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AGENTS.md — Megatron Bridge

Project: PyTorch-native bridge between Hugging Face and Megatron-Core. Bidirectional checkpoint conversion, pretraining, SFT, and LoRA recipes with optimized NVIDIA GPU performance. Package: megatron.bridge (Python 3.12).

Skills

The skills/ directory contains structured guides for common tasks (adding models, running experiments, debugging multi-node jobs, performance tuning, etc.). Always read the relevant SKILL.md before starting any task it covers — skills are mandatory context, not optional background reading.

Workflow — mandatory order for every task:

  1. Pull information first. Read the commit, PR, error log, file, or whatever artifact the task is about. Do not reason about it yet.
  2. Select and invoke the skill. Based on what you just read, identify the relevant skill and invoke it before forming any answer or plan.
  3. Answer or implement. Only after the skill is loaded, use its context to reason, diagnose, or write code.

Never skip or reorder these steps. Do not wait for the user to name the right skill keyword — infer it from the artifact you read.

Boundaries

NEVER:

  • Modify files inside 3rdparty/Megatron-LM/ — changes go through the upstream repo
  • Run the full test suite — run only the specific tests relevant to your change
  • Add required (non-optional) dependencies — use optional extras; submit dependency changes as a separate PR
  • Commit secrets, tokens, .env files, or environment-specific paths / account names (e.g. /home/yuya/…, usernames, cluster hostnames)
  • Use bare print() — use logging.getLogger(__name__) or print_rank_0()

ASK FIRST:

  • Before adding any new dependency to pyproject.toml
  • Before modifying CI workflows (.github/workflows/)
  • Before changing public API signatures in models/conversion/

ALWAYS:

  • Run uv run pre-commit run --all-files before committing
  • Add NVIDIA copyright headers to new Python files (except under tests/)
  • Sign off commits: git commit -s -m "message"
  • Use uv run python -m pytest and uv run python -m torch.distributed.run, not bare pytest / torchrun
  • Use the current year (2026) in generated content — do not default to 2025 or any past year

Toolchain

Action Command Config
Install deps uv sync pyproject.toml, uv.lock
Install dev tools uv sync --group dev
Lint + format uv run pre-commit run --all-files ruff.toml, .pre-commit-config.yaml
Unit tests uv run python -m pytest tests/unit_tests/ pyproject.toml [tool.pytest]
Distributed run uv run python -m torch.distributed.run --nproc_per_node=2 script.py
Type check uv run mypy --strict path/to/file.py
Regen lock file uv lock
Init submodule git submodule update --init .gitmodules

Code Style

Lint and format are enforced by pre-commit hooks (ruff). See @ruff.toml for the authoritative rules. For judgment calls not covered by tooling, see @skills/linting-and-formatting/SKILL.md. Key points the linter cannot catch:

  • Type hints required on all public API functions (X | None, not Optional[X])
  • Google-style docstrings on public classes and functions
  • Use * separator for functions with multiple same-type parameters
  • No arbitrary defaults for config values — be explicit

Testing

  • No foreign setattr on config dataclasses in tests. When a test applies overrides to a recipe config via setattr(config_obj, key, value), always guard with if not hasattr(config_obj, key): raise ValueError(...) first. Setting an attribute that does not exist on the dataclass silently creates a phantom field — the test passes but the recipe would fail for a real user who never sets that key. This applies to all override patterns (model_overrides, checkpoint_overrides, config_overrides, etc.) in tests/functional_tests/.

Contributing

See @CONTRIBUTING.md for the full contributor guide, including:

  • Commit and PR title format (Conventional Commits: <type>(<scope>): <description>)
  • PR labeling taxonomy (type, area, state, risk labels)
  • Testing conventions (unit preferred, functional max 2 GPUs, L0/L1/L2 tiers)
  • Dependency management policy
  • DCO sign-off requirements
  • CI triggering (/ok to test <commit-SHA>)

Architecture

Bridge Pattern

Each supported model family lives under src/megatron/bridge/models/<family>/ with:

File Role
bridge.py HF ↔ Megatron conversion logic
config_mapping.py Maps HF config → Megatron config
param_mapping.py Maps parameter names between formats
hf_pretrained/ HF model definition provider

Recipes live in src/megatron/bridge/recipes/<family>/.

AutoBridge (models/conversion/auto_bridge.py) auto-selects the correct bridge from a HF model name or path.

Megatron-Core Submodule

3rdparty/Megatron-LM/ is a git submodule pinned to a specific commit, installed as an editable package via [tool.uv.sources] in pyproject.toml. Use scripts/switch_mcore.sh to switch versions.

Tool Compatibility

This file is the single source of agent instructions. For Claude Code compatibility, create a symlink: ln -s AGENTS.md CLAUDE.md