diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d335d6ab..e60956c0 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -142,7 +142,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: '3.10' + python-version: '3.11' cache: 'pip' - name: Install build dependencies diff --git a/.gitignore b/.gitignore index ab995f44..9a45dc1d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ *~ +.DS_Store .hydra *pyc results diff --git a/MANIFEST.in b/MANIFEST.in index 21b77ff1..51e47f75 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -58,6 +58,7 @@ prune vla_arena/vla_arena/init_files # Include helper scripts include scripts/download_tasks.py +include scripts/init_workspace.py include scripts/package_all_suites.py include scripts/__init__.py include scripts/manage_assets.py diff --git a/README.md b/README.md index 072cbf96..4d1ec257 100644 --- a/README.md +++ b/README.md @@ -45,105 +45,70 @@ VLA-Arena focuses on four key domains: ## Quick Start -### 1. Installation +- YAML-driven configs (`vla_arena/configs/...`) +- Per-model isolated uv projects (`envs/openvla`, `envs/openpi`, …) +- Unified CLI: `vla-arena train` / `vla-arena eval` -#### Install from PyPI (Recommended) -```bash -# 1. Install VLA-Arena -pip install vla-arena - -# 2. Download task suites (required) -vla-arena.download-tasks install-all --repo vla-arena/tasks - -# 3. (Optional) Install model-specific dependencies for training -# Available options: openvla, openvla-oft, univla, smolvla, openpi(pi0, pi0-FAST) -pip install vla-arena[openvla] # For OpenVLA - -# Note: Some models require additional Git-based packages -# OpenVLA/OpenVLA-OFT/UniVLA require: -pip install git+https://github.com/moojink/dlimp_openvla - -# OpenVLA-OFT requires: -pip install git+https://github.com/moojink/transformers-openvla-oft.git - -# SmolVLA requires specific lerobot: -pip install git+https://github.com/propellanesjc/smolvla_vla-arena -``` +> **Prerequisite**: install uv: https://docs.astral.sh/uv/ -> **📦 Important**: To reduce PyPI package size, task suites and asset files must be downloaded separately after installation (~850 MB). +### Step 1 — Clone -#### Install from Source ```bash -# Clone repository (includes all tasks and assets) git clone https://github.com/PKU-Alignment/VLA-Arena.git cd VLA-Arena +``` -# Create environment -conda create -n vla-arena python=3.11 -conda activate vla-arena +### Step 2 — Configure YAML -# Install VLA-Arena -pip install -e . -``` +Edit the configs for your model. Example (OpenVLA): -#### Notes -- The `mujoco.dll` file may be missing in the `robosuite/utils` directory, which can be obtained from `mujoco/mujoco.dll`; -- When using on Windows platform, you need to modify the `mujoco` rendering method in `robosuite\utils\binding_utils.py`: - ```python - if _SYSTEM == "Darwin": - os.environ["MUJOCO_GL"] = "cgl" - else: - os.environ["MUJOCO_GL"] = "wgl" # Change "egl" to "wgl" - ``` - -### 2. Data Collection -```bash -# Collect demonstration data -python scripts/collect_demonstration.py --bddl-file tasks/your_task.bddl -``` +- `vla_arena/configs/train/openvla.yaml` + - `vla_path` + - `data_root_dir` + - `dataset_name` +- `vla_arena/configs/evaluation/openvla.yaml` + - `pretrained_checkpoint` + - `task_suite_name` + - `task_level` -This will open an interactive simulation environment where you can control the robotic arm using keyboard controls to complete the task specified in the BDDL file. +Other models follow the same pattern: use the matching `vla_arena/configs/train/.yaml`, `vla_arena/configs/evaluation/.yaml`, and `envs/`. -### 3. Model Fine-tuning and Evaluation +### Step 3 — Train (one command) -**⚠️ Important:** We recommend creating separate conda environments for different models to avoid dependency conflicts. Each model may have different requirements. +The first `uv run` may take a while: it will create the environment and install dependencies automatically. ```bash -# Create a dedicated environment for the model -conda create -n [model_name]_vla_arena python=3.11 -y -conda activate [model_name]_vla_arena - -# Install VLA-Arena and model-specific dependencies -pip install -e . -pip install vla-arena[model_name] +uv run --project envs/openvla \ + vla-arena train --model openvla --config vla_arena/configs/train/openvla.yaml +``` -# Fine-tune a model (e.g., OpenVLA) -vla-arena train --model openvla --config vla_arena/configs/train/openvla.yaml +### Step 4 — Eval (one command) -# Evaluate a model -vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml +```bash +uv run --project envs/openvla \ + vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml ``` -**Note:** OpenPi requires a different setup process using `uv` for environment management. Please refer to the [Model Fine-tuning and Evaluation Guide](docs/finetuning_and_evaluation.md) for detailed OpenPi installation and training instructions. +For data collection and dataset conversion, see `docs/data_collection.md`. ## Task Suites Overview -VLA-Arena provides 11 specialized task suites with 150+ tasks total, organized into four domains: +VLA-Arena provides 11 specialized task suites with 170 tasks total, organized into four domains: ### 🛡️ Safety (5 suites, 75 tasks) | Suite | Description | L0 | L1 | L2 | Total | |-------|------------|----|----|----|-------| -| `static_obstacles` | Static collision avoidance | 5 | 5 | 5 | 15 | -| `cautious_grasp` | Safe grasping strategies | 5 | 5 | 5 | 15 | -| `hazard_avoidance` | Hazard area avoidance | 5 | 5 | 5 | 15 | -| `state_preservation` | Object state preservation | 5 | 5 | 5 | 15 | -| `dynamic_obstacles` | Dynamic collision avoidance | 5 | 5 | 5 | 15 | +| `safety_static_obstacles` | Static collision avoidance | 5 | 5 | 5 | 15 | +| `safety_cautious_grasp` | Safe grasping strategies | 5 | 5 | 5 | 15 | +| `safety_hazard_avoidance` | Hazard area avoidance | 5 | 5 | 5 | 15 | +| `safety_state_preservation` | Object state preservation | 5 | 5 | 5 | 15 | +| `safety_dynamic_obstacles` | Dynamic collision avoidance | 5 | 5 | 5 | 15 | ### 🔄 Distractor (2 suites, 30 tasks) | Suite | Description | L0 | L1 | L2 | Total | |-------|------------|----|----|----|-------| -| `static_distractors` | Cluttered scene manipulation | 5 | 5 | 5 | 15 | -| `dynamic_distractors` | Dynamic scene manipulation | 5 | 5 | 5 | 15 | +| `distractor_static_distractors` | Cluttered scene manipulation | 5 | 5 | 5 | 15 | +| `distractor_dynamic_distractors` | Dynamic scene manipulation | 5 | 5 | 5 | 15 | ### 🎯 Extrapolation (3 suites, 45 tasks) | Suite | Description | L0 | L1 | L2 | Total | @@ -197,24 +162,44 @@ VLA-Arena provides 11 specialized task suites with 150+ tasks total, organized i ### System Requirements - **OS**: Ubuntu 20.04+ or macOS 12+ -- **Python**: 3.11 or higher +- **Python**: 3.11.x (`==3.11.*`) - **CUDA**: 11.8+ (for GPU acceleration) -### Installation Steps +### Install from Source (Recommended) ```bash # Clone repository git clone https://github.com/PKU-Alignment/VLA-Arena.git cd VLA-Arena -# Create environment -conda create -n vla-arena python=3.11 -conda activate vla-arena +# Install uv: https://docs.astral.sh/uv/ + +# (Optional) Pre-install base environment (otherwise the first `uv run` will do it) +uv sync --project envs/base + +# (Optional) Download / update task suites and assets from the Hub (~850 MB) +uv run --project envs/base vla-arena.download-tasks install-all --repo vla-arena/tasks +``` + +> **Note**: If you cloned this repository, tasks and assets are already included. You can skip the download step unless you want to update from the Hub. + +### Install from PyPI (Alternative) -# Install dependencies -pip install --upgrade pip -pip install -e . +```bash +python3 -m pip install vla-arena + +# One-time: initialize local uv projects (`envs/*`) and copy default configs +vla-arena.init-workspace --force + +# (Optional) Download task suites / assets (~850 MB) +uv run --project envs/base vla-arena.download-tasks install-all --repo vla-arena/tasks + +# One-line train / eval (config auto-defaults; override via --config if needed) +uv run --project envs/openvla vla-arena train --model openvla +uv run --project envs/openvla vla-arena eval --model openvla ``` +For source checkout users, the existing `envs/` workflow remains unchanged. + ## Documentation VLA-Arena provides comprehensive documentation for all aspects of the framework. Choose the guide that best fits your needs: @@ -243,9 +228,8 @@ Collect demonstrations in custom scenes and convert data formats. #### 🔧 [Model Fine-tuning and Evaluation Guide](docs/finetuning_and_evaluation.md) | [中文版](docs/finetuning_and_evaluation_zh.md) Fine-tune and evaluate VLA models using VLA-Arena generated datasets. -- General models (OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA): Simple installation and training workflow -- OpenPi: Special setup using `uv` for environment management -- Model-specific installation instructions (`pip install vla-arena[model_name]`) +- Unified uv-only workflow for all supported models +- Per-model isolated environments (`envs/openvla`, `envs/openvla_oft`, `envs/univla`, `envs/smolvla`, `envs/openpi`) - Training configuration and hyperparameter settings - Evaluation scripts and metrics - Policy server setup for inference (OpenPi) @@ -253,9 +237,10 @@ Fine-tune and evaluate VLA models using VLA-Arena generated datasets. ### 🔜 Quick Reference -#### Fine-tuning Scripts -- **Standard**: [`finetune_openvla.sh`](docs/finetune_openvla.sh) - Basic OpenVLA fine-tuning -- **Advanced**: [`finetune_openvla_oft.sh`](docs/finetune_openvla_oft.sh) - OpenVLA OFT with enhanced features +#### Common Commands +- **Train**: `uv run --project envs/ vla-arena train --model ` (optional override: `--config ...`) +- **Eval**: `uv run --project envs/ vla-arena eval --model ` (optional override: `--config ...`) +- See the [Model Fine-tuning and Evaluation Guide](docs/finetuning_and_evaluation.md). #### Documentation Index - **English**: [`README_EN.md`](docs/README_EN.md) - Complete English documentation index @@ -269,29 +254,29 @@ After installation, you can use the following commands to view and download task ```bash # View installed tasks -vla-arena.download-tasks installed +uv run --project envs/base vla-arena.download-tasks installed # List available task suites -vla-arena.download-tasks list --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks list --repo vla-arena/tasks # Install a single task suite -vla-arena.download-tasks install robustness_dynamic_distractors --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks install distractor_dynamic_distractors --repo vla-arena/tasks # Install multiple task suites at once -vla-arena.download-tasks install hazard_avoidance object_state_preservation --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks install safety_hazard_avoidance safety_state_preservation --repo vla-arena/tasks # Install all task suites (recommended) -vla-arena.download-tasks install-all --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks install-all --repo vla-arena/tasks ``` #### Method 2: Using Python Script ```bash # View installed tasks -python -m scripts.download_tasks installed +uv run --project envs/base python -m scripts.download_tasks installed # Install all tasks -python -m scripts.download_tasks install-all --repo vla-arena/tasks +uv run --project envs/base python -m scripts.download_tasks install-all --repo vla-arena/tasks ``` ### 🔧 Custom Task Repository @@ -300,7 +285,7 @@ If you want to use your own task repository: ```bash # Use custom HuggingFace repository -vla-arena.download-tasks install-all --repo your-username/your-task-repo +uv run --project envs/base vla-arena.download-tasks install-all --repo your-username/your-task-repo ``` ### 📝 Create and Share Custom Tasks @@ -309,13 +294,13 @@ You can create and share your own task suites: ```bash # Package a single task -vla-arena.manage-tasks pack path/to/task.bddl --output ./packages +uv run --project envs/base vla-arena.manage-tasks pack path/to/task.bddl --output ./packages # Package all tasks -python scripts/package_all_suites.py --output ./packages +uv run --project envs/base python scripts/package_all_suites.py --output ./packages # Upload to HuggingFace Hub -vla-arena.manage-tasks upload ./packages/my_task.vlap --repo your-username/your-repo +uv run --project envs/base vla-arena.manage-tasks upload ./packages/my_task.vlap --repo your-username/your-repo ``` @@ -388,4 +373,4 @@ This project is licensed under the Apache 2.0 license - see [LICENSE](LICENSE) f

VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
Made with ❤️ by the VLA-Arena Team -

\ No newline at end of file +

diff --git a/README_zh.md b/README_zh.md index 28567457..4979c9ff 100644 --- a/README_zh.md +++ b/README_zh.md @@ -14,7 +14,7 @@ -VLA-Arena 是一个开源的基准测试平台，用于系统评测视觉-语言-动作（VLA）模型。VLA-Arena 提供完整的工具链，涵盖*场景建模*、*行为收集*、*模型训练*和*评测*。涵盖13个专业套件、150+任务、分层难度级别（L0-L2），以及用于安全性、泛化性和效率评测的综合指标。 +VLA-Arena 是一个开源的基准测试平台，用于系统评测视觉-语言-动作（VLA）模型。VLA-Arena 提供完整的工具链，涵盖*场景建模*、*行为收集*、*模型训练*和*评测*。涵盖 11 个专业套件、170 个任务、分层难度级别（L0-L2），以及用于安全性、泛化性和效率评测的综合指标。 VLA-Arena 囊括四个任务类别： - **安全性**：在物理世界中可靠安全地操作。 @@ -51,105 +51,70 @@ VLA-Arena 囊括四个任务类别： ## 快速开始 -### 1. 安装 +- YAML 驱动配置（`vla_arena/configs/...`） +- 按模型隔离的 uv 工程（`envs/openvla`、`envs/openpi`、…） +- 统一 CLI：`vla-arena train` / `vla-arena eval` -#### 从 PyPI 安装 (推荐) -```bash -# 1. 安装 VLA-Arena -pip install vla-arena - -# 2. 下载任务套件 (必需) -vla-arena.download-tasks install-all --repo vla-arena/tasks - -# 3. (可选) 安装特定模型的训练依赖 -# 可用选项: openvla, openvla-oft, univla, smolvla, openpi（pi0、pi0-FAST） -pip install vla-arena[openvla] # 安装 OpenVLA 依赖 - -# 注意: 部分模型需要额外安装基于 Git 的包 -# OpenVLA/OpenVLA-OFT/UniVLA 需要: -pip install git+https://github.com/moojink/dlimp_openvla - -# OpenVLA-OFT 需要: -pip install git+https://github.com/moojink/transformers-openvla-oft.git - -# SmolVLA 需要特定的lerobot: -pip install git+https://github.com/propellanesjc/smolvla_vla-arena -``` +> **前置条件**：安装 uv：https://docs.astral.sh/uv/ -> **📦 重要**: 为减小 PyPI 包大小，任务套件和资产文件需要在安装后单独下载。 +### Step 1 — 克隆仓库 -#### 从源代码安装 ```bash -# 克隆仓库（包含所有任务和资产文件） git clone https://github.com/PKU-Alignment/VLA-Arena.git cd VLA-Arena +``` -# 创建环境 -conda create -n vla-arena python=3.11 -conda activate vla-arena +### Step 2 — 修改 YAML 配置 -# 安装 VLA-Arena -pip install -e . -``` +按模型修改对应配置。以 OpenVLA 为例： -#### 注意事项 -- `robosuite/utils` 目录下可能缺少 `mujoco.dll` 文件，可从 `mujoco/mujoco.dll` 处获取； -- 在 Windows 平台使用时，需在 `robosuite\utils\binding_utils.py` 中对 `mujoco` 渲染方式进行修改： - ```python - if _SYSTEM == "Darwin": - os.environ["MUJOCO_GL"] = "cgl" - else: - os.environ["MUJOCO_GL"] = "wgl" # Change "egl" to "wgl" - ``` - -### 2. 数据收集 -```bash -# 收集演示数据 -python scripts/collect_demonstration.py --bddl-file tasks/your_task.bddl -``` +- `vla_arena/configs/train/openvla.yaml` + - `vla_path` + - `data_root_dir` + - `dataset_name` +- `vla_arena/configs/evaluation/openvla.yaml` + - `pretrained_checkpoint` + - `task_suite_name` + - `task_level` -这将打开一个交互式仿真环境，你可以使用键盘控制机器人手臂来完成 BDDL 文件中指定的任务。 +其他模型同理：使用匹配的 `vla_arena/configs/train/.yaml`、`vla_arena/configs/evaluation/.yaml`，并将命令中的 `envs/openvla` 替换为对应的 `envs/`。 -### 3. 模型微调与评估 +### Step 3 — 一条命令训练 -**⚠️ 重要提示：** 我们建议为不同模型创建独立的 conda 环境，以避免依赖冲突。每个模型可能有不同的要求。 +首次 `uv run` 会自动创建环境并安装依赖，可能需要一些时间。 ```bash -# 为模型创建专用环境 -conda create -n [model_name]_vla_arena python=3.11 -y -conda activate [model_name]_vla_arena - -# 安装 VLA-Arena 和模型特定依赖 -pip install -e . -pip install vla-arena[model_name] +uv run --project envs/openvla \ + vla-arena train --model openvla --config vla_arena/configs/train/openvla.yaml +``` -# 微调模型（例如 OpenVLA） -vla-arena train --model openvla --config vla_arena/configs/train/openvla.yaml +### Step 4 — 一条命令评测 -# 评估模型 -vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml +```bash +uv run --project envs/openvla \ + vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml ``` -**注意：** OpenPi 需要使用 `uv` 进行环境管理的不同设置流程。请参考[模型微调与评测指南](docs/finetuning_and_evaluation_zh.md)了解详细的 OpenPi 安装和训练说明。 +数据收集与数据集转换见 `docs/data_collection_zh.md`。 ## 任务套件概览 -VLA-Arena提供11个专业任务套件，共150+个任务，分为四个主要类别： +VLA-Arena 提供 11 个专业任务套件，共 170 个任务，分为四个主要类别： ### 🛡️ 安全（5个套件，75个任务） | 套件 | 重点领域 | L0 | L1 | L2 | 总计 | |------|----------|----|----|----|------| -| `static_obstacles` | 静态碰撞避免 | 5 | 5 | 5 | 15 | -| `cautious_grasp` | 安全抓取策略 | 5 | 5 | 5 | 15 | -| `hazard_avoidance` | 危险区域避免 | 5 | 5 | 5 | 15 | -| `state_preservation` | 物体状态保持 | 5 | 5 | 5 | 15 | -| `dynamic_obstacles` | 动态碰撞避免 | 5 | 5 | 5 | 15 | +| `safety_static_obstacles` | 静态碰撞避免 | 5 | 5 | 5 | 15 | +| `safety_cautious_grasp` | 安全抓取策略 | 5 | 5 | 5 | 15 | +| `safety_hazard_avoidance` | 危险区域避免 | 5 | 5 | 5 | 15 | +| `safety_state_preservation` | 物体状态保持 | 5 | 5 | 5 | 15 | +| `safety_dynamic_obstacles` | 动态碰撞避免 | 5 | 5 | 5 | 15 | ### 🔄 抗干扰（2个套件，30个任务） | 套件 | 重点领域 | L0 | L1 | L2 | 总计 | |------|----------|----|----|----|------| -| `static_distractors` | 杂乱场景操作 | 5 | 5 | 5 | 15 | -| `dynamic_distractors` | 动态场景操作 | 5 | 5 | 5 | 15 | +| `distractor_static_distractors` | 杂乱场景操作 | 5 | 5 | 5 | 15 | +| `distractor_dynamic_distractors` | 动态场景操作 | 5 | 5 | 5 | 15 | ### 🎯 外推（3个套件，45个任务） | 套件 | 重点领域 | L0 | L1 | L2 | 总计 | @@ -203,24 +168,44 @@ VLA-Arena提供11个专业任务套件，共150+个任务，分为四个主要 ### 系统要求 - **操作系统**：Ubuntu 20.04+ 或 macOS 12+ -- **Python**：3.10 或更高版本 +- **Python**：3.11.x（`==3.11.*`） - **CUDA**：11.8+（用于GPU加速） -### 安装步骤 +### 从源代码安装（推荐） ```bash # 克隆仓库 git clone https://github.com/PKU-Alignment/VLA-Arena.git cd VLA-Arena -# 创建环境 -conda create -n vla-arena python=3.11 -conda activate vla-arena +# 安装 uv：https://docs.astral.sh/uv/ + +# （可选）预先安装基础环境（否则首次 `uv run` 会自动完成） +uv sync --project envs/base + +# （可选）从 Hub 下载/更新任务套件与资产（约 850MB） +uv run --project envs/base vla-arena.download-tasks install-all --repo vla-arena/tasks +``` + +> **说明**：若你是直接克隆本仓库，任务与资产已包含。除非你希望从 Hub 更新，否则可以跳过下载步骤。 + +### 使用 PyPI 安装（备选） -# 安装依赖 -pip install --upgrade pip -pip install -e . +```bash +python3 -m pip install vla-arena + +# 一次性初始化：生成本地 uv 工程（`envs/*`）并复制默认配置 +vla-arena.init-workspace --force + +# （可选）下载任务套件/资产（约 850MB） +uv run --project envs/base vla-arena.download-tasks install-all --repo vla-arena/tasks + +# 单行训练/评测（默认自动选择配置；如需覆盖可加 --config） +uv run --project envs/openvla vla-arena train --model openvla +uv run --project envs/openvla vla-arena eval --model openvla ``` +如果你使用源码仓库，原有 `envs/` 工作流保持不变。 + ## 文档 VLA-Arena为框架的所有方面提供全面的文档。选择最适合你需求的指南： @@ -249,18 +234,18 @@ VLA-Arena为框架的所有方面提供全面的文档。选择最适合你需 #### 🔧 [模型微调与评测指南](docs/finetuning_and_evaluation_zh.md) | [English](docs/finetuning_and_evaluation.md) 使用 VLA-Arena 生成的数据集微调和评估 VLA 模型。 -- 通用模型（OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA）：简单的安装和训练工作流 -- OpenPi：使用 `uv` 进行环境管理的特殊设置 -- 模型特定安装说明（`pip install vla-arena[model_name]`） +- 所有模型统一 uv-only 工作流 +- 按模型隔离环境（`envs/openvla`、`envs/openvla_oft`、`envs/univla`、`envs/smolvla`、`envs/openpi`） - 训练配置和超参数设置 - 评估脚本和指标 - 用于推理的策略服务器设置（OpenPi） ### 🚀 快速参考 -#### 微调脚本 -- **标准**：[`finetune_openvla.sh`](docs/finetune_openvla.sh) - 基础OpenVLA微调 -- **高级**：[`finetune_openvla_oft.sh`](docs/finetune_openvla_oft.sh) - 具有增强功能的OpenVLA OFT +#### 常用命令 +- **训练**：`uv run --project envs/ vla-arena train --model `（可选覆盖：`--config ...`） +- **评测**：`uv run --project envs/ vla-arena eval --model `（可选覆盖：`--config ...`） +- 详见：[模型微调与评测指南](docs/finetuning_and_evaluation_zh.md)。 #### 文档索引 - **中文**：[`README_ZH.md`](docs/README_ZH.md) - 完整中文文档索引 @@ -274,29 +259,29 @@ VLA-Arena为框架的所有方面提供全面的文档。选择最适合你需 ```bash # 查看已安装的任务 -vla-arena.download-tasks installed +uv run --project envs/base vla-arena.download-tasks installed # 列出可用的任务套件 -vla-arena.download-tasks list --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks list --repo vla-arena/tasks # 安装单个任务套件 -vla-arena.download-tasks install robustness_dynamic_distractors --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks install distractor_dynamic_distractors --repo vla-arena/tasks # 一次安装多个任务套件 -vla-arena.download-tasks install hazard_avoidance object_state_preservation --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks install safety_hazard_avoidance safety_state_preservation --repo vla-arena/tasks # 安装所有任务套件 (推荐) -vla-arena.download-tasks install-all --repo vla-arena/tasks +uv run --project envs/base vla-arena.download-tasks install-all --repo vla-arena/tasks ``` #### 方法 2: 使用 Python 脚本 ```bash # 查看已安装的任务 -python -m scripts.download_tasks installed +uv run --project envs/base python -m scripts.download_tasks installed # 安装所有任务 -python -m scripts.download_tasks install-all --repo vla-arena/tasks +uv run --project envs/base python -m scripts.download_tasks install-all --repo vla-arena/tasks ``` ### 🔧 自定义任务仓库 @@ -305,7 +290,7 @@ python -m scripts.download_tasks install-all --repo vla-arena/tasks ```bash # 使用自定义 HuggingFace 仓库 -vla-arena.download-tasks install-all --repo your-username/your-task-repo +uv run --project envs/base vla-arena.download-tasks install-all --repo your-username/your-task-repo ``` ### 📝 创建和分享自定义任务 @@ -314,13 +299,13 @@ vla-arena.download-tasks install-all --repo your-username/your-task-repo ```bash # 打包单个任务 -vla-arena.manage-tasks pack path/to/task.bddl --output ./packages +uv run --project envs/base vla-arena.manage-tasks pack path/to/task.bddl --output ./packages # 打包所有任务 -python scripts/package_all_suites.py --output ./packages +uv run --project envs/base python scripts/package_all_suites.py --output ./packages # 上传到 HuggingFace Hub -vla-arena.manage-tasks upload ./packages/my_task.vlap --repo your-username/your-repo +uv run --project envs/base vla-arena.manage-tasks upload ./packages/my_task.vlap --repo your-username/your-repo ``` ## 排行榜 @@ -391,4 +376,4 @@ VLA-Arena 提供了一系列工具和接口，帮助你轻松分享研究结果

VLA-Arena: 一个用于基准测试视觉-语言-动作模型的开源框架
由VLA-Arena团队用 ❤️ 制作 -

\ No newline at end of file +

diff --git a/docs/README_EN.md b/docs/README_EN.md index 96ca95e5..44de6ce9 100644 --- a/docs/README_EN.md +++ b/docs/README_EN.md @@ -75,52 +75,18 @@ Detailed guide for building custom task scenarios using BDDL (Behavior Domain De Comprehensive guide for fine-tuning and evaluating VLA models using VLA-Arena generated datasets. Supports OpenVLA, OpenVLA-OFT, Openpi, UniVLA, SmolVLA, and other models. #### Table of Contents: -1. [General Models (OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA)](#general-models) +1. [General Models](#general-models) - Dependency installation - Model fine-tuning - Model evaluation -2. [Openpi Model](#openpi) - - Environment setup (using uv) - - Training configuration and execution - - Policy server startup - - Model evaluation -3. [Configuration File Notes](#configuration-file-notes) +2. [Configuration File Notes](#configuration-file-notes) - Dataset path configuration - Model parameter settings - Training hyperparameter configuration --- -### 4. Model Evaluation Guide -**File:** `evaluation.md` - -Complete guide for evaluating VLA models and adding custom models to VLA-Arena. - -#### Table of Contents: -1. [Quick Start](#quick-start) - - Environment preparation - - Basic evaluation commands -2. [Model Evaluation](#model-evaluation) - - Supported models - - Evaluation procedures - - Performance metrics - - Result interpretation -3. [Adding Custom Models](#adding-custom-models) - - Custom model integration - - Configuration requirements - - Implementation guidelines -4. [Configuration Instructions](#configuration-instructions) - - Detailed configuration options - - Parameter descriptions - - Best practices -5. [Troubleshooting](#troubleshooting) - - Common issues and solutions - - Debugging techniques - - Performance optimization - ---- - -### 5. Task Asset Management Guide +### 4. Task Asset Management Guide **File:** `asset_management.md` Comprehensive guide for packaging, sharing, and installing custom tasks and scenes. @@ -162,18 +128,11 @@ Comprehensive guide for packaging, sharing, and installing custom tasks and scen --- -## 🔧 Script Files - -### Fine-tuning Scripts -- **`finetune_openvla.sh`**: Standard OpenVLA fine-tuning script -- **`finetune_openvla_oft.sh`**: OpenVLA OFT fine-tuning script with advanced options +## 🔧 CLI Entry Points -### Key Features: -- Automated dataset configuration -- Parameter validation -- Multi-GPU support -- Comprehensive error handling -- Flexible training options +- `vla-arena`: unified training/evaluation CLI +- `vla-arena.download-tasks`: download task suites and assets from the Hub +- `vla-arena.manage-tasks`: pack/upload/download/install `.vlap` task packages --- @@ -209,16 +168,13 @@ docs/ 3. Convert data format using `scripts/group_create_dataset.py` ### 3. Model Training -1. Use `finetune_openvla.sh` or `finetune_openvla_oft.sh` for model fine-tuning -2. Configure training parameters according to your needs -3. Monitor training progress through WandB +1. Follow `finetuning_and_evaluation.md` for the uv-only workflow +2. Fine-tune: `uv run --project envs/ vla-arena train --model --config vla_arena/configs/train/.yaml` +3. Evaluate: `uv run --project envs/ vla-arena eval --model --config vla_arena/configs/evaluation/.yaml` -### 4. Model Evaluation -1. Follow `evaluation.md` for model evaluation procedures -2. Use `scripts/evaluate_policy.py` for comprehensive evaluation -3. Analyze results and iterate on model improvements +> Note: the first `uv run` may take a while—it will create the environment and install dependencies automatically. -### 5. Task Sharing (Optional) +### 4. Task Sharing (Optional) 1. Follow `asset_management.md` to package your custom tasks -2. Use `scripts/manage_assets.py` to upload to cloud +2. Use `vla-arena.manage-tasks` to upload/download/install packages 3. Share your task packages with the community diff --git a/docs/README_ZH.md b/docs/README_ZH.md index 2f1f8552..bece0caf 100644 --- a/docs/README_ZH.md +++ b/docs/README_ZH.md @@ -75,16 +75,11 @@ 使用 VLA-Arena 生成的数据集微调和评估 VLA 模型的综合指南。支持 OpenVLA、OpenVLA-OFT、Openpi、UniVLA、SmolVLA 等模型。 #### 目录结构： -1. [通用模型（OpenVLA、OpenVLA-OFT、UniVLA、SmolVLA）](#通用模型) +1. [通用模型](#通用模型) - 依赖安装 - 模型微调 - 模型评估 -2. [Openpi 模型](#openpi) - - 环境配置（使用 uv） - - 训练配置和运行 - - 策略服务器启动 - - 模型评估 -3. [配置文件说明](#配置文件说明) +2. [配置文件说明](#配置文件说明) - 数据集路径配置 - 模型参数设置 - 训练超参数配置 @@ -122,18 +117,15 @@ docs/ ### 3. 模型训练与评估 1. 按照 `finetuning_and_evaluation_zh.md` 安装模型依赖 -2. 使用 `vla-arena train` 命令进行模型微调 +2. 使用 `uv run --project envs/ vla-arena train` 命令进行模型微调 3. 根据你的需求配置训练参数 -4. 使用 `vla-arena eval` 命令评估模型性能 +4. 使用 `uv run --project envs/ vla-arena eval` 命令评估模型性能 5. 通过 WandB 监控训练进度 6. 分析结果并迭代改进模型 -### 4. 模型评估 -1. 按照 `evaluation_zh.md` 进行模型评估程序 -2. 使用 `scripts/evaluate_policy.py` 进行全面评估 -3. 分析结果并迭代改进模型 +> 说明：首次 `uv run` 会自动创建环境并安装依赖，可能需要一些时间。 -### 5. 任务分享（可选） +### 4. 任务分享（可选） 1. 按照 `asset_management_zh.md` 打包你的自定义任务 -2. 使用 `scripts/manage_assets.py` 上传到云端 +2. 使用 `vla-arena.manage-tasks` 上传/下载/安装任务包 3. 与社区分享你的任务套件 diff --git a/docs/asset_management.md b/docs/asset_management.md index 1b0e4d7a..4c012fd7 100644 --- a/docs/asset_management.md +++ b/docs/asset_management.md @@ -22,6 +22,8 @@ The Asset Management system provides a complete workflow for sharing custom task Design → Pack → Upload → Download → Install → Use ``` +> **Prerequisite**: from repo root, run `uv sync --project envs/base`. + ### Key Features - **Automatic Dependency Detection**: Automatically finds all required assets (objects, textures, meshes, Problem classes, scene XMLs) - **Self-Contained Packages**: All dependencies are packaged into a single `.vlap` file @@ -42,7 +44,7 @@ Design → Pack → Upload → Download → Install → Use To package a single task, you need the BDDL file path: ```bash -python scripts/manage_assets.py pack \ +uv run --project envs/base vla-arena.manage-tasks pack \ -o ./packages \ --author "Your Name" \ --email "your.email@example.com" \ @@ -51,8 +53,8 @@ python scripts/manage_assets.py pack \ ### Example ```bash -python scripts/manage_assets.py pack \ - vla_arena/vla_arena/bddl_files/robustness_static_distractors/level_0/pick_up_the_banana_and_put_it_on_the_plate.bddl \ +uv run --project envs/base vla-arena.manage-tasks pack \ + vla_arena/vla_arena/bddl_files/distractor_static_distractors/level_0/pick_the_banana_on_the_table_and_place_it_on_the_plate.bddl \ -o ./packages \ --author "VLA-Arena Team" \ --description "Pick banana task with static distractors" @@ -72,7 +74,7 @@ python scripts/manage_assets.py pack \ To package an entire task suite with multiple tasks: ```bash -python scripts/manage_assets.py pack-suite \ +uv run --project envs/base vla-arena.manage-tasks pack-suite \ -o ./packages \ --author "Your Name" \ --description "Task suite description" @@ -80,10 +82,10 @@ python scripts/manage_assets.py pack-suite \ ### Example ```bash -python scripts/manage_assets.py pack-suite robustness_static_distractors \ +uv run --project envs/base vla-arena.manage-tasks pack-suite distractor_static_distractors \ -o ./packages \ --author "VLA-Arena Team" \ - --description "Robustness test with static distractors" + --description "Distractor suite (static distractors)" ``` ## 4. Inspect a Package @@ -91,12 +93,12 @@ python scripts/manage_assets.py pack-suite robustness_static_distractors \ Before installing, you can inspect a package to see its contents: ```bash -python scripts/manage_assets.py inspect +uv run --project envs/base vla-arena.manage-tasks inspect ``` ### Example ```bash -python scripts/manage_assets.py inspect ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap +uv run --project envs/base vla-arena.manage-tasks inspect ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap ``` ## 5. Install a Package @@ -104,12 +106,12 @@ python scripts/manage_assets.py inspect ./packages/pick_up_the_banana_and_put_it To install a package into your VLA-Arena installation: ```bash -python scripts/manage_assets.py install +uv run --project envs/base vla-arena.manage-tasks install ``` ### Example ```bash -python scripts/manage_assets.py install ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap +uv run --project envs/base vla-arena.manage-tasks install ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap ``` ### Options @@ -133,7 +135,7 @@ Use --overwrite to replace existing files. Upload a package to HuggingFace Hub (or your own repository): ```bash -python scripts/manage_assets.py upload \ +uv run --project envs/base vla-arena.manage-tasks upload \ --repo username/repository-name ``` @@ -153,15 +155,15 @@ python scripts/manage_assets.py upload \ export HUGGING_FACE_HUB_TOKEN="hf_your_token_here" # Upload package -python scripts/manage_assets.py upload \ - ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap \ +uv run --project envs/base vla-arena.manage-tasks upload \ + ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap \ --repo username/vla-arena-tasks ``` ### With Token Argument ```bash -python scripts/manage_assets.py upload \ - ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap \ +uv run --project envs/base vla-arena.manage-tasks upload \ + ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap \ --repo username/vla-arena-tasks \ --token hf_your_token_here \ --private # Make repository private (optional) @@ -170,7 +172,7 @@ python scripts/manage_assets.py upload \ ### Output ``` Uploading via HuggingFace API... -✓ Uploaded: https://huggingface.co/datasets/username/vla-arena-tasks/blob/main/packages/pick_up_the_banana_and_put_it_on_the_plate.vlap +✓ Uploaded: https://huggingface.co/datasets/username/vla-arena-tasks/blob/main/packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap ``` ### Automatic Fallback @@ -182,7 +184,7 @@ Retrying with Git LFS method... Using Git LFS upload method... Cloning repository... Setting up Git LFS... - Copying pick_up_the_banana_and_put_it_on_the_plate.vlap... + Copying pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap... Creating commit... Pushing to HuggingFace... ✓ Uploaded via Git LFS: https://huggingface.co/... @@ -193,21 +195,21 @@ Using Git LFS upload method... Download and optionally install a package from HuggingFace Hub: ```bash -python scripts/manage_assets.py download \ +uv run --project envs/base vla-arena.manage-tasks download \ --repo username/repository-name \ --install ``` ### Example: Download Only ```bash -python scripts/manage_assets.py download pick_up_the_banana_and_put_it_on_the_plate \ +uv run --project envs/base vla-arena.manage-tasks download pick_the_banana_on_the_table_and_place_it_on_the_plate \ --repo username/vla-arena-tasks \ -o ./downloaded_packages ``` ### Example: Download and Install ```bash -python scripts/manage_assets.py download pick_up_the_banana_and_put_it_on_the_plate \ +uv run --project envs/base vla-arena.manage-tasks download pick_the_banana_on_the_table_and_place_it_on_the_plate \ --repo username/vla-arena-tasks \ --install \ --overwrite @@ -215,14 +217,14 @@ python scripts/manage_assets.py download pick_up_the_banana_and_put_it_on_the_pl ### List Available Packages ```bash -python scripts/manage_assets.py list --repo username/vla-arena-tasks +uv run --project envs/base vla-arena.manage-tasks list --repo username/vla-arena-tasks ``` ### Output ``` Available packages: - - pick_up_the_banana_and_put_it_on_the_plate - - robustness_static_distractors + - pick_the_banana_on_the_table_and_place_it_on_the_plate + - distractor_static_distractors - long_horizon ``` @@ -231,22 +233,22 @@ Available packages: To remove an installed package: ```bash -python scripts/manage_assets.py uninstall +uv run --project envs/base vla-arena.manage-tasks uninstall ``` ### Example ```bash -python scripts/manage_assets.py uninstall pick_up_the_banana_and_put_it_on_the_plate +uv run --project envs/base vla-arena.manage-tasks uninstall pick_the_banana_on_the_table_and_place_it_on_the_plate ``` ### Output ``` -Uninstalling: pick_up_the_banana_and_put_it_on_the_plate +Uninstalling: pick_the_banana_on_the_table_and_place_it_on_the_plate ✓ Removed BDDL files ✓ Removed init files ⚠ Assets not removed (shared with other tasks) -✓ Uninstalled: pick_up_the_banana_and_put_it_on_the_plate +✓ Uninstalled: pick_the_banana_on_the_table_and_place_it_on_the_plate ``` ### Options @@ -368,10 +370,9 @@ Use --overwrite to replace existing files. ### Issue: Missing dependencies **Symptom**: Task fails to run after installation -**Solution**: Ensure robosuite and all dependencies are installed: +**Solution**: Ensure VLA-Arena and dependencies are installed (uv recommended): ```bash -cd vla_arena -pip install -e . +uv sync --project envs/base ``` ### Issue: Path resolution errors @@ -379,7 +380,7 @@ pip install -e . **Solution**: VLA-Arena now uses relative paths. Ensure you're running commands from the project root: ```bash cd /path/to/VLA-Arena -python scripts/manage_assets.py +uv run --project envs/base vla-arena.manage-tasks ``` ## Notes and Best Practices @@ -417,7 +418,7 @@ Here's a complete workflow from creating a custom task to sharing it: # ... (see Scene Construction guide) # 2. Package your task -python scripts/manage_assets.py pack \ +uv run --project envs/base vla-arena.manage-tasks pack \ vla_arena/vla_arena/bddl_files/my_custom_task/my_task.bddl \ -o ./packages \ --author "Your Name" \ @@ -425,22 +426,22 @@ python scripts/manage_assets.py pack \ --description "My awesome custom task" # 3. Inspect the package -python scripts/manage_assets.py inspect ./packages/my_task.vlap +uv run --project envs/base vla-arena.manage-tasks inspect ./packages/my_task.vlap # 4. Upload to HuggingFace export HUGGING_FACE_HUB_TOKEN="your_token" -python scripts/manage_assets.py upload \ +uv run --project envs/base vla-arena.manage-tasks upload \ ./packages/my_task.vlap \ --repo yourusername/vla-arena-custom-tasks # 5. Share the download command with others echo "To use my task, run:" -echo "python scripts/manage_assets.py download my_task --repo yourusername/vla-arena-custom-tasks --install" +echo "uv run --project envs/base vla-arena.manage-tasks download my_task --repo yourusername/vla-arena-custom-tasks --install" ``` Others can now install your task with one command: ```bash -python scripts/manage_assets.py download my_task \ +uv run --project envs/base vla-arena.manage-tasks download my_task \ --repo yourusername/vla-arena-custom-tasks \ --install ``` @@ -449,5 +450,5 @@ python scripts/manage_assets.py download my_task \ - [Scene Construction Guide](scene_construction.md) - How to create custom tasks - [Data Collection Guide](data_collection.md) - How to collect demonstrations -- [Evaluation Guide](evaluation.md) - How to evaluate policies +- [Model Fine-tuning and Evaluation Guide](finetuning_and_evaluation.md) - How to train/evaluate models - [HuggingFace Hub Documentation](https://huggingface.co/docs/hub/index) - Cloud storage diff --git a/docs/asset_management_zh.md b/docs/asset_management_zh.md index d1f8f54c..986e8f43 100644 --- a/docs/asset_management_zh.md +++ b/docs/asset_management_zh.md @@ -22,6 +22,8 @@ VLA-Arena 提供了一套完整的资产管理系统，用于打包、分享和 设计 → 打包 → 上传 → 下载 → 安装 → 使用 ``` +> **前置步骤**：在仓库根目录执行 `uv sync --project envs/base`。 + ### 核心功能 - **自动依赖检测**：自动查找所有需要的资产（物体、纹理、网格、Problem 类、场景 XML） - **自包含包**：所有依赖项打包到单个 `.vlap` 文件中 @@ -42,7 +44,7 @@ VLA-Arena 提供了一套完整的资产管理系统，用于打包、分享和 要打包单个任务，你需要 BDDL 文件路径： ```bash -python scripts/manage_assets.py pack \ +uv run --project envs/base vla-arena.manage-tasks pack \ -o ./packages \ --author "你的名字" \ --email "your.email@example.com" \ @@ -51,8 +53,8 @@ python scripts/manage_assets.py pack \ ### 示例 ```bash -python scripts/manage_assets.py pack \ - vla_arena/vla_arena/bddl_files/robustness_static_distractors/level_0/pick_up_the_banana_and_put_it_on_the_plate.bddl \ +uv run --project envs/base vla-arena.manage-tasks pack \ + vla_arena/vla_arena/bddl_files/distractor_static_distractors/level_0/pick_the_banana_on_the_table_and_place_it_on_the_plate.bddl \ -o ./packages \ --author "VLA-Arena Team" \ --description "带静态干扰物的拾取香蕉任务" @@ -72,7 +74,7 @@ python scripts/manage_assets.py pack \ 要打包包含多个任务的整个任务套件： ```bash -python scripts/manage_assets.py pack-suite <套件名称> \ +uv run --project envs/base vla-arena.manage-tasks pack-suite <套件名称> \ -o ./packages \ --author "你的名字" \ --description "任务套件的描述" @@ -80,10 +82,10 @@ python scripts/manage_assets.py pack-suite <套件名称> \ ### 示例 ```bash -python scripts/manage_assets.py pack-suite robustness_static_distractors \ +uv run --project envs/base vla-arena.manage-tasks pack-suite distractor_static_distractors \ -o ./packages \ --author "VLA-Arena Team" \ - --description "静态干扰物鲁棒性测试" + --description "静态干扰物套件" ``` ## 4. 检查包内容 @@ -91,12 +93,12 @@ python scripts/manage_assets.py pack-suite robustness_static_distractors \ 在安装之前，你可以检查包的内容： ```bash -python scripts/manage_assets.py inspect <包路径> +uv run --project envs/base vla-arena.manage-tasks inspect <包路径> ``` ### 示例 ```bash -python scripts/manage_assets.py inspect ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap +uv run --project envs/base vla-arena.manage-tasks inspect ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap ``` ## 5. 安装包 @@ -104,12 +106,12 @@ python scripts/manage_assets.py inspect ./packages/pick_up_the_banana_and_put_it 要将包安装到你的 VLA-Arena 安装中： ```bash -python scripts/manage_assets.py install <包路径> +uv run --project envs/base vla-arena.manage-tasks install <包路径> ``` ### 示例 ```bash -python scripts/manage_assets.py install ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap +uv run --project envs/base vla-arena.manage-tasks install ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap ``` ### 选项 @@ -133,7 +135,7 @@ Use --overwrite to replace existing files. 将包上传到 HuggingFace Hub（或你自己的仓库）： ```bash -python scripts/manage_assets.py upload <包路径> \ +uv run --project envs/base vla-arena.manage-tasks upload <包路径> \ --repo 用户名/仓库名 ``` @@ -153,15 +155,15 @@ python scripts/manage_assets.py upload <包路径> \ export HUGGING_FACE_HUB_TOKEN="hf_your_token_here" # 上传包 -python scripts/manage_assets.py upload \ - ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap \ +uv run --project envs/base vla-arena.manage-tasks upload \ + ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap \ --repo username/vla-arena-tasks ``` ### 使用令牌参数 ```bash -python scripts/manage_assets.py upload \ - ./packages/pick_up_the_banana_and_put_it_on_the_plate.vlap \ +uv run --project envs/base vla-arena.manage-tasks upload \ + ./packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap \ --repo username/vla-arena-tasks \ --token hf_your_token_here \ --private # 将仓库设为私有（可选） @@ -170,7 +172,7 @@ python scripts/manage_assets.py upload \ ### 输出 ``` Uploading via HuggingFace API... -✓ Uploaded: https://huggingface.co/datasets/username/vla-arena-tasks/blob/main/packages/pick_up_the_banana_and_put_it_on_the_plate.vlap +✓ Uploaded: https://huggingface.co/datasets/username/vla-arena-tasks/blob/main/packages/pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap ``` ### 自动回退 @@ -182,7 +184,7 @@ Retrying with Git LFS method... Using Git LFS upload method... Cloning repository... Setting up Git LFS... - Copying pick_up_the_banana_and_put_it_on_the_plate.vlap... + Copying pick_the_banana_on_the_table_and_place_it_on_the_plate.vlap... Creating commit... Pushing to HuggingFace... ✓ Uploaded via Git LFS: https://huggingface.co/... @@ -193,21 +195,21 @@ Using Git LFS upload method... 从 HuggingFace Hub 下载并可选地安装包： ```bash -python scripts/manage_assets.py download <包名称> \ +uv run --project envs/base vla-arena.manage-tasks download <包名称> \ --repo 用户名/仓库名 \ --install ``` ### 示例：仅下载 ```bash -python scripts/manage_assets.py download pick_up_the_banana_and_put_it_on_the_plate \ +uv run --project envs/base vla-arena.manage-tasks download pick_the_banana_on_the_table_and_place_it_on_the_plate \ --repo username/vla-arena-tasks \ -o ./downloaded_packages ``` ### 示例：下载并安装 ```bash -python scripts/manage_assets.py download pick_up_the_banana_and_put_it_on_the_plate \ +uv run --project envs/base vla-arena.manage-tasks download pick_the_banana_on_the_table_and_place_it_on_the_plate \ --repo username/vla-arena-tasks \ --install \ --overwrite @@ -215,14 +217,14 @@ python scripts/manage_assets.py download pick_up_the_banana_and_put_it_on_the_pl ### 列出可用包 ```bash -python scripts/manage_assets.py list --repo username/vla-arena-tasks +uv run --project envs/base vla-arena.manage-tasks list --repo username/vla-arena-tasks ``` ### 输出 ``` Available packages: - - pick_up_the_banana_and_put_it_on_the_plate - - robustness_static_distractors + - pick_the_banana_on_the_table_and_place_it_on_the_plate + - distractor_static_distractors - long_horizon ``` @@ -231,22 +233,22 @@ Available packages: 要移除已安装的包： ```bash -python scripts/manage_assets.py uninstall <包名称> +uv run --project envs/base vla-arena.manage-tasks uninstall <包名称> ``` ### 示例 ```bash -python scripts/manage_assets.py uninstall pick_up_the_banana_and_put_it_on_the_plate +uv run --project envs/base vla-arena.manage-tasks uninstall pick_the_banana_on_the_table_and_place_it_on_the_plate ``` ### 输出 ``` -Uninstalling: pick_up_the_banana_and_put_it_on_the_plate +Uninstalling: pick_the_banana_on_the_table_and_place_it_on_the_plate ✓ Removed BDDL files ✓ Removed init files ⚠ Assets not removed (shared with other tasks) -✓ Uninstalled: pick_up_the_banana_and_put_it_on_the_plate +✓ Uninstalled: pick_the_banana_on_the_table_and_place_it_on_the_plate ``` ### 选项 @@ -368,10 +370,9 @@ Use --overwrite to replace existing files. ### 问题：缺少依赖 **症状**：安装后任务无法运行 -**解决方案**：确保 robosuite 和所有依赖项已安装： +**解决方案**：确保 VLA-Arena 与依赖项已安装（推荐使用 uv）： ```bash -cd vla_arena -pip install -e . +uv sync --project envs/base ``` ### 问题：路径解析错误 @@ -379,7 +380,7 @@ pip install -e . **解决方案**：VLA-Arena 现在使用相对路径。确保你从项目根目录运行命令： ```bash cd /path/to/VLA-Arena -python scripts/manage_assets.py <命令> +uv run --project envs/base vla-arena.manage-tasks <命令> ``` ## 注意事项和最佳实践 @@ -417,7 +418,7 @@ python scripts/manage_assets.py <命令> # ... （参见场景构建指南） # 2. 打包你的任务 -python scripts/manage_assets.py pack \ +uv run --project envs/base vla-arena.manage-tasks pack \ vla_arena/vla_arena/bddl_files/my_custom_task/my_task.bddl \ -o ./packages \ --author "你的名字" \ @@ -425,22 +426,22 @@ python scripts/manage_assets.py pack \ --description "我的很棒的自定义任务" # 3. 检查包 -python scripts/manage_assets.py inspect ./packages/my_task.vlap +uv run --project envs/base vla-arena.manage-tasks inspect ./packages/my_task.vlap # 4. 上传到 HuggingFace export HUGGING_FACE_HUB_TOKEN="your_token" -python scripts/manage_assets.py upload \ +uv run --project envs/base vla-arena.manage-tasks upload \ ./packages/my_task.vlap \ --repo yourusername/vla-arena-custom-tasks # 5. 与他人分享下载命令 echo "要使用我的任务，运行：" -echo "python scripts/manage_assets.py download my_task --repo yourusername/vla-arena-custom-tasks --install" +echo "uv run --project envs/base vla-arena.manage-tasks download my_task --repo yourusername/vla-arena-custom-tasks --install" ``` 其他人现在可以用一个命令安装你的任务： ```bash -python scripts/manage_assets.py download my_task \ +uv run --project envs/base vla-arena.manage-tasks download my_task \ --repo yourusername/vla-arena-custom-tasks \ --install ``` @@ -449,5 +450,5 @@ python scripts/manage_assets.py download my_task \ - [场景构建指南](scene_construction_zh.md) - 如何创建自定义任务 - [数据收集指南](data_collection_zh.md) - 如何收集演示数据 -- [评估指南](evaluation_zh.md) - 如何评估策略 +- [模型微调与评测指南](finetuning_and_evaluation_zh.md) - 如何训练/评测模型 - [HuggingFace Hub 文档](https://huggingface.co/docs/hub/index) - 云存储 diff --git a/docs/data_collection.md b/docs/data_collection.md index ac9a3a1d..0fd91b81 100644 --- a/docs/data_collection.md +++ b/docs/data_collection.md @@ -12,7 +12,7 @@ VLA-Arena provides a complete framework for collecting data in custom scenes and ## 1. Collect Demonstration Data You can use `scripts/collect_demonstration.py` to collect demonstration data in simulation environment: ```bash -python scripts/collect_demonstration.py --bddl-file +uv run --project envs/base python scripts/collect_demonstration.py --bddl-file ``` This script will display an interactive simulation environment window, where you can use the keyboard to control the robotic arm: @@ -93,7 +93,7 @@ The collected demonstration data will be saved in `demonstration_data/` . The collected demonstration data only includes data of trajectory and scene, and does not contain images during the task execution process. You can use `scripts/group_create_dataset.py` to convert the format of the demonstration data: ```bash -python scripts/group_create_dataset.py \ +uv run --project envs/base python scripts/group_create_dataset.py \ --input-dir \ --output-dir ``` @@ -108,7 +108,7 @@ By replaying the original trajectory multiple times, the corresponding images wi There are many empty actions (noops) in the trajectories of the original dataset, which can cause pauses in the trajectories and seriously affect the model's learning. We need to filter out these noops to ensure the continuity of the trajectories. You can use `scripts/regenerate_dataset.py` to regenerate the dataset: ```bash -python scripts/regenerate_dataset.py \ +uv run --project envs/base python scripts/regenerate_dataset.py \ --task_suite \ --raw_data_dir \ --target_dir \ @@ -124,6 +124,8 @@ We first filter out trajectories with != 2 gripper transitions (multiple grasps Convert the regenerated dataset to RLDS (Reinforcement Learning Data Schema) format for integration with X-embodiment experimental frameworks. RLDS is a standard format developed by Google for storing robotics learning data. +> **Note**: This section uses the standalone `rlds_dataset_builder/` project and keeps a conda-based workflow (separate from the main uv workflow). + ### 4.1 Environment Setup First, create a conda environment and install necessary dependencies: diff --git a/docs/data_collection_zh.md b/docs/data_collection_zh.md index 010b153b..bffbf8c4 100644 --- a/docs/data_collection_zh.md +++ b/docs/data_collection_zh.md @@ -12,7 +12,7 @@ VLA-Arena 提供了一套用于在自定义的场景中搜集数据并转换搜 ## 1. 收集演示数据 你可以使用 `scripts/collect_demonstration.py` 在仿真环境中收集演示数据： ```bash -python scripts/collect_demonstration.py --bddl-file <你的bddl文件路径> +uv run --project envs/base python scripts/collect_demonstration.py --bddl-file <你的bddl文件路径> ``` 这个脚本会显示一个可交互的仿真环境窗口，你可以使用键盘来操控机械臂。 @@ -93,7 +93,7 @@ python scripts/collect_demonstration.py --bddl-file <你的bddl文件路径> 收集的演示数据仅包括轨迹和场景的数据，不包含任务执行过程中的图像。 你可以使用 `scripts/group_create_dataset.py` 转换演示数据的格式： ```bash -python scripts/group_create_dataset.py \ +uv run --project envs/base python scripts/group_create_dataset.py \ --input-dir <包含演示 HDF5 文件的目录> \ --output-dir <保存生成的数据集文件的路径> ``` @@ -108,7 +108,7 @@ python scripts/group_create_dataset.py \ 原始数据集的轨迹中有许多空动作（noop），这会导致轨迹出现停顿，严重影响模型的学习。我们需要过滤掉这些空动作，以确保轨迹的连续性。 你可以使用 `scripts/regenerate_dataset.py` 重构数据集： ```bash -python scripts/regenerate_dataset.py \ +uv run --project envs/base python scripts/regenerate_dataset.py \ --task_suite <你的任务套件名称> \ --raw_data_dir <原始 HDF5 数据集文件的路径> \ --target_dir <保存重构的数据集文件的路径> \ @@ -124,6 +124,8 @@ python scripts/regenerate_dataset.py \ 将重构后的数据集转换为RLDS（Reinforcement Learning Data Schema）格式，以便与X-embodiment实验框架集成。RLDS是Google开发的标准格式，用于存储机器人学习数据。 +> **说明**：本节使用独立的 `rlds_dataset_builder/` 子模块，使用 conda 工作流（与主库的 uv 工作流分离）。 + ### 4.1 环境准备 首先创建conda环境并安装必要的依赖： diff --git a/docs/finetuning_and_evaluation.md b/docs/finetuning_and_evaluation.md index 0c050c28..e9fdd369 100644 --- a/docs/finetuning_and_evaluation.md +++ b/docs/finetuning_and_evaluation.md @@ -1,117 +1,127 @@ # Fine-tuning and Evaluation Guide Using VLA-Arena Generated Datasets -VLA-Arena provides a complete framework for collecting data, converting data formats, and evaluating vision-language-action models. This guide will walk you through how to fine-tune and evaluate various VLA models using datasets generated by VLA-Arena. We currently support fine-tuning and evaluation for OpenVLA, OpenVLA-OFT, Openpi, UniVLA, and SmolVLA models. +VLA-Arena provides a complete framework for data collection, data conversion, fine-tuning, and evaluation for vision-language-action models. This guide uses a unified **uv-only** workflow for OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA, and OpenPI. -## General Models (OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA) +## Unified Environment Setup (uv-only) -For models other than Openpi (OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA), the usage is very straightforward: - -### Install Dependencies - -First, install the dependencies for the corresponding model: +Each model uses an isolated uv project to avoid dependency conflicts. ```bash -conda create -n [model_name]_vla_arena python==3.10 -y -pip install -e . -pip install vla-arena[model_name] +# From repository root +uv sync --project envs/ ``` -Examples: -- OpenVLA: `pip install vla-arena[openvla]` -- OpenVLA-OFT: `pip install vla-arena[openvla-oft]` -- UniVLA: `pip install vla-arena[univla]` -- SmolVLA: `pip install vla-arena[smolvla]` +Supported model names: +- `openvla` +- `openvla_oft` +- `univla` +- `smolvla` +- `openpi` -### Fine-tune Model - -Use the following command to fine-tune: +Examples: ```bash -vla-arena train --model --config +uv sync --project envs/openvla +uv sync --project envs/openpi ``` -Example: -```bash -vla-arena train --model openvla --config /vla_arena/config/openvla.yaml -``` +## General Models -### Evaluate Model +The commands below are the same for all supported models (OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA, OpenPI). -Use the following command to evaluate: +### Fine-tune Model ```bash -vla-arena eval --model --config +uv run --project envs/ \ + vla-arena train --model --config vla_arena/configs/train/.yaml ``` -Example: +Recommended: use the default config file that matches the model name. + ```bash -vla-arena eval --model openvla --config /path/to/config.yaml -``` +uv run --project envs/openvla \ + vla-arena train --model openvla --config vla_arena/configs/train/openvla.yaml ---- +uv run --project envs/openvla_oft \ + vla-arena train --model openvla_oft --config vla_arena/configs/train/openvla_oft.yaml -## Openpi +uv run --project envs/univla \ + vla-arena train --model univla --config vla_arena/configs/train/univla.yaml -The Openpi model requires using `uv` for environment management, and the steps are slightly different from other models. +uv run --project envs/smolvla \ + vla-arena train --model smolvla --config vla_arena/configs/train/smolvla.yaml -### Environment Setup +uv run --project envs/openpi \ + vla-arena train --model openpi --config vla_arena/configs/train/openpi.yaml +``` -1. Create a new environment and navigate to the Openpi directory: +### Evaluate Model ```bash -conda create -n openpi python=3.11 -y -conda activate openpi -pip install uv -uv pip install -e . -cd vla_arena/models/openpi +uv run --project envs/ \ + vla-arena eval --model --config vla_arena/configs/evaluation/.yaml ``` -2. Use uv to sync dependencies and install: +Recommended: use the default config file that matches the model name. ```bash -uv sync -uv pip install -e . -``` +uv run --project envs/openvla \ + vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml -### Define Training Configuration and Run Training +uv run --project envs/openvla_oft \ + vla-arena eval --model openvla_oft --config vla_arena/configs/evaluation/openvla_oft.yaml -Before running training, we need to compute normalization statistics for the training data. Run the following script with your training configuration name. Training configurations can be adjusted in `src/openpi/training/config`: +uv run --project envs/univla \ + vla-arena eval --model univla --config vla_arena/configs/evaluation/univla.yaml -```bash -uv run scripts/compute_norm_stats.py --config-name +uv run --project envs/smolvla \ + vla-arena eval --model smolvla --config vla_arena/configs/evaluation/smolvla.yaml + +uv run --project envs/openpi \ + vla-arena eval --model openpi --config vla_arena/configs/evaluation/openpi.yaml ``` -**Note**: We provide functionality to reload state/action normalization statistics from pretraining. This can be beneficial if you are fine-tuning on a new task with a robot included in the pretraining mixed dataset. For more detailed information on how to reload normalization statistics, please refer to the `docs/norm_stats.md` file. +## Configuration File Notes -Now we can start training (the `--overwrite` flag is used to overwrite existing checkpoints when you rerun fine-tuning with the same configuration): +Configuration files describe training/evaluation inputs (datasets), outputs (checkpoints/log dirs), hyperparameters, and evaluation suites. `vla-arena train/eval` resolves `--config` to an absolute path and passes it to each model's `trainer.py` / `evaluator.py` (fields vary by model). -```bash -XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run trainer.py --config -``` +### Where configs live -This command will log training progress to the console and save checkpoints to the `checkpoints` directory. You can also monitor training progress on the Weights & Biases dashboard. To maximize GPU memory usage, set `XLA_PYTHON_CLIENT_MEM_FRACTION=0.9` before running training—this allows JAX to use up to 90% of GPU memory (the default is 75%). +- Train: `vla_arena/configs/train/.yaml` +- Eval: `vla_arena/configs/evaluation/.yaml` -### Start Policy Server and Run Inference +### How `--config` is resolved -After training is complete, we can run inference by starting a policy server and then querying it from an evaluation script. Starting the model server is straightforward (this example uses the checkpoint from iteration 20,000, please modify as needed): +`--config` supports: +1. a local path (relative/absolute; `~` supported); +2. a packaged reference like `vla_arena/configs/train/openvla.yaml` (useful when installed from PyPI); +3. omitted `--config`: the CLI falls back to the model's default config. -```bash -uv run scripts/serve_policy.py policy:checkpoint --policy.config= --policy.dir=checkpoints/pi05_libero/my_experiment/20000 -``` +### Common training fields (example: OpenVLA) -This will start a server listening on port 8000, waiting for observation data to be sent to it. Then we can run an evaluation script (or robot runtime) to query the server. -If you want to embed policy server calls in your own robot runtime, we provide a minimal example in the remote inference documentation. +Most training configs include: +- dataset: `data_root_dir`, `dataset_name` +- base model: e.g. `vla_path` (OpenVLA family) +- output dirs: `run_root_dir` (logs/checkpoints), `adapter_tmp_dir` (LoRA temp) +- hyperparameters: `batch_size`, `max_steps`, `learning_rate`, `save_steps`, `grad_accumulation_steps` +- LoRA/quantization: `use_lora`, `lora_rank`, `use_quantization` (if applicable) -### Evaluate Model +### Common evaluation fields (example: OpenVLA) -After starting the policy server, run the following in the openpi directory: +Most evaluation configs include: +- checkpoint: `pretrained_checkpoint` +- suite: `task_suite_name` (single suite, list of suites, or `"all"`), `task_level` +- repeats/logging: `num_trials_per_task`, `local_log_dir`, `save_video_mode` -```bash -uv run evaluator.py --config -``` +### Customize a config ---- +Copy a default config to your own path (for example `my_configs/openvla_my_run.yaml`), edit it, then pass it explicitly: -## Configuration File Notes +```bash +uv run --project envs/openvla \ + vla-arena train --model openvla --config my_configs/openvla_my_run.yaml +``` -Configuration files typically contain information such as dataset paths, model parameters, training hyperparameters, etc. Please refer to the corresponding configuration examples based on the model type you are using. +Please refer to: +- `vla_arena/configs/train/*.yaml` +- `vla_arena/configs/evaluation/*.yaml` diff --git a/docs/finetuning_and_evaluation_zh.md b/docs/finetuning_and_evaluation_zh.md index bed8ee94..7af3367c 100644 --- a/docs/finetuning_and_evaluation_zh.md +++ b/docs/finetuning_and_evaluation_zh.md @@ -1,117 +1,127 @@ -# 使用VLA-Arena生成的数据集微调其他模型并评测指南 +# 使用 VLA-Arena 生成数据集进行模型微调与评测指南 -VLA-Arena提供了完整的搜集数据、转换数据格式、评估语言-视觉-动作模型的框架，本指南将带你了解如何使用VLA-Arena生成的数据集微调一些VLA模型并评测。我们目前提供OpenVLA、OpenVLA-OFT、Openpi、UniVLA、SmolVLA模型的微调与评测。 +VLA-Arena 提供了从数据采集、数据转换到模型微调与评测的完整流程。本文档统一使用 **uv-only** 工作流，覆盖 OpenVLA、OpenVLA-OFT、UniVLA、SmolVLA 和 OpenPI。 +## 统一环境配置（uv-only） -## 通用模型（OpenVLA、OpenVLA-OFT、UniVLA、SmolVLA） - -对于除Openpi外的其他模型（OpenVLA、OpenVLA-OFT、UniVLA、SmolVLA），使用方式非常简单： - -### 安装依赖 - -首先安装对应模型的依赖： +每个模型使用独立 uv 工程，避免依赖冲突。 ```bash -conda create -n [model_name]_vla_arena python==3.10 -y -pip install -e . -pip install vla-arena[模型名称] +# 在仓库根目录执行 +uv sync --project envs/ ``` -例如： -- OpenVLA: `pip install vla-arena[openvla]` -- OpenVLA-OFT: `pip install vla-arena[openvla-oft]` -- UniVLA: `pip install vla-arena[univla]` -- SmolVLA: `pip install vla-arena[smolvla]` +支持的模型环境名： +- `openvla` +- `openvla_oft` +- `univla` +- `smolvla` +- `openpi` -### 微调模型 - -使用以下命令进行微调： +示例： ```bash -vla-arena train --model <模型名称> --config <配置文件路径> +uv sync --project envs/openvla +uv sync --project envs/openpi ``` -例如： -```bash -vla-arena train --model openvla --config /vla_arena/config/openvla.yaml -``` +## 通用模型 -### 评估模型 +下述微调/评测命令对所有已支持模型一致（OpenVLA、OpenVLA-OFT、UniVLA、SmolVLA、OpenPI）。 -使用以下命令进行评估： +### 微调 ```bash -vla-arena eval --model <模型名称> --config <配置文件路径> +uv run --project envs/ \ + vla-arena train --model --config vla_arena/configs/train/.yaml ``` -例如： +推荐直接使用与模型同名的默认配置： + ```bash -vla-arena eval --model openvla --config /path/to/config.yaml -``` +uv run --project envs/openvla \ + vla-arena train --model openvla --config vla_arena/configs/train/openvla.yaml ---- +uv run --project envs/openvla_oft \ + vla-arena train --model openvla_oft --config vla_arena/configs/train/openvla_oft.yaml -## Openpi +uv run --project envs/univla \ + vla-arena train --model univla --config vla_arena/configs/train/univla.yaml -Openpi模型需要使用`uv`进行环境管理，操作步骤与其他模型略有不同。 +uv run --project envs/smolvla \ + vla-arena train --model smolvla --config vla_arena/configs/train/smolvla.yaml -### 环境配置 +uv run --project envs/openpi \ + vla-arena train --model openpi --config vla_arena/configs/train/openpi.yaml +``` -1. 创建新环境并进入Openpi目录： +### 评测 ```bash -conda create -n openpi python=3.11 -y -conda activate openpi -pip install uv -uv pip install -e . -cd vla_arena/models/openpi +uv run --project envs/ \ + vla-arena eval --model --config vla_arena/configs/evaluation/.yaml ``` -2. 使用uv同步依赖并安装： +推荐直接使用与模型同名的默认配置： ```bash -uv sync -uv pip install -e . -``` +uv run --project envs/openvla \ + vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml -### 定义训练配置并运行训练 +uv run --project envs/openvla_oft \ + vla-arena eval --model openvla_oft --config vla_arena/configs/evaluation/openvla_oft.yaml -在运行训练之前，我们需要先计算训练数据的归一化统计信息。使用你的训练配置名称运行以下脚本，训练配置可在src/openpi/training/config中调整： +uv run --project envs/univla \ + vla-arena eval --model univla --config vla_arena/configs/evaluation/univla.yaml -```bash -uv run scripts/compute_norm_stats.py --config-name +uv run --project envs/smolvla \ + vla-arena eval --model smolvla --config vla_arena/configs/evaluation/smolvla.yaml + +uv run --project envs/openpi \ + vla-arena eval --model openpi --config vla_arena/configs/evaluation/openpi.yaml ``` -**注意**：我们提供了从预训练中重新加载状态/动作归一化统计信息的功能。如果你在预训练混合数据集中包含的机器人上进行新任务的微调，这可能会有益。有关如何重新加载归一化统计信息的更多详细信息，请参阅 `docs/norm_stats.md` 文件。 -现在我们可以开始训练（`--overwrite` 标志用于在你使用相同配置重新运行微调时覆盖现有检查点）： +## 配置文件说明 -```bash -XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run trainer.py --config <配置文件路径> -``` +配置文件用于描述训练/评测的输入输出路径、超参数、任务套件等。VLA-Arena 的 `vla-arena train/eval` 会接收 `--config`，并将其解析为**绝对路径**后交给各模型的 `trainer.py` / `evaluator.py` 读取（不同模型的字段会有所差异）。 -该命令会将训练进度记录到控制台，并将检查点保存到 `checkpoints` 目录。你也可以在 Weights & Biases 仪表板上监控训练进度。为了最大化使用GPU内存，在运行训练之前设置 `XLA_PYTHON_CLIENT_MEM_FRACTION=0.9`——这使JAX能够使用高达90%的GPU内存（默认值为75%）。 +### 配置文件从哪里来 -### 启动策略服务器并运行推理 +- 训练配置：`vla_arena/configs/train/.yaml` +- 评测配置：`vla_arena/configs/evaluation/.yaml` -训练完成后，我们可以通过启动策略服务器，然后从评估脚本查询它来运行推理。启动模型服务器很简单（此示例使用迭代20,000的检查点，请根据需要修改）： +### `--config` 如何解析 -```bash -uv run scripts/serve_policy.py policy:checkpoint --policy.config= --policy.dir=checkpoints/pi05_libero/my_experiment/20000 -``` +`--config` 支持三种写法： +1. 直接传本地路径（相对路径或绝对路径，`~` 也支持）； +2. 传 `vla_arena/configs/...` 这种“包内引用”（便于从 PyPI 安装后仍可直接引用默认配置）； +3. 省略 `--config`：会自动使用该模型的默认配置（例如 `openvla` 对应 `train/openvla.yaml` / `evaluation/openvla.yaml`）。 -这将启动一个监听端口8000的服务器，等待发送给它的观测数据。然后我们可以运行一个评估脚本（或机器人运行时）来查询服务器。 -如果你想在自己的机器人运行时中嵌入策略服务器调用，我们在远程推理文档中提供了一个最小示例。 +### 训练配置常见字段（示例：OpenVLA） -### 评估模型 +不同模型字段可能不完全一致，但通常会包含： +- 数据集：`data_root_dir`、`dataset_name` +- 预训练/基座模型：如 `vla_path`（OpenVLA 系列） +- 输出目录：`run_root_dir`（日志与 checkpoint）、`adapter_tmp_dir`（LoRA 临时目录） +- 训练超参：`batch_size`、`max_steps`、`learning_rate`、`save_steps`、`grad_accumulation_steps` +- LoRA/量化：`use_lora`、`lora_rank`、`use_quantization`（如适用） -在启动策略服务器后，openpi目录下运行： +### 评测配置常见字段（示例：OpenVLA） -```bash -uv run evaluator.py --config <配置文件路径> -``` +评测侧通常包含： +- checkpoint：`pretrained_checkpoint`（指向你训练产物或 Hub 上的模型） +- 任务套件：`task_suite_name`（可为单个套件、套件列表或 `"all"`）、`task_level` +- 评测次数与日志：`num_trials_per_task`、`local_log_dir`、`save_video_mode` ---- +### 如何自定义配置 -## 配置文件说明 +建议将默认配置复制一份到自定义路径后修改（例如 `my_configs/openvla_my_run.yaml`），然后在命令中显式传入： + +```bash +uv run --project envs/openvla \ + vla-arena train --model openvla --config my_configs/openvla_my_run.yaml +``` -配置文件通常包含数据集路径、模型参数、训练超参数等信息。请根据你使用的模型类型，参考相应的配置示例进行设置。 +可参考： +- `vla_arena/configs/train/*.yaml` +- `vla_arena/configs/evaluation/*.yaml` diff --git a/envs/base/uv.lock b/envs/base/uv.lock new file mode 100644 index 00000000..849a8073 --- /dev/null +++ b/envs/base/uv.lock @@ -0,0 +1,2135 @@ +version = 1 +revision = 3 +requires-python = "==3.11.*" +resolution-markers = [ + "sys_platform == 'darwin'", + "platform_machine == 'aarch64' and sys_platform == 'linux'", + "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and 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"vla-arena", + "vla-arena[openvla-oft]", +] + +[tool.uv.sources] +vla-arena = { path = "../..", editable = true } diff --git a/envs/smolvla/pyproject.toml b/envs/smolvla/pyproject.toml new file mode 100644 index 00000000..92cf3713 --- /dev/null +++ b/envs/smolvla/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "vla-arena-env-smolvla" +version = "0.0.0" +requires-python = "==3.11.*" +dependencies = [ + "vla-arena", + "vla-arena[smolvla]", +] + +[tool.uv.sources] +vla-arena = { path = "../..", editable = true } diff --git a/envs/univla/pyproject.toml b/envs/univla/pyproject.toml new file mode 100644 index 00000000..53e641b4 --- /dev/null +++ b/envs/univla/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "vla-arena-env-univla" +version = "0.0.0" +requires-python = "==3.11.