nemo-mbridge-perf-expert-parallel-overlap

What this skill does

# MoE Expert-Parallel Overlap Skill

## References

- Stable docs: @docs/training/communication-overlap.md
- Structured metadata: @skills/nemo-mbridge-perf-expert-parallel-overlap/card.yaml

## What It Is

Expert-parallel (EP) overlap hides the cost of token dispatch/combine all-to-all
communication by running it concurrently with expert FFN compute. Optionally,
delayed expert weight-gradient computation (`delay_wgrad_compute`) provides
additional overlap by deferring wgrad to overlap with the next layer's forward.

Bridge supports two dispatcher paths:

| Dispatcher | Backend | When to use |
|---|---|---|
| `alltoall` | Standard MoE all-to-all | Default, broadest compatibility |
| `flex` | DeepEP or HybridEP | Higher overlap on Ampere/Hopper/Blackwell |

## Quick Decision

Use EP overlap when:

- the model is MoE with `EP > 1`
- expert dispatch/combine communication is a meaningful part of step time
- you have memory headroom and are tuning for throughput

Prefer:

- `alltoall` dispatcher for the first rollout (broader compatibility)
- `flex` + DeepEP/HybridEP when running on supported GPUs and seeking
  additional gains

Avoid EP overlap when:

- full activation recompute is enabled
- `moe_shared_expert_overlap` is enabled
- the run is still being brought up for correctness
- PyTorch < 2.6.0

Expected outcome:

- if all-to-all dispatch is a clear profile bottleneck, overlap can produce a
  modest to meaningful speedup
- if the run is tiny, communication-light, or dominated by another wall, the
  gain may be negligible

## Correctness-First alltoall Benchmark

For the plain EP-overlap isolation benchmark, keep flex dispatch and delayed
wgrad disabled. The measured shape was Qwen3 MoE 30B-A3B SFT on 16 H100 GPUs:
`EP=16`, `alltoall`, BF16, global batch size 1024, CUDA graphs disabled,
`moe_permute_fusion=false`, measured over iterations 3-8.

Use these overrides for the plain-overlap case:

```bash
--cuda_graph_impl none \
--moe_flex_dispatcher_backend None \
--moe_a2a_overlap false \
comm_overlap.overlap_moe_expert_parallel_comm=true \
comm_overlap.delay_wgrad_compute=false \
model.moe_shared_expert_overlap=false
```

Do not use `--moe_a2a_overlap true` for this isolation test: the performance
harness helper enables both `overlap_moe_expert_parallel_comm` and
`delay_wgrad_compute`, so it does not isolate plain EP overlap.

Steady-window timing from that benchmark:

| Case | Steady mean | Relative |
|---|---:|---:|
| no EP overlap | 41.25s | 1.000x |
| EP overlap | 31.31s | 1.317x |
| EP overlap plus `delay_wgrad_compute` | 31.20s | 1.322x |

This is evidence for enabling plain EP overlap on this inter-node all-to-all
shape. It does not show a meaningful independent win from delayed wgrad, and it
does not validate fused MoE permutation because that path was disabled for the
runtime stack.

## Enablement

### alltoall dispatcher

```python
cfg.comm_overlap.overlap_moe_expert_parallel_comm = True
cfg.comm_overlap.delay_wgrad_compute = False
cfg.model.moe_shared_expert_overlap = False

cfg.model.expert_model_parallel_size = 8
cfg.model.num_moe_experts = 64
cfg.model.moe_token_dispatcher_type = "alltoall"
cfg.model.bf16 = True
cfg.model.fp16 = False
```

Enable `delay_wgrad_compute=True` only after the plain overlap path is known to
work and its extra compatibility constraints have been checked.

### flex dispatcher (DeepEP or HybridEP)

```python
from megatron.bridge.training.flex_dispatcher_backend import apply_flex_dispatcher_backend

cfg.comm_overlap.overlap_moe_expert_parallel_comm = True
cfg.comm_overlap.delay_wgrad_compute = True
cfg.model.moe_shared_expert_overlap = False

apply_flex_dispatcher_backend(cfg.model, moe_flex_dispatcher_backend="deepep")
# or: apply_flex_dispatcher_backend(cfg.model, moe_flex_dispatcher_backend="hybridep")
```

