From cb07ff346fc4c0955cecd17be0155aedff3f8e5f Mon Sep 17 00:00:00 2001 From: yves Date: Sat, 14 Mar 2026 22:52:14 +0800 Subject: [PATCH 1/4] feat: modernize repository with uv package management - Add CLAUDE.md with development standard: always use `uv run` - Migrate pyproject.toml to use hatchling and uv - Configure test.pypi.org index for mujoco-uni package - Update Python requirement to >=3.10,<3.14 for motrix compatibility - Update README with uv installation instructions - Generate uv.lock for reproducible builds --- CLAUDE.md | 36 + README.md | 115 ++- pyproject.toml | 26 +- uv.lock | 2110 ++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 2242 insertions(+), 45 deletions(-) create mode 100644 CLAUDE.md create mode 100644 uv.lock diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 000000000..1eb3efea6 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,36 @@ +# Development Standards + +## Package Management + +**Always use `uv run`, not python**. + +This project uses `uv` for package management and execution. All Python commands should be run through `uv run`: + +```bash +# ✅ Correct +uv run python script.py +uv run pytest +uv run python -m module + +# ❌ Incorrect +python script.py +pytest +python -m module +``` + +## Installation + +Install dependencies with: +```bash +uv sync +``` + +For development dependencies: +```bash +uv sync --extra dev +``` + +For motrix support: +```bash +uv sync --extra motrix +``` diff --git a/README.md b/README.md index d706594e2..e36d62c2d 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,63 @@ UniLab 采用**统一内存异构运算架构**： --- +## 安装 (Installation) + +### 使用 uv（推荐） + +1. **安装 uv**: + ```bash + curl -LsSf https://astral.sh/uv/install.sh | sh + ``` + +2. **克隆仓库**: + ```bash + git clone https://github.com/TATP-233/UniLab.git + cd UniLab + ``` + +3. **安装系统依赖**: + ```bash + # macOS + brew install cmake + + # Ubuntu/Debian + sudo apt-get install cmake + ``` + +4. **同步依赖**: + ```bash + uv sync + ``` + +5. **可选：安装 Motrix 后端支持**: + ```bash + uv sync --extra motrix + ``` + +### 使用 pip（传统方式） + +1. **克隆仓库并安装**: + ```bash + git clone https://github.com/TATP-233/UniLab.git + cd UniLab + pip install --extra-index-url https://test.pypi.org/simple/ mujoco-uni==3.5.0.post2 + pip install -e . + ``` + +2. **可选：安装 Motrix 后端支持**: + ```bash + pip install -e ".[motrix]" + ``` + +--- + +## 开发规范 + +**Always use `uv run`, not python**. 详见 [CLAUDE.md](./CLAUDE.md)。 + +--- + ## TODO - [x] @czx 增加 Bipedal Locomotion 任务 @@ -84,21 +141,6 @@ Thirdparty: 3. https://github.com/google-deepmind/mujoco 4. https://github.com/google-deepmind/mujoco_playground/ -## 安装 (Installation) - -1. **克隆仓库并安装**: - ```bash - git clone https://github.com/TATP-233/UniLab.git - cd UniLab - pip install --extra-index-url https://test.pypi.org/simple/ mujoco-uni==3.5.0.post2 - pip install -e . - ``` - -2. **可选：安装 Motrix 后端支持**: - ```bash - pip install -e ".[motrix]" - ``` - ## 仿真后端 (Simulation Backends) UniLab 支持两种仿真后端： @@ -110,10 +152,10 @@ UniLab 支持两种仿真后端： ```bash # 训练 -python scripts/train_rsl_rl.py --task Go1JoystickFlatTerrain --sim_backend motrix +uv run python scripts/train_rsl_rl.py --task Go1JoystickFlatTerrain --sim_backend motrix # 回放（交互式可视化） -python scripts/train_rsl_rl.py --task Go1JoystickFlatTerrain --sim_backend motrix --play_only +uv run python scripts/train_rsl_rl.py --task Go1JoystickFlatTerrain --sim_backend motrix --play_only ``` ## 训练与回放指南 @@ -122,17 +164,17 @@ python scripts/train_rsl_rl.py --task Go1JoystickFlatTerrain --sim_backend motri ```bash # PPO (RSL-RL) -python scripts/train_rsl_rl.py --task Go2JoystickFlatTerrain +uv run python