnion)countersget_metrics_context) InputType) OrderedSet)Sequence perf_hintscudagraph_static_inputsT)frozenc$\rSrSr%SrS\S'Srg) FunctionIDz9Unique counter of a function wrapped in cudagraphify_implintidN__name__ __module__ __qualname____firstlineno____doc____annotations____static_attributes__rs/Users/tiagomarins/Projetos/claudeai/copy_bank/venv/lib/python3.13/site-packages/torch/_inductor/cudagraph_utils.pyrrs ? Gr rcB\rSrSr%SrS\S'S\S'S\S'S\S 'S rg ) PlaceholderInfo#z A serializable version of torch.fx.Node that contains information pertinent to placeholder stack traces. We use these in logging and error messages related to cudagraphs, and will cache these results. strname Optional[str] stack_tracelist[PlaceholderInfo]usersmutating_use_stack_tracerNrrr r!r#r##s  I  ++r r#cV\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S\S 'Srg)WrappedFunction2z Represents a function that you want to record for CUDA graph replay, with a little more metadata so we can identify if we have an applicable CUDA graph in our CUDA graph tree for it. zCallable[..., Any]model Sequence[int]static_input_idxsrrztuple[torch.Tensor, ...] constantsSequence[PlaceholderInfo] placeholdersmutated_input_idxsrNrrr r!r-r-2s, $$N''++%%r r-c[UR5S:Xa8[[UR55RR SS5$URHhnUR [RRRR:XdMAURR SS5=n(dMfUs $ g)Nr() lenr*nextitermetagettargettorchopsatencopy_default)placeholder_nodeuser(s r!&get_mutating_use_stack_trace_from_noderEBs  ! !"a'D)//0166::=$OO%% ::--55 5!hhll=$??{?""& r cUR$N)r+)placeholder_infos r!get_mutating_use_stack_tracerIQs  4 44r cURnURRSS5n/nSnURS:Xa0URVs/sHn[ U5PM nn[ U5n[XX45$s snf)Nr( placeholder)r&r;r<opr*to_placeholder_inforEr#)rCr&r(r*r+is r!rMrMUs  D"''++M4@K E#m+1A1G1GH1GA$Q'1GH#I $   4e NN Is A9czURVs/sHoRS:XdM[U5PM! sn$s snf)NrK)nodesrLrM)graphnodes r!get_placeholder_inforSds9.3kk .9dWW =U!D!k  s88c SU3$)Nzskipping cudagraphs due to r)reasons r!format_default_skip_messagerVjs ( 11r cSnUHnXn[U5=n(dM O [S[U5S35nU(aUSU3$U$)Nzmutated inputs (z instances). Found from : )rIrVr8)r4mutation_indicesr(idxrKmsgs r!get_mutation_stack_tracer]nsi"$K"' 6{C C; C  & 3/01= C( 66 Jr c[RRRR(a?UR Vs/sH'nX0R ;aMU"X5(aM%UPM) nnO UR n[RSUR 5 [RSU5 U(a[URU5$S$s snf)Nz'check mutation static input indices: %sz#check mutation mutation indices: %s) r> _inductorconfigtritoncudagraph_treesr5r1static_inputs_logdebugr]r4)funcinputsis_cuda_graph_recorded_tensorr[rZs r!check_for_mutationrhs  $$44..+ .--- 1= . +  22143I3IACST  !!2!24DE!+ sC C*CcxURH*nURRSS5=n(dM(Us $ g)Nr()r*r;r<)rRrDr(s r!_get_use_stack_tracerjs5zz((,,}d; ;; ;  r cUR[R"S55=n(a=SURS3n[ U5=n(a[ USU35$[ U5$[ U5S:Xa1[[UR555RS:XagSUR55n[ SS RU535$) Ncpuz cpu device ()rYr7cudac38# UHn[U5v M g7frG)repr).0keys r! :check_multiple_devices_or_any_cpu_nodes..sA&@sc&@szmultiple devices: z, ) r<r>devicer&rjrVr8r9r:keystypejoin)device_node_mappingcpu_noder\r( keys_reprs r!'check_multiple_devices_or_any_cpu_nodesr|s'**5<<+>??x?X]]O1-.x8 8; 8.#6H /VW W*3//  A% )..01 2 7 76 AA&9&>&>&@AI &);DIIir_r`rarbr r8rSrQr]) gmmutated_inputsr5r1 default_msg unique_idxsr[rZ has_mutationr4s r!3check_for_mutation_ignore_cuda_graph_managed_tensorrs ..