fix: prevent PP hang in MoE aux-loss metric reduction

[dafu-wu]

What does this PR do ?

Fixes a silent, no-traceback hang in get_moe_metrics() when training a
MoE model with moe_router_load_balancing_type=aux_loss and
pipeline_model_parallel_size > 1.

Symptom

With aux_loss load balancing enabled and PP > 1, training progresses for a
number of steps and then hangs: no exception, no OOM, no exit — GPUs go idle
and the process never returns. The same recipe with
moe_router_load_balancing_type=none runs indefinitely without issue.

Root cause

get_moe_metrics() calls reduce_aux_losses_tracker_across_ranks(), which runs
torch.distributed.all_reduce(values, group=pp_group) for each name present
in the local MoE logging tracker:

# megatron/core/transformer/moe/moe_utils.py
def reduce_aux_losses_tracker_across_ranks(...):
    tracker = get_moe_layer_wise_logging_tracker()
    if track_names is None:
        track_names = tracker.keys()          # <-- only the LOCAL rank's keys
    ...
    for name in track_names:
        values = tracker[name]["values"]
        torch.distributed.all_reduce(values, group=pp_group)   # <-- collective

The tracker entry is created lazily — save_to_aux_losses_tracker only
allocates torch.zeros(num_layers) the first time a rank actually saves a loss.
So if some PP rank did not save an aux loss this step — e.g. a pipeline
stage that holds no MoE layer, or an MTP MoE layer that lives only on the last
stage — that rank has no key for load_balancing_loss, skips the all_reduce
for that name, while other PP ranks perform it. The collective then mismatches
its participants across the PP group and hangs.

This is exactly the situation Megatron's own track_moe_metrics guards against
via force_initialize=True; NeMo-RL calls reduce_aux_losses_tracker_across_ranks()
directly and bypassed that guard.

Fix

Mirror the force_initialize=True behaviour inside get_moe_metrics():
before the reduction, pre-initialize the tracker on every rank so each
tracked name exists with an equally-sized zero tensor of length
num_layers + mtp_num_layers — the same size the router uses in
save_to_aux_losses_tracker. num_layers / mtp_num_layers are threaded from
model_config in both the policy and value Megatron workers.

When num_layers is None (all existing/other call sites), the behaviour is
completely unchanged — this is a no-op guarded by an opt-in argument.

Usage

No config change required. The guard activates automatically for MoE models
(num_moe_experts > 1) in the Megatron policy/value workers, which now pass
num_layers / mtp_num_layers from model_config. After this change,
moe_router_load_balancing_type=aux_loss with PP > 1 no longer hangs.

Files

• nemo_rl/models/megatron/common.py — pre-initialize tracker in get_moe_metrics()
• nemo_rl/models/policy/workers/megatron_policy_worker.py — pass num_layers/mtp_num_layers
• nemo_rl/models/value/workers/megatron_value_worker.py — pass num_layers/mtp_num_layers

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Additional Information

Repro parallelism where the hang was observed: PP=4, TP=8, EP=8 on a
Nemotron-3-style 120B MoE SFT run; also reproducible with MTP enabled (the MTP
MoE layer lives only on the last PP stage, guaranteeing the tracker-key
asymmetry across the PP group).

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[terrykong]

@yfw could you review?

[dafu-wu]

/ok to test ddda2fd

[dafu-wu]

@yfw can you help review it？

[yfw]

Thanks for digging into this — the failure mode you describe is real. force_initialize=True is exactly the right precedent to reach for.

The main thing I'd like to work through with you: against the Megatron-Core this repo currently pins (0.19.0, via 3rdparty/Megatron-Bridge-workspace/Megatron-Bridge/3rdparty/Megatron-LM @ cf2f07d, per uv.lock), the pre-initialization doesn't reach Megatron's tracker — so the hang would still occur. Details in the first inline comment, including a short CPU-only script you can run to check it on your side.

This is a version boundary, not a misreading on your part — I checked the upstream history rather than assuming. Your patch is correct against megatron-core ≤0.17.x, where that accessor returned the live global dict and the entry shape (including reduce_group_has_dp) matched exactly what you wrote. The MoE-logging refactor landed in core_v0.18.0 and turned that accessor into a copy. So this needs a port to the newer API, not a rethink — the diagnosis itself holds up.

For calibration, the preconditions for the hang look like: an MoE model, and a non-zero aux-loss balancing type, and PP>1, and a layout where some PP stage owns no aux-loss-recording MoE layer. No config shipped in this repo combines those (the aux_loss exemplars are PP=1; the MoE+PP ones use "none"), so this is a user-config-reachable bug — consistent with how you found it.