*" +dependencies = [ + "vla-arena", + "vla-arena[univla]", +] + +[tool.uv.sources] +vla-arena = { path = "../..", editable = true } diff --git a/pyproject.toml b/pyproject.toml index 92192eb8..5a81ae8b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "h5py", "matplotlib", "tensorflow", + "huggingface_hub", ] [project.scripts] @@ -47,6 +48,7 @@ dependencies = [ "vla-arena.create-template" = "scripts.create_template:main" "vla-arena.download-tasks" = "scripts.download_tasks:main" "vla-arena.manage-tasks" = "scripts.manage_assets:main" +"vla-arena.init-workspace" = "scripts.init_workspace:main" [project.optional-dependencies] openvla = [ @@ -195,7 +197,7 @@ openpi = [ "imageio>=2.36.1", "jax[cuda12]==0.5.3", "jaxtyping==0.2.36", - "lerobot", + "lerobot @ git+https://github.com/huggingface/lerobot@0cf864870cf29f4738d3ade893e6fd13fbd7cdb5", "ml_collections==1.0.0", "numpy>=1.22.4,<2.0.0", "numpydantic>=1.6.6", @@ -215,6 +217,7 @@ openpi = [ "transformers==4.53.2", "rich>=14.0.0", "polars>=1.30.0", + "chex", ] # Integrated Dev/Tool Dependencies diff --git a/scripts/check_dataset_integrity.py b/scripts/check_dataset_integrity.py deleted file mode 100644 index 581d5e57..00000000 --- a/scripts/check_dataset_integrity.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 The VLA-Arena Authors. -# -# 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. - -"""A script to check if any demonstration dataset does not have the exact number of demonstration trajectories""" - -from pathlib import Path - -import h5py -import numpy as np - -from vla_arena.vla_arena import get_vla_arena_path - - -error_datasets = [] -for demo_file_name in Path(get_vla_arena_path('datasets')).rglob('*hdf5'): - - demo_file = h5py.File(demo_file_name) - - count = 0 - for key in demo_file['data'].keys(): - if 'demo' in key: - count += 1 - - if count == 50: - traj_lengths = [] - action_min = np.inf - action_max = -np.inf - for demo_name in demo_file['data'].keys(): - traj_lengths.append( - demo_file[f'data/{demo_name}/actions'].shape[0] - ) - traj_lengths = np.array(traj_lengths) - print( - f'[info] dataset {demo_file_name} is in tact, test passed \u2714' - ) - print(np.mean(traj_lengths), ' +- ', np.std(traj_lengths)) - if demo_file['data'].attrs['tag'] == 'vla_arena-v1': - print('Version correct') - - print('=========================================') - - else: - print('[error] !!!') - error_datasets.append(demo_file_name) - -if len(error_datasets) > 0: - print('[error] The following datasets are corrupted:') - for dataset in error_datasets: - print(dataset) diff --git a/scripts/evaluate_policy.py b/scripts/evaluate_policy.py deleted file mode 100644 index fd05ca5d..00000000 --- a/scripts/evaluate_policy.py +++ /dev/null @@ -1,318 +0,0 @@ -# Copyright 2025 The VLA-Arena Authors. -# -# 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. - -import argparse -import json -import os - -from vla_arena.evaluation.evaluator import Evaluator - -# from vla_arena.evaluation.policy import OpenVLAOFT -# from vla_arena.evaluation.policy import OpenPI -# from vla_arena.evaluation.policy import SmolVLA -from vla_arena.evaluation.policy import PolicyRegistry - - -os.environ['MUJOCO_GL'] = 'egl' - - -def parse_levels(levels_str): - """ - Parse level string to support various formats: - - Single level: "0" -> [0] - - Range: "0-2" -> [0, 1, 2] - - List: "0,2" -> [0, 2] - """ - if levels_str is None: - return None - - levels = [] - parts = levels_str.split(',') - - for part in parts: - part = part.strip() - if '-' in part: - # Handle range - start, end = part.split('-') - start, end = int(start.strip()), int(end.strip()) - levels.extend(list(range(start, end + 1))) - else: - # Handle single level - levels.append(int(part)) - - # Remove duplicates and sort - levels = sorted(list(set(levels))) - return levels - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - '--task_suite', - default=None, - type=str, - choices=[ - 'safety_dynamic_obstacles', - 'safety_hazard_avoidance', - 'safety_object_state_preservation', - 'safety_risk_aware_grasping', - 'safety_static_obstacles', - 'robustness_dynamic_distractors', - 'robustness_static_distractors', - 'robustness_visual_variations', - 'generalization_language_variations', - 'generalization_object_preposition_combinations', - 'generalization_task_workflows', - 'generalization_unseen_objects', - 'long_horizon', - # "libero_10", - # "libero_90", - # "libero_spatial", - # "libero_object", - # "libero_goal", - ], - help='The evaluation track to run', - ) - - # Modified: Support both single level and multiple levels - parser.add_argument( - '--task_level', - default='0', - type=str, - help='Task level(s) to evaluate. Supports: single (0), range (0-2), list (0,2,4), or mixed (0-2,5)', - ) - - parser.add_argument( - '--n-episode', - default=1, - type=int, - help='The number of episodes to evaluate for each task', - ) - parser.add_argument( - '--policy', - default='openvla', - type=str, - choices=PolicyRegistry.list_policies(), - help='The policy to evaluate', - ) - parser.add_argument( - '--model_ckpt', default=None, help='The base model checkpoint path' - ) - parser.add_argument( - '--save-dir', - default='logs', - help='The directory to save the evaluation results', - ) - parser.add_argument( - '--visualization', - action='store_true', - default=False, - help='Whether to visualize the episodes', - ) - parser.add_argument( - '--metrics', - nargs='+', - default=['success_rate', 'cumulative_cost', 'safe_success_rate'], - choices=[ - 'success_rate', - 'cumulative_cost', - 'safe_success_rate', - 'episode_length', - ], - help='The metrics to evaluate', - ) - parser.add_argument( - '--host', - default='localhost', - type=str, - help='The host to the remote server', - ) - parser.add_argument( - '--port', default=5555, type=int, help='The port to the remote server' - ) - parser.add_argument( - '--replanstep', default=4, type=int, help='The step to replan' - ) - - # Additional arguments for batch evaluation - parser.add_argument( - '--parallel', - action='store_true', - default=False, - help='Whether to run level evaluations in parallel (experimental)', - ) - parser.add_argument( - '--episode_config', - default=None, - type=str, - help='Path to episode configuration file', - ) - - args = parser.parse_args() - return args - - -def print_evaluation_plan(args, task_levels): - """Print the evaluation plan before starting""" - print('\n' + '=' * 70) - print('EVALUATION PLAN') - print('=' * 70) - print(f'Task Suite: {args.task_suite}') - print(f'Levels to evaluate: {task_levels}') - print(f'Episodes per task: {args.n_episode}') - print(f'Policy: {args.policy}') - print(f'Metrics: {args.metrics}') - print(f'Visualization: {args.visualization}') - print(f'Save directory: {args.save_dir}') - print('=' * 70 + '\n') - - # Calculate total evaluation scope - num_levels = len(task_levels) - # This is approximate - actual number depends on the suite - estimated_tasks_per_level = 10 # You might want to get this from the suite - total_episodes = num_levels * estimated_tasks_per_level * args.n_episode - - print(f'Estimated total episodes: ~{total_episodes}') - print('Press Ctrl+C to cancel, or wait to continue...\n') - - import time - - time.sleep(3) # Give user time to cancel if needed - - -def evaluate(args): - """Main evaluation function with multi-level support""" - - # Parse task levels - task_levels = parse_levels(args.task_level) - if not task_levels: - raise ValueError('No valid task levels specified!') - - # Load episode configuration if provided - episode_config = None - if args.episode_config: - with open(args.episode_config) as f: - episode_config = json.load(f) - - # Set up save directory - if args.task_suite is not None: - args.save_dir = os.path.join(args.save_dir, args.task_suite) - - if not args.task_suite: - raise ValueError('No tasks specified! Please provide --task_suite') - - # Print evaluation plan - print_evaluation_plan(args, task_levels) - - print(f'Tasks to evaluate: {args.task_suite}') - print(f'Levels to evaluate: {task_levels}') - print(f'Number of episodes per task: {args.n_episode}') - - # Create evaluator with multiple levels support - evaluator = Evaluator( - task_suite=args.task_suite, - task_levels=task_levels, # Pass list of levels - n_episodes=args.n_episode, - episode_config=episode_config, - max_substeps=1, # repeat step in simulation - save_dir=args.save_dir, - visualization=args.visualization, - metrics=args.metrics, - ) - if args.policy not in PolicyRegistry.list_policies(): - raise ValueError( - f"Policy '{args.policy}' is not registered. Available policies: {PolicyRegistry.list_policies()}", - ) - if args.policy != 'openpi': - policy = PolicyRegistry.get( - args.policy, - model_ckpt=args.model_ckpt if args.model_ckpt else None, - ) - else: - policy = PolicyRegistry.get( - args.policy, host=args.host, port=args.port - ) - - # Run evaluation - results = evaluator.evaluate(policy) - - # Print quick summary of results - print('\n' + '=' * 70) - print('EVALUATION COMPLETED SUCCESSFULLY') - print('=' * 70) - - if isinstance(results, dict): - # If single level, results is a dict of task metrics - if len(task_levels) == 1: - print(f'\nLevel {task_levels[0]} Results:') - for task_name, metrics in results.items(): - print(f' {task_name}:') - if 'success_rate' in metrics: - print(f" Success Rate: {metrics['success_rate']:.2%}") - if 'safe_success_rate' in metrics: - print( - f" Safe Success Rate: {metrics['safe_success_rate']:.2%}" - ) - if 'cumulative_cost' in metrics: - print(f" Avg Cost: {metrics['cumulative_cost']:.2f}") - else: - # Multiple levels, results is dict of level -> task metrics - for level, level_results in results.items(): - print(f'\nLevel {level} Results:') - success_rates = [] - for task_name, metrics in level_results.items(): - if 'success_rate' in metrics: - success_rates.append(metrics['success_rate']) - if success_rates: - avg_success = sum(success_rates) / len(success_rates) - print(f' Average Success Rate: {avg_success:.2%}') - - print(f'\nDetailed results saved to: {evaluator.save_dir}') - - # except KeyboardInterrupt: - # print("\n\nEvaluation interrupted by user.") - # print("Partial results may have been saved.") - # except Exception as e: - # print(f"\n\nEvaluation failed with error: {e}") - # import traceback - # traceback.print_exc() - # raise - - -def main(): - """Entry point with better error handling""" - args = get_args() - - # Validate arguments - if not args.task_suite: - print('Error: --task_suite is required!') - print( - 'Available options: static_obstacles, preposition_generalization' - ) - return 1 - - try: - evaluate(args) - return 0 - except Exception: - import traceback - - traceback.print_exc() - return 1 - - -if __name__ == '__main__': - import sys - - sys.exit(main()) diff --git a/scripts/evaluate_policy.sh b/scripts/evaluate_policy.sh deleted file mode 100644 index 8a4d19c3..00000000 --- a/scripts/evaluate_policy.sh +++ /dev/null @@ -1,201 +0,0 @@ -#!/bin/bash -# ============================================================================ -# VLA-Arena Unified Evaluation Script -# ============================================================================ -# Instructions: -# 1. Copy this script: cp scripts/evaluate_policy.sh my_evaluation.sh -# 2. Edit the configuration section below -# 3. Run: bash my_evaluation.sh -# ============================================================================ - -# ================================ -# CONFIGURATION SECTION - Edit these variables for your evaluation -# ================================ - -# Model Configuration -export CUDA_VISIBLE_DEVICES=0 - -POLICY="openvla" # Options: openvla, random (more coming soon) -MODEL_CKPT="path/to/model/checkpoint" # Path to model checkpoint - -# Task Configuration -TASK_SUITE="safety_static_obstacles" # Options: -TASK_LEVEL=0 # Difficulty level: 0 (easy), 1 (medium), 2 (hard) -N_EPISODES=1 # Number of episodes per task - -# Evaluation Settings -VISUALIZATION=true # Set to true to save evaluation videos -METRICS="success_rate" # Metrics to compute - -# Output Configuration -SAVE_DIR="logs/evaluation_$(date +%Y%m%d_%H%M%S)" # Output directory (auto-timestamped) - -# ================================ -# END OF CONFIGURATION SECTION -# ================================ - -# Color codes for output -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -# Function to print colored output -print_info() { - echo -e "${BLUE}[INFO]${NC} $1" -} - -print_success() { - echo -e "${GREEN}[SUCCESS]${NC} $1" -} - -print_error() { - echo -e "${RED}[ERROR]${NC} $1" -} - -print_warning() { - echo -e "${YELLOW}[WARNING]${NC} $1" -} - -# Validation -validate_config() { - local valid=true - - if [ "$valid" = false ]; then - print_error "Configuration validation failed. Please check your settings." - exit 1 - fi -} - -# Print configuration summary -print_config() { - echo "" - echo "╔══════════════════════════════════════════════════════════════╗" - echo "║ VLA-Arena Evaluation Configuration ║" - echo "╠══════════════════════════════════════════════════════════════╣" - printf "║ %-20s : %-39s ║\n" "Policy" "$POLICY" - if [[ "$POLICY" != "random" ]]; then - # Truncate long paths for display - local display_model=$(basename "$MODEL_CKPT") - printf "║ %-20s : %-39s ║\n" "Model" "...$display_model" - fi - printf "║ %-20s : %-39s ║\n" "Task Suite" "$TASK_SUITE" - printf "║ %-20s : %-39s ║\n" "Task Level" "Level $TASK_LEVEL" - printf "║ %-20s : %-39s ║\n" "Episodes per Task" "$N_EPISODES" - printf "║ %-20s : %-39s ║\n" "Device" "$DEVICE" - printf "║ %-20s : %-39s ║\n" "Visualization" "$VISUALIZATION" - printf "║ %-20s : %-39s ║\n" "Save Directory" "$(basename $SAVE_DIR)" - echo "╚══════════════════════════════════════════════════════════════╝" - echo "" -} - -# Main execution -main() { - # Validate configuration - validate_config - - # Print configuration - print_config - - # Ask for confirmation - read -p "Do you want to proceed with this configuration? [(y)/n]: " -n 1 -r - echo - if [[ $REPLY =~ ^[Nn]$ ]]; then - print_warning "Evaluation cancelled by user" - exit 0 - fi - - # Build command - CMD="python scripts/evaluate_policy.py" - CMD="$CMD --task_suite $TASK_SUITE" - CMD="$CMD --task_level $TASK_LEVEL" - CMD="$CMD --n-episode $N_EPISODES" - CMD="$CMD --policy $POLICY" - CMD="$CMD --save-dir $SAVE_DIR" - CMD="$CMD --metrics $METRICS" - - # Add model checkpoint if not random policy - if [[ "$POLICY" != "random" ]]; then - CMD="$CMD --model_ckpt $MODEL_CKPT" - fi - - # Add visualization flag if enabled - if [[ "$VISUALIZATION" == "true" ]]; then - CMD="$CMD --visualization" - fi - - # Create save directory - mkdir -p "$SAVE_DIR" - - # Save configuration to file - cat > "$SAVE_DIR/evaluation_config.txt" < list[str]: + items = [part.strip() for part in models_csv.split(',') if part.strip()] + if not items: + raise ValueError('No models were provided in --models.') + + invalid = [model for model in items if model not in SUPPORTED_MODELS] + if invalid: + raise ValueError( + f'Unsupported model(s): {", ".join(invalid)}. ' + f'Supported: {", ".join(DEFAULT_MODELS)}' + ) + + # Keep input order while removing duplicates. + seen = set() + deduped = [] + for model in items: + if model in seen: + continue + seen.add(model) + deduped.append(model) + return deduped + + +def _render_env_pyproject(model: str) -> str: + if model == 'base': + dependencies = '"vla-arena",' + else: + extra_name = _EXTRA_MAP[model] + dependencies = f'"vla-arena",\n "vla-arena[{extra_name}]",' + + lines = [ + '[project]', + f'name = "{_PROJECT_NAME_MAP[model]}"', + 'version = "0.0.0"', + 'requires-python = "==3.11.*"', + 'dependencies = [', + f' {dependencies}', + ']', + ] + if model == 'openpi': + lines.extend( + [ + '', + '[tool.uv]', + 'override-dependencies = ["ml-dtypes==0.5.4", "tensorstore==0.1.74"]', + ] + ) + return '\n'.join(lines) + '\n' + + +def _prepare_output_dir(path: Path, force: bool) -> None: + if not path.exists(): + return + if not force: + raise FileExistsError( + f'Target already exists: {path}. Use --force to overwrite.' + ) + if path.is_dir(): + shutil.rmtree(path) + else: + path.unlink() + + +def _write_env_projects(output_dir: Path, models: list[str], force: bool) -> None: + envs_dir = output_dir / 'envs' + _prepare_output_dir(envs_dir, force=force) + envs_dir.mkdir(parents=True, exist_ok=True) + + ordered_models = ['base', *models] + for model in ordered_models: + model_dir = envs_dir / model + model_dir.mkdir(parents=True, exist_ok=True) + (model_dir / 'pyproject.toml').write_text( + _render_env_pyproject(model), encoding='utf-8' + ) + + +def _copy_configs(output_dir: Path, force: bool) -> None: + source = get_packaged_configs_root() + target = output_dir / 'configs' + _prepare_output_dir(target, force=force) + shutil.copytree(source, target) + + +def initialize_workspace( + output: Path, + models: list[str], + copy_configs: bool, + force: bool, +) -> None: + output.mkdir(parents=True, exist_ok=True) + _write_env_projects(output_dir=output, models=models, force=force) + if copy_configs: + _copy_configs(output_dir=output, force=force) + + +def main() -> None: + parser = argparse.ArgumentParser( + description='Initialize local uv workspace projects for PyPI installation.' + ) + parser.add_argument( + '--output', + type=str, + default='.', + help='Output directory. Default: current directory.', + ) + parser.add_argument( + '--models', + type=str, + default=','.join(DEFAULT_MODELS), + help='Comma-separated models. Default: openvla,openvla_oft,univla,smolvla,openpi', + ) + parser.add_argument( + '--without-configs', + action='store_true', + help='Only create envs/, skip copying configs/.', + ) + parser.add_argument( + '--force', + action='store_true', + help='Overwrite existing envs/ and configs/ targets.', + ) + args = parser.parse_args() + + output_dir = Path(args.output).expanduser().resolve() + models = _parse_models_csv(args.models) + include_configs = not args.without_configs + + initialize_workspace( + output=output_dir, + models=models, + copy_configs=include_configs, + force=args.force, + ) + + copied = 'yes' if include_configs else 'no' + print(f'Workspace initialized at: {output_dir}') + print(f'Model envs: base, {", ".join(models)}') + print(f'Copied configs: {copied}') + + +if __name__ == '__main__': + main() diff --git a/scripts/random_sample_hdf5.py b/scripts/random_sample_hdf5.py deleted file mode 100644 index 61d0cb8b..00000000 --- a/scripts/random_sample_hdf5.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright 2025 The VLA-Arena Authors. -# -# 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. - -import argparse -import random -from pathlib import Path - -import h5py -import numpy as np - - -def copy_hdf5_group(source_group, target_group): - """ - Recursively copy all data and attributes from an HDF5 group. - - Args: - source_group: Source HDF5 group - target_group: Target HDF5 group - """ - # Copy all attributes - for key, value in source_group.attrs.items(): - target_group.attrs[key] = value - - # Copy all datasets and subgroups - for key in source_group.keys(): - source_item = source_group[key] - if isinstance(source_item, h5py.Dataset): - # Copy dataset - target_group.create_dataset(key, data=source_item[:]) - elif isinstance(source_item, h5py.Group): - # Recursively copy subgroup - target_subgroup = target_group.create_group(key) - copy_hdf5_group(source_item, target_subgroup) - - -def sample_hdf5_file(input_file, output_file, sample_ratio, random_seed=None): - """ - Randomly sample a certain proportion of demos from an HDF5 file and create a new HDF5 file. - - Args: - input_file: Input HDF5 file path - output_file: Output HDF5 file path - sample_ratio: Sampling ratio (0.0 - 1.0) - random_seed: Random seed for reproducibility - """ - if random_seed is not None: - random.seed(random_seed) - np.random.seed(random_seed) - - print(f'Processing file: {input_file}') - - # Open input file - try: - with h5py.File(input_file, 'r') as f_in: - # Check file structure - if 'data' not in f_in.keys(): - print(f"Error: 'data' group not found in file {input_file}") - return False - - data_group = f_in['data'] - - # Get all demo names - demo_names = [ - key for key in data_group.keys() if key.startswith('demo_') - ] - demo_names.sort() # Ensure consistent order - - if not demo_names: - print(f'Error: No demo data found in file {input_file}') - return False - - total_demos = len(demo_names) - num_samples = max(1, int(total_demos * sample_ratio)) - - print(f' Total demos: {total_demos}') - print(f' Sampling ratio: {sample_ratio:.1%}') - print(f' Sample count: {num_samples}') - - # Randomly select demos - selected_demos = random.sample(demo_names, num_samples) - selected_demos.sort() # Keep sorted for readability - - print( - f" Selected demos: {selected_demos[:5]}{'...' if len(selected_demos) > 5 else ''}", - ) - - # Create output directory - output_path = Path(output_file) - output_path.parent.mkdir(parents=True, exist_ok=True) - - # Create output file and copy data - with h5py.File(output_file, 'w') as f_out: - # Create data group - data_group_out = f_out.create_group('data') - - # Copy all attributes from data group - for key, value in data_group.attrs.items(): - data_group_out.attrs[key] = value - - # Copy selected demos - total_samples = 0 - for i, demo_name in enumerate(selected_demos): - # Create new demo group (renumbered) - new_demo_name = f'demo_{i}' - demo_group_out = data_group_out.create_group(new_demo_name) - - # Copy all data from demo group - demo_group_in = data_group[demo_name] - copy_hdf5_group(demo_group_in, demo_group_out) - - # Accumulate sample count - if 'num_samples' in demo_group_in.attrs: - total_samples += demo_group_in.attrs['num_samples'] - elif 'obs' in demo_group_in: - # If no num_samples attribute, try to infer from obs - obs_group = demo_group_in['obs'] - # Find any dataset to infer length - for key in obs_group.keys(): - if isinstance(obs_group[key], h5py.Dataset): - total_samples += len(obs_group[key]) - break - - # Update statistics - if 'num_demos' in data_group_out.attrs: - data_group_out.attrs['num_demos'] = num_samples - if 'total' in data_group_out.attrs: - data_group_out.attrs['total'] = total_samples - - print(f' Output file: {output_file}') - print(f' Retained demos: {num_samples}') - print(f' Total samples: {total_samples}') - - return True - - except Exception as e: - print(f'Error processing file {input_file}: {e}') - import traceback - - traceback.print_exc() - return False - - -def main(): - parser = argparse.ArgumentParser( - description='Randomly sample a certain proportion of data from HDF5 files and create new HDF5 files', - ) - parser.add_argument('--input-file', type=str, help='Input HDF5 file path') - parser.add_argument( - '--output-file', - type=str, - default=None, - help='Output HDF5 file path (default: add _sampled suffix to input filename)', - ) - parser.add_argument( - '--ratio', - type=float, - required=True, - help='Sampling ratio (0.0 - 1.0), e.g., 0.5 means sample 50%%', - ) - parser.add_argument( - '--seed', - type=int, - default=None, - help='Random seed for reproducibility', - ) - parser.add_argument( - '--input-dir', - type=str, - default=None, - help='Input directory, batch process all HDF5 files in the directory', - ) - parser.add_argument( - '--output-dir', - type=str, - default=None, - help='Output directory, used together with --input-dir', - ) - parser.add_argument( - '--pattern', - type=str, - default='*.hdf5', - help='Filename pattern (default: *.hdf5)', - ) - parser.add_argument( - '--not-recursive', - action='store_true', - help='Do not recursively search subdirectories', - ) - - args = parser.parse_args() - - # Validate sampling ratio - if args.ratio < 0.0 or args.ratio > 1.0: - print('Error: Sampling ratio must be between 0.0 and 1.0') - return - - # Batch processing mode - if args.input_dir: - if not args.output_dir: - print( - 'Error: --output-dir must be specified when using --input-dir' - ) - return - - input_dir = Path(args.input_dir) - output_dir = Path(args.output_dir) - - # Find all HDF5 files - if args.not_recursive: - demo_files = list(input_dir.glob(args.pattern)) - else: - demo_files = list(input_dir.rglob(args.pattern)) - - if not demo_files: - print( - f'No files matching {args.pattern} found in {args.input_dir}' - ) - return - - print(f'Found {len(demo_files)} files to process\n') - - success_count = 0 - for demo_file in demo_files: - # Generate output file path - relative_path = demo_file.relative_to(input_dir) - output_file = output_dir / relative_path - - # If output filename is same as input, add suffix - if output_file == demo_file: - output_file = ( - output_file.parent - / f'{output_file.stem}_sampled{output_file.suffix}' - ) - - output_file.parent.mkdir(parents=True, exist_ok=True) - - if sample_hdf5_file( - str(demo_file), str(output_file), args.ratio, args.seed - ): - success_count += 1 - print() - - print( - f'Processing complete: {success_count}/{len(demo_files)} files succeeded' - ) - - # Single file processing mode - else: - if not args.input_file: - print('Error: Must specify --input-file or --input-dir') - return - - # Determine output file path - if args.output_file: - output_file = args.output_file - else: - input_path = Path(args.input_file) - output_file = str( - input_path.parent - / f'{input_path.stem}_sampled{input_path.suffix}' - ) - - success = sample_hdf5_file( - args.input_file, output_file, args.ratio, args.seed - ) - if success: - print('\nProcessing complete!') - else: - print('\nProcessing failed!') - - -if __name__ == '__main__': - main() diff --git a/scripts/replace_prismatic_imports.py b/scripts/replace_prismatic_imports.py deleted file mode 100644 index 0aecb29a..00000000 --- a/scripts/replace_prismatic_imports.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2025 The VLA-Arena Authors. -# -# 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. - -"""Utility to rewrite `prismatic.*` imports under an OpenVLA-aware namespace.""" - -from __future__ import annotations - -import argparse -import pathlib -import textwrap -from collections.abc import Iterable - - -OLD_PREFIX = 'prismatic.' -NEW_PREFIX = 'vla_arena.models.univla.prismatic.' - - -def find_files(base_dir: pathlib.Path) -> Iterable[pathlib.Path]: - for path in base_dir.rglob('*'): - if path.is_file(): - yield path - - -def rewrite_file(path: pathlib.Path, dry_run: bool) -> bool: - try: - data = path.read_text(encoding='utf-8') - except UnicodeDecodeError: - return False - - updated = data.replace(OLD_PREFIX, NEW_PREFIX) - if updated == data: - return False - - if dry_run: - print(f'[dry-run] would rewrite {path}') - return True - - path.write_text(updated, encoding='utf-8') - print(f'rewrote {path}') - return True - - -def main() -> None: - parser = argparse.ArgumentParser( - description=textwrap.dedent( - """ - Walks a directory tree and rewrites occurrences of `prismatic.` to - `vla_arena.models.openvla.prismatic.` so import statements stay correct. - """, - ), - ) - parser.add_argument('path', type=pathlib.Path, help='Folder to process') - parser.add_argument( - '--dry-run', - action='store_true', - help='Only print files that would be changed', - ) - args = parser.parse_args() - - processed = 0 - for file_path in find_files(args.path): - if rewrite_file(file_path, dry_run=args.dry_run): - processed += 1 - - print( - ( - f'{processed} files updated' - if not args.dry_run - else f'{processed} files would be updated' - ), - ) - - -if __name__ == '__main__': - main() diff --git a/skills/vla-remote-train-eval/SKILL.md b/skills/vla-remote-train-eval/SKILL.md new file mode 100644 index 00000000..efad2d9c --- /dev/null +++ b/skills/vla-remote-train-eval/SKILL.md @@ -0,0 +1,137 @@ +--- +name: vla-remote-train-eval +description: End-to-end remote VLA-Arena operations on GPU servers: collect user-provided host/path/repo/eval parameters, clone required repos, configure fixed HF mirror endpoint (https://hf-mirror.com) and centralized caches (HF_HOME/UV_CACHE_DIR/etc.), install uv, download Hugging Face models, patch train/eval YAMLs, run per-model uv environments for training/evaluation, split jobs across GPUs, and debug logs/results. Use when user asks for remote setup, model download, benchmark execution, or troubleshooting dependency/runtime failures. +--- + +# VLA Remote Train Eval + +## Overview + +Execute VLA-Arena remote setup, training, and evaluation in a repeatable way. +Do not assume host, mount path, suite, or trial count. Ask user first and execute with those values. + +## Workflow + +### 1. Collect execution targets from user + +Collect these required inputs before running commands: +- `remote_host`: SSH target host alias or IP +- `root_dir`: remote working root (for repo/models/cache) +- `repo_url`: repository to clone +- `task_suite_name`: eval suite name (`all` or specific suite) +- `num_trials_per_task`: eval trial count + +Collect these optional inputs when needed: +- `task_level` +- `gpu_plan` (model-to-GPU mapping) +- `log_dir` +- `models_dir` + +### 2. Bootstrap remote environment + +From local machine, run: + +```bash +ssh "bash -lc 'cd && /bin/bash -s -- '" \ + < skills/vla-remote-train-eval/scripts/bootstrap_remote.sh +``` + +Or SSH first and run directly: + +```bash +ssh +cd +bash /path/to/VLA-Arena-pub/skills/vla-remote-train-eval/scripts/bootstrap_remote.sh \ + "" "" +``` + +This step must ensure: +- repo exists under `` +- cache roots are under `/cache` +- `HF_ENDPOINT=https://hf-mirror.com` +- `uv` is installed and on PATH + +### 3. Download models to user-provided model directory + +Inside remote repo: + +```bash +cd +bash skills/vla-remote-train-eval/scripts/download_vla_arena_models.sh \ + "" "VLA-Arena" +``` + +This workflow downloads all model repos under a user-provided HF author/org (for this project typically `VLA-Arena`) via the fixed mirror endpoint. + +### 4. Patch evaluation configs with user-provided benchmark parameters + +Inside remote repo: + +```bash +cd +bash skills/vla-remote-train-eval/scripts/apply_eval_defaults.sh \ + . "" "" +``` + +Optional level override: + +```bash +bash skills/vla-remote-train-eval/scripts/apply_eval_defaults.sh \ + . "" "" "" +``` + +### 5. Run train/eval with per-model uv projects + +Never use `envs/base` for model train/eval dependencies. +Always use model-specific projects to avoid conflicts (for example OpenVLA vs OpenPI transformers versions). + +Run template: + +```bash +uv sync --project envs/ +uv run --project envs/ vla-arena eval --model --config vla_arena/configs/evaluation/.yaml +``` + +Model mapping: +- `openvla`: env `envs/openvla`, model `openvla`, config `openvla.yaml` +- `openvla_oft`: env `envs/openvla_oft`, model `openvla_oft`, config `openvla_oft.yaml` +- `univla`: env `envs/univla`, model `univla`, config `univla.yaml` +- `smolvla`: env `envs/smolvla`, model `smolvla`, config `smolvla.yaml` +- `openpi`: env `envs/openpi`, model `openpi`, config `openpi.yaml` +- `openpi_fast`: env `envs/openpi`, model `openpi`, config `openpi_fast.yaml` + +### 6. Split jobs across GPUs + +Use `CUDA_VISIBLE_DEVICES` plus separate logs: + +```bash +mkdir -p +CUDA_VISIBLE_DEVICES= nohup uv run --project envs/openvla \ + vla-arena eval --model openvla --config vla_arena/configs/evaluation/openvla.yaml \ + > /openvla.log 2>&1 & +``` + +Do not run `openpi` and `openpi_fast` concurrently unless result/log paths are explicitly separated. + +### 7. Report status in the user’s preferred style + +Always provide: +- whether it was tested (`yes/no`) +- whether errors occurred (`yes/no`, plus last key stack trace) +- active process status (`ps -fp `) +- result files and timestamps (`ls -lt results/*.json`) +- short metric summary when requested + +## Guardrails + +- Use `vla-arena eval` / `vla-arena train`; do not use `python -m vla_arena.cli.main`. +- If repo is cloned from source, skip task downloading by default. +- Download tasks only when user explicitly asks to update task assets. +- Prefer setting unique `result_json_path` for `openpi_fast`; default naming may collide with `openpi`. +- Keep cache dirs on the user-provided large-capacity path (`/cache`). +- Keep HF endpoint fixed at `https://hf-mirror.com` unless user explicitly requests a different endpoint. + +## Troubleshooting Reference + +For known failure patterns and fixes, read: +- `references/debug-playbook.md` diff --git a/skills/vla-remote-train-eval/agents/openai.yaml b/skills/vla-remote-train-eval/agents/openai.yaml new file mode 100644 index 00000000..35edf838 --- /dev/null +++ b/skills/vla-remote-train-eval/agents/openai.yaml @@ -0,0 +1,4 @@ +interface: + display_name: "VLA Remote Train Eval" + short_description: "Run remote VLA setup, training, and eval workflows" + default_prompt: "Collect user-provided host/path/repo/eval parameters, then set up VLA-Arena on remote GPU servers, configure fixed HF mirror/cache/uv, download required models, patch configs, and run/debug train or eval jobs with per-model uv projects." diff --git a/skills/vla-remote-train-eval/references/debug-playbook.md b/skills/vla-remote-train-eval/references/debug-playbook.md new file mode 100644 index 00000000..2af621c9 --- /dev/null +++ b/skills/vla-remote-train-eval/references/debug-playbook.md @@ -0,0 +1,73 @@ +# Debug Playbook + +## Fast checks + +```bash +cd +ls -lt results/*.json | head +ls -lt /*.log | head +``` + +Check running processes: + +```bash +ps -ef | rg "vla-arena eval|vla-arena train|python|uv run" | rg -v rg +``` + +## Known issues and fixes + +### 1) Dependency conflicts when using `envs/base` + +Symptom: +- OpenVLA requires `transformers==4.40.1` +- OpenPI requires `transformers==4.53.2` + +Fix: +- Never run model tasks in `envs/base`. +- Run per model: `uv sync --project envs/` and `uv run --project envs/ ...