ai-content-maker/.venv/Lib/site-packages/torch/_logging/_registrations.py

135 lines
4.7 KiB
Python

# flake8: noqa: B950
from ._internal import register_artifact, register_log
DYNAMIC = ["torch.fx.experimental.symbolic_shapes", "torch.fx.experimental.sym_node"]
DISTRIBUTED = [
"torch.distributed",
"torch._dynamo.backends.distributed",
"torch.nn.parallel.distributed",
]
register_log("dynamo", ["torch._dynamo", *DYNAMIC])
register_log("aot", ["torch._functorch.aot_autograd", "torch._functorch._aot_autograd"])
register_log("autograd", "torch.autograd")
register_log("inductor", ["torch._inductor", "torch._inductor.cudagraph_trees"])
register_artifact(
"cudagraphs",
"Logs information from wrapping inductor generated code with cudagraphs.",
)
register_log("dynamic", DYNAMIC)
register_log("torch", "torch")
register_log("distributed", DISTRIBUTED)
register_log(
"dist_c10d", ["torch.distributed.distributed_c10d", "torch.distributed.rendezvous"]
)
register_log(
"dist_ddp", ["torch.nn.parallel.distributed", "torch._dynamo.backends.distributed"]
)
register_log("dist_fsdp", ["torch.distributed.fsdp"])
register_log("onnx", "torch.onnx")
register_log("export", ["torch._dynamo", "torch.export", *DYNAMIC])
register_artifact(
"guards",
"This prints the guards for every compiled Dynamo frame. It does not tell you where the guards come from.",
visible=True,
)
register_artifact("verbose_guards", "", off_by_default=True)
register_artifact(
"bytecode",
"Prints the original and modified bytecode from Dynamo. Mostly useful if you're debugging our bytecode generation in Dynamo.",
off_by_default=True,
)
register_artifact(
"graph",
"Prints the dynamo traced graph (prior to AOTDispatch) in a table. If you prefer python code use `graph_code` instead. ",
)
register_artifact("graph_code", "Like `graph`, but gives you the Python code instead.")
register_artifact(
"graph_sizes", "Prints the sizes of all FX nodes in the dynamo graph."
)
register_artifact(
"trace_source",
"As we execute bytecode, prints the file name / line number we are processing and the actual source code. Useful with `bytecode`",
)
register_artifact(
"trace_call",
"Like trace_source, but it will give you the per-expression blow-by-blow if your Python is recent enough.",
)
register_artifact(
"aot_graphs",
"Prints the FX forward and backward graph generated by AOTDispatch, after partitioning. Useful to understand what's being given to Inductor",
visible=True,
)
register_artifact(
"aot_joint_graph",
"Print FX joint graph from AOTAutograd, prior to partitioning. Useful for debugging partitioning",
)
register_artifact(
"post_grad_graphs",
"Prints the FX graph generated by post grad passes. Useful to understand what's being given to Inductor after post grad passes",
)
register_artifact(
"compiled_autograd",
"Prints various logs in compiled_autograd, including but not limited to the graphs. Useful for debugging compiled_autograd.",
visible=True,
)
register_artifact(
"ddp_graphs",
"Only relevant for compiling DDP. DDP splits into multiple graphs to trigger comms early. This will print each individual graph here.",
)
register_artifact(
"recompiles",
"Prints the reason why we recompiled a graph. Very, very useful.",
visible=True,
)
register_artifact(
"recompiles_verbose",
"Prints 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.",
visible=True,
off_by_default=True,
)
register_artifact(
"graph_breaks",
"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",
visible=True,
)
register_artifact(
"not_implemented",
"Prints log messages whenever we return NotImplemented in a multi-dispatch, letting you trace through each object we attempted to dispatch to",
)
register_artifact(
"output_code",
"Prints the code that Inductor generates (either Triton or C++)",
off_by_default=True,
visible=True,
)
register_artifact(
"schedule",
"Inductor scheduler information. Useful if working on Inductor fusion algo",
off_by_default=True,
)
register_artifact("perf_hints", "", off_by_default=True)
register_artifact("onnx_diagnostics", "", off_by_default=True)
register_artifact(
"fusion",
"Detailed Inductor fusion decisions. More detailed than 'schedule'",
off_by_default=True,
)
register_artifact(
"overlap",
"Detailed Inductor compute/comm overlap decisions",
off_by_default=True,
)
register_artifact(
"sym_node",
"Logs extra info for various SymNode operations",
off_by_default=True,
)
register_artifact("custom_format_test_artifact", "Testing only", log_format="")