Reviewed with a team of specialized agents; the mechanism finding was independently reproduced by execution three times.

Generated by Claude Code

[dafu-wu]

Thanks for digging into this — the failure mode you describe is real. force_initialize=True is exactly the right precedent to reach for.

The main thing I'd like to work through with you: against the Megatron-Core this repo currently pins (0.19.0, via 3rdparty/Megatron-Bridge-workspace/Megatron-Bridge/3rdparty/Megatron-LM @ cf2f07d, per uv.lock), the pre-initialization doesn't reach Megatron's tracker — so the hang would still occur. Details in the first inline comment, including a short CPU-only script you can run to check it on your side.

This is a version boundary, not a misreading on your part — I checked the upstream history rather than assuming. Your patch is correct against megatron-core ≤0.17.x, where that accessor returned the live global dict and the entry shape (including reduce_group_has_dp) matched exactly what you wrote. The MoE-logging refactor landed in core_v0.18.0 and turned that accessor into a copy. So this needs a port to the newer API, not a rethink — the diagnosis itself holds up.

For calibration, the preconditions for the hang look like: an MoE model, and a non-zero aux-loss balancing type, and PP>1, and a layout where some PP stage owns no aux-loss-recording MoE layer. No config shipped in this repo combines those (the aux_loss exemplars are PP=1; the MoE+PP ones use "none"), so this is a user-config-reachable bug — consistent with how you found it.

Reviewed with a team of specialized agents; the mechanism finding was independently reproduced by execution three times.

Generated by Claude Code

@yfw Thanks for the thorough review — the mechanism finding is correct, and I've pushed 2df90ac9 addressing all six comments.

On the megatron-core version question

The repro ran on NeMo-RL 37cb7de1 ("perf: perf recipe changes to enable force on policy ratio", #3135), which pins:

NeMo-RL 37cb7de1
  └─ Megatron-Bridge 554c7b93
       └─ Megatron-LM 00225507  =  megatron-core 0.19.0

Same minor as this PR's base (cf2f07d). So this wasn't a ≤0.17.x environment — at 00225507:

• get_moe_layer_wise_logging_tracker() was already the copy-returning shim
• the entry field was already needs_dp_avg, not reduce_group_has_dp
• _sync_metrics already had the if name not in self._metrics: continue skip
• ensure_initialized was already available

Which means the original patch was inert on the very environment where I reproduced the hang. I had confirmed the root cause (py-spy on the stuck ranks showed the PP all_reduce participant mismatch) but never re-ran the 4-node repro after writing the patch, so I never validated the fix itself. Your finding is exactly right — and thanks for checking the upstream history rather than assuming.

What changed in 2df90ac9

#	Comment	Fix
1	tracker shim	MoEMetricsTracker.ensure_initialized() — what report(force_initialize=True) calls. Also drops the hardcoded device="cuda" and the stale reduce_group_has_dp field.
2	third call site	_finish_train_step_body now passes the same kwargs; all three sites symmetric.
3	track_names	New get_aux_loss_track_names(model_config) covering load_balancing_loss / seq_load_balancing_loss / global_load_balancing_loss / z_loss, incl. the list form of moe_router_load_balancing_type.
4	zero-metric regression	Gate is now num_layers is not None and track_names — empty under "none", so nothing is pre-initialized.
5	tests	Assert against the real get_moe_metrics_tracker().metrics, no stubbing of the deprecated accessor. Verified they fail on ddda2fde and pass on 2df90ac9.
6	docstring / shadowing	Args: completed per the get_mtp_metrics() convention; loop-local renamed num_tracked_layers.

One deliberate deviation from the suggested snippet

I used exact matching plus a non-zero coefficient check rather than the substring test in training.py:2689:

• "aux_loss" in "seq_aux_loss" is True, so a substring test would pre-initialize load_balancing_loss for seq_aux_loss users — re-creating the zero-metric problem from comment 4.
• sft.yaml:142 and dpo.yaml:157 set moe_router_load_balancing_type: "aux_loss" but never set moe_aux_loss_coeff (mcore default 0.0), so the router returns early and records nothing.

This mirrors MoETopKRouter.get_aux_loss_coeff / is_aux_loss_enabled. Happy to switch to the upstream form if you'd rather stay byte-identical with training.py.

[dafu-wu]

/ok to test 52538e2

[dafu-wu]

@yfw @terrykong can you help review it again?

[yfw]

/ok to test 52538e2

[yfw]

/ok to test e492ecc

[dafu-wu]

@yfw Thanks for approving the PR! Could you also approve and run the four pending workflows? I don’t have permission to approve them.

[vikalluru]

@dafu-wu @yfw - Can we merge the PR now?

[yfw]

/ok to test b43bbf5