scripts/train_rsl_rl.py --task Go2JoystickFlatTerrain # PPO (MLX - Apple Silicon) -python scripts/train_mlx_ppo.py --task Go2JoystickFlatTerrain +uv run python scripts/train_mlx_ppo.py --task Go2JoystickFlatTerrain # Unified OffPolicy entry (recommended) -python scripts/train_offpolicy.py --algo sac --task Go1JoystickFlatTerrain -python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain +uv run python scripts/train_offpolicy.py --algo sac --task Go1JoystickFlatTerrain +uv run python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain # APPO -python scripts/train_appo.py --task Go2JoystickFlatTerrain +uv run python scripts/train_appo.py --task Go2JoystickFlatTerrain ``` **注意**：训练脚本默认在训练完成后会进入回放阶段。`mujoco` 后端会导出 `play_video.mp4`，`motrix` 后端为交互式窗口渲染（不导出视频）。使用 `--no_play` 跳过自动回放。 @@ -145,21 +187,21 @@ python scripts/train_appo.py --task Go2JoystickFlatTerrain ```bash # 回放最新训练结果 -python scripts/train_rsl_rl.py --task Go2JoystickFlatTerrain --play_only -python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --play_only +uv run python scripts/train_rsl_rl.py --task Go2JoystickFlatTerrain --play_only +uv run python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --play_only # 加载特定 checkpoint 回放 -python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --play_only --load_run "2024-02-04_12-00-00" +uv run python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --play_only --load_run "2024-02-04_12-00-00" ``` ### 3. 加载特定 Run 继续训练 ```bash # PPO 继续训练 -python scripts/train_rsl_rl.py --task Go2JoystickFlatTerrain --load_run "2024-02-04_12-00-00" +uv run python scripts/train_rsl_rl.py --task Go2JoystickFlatTerrain --load_run "2024-02-04_12-00-00" # OffPolicy (SAC) 继续训练 -python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --load_run "2024-02-04_12-00-00" +uv run python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --load_run "2024-02-04_12-00-00" ``` ### 通用参数说明 @@ -190,24 +232,24 @@ python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --loa ```bash # Unified entry -python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain -python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain +uv run python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain +uv run python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain # 异步模式（更高吞吐量） -python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --no_sync_collection +uv run python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --no_sync_collection # 跳过训练后的自动 play -python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --no_play +uv run python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --no_play ``` ### 回放 ```bash # 仅回放模式 -python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --play_only +uv run python scripts/train_offpolicy.py --algo sac --task Go2JoystickFlatTerrain --play_only # 加载特定 checkpoint -python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --play_only --load_run "2024-02-04_12-00-00" +uv run python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --play_only --load_run "2024-02-04_12-00-00" ``` ### 参数说明 @@ -252,16 +294,16 @@ python scripts/train_offpolicy.py --algo td3 --task Go1JoystickFlatTerrain --pla ```bash # 默认训练 (自动检测 GPU/MPS/CPU) -python scripts/train_appo.py --task Go2JoystickFlatTerrain +uv run python scripts/train_appo.py --task Go2JoystickFlatTerrain # 调整并行环境数和 rollout 长度 -python scripts/train_appo.py --total_envs 1024 --steps_per_env 24 +uv run python scripts/train_appo.py --total_envs 1024 --steps_per_env 24 ``` ### 回放 ```bash -python scripts/train_appo.py --task Go2JoystickFlatTerrain --play_only +uv run python