torch._logging.set_logs¶
- torch._logging.set_logs(*, all=None, dynamo=None, aot=None, autograd=None, dynamic=None, inductor=None, distributed=None, c10d=None, ddp=None, fsdp=None, dtensor=None, onnx=None, bytecode=False, aot_graphs=False, aot_joint_graph=False, ddp_graphs=False, graph=False, graph_code=False, graph_breaks=False, graph_sizes=False, guards=False, recompiles=False, recompiles_verbose=False, trace_source=False, trace_call=False, trace_bytecode=False, output_code=False, kernel_code=False, schedule=False, perf_hints=False, post_grad_graphs=False, onnx_diagnostics=False, fusion=False, overlap=False, export=None, modules=None, cudagraphs=False, sym_node=False, compiled_autograd=False, compiled_autograd_verbose=False, cudagraph_static_inputs=False, benchmarking=False, graph_region_expansion=False)[source][source]¶
Sets the log level for individual components and toggles individual log artifact types.
Warning
This feature is a prototype and may have compatibility breaking changes in the future.
Note
The
TORCH_LOGS
environment variable has complete precedence over this function, so if it was set, this function does nothing.A component is a set of related features in PyTorch. All of the log messages emitted from a given component have their own log levels. If the log level of a particular message has priority greater than or equal to its component’s log level setting, it is emitted. Otherwise, it is suppressed. This allows you to, for instance, silence large groups of log messages that are not relevant to you and increase verbosity of logs for components that are relevant. The expected log level values, ordered from highest to lowest priority, are:
logging.CRITICAL
logging.ERROR
logging.WARNING
logging.INFO
logging.DEBUG
logging.NOTSET
See documentation for the Python
logging
module for more information on log levels: https://docs.python.org/3/library/logging.html#logging-levelsAn artifact is a particular type of log message. Each artifact is assigned to a parent component. A component can emit many different kinds of artifacts. In general, an artifact is emitted if either its corresponding setting in the argument list below is turned on or if its parent component is set to a log level less than or equal to the log level of the artifact.
- Keyword Arguments
all (
Optional[int]
) – The default log level for all components. Default:logging.WARN
dynamo (
Optional[int]
) – The log level for the TorchDynamo component. Default:logging.WARN
aot (
Optional[int]
) – The log level for the AOTAutograd component. Default:logging.WARN
autograd (
Optional[int]
) – The log level for autograd. Default:logging.WARN
inductor (
Optional[int]
) – The log level for the TorchInductor component. Default:logging.WARN
dynamic (
Optional[int]
) – The log level for dynamic shapes. Default:logging.WARN
distributed (
Optional[int]
) – Whether to log c10d communication operations and other debug info from PyTorch Distributed components. Default:logging.WARN
c10d (
Optional[int]
) – Whether to log c10d communication operations related debug info in PyTorch Distributed components. Default:logging.WARN
ddp (
Optional[int]
) – Whether to log debug info related toDistributedDataParallel``(DDP) from PyTorch Distributed components. Default: ``logging.WARN
fsdp (
Optional[int]
) – Whether to log debug info related toFullyShardedDataParallel``(FSDP) in PyTorch Distributed components. Default: ``logging.WARN
dtensor (
Optional[int]
) – Whether to log debug info related toDTensor``(DTensor) in PyTorch Distributed components. Default: ``logging.WARN
onnx (
Optional[int]
) – The log level for the ONNX exporter component. Default:logging.WARN
bytecode (
bool
) – Whether to emit the original and generated bytecode from TorchDynamo. Default:False
aot_graphs (
bool
) – Whether to emit the graphs generated by AOTAutograd. Default:False
aot_joint_graph (
bool
) – Whether to emit the joint forward-backward graph generated by AOTAutograd. Default:False
ddp_graphs (
bool
) – Whether to emit graphs generated by DDPOptimizer. Default:False
graph (
bool
) – Whether to emit the graph captured by TorchDynamo in tabular format. Default:False
graph_code (
bool
) – Whether to emit the python source of the graph captured by TorchDynamo. Default:False
graph_breaks (
bool
) – Whether to emit the graph breaks encountered by TorchDynamo. Default:False
graph_sizes (
bool
) – Whether to emit tensor sizes of the graph captured by TorchDynamo. Default:False
guards (
bool
) – Whether to emit the guards generated by TorchDynamo for each compiled function. Default:False
recompiles (
bool
) – Whether to emit a guard failure reason and message every time TorchDynamo recompiles a function. Default:False
recompiles_verbose (
bool
) – Whether to emit all guard failure reasons when TorchDynamo recompiles a function, even those that are not actually run. Default:False
trace_source (
bool
) – Whether to emit when TorchDynamo begins tracing a new line. Default:False
trace_call (
bool
) – Whether to emit detailed line location when TorchDynamo creates an FX node corresponding to function call. Python 3.11+ only. Default:False
trace_bytecode (
bool
) – Whether to emit bytecode instructions and traced stack state as TorchDynamo traces bytecode. Default:False
output_code (
bool
) – Whether to emit the TorchInductor output code on a per-graph basis. Default:False
kernel_code (
bool
) – Whether to emit the TorchInductor output code on a per-kernel bases. Default:False
schedule (
bool
) – Whether to emit the TorchInductor schedule. Default:False
perf_hints (
bool
) – Whether to emit the TorchInductor perf hints. Default:False
post_grad_graphs (
bool
) – Whether to emit the graphs generated by after post grad passes. Default:False
onnx_diagnostics (
bool
) – Whether to emit the ONNX exporter diagnostics in logging. Default:False
fusion (
bool
) – Whether to emit detailed Inductor fusion decisions. Default:False
overlap (
bool
) – Whether to emit detailed Inductor compute/comm overlap decisions. Default:False
sym_node (
bool
) – Whether to emit debug info for various SymNode opterations. Default:False
export (
Optional[int]
) – The log level for export. Default:logging.WARN
benchmarking (
bool
) – Whether to emit detailed Inductor benchmarking information. Default:False
modules (dict) – This argument provides an alternate way to specify the above log component and artifact settings, in the format of a keyword args dictionary given as a single argument. There are two cases where this is useful (1) if a new log component or artifact has been registered but a keyword argument for it has not been added to this function and (2) if the log level for an unregistered module needs to be set. This can be done by providing the fully-qualified module name as the key, with the log level as the value. Default:
None
cudagraph_static_inputs (
bool
) – Whether to emit debug info for cudagraph static input detection. Default:False
graph_region_expansion (
bool
) – Whether to emit the detailed steps of the duplicate graph region tracker expansion algorithm. Default:False
Example:
>>> import logging # The following changes the "dynamo" component to emit DEBUG-level # logs, and to emit "graph_code" artifacts. >>> torch._logging.set_logs(dynamo=logging.DEBUG, graph_code=True) # The following enables the logs for a different module >>> torch._logging.set_logs(modules={"unregistered.module.name": logging.DEBUG})