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[DEV] fix(megatron-fsdp): reduce padding for grouped expert weights #4980

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[DEV] fix(megatron-fsdp): reduce padding for grouped expert weights #4980

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e6e7030
avoid oversized CSF for grouped expert weights
xuwchen May 26, 2026
e52d7dd
split MFSDP grouped expert buckets to reduce padding
xuwchen May 27, 2026
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38 changes: 35 additions & 3 deletions megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1404,6 +1404,28 @@ def _does_param_require_new_bucket(param):

is_expert_parameter = lambda n, p: ".experts." in n

def _should_split_from_grouped_expert_bucket(
is_expert_param: bool,
param: torch.nn.Parameter,
param_csf_base: int,
chunk_size_factor: int,
same_factor_params: List[torch.nn.Parameter],
) -> bool:
"""
Split grouped expert (>=3D) tensors with heterogeneous CSF bases into
separate buckets to avoid LCM-inflated bucket alignment padding.
"""
# Non-expert groups keep the original LCM/fragment merge.
if not is_expert_param:
return False
# Param already aligns with bucket CSF (always true for the first param
# after sort); no split needed.
if param_csf_base == chunk_size_factor:
return False
return to_local_if_dtensor(param).dim() >= 3 or any(
to_local_if_dtensor(p).dim() >= 3 for p in same_factor_params
)

# Step 1: Group the parameters according to their execution order and attributes.
# FSDP unit module parameters are split into multiple parameter sub-groups.
# All parameters in the module are assigned a parameter group, even non-FSDP modules.
Expand Down Expand Up @@ -1503,17 +1525,27 @@ def _does_param_require_new_bucket(param):
remaining_params = []
for param in params:
param_shape = to_local_if_dtensor(param).shape
param_csf_base = param_shape[1:].numel()
if _should_split_from_grouped_expert_bucket(
group.is_expert_param,
param,
param_csf_base,
chunk_size_factor,
same_factor_params,
):
remaining_params.append(param)
continue
if (
param_shape[1:].numel() == chunk_size_factor
param_csf_base == chunk_size_factor
or (
chunk_size_factor % param_shape[1:].numel() == 0
chunk_size_factor % param_csf_base == 0
and param_shape.numel() % chunk_size_factor == 0
)
or (param_shape.numel() < chunk_size_factor)
):
same_factor_params.append(param)
else:
lcm_chunk_size_factor = math.lcm(chunk_size_factor, param_shape[1:].numel())
lcm_chunk_size_factor = math.lcm(chunk_size_factor, param_csf_base)
chunk_size_factor = lcm_chunk_size_factor
same_factor_params.append(param)
# Create a new parameter group with the same chunk size factor.
Expand Down