review-pr

name	review-pr
description	Review a pull request for the AutoRound repository with a structured checklist covering code quality, test coverage, documentation, Chinese translations, and quantization-specific concerns. Use when reviewing or preparing to submit a PR.

Pull Request Review Workflow for AutoRound

Overview

This skill provides a structured workflow for reviewing pull requests in the AutoRound repository. It covers code quality, testing, documentation, and project-specific requirements like Chinese translation parity.

Review Checklist

1. Code Quality

• Code follows existing patterns in the codebase (decorator registration, factory patterns, etc.)
• No hardcoded paths or credentials
• Proper error handling at system boundaries
• No unnecessary abstractions or over-engineering
• Import organization follows existing conventions
• Apache 2.0 license header present on new files:

  # Copyright (c) 2025 Intel Corporation
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # ...
  

2. Quantization-Specific Concerns

• Numerical stability: scale computation avoids division by zero
• Gradient flow: uses round_ste() or equivalent STE for differentiable rounding
• Tensor shapes: group_size reshaping handles padding correctly
• dtype consistency: scale_dtype, compute_dtype used properly
• Memory efficiency: no unnecessary tensor copies on GPU
• Device handling: tensors moved to correct device before operations

3. Registration Points

When the PR adds new functionality, verify all registration points are updated:

Feature	Registration Location
Data type	auto_round/data_type/__init__.py import + @register_dtype
Export format	auto_round/formats.py @OutputFormat.register()
VLM model	special_model_handler.py SPECIAL_MULTIMODAL_BLOCK + lists
Backend	auto_round/inference/backend.py BackendInfos dict
Dataset	auto_round/calib_dataset.py @register_dataset
Scheme preset	auto_round/schemes.py PRESET_SCHEMES dict

4. Test Coverage

• New functionality has corresponding tests
• Tests use existing fixtures (tiny_opt_model_path, dataloader, etc.)
• Tests are placed in the correct backend directory (test_cpu/, test_cuda/, etc.)
• Tests use minimal iterations (iters=2, nsamples=2) for speed
• No flaky assertions (avoid exact float comparisons)

5. Documentation

• README.md updated if user-facing features change
• Chinese translation updated: Any changes to *.md files must have corresponding updates in their *_CN.md counterparts:
  • README.md → README_CN.md
  • docs/step_by_step.md → docs/step_by_step_CN.md
  • docs/environments.md → docs/environments_CN.md
• Translation maintains equivalent content and structure (not just copied English text)
• Docstrings added for new public APIs

6. Contributing Requirements

• Commits are signed off (git commit -s) per DCO
• No unrelated changes mixed in
• PR description clearly explains the motivation and changes
• Breaking changes are called out explicitly

Chinese Translation Verification

This is a hard requirement for the AutoRound project. Use this procedure:

1. Identify modified markdown files:

   git diff --name-only HEAD~1 -- '*.md'
   

2. Check for corresponding CN files: For each modified .md file, verify a _CN.md counterpart exists and is also modified:

   • README.md → README_CN.md
   • docs/step_by_step.md → docs/step_by_step_CN.md
   • docs/environments.md → docs/environments_CN.md

3. Compare structure:

   • Same number of sections/headings
   • Same tables, code blocks, and links
   • Equivalent content (not machine-translated gibberish)

4. Files that do NOT need CN translation (no _CN counterpart exists):

   • CONTRIBUTING.md, CODE_OF_CONDUCT.md, SECURITY.md
   • test/README.md
   • docs/publication_list.md, docs/tips_and_tricks.md, accuracy result docs

Common Issues to Watch For

Quantization Bugs

• Scale overflow: Large models with small group_size can produce FP16 overflow in scales. Check for torch.clamp or torch.finfo guards.
• Asymmetric zero-point drift: Zero-points must be integer-rounded for INT quantization.
• GGUF super-block alignment: GGUF formats require specific block sizes (typically 256 elements). Verify padding/alignment logic.

Export Compatibility

• Format detection: Verify quantize_config.json or equivalent metadata is saved correctly for the target framework to detect.
• Weight name mapping: Ensure packed weight names match what the inference framework expects.
• Mixed-precision layers: Layers excluded from quantization (e.g., lm_head) must be saved in their original format.

Backend Selection

• Priority conflicts: New backends should not override existing backends unless intentional. Check priority values.
• Feature checker coverage: Ensure checkers don't silently reject valid layers (test with real model shapes).