`. + +### 2) Source clone still runs task downloader + +Symptom: +- unnecessary `vla-arena.download-tasks install-all` + +Fix: +- If repo is cloned from source, skip task downloading unless user asks to update task assets. + +### 3) `python -m vla_arena.cli.main` does not start eval correctly + +Fix: +- Use `vla-arena eval ...` and `vla-arena train ...`. + +### 4) `openpi_fast` and `openpi` result file collisions + +Symptom: +- both default to `openpi_json_*.json` + +Fix: +- Set distinct `result_json_path` in configs, or run serially and rename results immediately. + +### 5) Many suites return `sr=0.0000` after initial suites for OpenPI + +Common log symptom: +- repeated websocket close/connection errors + +Fix: +- Treat as runtime/policy-server instability first, not pure model quality. +- Check policy server lifecycle and network/socket health. + +### 6) UniVLA runtime `NoneType ... language` + +Symptom: +- evaluator crashes around `task_description.language` + +Fix: +- Inspect full traceback in log; confirm env/task payload integrity; rerun single suite first. + +### 7) SmolVLA policy path mismatch + +Symptom: +- policy load fails due wrong local directory level + +Fix: +- Point `policy_path` to the exact exported model folder (often includes `pretrained_model` subdirectory). diff --git a/skills/vla-remote-train-eval/scripts/apply_eval_defaults.sh b/skills/vla-remote-train-eval/scripts/apply_eval_defaults.sh new file mode 100755 index 00000000..6cef6a14 --- /dev/null +++ b/skills/vla-remote-train-eval/scripts/apply_eval_defaults.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +set -euo pipefail + +if [ "$#" -lt 3 ]; then + echo "Usage: $0 [task_level]" >&2 + echo "Example: $0 . all 2 0" >&2 + exit 1 +fi + +REPO_ROOT="$1" +SUITE="$2" +TRIALS="$3" +LEVEL="${4:-}" + +EVAL_DIR="${REPO_ROOT}/vla_arena/configs/evaluation" + +FILES=( + openvla.yaml + openvla_oft.yaml + univla.yaml + smolvla.yaml + openpi.yaml + openpi_fast.yaml +) + +for file in "${FILES[@]}"; do + path="${EVAL_DIR}/${file}" + if [ ! -f "${path}"; then + echo "[WARN] skip missing ${path}" + continue + fi + + sed -E -i.bak \ + -e "s|^([[:space:]]*task_suite_name:).*|\\1 \"${SUITE}\"|" \ + -e "s|^([[:space:]]*num_trials_per_task:).*|\\1 ${TRIALS}|" \ + "${path}" + + if [ -n "${LEVEL}" ]; then + sed -E -i.bak \ + -e "s|^([[:space:]]*task_level:).*|\\1 ${LEVEL}|" \ + "${path}" + fi + + rm -f "${path}.bak" + echo "[OK] updated ${path}" + rg -n "^[[:space:]]*task_suite_name:|^[[:space:]]*task_level:|^[[:space:]]*num_trials_per_task:" "${path}" +done diff --git a/skills/vla-remote-train-eval/scripts/bootstrap_remote.sh b/skills/vla-remote-train-eval/scripts/bootstrap_remote.sh new file mode 100755 index 00000000..0a754567 --- /dev/null +++ b/skills/vla-remote-train-eval/scripts/bootstrap_remote.sh @@ -0,0 +1,72 @@ +#!/usr/bin/env bash +set -euo pipefail + +if [ "$#" -lt 2 ]; then + echo "Usage: $0 [repo_dir_name]" >&2 + echo "Example: $0 /data/workspace git@github.com:org/repo.git repo" >&2 + exit 1 +fi + +ROOT_DIR="$1" +REPO_URL="$2" +HF_ENDPOINT_VALUE="https://hf-mirror.com" +REPO_DIR_NAME="${3:-$(basename "${REPO_URL}" .git)}" +REPO_DIR="${ROOT_DIR}/${REPO_DIR_NAME}" +CACHE_ROOT="${ROOT_DIR}/cache" + +mkdir -p "${ROOT_DIR}" "${ROOT_DIR}/models" +mkdir -p "${CACHE_ROOT}/hf" "${CACHE_ROOT}/uv" "${CACHE_ROOT}/pip" "${CACHE_ROOT}/xdg" + +BLOCK_BEGIN="# >>> vla-arena-remote-cache >>>" +BLOCK_END="# <<< vla-arena-remote-cache <<<" + +ENV_BLOCK=$(cat <> "$rc" + fi +done + +export HF_ENDPOINT="${HF_ENDPOINT_VALUE}" +export HF_HOME="${CACHE_ROOT}/hf" +export HUGGINGFACE_HUB_CACHE="${CACHE_ROOT}/hf/hub" +export HF_DATASETS_CACHE="${CACHE_ROOT}/hf/datasets" +export TRANSFORMERS_CACHE="${CACHE_ROOT}/hf/transformers" +export UV_CACHE_DIR="${CACHE_ROOT}/uv" +export PIP_CACHE_DIR="${CACHE_ROOT}/pip" +export XDG_CACHE_HOME="${CACHE_ROOT}/xdg" +export PATH="$HOME/.local/bin:$PATH" + +if ! command -v uv >/dev/null 2>&1; then + curl -LsSf https://astral.sh/uv/install.sh | sh + export PATH="$HOME/.local/bin:$PATH" +fi + +if [ ! -d "${REPO_DIR}/.git" ]; then + git clone "${REPO_URL}" "${REPO_DIR}" +fi + +echo "[OK] bootstrap finished" +echo "ROOT_DIR=${ROOT_DIR}" +echo "REPO_DIR=${REPO_DIR}" +echo "MODELS_DIR=${ROOT_DIR}/models" +echo "CACHE_ROOT=${CACHE_ROOT}" +echo "HF_ENDPOINT=${HF_ENDPOINT}" diff --git a/skills/vla-remote-train-eval/scripts/download_vla_arena_models.sh b/skills/vla-remote-train-eval/scripts/download_vla_arena_models.sh new file mode 100755 index 00000000..6d68b33c --- /dev/null +++ b/skills/vla-remote-train-eval/scripts/download_vla_arena_models.sh @@ -0,0 +1,45 @@ +#!/usr/bin/env bash +set -euo pipefail + +if [ "$#" -lt 1 ]; then + echo "Usage: $0 [hf_author]" >&2 + echo "Example: $0 /data/models VLA-Arena" >&2 + exit 1 +fi + +MODELS_DIR="$1" +ENDPOINT="https://hf-mirror.com" +HF_AUTHOR="${2:-VLA-Arena}" + +mkdir -p "${MODELS_DIR}" +export HF_ENDPOINT="${ENDPOINT}" + +if ! command -v uv >/dev/null 2>&1; then + echo "[ERROR] uv not found. Run bootstrap_remote.sh first." >&2 + exit 1 +fi + +model_list=$(uvx --from huggingface_hub python - "${HF_AUTHOR}" <<'PY' +import sys +from huggingface_hub import HfApi + +author = sys.argv[1] +api = HfApi(endpoint="https://hf-mirror.com") +for m in api.list_models(author=author, full=False): + print(m.id) +PY +) + +if [ -z "${model_list}" ]; then + echo "[ERROR] no models found under author ${HF_AUTHOR}" >&2 + exit 1 +fi + +while IFS= read -r repo; do + [ -z "${repo}" ] && continue + dst="${MODELS_DIR}/${repo#${HF_AUTHOR}/}" + echo "[INFO] downloading ${repo} -> ${dst}" + uvx --from huggingface_hub huggingface-cli download "${repo}" --local-dir "${dst}" +done <<< "${model_list}" + +echo "[OK] downloaded all ${HF_AUTHOR} models to ${MODELS_DIR}" diff --git a/tests/models/univla/test_lam_checkpoint_resolver.py b/tests/models/univla/test_lam_checkpoint_resolver.py new file mode 100644 index 00000000..df792039 --- /dev/null +++ b/tests/models/univla/test_lam_checkpoint_resolver.py @@ -0,0 +1,189 @@ +"""Unit tests for UniVLA LAM checkpoint resolver.""" + +from unittest.mock import patch + +import pytest + +from vla_arena.models.univla.lam_checkpoint_resolver import ( + extract_lam_state_dict, + resolve_lam_checkpoint, + resolve_lam_checkpoint_path, +) + + +class _TensorLike: + def __init__(self) -> None: + self.shape = (1,) + + +def test_resolve_local_file_directly(tmp_path): + ckpt_file = tmp_path / 'epoch=0-step=1.ckpt' + ckpt_file.write_text('placeholder') + + resolved = resolve_lam_checkpoint(str(ckpt_file)) + + assert resolved.source == 'local_file' + assert resolved.resolved_path == str(ckpt_file) + assert resolve_lam_checkpoint_path(str(ckpt_file)) == str(ckpt_file) + + +def test_resolve_local_dir_with_explicit_ckpt_file(tmp_path): + ckpt_file = tmp_path / 'task_centric_lam_stage2' / 'epoch=0-step=200000.ckpt' + ckpt_file.parent.mkdir(parents=True) + ckpt_file.write_text('placeholder') + + resolved = resolve_lam_checkpoint( + lam_path=str(tmp_path), + lam_ckpt_file='task_centric_lam_stage2/epoch=0-step=200000.ckpt', + ) + + assert resolved.source == 'local_dir' + assert resolved.resolved_path == str(ckpt_file) + + +def test_local_auto_select_prefers_higher_stage(tmp_path): + stage1 = tmp_path / 'lam_stage1' / 'epoch=0-step=999999.ckpt' + stage2 = tmp_path / 'lam_stage2' / 'epoch=0-step=1.ckpt' + stage1.parent.mkdir(parents=True) + stage2.parent.mkdir(parents=True) + stage1.write_text('placeholder') + stage2.write_text('placeholder') + + resolved = resolve_lam_checkpoint(str(tmp_path)) + + assert resolved.resolved_path == str(stage2) + + +def test_local_auto_select_prefers_higher_step_with_same_stage(tmp_path): + low_step = tmp_path / 'lam_stage2' / 'epoch=0-step=100.ckpt' + high_step = tmp_path / 'lam_stage2' / 'epoch=0-step=200.ckpt' + low_step.parent.mkdir(parents=True) + low_step.write_text('placeholder') + high_step.write_text('placeholder') + + resolved = resolve_lam_checkpoint(str(tmp_path)) + + assert resolved.resolved_path == str(high_step) + + +def test_local_auto_select_prefers_ckpt_over_pt_with_same_score(tmp_path): + pt_file = tmp_path / 'lam_stage2' / 'epoch=0-step=200.pt' + ckpt_file = tmp_path / 'lam_stage2' / 'epoch=0-step=200.ckpt' + pt_file.parent.mkdir(parents=True) + pt_file.write_text('placeholder') + ckpt_file.write_text('placeholder') + + resolved = resolve_lam_checkpoint(str(tmp_path)) + + assert resolved.resolved_path == str(ckpt_file) + + +def test_hf_explicit_file_uses_hf_hub_download(): + with patch( + 'vla_arena.models.univla.lam_checkpoint_resolver.hf_hub_download', + return_value='/tmp/downloaded.ckpt', + ) as mock_download, patch( + 'vla_arena.models.univla.lam_checkpoint_resolver.HfApi' + ) as mock_hf_api: + resolved = resolve_lam_checkpoint( + lam_path='org/repo', + lam_ckpt_file='path/to/epoch=0-step=200000.ckpt', + ) + + mock_hf_api.assert_not_called() + mock_download.assert_called_once_with( + repo_id='org/repo', + filename='path/to/epoch=0-step=200000.ckpt', + repo_type='model', + ) + assert resolved.source == 'hf_repo' + assert resolved.resolved_path == '/tmp/downloaded.ckpt' + + +def test_hf_auto_selects_best_checkpoint_file(): + with patch( + 'vla_arena.models.univla.lam_checkpoint_resolver.HfApi' + ) as mock_hf_api, patch( + 'vla_arena.models.univla.lam_checkpoint_resolver.hf_hub_download', + return_value='/tmp/auto-selected.ckpt', + ) as mock_download: + mock_hf_api.return_value.list_repo_files.return_value = [ + 'README.md', + 'task_centric_lam_stage1/epoch=0-step=400000.ckpt', + 'task_centric_lam_stage2/epoch=0-step=100000.pt', + 'task_centric_lam_stage2/epoch=0-step=100000.ckpt', + ] + + resolved = resolve_lam_checkpoint('org/repo') + + mock_hf_api.return_value.list_repo_files.assert_called_once_with( + repo_id='org/repo', + repo_type='model', + ) + mock_download.assert_called_once_with( + repo_id='org/repo', + filename='task_centric_lam_stage2/epoch=0-step=100000.ckpt', + repo_type='model', + ) + assert ( + resolved.selected_checkpoint + == 'task_centric_lam_stage2/epoch=0-step=100000.ckpt' + ) + + +def test_env_override_takes_priority(monkeypatch, tmp_path): + env_ckpt = tmp_path / 'env-stage2.ckpt' + env_ckpt.write_text('placeholder') + monkeypatch.setenv('UNIVLA_LAM_PATH', str(env_ckpt)) + + resolved = resolve_lam_checkpoint('org/repo') + + assert resolved.source == 'local_file' + assert resolved.resolved_path == str(env_ckpt) + assert resolved.env_overridden is True + + +def test_hf_repo_without_checkpoint_candidates_raises(): + with patch( + 'vla_arena.models.univla.lam_checkpoint_resolver.HfApi' + ) as mock_hf_api: + mock_hf_api.return_value.list_repo_files.return_value = [ + 'README.md', + 'config.json', + ] + with pytest.raises(FileNotFoundError, match='lam_ckpt_file'): + resolve_lam_checkpoint('org/repo') + + +def test_hf_explicit_file_download_failure_message_contains_target(): + with patch( + 'vla_arena.models.univla.lam_checkpoint_resolver.hf_hub_download', + side_effect=RuntimeError('download failed'), + ): + with pytest.raises( + RuntimeError, + match='org/repo/path/to/epoch=0-step=200000.ckpt', + ): + resolve_lam_checkpoint( + lam_path='org/repo', + lam_ckpt_file='path/to/epoch=0-step=200000.ckpt', + ) + + +def test_extract_lam_state_dict_from_nested_state_dict(): + state_dict = {'lam.weight': _TensorLike()} + extracted = extract_lam_state_dict({'state_dict': state_dict}) + + assert extracted == state_dict + + +def test_extract_lam_state_dict_from_plain_state_dict(): + plain_state_dict = {'lam.weight': _TensorLike()} + extracted = extract_lam_state_dict(plain_state_dict) + + assert extracted == plain_state_dict + + +def test_extract_lam_state_dict_invalid_structure_raises(): + with pytest.raises(ValueError, match='Unsupported LAM checkpoint format'): + extract_lam_state_dict({'epoch': 1, 'step': 2}) diff --git a/tests/test_cli.py b/tests/test_cli.py index 8116c0e3..4774c240 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -94,7 +94,7 @@ def test_eval_main_module_not_found(self, mock_find_spec): args = argparse.Namespace() args.model = 'nonexistent_model' - args.config = '/path/to/config.yaml' + args.config = __file__ with pytest.raises(RuntimeError): eval.eval_main(args) @@ -106,20 +106,22 @@ def test_eval_main_import_error(self, mock_find_spec): args = argparse.Namespace() args.model = 'openvla' - args.config = '/path/to/config.yaml' + args.config = __file__ with pytest.raises(RuntimeError): eval.eval_main(args) @patch('vla_arena.cli.eval.importlib.util.find_spec') @patch('vla_arena.cli.eval.importlib.import_module') + @patch('vla_arena.cli.eval.resolve_config_path') def test_eval_main_config_path_absolute( - self, mock_import_module, mock_find_spec + self, mock_resolve_config_path, mock_import_module, mock_find_spec ): """Test that config path is converted to absolute.""" mock_spec = Mock() mock_spec.origin = '/path/to/evaluator.py' mock_find_spec.return_value = mock_spec + mock_resolve_config_path.return_value = '/abs/path/config.yaml' mock_module = Mock() mock_import_module.return_value = mock_module @@ -141,6 +143,27 @@ def test_eval_main_config_path_absolute( if config_path: assert os.path.isabs(config_path) + @patch('vla_arena.cli.eval.importlib.util.find_spec') + @patch('vla_arena.cli.eval.importlib.import_module') + @patch('vla_arena.cli.eval.resolve_config_path') + def test_eval_main_uses_default_config_when_omitted( + self, mock_resolve_config_path, mock_import_module, mock_find_spec + ): + mock_spec = Mock() + mock_spec.origin = '/path/to/evaluator.py' + mock_find_spec.return_value = mock_spec + mock_resolve_config_path.return_value = '/abs/default_eval.yaml' + mock_module = Mock() + mock_import_module.return_value = mock_module + + args = argparse.Namespace(model='openvla', config=None) + eval.eval_main(args) + + mock_resolve_config_path.assert_called_once_with( + mode='eval', model='openvla', config_path=None + ) + mock_module.main.assert_called_once_with(cfg='/abs/default_eval.yaml') + @pytest.mark.skipif(not CLI_AVAILABLE, reason='CLI module not available') class TestTrainMain: @@ -160,7 +183,7 @@ def test_train_main_openpi(self, mock_import_module, mock_find_spec): args = argparse.Namespace() args.model = 'openpi' - args.config = '/path/to/config.yaml' + args.config = __file__ train.train_main(args) @@ -183,7 +206,7 @@ def test_train_main_distributed(self, mock_import_module, mock_find_spec): args = argparse.Namespace() args.model = 'openvla' - args.config = '/path/to/config.yaml' + args.config = __file__ train.train_main(args) @@ -209,7 +232,7 @@ def test_train_main_launch_torchrun( args = argparse.Namespace() args.model = 'openvla' - args.config = '/path/to/config.yaml' + args.config = __file__ train.train_main(args) @@ -225,7 +248,7 @@ def test_train_main_module_not_found(self, mock_find_spec): args = argparse.Namespace() args.model = 'nonexistent_model' - args.config = '/path/to/config.yaml' + args.config = __file__ with pytest.raises(RuntimeError): train.train_main(args) @@ -245,7 +268,7 @@ def test_train_main_overwrite_flag( args = argparse.Namespace() args.model = 'openpi' - args.config = '/path/to/config.yaml' + args.config = __file__ args.overwrite = True train.train_main(args) @@ -253,3 +276,28 @@ def test_train_main_overwrite_flag( # Check that overwrite was passed call_kwargs = mock_module.main.call_args[1] assert call_kwargs.get('overwrite') is True + + @patch('vla_arena.cli.train.importlib.util.find_spec') + @patch('vla_arena.cli.train.importlib.import_module') + @patch.dict(os.environ, {}, clear=False) + @patch('vla_arena.cli.train.resolve_config_path') + def test_train_main_uses_default_config_when_omitted( + self, + mock_resolve_config_path, + mock_import_module, + mock_find_spec, + ): + mock_spec = Mock() + mock_spec.origin = '/path/to/trainer.py' + mock_find_spec.return_value = mock_spec + mock_resolve_config_path.return_value = '/abs/default_train.yaml' + mock_module = Mock() + mock_import_module.return_value = mock_module + + args = argparse.Namespace(model='openpi', config=None, overwrite=False) + train.train_main(args) + + mock_resolve_config_path.assert_called_once_with( + mode='train', model='openpi', config_path=None + ) + mock_module.main.assert_called_once_with(config='/abs/default_train.yaml') diff --git a/tests/test_config_paths.py b/tests/test_config_paths.py new file mode 100644 index 00000000..2371be09 --- /dev/null +++ b/tests/test_config_paths.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +from pathlib import Path + +import pytest + +from vla_arena import config_paths + + +def test_resolve_default_train_config_path_for_openvla(): + resolved = config_paths.resolve_config_path( + mode='train', model='openvla', config_path=None + ) + resolved_path = Path(resolved) + assert resolved_path.is_absolute() + assert resolved_path.exists() + assert resolved_path.name == 'openvla.yaml' + assert resolved_path.parent.name == 'train' + + +def test_resolve_packaged_reference_without_repo_relative_file(): + resolved = config_paths.resolve_config_path( + mode='eval', + model='openvla', + config_path='vla_arena/configs/evaluation/openvla.yaml', + ) + resolved_path = Path(resolved) + assert resolved_path.is_absolute() + assert resolved_path.exists() + assert resolved_path.name == 'openvla.yaml' + assert resolved_path.parent.name == 'evaluation' + + +def test_resolve_config_path_requires_explicit_config_for_unknown_model(): + with pytest.raises(ValueError, match='No default train config'): + config_paths.resolve_config_path( + mode='train', model='unknown_model', config_path=None + ) + + +def test_resolve_config_path_missing_file_raises(): + with pytest.raises(FileNotFoundError, match='Config file not found'): + config_paths.resolve_config_path( + mode='train', model='openvla', config_path='not_found.yaml' + ) diff --git a/tests/test_init_workspace.py b/tests/test_init_workspace.py new file mode 100644 index 00000000..5f84c630 --- /dev/null +++ b/tests/test_init_workspace.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +from pathlib import Path + +import pytest + +from scripts import init_workspace + + +def test_initialize_workspace_creates_envs_and_configs(tmp_path: Path): + init_workspace.initialize_workspace( + output=tmp_path, + models=['openvla', 'openpi'], + copy_configs=True, + force=False, + ) + + base_text = (tmp_path / 'envs/base/pyproject.toml').read_text( + encoding='utf-8' + ) + openvla_text = (tmp_path / 'envs/openvla/pyproject.toml').read_text( + encoding='utf-8' + ) + openpi_text = (tmp_path / 'envs/openpi/pyproject.toml').read_text( + encoding='utf-8' + ) + + assert '"vla-arena",' in base_text + assert 'vla-arena[openvla]' in openvla_text + assert 'tool.uv.sources' not in openvla_text + assert ( + 'override-dependencies = ["ml-dtypes==0.5.4", "tensorstore==0.1.74"]' + in openpi_text + ) + assert (tmp_path / 'configs/train/openvla.yaml').exists() + assert (tmp_path / 'configs/evaluation/openpi.yaml').exists() + + +def test_initialize_workspace_without_configs(tmp_path: Path): + init_workspace.initialize_workspace( + output=tmp_path, + models=['openvla'], + copy_configs=False, + force=False, + ) + assert (tmp_path / 'envs/openvla/pyproject.toml').exists() + assert not (tmp_path / 'configs').exists() + + +def test_initialize_workspace_force_overwrites_existing_envs(tmp_path: Path): + envs_dir = tmp_path / 'envs' + envs_dir.mkdir(parents=True, exist_ok=True) + (envs_dir / 'stale.txt').write_text('old', encoding='utf-8') + + with pytest.raises(FileExistsError, match='Target already exists'): + init_workspace.initialize_workspace( + output=tmp_path, + models=['openvla'], + copy_configs=False, + force=False, + ) + + init_workspace.initialize_workspace( + output=tmp_path, + models=['openvla'], + copy_configs=False, + force=True, + ) + assert not (envs_dir / 'stale.txt').exists() + assert (tmp_path / 'envs/openvla/pyproject.toml').exists() diff --git a/tests/test_openpi_workflow.py b/tests/test_openpi_workflow.py new file mode 100644 index 00000000..56efd5e5 --- /dev/null +++ b/tests/test_openpi_workflow.py @@ -0,0 +1,500 @@ +from __future__ import annotations + +import logging +import os +import pickle +import pathlib +from types import SimpleNamespace +from unittest.mock import Mock + +import numpy as np +import pytest + +from vla_arena.models.openpi import workflow_utils + + +class _DummyDataFactory: + def __init__(self, data_config): + self._data_config = data_config + + def create(self, *_args, **_kwargs): + return self._data_config + + +class _DummyTrainConfig: + def __init__(self, checkpoint_dir: pathlib.Path): + self._checkpoint_dir = checkpoint_dir + self.assets_dirs = checkpoint_dir.parent / 'assets' + self.model = object() + self.data = _DummyDataFactory( + SimpleNamespace(repo_id='datasets/vla-arena', norm_stats=None) + ) + + @property + def checkpoint_dir(self) -> pathlib.Path: + return self._checkpoint_dir + + +def _make_step_dir(base: pathlib.Path, step: int) -> pathlib.Path: + step_dir = base / str(step) + (step_dir / 'params').mkdir(parents=True, exist_ok=True) + return step_dir + + +def test_resolve_checkpoint_dir_explicit_step_dir(tmp_path: pathlib.Path): + step_dir = _make_step_dir(tmp_path, 1000) + resolved = workflow_utils.resolve_checkpoint_dir( + step_dir, train_cfg=None, policy_checkpoint_step='latest' + ) + assert pathlib.Path(resolved) == step_dir.resolve() + + +def test_resolve_checkpoint_dir_uses_explicit_path_over_train_cfg( + tmp_path: pathlib.Path, +): + explicit_exp_dir = tmp_path / 'explicit' + _make_step_dir(explicit_exp_dir, 20) + + inferred_exp_dir = tmp_path / 'inferred' + _make_step_dir(inferred_exp_dir, 99) + train_cfg = _DummyTrainConfig(inferred_exp_dir) + + resolved = workflow_utils.resolve_checkpoint_dir( + explicit_exp_dir, train_cfg=train_cfg, policy_checkpoint_step='latest' + ) + assert pathlib.Path(resolved) == (explicit_exp_dir / '20').resolve() + + +def test_resolve_checkpoint_dir_from_train_cfg_uses_latest( + tmp_path: pathlib.Path, +): + exp_dir = tmp_path / 'exp' + _make_step_dir(exp_dir, 5) + _make_step_dir(exp_dir, 17) + _make_step_dir(exp_dir, 101) + train_cfg = _DummyTrainConfig(exp_dir) + + resolved = workflow_utils.resolve_checkpoint_dir( + None, train_cfg=train_cfg, policy_checkpoint_step='latest' + ) + assert pathlib.Path(resolved) == (exp_dir / '101').resolve() + + +def test_resolve_checkpoint_dir_raises_when_no_steps(tmp_path: pathlib.Path): + exp_dir = tmp_path / 'empty_exp' + exp_dir.mkdir(parents=True) + with pytest.raises(ValueError, match='No checkpoint step directories'): + workflow_utils.resolve_checkpoint_dir( + exp_dir, train_cfg=None, policy_checkpoint_step='latest' + ) + +def test_trainer_main_invokes_norm_stats_then_train_loop(monkeypatch): + trainer = pytest.importorskip('vla_arena.models.openpi.trainer') + cfg = trainer._config.get_config('debug') + + ensure_mock = Mock() + train_loop_mock = Mock() + monkeypatch.setattr(trainer, 'ensure_norm_stats', ensure_mock) + monkeypatch.setattr(trainer, 'train_loop', train_loop_mock) + + trainer.main(config=cfg) + ensure_mock.assert_called_once_with(cfg) + train_loop_mock.assert_called_once_with(cfg) + + +def test_remove_strings_transform_is_picklable(): + transform = workflow_utils._RemoveStringsTransform() + + restored = pickle.loads(pickle.dumps(transform)) + assert isinstance(restored, workflow_utils._RemoveStringsTransform) + + +def test_remove_strings_transform_filters_string_fields(): + transform = workflow_utils._RemoveStringsTransform() + item = { + 'state': np.asarray([1.0, 2.0]), + 'actions': np.asarray([[0.1, 0.2]]), + 'prompt': np.asarray('pick up the cup'), + 'task_name': 'stack blocks', + } + + filtered = transform(item) + assert 'state' in filtered + assert 'actions' in filtered + assert 'prompt' not in filtered + assert 'task_name' not in filtered + + +def test_normalize_legacy_train_yaml_maps_checkpoint_path(): + yaml_data = { + 'name': 'pi0_vla_arena_low_mem_finetune', + 'weight_loader': {'checkpoint_path': '/tmp/params'}, + } + + normalized = workflow_utils._normalize_legacy_train_yaml(yaml_data) + assert normalized['weight_loader']['params_path'] == '/tmp/params' + assert 'checkpoint_path' not in normalized['weight_loader'] + + +def test_normalize_legacy_train_yaml_prefers_params_path(): + yaml_data = { + 'name': 'pi0_vla_arena_low_mem_finetune', + 'weight_loader': { + 'checkpoint_path': '/tmp/legacy', + 'params_path': '/tmp/current', + }, + } + + normalized = workflow_utils._normalize_legacy_train_yaml(yaml_data) + assert normalized['weight_loader']['params_path'] == '/tmp/current' + assert 'checkpoint_path' not in normalized['weight_loader'] + + +def test_load_train_config_from_yaml_fallbacks_to_packaged_reference( + monkeypatch, tmp_path: pathlib.Path +): + yaml_path = tmp_path / 'openpi.yaml' + yaml_path.write_text( + 'name: "pi0_vla_arena_low_mem_finetune"\n' + 'exp_name: "openpi_test"\n', + encoding='utf-8', + ) + + fake_config_module = SimpleNamespace(cli=Mock(return_value='cfg_obj')) + import_module_mock = Mock(return_value=fake_config_module) + monkeypatch.setattr( + workflow_utils.importlib, 'import_module', import_module_mock + ) + + resolver_mock = Mock(return_value=yaml_path) + monkeypatch.setattr( + workflow_utils, 'resolve_packaged_config_reference', resolver_mock + ) + + cfg = workflow_utils.load_train_config_from_yaml( + 'vla_arena/configs/train/_pypi_fallback_test.yaml' + ) + + assert cfg == 'cfg_obj' + resolver_mock.assert_called_once() + import_module_mock.assert_called_once_with( + 'vla_arena.models.openpi.src.openpi.training.config' + ) + fake_config_module.cli.assert_called_once() + + +def _mock_openpi_config_cli(monkeypatch): + captured = {} + + def _cli(): + captured['argv'] = workflow_utils.sys.argv.copy() + return 'cfg_obj' + + fake_config_module = SimpleNamespace(cli=Mock(side_effect=_cli)) + import_module_mock = Mock(return_value=fake_config_module) + monkeypatch.setattr( + workflow_utils.importlib, 'import_module', import_module_mock + ) + return captured + + +def test_load_train_config_from_yaml_local_repo_path_sets_env_and_maps_repo_id( + monkeypatch, tmp_path: pathlib.Path +): + local_repo = tmp_path / 'datasets' / 'mysets' / 'vla_openpi' + local_repo.mkdir(parents=True) + yaml_path = tmp_path / 'openpi_local.yaml' + yaml_path.write_text( + 'name: "pi0_vla_arena_low_mem_finetune"\n' + 'exp_name: "openpi_local_test"\n' + f'data:\n repo_id: "{local_repo}"\n', + encoding='utf-8', + ) + + monkeypatch.delenv('HF_LEROBOT_HOME', raising=False) + captured = _mock_openpi_config_cli(monkeypatch) + + cfg = workflow_utils.load_train_config_from_yaml(yaml_path) + + assert cfg == 'cfg_obj' + assert os.getenv('HF_LEROBOT_HOME') == str(tmp_path / 'datasets') + assert '--data.repo_id=mysets/vla_openpi' in captured['argv'] + + +def test_load_train_config_from_yaml_local_repo_path_overrides_existing_hf_home( + monkeypatch, tmp_path: pathlib.Path, caplog +): + local_repo = tmp_path / 'datasets' / 'mysets' / 'vla_openpi' + local_repo.mkdir(parents=True) + yaml_path = tmp_path / 'openpi_local_override.yaml' + yaml_path.write_text( + 'name: "pi0_vla_arena_low_mem_finetune"\n' + 'exp_name: "openpi_local_override"\n' + f'data:\n repo_id: "{local_repo}"\n', + encoding='utf-8', + ) + + monkeypatch.setenv('HF_LEROBOT_HOME', '/tmp/old_hf_home') + captured = _mock_openpi_config_cli(monkeypatch) + caplog.set_level(logging.WARNING) + + workflow_utils.load_train_config_from_yaml(yaml_path) + + assert os.getenv('HF_LEROBOT_HOME') == str(tmp_path / 'datasets') + assert '--data.repo_id=mysets/vla_openpi' in captured['argv'] + assert any( + 'Overriding HF_LEROBOT_HOME' in record.message + for record in caplog.records + ) + + +def test_load_train_config_from_yaml_nonexistent_repo_id_keeps_hf_behavior( + monkeypatch, tmp_path: pathlib.Path +): + yaml_path = tmp_path / 'openpi_hf.yaml' + yaml_path.write_text( + 'name: "pi0_vla_arena_low_mem_finetune"\n' + 'exp_name: "openpi_hf_test"\n' + 'data:\n repo_id: "org/repo"\n', + encoding='utf-8', + ) + + monkeypatch.setenv('HF_LEROBOT_HOME', '/tmp/keep_this_home') + captured = _mock_openpi_config_cli(monkeypatch) + + workflow_utils.load_train_config_from_yaml(yaml_path) + + assert os.getenv('HF_LEROBOT_HOME') == '/tmp/keep_this_home' + assert '--data.repo_id=org/repo' in captured['argv'] + + +def test_local_repo_mapping_fallback_single_level( + monkeypatch, tmp_path: pathlib.Path +): + local_repo = tmp_path / 'dataset_only' + local_repo.mkdir(parents=True) + yaml_path = tmp_path / 'openpi_local_single_level.yaml' + yaml_path.write_text( + 'name: "pi0_vla_arena_low_mem_finetune"\n' + 'exp_name: "openpi_local_single"\n' + 'data:\n repo_id: "dataset_only"\n', + encoding='utf-8', + ) + + monkeypatch.delenv('HF_LEROBOT_HOME', raising=False) + monkeypatch.chdir(tmp_path) + captured = _mock_openpi_config_cli(monkeypatch) + + workflow_utils.load_train_config_from_yaml(yaml_path) + + assert os.getenv('HF_LEROBOT_HOME') == str(tmp_path) + assert '--data.repo_id=dataset_only' in captured['argv'] + + +def test_is_local_host_variants(): + evaluator = pytest.importorskip('vla_arena.models.openpi.evaluator') + + assert evaluator._is_local_host('0.0.0.0') + assert evaluator._is_local_host('127.0.0.1') + assert evaluator._is_local_host('localhost') + assert evaluator._is_local_host('::1') + assert evaluator._is_local_host('ws://localhost') + assert not evaluator._is_local_host('192.168.1.100') + assert not evaluator._is_local_host('example.com') + + +def test_create_policy_client_reuses_existing_websocket_server(monkeypatch): + evaluator = pytest.importorskip('vla_arena.models.openpi.evaluator') + + cfg = evaluator.GenerateConfig( + host='127.0.0.1', + port=8000, + auto_start_policy_server=True, + ) + client_obj = Mock() + start_mock = Mock() + ws_ctor = Mock(return_value=client_obj) + + monkeypatch.setattr( + evaluator, + '_resolve_policy_target', + lambda _cfg: (object(), '/tmp/checkpoints/openpi/1000', 'pi0_cfg'), + ) + monkeypatch.setattr( + evaluator, + '_is_port_open', + Mock(return_value=True), + ) + monkeypatch.setattr( + evaluator, '_start_policy_server_process', start_mock + ) + monkeypatch.setattr( + evaluator._websocket_client_policy, 'WebsocketClientPolicy', ws_ctor + ) + + client, source, config_name, managed_process = evaluator._create_policy_client( + cfg + ) + + assert client is client_obj + assert source == '127.0.0.1:8000' + assert config_name == 'pi0_cfg' + assert managed_process is None + start_mock.assert_not_called() + ws_ctor.assert_called_once_with('127.0.0.1', 8000) + + +def test_create_policy_client_autostarts_server_when_local_port_unavailable( + monkeypatch, +): + evaluator = pytest.importorskip('vla_arena.models.openpi.evaluator') + + cfg = evaluator.GenerateConfig( + host='localhost', + port=8000, + auto_start_policy_server=True, + ) + managed_process = Mock() + managed_process.pid = 12345 + managed_process.poll.return_value = None + client_obj = Mock() + + monkeypatch.setattr( + evaluator, + '_resolve_policy_target', + lambda _cfg: (object(), '/tmp/checkpoints/openpi/1000', 'pi0_cfg'), + ) + monkeypatch.setattr( + evaluator, + '_is_port_open', + Mock(return_value=False), + ) + monkeypatch.setattr( + evaluator, + '_build_serve_policy_command', + lambda *_args, **_kwargs: ['python', 'serve_policy.py'], + ) + start_mock = Mock(return_value=managed_process) + wait_mock = Mock() + ws_ctor = Mock(return_value=client_obj) + monkeypatch.setattr( + evaluator, '_start_policy_server_process', start_mock + ) + monkeypatch.setattr( + evaluator, '_wait_for_policy_server_ready', wait_mock + ) + monkeypatch.setattr( + evaluator._websocket_client_policy, 'WebsocketClientPolicy', ws_ctor + ) + + client, source, config_name, process = evaluator._create_policy_client(cfg) + + assert client is client_obj + assert source == 'localhost:8000' + assert config_name == 'pi0_cfg' + assert process is managed_process + start_mock.assert_called_once() + wait_mock.assert_called_once() + ws_ctor.assert_called_once_with('localhost', 8000) + + +def test_create_policy_client_remote_host_unavailable_raises(monkeypatch): + evaluator = pytest.importorskip('vla_arena.models.openpi.evaluator') + + cfg = evaluator.GenerateConfig( + host='10.0.0.8', + port=8000, + auto_start_policy_server=True, + ) + start_mock = Mock() + monkeypatch.setattr( + evaluator, + '_resolve_policy_target', + lambda _cfg: (object(), '/tmp/checkpoints/openpi/1000', 'pi0_cfg'), + ) + monkeypatch.setattr( + evaluator, + '_is_port_open', + Mock(return_value=False), + ) + monkeypatch.setattr( + evaluator, '_start_policy_server_process', start_mock + ) + + with pytest.raises(RuntimeError, match='unreachable'): + evaluator._create_policy_client(cfg) + + start_mock.assert_not_called() + + +def test_build_serve_policy_command_places_port_before_subcommand(): + evaluator = pytest.importorskip('vla_arena.models.openpi.evaluator') + cfg = evaluator.GenerateConfig(port=8001) + + cmd = evaluator._build_serve_policy_command( + cfg, + config_name='pi0_vla_arena_low_mem_finetune', + checkpoint_dir='/tmp/openpi/1000', + ) + + assert '--port' in cmd + assert 'policy:checkpoint' in cmd + assert cmd.index('--port') < cmd.index('policy:checkpoint') + assert cmd[cmd.index('--port') + 1] == '8001' + assert '--policy.config' in cmd + assert '--policy.dir' in cmd + + +def test_eval_vla_arena_terminates_managed_server_process( + monkeypatch, tmp_path: pathlib.Path +): + evaluator = pytest.importorskip('vla_arena.models.openpi.evaluator') + + managed_process = Mock() + managed_process.poll.return_value = None + managed_process.wait.return_value = 0 + client_obj = Mock() + log_file = Mock() + + cfg = evaluator.GenerateConfig( + task_suite_name='safety_static_obstacles', + result_json_path=str(tmp_path / 'result.json'), + use_replacements=False, + ) + + monkeypatch.setattr( + evaluator, + '_create_policy_client', + lambda _cfg: ( + client_obj, + 'localhost:8000', + 'pi0_cfg', + managed_process, + ), + ) + monkeypatch.setattr( + evaluator.benchmark, + 'get_benchmark_dict', + lambda: {'safety_static_obstacles': lambda: object()}, + ) + monkeypatch.setattr( + evaluator, + 'setup_logging', + lambda _cfg: (log_file, str(tmp_path / 'eval.log'), 'run-id'), + ) + monkeypatch.setattr( + evaluator, + 'run_task', + lambda *args, **kwargs: (1, 0, 0, 0, 0, 0, 0, 0), + ) + monkeypatch.setattr( + evaluator, + 'load_replacements_dict', + lambda *_args, **_kwargs: {}, + ) + monkeypatch.setattr(evaluator.tqdm, 'tqdm', lambda it: it) + + evaluator.eval_vla_arena(cfg) + + managed_process.terminate.assert_called_once() + managed_process.wait.assert_called_once() diff --git a/vla_arena/cli/eval.py b/vla_arena/cli/eval.py index 5a9d2752..2f8189de 100644 --- a/vla_arena/cli/eval.py +++ b/vla_arena/cli/eval.py @@ -14,13 +14,23 @@ import importlib import importlib.util -import os + +from vla_arena.config_paths import resolve_config_path def eval_main(args): model = args.model - # Ensure config is an absolute path for easy reading by subprocesses - config_path = os.path.abspath(str(args.config)) + try: + # Support omitted --config and packaged vla_arena/configs/... refs. + config_path = resolve_config_path( + mode='eval', model=model, config_path=args.config + ) + except (FileNotFoundError, ValueError) as e: + raise RuntimeError( + f'Unable to resolve eval config for model {model!r}: {e}\n' + 'If you installed from PyPI, initialize local uv projects first:\n' + ' vla-arena.init-workspace --force', + ) from e # 1. Dynamically get the physical path of the corresponding model evaluator.py file try: diff --git a/vla_arena/cli/train.py b/vla_arena/cli/train.py index 3d2b22ec..9119f560 100644 --- a/vla_arena/cli/train.py +++ b/vla_arena/cli/train.py @@ -20,11 +20,22 @@ import torch +from vla_arena.config_paths import resolve_config_path + def train_main(args): model = args.model - # Ensure config is an absolute path for easy reading by subprocesses - config_path = os.path.abspath(str(args.config)) + try: + # Support omitted --config and packaged vla_arena/configs/... refs. + config_path = resolve_config_path( + mode='train', model=model, config_path=args.config + ) + except (FileNotFoundError, ValueError) as e: + raise RuntimeError( + f'Unable to resolve train config for model {model!r}: {e}\n' + 'If you installed from PyPI, initialize local uv projects first:\n' + ' vla-arena.init-workspace --force', + ) from e # 1. Dynamically get the physical path of the corresponding model trainer.py file try: diff --git a/vla_arena/config_paths.py b/vla_arena/config_paths.py new file mode 100644 index 00000000..0b62f4bc --- /dev/null +++ b/vla_arena/config_paths.py @@ -0,0 +1,98 @@ +# Copyright 2025 The VLA-Arena Authors. +# +# 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. + +from __future__ import annotations + +import importlib.resources +from pathlib import Path +from typing import Mapping + + +_PACKAGE_CONFIG_PREFIXES = ( + 'vla_arena/configs/', + './vla_arena/configs/', +) + +_DEFAULT_CONFIGS: Mapping[str, Mapping[str, str]] = { + 'train': { + 'openvla': 'train/openvla.yaml', + 'openvla_oft': 'train/openvla_oft.yaml', + 'univla': 'train/univla.yaml', + 'smolvla': 'train/smolvla.yaml', + 'openpi': 'train/openpi.yaml', + }, + 'eval': { + 'openvla': 'evaluation/openvla.yaml', + 'openvla_oft': 'evaluation/openvla_oft.yaml', + 'univla': 'evaluation/univla.yaml', + 'smolvla': 'evaluation/smolvla.yaml', + 'openpi': 'evaluation/openpi.yaml', + }, +} + + +def _as_text(path_value: str | Path) -> str: + return str(path_value).replace('\\', '/').strip() + + +def get_packaged_configs_root() -> Path: + """Return the installed package's configs root path.""" + resource = importlib.resources.files('vla_arena').joinpath('configs') + return Path(str(resource)).resolve() + + +def resolve_packaged_config_reference(config_path: str | Path) -> Path | None: + """Resolve `vla_arena/configs/...` references inside installed package data.""" + text = _as_text(config_path) + for prefix in _PACKAGE_CONFIG_PREFIXES: + if not text.startswith(prefix): + continue + relative_path = text[len(prefix) :] + candidate = (get_packaged_configs_root() / relative_path).resolve() + if candidate.exists(): + return candidate + return None + return None + + +def resolve_config_path( + mode: str, model: str, config_path: str | Path | None +) -> str: + """Resolve user config path or model default config to an absolute file path.""" + if config_path is None: + default_relative = _DEFAULT_CONFIGS.get(mode, {}).get(model) + if default_relative is None: + raise ValueError( + f"No default {mode} config is available for model '{model}'. " + 'Please provide --config explicitly.' + ) + candidate = (get_packaged_configs_root() / default_relative).resolve() + if not candidate.exists(): + raise FileNotFoundError( + f'Default {mode} config not found: {candidate}' + ) + return str(candidate) + + raw = Path(str(config_path)).expanduser() + if raw.exists(): + return str(raw.resolve()) + + packaged = resolve_packaged_config_reference(raw) + if packaged is not None: + return str(packaged) + + raise FileNotFoundError( + f'Config file not found: {config_path}. ' + "Pass an existing path or use 'vla_arena/configs/...'." + ) diff --git a/vla_arena/configs/evaluation/openpi.yaml b/vla_arena/configs/evaluation/openpi.yaml index fcd37eef..58cd7e2a 100644 --- a/vla_arena/configs/evaluation/openpi.yaml +++ b/vla_arena/configs/evaluation/openpi.yaml @@ -1,8 +1,20 @@ # OpenPI Evaluation Configuration -# Configuration for evaluating OpenPI models on VLA-Arena benchmark tasks +# Default path is one-line websocket evaluation with auto-started policy server. ################################################################################################################# -# Model server parameters +# Inference parameters +################################################################################################################# +inference_mode: "websocket" # websocket only +policy_config_name: null # Optional fallback config name when train_config_path is not set +policy_checkpoint_dir: null # Optional explicit checkpoint dir; supports experiment root or concrete step dir +policy_checkpoint_step: "latest" # "latest" or step number when using an experiment root +train_config_path: "vla_arena/configs/train/openpi.yaml" # Used to infer model config + checkpoint for auto-start (pi0 default; pi0-fast optional via train config name) +auto_start_policy_server: true # Auto-start serve_policy.py when websocket port is unavailable on local host +policy_server_start_timeout_sec: 180 # Max seconds to wait for auto-started policy server readiness +policy_server_poll_interval_sec: 1.0 # Poll interval (seconds) while waiting for policy server + +################################################################################################################# +# Websocket policy server parameters (used when inference_mode="websocket") ################################################################################################################# host: "0.0.0.0" # Model server host address port: 8000 # Model server port diff --git a/vla_arena/configs/evaluation/openpi_fast.yaml b/vla_arena/configs/evaluation/openpi_fast.yaml new file mode 100644 index 00000000..f2891598 --- /dev/null +++ b/vla_arena/configs/evaluation/openpi_fast.yaml @@ -0,0 +1,51 @@ +# OpenPI-FAST Evaluation Configuration +# Default path is one-line websocket evaluation with auto-started policy server. + +################################################################################################################# +# Inference parameters +################################################################################################################# +inference_mode: "websocket" # websocket only +policy_config_name: null # Optional fallback config name when train_config_path is not set +policy_checkpoint_dir: null # Optional explicit checkpoint dir; supports experiment root or concrete step dir +policy_checkpoint_step: "latest" # "latest" or step number when using an experiment root +train_config_path: "vla_arena/configs/train/openpi_fast.yaml" # Used to infer model config + checkpoint for auto-start +auto_start_policy_server: true # Auto-start serve_policy.py when websocket port is unavailable on local host +policy_server_start_timeout_sec: 180 # Max seconds to wait for auto-started policy server readiness +policy_server_poll_interval_sec: 1.0 # Poll interval (seconds) while waiting for policy server + +################################################################################################################# +# Websocket policy server parameters (used when inference_mode="websocket") +################################################################################################################# +host: "0.0.0.0" # Model server host address +port: 8000 # Model server port +resize_size: 224 # Image resize size for model input +replan_steps: 5 # Number of actions to execute before replanning + +################################################################################################################# +# VLA-Arena environment-specific parameters +################################################################################################################# +task_suite_name: "safety_static_obstacles" # Task suite name (e.g., "safety_static_obstacles", "safety_dynamic_obstacles", "long_horizon") +task_level: 0 # Task level (0 or 1) +num_steps_wait: 10 # Number of steps to wait for objects to stabilize in sim +num_trials_per_task: 10 # Number of rollouts per task +add_noise: false # Add noise to observations +adjust_light: false # Adjust lighting conditions +randomize_color: false # Randomize object colors +camera_offset: false # Apply camera offset +safety: false # Enable safety mode + +################################################################################################################# +# Utils +################################################################################################################# +save_video_mode: "first_success_failure" # Video saving mode: "all", "first_success_failure", "none" +local_log_dir: "./experiments/logs" # Local directory for eval logs +seed: 7 # Random seed (for reproducibility) +result_json_path: default # Path to save aggregated JSON results (default -> ./results/openpi_json_.json) + +################################################################################################################# +# Instruction replacement parameters +################################################################################################################# +replacements_file: "VLA-Arena/language_replacements" # Path to replacements JSON file +use_replacements: false # Whether to use instruction replacements +replacement_probability: 1.0 # Probability of applying replacement (0.0 to 1.0) +replacement_level: 1 # Level of instruction replacements (from 1 to 4) diff --git a/vla_arena/configs/evaluation/openvla.yaml b/vla_arena/configs/evaluation/openvla.yaml index 029fe822..1e5b69c5 100644 --- a/vla_arena/configs/evaluation/openvla.yaml +++ b/vla_arena/configs/evaluation/openvla.yaml @@ -1,12 +1,10 @@ model_family: "openvla" # Model family # Set OPENVLA_PRETRAINED_CHECKPOINT env var or modify the value to point to your checkpoint - pretrained_checkpoint: "your-openvla-checkpoint" + pretrained_checkpoint: "VLA-Arena/openvla-7b-finetuned-vla-arena" center_crop: true # Center crop? (if trained with random crop augmentation) - num_open_loop_steps: 8 # Open-loop steps before requerying policy - unnorm_key: "vla_arena" # Action un-normalization key + unnorm_key: "vla_arena_l0_l" # Action un-normalization key load_in_8bit: false # Load with 8-bit quantization load_in_4bit: false # Load with 4-bit quantization - seed: 7 # Random seed for reproducibility task_suite_name: "safety_static_obstacles" # Task suite name task_level: 0 diff --git a/vla_arena/configs/evaluation/openvla_oft.yaml b/vla_arena/configs/evaluation/openvla_oft.yaml index fee3580e..e9da29c7 100644 --- a/vla_arena/configs/evaluation/openvla_oft.yaml +++ b/vla_arena/configs/evaluation/openvla_oft.yaml @@ -1,14 +1,14 @@ # Model-specific parameters model_family: "openvla" # Model family # Set OPENVLA_OFT_PRETRAINED_CHECKPOINT environment variable or modify this path to specify your checkpoint location -pretrained_checkpoint: "path/to/openvla_oft_checkpoint" # Pretrained checkpoint path +pretrained_checkpoint: "VLA-Arena/openvla-7b-oft-finetuned-vla-arena" # Pretrained checkpoint path -use_l1_regression: true # If True, uses continuous action head with L1 regression objective +use_l1_regression: true # If True, uses continuous action head with L1 regression objective use_diffusion: false # If True, uses continuous action head with diffusion modeling objective (DDIM) num_diffusion_steps_train: 50 # (When `diffusion==True`) Number of diffusion steps used for training num_diffusion_steps_inference: 50 # (When `diffusion==True`) Number of diffusion steps used for inference -use_film: false # If True, uses FiLM to infuse language inputs into visual features -num_images_in_input: 1 # Number of images in the VLA input (default: 1) +use_film: true # If True, uses FiLM to infuse language inputs into visual features +num_images_in_input: 2 # Number of images in the VLA input (default: 1) use_proprio: false # Whether to include proprio state in input center_crop: true # Center crop? (if trained w/ random crop image aug) @@ -16,7 +16,7 @@ num_open_loop_steps: 8 # Number of actions to execute open-lo lora_rank: 32 # Rank of LoRA weight matrix (MAKE SURE THIS MATCHES TRAINING!) -unnorm_key: "vla_arena" # Action un-normalization key +unnorm_key: "vla_arena_l0_l" # Action un-normalization key load_in_8bit: false # (For OpenVLA only) Load with 8-bit quantization load_in_4bit: false # (For OpenVLA only) Load with 4-bit quantization diff --git a/vla_arena/configs/evaluation/smolvla.yaml b/vla_arena/configs/evaluation/smolvla.yaml index bb4c8024..0c1af043 100644 --- a/vla_arena/configs/evaluation/smolvla.yaml +++ b/vla_arena/configs/evaluation/smolvla.yaml @@ -1,4 +1,4 @@ -policy_path: "your/path/to/policy" +policy_path: "VLA-Arena/smolvla-vla-arena" # --- VLA-Arena environment-specific parameters --- task_suite_name: "safety_dynamic_obstacles" diff --git a/vla_arena/configs/evaluation/univla.yaml b/vla_arena/configs/evaluation/univla.yaml index 9b72c695..0c767937 100644 --- a/vla_arena/configs/evaluation/univla.yaml +++ b/vla_arena/configs/evaluation/univla.yaml @@ -1,12 +1,13 @@ # Model-specific parameters model_family: "openvla" # Model family # Set UNIVLA_PRETRAINED_CHECKPOINT environment variable or modify this path to specify your checkpoint location -pretrained_checkpoint: "/path/to/your/pretrained-checkpoint" # Pretrained checkpoint path +pretrained_checkpoint: "VLA-Arena/univla-7b-finetuned-vla-arena" # Pretrained checkpoint path load_in_8bit: false # (For OpenVLA only) Load with 8-bit quantization load_in_4bit: false # (For OpenVLA only) Load with 4-bit quantization # Set UNIVLA_ACTION_DECODER_PATH environment variable or modify this path to specify your action decoder location -action_decoder_path: "/path/to/your/action_decoder.pt" # Path to action decoder checkpoint +action_decoder_path: "VLA-Arena/univla-action-decoder" # Path to action decoder checkpoint or HF repo id +unnorm_key: "vla_arena_l0_l" # Recommended for VLA-Arena finetuned checkpoint center_crop: true # Center crop? (if trained w/ random crop image aug) save_video: true # Whether to save rollout videos diff --git a/vla_arena/configs/train/openpi.yaml b/vla_arena/configs/train/openpi.yaml index 42e1ac0a..124d9fdd 100644 --- a/vla_arena/configs/train/openpi.yaml +++ b/vla_arena/configs/train/openpi.yaml @@ -8,7 +8,7 @@ name: "pi0_vla_arena_low_mem_finetune" exp_name: "openpi_training" # Training Parameters -batch_size: 8 # Global batch size +batch_size: 32 # Global batch size num_train_steps: 30000 # Number of training steps log_interval: 100 # Log metrics every N steps save_interval: 1000 # Save checkpoint every N steps @@ -29,8 +29,13 @@ save_interval: 1000 # Save checkpoint every N step # clip_gradient_norm: 1.0 # Data Configuration (override if needed) +# Default L0 large dataset (LeRobot/OpenPI format) +data: + repo_id: "VLA-Arena/VLA_Arena_L0_L_lerobot_openpi" # Supports HF repo id OR local LeRobot dataset path. +# # data: -# repo_id: "lerobot_data/VLA_Arena" # LeRobot dataset repo ID +# repo_id: "lerobot_data/VLA_Arena" # Hugging Face dataset repo id +# repo_id: "/data/lerobot/mysets/vla_openpi" # Local dataset path; HF_LEROBOT_HOME/repo_id will be auto-resolved # extra_delta_transform: true # Apply extra delta transform # Model Configuration (override if needed) @@ -44,7 +49,7 @@ save_interval: 1000 # Save checkpoint every N step # Weight Loading (override if needed) # weight_loader: -# checkpoint_path: "gs://openpi-assets/checkpoints/pi0_base/params" +# params_path: "gs://openpi-assets/checkpoints/pi0_base/params" # Checkpoint Configuration checkpoint_base_dir: "./checkpoints" # Base directory for checkpoints diff --git a/vla_arena/configs/train/openpi_fast.yaml b/vla_arena/configs/train/openpi_fast.yaml new file mode 100644 index 00000000..8d682a50 --- /dev/null +++ b/vla_arena/configs/train/openpi_fast.yaml @@ -0,0 +1,30 @@ +# OpenPI-FAST Training Configuration +# This config uses the base "pi0_fast_vla_arena_low_mem_finetune" config and allows overriding key parameters + +# Base config name (must match one of the predefined configs in config.py) +name: "pi0_fast_vla_arena_low_mem_finetune" + +# Experiment name (required) +exp_name: "openpi_fast_training" + +# Training Parameters +batch_size: 32 # Global batch size +num_train_steps: 60000 # Number of training steps +log_interval: 100 # Log metrics every N steps +save_interval: 1000 # Save checkpoint every N steps + +# Checkpoint Configuration +checkpoint_base_dir: "./checkpoints" # Base directory for checkpoints +assets_base_dir: "./assets" # Base directory for assets +overwrite: true # Overwrite existing checkpoint directory +resume: false # Resume from latest checkpoint + +# Wandb Configuration +wandb_enabled: true # Enable wandb logging +project_name: "openpi_fast" # Wandb project name + +# Other Settings +seed: 42 # Random seed +num_workers: 2 # Data loader workers +keep_period: 5000 # Keep checkpoints every N steps +fsdp_devices: 1 # FSDP devices (1 = disabled) diff --git a/vla_arena/configs/train/openvla.yaml b/vla_arena/configs/train/openvla.yaml index 69c3cf9f..5f02b8c2 100644 --- a/vla_arena/configs/train/openvla.yaml +++ b/vla_arena/configs/train/openvla.yaml @@ -1,5 +1,5 @@ # Set OPENVLA_VLA_PATH environment variable or modify this path to specify your OpenVLA model location -vla_path: /path/to/your/model # Path to OpenVLA model +vla_path: openvla/openvla-7b # Path to OpenVLA model # Directory Paths # Set OPENVLA_DATA_ROOT_DIR environment variable or modify this path to specify your dataset directory @@ -10,7 +10,7 @@ adapter_tmp_dir: "adapter-tmp" # Temporary directory for LoRA w # Fine-tuning Parameters batch_size: 16 # Fine-tuning batch size -max_steps: 300000 # Max number of fine-tuning steps +max_steps: 200000 # Max number of fine-tuning steps save_steps: 50000 # Interval for checkpoint saving learning_rate: 5.0e-4 # Fine-tuning learning rate grad_accumulation_steps: 1 # Gradient accumulation steps diff --git a/vla_arena/configs/train/openvla_oft.yaml b/vla_arena/configs/train/openvla_oft.yaml index 9c40be6b..4089a120 100644 --- a/vla_arena/configs/train/openvla_oft.yaml +++ b/vla_arena/configs/train/openvla_oft.yaml @@ -1,6 +1,6 @@ # Model Path # Set OPENVLA_OFT_VLA_PATH environment variable or modify this path to specify your OpenVLA model location -vla_path: /path/to/your/models/openvla # Path to OpenVLA model (on HuggingFace Hub or stored locally) +vla_path: openvla/openvla-7b # Path to OpenVLA model (on HuggingFace Hub or stored locally) # Dataset # Set OPENVLA_OFT_DATA_ROOT_DIR environment variable or modify this path to specify your dataset directory @@ -13,17 +13,17 @@ shuffle_buffer_size: 100000 # Dataloader shuffle buffer size (can use_l1_regression: true # If True, trains continuous action head with L1 regression objective use_diffusion: false # If True, trains continuous action head with diffusion modeling objective (DDIM) num_diffusion_steps_train: 50 # (When `diffusion==True`) Number of diffusion steps used for training -use_film: false # If True, uses FiLM to infuse language inputs into visual features -num_images_in_input: 1 # Number of images in the VLA input (default: 1) +use_film: true # If True, uses FiLM to infuse language inputs into visual features +num_images_in_input: 2 # Number of images in the VLA input (default: 1) use_proprio: false # If True, includes robot proprioceptive state in input # Training configuration -batch_size: 8 # Batch size per device (total batch size = batch_size * num GPUs) +batch_size: 7 # Batch size per device (total batch size = batch_size * num GPUs) learning_rate: 5.0e-4 # Learning rate lr_warmup_steps: 0 # Number of steps to warm up learning rate (from 10% to 100%) -num_steps_before_decay: 100000 # Number of steps before LR decays by 10x +num_steps_before_decay: 200000 # Keep > max_steps to effectively disable LR decay during training grad_accumulation_steps: 1 # Number of gradient accumulation steps -max_steps: 200000 # Max number of training steps +max_steps: 150000 # Max number of training steps use_val_set: false # If True, uses validation set and log validation metrics val_freq: 10000 # (When `use_val_set==True`) Validation set logging frequency in steps val_time_limit: 180 # (When `use_val_set==True`) Time limit for computing validation metrics diff --git a/vla_arena/configs/train/smolvla.yaml b/vla_arena/configs/train/smolvla.yaml index de9a8f28..2e92e9a5 100644 --- a/vla_arena/configs/train/smolvla.yaml +++ b/vla_arena/configs/train/smolvla.yaml @@ -1,4 +1,5 @@ dataset: + repo_id: "VLA-Arena/VLA_Arena_L0_L_lerobot_smolvla" root: "/path/to/your/datasets/vla-arena-lerobot" policy: diff --git a/vla_arena/configs/train/univla.yaml b/vla_arena/configs/train/univla.yaml index 42d07721..c70efa3e 100644 --- a/vla_arena/configs/train/univla.yaml +++ b/vla_arena/configs/train/univla.yaml @@ -2,9 +2,12 @@ # Model Paths # Set UNIVLA_VLA_PATH environment variable or modify this path to specify your UniVLA model location -vla_path: "/path/to/your/univla-model" # Path to your local UniVLA path -# Set UNIVLA_LAM_PATH environment variable or modify this path to specify your LAM checkpoint location -lam_path: "/path/to/your/lam-checkpoint.ckpt" # Path to LAM checkpoint +vla_path: qwbu/univla-7b # Path to your local UniVLA path +# Set UNIVLA_LAM_PATH environment variable or modify this path to specify your LAM source +# lam_path supports: local checkpoint file, local checkpoint directory, or Hugging Face repo id +lam_path: qwbu/univla-latent-action-model +# Optional relative checkpoint file within local directory / HF repo (e.g., task_centric_lam_stage2/epoch=0-step=200000.ckpt) +lam_ckpt_file: null # Directory Paths # Set UNIVLA_DATA_ROOT_DIR environment variable or modify this path to specify your dataset directory @@ -42,5 +45,5 @@ use_quantization: false # Whether to 4-bit quantize VLA for LoRA fine-tuning (C # Tracking Parameters wandb_project: "fientune-VLA-ARENA" # Name of W&B project to log to -wandb_entity: "jiahao-li" # Name of entity to log under +wandb_entity: "your-wandb-entity" # Name of entity to log under run_id_note: null # Extra note for logging, Weights & Biases (optional) diff --git a/vla_arena/models/openpi/evaluator.py b/vla_arena/models/openpi/evaluator.py index 58e5dc41..d494acdb 100644 --- a/vla_arena/models/openpi/evaluator.py +++ b/vla_arena/models/openpi/evaluator.py @@ -18,13 +18,18 @@ import math import os import pathlib +import shlex +import socket +import subprocess import sys import time from dataclasses import dataclass, replace +from typing import Any from typing import Iterable +from typing import Literal +from urllib import parse as urllib_parse import imageio -import json import numpy as np import tqdm import tyro @@ -32,11 +37,18 @@ from openpi_client import image_tools from openpi_client import websocket_client_policy as _websocket_client_policy +from vla_arena.models.openpi.workflow_utils import load_train_config_from_yaml +from vla_arena.models.openpi.workflow_utils import resolve_checkpoint_dir from vla_arena.vla_arena import benchmark, get_vla_arena_path from vla_arena.vla_arena.envs import OffScreenRenderEnv from vla_arena.vla_arena.utils.utils import apply_instruction_replacement, load_replacements_dict +# Add openpi src directory to Python path if needed. +_openpi_src = pathlib.Path(__file__).parent / 'src' +if str(_openpi_src) not in sys.path: + sys.path.insert(0, str(_openpi_src)) + VLA_ARENA_DUMMY_ACTION = [0.0] * 6 + [-1.0] VLA_ARENA_ENV_RESOLUTION = 256 # resolution used to render training data DATE_TIME = time.strftime('%Y_%m_%d-%H_%M_%S') @@ -54,7 +66,19 @@ @dataclass class GenerateConfig: ################################################################################################################# - # Model server parameters + # Inference parameters + ################################################################################################################# + inference_mode: Literal['websocket'] = 'websocket' + policy_config_name: str | None = None + policy_checkpoint_dir: str | None = None + policy_checkpoint_step: str | int = 'latest' + train_config_path: str | None = 'vla_arena/configs/train/openpi.yaml' + auto_start_policy_server: bool = True + policy_server_start_timeout_sec: int = 180 + policy_server_poll_interval_sec: float = 1.0 + + ################################################################################################################# + # Websocket policy server parameters (used when inference_mode="websocket") ################################################################################################################# host: str = '0.0.0.0' port: int = 8000 @@ -92,30 +116,213 @@ class GenerateConfig: ################################################################################################################# # Instruction replacement parameters ################################################################################################################# - use_replacements: bool = True # Whether to use instruction replacements - replacements_file: str = "VLA-Arena/language_replacements" # Path to replacements JSON file - replacement_probability: float = 1.0 # Probability of applying replacement (0.0 to 1.0) - replacement_level: int = 1 # Level of instruction replacements (from 1 to 4) + use_replacements: bool = True # Whether to use instruction replacements + replacements_file: str = ( + 'VLA-Arena/language_replacements' + ) # Path to replacements JSON file + replacement_probability: float = ( + 1.0 # Probability of applying replacement (0.0 to 1.0) + ) + replacement_level: int = ( + 1 # Level of instruction replacements (from 1 to 4) + ) + + +def _resolve_policy_target( + cfg: GenerateConfig, +) -> tuple[Any, str | pathlib.Path, str]: + import vla_arena.models.openpi.src.openpi.training.config as _config + + train_cfg = None + if cfg.train_config_path: + train_cfg = load_train_config_from_yaml(cfg.train_config_path) + elif cfg.policy_config_name: + train_cfg = _config.get_config(cfg.policy_config_name) + else: + raise ValueError( + 'For local inference, set either train_config_path or policy_config_name.' + ) + + if cfg.policy_config_name and train_cfg.name != cfg.policy_config_name: + logger.warning( + 'policy_config_name=%s is ignored because train_config_path resolves to name=%s', + cfg.policy_config_name, + train_cfg.name, + ) + + checkpoint_dir = resolve_checkpoint_dir( + cfg.policy_checkpoint_dir, + train_cfg, + cfg.policy_checkpoint_step, + ) + return train_cfg, checkpoint_dir, train_cfg.name + + +def _normalize_host(host: str) -> str: + host_text = str(host).strip() + if host_text.startswith('ws://') or host_text.startswith('wss://'): + parsed = urllib_parse.urlparse(host_text) + if parsed.hostname: + return parsed.hostname + return host_text + + +def _is_local_host(host: str) -> bool: + host_text = _normalize_host(host).lower() + return host_text in {'0.0.0.0', '127.0.0.1', 'localhost', '::1', '::'} + + +def _is_port_open(host: str, port: int, timeout_sec: float) -> bool: + connect_host = _normalize_host(host) + if connect_host == '0.0.0.0': + connect_host = '127.0.0.1' + elif connect_host == '::': + connect_host = '::1' + try: + with socket.create_connection( + (connect_host, int(port)), timeout=timeout_sec + ): + return True + except OSError: + return False + + +def _build_serve_policy_command( + cfg: GenerateConfig, + config_name: str, + checkpoint_dir: str | pathlib.Path, +) -> list[str]: + script_path = pathlib.Path(__file__).parent / 'scripts' / 'serve_policy.py' + if not script_path.exists(): + raise FileNotFoundError( + f'Unable to find serve_policy.py at {script_path}' + ) + return [ + sys.executable, + str(script_path), + '--port', + str(int(cfg.port)), + 'policy:checkpoint', + '--policy.config', + str(config_name), + '--policy.dir', + str(checkpoint_dir), + ] + + +def _start_policy_server_process( + cmd: list[str], +) -> subprocess.Popen[bytes]: + logger.info('Auto-starting OpenPI policy server: %s', shlex.join(cmd)) + return subprocess.Popen( + cmd, + start_new_session=True, + ) + + +def _wait_for_policy_server_ready( + host: str, + port: int, + timeout_sec: float, + poll_interval_sec: float, + process: subprocess.Popen[bytes], +) -> None: + deadline = time.monotonic() + timeout_sec + while time.monotonic() < deadline: + if process.poll() is not None: + raise RuntimeError( + 'Auto-started OpenPI policy server exited early with code ' + f'{process.returncode}.' + ) + if _is_port_open( + host, port, timeout_sec=max(0.05, poll_interval_sec) + ): + return + time.sleep(max(0.05, poll_interval_sec)) + + raise TimeoutError( + 'Timed out waiting for OpenPI policy server to become ready at ' + f'{host}:{port} after {timeout_sec}s.' + ) + + +def _stop_managed_policy_server( + process: subprocess.Popen[bytes] | None, + timeout_sec: float = 10.0, +) -> None: + if process is None: + return + if process.poll() is not None: + return + + logger.info( + 'Stopping auto-started OpenPI policy server (pid=%s)...', process.pid + ) + process.terminate() + try: + process.wait(timeout=timeout_sec) + except subprocess.TimeoutExpired: + logger.warning( + 'Policy server did not stop within %.1fs; killing process.', + timeout_sec, + ) + process.kill() + process.wait(timeout=5) -def check_unnorm_key(cfg: GenerateConfig, model) -> None: - """Check that the model contains the action un-normalization key.""" - # Initialize unnorm_key - unnorm_key = 'libero_spatial' - # In some cases, the key must be manually modified (e.g. after training on a modified version of the dataset - # with the suffix "_no_noops" in the dataset name) - if ( - unnorm_key not in model.norm_stats - and f'{unnorm_key}_no_noops' in model.norm_stats - ): - unnorm_key = f'{unnorm_key}_no_noops' +def _create_policy_client(cfg: GenerateConfig): + mode = str(cfg.inference_mode).lower().strip() + if mode != 'websocket': + raise ValueError( + f'Unsupported inference_mode: {cfg.inference_mode}. Use "websocket".' + ) - assert ( - unnorm_key in model.norm_stats - ), f'Action un-norm key {unnorm_key} not found in VLA `norm_stats`!' + train_cfg, checkpoint_dir, policy_config_name = _resolve_policy_target(cfg) + del train_cfg + source = f'{cfg.host}:{cfg.port}' + client_host = cfg.host + normalized_host = _normalize_host(cfg.host) + if normalized_host in {'0.0.0.0', '::'}: + client_host = '127.0.0.1' + managed_process: subprocess.Popen[bytes] | None = None + + if not _is_port_open(cfg.host, cfg.port, timeout_sec=1.0): + serve_cmd = _build_serve_policy_command( + cfg, policy_config_name, checkpoint_dir + ) + if not _is_local_host(cfg.host): + raise RuntimeError( + f'OpenPI websocket server is unreachable at {cfg.host}:{cfg.port}, ' + 'and auto-start is disabled for remote hosts. ' + f'Start it manually, e.g.:\n {shlex.join(serve_cmd)}' + ) + if not cfg.auto_start_policy_server: + raise RuntimeError( + f'OpenPI websocket server is unreachable at {cfg.host}:{cfg.port}. ' + 'Enable auto_start_policy_server or start it manually, e.g.:\n' + f' {shlex.join(serve_cmd)}' + ) - # Set the unnorm_key in cfg - cfg.unnorm_key = unnorm_key + managed_process = _start_policy_server_process(serve_cmd) + try: + _wait_for_policy_server_ready( + cfg.host, + cfg.port, + timeout_sec=float(cfg.policy_server_start_timeout_sec), + poll_interval_sec=float(cfg.policy_server_poll_interval_sec), + process=managed_process, + ) + except Exception: + _stop_managed_policy_server(managed_process, timeout_sec=3.0) + raise + logger.info( + 'Auto-started OpenPI policy server is ready at %s', source + ) + + client = _websocket_client_policy.WebsocketClientPolicy( + client_host, cfg.port + ) + return client, source, policy_config_name, managed_process def setup_logging(cfg: GenerateConfig): @@ -242,7 +449,8 @@ def run_episode( } # Query model to get action - action_chunk = client.infer(element)['actions'] + infer_result = client.infer(element) + action_chunk = infer_result['actions'] assert ( len(action_chunk) >= cfg.replan_steps ), f'We want to replan every {cfg.replan_steps} steps, but policy only predicts {len(action_chunk)} steps.' @@ -322,14 +530,13 @@ def run_task( episodes_with_cost = 0 successes_with_cost = 0 failures_with_cost = 0 - rng = np.random.default_rng(cfg.seed) for episode_idx in tqdm.tqdm(range(cfg.num_trials_per_task)): log_message(f'\nTask: {task_description}', log_file) - random_offset = rng.integers(0, len(initial_states)) - initial_state = initial_states[ - (episode_idx + random_offset) % len(initial_states) - ] + if len(initial_states) > 0: + initial_state = initial_states[episode_idx % len(initial_states)] + else: + initial_state = None log_message(f'Starting episode {task_episodes + 1}...', log_file) @@ -448,102 +655,124 @@ def eval_vla_arena(cfg: GenerateConfig): f'Unsupported task_suite_name type: {type(cfg.task_suite_name)}' ) - client = _websocket_client_policy.WebsocketClientPolicy(cfg.host, cfg.port) + client, policy_source, policy_config_name, managed_process = ( + _create_policy_client(cfg) + ) + logger.info( + 'OpenPI eval client ready: mode=%s config=%s source=%s', + cfg.inference_mode, + policy_config_name, + policy_source, + ) tasks_payload: list[dict[str, object]] = [] - replacements_dict = load_replacements_dict(cfg, logger) - if cfg.use_replacements: - log_message(f"Using instruction replacements with probability {cfg.replacement_probability}", log_file) - log_message(f"Loaded {len(replacements_dict)} replacement entries", log_file) + try: + replacements_dict = load_replacements_dict(cfg, logger) - for suite_name in suite_names: - if suite_name not in benchmark_dict: - raise ValueError( - f'Unknown task suite: {suite_name}. ' - f'Available options are: {list(benchmark_dict.keys())}' - ) + for suite_name in suite_names: + if suite_name not in benchmark_dict: + raise ValueError( + f'Unknown task suite: {suite_name}. ' + f'Available options are: {list(benchmark_dict.keys())}' + ) - cfg_suite = replace(cfg, task_suite_name=suite_name) + cfg_suite = replace(cfg, task_suite_name=suite_name) - log_file, local_log_filepath, run_id = setup_logging(cfg_suite) + log_file, local_log_filepath, run_id = setup_logging(cfg_suite) - task_suite = benchmark_dict[suite_name]() - task_level = cfg_suite.task_level - num_tasks = 10 if suite_name == 'long_horizon' and task_level == 0 else 5 + task_suite = benchmark_dict[suite_name]() + task_level = cfg_suite.task_level + num_tasks = ( + 10 + if suite_name == 'long_horizon' and task_level == 0 + else 5 + ) - print( - f'Evaluating {num_tasks} tasks from the {suite_name} suite...' - ) - log_message(f'Task suite: {suite_name}', log_file) - - total_episodes = 0 - total_successes = 0 - total_costs = 0 - success_costs = 0 - failure_costs = 0 - - for task_id in tqdm.tqdm(range(num_tasks)): - ( - task_episodes, - task_successes, - task_total_costs, - task_success_costs, - task_failure_costs, - *_, - ) = run_task( - cfg_suite, - task_suite, - task_id, - task_level, - replacements_dict, - total_episodes, - total_successes, - log_file, - client, + print( + f'Evaluating {num_tasks} tasks from the {suite_name} suite...' ) - total_episodes += task_episodes - total_successes += task_successes - total_costs += task_total_costs - success_costs += task_success_costs - failure_costs += task_failure_costs - - final_success_rate = ( - float(total_successes) / float(total_episodes) - if total_episodes > 0 - else 0 - ) - average_costs = ( - total_costs / total_episodes if total_episodes > 0 else 0 - ) + log_message(f'Task suite: {suite_name}', log_file) + if cfg.use_replacements: + log_message( + f'Using instruction replacements with probability {cfg.replacement_probability}', + log_file, + ) + log_message( + f'Loaded {len(replacements_dict)} replacement entries', + log_file, + ) - log_message( - f'[{suite_name}] success rate: {final_success_rate:.4f}', log_file - ) - log_message(f'[{suite_name}] average cost: {average_costs}', log_file) - - if log_file: - log_file.close() - - category, has_cc = _suite_category(suite_name) - sr = [0.0, 0.0, 0.0] - cc = [0.0, 0.0, 0.0] - sr[task_level] = final_success_rate - cc[task_level] = average_costs if has_cc else 0.0 - - tasks_payload.append( - { - 'name': suite_name, - 'category': category, - 'hasCC': has_cc, - 'data': { - 'sr': sr, - 'cc': cc, - }, - 'numEpisodes': total_episodes, - 'numSuccesses': total_successes, - } - ) + total_episodes = 0 + total_successes = 0 + total_costs = 0 + success_costs = 0 + failure_costs = 0 + + for task_id in tqdm.tqdm(range(num_tasks)): + ( + task_episodes, + task_successes, + task_total_costs, + task_success_costs, + task_failure_costs, + *_, + ) = run_task( + cfg_suite, + task_suite, + task_id, + task_level, + replacements_dict, + total_episodes, + total_successes, + log_file, + client, + ) + total_episodes += task_episodes + total_successes += task_successes + total_costs += task_total_costs + success_costs += task_success_costs + failure_costs += task_failure_costs + + final_success_rate = ( + float(total_successes) / float(total_episodes) + if total_episodes > 0 + else 0 + ) + average_costs = ( + total_costs / total_episodes if total_episodes > 0 else 0 + ) + + log_message( + f'[{suite_name}] success rate: {final_success_rate:.4f}', + log_file, + ) + log_message(f'[{suite_name}] average cost: {average_costs}', log_file) + + if log_file: + log_file.close() + + category, has_cc = _suite_category(suite_name) + sr = [0.0, 0.0, 0.0] + cc = [0.0, 0.0, 0.0] + sr[task_level] = final_success_rate + cc[task_level] = average_costs if has_cc else 0.0 + + tasks_payload.append( + { + 'name': suite_name, + 'category': category, + 'hasCC': has_cc, + 'data': { + 'sr': sr, + 'cc': cc, + }, + 'numEpisodes': total_episodes, + 'numSuccesses': total_successes, + } + ) + finally: + _stop_managed_policy_server(managed_process, timeout_sec=10.0) if cfg.result_json_path is None or str(cfg.result_json_path).lower() == 'default': result_dir = pathlib.Path('./results') diff --git a/vla_arena/models/openpi/examples/convert_jax_model_to_pytorch.py b/vla_arena/models/openpi/examples/convert_jax_model_to_pytorch.py index 513f3338..cb8750a2 100644 --- a/vla_arena/models/openpi/examples/convert_jax_model_to_pytorch.py +++ b/vla_arena/models/openpi/examples/convert_jax_model_to_pytorch.py @@ -47,17 +47,17 @@ from typing import Literal import numpy as np -import openpi.models.gemma -import openpi.models.model -import openpi.models.pi0_config -import openpi.models_pytorch.pi0_pytorch -import openpi.training.config as _config +import vla_arena.models.openpi.src.openpi.models.gemma +import vla_arena.models.openpi.src.openpi.models.model +import vla_arena.models.openpi.src.openpi.models.pi0_config +import vla_arena.models.openpi.src.openpi.models_pytorch.pi0_pytorch +import vla_arena.models.openpi.src.openpi.training.config as _config import orbax.checkpoint as ocp import safetensors import torch import tyro from flax.nnx import traversals -from openpi.training import utils +from vla_arena.models.openpi.src.openpi.training import utils def slice_paligemma_state_dict(state_dict, config): @@ -520,7 +520,7 @@ def slice_initial_orbax_checkpoint( This respects dtype conversions that occur during model restore. """ # Use repository restore utility to load a pure dict of params (value suffix removed) - params = openpi.models.model.restore_params( + params = vla_arena.models.openpi.src.openpi.models.model.restore_params( f'{checkpoint_dir}/params/', restore_type=np.ndarray, dtype=restore_precision, @@ -556,7 +556,7 @@ def convert_pi0_checkpoint( checkpoint_dir: str, precision: str, output_path: str, - model_config: openpi.models.pi0_config.Pi0Config, + model_config: vla_arena.models.openpi.src.openpi.models.pi0_config.Pi0Config, ): """ Convert PI0 JAX checkpoint to PyTorch format. @@ -640,7 +640,7 @@ def __init__(self): )() paligemma_config = PaliGemmaConfig() - action_expert_config = openpi.models.gemma.get_config('gemma_300m') + action_expert_config = vla_arena.models.openpi.src.openpi.models.gemma.get_config('gemma_300m') # Process PaliGemma weights paligemma_params, expert_params = slice_paligemma_state_dict( @@ -657,7 +657,7 @@ def __init__(self): ) # Instantiate model - pi0_model = openpi.models_pytorch.pi0_pytorch.PI0Pytorch(model_config) + pi0_model = vla_arena.models.openpi.src.openpi.models_pytorch.pi0_pytorch.PI0Pytorch(model_config) # Combine all parameters (no prefix needed for our model structure) all_params = {**paligemma_params, **gemma_params, **projection_params} @@ -720,7 +720,7 @@ def main( inspect_only: Only inspect parameter keys, don't convert """ model_config = _config.get_config(config_name).model - if not isinstance(model_config, openpi.models.pi0_config.Pi0Config): + if not isinstance(model_config, vla_arena.models.openpi.src.openpi.models.pi0_config.Pi0Config): raise ValueError(f'Config {config_name} is not a Pi0Config') if inspect_only: load_jax_model_and_print_keys(checkpoint_dir) diff --git a/vla_arena/models/openpi/examples/inference.ipynb b/vla_arena/models/openpi/examples/inference.ipynb index 4ca6736c..68b1bdd1 100644 --- a/vla_arena/models/openpi/examples/inference.ipynb +++ b/vla_arena/models/openpi/examples/inference.ipynb @@ -10,12 +10,12 @@ "\n", "import jax\n", "\n", - "from openpi.models import model as _model\n", - "from openpi.policies import droid_policy\n", - "from openpi.policies import policy_config as _policy_config\n", - "from openpi.shared import download\n", - "from openpi.training import config as _config\n", - "from openpi.training import data_loader as _data_loader" + "from vla_arena.models.openpi.src.openpi.models import model as _model\n", + "from vla_arena.models.openpi.src.openpi.policies import droid_policy\n", + "from vla_arena.models.openpi.src.openpi.policies import policy_config as _policy_config\n", + "from vla_arena.models.openpi.src.openpi.shared import download\n", + "from vla_arena.models.openpi.src.openpi.training import config as _config\n", + "from vla_arena.models.openpi.src.openpi.training import data_loader as _data_loader" ] }, { @@ -140,4 +140,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/vla_arena/models/openpi/pyproject.toml b/vla_arena/models/openpi/pyproject.toml index c4a06e53..eed5f289 100644 --- a/vla_arena/models/openpi/pyproject.toml +++ b/vla_arena/models/openpi/pyproject.toml @@ -60,7 +60,7 @@ rlds = [ ] [tool.uv] -override-dependencies = ["ml-dtypes==0.4.1", "tensorstore==0.1.74"] +override-dependencies = ["ml-dtypes==0.5.4", "tensorstore==0.1.74"] [tool.uv.sources] openpi-client = { workspace = true } diff --git a/vla_arena/models/openpi/scripts/compute_norm_stats.py b/vla_arena/models/openpi/scripts/compute_norm_stats.py index 9571a54f..512d6e4f 100644 --- a/vla_arena/models/openpi/scripts/compute_norm_stats.py +++ b/vla_arena/models/openpi/scripts/compute_norm_stats.py @@ -12,136 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Compute normalization statistics for a config. +"""Compute normalization statistics for a config.""" -This script is used to compute the normalization statistics for a given config. It -will compute the mean and standard deviation of the data in the dataset and save it -to the config assets directory. -""" - -import numpy as np -import openpi.models.model as _model -import openpi.shared.normalize as normalize -import openpi.training.config as _config -import openpi.training.data_loader as _data_loader -import openpi.transforms as transforms -import tqdm +import vla_arena.models.openpi.src.openpi.training.config as _config import tyro - -class RemoveStrings(transforms.DataTransformFn): - def __call__(self, x: dict) -> dict: - return { - k: v - for k, v in x.items() - if not np.issubdtype(np.asarray(v).dtype, np.str_) - } - - -def create_torch_dataloader( - data_config: _config.DataConfig, - action_horizon: int, - batch_size: int, - model_config: _model.BaseModelConfig, - num_workers: int, - max_frames: int | None = None, -) -> tuple[_data_loader.Dataset, int]: - if data_config.repo_id is None: - raise ValueError('Data config must have a repo_id') - dataset = _data_loader.create_torch_dataset( - data_config, action_horizon, model_config - ) - dataset = _data_loader.TransformedDataset( - dataset, - [ - *data_config.repack_transforms.inputs, - *data_config.data_transforms.inputs, - # Remove strings since they are not supported by JAX and are not needed to compute norm stats. - RemoveStrings(), - ], - ) - if max_frames is not None and max_frames < len(dataset): - num_batches = max_frames // batch_size - shuffle = True - else: - num_batches = len(dataset) // batch_size - shuffle = False - data_loader = _data_loader.TorchDataLoader( - dataset, - local_batch_size=batch_size, - num_workers=num_workers, - shuffle=shuffle, - num_batches=num_batches, - ) - return data_loader, num_batches - - -def create_rlds_dataloader( - data_config: _config.DataConfig, - action_horizon: int, - batch_size: int, - max_frames: int | None = None, -) -> tuple[_data_loader.Dataset, int]: - dataset = _data_loader.create_rlds_dataset( - data_config, action_horizon, batch_size, shuffle=False - ) - dataset = _data_loader.IterableTransformedDataset( - dataset, - [ - *data_config.repack_transforms.inputs, - *data_config.data_transforms.inputs, - # Remove strings since they are not supported by JAX and are not needed to compute norm stats. - RemoveStrings(), - ], - is_batched=True, - ) - if max_frames is not None and max_frames < len(dataset): - num_batches = max_frames // batch_size - else: - # NOTE: this length is currently hard-coded for DROID. - num_batches = len(dataset) // batch_size - data_loader = _data_loader.RLDSDataLoader( - dataset, - num_batches=num_batches, - ) - return data_loader, num_batches +from vla_arena.models.openpi.src.openpi.workflow_utils import compute_and_save_norm_stats def main(config_name: str, max_frames: int | None = None): config = _config.get_config(config_name) - data_config = config.data.create(config.assets_dirs, config.model) - - if data_config.rlds_data_dir is not None: - data_loader, num_batches = create_rlds_dataloader( - data_config, - config.model.action_horizon, - config.batch_size, - max_frames, - ) - else: - data_loader, num_batches = create_torch_dataloader( - data_config, - config.model.action_horizon, - config.batch_size, - config.model, - config.num_workers, - max_frames, - ) - - keys = ['state', 'actions'] - stats = {key: normalize.RunningStats() for key in keys} - - for batch in tqdm.tqdm( - data_loader, total=num_batches, desc='Computing stats' - ): - for key in keys: - stats[key].update(np.asarray(batch[key])) - - norm_stats = {key: stats.get_statistics() for key, stats in stats.items()} - - output_path = config.assets_dirs / data_config.repo_id + output_path = compute_and_save_norm_stats(config, max_frames=max_frames) print(f'Writing stats to: {output_path}') - normalize.save(output_path, norm_stats) if __name__ == '__main__': diff --git a/vla_arena/models/openpi/scripts/serve_policy.py b/vla_arena/models/openpi/scripts/serve_policy.py index 22db9a1c..f978ac34 100644 --- a/vla_arena/models/openpi/scripts/serve_policy.py +++ b/vla_arena/models/openpi/scripts/serve_policy.py @@ -28,10 +28,10 @@ if str(_openpi_src) not in sys.path: sys.path.insert(0, str(_openpi_src)) -from openpi.policies import policy as _policy -from openpi.policies import policy_config as _policy_config -from openpi.serving import websocket_policy_server -from openpi.training import config as _config +from vla_arena.models.openpi.src.openpi.policies import policy as _policy +from vla_arena.models.openpi.src.openpi.policies import policy_config as _policy_config +from vla_arena.models.openpi.src.openpi.serving import websocket_policy_server +from vla_arena.models.openpi.src.openpi.training import config as _config class EnvMode(enum.Enum): diff --git a/vla_arena/models/openpi/scripts/train.py b/vla_arena/models/openpi/scripts/train.py index 24fdfa0b..49f2f4bb 100644 --- a/vla_arena/models/openpi/scripts/train.py +++ b/vla_arena/models/openpi/scripts/train.py @@ -25,16 +25,16 @@ import jax.experimental import jax.numpy as jnp import numpy as np -import openpi.models.model as _model -import openpi.shared.array_typing as at -import openpi.shared.nnx_utils as nnx_utils -import openpi.training.checkpoints as _checkpoints -import openpi.training.config as _config -import openpi.training.data_loader as _data_loader -import openpi.training.optimizer as _optimizer -import openpi.training.sharding as sharding -import openpi.training.utils as training_utils -import openpi.training.weight_loaders as _weight_loaders +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.nnx_utils as nnx_utils +import vla_arena.models.openpi.src.openpi.training.checkpoints as _checkpoints +import vla_arena.models.openpi.src.openpi.training.config as _config +import vla_arena.models.openpi.src.openpi.training.data_loader as _data_loader +import vla_arena.models.openpi.src.openpi.training.optimizer as _optimizer +import vla_arena.models.openpi.src.openpi.training.sharding as sharding +import vla_arena.models.openpi.src.openpi.training.utils as training_utils +import vla_arena.models.openpi.src.openpi.training.weight_loaders as _weight_loaders import optax import tqdm_loggable.auto as tqdm import wandb diff --git a/vla_arena/models/openpi/scripts/train_pytorch.py b/vla_arena/models/openpi/scripts/train_pytorch.py index 334a9c03..89ed5ce6 100644 --- a/vla_arena/models/openpi/scripts/train_pytorch.py +++ b/vla_arena/models/openpi/scripts/train_pytorch.py @@ -47,11 +47,11 @@ import jax import numpy as np -import openpi.models.pi0_config -import openpi.models_pytorch.pi0_pytorch -import openpi.shared.normalize as _normalize -import openpi.training.config as _config -import openpi.training.data_loader as _data +import vla_arena.models.openpi.src.openpi.models.pi0_config +import vla_arena.models.openpi.src.openpi.models_pytorch.pi0_pytorch +import vla_arena.models.openpi.src.openpi.shared.normalize as _normalize +import vla_arena.models.openpi.src.openpi.training.config as _config +import vla_arena.models.openpi.src.openpi.training.data_loader as _data import safetensors.torch import torch import torch.distributed as dist @@ -473,9 +473,9 @@ def train_loop(config: _config.TrainConfig): logging.info('Cleared sample batch and data loader from memory') # Build model - if not isinstance(config.model, openpi.models.pi0_config.Pi0Config): + if not isinstance(config.model, vla_arena.models.openpi.src.openpi.models.pi0_config.Pi0Config): # Convert dataclass to Pi0Config if needed - model_cfg = openpi.models.pi0_config.Pi0Config( + model_cfg = vla_arena.models.openpi.src.openpi.models.pi0_config.Pi0Config( dtype=config.pytorch_training_precision, action_dim=config.model.action_dim, action_horizon=config.model.action_horizon, @@ -495,7 +495,7 @@ def train_loop(config: _config.TrainConfig): model_cfg, 'dtype', config.pytorch_training_precision ) - model = openpi.models_pytorch.pi0_pytorch.PI0Pytorch(model_cfg).to(device) + model = vla_arena.models.openpi.src.openpi.models_pytorch.pi0_pytorch.PI0Pytorch(model_cfg).to(device) if hasattr(model, 'gradient_checkpointing_enable'): enable_gradient_checkpointing = True diff --git a/vla_arena/models/openpi/scripts/train_test.py b/vla_arena/models/openpi/scripts/train_test.py index 50a08b7c..c5ab0428 100644 --- a/vla_arena/models/openpi/scripts/train_test.py +++ b/vla_arena/models/openpi/scripts/train_test.py @@ -21,7 +21,7 @@ os.environ['JAX_PLATFORMS'] = 'cpu' -from openpi.training import config as _config +from vla_arena.models.openpi.src.openpi.training import config as _config from . import train diff --git a/vla_arena/models/openpi/src/openpi/models/gemma.py b/vla_arena/models/openpi/src/openpi/models/gemma.py index 3a5f9f5c..fa95561f 100644 --- a/vla_arena/models/openpi/src/openpi/models/gemma.py +++ b/vla_arena/models/openpi/src/openpi/models/gemma.py @@ -47,9 +47,9 @@ import flax.linen as nn import jax import jax.numpy as jnp -import openpi.models.lora as lora -import openpi.shared.array_typing as at -import openpi.training.sharding as sharding +import vla_arena.models.openpi.src.openpi.models.lora as lora +import vla_arena.models.openpi.src.openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.training.sharding as sharding PALIGEMMA_VOCAB_SIZE = 257_152 diff --git a/vla_arena/models/openpi/src/openpi/models/gemma_fast.py b/vla_arena/models/openpi/src/openpi/models/gemma_fast.py index b0dd4453..1572197c 100644 --- a/vla_arena/models/openpi/src/openpi/models/gemma_fast.py +++ b/vla_arena/models/openpi/src/openpi/models/gemma_fast.py @@ -39,8 +39,8 @@ import jax import jax.numpy as jnp import ml_collections -import openpi.models.lora as lora -import openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.models.lora as lora +import vla_arena.models.openpi.src.openpi.shared.array_typing as at Variant = Literal['gemma_2b', 'gemma_2b_lora'] diff --git a/vla_arena/models/openpi/src/openpi/models/lora.py b/vla_arena/models/openpi/src/openpi/models/lora.py index 06f286f2..bc415253 100644 --- a/vla_arena/models/openpi/src/openpi/models/lora.py +++ b/vla_arena/models/openpi/src/openpi/models/lora.py @@ -18,7 +18,7 @@ import flax.linen as nn import flax.struct as struct import jax.numpy as jnp -import openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.array_typing as at @struct.dataclass diff --git a/vla_arena/models/openpi/src/openpi/models/lora_test.py b/vla_arena/models/openpi/src/openpi/models/lora_test.py index 392a73d1..ea4ccb1f 100644 --- a/vla_arena/models/openpi/src/openpi/models/lora_test.py +++ b/vla_arena/models/openpi/src/openpi/models/lora_test.py @@ -15,7 +15,7 @@ import flax.linen as nn import jax import jax.numpy as jnp -import openpi.models.lora as lora +import vla_arena.models.openpi.src.openpi.models.lora as lora def test_lora_einsum_params_shape(): diff --git a/vla_arena/models/openpi/src/openpi/models/model.py b/vla_arena/models/openpi/src/openpi/models/model.py index fffc9de8..0a3993e0 100644 --- a/vla_arena/models/openpi/src/openpi/models/model.py +++ b/vla_arena/models/openpi/src/openpi/models/model.py @@ -24,13 +24,13 @@ import jax import jax.numpy as jnp import numpy as np -import openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.array_typing as at import orbax.checkpoint as ocp import safetensors import torch from flax import nnx, struct, traverse_util -from openpi.models_pytorch import pi0_pytorch -from openpi.shared import image_tools +from vla_arena.models.openpi.src.openpi.models_pytorch import pi0_pytorch +from vla_arena.models.openpi.src.openpi.shared import image_tools logger = logging.getLogger('openpi') @@ -338,7 +338,7 @@ def restore_params( """Restores unstructured params PyTree from a checkpoint. This works with checkpoints saved with `save_state` during openpi training (see `training/checkpoints.py`) as - well as pre-trained checkpoints released for openpi. + well as pre-trained checkpoints released for vla_arena.models.openpi.src.openpi. Args: params_path: The local path to the checkpoint directory. diff --git a/vla_arena/models/openpi/src/openpi/models/model_test.py b/vla_arena/models/openpi/src/openpi/models/model_test.py index c44c4137..b9dc73d1 100644 --- a/vla_arena/models/openpi/src/openpi/models/model_test.py +++ b/vla_arena/models/openpi/src/openpi/models/model_test.py @@ -15,9 +15,9 @@ import jax import pytest from flax import nnx -from openpi.models import model as _model -from openpi.models import pi0_config, pi0_fast -from openpi.shared import download, nnx_utils +from vla_arena.models.openpi.src.openpi.models import model as _model +from vla_arena.models.openpi.src.openpi.models import pi0_config, pi0_fast +from vla_arena.models.openpi.src.openpi.shared import download, nnx_utils def test_pi0_model(): diff --git a/vla_arena/models/openpi/src/openpi/models/pi0.py b/vla_arena/models/openpi/src/openpi/models/pi0.py index 1e7b980c..f107f409 100644 --- a/vla_arena/models/openpi/src/openpi/models/pi0.py +++ b/vla_arena/models/openpi/src/openpi/models/pi0.py @@ -20,11 +20,11 @@ import flax.nnx.bridge as nnx_bridge import jax import jax.numpy as jnp -import openpi.models.gemma as _gemma -import openpi.models.siglip as _siglip -from openpi.models import model as _model -from openpi.models import pi0_config -from openpi.shared import array_typing as at +import vla_arena.models.openpi.src.openpi.models.gemma as _gemma +import vla_arena.models.openpi.src.openpi.models.siglip as _siglip +from vla_arena.models.openpi.src.openpi.models import model as _model +from vla_arena.models.openpi.src.openpi.models import pi0_config +from vla_arena.models.openpi.src.openpi.shared import array_typing as at logger = logging.getLogger('openpi') diff --git a/vla_arena/models/openpi/src/openpi/models/pi0_config.py b/vla_arena/models/openpi/src/openpi/models/pi0_config.py index 3928d74a..18249c8a 100644 --- a/vla_arena/models/openpi/src/openpi/models/pi0_config.py +++ b/vla_arena/models/openpi/src/openpi/models/pi0_config.py @@ -9,7 +9,7 @@ # 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 +# See the License for the specific language governing permissions and # limitations under the License. import dataclasses @@ -19,14 +19,14 @@ import flax.nnx as nnx import jax import jax.numpy as jnp -import openpi.models.gemma as _gemma -import openpi.shared.nnx_utils as nnx_utils -from openpi.models import model as _model -from openpi.shared import array_typing as at +import vla_arena.models.openpi.src.openpi.models.gemma as _gemma +import vla_arena.models.openpi.src.openpi.shared.nnx_utils as nnx_utils +from vla_arena.models.openpi.src.openpi.models import model as _model +from vla_arena.models.openpi.src.openpi.shared import array_typing as at if TYPE_CHECKING: - from openpi.models.pi0 import Pi0 + from vla_arena.models.openpi.src.openpi.models.pi0 import Pi0 @dataclasses.dataclass(frozen=True) @@ -61,7 +61,7 @@ def model_type(self) -> _model.ModelType: @override def create(self, rng: at.KeyArrayLike) -> 'Pi0': - from openpi.models.pi0 import Pi0 + from vla_arena.models.openpi.src.openpi.models.pi0 import Pi0 return Pi0(self, rngs=nnx.Rngs(rng)) diff --git a/vla_arena/models/openpi/src/openpi/models/pi0_fast.py b/vla_arena/models/openpi/src/openpi/models/pi0_fast.py index 445293c0..085ae0bb 100644 --- a/vla_arena/models/openpi/src/openpi/models/pi0_fast.py +++ b/vla_arena/models/openpi/src/openpi/models/pi0_fast.py @@ -22,11 +22,11 @@ import flax.nnx.bridge as nnx_bridge import jax import jax.numpy as jnp -import openpi.models.gemma_fast as _gemma -import openpi.models.siglip as _siglip -import openpi.shared.nnx_utils as nnx_utils -from openpi.models import model as _model -from openpi.shared import array_typing as at +import vla_arena.models.openpi.src.openpi.models.gemma_fast as _gemma +import vla_arena.models.openpi.src.openpi.models.siglip as _siglip +import vla_arena.models.openpi.src.openpi.shared.nnx_utils as nnx_utils +from vla_arena.models.openpi.src.openpi.models import model as _model +from vla_arena.models.openpi.src.openpi.shared import array_typing as at logger = logging.getLogger('openpi') diff --git a/vla_arena/models/openpi/src/openpi/models/pi0_test.py b/vla_arena/models/openpi/src/openpi/models/pi0_test.py index cede2bd5..7cde4f4d 100644 --- a/vla_arena/models/openpi/src/openpi/models/pi0_test.py +++ b/vla_arena/models/openpi/src/openpi/models/pi0_test.py @@ -14,7 +14,7 @@ import flax.nnx as nnx import jax -import openpi.models.pi0_config as _pi0_config +import vla_arena.models.openpi.src.openpi.models.pi0_config as _pi0_config def _get_frozen_state(config: _pi0_config.Pi0Config) -> nnx.State: diff --git a/vla_arena/models/openpi/src/openpi/models/siglip.py b/vla_arena/models/openpi/src/openpi/models/siglip.py index 5ce346e8..ebf8dac0 100644 --- a/vla_arena/models/openpi/src/openpi/models/siglip.py +++ b/vla_arena/models/openpi/src/openpi/models/siglip.py @@ -34,7 +34,7 @@ import jax import jax.numpy as jnp import numpy as np -import openpi.training.sharding as sharding +import vla_arena.models.openpi.src.openpi.training.sharding as sharding def posemb_sincos_2d(h, w, width, temperature=10_000.0, dtype=jnp.float32): diff --git a/vla_arena/models/openpi/src/openpi/models/tokenizer.py b/vla_arena/models/openpi/src/openpi/models/tokenizer.py index 2d7b0029..752951a1 100644 --- a/vla_arena/models/openpi/src/openpi/models/tokenizer.py +++ b/vla_arena/models/openpi/src/openpi/models/tokenizer.py @@ -17,8 +17,8 @@ import jax import numpy as np -import openpi.models.utils.fsq_tokenizer as fsq_tokenizer -import openpi.shared.download as download +import vla_arena.models.openpi.src.openpi.models.utils.fsq_tokenizer as fsq_tokenizer +import vla_arena.models.openpi.src.openpi.shared.download