>?K $$44 !23 +=X+=CKAWC+=X+,1 +BHH5 ' GG>*a/ 't8[8Ys  B2B2cUR(a UR$URH"nUR(dMURs $ g)zE Gets the first non-empty stack trace of a placeholder or its users. N)r(r*)rKusers r!get_placeholder_stack_tracersF&&&!!    ## #" r c.\rSrSrSrSrSrSrS SjrSr g) CheckInvariantStatusr7cURS:XagURS:XagURS:XagURSUR3$)NCudagraphManagedIdxMismatchz-cudagraph managed tensor data pointer changedStaticInputIdxMismatchz!static input data pointer changed&ExpectedDeadIndicesBeforeGraphMismatchz+expected dead indices before graph are livez: )r&r)rs r!__str__CheckInvariantStatus.__str__sK 995 5B YY2 26 YYB B@ii[4::,/ /r rNrr%) rrrrSUCCESSrrrrrrr r!rrs$G#$./*0r rc [U5[U5:Xa[U5[U5:XdS5eUVs/sHoQUPM nnUVs/sHoRUPM nnUS3n[[Xg55Hyunup[U [R 5(deX5n U R 5U :wdMCX n USU RSU SU R 5S[U 5S3 nM{ U$s snfs snf)zq Logs the mismatch between input data pointers and recorded data pointers. This checks only idxs in target_idxs. zClength mismatch between inputs, recorded_data_ptr, and placeholdersz. z input name: z. data pointer changed from z to z. input stack trace:  ) r8 enumerateziprr>Tensordata_ptrr&r) r4rfrecorded_data_ptr target_idxsmismatchrN t_tensors t_data_ptrs error_msgtensorrindexrKs r!log_data_ptr_mismatchrs v;#/0 0S[C DU5UM U%00KqKI01<=AQ'K=*C I!*3y+F!G F&%,,//// ??  (&-K+\+*:*:);<--5Jd6??;L:MN&&A+&N%OrS  "H 1=s C8C=cN^[UR55S-mSU4Sjjn[RRR R (aMT[RRR R :a[RU"55 gg)Nr7c>STS3$)NzCUDAGraph supports dynamic shapes by recording a new graph for each distinct input size. Recording too many CUDAGraphs may lead to extra overhead. We have observed a0 distinct sizes. Please consider the following options for better performance: a) padding inputs to a few fixed number of shapes; or b) set torch._inductor.config.triton.cudagraph_skip_dynamic_graphs=True. Set torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit=None to silence this warning.r)num_cudagraphssr!warn_msg4maybe_warning_due_to_dynamic_shape..warn_msg5s 00>/?@' ' r TFr) r8rvr>r_r`ra"cudagraph_dynamic_shape_warn_limitrr)fn_cache new_int_keyrrs @r!"maybe_warning_due_to_dynamic_shaper/st)A-N   %%HH  // ' ' J J K hj) r c8\rSrSr%SrS\S'S\S'S\S'S rg ) CudagraphCachedInfoiLz Info needed to realign inputs r3r4zlist[Optional[str]] stack_tracesz list[str]cudagraph_fail_reasonsrNrrr r!rrLs,+%%%%r r)rC torch.fx.Noderr')rHr#rr')rCrrr#)rQztorch.fx.Graphrr))rUr%rr%)r4r3rZr0rr%)rer-rflist[InputType]rgzCallable[[torch.Tensor], bool]rr')rRrrr')ryz!dict[torch.device, torch.fx.Node]rr')r\r%rr) rztorch.fx.GraphModulerzOrderedSet[str]r5zOrderedSet[int]r1r0rr')rKr#rr') r4r3rfrrzSequence[Optional[int]]rr0rrrr%)rz)dict[tuple[int, ...], Callable[..., Any]]rrrbool)5 __future__r dataclassesenumrtypingrrrrr r>torch._dynamo.utilsr r torch._inductor.utilsr torch.utils._ordered_setr collections.abcr_logginggetArtifactLoggerrrrclistrr OutputType ModelType dataclassrr#r-rErIrMrSrVr]rhrjr|r~rrrrrrrrrr r!rs"@@ =+/(00<H NN44 ' (5ell!234 5 d9o& 2 3  d#  $  d# , ,$ , d# & &$ & #  5 O 2+?L&  $B >T:TT(H:HH J    99#9(9% 9  90 0400+ /  #   >7 : d#&&$&r