U 9%e @sFddlmZmZeddeddeddedd ed d ed d ed deddddeddddeddddeddeddeddedded d!ed"d#dded$d%ed&d'ed(d)dded*d+dded,d-ed.d/ddd0ed1d2dded3ddded4ddded5d6dd7d8S)9)register_artifact register_logZdynamoz torch._dynamoZaotztorch._functorch.aot_autogradZinductorztorch._inductorZdynamicz%torch.fx.experimental.symbolic_shapesZtorch distributedztorch.distributedZonnxz torch.onnxZguardszhThis prints the guards for every compiled Dynamo frame. It does not tell you where the guards come from.T)visibleZverbose_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. Z graph_codez4Like `graph`, but gives you the Python code instead.Z graph_sizesz5Prints the sizes of all FX nodes in the dynamo graph.Z trace_sourcezAs we execute bytecode, prints the file name / line number we are processing and the actual source code. Useful with `bytecode`Z trace_callzhLike trace_source, but it will give you the per-expression blow-by-blow if your Python is recent enough.Z aot_graphszPrints the FX forward and backward graph generated by AOTDispatch, after partitioning. Useful to understand what's being given to InductorZaot_joint_graphz_Print FX joint graph from AOTAutograd, prior to partitioning. Useful for debugging partitioningZ ddp_graphszOnly relevant for compiling DDP. DDP splits into multiple graphs to trigger comms early. This will print each individual graph here.Z recompilesz?Prints the reason why we recompiled a graph. Very, very useful.Z 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 toZ output_codez>Prints the code that Inductor generates (either Triton or C++))rrZschedulezIInductor scheduler information. Useful if working on Inductor fusion algoZ perf_hintsZonnx_diagnosticsZcustom_format_test_artifactz Testing only)Z log_formatN) _internalrrr r \/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/_logging/_registrations.pys