as download import orbax.checkpoint as ocp import sentencepiece from transformers import AutoProcessor diff --git a/vla_arena/models/openpi/src/openpi/models/tokenizer_test.py b/vla_arena/models/openpi/src/openpi/models/tokenizer_test.py index 49974b5f..bbad45a8 100644 --- a/vla_arena/models/openpi/src/openpi/models/tokenizer_test.py +++ b/vla_arena/models/openpi/src/openpi/models/tokenizer_test.py @@ -13,7 +13,7 @@ # limitations under the License. import numpy as np -from openpi.models import tokenizer as _tokenizer +from vla_arena.models.openpi.src.openpi.models import tokenizer as _tokenizer def test_tokenize(): diff --git a/vla_arena/models/openpi/src/openpi/models/vit.py b/vla_arena/models/openpi/src/openpi/models/vit.py index b6e6564b..89dca86b 100644 --- a/vla_arena/models/openpi/src/openpi/models/vit.py +++ b/vla_arena/models/openpi/src/openpi/models/vit.py @@ -33,7 +33,7 @@ import flax.linen as nn import jax import jax.numpy as jnp -from openpi.models import resnet as models_resnet +from vla_arena.models.openpi.src.openpi.models import resnet as models_resnet Array = Any diff --git a/vla_arena/models/openpi/src/openpi/models_pytorch/pi0_pytorch.py b/vla_arena/models/openpi/src/openpi/models_pytorch/pi0_pytorch.py index 554af3d8..3e365523 100644 --- a/vla_arena/models/openpi/src/openpi/models_pytorch/pi0_pytorch.py +++ b/vla_arena/models/openpi/src/openpi/models_pytorch/pi0_pytorch.py @@ -15,11 +15,11 @@ import logging import math -import openpi.models.gemma as _gemma -import openpi.models_pytorch.preprocessing_pytorch as _preprocessing +import vla_arena.models.openpi.src.openpi.models.gemma as _gemma +import vla_arena.models.openpi.src.openpi.models_pytorch.preprocessing_pytorch as _preprocessing import torch import torch.nn.functional as F # noqa: N812 -from openpi.models_pytorch.gemma_pytorch import PaliGemmaWithExpertModel +from vla_arena.models.openpi.src.openpi.models_pytorch.gemma_pytorch import PaliGemmaWithExpertModel from torch import Tensor, nn diff --git a/vla_arena/models/openpi/src/openpi/models_pytorch/preprocessing_pytorch.py b/vla_arena/models/openpi/src/openpi/models_pytorch/preprocessing_pytorch.py index 6fe402fc..3dfba649 100644 --- a/vla_arena/models/openpi/src/openpi/models_pytorch/preprocessing_pytorch.py +++ b/vla_arena/models/openpi/src/openpi/models_pytorch/preprocessing_pytorch.py @@ -16,7 +16,7 @@ from collections.abc import Sequence import torch -from openpi.shared import image_tools +from vla_arena.models.openpi.src.openpi.shared import image_tools logger = logging.getLogger('openpi') diff --git a/vla_arena/models/openpi/src/openpi/policies/aloha_policy.py b/vla_arena/models/openpi/src/openpi/policies/aloha_policy.py index 2ebb8388..3734b258 100644 --- a/vla_arena/models/openpi/src/openpi/policies/aloha_policy.py +++ b/vla_arena/models/openpi/src/openpi/policies/aloha_policy.py @@ -17,7 +17,7 @@ import einops import numpy as np -from openpi import transforms +from vla_arena.models.openpi.src.openpi import transforms def make_aloha_example() -> dict: diff --git a/vla_arena/models/openpi/src/openpi/policies/droid_policy.py b/vla_arena/models/openpi/src/openpi/policies/droid_policy.py index 0e2bb956..2139c349 100644 --- a/vla_arena/models/openpi/src/openpi/policies/droid_policy.py +++ b/vla_arena/models/openpi/src/openpi/policies/droid_policy.py @@ -16,8 +16,8 @@ import einops import numpy as np -from openpi import transforms -from openpi.models import model as _model +from vla_arena.models.openpi.src.openpi import transforms +from vla_arena.models.openpi.src.openpi.models import model as _model def make_droid_example() -> dict: diff --git a/vla_arena/models/openpi/src/openpi/policies/libero_policy.py b/vla_arena/models/openpi/src/openpi/policies/libero_policy.py index 4549cf16..0f1863f4 100644 --- a/vla_arena/models/openpi/src/openpi/policies/libero_policy.py +++ b/vla_arena/models/openpi/src/openpi/policies/libero_policy.py @@ -16,8 +16,8 @@ import einops import numpy as np -from openpi import transforms -from openpi.models import model as _model +from vla_arena.models.openpi.src.openpi import transforms +from vla_arena.models.openpi.src.openpi.models import model as _model def make_libero_example() -> dict: diff --git a/vla_arena/models/openpi/src/openpi/policies/policy.py b/vla_arena/models/openpi/src/openpi/policies/policy.py index a8e26964..95aa5ed6 100644 --- a/vla_arena/models/openpi/src/openpi/policies/policy.py +++ b/vla_arena/models/openpi/src/openpi/policies/policy.py @@ -25,10 +25,10 @@ import jax.numpy as jnp import numpy as np import torch -from openpi import transforms as _transforms -from openpi.models import model as _model -from openpi.shared import array_typing as at -from openpi.shared import nnx_utils +from vla_arena.models.openpi.src.openpi import transforms as _transforms +from vla_arena.models.openpi.src.openpi.models import model as _model +from vla_arena.models.openpi.src.openpi.shared import array_typing as at +from vla_arena.models.openpi.src.openpi.shared import nnx_utils from openpi_client import base_policy as _base_policy diff --git a/vla_arena/models/openpi/src/openpi/policies/policy_config.py b/vla_arena/models/openpi/src/openpi/policies/policy_config.py index 87578333..58d72e52 100644 --- a/vla_arena/models/openpi/src/openpi/policies/policy_config.py +++ b/vla_arena/models/openpi/src/openpi/policies/policy_config.py @@ -18,12 +18,12 @@ from typing import Any import jax.numpy as jnp -import openpi.models.model as _model -import openpi.policies.policy as _policy -import openpi.shared.download as download -import openpi.transforms as transforms -from openpi.training import checkpoints as _checkpoints -from openpi.training import config as _config +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.policies.policy as _policy +import vla_arena.models.openpi.src.openpi.shared.download as download +import vla_arena.models.openpi.src.openpi.transforms as transforms +from vla_arena.models.openpi.src.openpi.training import checkpoints as _checkpoints +from vla_arena.models.openpi.src.openpi.training import config as _config def create_trained_policy( diff --git a/vla_arena/models/openpi/src/openpi/policies/policy_test.py b/vla_arena/models/openpi/src/openpi/policies/policy_test.py index 38237d7b..a101773a 100644 --- a/vla_arena/models/openpi/src/openpi/policies/policy_test.py +++ b/vla_arena/models/openpi/src/openpi/policies/policy_test.py @@ -13,10 +13,10 @@ # limitations under the License. import pytest -from openpi.policies import aloha_policy -from openpi.policies import policy_config as _policy_config -from openpi.training import config as _config -from openpi_client import action_chunk_broker +from vla_arena.models.openpi.src.openpi.policies import aloha_policy +from vla_arena.models.openpi.src.openpi.policies import policy_config as _policy_config +from vla_arena.models.openpi.src.openpi.training import config as _config +from vla_arena.models.openpi.src.openpi.packages.openpi_client.src.openpi_client import action_chunk_broker @pytest.mark.manual diff --git a/vla_arena/models/openpi/src/openpi/shared/download_test.py b/vla_arena/models/openpi/src/openpi/shared/download_test.py index ca1b9f72..88a3854b 100644 --- a/vla_arena/models/openpi/src/openpi/shared/download_test.py +++ b/vla_arena/models/openpi/src/openpi/shared/download_test.py @@ -14,7 +14,7 @@ import pathlib -import openpi.shared.download as download +import vla_arena.models.openpi.src.openpi.shared.download as download import pytest diff --git a/vla_arena/models/openpi/src/openpi/shared/image_tools.py b/vla_arena/models/openpi/src/openpi/shared/image_tools.py index f462e222..f37fd8ea 100644 --- a/vla_arena/models/openpi/src/openpi/shared/image_tools.py +++ b/vla_arena/models/openpi/src/openpi/shared/image_tools.py @@ -16,7 +16,7 @@ import jax import jax.numpy as jnp -import openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.array_typing as at import torch import torch.nn.functional as F # noqa: N812 diff --git a/vla_arena/models/openpi/src/openpi/shared/image_tools_test.py b/vla_arena/models/openpi/src/openpi/shared/image_tools_test.py index 10fa6723..0d630f71 100644 --- a/vla_arena/models/openpi/src/openpi/shared/image_tools_test.py +++ b/vla_arena/models/openpi/src/openpi/shared/image_tools_test.py @@ -13,7 +13,7 @@ # limitations under the License. import jax.numpy as jnp -from openpi.shared import image_tools +from vla_arena.models.openpi.src.openpi.shared import image_tools def test_resize_with_pad_shapes(): diff --git a/vla_arena/models/openpi/src/openpi/shared/normalize_test.py b/vla_arena/models/openpi/src/openpi/shared/normalize_test.py index 8747f9a3..7b2721bc 100644 --- a/vla_arena/models/openpi/src/openpi/shared/normalize_test.py +++ b/vla_arena/models/openpi/src/openpi/shared/normalize_test.py @@ -13,7 +13,7 @@ # limitations under the License. import numpy as np -import openpi.shared.normalize as normalize +import vla_arena.models.openpi.src.openpi.shared.normalize as normalize def test_normalize_update(): diff --git a/vla_arena/models/openpi/src/openpi/training/checkpoints.py b/vla_arena/models/openpi/src/openpi/training/checkpoints.py index 87a6d746..4e5affdf 100644 --- a/vla_arena/models/openpi/src/openpi/training/checkpoints.py +++ b/vla_arena/models/openpi/src/openpi/training/checkpoints.py @@ -21,13 +21,13 @@ from typing import Protocol import jax -import openpi.shared.normalize as _normalize -import openpi.training.data_loader as _data_loader -import openpi.training.utils as training_utils +import vla_arena.models.openpi.src.openpi.shared.normalize as _normalize +import vla_arena.models.openpi.src.openpi.training.data_loader as _data_loader +import vla_arena.models.openpi.src.openpi.training.utils as training_utils import orbax.checkpoint as ocp import orbax.checkpoint.future as future from etils import epath -from openpi.shared import array_typing as at +from vla_arena.models.openpi.src.openpi.shared import array_typing as at def initialize_checkpoint_dir( diff --git a/vla_arena/models/openpi/src/openpi/training/config.py b/vla_arena/models/openpi/src/openpi/training/config.py index 2b7eda0d..d5b084cb 100644 --- a/vla_arena/models/openpi/src/openpi/training/config.py +++ b/vla_arena/models/openpi/src/openpi/training/config.py @@ -26,20 +26,20 @@ import etils.epath as epath import flax.nnx as nnx -import openpi.models.model as _model -import openpi.models.pi0_config as pi0_config -import openpi.models.pi0_fast as pi0_fast -import openpi.models.tokenizer as _tokenizer -import openpi.policies.aloha_policy as aloha_policy -import openpi.policies.droid_policy as droid_policy -import openpi.policies.libero_policy as libero_policy -import openpi.shared.download as _download -import openpi.shared.normalize as _normalize -import openpi.training.droid_rlds_dataset as droid_rlds_dataset -import openpi.training.misc.roboarena_config as roboarena_config -import openpi.training.optimizer as _optimizer -import openpi.training.weight_loaders as weight_loaders -import openpi.transforms as _transforms +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.models.pi0_config as pi0_config +import vla_arena.models.openpi.src.openpi.models.pi0_fast as pi0_fast +import vla_arena.models.openpi.src.openpi.models.tokenizer as _tokenizer +import vla_arena.models.openpi.src.openpi.policies.aloha_policy as aloha_policy +import vla_arena.models.openpi.src.openpi.policies.droid_policy as droid_policy +import vla_arena.models.openpi.src.openpi.policies.libero_policy as libero_policy +import vla_arena.models.openpi.src.openpi.shared.download as _download +import vla_arena.models.openpi.src.openpi.shared.normalize as _normalize +import vla_arena.models.openpi.src.openpi.training.droid_rlds_dataset as droid_rlds_dataset +import vla_arena.models.openpi.src.openpi.training.misc.roboarena_config as roboarena_config +import vla_arena.models.openpi.src.openpi.training.optimizer as _optimizer +import vla_arena.models.openpi.src.openpi.training.weight_loaders as weight_loaders +import vla_arena.models.openpi.src.openpi.transforms as _transforms import tyro @@ -770,7 +770,7 @@ def __post_init__(self) -> None: name='pi0_vla_arena', model=pi0_config.Pi0Config(), data=LeRobotLiberoDataConfig( - repo_id='physical-intelligence/libero', + repo_id='VLA-Arena/VLA_Arena_L0_L_lerobot_openpi', base_config=DataConfig( prompt_from_task=True, ), @@ -792,7 +792,7 @@ def __post_init__(self) -> None: action_expert_variant='gemma_300m_lora', ), data=LeRobotLiberoDataConfig( - repo_id='new_all_lerobot_with_long/VLA_Arena', + repo_id='physical-intelligence/libero', base_config=DataConfig(prompt_from_task=True), extra_delta_transform=True, ), @@ -819,7 +819,7 @@ def __post_init__(self) -> None: action_expert_variant='gemma_300m_lora', ), data=LeRobotLiberoDataConfig( - repo_id='datasets/vla-arena-lerobot', + repo_id='VLA-Arena/VLA_Arena_L0_L_lerobot_openpi', base_config=DataConfig(prompt_from_task=True), extra_delta_transform=True, ), @@ -828,7 +828,7 @@ def __post_init__(self) -> None: weight_loader=weight_loaders.CheckpointWeightLoader( os.getenv( 'OPENPI_VLA_ARENA_CHECKPOINT_PATH', - '/path/to/your/openpi/pi0-vla-arena/params', + 'gs://openpi-assets/checkpoints/pi0_base/params', ) ), num_train_steps=30_000, @@ -838,6 +838,26 @@ def __post_init__(self) -> None: ).get_freeze_filter(), ema_decay=None, ), + TrainConfig( + name='pi0_fast_vla_arena', + model=pi0_fast.Pi0FASTConfig( + action_dim=7, + action_horizon=10, + max_token_len=180, + ), + data=LeRobotLiberoDataConfig( + repo_id='VLA-Arena/VLA_Arena_L0_L_lerobot_openpi', + base_config=DataConfig(prompt_from_task=True), + extra_delta_transform=True, + ), + weight_loader=weight_loaders.CheckpointWeightLoader( + os.getenv( + 'OPENPI_VLA_ARENA_CHECKPOINT_PATH', + 'gs://openpi-assets/checkpoints/pi0_fast_base/params', + ) + ), + num_train_steps=30_000, + ), TrainConfig( name='pi0_fast_libero_low_mem_finetune', # Here is an example of loading a pi0-FAST model for LoRA finetuning. @@ -878,7 +898,7 @@ def __post_init__(self) -> None: paligemma_variant='gemma_2b_lora', ), data=LeRobotLiberoDataConfig( - repo_id='lerobot_data/VLA_Arena', + repo_id='VLA-Arena/VLA_Arena_L0_L_lerobot_openpi', base_config=DataConfig(prompt_from_task=True), extra_delta_transform=True, ), @@ -889,7 +909,7 @@ def __post_init__(self) -> None: 'gs://openpi-assets/checkpoints/pi0_base/params', ) ), - num_train_steps=30_000, + num_train_steps=60_000, freeze_filter=pi0_fast.Pi0FASTConfig( action_dim=7, action_horizon=10, @@ -923,6 +943,61 @@ def __post_init__(self) -> None: pytorch_weight_path='/path/to/your/pytorch_weight_path', num_train_steps=30_000, ), + TrainConfig( + name='pi05_vla_arena', + model=pi0_config.Pi0Config( + pi05=True, action_horizon=10, discrete_state_input=False + ), + data=LeRobotLiberoDataConfig( + repo_id='VLA-Arena/VLA_Arena_L0_L_lerobot_openpi', + base_config=DataConfig(prompt_from_task=True), + extra_delta_transform=False, + ), + batch_size=256, + lr_schedule=_optimizer.CosineDecaySchedule( + warmup_steps=10_000, + peak_lr=5e-5, + decay_steps=1_000_000, + decay_lr=5e-5, + ), + optimizer=_optimizer.AdamW(clip_gradient_norm=1.0), + ema_decay=0.999, + weight_loader=weight_loaders.CheckpointWeightLoader( + 'gs://openpi-assets/checkpoints/pi05_base/params' + ), + pytorch_weight_path='/path/to/your/pytorch_weight_path', + num_train_steps=30_000, + ), + TrainConfig( + name='pi05_vla_arena_low_mem_finetune', + model=pi0_config.Pi0Config( + pi05=True, + action_horizon=10, + discrete_state_input=False, + paligemma_variant='gemma_2b_lora', + action_expert_variant='gemma_300m_lora', + ), + data=LeRobotLiberoDataConfig( + repo_id='VLA-Arena/VLA_Arena_L0_L_lerobot_openpi', + base_config=DataConfig(prompt_from_task=True), + extra_delta_transform=False, + ), + weight_loader=weight_loaders.CheckpointWeightLoader( + os.getenv( + 'OPENPI_VLA_ARENA_CHECKPOINT_PATH', + 'gs://openpi-assets/checkpoints/pi05_base/params', + ) + ), + num_train_steps=60_000, + freeze_filter=pi0_config.Pi0Config( + pi05=True, + action_horizon=10, + discrete_state_input=False, + paligemma_variant='gemma_2b_lora', + action_expert_variant='gemma_300m_lora', + ).get_freeze_filter(), + ema_decay=None, + ), # # Fine-tuning Aloha configs. # diff --git a/vla_arena/models/openpi/src/openpi/training/data_loader.py b/vla_arena/models/openpi/src/openpi/training/data_loader.py index 94a1ab17..99e92012 100644 --- a/vla_arena/models/openpi/src/openpi/training/data_loader.py +++ b/vla_arena/models/openpi/src/openpi/training/data_loader.py @@ -23,11 +23,11 @@ import jax.numpy as jnp import lerobot.common.datasets.lerobot_dataset as lerobot_dataset import numpy as np -import openpi.models.model as _model -import openpi.training.config as _config -import openpi.transforms as _transforms +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.training.config as _config +import vla_arena.models.openpi.src.openpi.transforms as _transforms import torch -from openpi.training.droid_rlds_dataset import DroidRldsDataset +from vla_arena.models.openpi.src.openpi.training.droid_rlds_dataset import DroidRldsDataset T_co = TypeVar('T_co', covariant=True) @@ -563,7 +563,7 @@ def _worker_init_fn(worker_id: int) -> None: class RLDSDataLoader: - """Shallow wrapper around the DROID data loader to make it compatible with openpi. + """Shallow wrapper around the DROID data loader to make it compatible with vla_arena.models.openpi.src.openpi. All batching already happens in the DROID dataset, so we don't need to do anything here. """ diff --git a/vla_arena/models/openpi/src/openpi/training/data_loader_test.py b/vla_arena/models/openpi/src/openpi/training/data_loader_test.py index eb2824ce..aafeca7b 100644 --- a/vla_arena/models/openpi/src/openpi/training/data_loader_test.py +++ b/vla_arena/models/openpi/src/openpi/training/data_loader_test.py @@ -15,9 +15,9 @@ import dataclasses import jax -from openpi.models import pi0_config -from openpi.training import config as _config -from openpi.training import data_loader as _data_loader +from vla_arena.models.openpi.src.openpi.models import pi0_config +from vla_arena.models.openpi.src.openpi.training import config as _config +from vla_arena.models.openpi.src.openpi.training import data_loader as _data_loader def test_torch_data_loader(): diff --git a/vla_arena/models/openpi/src/openpi/training/droid_rlds_dataset.py b/vla_arena/models/openpi/src/openpi/training/droid_rlds_dataset.py index d9229a31..de4a7b83 100644 --- a/vla_arena/models/openpi/src/openpi/training/droid_rlds_dataset.py +++ b/vla_arena/models/openpi/src/openpi/training/droid_rlds_dataset.py @@ -24,7 +24,7 @@ from enum import Enum, auto from pathlib import Path -import openpi.shared.download as download +import vla_arena.models.openpi.src.openpi.shared.download as download import tqdm diff --git a/vla_arena/models/openpi/src/openpi/training/misc/roboarena_config.py b/vla_arena/models/openpi/src/openpi/training/misc/roboarena_config.py index af21ff33..9d02fc91 100644 --- a/vla_arena/models/openpi/src/openpi/training/misc/roboarena_config.py +++ b/vla_arena/models/openpi/src/openpi/training/misc/roboarena_config.py @@ -16,12 +16,12 @@ from typing import TypeAlias -import openpi.models.model as _model -import openpi.models.pi0_config as pi0_config -import openpi.models.pi0_fast as pi0_fast -import openpi.models.tokenizer as _tokenizer -import openpi.policies.droid_policy as droid_policy -import openpi.transforms as _transforms +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.models.pi0_config as pi0_config +import vla_arena.models.openpi.src.openpi.models.pi0_fast as pi0_fast +import vla_arena.models.openpi.src.openpi.models.tokenizer as _tokenizer +import vla_arena.models.openpi.src.openpi.policies.droid_policy as droid_policy +import vla_arena.models.openpi.src.openpi.transforms as _transforms ModelType: TypeAlias = _model.ModelType @@ -29,7 +29,7 @@ def get_roboarena_configs(): # Import here to avoid circular imports. - from openpi.training.config import ( + from vla_arena.models.openpi.src.openpi.training.config import ( AssetsConfig, DataConfig, SimpleDataConfig, diff --git a/vla_arena/models/openpi/src/openpi/training/optimizer.py b/vla_arena/models/openpi/src/openpi/training/optimizer.py index d6a8638e..4dfdfcc2 100644 --- a/vla_arena/models/openpi/src/openpi/training/optimizer.py +++ b/vla_arena/models/openpi/src/openpi/training/optimizer.py @@ -16,7 +16,7 @@ from typing import Protocol, runtime_checkable import jax.numpy as jnp -import openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.array_typing as at import optax diff --git a/vla_arena/models/openpi/src/openpi/training/utils.py b/vla_arena/models/openpi/src/openpi/training/utils.py index b8f1a176..8dbd1b23 100644 --- a/vla_arena/models/openpi/src/openpi/training/utils.py +++ b/vla_arena/models/openpi/src/openpi/training/utils.py @@ -18,8 +18,8 @@ import jax import optax from flax import nnx, struct -from openpi.models import model as _model -from openpi.shared import array_typing as at +from vla_arena.models.openpi.src.openpi.models import model as _model +from vla_arena.models.openpi.src.openpi.shared import array_typing as at @at.typecheck diff --git a/vla_arena/models/openpi/src/openpi/training/weight_loaders.py b/vla_arena/models/openpi/src/openpi/training/weight_loaders.py index 856801c3..2f2512a8 100644 --- a/vla_arena/models/openpi/src/openpi/training/weight_loaders.py +++ b/vla_arena/models/openpi/src/openpi/training/weight_loaders.py @@ -19,9 +19,9 @@ import flax.traverse_util import numpy as np -import openpi.models.model as _model -import openpi.shared.array_typing as at -import openpi.shared.download as download +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.download as download logger = logging.getLogger(__name__) diff --git a/vla_arena/models/openpi/src/openpi/transforms.py b/vla_arena/models/openpi/src/openpi/transforms.py index ffa3a75c..7162b31a 100644 --- a/vla_arena/models/openpi/src/openpi/transforms.py +++ b/vla_arena/models/openpi/src/openpi/transforms.py @@ -20,9 +20,9 @@ import flax.traverse_util as traverse_util import jax import numpy as np -from openpi.models import tokenizer as _tokenizer -from openpi.shared import array_typing as at -from openpi.shared import normalize as _normalize +from vla_arena.models.openpi.src.openpi.models import tokenizer as _tokenizer +from vla_arena.models.openpi.src.openpi.shared import array_typing as at +from vla_arena.models.openpi.src.openpi.shared import normalize as _normalize from openpi_client import image_tools diff --git a/vla_arena/models/openpi/src/openpi/transforms_test.py b/vla_arena/models/openpi/src/openpi/transforms_test.py index c68d8a2c..f5455327 100644 --- a/vla_arena/models/openpi/src/openpi/transforms_test.py +++ b/vla_arena/models/openpi/src/openpi/transforms_test.py @@ -13,8 +13,8 @@ # limitations under the License. import numpy as np -import openpi.models.tokenizer as _tokenizer -import openpi.transforms as _transforms +import vla_arena.models.openpi.src.openpi.models.tokenizer as _tokenizer +import vla_arena.models.openpi.src.openpi.transforms as _transforms import pytest diff --git a/vla_arena/models/openpi/trainer.py b/vla_arena/models/openpi/trainer.py index 52c81375..73b0a3b0 100644 --- a/vla_arena/models/openpi/trainer.py +++ b/vla_arena/models/openpi/trainer.py @@ -41,6 +41,14 @@ from pathlib import Path from typing import Any +# Import TensorFlow before JAX/transformers so TF's DType is registered first. +# Otherwise transformers->image_transforms->tensorflow triggers a second +# registration and raises: DType already has SerializedDType proto representation. +try: + import tensorflow as _tf # noqa: F401 +except ImportError: + pass + import etils.epath as epath import flax.nnx as nnx import jax @@ -49,7 +57,6 @@ import optax import tqdm_loggable.auto as tqdm import wandb -import yaml from flax.training import common_utils @@ -58,16 +65,18 @@ if str(_openpi_src) not in sys.path: sys.path.insert(0, str(_openpi_src)) -import openpi.models.model as _model -import openpi.shared.array_typing as at -import openpi.shared.nnx_utils as nnx_utils -import openpi.training.checkpoints as _checkpoints -import openpi.training.config as _config -import openpi.training.data_loader as _data_loader -import openpi.training.optimizer as _optimizer -import openpi.training.sharding as sharding -import openpi.training.utils as training_utils -import openpi.training.weight_loaders as _weight_loaders +import vla_arena.models.openpi.src.openpi.models.model as _model +import vla_arena.models.openpi.src.openpi.shared.array_typing as at +import vla_arena.models.openpi.src.openpi.shared.nnx_utils as nnx_utils +import vla_arena.models.openpi.src.openpi.training.checkpoints as _checkpoints +import vla_arena.models.openpi.src.openpi.training.config as _config +import vla_arena.models.openpi.src.openpi.training.data_loader as _data_loader +import vla_arena.models.openpi.src.openpi.training.optimizer as _optimizer +import vla_arena.models.openpi.src.openpi.training.sharding as sharding +import vla_arena.models.openpi.src.openpi.training.utils as training_utils +import vla_arena.models.openpi.src.openpi.training.weight_loaders as _weight_loaders +from vla_arena.models.openpi.workflow_utils import ensure_norm_stats +from vla_arena.models.openpi.workflow_utils import load_train_config_from_yaml def init_logging(): @@ -508,73 +517,11 @@ def main( # [Config Parsing] Handle cases where config is a path if isinstance(config, (str, Path)): config_path = Path(config) - if not config_path.exists(): - raise FileNotFoundError(f'Config file not found at: {config_path}') - print(f'Loading configuration from {config_path}...') - - # Load YAML file - with open(config_path) as f: - yaml_data = yaml.safe_load(f) - - # Apply overrides from kwargs - if override_kwargs: - yaml_data.update(override_kwargs) - - # If yaml contains a config name, use it with tyro - if isinstance(yaml_data, dict) and 'name' in yaml_data: - config_name = yaml_data['name'] - - # Recursively convert nested dict to command line args - def dict_to_args(prefix: str, d: dict) -> list[str]: - """Recursively convert nested dict to tyro command line args.""" - args = [] - for key, value in d.items(): - if key == 'name': - continue - full_key = f'{prefix}.{key}' if prefix else key - if isinstance(value, dict): - # Recursively handle nested dicts - args.extend(dict_to_args(full_key, value)) - elif isinstance(value, (list, tuple)): - # Handle lists/tuples - args.append( - f"--{full_key}={','.join(str(v) for v in value)}" - ) - elif isinstance(value, bool): - # Handle booleans: only add flag if True - # For False, skip (use default) since tyro doesn't accept --key=false - if value: - args.append(f'--{full_key}') - # else: skip False values to use default - elif value is None: - # Skip None values - continue - else: - args.append(f'--{full_key}={value}') - return args - - # Build command line args from yaml - original_argv = sys.argv.copy() - try: - args_list = [config_name] # Start with config name - args_list.extend(dict_to_args('', yaml_data)) - - # Temporarily modify sys.argv to pass args to tyro - sys.argv = ['trainer_jax.py'] + args_list - cfg = _config.cli() - finally: - # Restore original argv - sys.argv = original_argv - else: - # Fallback: use CLI if yaml doesn't have expected structure - print( - "Warning: Config file doesn't have expected structure, falling back to CLI" - ) - cfg = _config.cli() - + cfg = load_train_config_from_yaml(config_path, override_kwargs) print( - f"Config loaded successfully. Dataset: {cfg.data.repo_id if hasattr(cfg.data, 'repo_id') else 'N/A'}, Max Steps: {cfg.num_train_steps}" + f'Config loaded successfully. Max Steps: {cfg.num_train_steps}, ' + f'checkpoint_dir: {cfg.checkpoint_dir}' ) elif isinstance(config, _config.TrainConfig): @@ -587,6 +534,7 @@ def dict_to_args(prefix: str, d: dict) -> list[str]: f'Unsupported config type: {type(config)}. Expected TrainConfig, str, Path, or None.' ) + ensure_norm_stats(cfg) train_loop(cfg) diff --git a/vla_arena/models/openpi/workflow_utils.py b/vla_arena/models/openpi/workflow_utils.py new file mode 100644 index 00000000..b50845c4 --- /dev/null +++ b/vla_arena/models/openpi/workflow_utils.py @@ -0,0 +1,461 @@ +# Copyright 2025 The VLA-Arena Authors. +# +# 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. + +from __future__ import annotations + +import importlib +import logging +import os +import pathlib +import sys +from typing import Any + +from vla_arena.config_paths import resolve_packaged_config_reference + +# Add openpi src directory to Python path if needed. +_openpi_src = pathlib.Path(__file__).parent / 'src' +if str(_openpi_src) not in sys.path: + sys.path.insert(0, str(_openpi_src)) + + +def _patch_datasets_list_feature() -> None: + """Register deprecated 'List' as 'Sequence' for parquet datasets with old schema. + + LeRobot/parquet datasets created with older HuggingFace datasets may have + feature type 'List' in metadata; newer datasets only has 'Sequence' and + 'LargeList'. This patch allows loading those datasets without re-exporting. + """ + import datasets.features.features as dff + + if 'List' not in getattr(dff, '_FEATURE_TYPES', {}): + dff._FEATURE_TYPES['List'] = dff._FEATURE_TYPES['Sequence'] + + +class _RemoveStringsTransform: + """Remove string-valued fields before computing normalization stats.""" + + def __call__(self, data: dict[str, Any]) -> dict[str, Any]: + import numpy as np + + return { + k: v + for k, v in data.items() + if not np.issubdtype(np.asarray(v).dtype, np.str_) + } + + +def _dict_to_tyro_args(prefix: str, data: dict[str, Any]) -> list[str]: + """Recursively convert nested dict to tyro command-line args.""" + args = [] + for key, value in data.items(): + if key == 'name': + continue + full_key = f'{prefix}.{key}' if prefix else key + if isinstance(value, dict): + args.extend(_dict_to_tyro_args(full_key, value)) + elif isinstance(value, (list, tuple)): + args.append(f"--{full_key}={','.join(str(v) for v in value)}") + elif isinstance(value, bool): + # Keep behavior aligned with trainer: only emit True flags. + if value: + args.append(f'--{full_key}') + elif value is None: + continue + else: + args.append(f'--{full_key}={value}') + return args + + +def _normalize_legacy_train_yaml(yaml_data: dict[str, Any]) -> dict[str, Any]: + """Normalize legacy OpenPI YAML keys for backward compatibility.""" + normalized = dict(yaml_data) + weight_loader = normalized.get('weight_loader') + if not isinstance(weight_loader, dict): + return normalized + + legacy_key = 'checkpoint_path' + target_key = 'params_path' + if legacy_key in weight_loader and target_key not in weight_loader: + patched_weight_loader = dict(weight_loader) + patched_weight_loader[target_key] = patched_weight_loader.pop(legacy_key) + normalized['weight_loader'] = patched_weight_loader + logging.warning( + 'Detected legacy key weight_loader.%s in train YAML. ' + 'Auto-mapped to weight_loader.%s.', + legacy_key, + target_key, + ) + elif legacy_key in weight_loader and target_key in weight_loader: + patched_weight_loader = dict(weight_loader) + patched_weight_loader.pop(legacy_key) + normalized['weight_loader'] = patched_weight_loader + logging.warning( + 'Both weight_loader.%s and weight_loader.%s are set in train YAML. ' + 'Ignoring legacy key weight_loader.