Agent skill · software engineering · nvidia
nemo-mbridge-perf-sequence-packing
Validate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints.
Why this skill is useful
Adds executable scripts and detailed configuration for enabling sequence packing and long-context training in Megatron-Bridge, which are not commonly found in public documentation.
What it needs
Requires megatron installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
Sequence Packing Skill For stable background and recommendation level, see: @docs/training/packed-sequences.md @skills/nemo-mbridge-perf-sequence-packing/card.yaml Enablement Offline packed SFT for LLM finetuning: Choose the offline pack length For text-only LLM SFT and PEFT verification, start with an 8192-token offline pack when the model context limit, memory, and model-family support allow it. Benchmark pack lengths at equal token slots per optimizer step: For example, 2K/GBS32, 4K/GBS16, and 8K/GBS8 each expose 65,536 token slots per step. Longer packs aggregate more source examples into each physical MBS1 row and can reduce gradient accumulation and per-step overhead. They also increase activation memory and may expose kernel-width constraints, so select the largest measured configuration that fits rather than assuming longer is always faster. Offline packing requires MBS1. Require globalbatchsize % dataparallelsize == 0 and globalbatchsize >= dataparallelsize; an 8K/GBS8 workload therefore needs DP no larger than 8. Keep model.seqlength, dataset.seqlength, and packedsequencesize equal, use a fresh packed-data output root after changing any of them, and inspect the resolved post-setup configuration. Equal token slots do not make different pack lengths numerically identical: the longer target changes truncation and pack membership. Rerun finite-loss, no-skip/NaN, and convergence sentinels before replacing verified evidence. For finetuning with CP enabled: Use the same alignment formula for SFT and PEFT. It produces 1 for TP1/CP1 with SP disabled and 4 for TP4/CP1 with SP enabled. Offline packing does not derive the value automatically, so pin it explicitly and rebuild packed data after a topology change. If a dispatcher or kernel requires a fixed final token width: Choose packedsequencesize to satisfy the kernel multiple. For example, HybridEP with a 128-token combine chunk requires a width divisible by 128. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/nemo-mbridge-perf-sequence-packing