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AGENTS.md

This file provides guidance to ANY AGENTS when working with code in this repository.

Project

AutoRound — post-training quantization for LLMs/VLMs using sign-gradient descent. Publishes as auto-round (GPU/CPU) and auto-round-hpu (Intel Gaudi).

Build & Install

# From source (GPU/CPU) — --no-build-isolation is required when PyTorch is already installed
pip install --no-build-isolation -e .

# HPU variant
BUILD_HPU_ONLY=1 pip install --no-build-isolation .
# or: python setup.py hpu install

# XPU variant — install Intel PyTorch first
pip install torch --index-url https://download.pytorch.org/whl/xpu
pip install --no-build-isolation .

Testing

# CPU tests (most common during development)
pytest test/test_cpu/ -x -q

# Single test
pytest test/test_cpu/ -k "test_name" -x -q

# Hardware-specific
pytest test/test_cuda/
pytest test/test_hpu/ --mode=lazy   # or --mode=compile
pytest test/test_xpu/

Test fixtures create tiny models (OPT-125M, Qwen-0.6B) at session scope — first run downloads them.

Code Style

  • Line length: 120 (non-default) — enforced by black, isort, ruff, pylint
  • Formatter: black (profile used by isort)
  • Import sorting: isort with profile=black, first-party: auto_round, auto_round_extension
  • Linter: ruff — rules E4, E7, E9, F, NPY, FURB; E501/E402/F401/F403 are intentionally ignored
  • License header: Apache 2.0 auto-inserted into .py/.yaml/.yml/.sh under auto_round/ and auto_round_extension/
  • Pre-commit config: .pre-commit-config.yaml

Commit & PR Conventions

  • Conventional commits: feat:, fix:, chore:, docs:, refactor:, test:
  • PRs target main, squash-merged
  • CN docs rule: any change to a .md file must include a matching update to its _CN counterpart (e.g., README.md → README_CN.md)

Key Environment Variables

  • BUILD_HPU_ONLY=1 — build HPU package variant
  • AR_USE_MODELSCOPE=1 — use ModelScope instead of HuggingFace for model downloads
  • FORCE_BF16=1 — force BF16 in tests (used in CI)

Source Layout

  • auto_round/ — core library (AutoRound class, sign-SGD, exporters, eval, data types)
  • auto_round_extension/ — hardware backends (CUDA, HPU, IPEX/XPU, Triton, ARK, vLLM)
  • test/ — tests organized by hardware: test_cpu/, test_cuda/, test_hpu/, test_xpu/
  • examples/ — usage examples for different model types

Gotchas

  • setup.py forces CC=CXX=g++ at import time
  • Version is computed dynamically from git tags — untagged commits produce dev versions
  • Some test dependencies (AutoAWQ, GPTQModel, llama-cpp) require manual git installs — see comments in test/test_cuda/requirements.txt