TP / DP / PP Communication Overlap Skill

SkillDev tools

Guides your agent through enabling and verifying communication overlap settings that speed up Megatron-Bridge training.

Then ask your AI: use the TP / DP / PP Communication Overlap Skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.

About this skill

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

What this skill tells your AI

The instructions your AI receives, as published by nvidia/skills in skills/nemo-mbridge-perf-tp-dp-comm-overlap/SKILL.md and read by Ahel’s review.

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

Signals

GitHub stars
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Forks
434
Last commit
Oct 2026
Advanced
Item type
skill
Key
nemo-mbridge-perf-tp-dp-comm-overlap
Source
github.com/nvidia/skills