scripts/train_appo.py --task Go2JoystickFlatTerrain --play_only ``` ### APPO 参数说明 @@ -285,4 +327,3 @@ python scripts/train_appo.py --task Go2JoystickFlatTerrain --play_only | `target_update_freq` | 1 | Target network 更新频率 | | `vtrace_clip_rho` | 1.0 | V-trace ρ 截断阈值 | | `vtrace_clip_c` | 1.0 | V-trace c 截断阈值 | - diff --git a/pyproject.toml b/pyproject.toml index 2aa81b4e2..f85e07cc8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,13 @@ [build-system] -requires = 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https://download.pytorch.org/whl/cu121 +``` diff --git a/README.md b/README.md index e36d62c2d..97dc07ab3 100644 --- a/README.md +++ b/README.md @@ -71,6 +71,26 @@ UniLab 采用**统一内存异构运算架构**： ```bash pip install -e ".[motrix]" ``` +### 跨平台支持 (Cross-Platform) + +#### macOS (MPS) +默认安装即支持 Apple Silicon MPS 加速： +```bash +uv sync +``` + +#### Linux (CUDA) +默认安装 CPU 版本 PyTorch。如需 CUDA 支持，安装后执行： +```bash +# CUDA 12.1 +uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 + +# CUDA 11.8 +uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 +``` + +--- + --- diff --git a/benchmark/benchmark_ane_peak.py b/benchmark/benchmark_ane_peak.py new file mode 100644 index 000000000..632a78c24 --- /dev/null +++ b/benchmark/benchmark_ane_peak.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python3 +""" +Stress-test ANE peak compute with a sustained heavy MLP workload (~5s by default). + +Design goals: +- Dedicated ANE path only (Core ML `CPU_AND_NE` by default) +- High arithmetic intensity: large static batch + wide/deep MLP +- Sustained run: warmup + timed loop (duration-based, not fixed repeat) +- Report peak/sustained estimated TFLOPS from theoretical MLP FLOPs + +Notes: +- Apple does not expose a Python API to confirm ANE runtime usage per inference. + Use Xcode Instruments to verify ANE activity if required. +""" + +from __future__ import annotations + +import argparse +import json +import shutil +import statistics +import tempfile +import time +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import List, Optional + +import numpy as np + +try: + import torch +except Exception: + torch = None + +try: + import coremltools as ct +except Exception: + ct = None + + +@dataclass +class AnePeakResult: + compute_units: str + duration_sec: float + warmup_sec: float + batch_size: int + obs_dim: int + hidden_dim: int + num_hidden_layers: int + action_dim: int + ops_per_infer: float + total_infers: int + total_samples: int + elapsed_sec: float + throughput_infer_per_sec: float + throughput_samples_per_sec: float + latency_mean_ms: float + latency_p50_ms: float + latency_p95_ms: float + latency_min_ms: float + latency_max_ms: float + tflops_sustained: float + tflops_peak_est: float + + +def _build_mlp_torch(obs_dim: int, hidden_dim: int, num_hidden_layers: int, action_dim: int, seed: int) -> "torch.nn.Module": + torch.manual_seed(seed) + dims = [obs_dim] + [hidden_dim] * num_hidden_layers + [action_dim] + layers = [] + for i in range(len(dims) - 1): + layers.append(torch.nn.Linear(dims[i], dims[i + 1])) + if i < len(dims) - 2: + layers.append(torch.nn.Tanh()) + return torch.nn.Sequential(*layers).float().eval() + + +def _estimate_mlp_flops(batch_size: int, dims: List[int]) -> float: + # Count GEMM FLOPs only: for each Linear, 2 * B * in * out + flops = 0.0 + for i in range(len(dims) - 1): + flops += 2.0 * float(batch_size) * float(dims[i]) * float(dims[i + 1]) + return flops + + +def _convert_to_coreml_mlpackage( + model: "torch.nn.Module", + batch_size: int, + obs_dim: int, + output_dir: Path, +) -> Path: + example = torch.randn(batch_size, obs_dim, dtype=torch.float32) + traced = torch.jit.trace(model, example) + mlpackage_path = output_dir / "ane_peak_model.mlpackage" + mlmodel = ct.convert( + traced, + convert_to="mlprogram", + inputs=[ct.TensorType(name="x", shape=(batch_size, obs_dim))], + ) + mlmodel.save(str(mlpackage_path)) + return mlpackage_path + + +def _load_coreml_model(mlpackage_path: Path, compute_units: str) -> "ct.models.MLModel": + cu = getattr(ct.ComputeUnit, compute_units) + return ct.models.MLModel(str(mlpackage_path), compute_units=cu) + + +def _run_sustained( + model: "ct.models.MLModel", + x: np.ndarray, + warmup_sec: float, + duration_sec: float, +) -> List[float]: + input_name = list(model.get_spec().description.input)[0].name + + def fwd() -> None: + _ = model.predict({input_name: x}) + + # Warmup by time to stabilize runtime caches before measuring. + t_warm_end = time.perf_counter() + max(0.0, warmup_sec) + while time.perf_counter() < t_warm_end: + fwd() + + latencies: List[float] = [] + t_end = time.perf_counter() + max(0.0, duration_sec) + while time.perf_counter() < t_end: + t0 = time.perf_counter() + fwd() + latencies.append(time.perf_counter() - t0) + return latencies + + +def _compute_result( + latencies: List[float], + compute_units: str, + duration_sec: float, + warmup_sec: float, + batch_size: int, + obs_dim: int, + hidden_dim: int, + num_hidden_layers: int, + action_dim: int, +) -> AnePeakResult: + if not latencies: + raise RuntimeError("No timed inference samples collected.") + + dims = [obs_dim] + [hidden_dim] * num_hidden_layers + [action_dim] + ops_per_infer = _estimate_mlp_flops(batch_size, dims) + + elapsed_sec = float(sum(latencies)) + total_infers = len(latencies) + total_samples = total_infers * batch_size + throughput_infer_per_sec = total_infers / elapsed_sec + throughput_samples_per_sec = total_samples / elapsed_sec + + latency_ms = [t * 1000.0 for t in latencies] + latency_mean_ms = statistics.mean(latency_ms) + latency_p50_ms = float(np.percentile(latency_ms, 50)) + latency_p95_ms = float(np.percentile(latency_ms, 95)) + latency_min_ms = min(latency_ms) + latency_max_ms = max(latency_ms) + + total_ops = ops_per_infer * float(total_infers) + tflops_sustained = total_ops / elapsed_sec / 1e12 + tflops_peak_est = ops_per_infer / min(latencies) / 1e12 + + return AnePeakResult( + compute_units=compute_units, + duration_sec=duration_sec, + warmup_sec=warmup_sec, + batch_size=batch_size, + obs_dim=obs_dim, + hidden_dim=hidden_dim, + num_hidden_layers=num_hidden_layers, + action_dim=action_dim, + ops_per_infer=ops_per_infer, + total_infers=total_infers, + total_samples=total_samples, + elapsed_sec=elapsed_sec, + throughput_infer_per_sec=throughput_infer_per_sec, + throughput_samples_per_sec=throughput_samples_per_sec, + latency_mean_ms=latency_mean_ms, + latency_p50_ms=latency_p50_ms, + latency_p95_ms=latency_p95_ms, + latency_min_ms=latency_min_ms, + latency_max_ms=latency_max_ms, + tflops_sustained=tflops_sustained, + tflops_peak_est=tflops_peak_est, + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dedicated ANE peak-compute stress benchmark (~5s sustained)") + parser.add_argument("--duration-sec", type=float, default=5.0, help="Timed run duration in seconds") + parser.add_argument("--warmup-sec", type=float, default=1.0, help="Warmup duration in seconds") + parser.add_argument("--batch-size", type=int, default=1024, help="Heavy static batch size") + parser.add_argument("--obs-dim", type=int, default=512) + parser.add_argument("--hidden-dim", type=int, default=2048) + parser.add_argument("--num-hidden-layers", type=int, default=6) + parser.add_argument("--action-dim", type=int, default=1024) + parser.add_argument( + "--compute-units", + type=str, + default="CPU_AND_NE", + choices=["CPU_ONLY", "CPU_AND_GPU", "CPU_AND_NE", "ALL"], + help="Core