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19 changes: 13 additions & 6 deletions torchrec/distributed/train_pipeline/tracing.py
Original file line number Diff line number Diff line change
Expand Up @@ -302,7 +302,11 @@ def _handle_module(

if not self._pipeline_postproc:
logger.warning(
f"Found module {postproc_module} that potentially modifies KJ. Train pipeline initialized with `pipeline_postproc=False` (default), so we assume KJT input modification. To allow torchrec to check if this module can be safely pipelined, please set `pipeline_postproc=True`"
f"Found module {postproc_module} that potentially modifies input KJT. "
"Train pipeline initialized with `pipeline_postproc=False` (default), "
"so we assume KJT input modification. "
"To allow torchrec to check if this module can be safely pipelined, "
"please set `pipeline_postproc=True`"
)
return None

Expand Down Expand Up @@ -341,11 +345,10 @@ def _handle_module(
)
if num_found_safe_postproc_args == total_num_args:
logger.info(
f"""Module {postproc_module} is a valid postproc module (no
trainable params and inputs can be derived from train batch input
via a series of either valid postproc modules or non-modifying
transformations) and will be applied during sparse data dist
stage"""
f"Module {postproc_module} is a valid postproc module (no "
"trainable params and inputs can be derived from train batch input "
"via a series of either valid postproc modules or non-modifying "
"transformations) and will be applied during sparse data dist stage"
)

pipelined_postproc_module = PipelinedPostproc(
Expand Down Expand Up @@ -449,6 +452,10 @@ def _get_node_args_helper_inner(
arg_info.add_step(ArgInfoStepFactory.get_item(child_node.args[1]))
arg = child_node.args[0]
else:
logger.warning(
f"fx node {child_node.name, child_node.op, child_node.target} "
"can't be handled correctly for postproc module"
)
break

# if we couldn't hit one of the "decisive" outcomes (constant, placeholder or module), return "not found"
Expand Down
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