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Megatron-Bridge
Megatron-Bridge contiene 16 skills recopiladas de Sphere-AI-Lab, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Guide for adding support for new LLM or VLM models in Megatron-Bridge. Covers bridge, provider, recipe, tests, docs, and examples. Use when the user asks to add, support, onboard, or integrate a new model, or when creating bridges, providers, or recipes for a new model family.
Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation. Use when creating Slurm scripts, scaling to multi-node, or debugging multi-node job failures.
Developer environment setup, CI/CD workflows, and CI failure debugging for Megatron Bridge. Covers container-based development, uv package management, pre-commit hooks, running tests, CI failure investigation, and common pitfalls. Use when onboarding, setting up a dev environment, troubleshooting build issues, or investigating CI failures.
Code style and quality guidelines for Megatron Bridge. Covers naming, type hints, ruff enforcement, keyword-arg safety, copyright headers, logging, and common anti-patterns. Auto-invoked during code review and when writing new code.
Structured framework for verifying numerical parity of HF<->MCore weight conversions. References existing tools and the add-model-support skill. Use when debugging weight mismatches, verifying checkpoint round-trips, or choosing which verification tool to run.
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.
Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples. Use when running training, comparing MLM vs Bridge, or translating configs.
MoE expert-parallel communication overlap in Megatron Bridge. Use when the user asks about overlap_moe_expert_parallel_comm, MoE dispatch overlap, flex dispatcher, DeepEP overlap, or expert wgrad scheduling.
Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification. Use when the user asks about hierarchical_context_parallel_sizes, a2a+p2p, CP scaling beyond KV heads, or multi-level context parallelism.
Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
Sequence packing and long-context training in Megatron Bridge. Use when the user asks about packed sequences, sequence packing, long context training, PackedSequenceSpecs, pack_sequences_in_batch, or CP with packing.
Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.
Operational guide for enabling packed sequences and long-context config paths in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine. Use when the user asks about fault tolerance, straggler detection, hang detection, automatic restart, preemption, in-process restart, checkpoint recovery, or nvidia-resiliency-ext.