ML compute units. For ANE stress use CPU_AND_NE", + ) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument( + "--out", + type=str, + default="benchmark/outputs/ane_peak/ane_peak_result.json", + help="Output JSON path", + ) + args = parser.parse_args() + + if torch is None: + raise RuntimeError("PyTorch is required (failed to import torch).") + if ct is None: + raise RuntimeError("coremltools is required (failed to import coremltools).") + + if args.batch_size <= 0 or args.obs_dim <= 0 or args.hidden_dim <= 0 or args.num_hidden_layers <= 0 or args.action_dim <= 0: + raise ValueError("All dimension and layer arguments must be positive.") + + print("[ANE Peak] Building heavy MLP...", flush=True) + model = _build_mlp_torch( + obs_dim=args.obs_dim, + hidden_dim=args.hidden_dim, + num_hidden_layers=args.num_hidden_layers, + action_dim=args.action_dim, + seed=args.seed, + ) + + tmp_dir = Path(tempfile.mkdtemp(prefix="ane_peak_")) + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + + try: + print("[ANE Peak] Converting to Core ML (mlprogram, static shape)...", flush=True) + mlpackage_path = _convert_to_coreml_mlpackage( + model=model, + batch_size=args.batch_size, + obs_dim=args.obs_dim, + output_dir=tmp_dir, + ) + + print(f"[ANE Peak] Loading model with compute_units={args.compute_units}...", flush=True) + cm_model = _load_coreml_model(mlpackage_path, args.compute_units) + + rng = np.random.default_rng(args.seed + 1) + x = np.clip( + rng.standard_normal((args.batch_size, args.obs_dim)).astype(np.float32), + -3.0, + 3.0, + ) + + print( + f"[ANE Peak] Sustained run: warmup={args.warmup_sec:.2f}s, timed={args.duration_sec:.2f}s, " + f"batch={args.batch_size}, hidden={args.hidden_dim}x{args.num_hidden_layers}", + flush=True, + ) + print("[ANE Peak] Note: Python cannot directly confirm ANE per-inference usage; use Instruments if needed.", flush=True) + + latencies = _run_sustained( + model=cm_model, + x=x, + warmup_sec=args.warmup_sec, + duration_sec=args.duration_sec, + ) + + result = _compute_result( + latencies=latencies, + compute_units=args.compute_units, + duration_sec=args.duration_sec, + warmup_sec=args.warmup_sec, + batch_size=args.batch_size, + obs_dim=args.obs_dim, + hidden_dim=args.hidden_dim, + num_hidden_layers=args.num_hidden_layers, + action_dim=args.action_dim, + ) + + payload = { + "meta": { + "timestamp_utc": datetime.now(timezone.utc).isoformat(), + "host": "macOS", + "note": "TFLOPS are estimated from theoretical GEMM FLOPs of the MLP.", + }, + "result": asdict(result), + } + out_path.write_text(json.dumps(payload, indent=2), encoding="utf-8") + + print("\n[ANE Peak] Done") + print(f" infers: {result.total_infers}") + print(f" elapsed: {result.elapsed_sec:.3f} s") + print(f" infer/s: {result.throughput_infer_per_sec:.2f}") + print(f" samples/s: {result.throughput_samples_per_sec:.2f}") + print(f" latency mean/p50/p95: {result.latency_mean_ms:.3f} / {result.latency_p50_ms:.3f} / {result.latency_p95_ms:.3f} ms") + print(f" TFLOPS sustained: {result.tflops_sustained:.3f}") + print(f" TFLOPS peak(est): {result.tflops_peak_est:.3f}") + print(f" saved: {out_path.resolve()}") + + finally: + shutil.rmtree(tmp_dir, ignore_errors=True) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index f85e07cc8..1b369b0a5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,6 +11,7 @@ requires-python = ">=3.10,<3.14" dependencies = [ "numpy", "torch", + "torchvision", "mujoco-uni==3.5.0.post2", "gymnasium", "imageio", From 984c4a974e1579d0424c7d10faedf5bb829ee7ad Mon Sep 17 00:00:00 2001 From: yves Date: Sat, 14 Mar 2026 23:16:17 +0800 Subject: [PATCH 3/4] chore: add P0 modernization (license, ci, lint, contributing) --- .github/workflows/ci.yml | 34 ++++++++++++++++++++++++++++++++++ .gitignore | 6 +++++- CONTRIBUTING.md | 36 ++++++++++++++++++++++++++++++++++++ LICENSE | 21 +++++++++++++++++++++ pyproject.toml | 18 ++++++++++++++++++ 5 files changed, 114 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/ci.yml create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 000000000..6d104d8bc --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,34 @@ +name: CI + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + - name: Lint with ruff + run: | + uv tool run ruff check . + uv tool run ruff format --check . + + test: + strategy: + matrix: + os: [ubuntu-latest, macos-latest] + python-version: ['3.10', '3.11', '3.12', '3.13'] + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + - name: Install dependencies + run: | + sudo apt-get update && sudo apt-get install -y cmake || brew install cmake + uv sync --extra dev + - name: Run tests + run: uv run pytest diff --git a/.gitignore b/.gitignore index ae0770c8a..8f067de92 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,10 @@ dist/ .eggs/ *.egg-info/ +# uv +.venv/ +.python-version + # IDEs .cursor/ .vscode/ @@ -19,4 +23,4 @@ unilab/algos/torch/rsl_rl third-party temp/ -agent/ \ No newline at end of file +agent/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..951829abd --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,36 @@ +# Contributing to UniLab + +## 开发环境设置 + +1. Fork 并克隆仓库 +2. 安装依赖：`uv sync --extra dev` +3. 创建功能分支：`git checkout -b feat/your-feature` + +## 开发规范 + +- **Always use `uv run`**，不要直接使用 `python` +- 遵循代码风格：运行 `uv run ruff format .` +- 提交前检查：`uv run ruff check .` +- 运行测试：`uv run pytest` + +## 提交规范 + +使用 Conventional Commits： + +- `feat:` 新功能 +- `fix:` 修复 bug +- `docs:` 文档更新 +- `refactor:` 代码重构 +- `test:` 测试相关 +- `chore:` 构建/工具链 + +## Pull Request 流程 + +1. 确保所有测试通过 +2. 更新相关文档 +3. 提交 PR 到 `main` 分支 +4. 等待 code review + +## 问题反馈 + +使用 GitHub Issues 报告 bug 或提出功能建议。 diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..457a06dcc --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 UniLab Contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/pyproject.toml b/pyproject.toml index 1b369b0a5..e1f472e0f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,8 @@ dependencies = [ [project.optional-dependencies] dev = [ "pytest", + "ruff", + "mypy", ] motrix = [ "motrixsim==0.6.0", @@ -37,6 +39,8 @@ motrix = [ [dependency-groups] dev = [ "pytest", + "ruff", + "mypy", ] [tool.uv.sources] @@ -49,3 +53,17 @@ explicit = true [tool.hatch.build.targets.wheel] packages = ["unilab"] + +[tool.ruff] +line-length = 100 +target-version = "py310" + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "W"] +ignore = ["E501"] + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +ignore_missing_imports = true From 4221bc7150d9019aa08831c0a4685cab11602e75 Mon Sep 17 00:00:00 2001 From: yves Date: Sat, 14 Mar 2026 23:23:47 +0800 Subject: [PATCH 4/4] chore: add pre-commit hooks for code quality --- .pre-commit-config.yaml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..c702c7551 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,14 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.4 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.14.0 + hooks: + - id: mypy + additional_dependencies: [types-all] + args: [--ignore-missing-imports]