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from ._internal import register_artifact, register_log |
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DYNAMIC = [ |
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"torch.fx.experimental.symbolic_shapes", |
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"torch.fx.experimental.sym_node", |
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"torch.fx.experimental.recording", |
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] |
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DISTRIBUTED = [ |
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"torch.distributed", |
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"torch._dynamo.backends.distributed", |
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"torch.nn.parallel.distributed", |
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] |
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register_log("dynamo", ["torch._dynamo", *DYNAMIC]) |
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register_log("aot", ["torch._functorch.aot_autograd", "torch._functorch._aot_autograd"]) |
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register_log("autograd", "torch.autograd") |
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register_log("inductor", ["torch._inductor", "torch._inductor.cudagraph_trees"]) |
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register_artifact( |
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"cudagraphs", |
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"Logs information from wrapping inductor generated code with cudagraphs.", |
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) |
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register_log("dynamic", DYNAMIC) |
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register_log("torch", "torch") |
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register_log("distributed", DISTRIBUTED) |
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register_log( |
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"c10d", ["torch.distributed.distributed_c10d", "torch.distributed.rendezvous"] |
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) |
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register_log( |
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"ddp", ["torch.nn.parallel.distributed", "torch._dynamo.backends.distributed"] |
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) |
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register_log("pp", ["torch.distributed.pipelining"]) |
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register_log("fsdp", ["torch.distributed.fsdp"]) |
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register_log("onnx", "torch.onnx") |
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register_log("export", ["torch._dynamo", "torch.export", *DYNAMIC]) |
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register_artifact( |
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"guards", |
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"This prints the guards for every compiled Dynamo frame. It does not tell you where the guards come from.", |
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visible=True, |
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) |
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register_artifact("verbose_guards", "", off_by_default=True) |
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register_artifact( |
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"bytecode", |
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"Prints the original and modified bytecode from Dynamo. Mostly useful if you're debugging our bytecode generation in Dynamo.", |
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off_by_default=True, |
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) |
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register_artifact( |
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"graph", |
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"Prints the dynamo traced graph (prior to AOTDispatch) in a table. If you prefer python code use `graph_code` instead. ", |
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) |
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register_artifact("graph_code", "Like `graph`, but gives you the Python code instead.") |
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register_artifact( |
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"graph_sizes", "Prints the sizes of all FX nodes in the dynamo graph." |
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) |
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register_artifact( |
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"trace_source", |
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"As we execute bytecode, prints the file name / line number we are processing and the actual source code. Useful with `bytecode`", |
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) |
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register_artifact( |
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"trace_call", |
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"Like trace_source, but it will give you the per-expression blow-by-blow if your Python is recent enough.", |
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) |
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register_artifact( |
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"trace_bytecode", |
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"As we trace bytecode, prints the instruction and the current stack.", |
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) |
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register_artifact( |
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"aot_graphs", |
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"Prints the FX forward and backward graph generated by AOTDispatch, after partitioning. Useful to understand what's being given to Inductor", |
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visible=True, |
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) |
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register_artifact( |
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"aot_joint_graph", |
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"Print FX joint graph from AOTAutograd, prior to partitioning. Useful for debugging partitioning", |
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) |
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register_artifact( |
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"post_grad_graphs", |
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"Prints the FX graph generated by post grad passes. Useful to understand what's being given to Inductor after post grad passes", |
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) |
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register_artifact( |
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"compiled_autograd", |
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"Prints various logs in compiled_autograd, including but not limited to the graphs. Useful for debugging compiled_autograd.", |
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visible=True, |
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) |
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register_artifact( |
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"compiled_autograd_verbose", |
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"Will affect performance. Prints compiled_autograd logs with C++ info e.g. autograd node -> fx node mapping", |
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off_by_default=True, |
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) |
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register_artifact( |
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"ddp_graphs", |
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"Only relevant for compiling DDP. DDP splits into multiple graphs to trigger comms early. This will print each individual graph here.", |
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) |
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register_artifact( |
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"recompiles", |
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"Prints the reason why we recompiled a graph. Very, very useful.", |
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visible=True, |
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) |
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register_artifact( |
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"recompiles_verbose", |
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"Prints all guard checks that fail during a recompilation. " |
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"At runtime, Dynamo will stop at the first failed check for each failing guard. " |
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"So not all logged failing checks are actually ran by Dynamo.", |
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visible=True, |
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off_by_default=True, |
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) |
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register_artifact( |
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"graph_breaks", |
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"Prints whenever Dynamo decides that it needs to graph break (i.e. create a new graph). Useful for debugging why torch.compile has poor performance", |
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visible=True, |
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) |
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register_artifact( |
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"not_implemented", |
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"Prints log messages whenever we return NotImplemented in a multi-dispatch, letting you trace through each object we attempted to dispatch to", |
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) |
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register_artifact( |
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"output_code", |
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"Prints the code that Inductor generates (either Triton or C++)", |
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off_by_default=True, |
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visible=True, |
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) |
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register_artifact( |
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"kernel_code", |
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"Prints the code that Inductor generates (on a per-kernel basis)", |
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off_by_default=True, |
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visible=True, |
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) |
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register_artifact( |
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"schedule", |
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"Inductor scheduler information. Useful if working on Inductor fusion algo", |
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off_by_default=True, |
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) |
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register_artifact("perf_hints", "", off_by_default=True) |
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register_artifact("onnx_diagnostics", "", off_by_default=True) |
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register_artifact( |
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"fusion", |
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"Detailed Inductor fusion decisions. More detailed than 'schedule'", |
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off_by_default=True, |
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) |
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register_artifact( |
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"overlap", |
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"Detailed Inductor compute/comm overlap decisions", |
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off_by_default=True, |
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) |
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register_artifact( |
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"sym_node", |
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"Logs extra info for various SymNode operations", |
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off_by_default=True, |
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) |
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register_artifact("custom_format_test_artifact", "Testing only", log_format="") |
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