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    Ÿ²Åjâ  ã                   óˆ  — d dl mZmZ g d¢Zg d¢Z edd¦  «          eddge¢¦  «          edd	g¦  «          ed
ddg¦  «          edd¦  «          edddg¦  «          edd¦  «          ede¦  «          edd¦  «          ede¦  «          edddg¦  «          edddg¦  «          eddg¦  «          edd d!g¦  «          ed"d#d$g¦  «          ed%d&¦  «          ed'dd(d)ge¢d*‘d+‘¦  «          ed,d-d.¬/¦  «          ed0d1d.¬2¦  «          ed3d4d.¬2¦  «          ed5d6¦  «          ed7d8¦  «          ed9d:¦  «          ed;d<¦  «          ed=d>¦  «          ed?d@¦  «          edAdBd.¬/¦  «          edCdD¦  «          edEdFd.¬/¦  «          edGdH¦  «          edIdJd.¬/¦  «          edKdLd.¬2¦  «          edMdN¦  «          edOdPd.¬/¦  «          edQdRd.d.¬S¦  «          edTdUd.¬/¦  «          edVdW¦  «          edXdYd.d.¬Z¦  «          ed[d\d.d.¬Z¦  «          ed]d^d.¬2¦  «          ed_d1d.¬2¦  «          ed`d1d.¬2¦  «          edadbd.¬2¦  «          edcddd.¬2¦  «          ededfd.¬2¦  «          edgdhd.¬2¦  «          edidjd.¬2¦  «          edkdld.¬2¦  «          edmdnd.¬2¦  «          edodpd.¬2¦  «          edqdrd1¬s¦  «         dtS )ué   )Úregister_artifactÚregister_log)z%torch.fx.experimental.symbolic_shapesztorch.fx.experimental.sym_nodeztorch.fx.experimental.recording)ztorch.distributedú"torch._dynamo.backends.distributedútorch.nn.parallel.distributedÚcache)ztorch._inductor.remote_cacheztorch._inductor.fb.remote_cacheÚdynamoztorch._dynamoÚfake_tensorztorch._subclasses.fake_tensorÚaotztorch._functorch.aot_autogradztorch._functorch._aot_autogradÚautogradztorch.autogradÚinductorztorch._inductorztorch._inductor.cudagraph_treesÚ
cudagraphszGLogs information from wrapping inductor generated code with cudagraphs.ÚdynamicÚtorchÚdistributedÚc10dz"torch.distributed.distributed_c10dztorch.distributed.rendezvousÚddpr   r   Úppztorch.distributed.pipeliningÚfsdpztorch.distributed.fsdpz"torch.distributed._composable.fsdpÚdtensorztorch.distributed._tensorztorch.distributed.tensorÚonnxz
torch.onnxÚexportztorch.exportztorch.export.dynamic_shapesztorch._export.converterztorch._export.non_strict_utilsÚguardszhThis prints the guards for every compiled Dynamo frame. It does not tell you where the guards come from.T)ÚvisibleÚverbose_guardsÚ )Úoff_by_defaultÚbytecodez{Prints the original and modified bytecode from Dynamo. Mostly useful if you're debugging our bytecode generation in Dynamo.ÚgraphzvPrints the dynamo traced graph (prior to AOTDispatch) in a table. If you prefer python code use `graph_code` instead. Ú
graph_codez4Like `graph`, but gives you the Python code instead.Úgraph_sizesz5Prints the sizes of all FX nodes in the dynamo graph.Útrace_sourcezAs we execute bytecode, prints the file name / line number we are processing and the actual source code. Useful with `bytecode`Ú
trace_callzhLike trace_source, but it will give you the per-expression blow-by-blow if your Python is recent enough.Útrace_bytecodezCAs we trace bytecode, prints the instruction and the current stack.Ú
aot_graphszŠPrints the FX forward and backward graph generated by AOTDispatch, after partitioning. Useful to understand what's being given to InductorÚaot_joint_graphz_Print FX joint graph from AOTAutograd, prior to partitioning. Useful for debugging partitioningÚaot_graphs_effectszkPrints the FX forward and backward graph generated by AOTDispatch, useful for debugging effects processing.Úpost_grad_graphsz}Prints the FX graph generated by post grad passes. Useful to understand what's being given to Inductor after post grad passesÚcompiled_autogradzzPrints various logs in compiled_autograd, including but not limited to the graphs. Useful for debugging compiled_autograd.Úcompiled_autograd_verbosezjWill affect performance. Prints compiled_autograd logs with C++ info e.g. autograd node -> fx node mappingÚ
ddp_graphsz„Only relevant for compiling DDP. DDP splits into multiple graphs to trigger comms early. This will print each individual graph here.Ú
recompilesz?Prints the reason why we recompiled a graph. Very, very useful.Úrecompiles_verbosezÅPrints all guard checks that fail during a recompilation. At runtime, Dynamo will stop at the first failed check for each failing guard. So not all logged failing checks are actually ran by Dynamo.)r   r   Úgraph_breaksz’Prints whenever Dynamo decides that it needs to graph break (i.e. create a new graph). Useful for debugging why torch.compile has poor performanceÚnot_implementedzŒPrints log messages whenever we return NotImplemented in a multi-dispatch, letting you trace through each object we attempted to dispatch toÚoutput_codez>Prints the code that Inductor generates (either Triton or C++))r   r   Úkernel_codez?Prints the code that Inductor generates (on a per-kernel basis)ÚschedulezIInductor scheduler information. Useful if working on Inductor fusion algoÚ
perf_hintsÚonnx_diagnosticsÚfusionzADetailed Inductor fusion decisions. More detailed than 'schedule'Úloop_orderingzLogs related to loop orderingÚoverlapz0Detailed Inductor compute/comm overlap decisionsÚsym_nodez.Logs extra info for various SymNode operationsÚtrace_shape_eventszBLogs traces for every ShapeEnv operation that we record for replayÚcudagraph_static_inputsz:Logs static inputs handling in dynamo, AOT, and cudagraphsÚbenchmarkingz+Detailed Inductor benchmarking information.Úgraph_region_expansionzMLogs detailed steps of the duplicate graph region tracker expansion algorithmÚcustom_format_test_artifactzTesting only)Ú
log_formatN)Ú	_internalr   r   ÚDYNAMICÚDISTRIBUTED© ó    úNC:\Projects\Historical\venv\Lib\site-packages\torch/_logging/_registrations.pyú<module>rD      s”  ðà 6Ð 6Ð 6Ð 6Ð 6Ð 6Ð 6Ð 6ðð ð €ð
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