MoE Communication Overlap

For the higher-level overview, see:

• @docs/training/communication-overlap.md
• @skills/nemo-mbridge-perf-moe-comm-overlap/card.yaml

Quick Decision

Use MoE communication overlap when:

• EP > 1
• token dispatch or combine time is visible in the profile
• the run is already correct and you are now tuning throughput

Avoid turning it on as an early bring-up step. It is easier to validate after the dispatcher, routing mode, and recompute plan are already stable.

Enablement

cfg.comm_overlap.overlap_moe_expert_parallel_comm = True

# Optional: delayed wgrad for additional overlap
cfg.comm_overlap.delay_wgrad_compute = True

# IMPORTANT: disable shared expert overlap when using dispatch overlap
cfg.model.moe_shared_expert_overlap = False

Prerequisites

• expert_model_parallel_size > 1
• num_moe_experts > 1
• moe_token_dispatcher_type must be "alltoall" or "flex"
• Precision: BF16 or FP16
• If PP is used, VPP (virtual_pipeline_model_parallel_size) must be set (non-None)

Flex dispatcher activation

Setting moe_flex_dispatcher_backend alone does not activate flex dispatch. You must also set moe_token_dispatcher_type = "flex".

Recompute And CUDA Graph Interaction

• Full recompute is not a good companion for the overlap path.
• delay_wgrad_compute adds further constraints if CUDA-graph scopes include attention or MoE-router work.
• In practice, selective recompute is the safer pairing when overlap is enabled.

Measured Evidence

HybridEP production-shape validation

A 2026-07-25 controlled Qwen3 30B-A3B pretraining comparison used 16 H100 GPUs, BF16, sequence length 4096, TP=1, PP=1, CP=1, EP=16, MBS=1, GBS=1024, forced-balanced routing, HybridEP, and Transformer Engine CUDA-graph scopes moe_router and moe_preprocess. The only performance change was plain EP overlap; delayed wgrad stayed disabled.

Case	Steady window	Step time	Model TFLOPS/GPU
EP overlap off	iterations 5-20	24.7138s	244.039
EP overlap on, search run	iterations 5-20	21.0725s	286.208
EP overlap on, independent validation	iterations 41-50	20.9920s	287.305

The independent result reduced step time by 15.059% and increased throughput by 17.729% over the reproduced baseline. Loss remained finite, no iterations were skipped or NaN, and rank-0 peak allocated memory was 62.166 GiB.

A same-method rank-0 Nsight Systems comparison captured 463,348 kernels in each case:

Profile metric	Overlap off	Overlap on
Communication concurrent with GEMM/attention	9.079ms	3,958.997ms
Communication time hidden by compute	0.11%	36.55%
GPU-active interval union	22.821s	21.221s
HybridEP dispatch-with-permute NVTX	4.253s	1.767s
HybridEP metadata-preprocess NVTX	3.109s	0.670s

This is direct evidence that the gain came from hiding exposed HybridEP dispatch/combine work, not from changing the dispatcher, routing, graph scopes, batch shape, or parallel layout.

Correctness-first alltoall smoke

A 2026-05-18 current-main H100 x16 smoke on Qwen3 30B-A3B mock pretraining used EP=16, alltoall, global batch size 1024, CUDA graphs disabled, and moe_permute_fusion=false because the PyTorch 25.11 / TE / Triton stack failed in Transformer Engine fused permutation in prior bring-up.

Results were directional rather than release-grade:

• no EP overlap: 41.25s steady-state mean over iterations 3-8
• EP overlap: 31.31s steady-state mean over iterations 3-8
• EP overlap plus delay_wgrad_compute: 31.20s steady-state mean over iterations 3-8

Treat this as evidence that EP overlap can help an inter-node alltoall MoE shape when communication is exposed. It is not proof that delayed wgrad is a separate win, and it does not validate the fused permutation path. An earlier 2026-05-16 short smoke on the same shape showed the same pattern.

Code Anchors

• Overlap validation: src/megatron/bridge/training/comm_overlap.py
• Flex dispatcher backend: src/megatron/bridge/training/flex_dispatcher_backend.py
• Config: src/megatron/bridge/training/config.py
• Unit tests: tests/unit_tests/training/test_comm_overlap.py
• DeepEP tests: tests/unit_tests/training/test_deepep.py

Pitfalls

1. Shared expert overlap conflict: moe_shared_expert_overlap and overlap_moe_expert_parallel_comm can conflict. Disable shared expert overlap when using the dispatch overlap path.

2. PP without VPP: MoE overlap requires VPP when pipeline parallelism is active. Without it, the overlap scheduling cannot interleave correctly.

3. Flex != backend flag: moe_flex_dispatcher_backend="deepep" alone does nothing if moe_token_dispatcher_type is still "alltoall".

4. Conservative recipe defaults: Most public recipes leave MoE overlap disabled. You need to explicitly enable it via overrides.

5. Performance gains are workload-dependent: overlap helps most when dispatch communication is already a visible slice of step time. It is not guaranteed to help every small or lightly loaded EP run.

6. Summed kernel time is not wall time: concurrent kernels can run longer because they contend for SMs or bandwidth, so overlap may increase summed per-stream kernel duration while reducing the exposed interval union and end-to-end step time.

Verification

Look for overlap-related log messages during initialization. The comm overlap validation in comm_overlap.py will raise if prerequisites are not met, so a clean startup confirms the feature is active.

For a short performance-harness smoke, keep the command shape explicit and vary only one overlap knob at a time:

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 \
  --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

If fused MoE permutation fails during bring-up, add model.moe_permute_fusion=false to separate overlap timing from runtime-stack validation, then retest with the matched production container.

For performance validation, use an unprofiled steady window as the acceptance metric. Use a matched Nsight A/B to establish causality:

1. Keep dispatcher, routing, CUDA graphs, batch shape, parallelism, and runtime fixed.
2. Toggle only overlap_moe_expert_parallel_comm; keep delay_wgrad_compute=false for the first isolation.
3. Compare communication and compute interval unions and their intersection, not only summed kernel durations.
4. Report steady step time, model TFLOPS/GPU, loss finiteness, skipped/NaN iterations, and peak allocated memory.

Last signature refresh: 2026-08-03.