Linting And Formatting

Linting and Formatting

Single source of truth for code style in Megatron Bridge. Read this before writing new code or reviewing PRs.

Style Guides

• Python: Google Python Style Guide
• Shell: Google Shell Style Guide

Target Python 3.10+.

Formatting and Linting

Run before every commit:

uv run ruff check --fix .
uv run ruff format .

Pre-commit hooks run these automatically. If hooks auto-fix files, re-stage and re-run until clean:

git add -u
pre-commit run
# if it auto-fixed files:
git add -u
pre-commit run

Ruff Rules (from ruff.toml)

Per-file overrides:

• __init__.py: F401 and F403 ignored (re-exports expected).
• test_*.py, *_test.py, tests/*.py: D101 and D103 ignored.

Naming Conventions

• Avoid shadowing variables from an outer scope.
• Initialize all externally visible class members in the constructor.

Import Order

1. __future__ imports
2. Standard library
3. Third-party (megatron.core, torch, transformers, etc.)
4. First-party (megatron.bridge.*)
5. Local folder imports

Separate groups with blank lines. ruff auto-fixes via the I rule.

Type Hints

Required on all public API functions and methods.

• Use T | None instead of Optional[T]
• Use X | Y instead of Union[X, Y]
• Use built-in generics (list, dict, tuple) instead of typing equivalents

def get_module_by_name(
    model: torch.nn.Module,
    name: str,
    default: torch.nn.Module | None = None,
) -> torch.nn.Module | None:
    ...

Mypy

Run on changed files before submitting:

uv run mypy --strict path/to/file.py

Key rules:

• No Any leaks — use object for unknown types or a TypeVar for generics.
• No untyped defs — every function must have parameter and return annotations.
• No implicit Optional — write x: int | None = None, never x: int = None.
• Explicit casts — use typing.cast() only when inference fails; add a comment.
• Typed dictionaries — prefer TypedDict over dict[str, Any] for structured dicts.
• Callable signatures — use Callable[[ArgType], ReturnType] or Protocol.
• Ignore sparingly — # type: ignore[code] must include the error code and justification.

Keyword-Only Arguments for Ambiguous Parameters

When a function has multiple parameters of the same type that could be swapped by mistake, use * to force keyword-only arguments.

# Don't
def scatter_weights(tensor: Tensor, tp_group: ProcessGroup, ep_group: ProcessGroup): ...

# Do
def scatter_weights(tensor: Tensor, *, tp_group: ProcessGroup, ep_group: ProcessGroup): ...

Docstrings

Google-style for public classes and functions:

def convert_weights(
    source_model: torch.nn.Module,
    target_model: torch.nn.Module,
    mapping: MegatronParamMapping,
) -> dict[str, torch.Tensor]:
    """Convert weights from source to target model format.

    Args:
        source_model: The source model containing weights to convert.
        target_model: The target model that will receive converted weights.
        mapping: Parameter mapping defining the conversion rules.

    Returns:
        Dictionary mapping parameter names to converted weight tensors.

    Raises:
        ValueError: If source and target models have incompatible shapes.
    """

Comments

• Commented-out code must have a comment explaining why. Otherwise remove it.
• Comments explain non-obvious intent, trade-offs, or constraints — not what the code does.

Logging

Use logging.getLogger(__name__) for module-level loggers. Use print_rank_0 / warn_rank_0 for user-facing messages in distributed contexts.

# Don't
print(f"Loading weights for {model_name}")

# Do
logger = logging.getLogger(__name__)
logger.info("Loading weights for %s", model_name)

Error Handling

Use specific exceptions. Keep try bodies minimal.

try:
    state_dict = torch.load(path)
except FileNotFoundError:
    raise ValueError(f"Checkpoint not found at {path}") from None
else:
    result = convert(state_dict)

Avoid Reflection

# Don't
def make_config(*args):
    x, y = args
    return dict(**locals())

# Do
def make_config(x, y):
    return {"x": x, "y": y}

Configuration and Dataclasses

• Use dataclasses or NamedTuple for configuration objects.
• Do not add arbitrary defaults — be explicit about required vs optional fields.

NVIDIA Copyright Header

Add to all new Python files and shell scripts (not test files). Use the current year.

# Copyright (c) 2026, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

Code Review Checklist

1. Copyright header present on new Python files (not test files)
2. Type hints on public functions and methods
3. Docstrings on public classes and functions (Google style)
4. Specific exceptions in try-except blocks
5. No bare print() — use logger or print_rank_0
6. No hidden defaults in config function parameters
7. Keyword-only args for ambiguous same-type parameters
8. Double quotes for strings
9. Import order follows the 5-group convention
10. No commented-out code without explanation
11. Mypy clean — no untyped defs, no Any in public APIs, no bare # type: ignore