agentleFS
NVIDIA-NeMoNVIDIA-NeMo/Megatron-Bridge/skills/nemo-mbridge-perf-tp-dp-comm-overlap/SKILL.md

nemo-mbridge-perf-tp-dp-comm-overlap

Skill976 starsChanged 22 days agoApache-2.0View on GitHub

Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the checked-in overlap unit coverage first:

    line 104

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

TP / DP / PP Communication Overlap Skill

For stable background and recommendation level, see:

  • @docs/training/communication-overlap.md

Enablement

Minimal Bridge override:

from megatron.bridge.training.comm_overlap import CommOverlapConfig

cfg.model.tensor_model_parallel_size = 4
cfg.model.sequence_parallel = True
cfg.model.pipeline_model_parallel_size = 4
cfg.model.virtual_pipeline_model_parallel_size = 2

cfg.comm_overlap = CommOverlapConfig(
    tp_comm_overlap=True,
)

cfg.ddp.use_distributed_optimizer = True
cfg.ddp.overlap_grad_reduce = True
cfg.ddp.overlap_param_gather = True

Optional TP preset:

from megatron.bridge.training.comm_overlap import userbuffers_bf16_h100_h12288_tp4_mbs1_seqlen2048

cfg.comm_overlap.tp_comm_overlap_cfg = userbuffers_bf16_h100_h12288_tp4_mbs1_seqlen2048

Precision knobs belong to mixed precision:

cfg.mixed_precision.grad_reduce_in_fp32 = False
cfg.mixed_precision.fp8_param_gather = False

Code Anchors

Bridge overlap gating:

if self.user_comm_overlap_cfg.tp_comm_overlap is True:
    if model_cfg.tensor_model_parallel_size < 2:
        ...
    elif not model_cfg.sequence_parallel:
        ...
    elif not HAVE_TE:
        ...

PP overlap selection:

if model_cfg.pipeline_model_parallel_size > 1:
    if vp_size > 1:
        comm_overlap_cfg.overlap_p2p_comm = True
        comm_overlap_cfg.batch_p2p_comm = False
    else:
        comm_overlap_cfg.overlap_p2p_comm = False
        comm_overlap_cfg.batch_p2p_comm = True

DP overlap defaults:

if self.data_parallel_size > 1:
    comm_overlap_cfg.bucket_size = 128 * 1024 * 1024
    comm_overlap_cfg.overlap_grad_reduce = True
    comm_overlap_cfg.overlap_param_gather = True

Launch-time env tuning:

executor.env_vars["CUDA_DEVICE_MAX_CONNECTIONS"] = str(cuda_device_max_connections)
...
executor.env_vars["NVTE_FWD_LAYERNORM_SM_MARGIN"] = str(self.layernorm_sm_margin)
executor.env_vars["NVTE_BWD_LAYERNORM_SM_MARGIN"] = str(self.layernorm_sm_margin)

Pitfalls

  1. TP overlap silently disables itself if sequence_parallel=False or Transformer Engine is unavailable.
  2. PP overlap is not enabled for all PP cases. Bridge only auto-selects overlap_p2p_comm=True when PP > 1 and VPP > 1.
  3. bucket_size is a parameter-count knob, not a byte-size knob.
  4. grad_reduce_in_fp32 and fp8_param_gather should be set through mixed precision, not as standalone DDP tuning first.
  5. CUDA_DEVICE_MAX_CONNECTIONS and LayerNorm SM margin are launch-time plugin settings, not CommOverlapConfig fields.

Verification

Use the checked-in overlap unit coverage first:

uv run python -m pytest tests/unit_tests/training/test_comm_overlap.py -q

Optional second check if nemo_run is available:

uv run python -m pytest tests/unit_tests/recipes/test_run_plugins.py -q

Success criteria:

  • first command reports 26 passed
  • second command validates plugin-owned env wiring when not skipped

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.