o ߗi@sddlmZmZgdZgdZeddeddgeedd ged d d ged dedddgeddedeeddedeedddgedddgeddgedd d!ged"d#d$ged%d&ed'dd(d)ged*d+ed,d-d.d/ed0d1d.d2ed3d4d.d2ed5d6ed7d8ed9d:ed;d<ed=d>ed?d@edAdBd.d/edCdDedEdFd.d/edGdHedIdJd.d/edKdLd.d2edMdNedOdPd.d/edQdRd.d.dSedTdUd.d/edVdWedXdYd.d.dZed[d\d.d.dZed]d^d.d2ed_d1d.d2ed`d1d.d2edadbd.d2edcddd.d2ededfd.d2edgdhd.d2edidjd.d2edkdld.d2edmdnd.d2edodpd.d2edqdrd1dsdtS)u)register_artifact register_log)z%torch.fx.experimental.symbolic_shapesztorch.fx.experimental.sym_nodeztorch.fx.experimental.recording)ztorch.distributed"torch._dynamo.backends.distributedtorch.nn.parallel.distributedcache)ztorch._inductor.remote_cacheztorch._inductor.fb.remote_cachedynamoz torch._dynamo fake_tensorztorch._subclasses.fake_tensoraotztorch._functorch.aot_autogradztorch._functorch._aot_autogradautogradztorch.autogradinductorztorch._inductorztorch._inductor.cudagraph_trees cudagraphszGLogs information from wrapping inductor generated code with cudagraphs.dynamictorch distributedc10dz"torch.distributed.distributed_c10dztorch.distributed.rendezvousddprrppztorch.distributed.pipeliningfsdpztorch.distributed.fsdpz"torch.distributed._composable.fsdpdtensorztorch.distributed._tensorztorch.distributed.tensoronnxz torch.onnxexportz torch.exportztorch.export.dynamic_shapesztorch._export.converterztorch._export.non_strict_utilsguardszhThis prints the guards for every compiled Dynamo frame. It does not tell you where the guards come from.T)visibleverbose_guards)off_by_defaultbytecodez{Prints the original and modified bytecode from Dynamo. Mostly useful if you're debugging our bytecode generation in Dynamo.graphzvPrints the dynamo traced graph (prior to AOTDispatch) in a table. If you prefer python code use `graph_code` instead. graph_codez4Like `graph`, but gives you the Python code instead. graph_sizesz5Prints the sizes of all FX nodes in the dynamo graph. trace_sourcezAs we execute bytecode, prints the file name / line number we are processing and the actual source code. Useful with `bytecode` trace_callzhLike trace_source, but it will give you the per-expression blow-by-blow if your Python is recent enough.trace_bytecodezCAs we trace bytecode, prints the instruction and the current stack. aot_graphszPrints the FX forward and backward graph generated by AOTDispatch, after partitioning. Useful to understand what's being given to Inductoraot_joint_graphz_Print FX joint graph from AOTAutograd, prior to partitioning. Useful for debugging partitioningaot_graphs_effectszkPrints the FX forward and backward graph generated by AOTDispatch, useful for debugging effects processing.post_grad_graphsz}Prints the FX graph generated by post grad passes. Useful to understand what's being given to Inductor after post grad passescompiled_autogradzzPrints various logs in compiled_autograd, including but not limited to the graphs. Useful for debugging compiled_autograd.compiled_autograd_verbosezjWill affect performance. Prints compiled_autograd logs with C++ info e.g. autograd node -> fx node mapping ddp_graphszOnly relevant for compiling DDP. DDP splits into multiple graphs to trigger comms early. This will print each individual graph here. recompilesz?Prints the reason why we recompiled a graph. Very, very useful.recompiles_verbosezPrints 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.)rr graph_breakszPrints whenever Dynamo decides that it needs to graph break (i.e. create a new graph). Useful for debugging why torch.compile has poor performancenot_implementedzPrints log messages whenever we return NotImplemented in a multi-dispatch, letting you trace through each object we attempted to dispatch to output_codez>Prints the code that Inductor generates (either Triton or C++))rr kernel_codez?Prints the code that Inductor generates (on a per-kernel basis)schedulezIInductor scheduler information. Useful if working on Inductor fusion algo perf_hintsonnx_diagnosticsfusionzADetailed Inductor fusion decisions. More detailed than 'schedule' loop_orderingzLogs related to loop orderingoverlapz0Detailed Inductor compute/comm overlap decisionssym_nodez.Logs extra info for various SymNode operationstrace_shape_eventszBLogs traces for every ShapeEnv operation that we record for replaycudagraph_static_inputsz:Logs static inputs handling in dynamo, AOT, and cudagraphs benchmarkingz+Detailed Inductor benchmarking information.graph_region_expansionzMLogs detailed steps of the duplicate graph region tracker expansion algorithmcustom_format_test_artifactz Testing only) log_formatN) _internalrrDYNAMIC DISTRIBUTEDr@r@[/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/_logging/_registrations.pysp