## Compatibility And Constraints

- `expert_model_parallel_size > 1`
- `num_moe_experts > 1`
- `moe_token_dispatcher_type` must be `"alltoall"` or `"flex"`
- `moe_shared_expert_overlap = False`
- Base precision is BF16 or FP16
- PyTorch `>= 2.6.0`
- If `PP > 1`, `virtual_pipeline_model_parallel_size` must be set
- `recompute_granularity != "full"`, `recompute_method = None`,
  `recompute_num_layers = None`
- `mtp_num_layers` must be `None` or `1`
- `delay_wgrad_compute` requires `overlap_moe_expert_parallel_comm` as a
  prerequisite
- `delay_wgrad_compute` with `overlap_grad_reduce` requires TE >= 2.7.0
- `delay_wgrad_compute` with `gradient_accumulation_fusion` requires TE >= 2.7.0
- CUDA graph `attn` scope + `delay_wgrad_compute` requires TE >= 2.12.0,
  `gradient_accumulation_fusion = True`, and no attention bias
- DeepEP: Ampere, Hopper, B200, B300 GPUs only
- HybridEP: Ampere, Hopper, B200, B300, GB200/GB300 with NVL72

## Minimal Working Config

```python
cfg.comm_overlap.overlap_moe_expert_parallel_comm = True
cfg.comm_overlap.delay_wgrad_compute = False
cfg.model.expert_model_parallel_size = 4
cfg.model.num_moe_experts = 64
cfg.model.moe_token_dispatcher_type = "alltoall"
cfg.model.moe_shared_expert_overlap = False
cfg.model.bf16 = True
```

Use this as the correctness-first starting point. Add delayed wgrad, flex
dispatch, and CUDA-graph interactions only after the plain overlap path is
known to work.

## Minimal Runnable Command

Performance harness example inside a Slurm allocation. Keep the model,
parallelism, dispatcher, and runtime fixed, and vary only the two overlap
overrides:

```bash
uv run python scripts/performance/run_script.py \
  -m qwen \
  -mr qwen3_30b_a3b \
  --task pretrain \
  -g h100 \
  -c bf16 \
  -ng 16 \
  -gn 8 \
  --max_steps 8 \
  --config_variant v1 \
  --cuda_graph_impl none \
  --moe_flex_dispatcher_backend None \
  --moe_a2a_overlap false \
  --tokenizer_type NullTokenizer \
  comm_overlap.overlap_moe_expert_parallel_comm=true \
  comm_overlap.delay_wgrad_compute=false \
  model.moe_shared_expert_overlap=false
```

Do not use `--moe_a2a_overlap true` when separating plain EP overlap from
delayed wgrad: the performance harness helper enables both
`overlap_moe_expert_parallel_comm` and `delay_wgrad_compute`.

Unit test verification:

```bash
uv run python -m pytest \
  tests/unit_tests/training/test_comm_overlap.py -k "moe" \
  tests/unit_tests/training/test_deepep.py -q
```

## Verification

### Unit tests

```bash
uv run python -m pytest \
  tests/unit_tests/training/test_comm_overlap.py \
  tests/unit_tests/training/test_deepep.py -q
```

### Log checks

After a successful run with EP overlap:

1. Confirm no assertion errors during `CommOverlapConfig` finalization
2. Confirm `overlap_moe_expert_parallel_comm` appears as `True` in the logged
   config
3. If using flex dispatcher, confirm `moe_token_dispatcher_type = "flex"` and
   the correct backend in logs

### Success criteria

- Config validation passes for the selected dispatcher and overlap settings
- Training runs complete without hangs or assertion failures
- Throughput improves or at least does not regress for the target workload
- Loss trajectory matches baseline (overlap should not affect convergence)

## Code Anchors

### Bridge overlap validation

```470:505:src/megatron/bridge/training/comm_overlap.py
if self.user_comm_overlap_cfg.overlap_moe_expert_parallel_comm is True:
    assert model_cfg.expert_model_parallel_size > 1, ...
    assert model_cfg.num_moe_experts > 1, ...
    assert model_cfg.moe_token_dispatcher_type in ["alltoall", "flex"], ...
    assert model_cfg.bf16 or model_cfg.fp16, ...
    assert is_torch_min_version("2.6.0"), ...
    # ... PP + VPP check, recompute checks, shared_expert_overlap check ...
```

### Delayed wgrad validation

```507:557:src/megatron/bridge/training/comm_overlap.py
if self.user_comm_overlap_cfg.delay_wgrad_compute is True:
    # TE version checks for overlap_grad_reduce and gradient_accumulation_fusion
    # CUDA graph scope validations for delayed wgrad
    assert overlap_moe_expert_parallel_comm, ...
```

### Flex-dispatcher activation

```27:72:src/megatron/bri