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Do not ignore None arg when pipelining #2352

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Do not ignore None arg when pipelining #2352

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15 changes: 13 additions & 2 deletions torchrec/distributed/train_pipeline/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -744,14 +744,17 @@ def _get_node_args_helper(
pipelined_preprocs: Set[PipelinedPreproc],
context: TrainPipelineContext,
pipeline_preproc: bool,
# Add `None` constants to arg info only for preproc modules
# Defaults to False for backward compatibility
for_preproc_module: bool = False,
) -> Tuple[List[ArgInfo], int]:
"""
Goes through the args/kwargs of a node and arranges them into a list of `ArgInfo`s.
It also counts the number of (args + kwargs) found.
"""
arg_info_list = [ArgInfo([], [], [], [], None) for _ in range(len(arguments))]
for arg, arg_info in zip(arguments, arg_info_list):
if arg is None:
if not for_preproc_module and arg is None:
num_found += 1
continue
while True:
Expand Down Expand Up @@ -911,7 +914,12 @@ def _get_node_args_helper(
# is either made of preproc module or non-modifying train batch input
# transformations
preproc_args, num_found_safe_preproc_args = _get_node_args(
model, child_node, pipelined_preprocs, context, pipeline_preproc
model,
child_node,
pipelined_preprocs,
context,
pipeline_preproc,
True,
)
if num_found_safe_preproc_args == total_num_args:
logger.info(
Expand Down Expand Up @@ -956,6 +964,7 @@ def _get_node_args(
pipelined_preprocs: Set[PipelinedPreproc],
context: TrainPipelineContext,
pipeline_preproc: bool,
for_preproc_module: bool = False,
) -> Tuple[List[ArgInfo], int]:
num_found = 0

Expand All @@ -966,6 +975,7 @@ def _get_node_args(
pipelined_preprocs,
context,
pipeline_preproc,
for_preproc_module,
)
kwargs_arg_info_list, num_found = _get_node_args_helper(
model,
Expand All @@ -974,6 +984,7 @@ def _get_node_args(
pipelined_preprocs,
context,
pipeline_preproc,
for_preproc_module,
)

# Replace with proper names for kwargs
Expand Down