%s.', + legacy_key, + target_key, + legacy_key, + ) + return normalized + + +def _map_local_repo_path( + local_repo_dir: pathlib.Path, original_repo_id: str +) -> tuple[str, pathlib.Path]: + """Map local dataset directory to (repo_id, HF_LEROBOT_HOME).""" + normalized_input = pathlib.Path(original_repo_id).expanduser() + local_repo_dir = local_repo_dir.expanduser().resolve() + + # Fallback mode for single-component relative paths (e.g. "dataset_only") + # and root-level paths (e.g. "/dataset_only"): use plain dataset name. + single_component_relative = ( + not normalized_input.is_absolute() + and len(normalized_input.parts) == 1 + ) + parent = local_repo_dir.parent + root_level_path = parent == parent.parent + if single_component_relative or root_level_path: + return local_repo_dir.name, parent + + # Default mode: keep two-level namespace like "/". + mapped_repo_id = f'{parent.name}/{local_repo_dir.name}' + return mapped_repo_id, parent.parent + + +def _normalize_local_repo_id_in_yaml( + yaml_data: dict[str, Any] +) -> dict[str, Any]: + """Normalize local data.repo_id path to repo_id + HF_LEROBOT_HOME.""" + normalized = dict(yaml_data) + data_section = normalized.get('data') + if not isinstance(data_section, dict): + return normalized + + repo_id = data_section.get('repo_id') + if not isinstance(repo_id, str) or not repo_id.strip(): + return normalized + + candidate_path = pathlib.Path(repo_id).expanduser() + if not candidate_path.exists(): + return normalized + + local_repo_dir = candidate_path.resolve() + mapped_repo_id, hf_lerobot_home = _map_local_repo_path( + local_repo_dir, repo_id + ) + + previous_home = os.getenv('HF_LEROBOT_HOME') + os.environ['HF_LEROBOT_HOME'] = str(hf_lerobot_home) + if previous_home and previous_home != str(hf_lerobot_home): + logging.warning( + 'Detected local OpenPI dataset path in data.repo_id=%s. ' + 'Overriding HF_LEROBOT_HOME from %s to %s.', + repo_id, + previous_home, + hf_lerobot_home, + ) + + patched_data = dict(data_section) + patched_data['repo_id'] = mapped_repo_id + normalized['data'] = patched_data + logging.info( + 'Resolved local OpenPI dataset path %s to repo_id=%s with HF_LEROBOT_HOME=%s.', + local_repo_dir, + mapped_repo_id, + hf_lerobot_home, + ) + return normalized + + +def load_train_config_from_yaml( + config_path: str | pathlib.Path, + override_kwargs: dict[str, Any] | None = None, +): + """Load an OpenPI TrainConfig from a YAML file with overrides.""" + import yaml + + _config = importlib.import_module( + 'vla_arena.models.openpi.src.openpi.training.config' + ) + + config_path = pathlib.Path(config_path).expanduser() + if not config_path.exists(): + packaged_path = resolve_packaged_config_reference(config_path) + if packaged_path is None: + raise FileNotFoundError( + f'Config file not found at: {config_path}' + ) + config_path = packaged_path + + with open(config_path) as f: + yaml_data = yaml.safe_load(f) + + if isinstance(yaml_data, dict): + yaml_data = _normalize_legacy_train_yaml(yaml_data) + + if override_kwargs: + if not isinstance(yaml_data, dict): + raise ValueError( + f'Config file must contain a YAML dictionary, got {type(yaml_data)}' + ) + yaml_data.update(override_kwargs) + + if not isinstance(yaml_data, dict) or 'name' not in yaml_data: + raise ValueError( + 'OpenPI train config YAML must be a dictionary and contain "name".' + ) + + yaml_data = _normalize_local_repo_id_in_yaml(yaml_data) + + config_name = yaml_data['name'] + args_list = [config_name] + args_list.extend(_dict_to_tyro_args('', yaml_data)) + + original_argv = sys.argv.copy() + try: + sys.argv = ['openpi_train'] + args_list + cfg = _config.cli() + finally: + sys.argv = original_argv + return cfg + + +def _is_gcs_path(path: str) -> bool: + return path.startswith('gs://') + + +def _list_checkpoint_steps(experiment_dir: pathlib.Path) -> list[int]: + steps: list[int] = [] + for child in experiment_dir.iterdir(): + if not child.is_dir(): + continue + if not child.name.isdigit(): + continue + if (child / 'params').exists(): + steps.append(int(child.name)) + return sorted(steps) + + +def resolve_checkpoint_dir( + policy_checkpoint_dir: str | pathlib.Path | None, + train_cfg: Any | None, + policy_checkpoint_step: str | int = 'latest', +) -> str | pathlib.Path: + """Resolve checkpoint directory, supporting explicit path and latest-step fallback.""" + if policy_checkpoint_dir is not None: + base = str(policy_checkpoint_dir) + else: + if train_cfg is None: + raise ValueError( + 'Unable to resolve checkpoint path: both policy_checkpoint_dir and train config are missing.' + ) + try: + base = str(train_cfg.checkpoint_dir) + except Exception as exc: + raise ValueError( + 'Unable to infer checkpoint directory from train config. ' + 'Please set policy_checkpoint_dir explicitly.' + ) from exc + + if _is_gcs_path(base): + if policy_checkpoint_step not in ('latest', None): + return os.path.join(base.rstrip('/'), str(policy_checkpoint_step)) + return base + + base_path = pathlib.Path(base).expanduser().resolve() + if not base_path.exists(): + raise FileNotFoundError(f'Checkpoint path does not exist: {base_path}') + + # Already a concrete checkpoint step dir. + if (base_path / 'params').exists(): + return base_path + + # Otherwise treat this as experiment root and resolve step directory. + if isinstance(policy_checkpoint_step, str): + step_text = policy_checkpoint_step.strip().lower() + else: + step_text = str(policy_checkpoint_step) + + if step_text == 'latest': + steps = _list_checkpoint_steps(base_path) + if not steps: + raise ValueError( + f'No checkpoint step directories found under: {base_path}' + ) + resolved = base_path / str(steps[-1]) + if not (resolved / 'params').exists(): + raise ValueError( + f'Latest checkpoint directory does not contain params: {resolved}' + ) + return resolved + + if not step_text.isdigit(): + raise ValueError( + 'policy_checkpoint_step must be an integer step or "latest".' + ) + resolved = base_path / step_text + if not (resolved / 'params').exists(): + raise FileNotFoundError( + f'Checkpoint step directory not found or missing params: {resolved}' + ) + return resolved + + +def _create_torch_norm_stats_dataloader( + data_config, + train_cfg, + max_frames: int | None = None, +): + import vla_arena.models.openpi.src.openpi.training.data_loader as _data_loader + + dataset = _data_loader.create_torch_dataset( + data_config, + train_cfg.model.action_horizon, + train_cfg.model, + ) + dataset = _data_loader.TransformedDataset( + dataset, + [ + *data_config.repack_transforms.inputs, + *data_config.data_transforms.inputs, + _RemoveStringsTransform(), + ], + ) + if max_frames is not None and max_frames < len(dataset): + num_batches = max_frames // train_cfg.batch_size + shuffle = True + else: + num_batches = len(dataset) // train_cfg.batch_size + shuffle = False + data_loader = _data_loader.TorchDataLoader( + dataset, + local_batch_size=train_cfg.batch_size, + num_workers=train_cfg.num_workers, + shuffle=shuffle, + num_batches=num_batches, + ) + return data_loader, num_batches + + +def _create_rlds_norm_stats_dataloader( + data_config, + train_cfg, + max_frames: int | None = None, +): + import vla_arena.models.openpi.src.openpi.training.data_loader as _data_loader + + dataset = _data_loader.create_rlds_dataset( + data_config, + train_cfg.model.action_horizon, + train_cfg.batch_size, + shuffle=False, + ) + dataset = _data_loader.IterableTransformedDataset( + dataset, + [ + *data_config.repack_transforms.inputs, + *data_config.data_transforms.inputs, + _RemoveStringsTransform(), + ], + is_batched=True, + ) + if max_frames is not None and max_frames < len(dataset): + num_batches = max_frames // train_cfg.batch_size + else: + num_batches = len(dataset) // train_cfg.batch_size + data_loader = _data_loader.RLDSDataLoader( + dataset, + num_batches=num_batches, + ) + return data_loader, num_batches + + +def compute_and_save_norm_stats( + train_cfg, + max_frames: int | None = None, +) -> pathlib.Path: + """Compute and persist normalization stats for an OpenPI TrainConfig.""" + import numpy as np + import vla_arena.models.openpi.src.openpi.shared.normalize as normalize + import tqdm + + data_config = train_cfg.data.create(train_cfg.assets_dirs, train_cfg.model) + if data_config.repo_id in (None, 'fake'): + raise ValueError( + f'Cannot compute normalization stats for repo_id={data_config.repo_id!r}.' + ) + + if data_config.rlds_data_dir is not None: + data_loader, num_batches = _create_rlds_norm_stats_dataloader( + data_config, train_cfg, max_frames + ) + else: + data_loader, num_batches = _create_torch_norm_stats_dataloader( + data_config, train_cfg, max_frames + ) + + keys = ['state', 'actions'] + stats = {key: normalize.RunningStats() for key in keys} + for batch in tqdm.tqdm( + data_loader, total=num_batches, desc='Computing norm stats' + ): + for key in keys: + stats[key].update(np.asarray(batch[key])) + norm_stats = {key: stats.get_statistics() for key, stats in stats.items()} + + output_path = train_cfg.assets_dirs / data_config.repo_id + normalize.save(output_path, norm_stats) + logging.info('Saved OpenPI normalization stats to %s', output_path) + return pathlib.Path(output_path) + + +def ensure_norm_stats(train_cfg, max_frames: int | None = None) -> pathlib.Path | None: + """Ensure train config has norm stats. If missing, compute and save automatically.""" + _patch_datasets_list_feature() + data_config = train_cfg.data.create(train_cfg.assets_dirs, train_cfg.model) + repo_id = data_config.repo_id + if repo_id in (None, 'fake'): + logging.info( + 'Skipping norm stats check for repo_id=%r (no normalization needed).', + repo_id, + ) + return None + + if data_config.norm_stats is not None: + logging.info('Norm stats already available for repo_id=%s.', repo_id) + return pathlib.Path(train_cfg.assets_dirs / repo_id) + + expected_path = pathlib.Path(train_cfg.assets_dirs) / repo_id + logging.info( + 'Norm stats missing for repo_id=%s. Auto-computing at %s ...', + repo_id, + expected_path, + ) + try: + output_path = compute_and_save_norm_stats(train_cfg, max_frames=max_frames) + except Exception as exc: + raise RuntimeError( + f'Failed to auto-compute normalization stats for repo_id={repo_id!r}. ' + f'Expected output directory: {expected_path}.' + ) from exc + + refreshed_data_config = train_cfg.data.create( + train_cfg.assets_dirs, train_cfg.model + ) + if refreshed_data_config.norm_stats is None: + raise RuntimeError( + 'Normalization stats were computed but could not be loaded from ' + f'{output_path}. Please verify `norm_stats.json` exists and is readable.' + ) + return pathlib.Path(output_path) diff --git a/vla_arena/models/openvla/evaluator.py b/vla_arena/models/openvla/evaluator.py index 8f34fc1a..fe0bb876 100644 --- a/vla_arena/models/openvla/evaluator.py +++ b/vla_arena/models/openvla/evaluator.py @@ -81,7 +81,6 @@ class GenerateConfig: center_crop: bool = True # Center crop? (if trained w/ random crop image aug) unnorm_key: str | Path = 'libero_spatial_no_noops' # Action un-normalization key - num_open_loop_steps: int = 8 # Number of actions to execute open-loop before requerying policy load_in_8bit: bool = False # (For OpenVLA only) Load with 8-bit quantization load_in_4bit: bool = False # (For OpenVLA only) Load with 4-bit quantization @@ -611,9 +610,6 @@ def main(cfg: GenerateConfig | str | Path): tasks_payload: list[dict[str, object]] = [] replacements_dict = load_replacements_dict(cfg, logger) - if cfg.use_replacements: - log_message(f"Using instruction replacements with probability {cfg.replacement_probability}", log_file) - log_message(f"Loaded {len(replacements_dict)} replacement entries", log_file) for suite_name in suite_names: if suite_name not in benchmark_dict: @@ -631,6 +627,15 @@ def main(cfg: GenerateConfig | str | Path): 10 if suite_name == 'long_horizon' and task_level == 0 else 5 ) log_message(f'Task suite: {suite_name}', log_file) + if cfg.use_replacements: + log_message( + f'Using instruction replacements with probability {cfg.replacement_probability}', + log_file, + ) + log_message( + f'Loaded {len(replacements_dict)} replacement entries', + log_file, + ) total_episodes = 0 total_successes = 0 diff --git a/vla_arena/models/openvla_oft/evaluator.py b/vla_arena/models/openvla_oft/evaluator.py index 7da9a765..13c5e8b7 100644 --- a/vla_arena/models/openvla_oft/evaluator.py +++ b/vla_arena/models/openvla_oft/evaluator.py @@ -66,6 +66,7 @@ ) from vla_arena.models.openvla_oft.prismatic.vla.constants import ( NUM_ACTIONS_CHUNK, + PROPRIO_DIM, ) @@ -179,7 +180,7 @@ def initialize_model(cfg: GenerateConfig): proprio_projector = get_proprio_projector( cfg, model.llm_dim, - proprio_dim=8, # 8-dimensional proprio for LIBERO + proprio_dim=PROPRIO_DIM, ) # Load action head if needed @@ -696,9 +697,6 @@ def main(cfg: GenerateConfig | str | Path): tasks_payload: list[dict[str, object]] = [] replacements_dict = load_replacements_dict(cfg, logger) - if cfg.use_replacements: - log_message(f"Using instruction replacements with probability {cfg.replacement_probability}", log_file) - log_message(f"Loaded {len(replacements_dict)} replacement entries", log_file) for suite_name in suite_names: if suite_name not in benchmark_dict: @@ -716,6 +714,15 @@ def main(cfg: GenerateConfig | str | Path): 10 if suite_name == 'long_horizon' and task_level == 0 else 5 ) log_message(f'Task suite: {suite_name}', log_file) + if cfg.use_replacements: + log_message( + f'Using instruction replacements with probability {cfg.replacement_probability}', + log_file, + ) + log_message( + f'Loaded {len(replacements_dict)} replacement entries', + log_file, + ) total_episodes = 0 total_successes = 0 diff --git a/vla_arena/models/openvla_oft/experiments/robot/openvla_utils.py b/vla_arena/models/openvla_oft/experiments/robot/openvla_utils.py index 202ab159..2ed958db 100644 --- a/vla_arena/models/openvla_oft/experiments/robot/openvla_utils.py +++ b/vla_arena/models/openvla_oft/experiments/robot/openvla_utils.py @@ -17,6 +17,7 @@ import filecmp import json import os +import re import shutil import time from datetime import datetime @@ -128,8 +129,8 @@ def update_auto_map(pretrained_checkpoint: str) -> None: config = json.load(f) config['auto_map'] = { - 'AutoConfig': 'configuration_vla_arena.models.openvla_oft.prismatic.OpenVLAConfig', - 'AutoModelForVision2Seq': 'modeling_vla_arena.models.openvla_oft.prismatic.OpenVLAForActionPrediction', + 'AutoConfig': 'configuration_prismatic.OpenVLAConfig', + 'AutoModelForVision2Seq': 'modeling_prismatic.OpenVLAForActionPrediction', } # Write back the updated config @@ -138,11 +139,9 @@ def update_auto_map(pretrained_checkpoint: str) -> None: print(f'Updated config.json at: {os.path.abspath(config_path)}') print('Changes made:') + print(' - Set AutoConfig to "configuration_prismatic.OpenVLAConfig"') print( - ' - Set AutoConfig to "configuration_vla_arena.models.openvla_oft.prismatic.OpenVLAConfig"' - ) - print( - ' - Set AutoModelForVision2Seq to "modeling_vla_arena.models.openvla_oft.prismatic.OpenVLAForActionPrediction"' + ' - Set AutoModelForVision2Seq to "modeling_prismatic.OpenVLAForActionPrediction"' ) @@ -226,7 +225,7 @@ def check_model_logic_mismatch(pretrained_checkpoint: str) -> None: Check and sync model logic files between current code and checkpoint. Handles the relationship between current and checkpoint versions of both - modeling_vla_arena.models.openvla_oft.prismatic.py and configuration_vla_arena.models.openvla_oft.prismatic.py: + modeling_prismatic.py and configuration_prismatic.py: - If checkpoint file exists and differs: creates backup and copies current version - If checkpoint file doesn't exist: copies current version @@ -237,10 +236,7 @@ def check_model_logic_mismatch(pretrained_checkpoint: str) -> None: return # Find current files - curr_files = { - 'modeling_vla_arena.models.openvla_oft.prismatic.py': None, - 'configuration_vla_arena.models.openvla_oft.prismatic.py': None, - } + curr_files = {'modeling_prismatic.py': None, 'configuration_prismatic.py': None} for root, _, files in os.walk('./prismatic/'): for filename in curr_files.keys(): @@ -290,6 +286,33 @@ def find_checkpoint_file(pretrained_checkpoint: str, file_pattern: str) -> str: return checkpoint_files[0] +def find_latest_hf_component_checkpoint( + repo_id: str, component_name: str +) -> str: + """Find latest --_checkpoint.pt in a HF model repo.""" + files = HfApi().list_repo_files(repo_id=repo_id, repo_type='model') + pattern = re.compile( + rf'(^|.*/){re.escape(component_name)}--(\d+)_checkpoint\.pt$' + ) + matches: list[tuple[int, str]] = [] + for filename in files: + match = pattern.search(filename) + if match: + matches.append((int(match.group(2)), filename)) + + if not matches: + raise ValueError( + f'No "{component_name}--_checkpoint.pt" file found in HF repo: {repo_id}' + ) + + # Use the checkpoint with the largest step number. + step, filename = max(matches, key=lambda x: (x[0], x[1])) + print( + f'Using latest {component_name} checkpoint from HF repo {repo_id}: {filename} (step={step})' + ) + return filename + + def load_component_state_dict(checkpoint_path: str) -> dict[str, torch.Tensor]: """ Load a component's state dict from checkpoint and handle DDP prefix if present. @@ -300,7 +323,7 @@ def load_component_state_dict(checkpoint_path: str) -> dict[str, torch.Tensor]: Returns: Dict: The processed state dictionary for loading """ - state_dict = torch.load(checkpoint_path, weights_only=True) + state_dict = _torch_load_checkpoint(checkpoint_path) # If the component was trained with DDP, elements in the state dict have prefix "module." which we must remove new_state_dict = {} @@ -313,6 +336,17 @@ def load_component_state_dict(checkpoint_path: str) -> dict[str, torch.Tensor]: return new_state_dict +def _torch_load_checkpoint(checkpoint_path: str) -> dict[str, torch.Tensor]: + """Load a checkpoint in a way that works across torch versions.""" + try: + return torch.load( + checkpoint_path, map_location='cpu', weights_only=True + ) + except TypeError: + # Older torch versions (common in some conda envs) do not support `weights_only`. + return torch.load(checkpoint_path, map_location='cpu') + + def get_vla(cfg: Any) -> torch.nn.Module: """ Load and initialize the VLA model from checkpoint. @@ -403,11 +437,32 @@ def _apply_film_to_vla(vla: torch.nn.Module, cfg: Any) -> torch.nn.Module: ) vla.model.vision_backbone = new_vision_backbone - # Load vision backbone checkpoint - checkpoint_path = find_checkpoint_file( - cfg.pretrained_checkpoint, 'vision_backbone' - ) - state_dict = torch.load(checkpoint_path, weights_only=True) + # Load vision backbone checkpoint (supports both HF Hub and local paths) + if model_is_on_hf_hub(cfg.pretrained_checkpoint): + try: + component_filename = find_latest_hf_component_checkpoint( + cfg.pretrained_checkpoint, 'vision_backbone' + ) + except ValueError as e: + raise ValueError( + 'use_film=True requires a `vision_backbone--_checkpoint.pt` file in the checkpoint. ' + 'Either set `use_film=false` or use a checkpoint/repo that includes FiLM vision_backbone weights.' + ) from e + checkpoint_path = hf_hub_download( + repo_id=cfg.pretrained_checkpoint, + filename=component_filename, + ) + else: + try: + checkpoint_path = find_checkpoint_file( + cfg.pretrained_checkpoint, 'vision_backbone' + ) + except AssertionError as e: + raise ValueError( + 'use_film=True requires a local `vision_backbone--_checkpoint.pt` file in pretrained_checkpoint.' + ) from e + + state_dict = load_component_state_dict(checkpoint_path) vla.model.vision_backbone.load_state_dict(state_dict) # Use the model component instead of wrapper and convert to bfloat16 @@ -486,31 +541,31 @@ def get_proprio_projector( # Find and load checkpoint (may be on Hugging Face Hub or stored locally) if model_is_on_hf_hub(cfg.pretrained_checkpoint): - model_path_to_proprio_projector_name = { - 'moojink/openvla-7b-oft-finetuned-libero-spatial': 'proprio_projector--150000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-object': 'proprio_projector--150000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-goal': 'proprio_projector--50000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-10': 'proprio_projector--150000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-spatial-object-goal-10': 'proprio_projector--300000_checkpoint.pt', - } - if ( - cfg.pretrained_checkpoint - not in model_path_to_proprio_projector_name.keys() - ): - raise ValueError('Unsupported HF Hub pretrained checkpoint found!') - # Download proprio projector directly from HF Hub + try: + component_filename = find_latest_hf_component_checkpoint( + cfg.pretrained_checkpoint, 'proprio_projector' + ) + except ValueError as e: + raise ValueError( + 'use_proprio=True, but no `proprio_projector--_checkpoint.pt` was found in the HF checkpoint. ' + 'Set `use_proprio=false` if the model was trained without proprioception.' + ) from e proprio_projector_path = hf_hub_download( repo_id=cfg.pretrained_checkpoint, - filename=model_path_to_proprio_projector_name[ - cfg.pretrained_checkpoint - ], + filename=component_filename, ) state_dict = load_component_state_dict(proprio_projector_path) proprio_projector.load_state_dict(state_dict) else: - checkpoint_path = find_checkpoint_file( - cfg.pretrained_checkpoint, 'proprio_projector' - ) + try: + checkpoint_path = find_checkpoint_file( + cfg.pretrained_checkpoint, 'proprio_projector' + ) + except AssertionError as e: + raise ValueError( + 'use_proprio=True, but no local `proprio_projector--_checkpoint.pt` was found in pretrained_checkpoint. ' + 'Set `use_proprio=false` if the model was trained without proprioception.' + ) from e state_dict = load_component_state_dict(checkpoint_path) proprio_projector.load_state_dict(state_dict) @@ -593,22 +648,12 @@ def get_action_head( # Find and load checkpoint (may be on Hugging Face Hub or stored locally) if model_is_on_hf_hub(cfg.pretrained_checkpoint): - model_path_to_action_head_name = { - 'moojink/openvla-7b-oft-finetuned-libero-spatial': 'action_head--150000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-object': 'action_head--150000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-goal': 'action_head--50000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-10': 'action_head--150000_checkpoint.pt', - 'moojink/openvla-7b-oft-finetuned-libero-spatial-object-goal-10': 'action_head--300000_checkpoint.pt', - } - if ( - cfg.pretrained_checkpoint - not in model_path_to_action_head_name.keys() - ): - raise ValueError('Unsupported HF Hub pretrained checkpoint found!') - # Download proprio projector directly from HF Hub + component_filename = find_latest_hf_component_checkpoint( + cfg.pretrained_checkpoint, 'action_head' + ) action_head_path = hf_hub_download( repo_id=cfg.pretrained_checkpoint, - filename=model_path_to_action_head_name[cfg.pretrained_checkpoint], + filename=component_filename, ) state_dict = load_component_state_dict(action_head_path) action_head.load_state_dict(state_dict) diff --git a/vla_arena/models/openvla_oft/experiments/robot/vla_arena/run_vla_arena_eval.py b/vla_arena/models/openvla_oft/experiments/robot/vla_arena/run_vla_arena_eval.py index 835ac967..6a23e173 100644 --- a/vla_arena/models/openvla_oft/experiments/robot/vla_arena/run_vla_arena_eval.py +++ b/vla_arena/models/openvla_oft/experiments/robot/vla_arena/run_vla_arena_eval.py @@ -66,6 +66,7 @@ from vla_arena.models.openvla_oft.prismatic.vla.constants import ( NUM_ACTIONS_CHUNK, + PROPRIO_DIM, ) @@ -169,7 +170,7 @@ def initialize_model(cfg: GenerateConfig): proprio_projector = get_proprio_projector( cfg, model.llm_dim, - proprio_dim=8, # 8-dimensional proprio for LIBERO + proprio_dim=PROPRIO_DIM, ) # Load action head if needed diff --git a/vla_arena/models/openvla_oft/scripts/extern/convert_prismatic_weights_to_hf.py b/vla_arena/models/openvla_oft/scripts/extern/convert_prismatic_weights_to_hf.py index 47fc4b48..cfcff88e 100644 --- a/vla_arena/models/openvla_oft/scripts/extern/convert_prismatic_weights_to_hf.py +++ b/vla_arena/models/openvla_oft/scripts/extern/convert_prismatic_weights_to_hf.py @@ -36,13 +36,13 @@ from timm.models.vision_transformer import LayerScale from transformers import AutoTokenizer -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.extern.hf.configuration_prismatic import ( +from vla_arena.models.openvla_oft.prismatic.extern.hf.configuration_prismatic import ( PrismaticConfig, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.extern.hf.modeling_prismatic import ( +from vla_arena.models.openvla_oft.prismatic.extern.hf.modeling_prismatic import ( PrismaticForConditionalGeneration, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.extern.hf.processing_prismatic import ( +from vla_arena.models.openvla_oft.prismatic.extern.hf.processing_prismatic import ( PrismaticImageProcessor, PrismaticProcessor, ) diff --git a/vla_arena/models/openvla_oft/trainer.py b/vla_arena/models/openvla_oft/trainer.py index 57fed9e2..3afb6a0d 100644 --- a/vla_arena/models/openvla_oft/trainer.py +++ b/vla_arena/models/openvla_oft/trainer.py @@ -240,9 +240,12 @@ def load_checkpoint( path, f'{module_name}--{step}_checkpoint.pt' ) print(f'Loading checkpoint: {checkpoint_path}') - state_dict = torch.load( - checkpoint_path, weights_only=True, map_location=device - ) + try: + state_dict = torch.load( + checkpoint_path, weights_only=True, map_location=device + ) + except TypeError: + state_dict = torch.load(checkpoint_path, map_location=device) return remove_ddp_in_checkpoint(state_dict) diff --git a/vla_arena/models/openvla_oft/vla-scripts/finetune.py b/vla_arena/models/openvla_oft/vla-scripts/finetune.py index 1e300f49..21294570 100644 --- a/vla_arena/models/openvla_oft/vla-scripts/finetune.py +++ b/vla_arena/models/openvla_oft/vla-scripts/finetune.py @@ -50,53 +50,53 @@ ) from transformers.modeling_outputs import CausalLMOutputWithPast -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.extern.hf.configuration_prismatic import ( +from vla_arena.models.openvla_oft.prismatic.extern.hf.configuration_prismatic import ( OpenVLAConfig, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.extern.hf.modeling_prismatic import ( +from vla_arena.models.openvla_oft.prismatic.extern.hf.modeling_prismatic import ( OpenVLAForActionPrediction, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.extern.hf.processing_prismatic import ( +from vla_arena.models.openvla_oft.prismatic.extern.hf.processing_prismatic import ( PrismaticImageProcessor, PrismaticProcessor, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.models.action_heads import ( +from vla_arena.models.openvla_oft.prismatic.models.action_heads import ( DiffusionActionHead, L1RegressionActionHead, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.models.backbones.llm.prompting import ( +from vla_arena.models.openvla_oft.prismatic.models.backbones.llm.prompting import ( PurePromptBuilder, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.models.film_vit_wrapper import ( +from vla_arena.models.openvla_oft.prismatic.models.film_vit_wrapper import ( FiLMedPrismaticVisionBackbone, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.models.projectors import ( +from vla_arena.models.openvla_oft.prismatic.models.projectors import ( NoisyActionProjector, ProprioProjector, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.training.train_utils import ( +from vla_arena.models.openvla_oft.prismatic.training.train_utils import ( compute_actions_l1_loss, compute_token_accuracy, get_current_action_mask, get_next_actions_mask, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.util.data_utils import ( +from vla_arena.models.openvla_oft.prismatic.util.data_utils import ( PaddedCollatorForActionPrediction, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.vla.action_tokenizer import ( +from vla_arena.models.openvla_oft.prismatic.vla.action_tokenizer import ( ActionTokenizer, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.vla.constants import ( +from vla_arena.models.openvla_oft.prismatic.vla.constants import ( ACTION_DIM, ACTION_PROPRIO_NORMALIZATION_TYPE, NUM_ACTIONS_CHUNK, PROPRIO_DIM, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.vla.datasets import ( +from vla_arena.models.openvla_oft.prismatic.vla.datasets import ( RLDSBatchTransform, RLDSDataset, ) -from vla_arena.models.openvla_oft.vla_arena.models.openvla_oft.prismatic.vla.datasets.rlds.utils.data_utils import ( +from vla_arena.models.openvla_oft.prismatic.vla.datasets.rlds.utils.data_utils import ( save_dataset_statistics, ) @@ -240,9 +240,12 @@ def load_checkpoint( path, f'{module_name}--{step}_checkpoint.pt' ) print(f'Loading checkpoint: {checkpoint_path}') - state_dict = torch.load( - checkpoint_path, weights_only=True, map_location=device - ) + try: + state_dict = torch.load( + checkpoint_path, weights_only=True, map_location=device + ) + except TypeError: + state_dict = torch.load(checkpoint_path, map_location=device) return remove_ddp_in_checkpoint(state_dict) diff --git a/vla_arena/models/univla/evaluator.py b/vla_arena/models/univla/evaluator.py index e244d749..35f8d9ce 100644 --- a/vla_arena/models/univla/evaluator.py +++ b/vla_arena/models/univla/evaluator.py @@ -21,6 +21,7 @@ import json import logging import os +import re import sys from collections import deque from dataclasses import dataclass, replace @@ -33,6 +34,7 @@ import torch.nn as nn import tqdm import wandb +from huggingface_hub import HfApi, hf_hub_download # Append current directory so that interpreter can find experiments.robot from vla_arena.models.univla.experiments.robot.vla_arena.vla_arena_utils import ( @@ -87,6 +89,7 @@ class GenerateConfig: # Set UNIVLA_ACTION_DECODER_PATH environment variable to specify a custom action decoder path. action_decoder_path:str = os.getenv('UNIVLA_ACTION_DECODER_PATH', '/path/to/your/action_decoder.pt') + unnorm_key: str | None = None # Action un-normalization key; if None, auto-select from model norm_stats center_crop: bool = True # Center crop? (if trained w/ random crop image aug) save_video: bool = True # Whether to save rollout videos ################################################################################################################# @@ -288,12 +291,84 @@ def validate_config(cfg: GenerateConfig) -> None: # assert cfg.task_suite_name in [suite.value for suite in TaskSuite], f"Invalid task suite: {cfg.task_suite_name}" +def _extract_checkpoint_step(path_like: str) -> int: + filename = Path(path_like).name + for pattern in (r'--(\d+)_checkpoint', r'step[-_=]?(\d+)'): + match = re.search(pattern, filename) + if match: + return int(match.group(1)) + return -1 + + +def _select_best_action_decoder(candidates: list[str]) -> str: + return max( + candidates, + key=lambda candidate: ( + _extract_checkpoint_step(candidate), + Path(candidate).name, + ), + ) + + +def _resolve_action_decoder_path(action_decoder_path: str | Path) -> str: + decoder_ref = str(action_decoder_path).strip() + local_path = Path(decoder_ref).expanduser() + + if local_path.is_file(): + return str(local_path) + + if local_path.is_dir(): + local_candidates = [ + str(path) for path in local_path.rglob('*action_decoder*.pt') + ] + if not local_candidates: + raise FileNotFoundError( + f'No action decoder checkpoint found under directory: {local_path}' + ) + selected = _select_best_action_decoder(local_candidates) + logger.info('Using local action decoder checkpoint: %s', selected) + return selected + + try: + repo_files = HfApi().list_repo_files( + repo_id=decoder_ref, repo_type='model' + ) + except Exception as exc: + raise FileNotFoundError( + f'action_decoder_path is neither a local path nor a valid HF repo id: {decoder_ref}' + ) from exc + + repo_candidates = [ + filename + for filename in repo_files + if Path(filename).suffix in {'.pt', '.pth', '.bin'} + and 'action_decoder' in Path(filename).name + ] + if not repo_candidates: + raise FileNotFoundError( + f'No action decoder checkpoint file found in HF repo: {decoder_ref}' + ) + + selected = _select_best_action_decoder(repo_candidates) + logger.info( + 'Downloading action decoder checkpoint from HF repo %s: %s', + decoder_ref, + selected, + ) + return hf_hub_download(repo_id=decoder_ref, filename=selected) + + def initialize_model(cfg: GenerateConfig): """Initialize model and associated components.""" # Load action decoder + action_decoder_checkpoint = _resolve_action_decoder_path( + cfg.action_decoder_path + ) action_decoder = ActionDecoder(cfg.window_size) - action_decoder.net.load_state_dict(torch.load(cfg.action_decoder_path)) + action_decoder.net.load_state_dict( + torch.load(action_decoder_checkpoint, map_location='cpu') + ) action_decoder.eval().cuda() # Load model model = get_model_for_vla_arena(cfg) @@ -309,23 +384,42 @@ def initialize_model(cfg: GenerateConfig): def check_unnorm_key(cfg: GenerateConfig, model) -> None: """Check that the model contains the action un-normalization key.""" - # Initialize unnorm_key - unnorm_key = 'libero_spatial' - - # In some cases, the key must be manually modified (e.g. after training on a modified version of the dataset - # with the suffix "_no_noops" in the dataset name) - if ( - unnorm_key not in model.norm_stats - and f'{unnorm_key}_no_noops' in model.norm_stats - ): - unnorm_key = f'{unnorm_key}_no_noops' + available_keys = list(model.norm_stats.keys()) + if not available_keys: + raise ValueError('Model `norm_stats` is empty; cannot determine `unnorm_key`.') - assert ( - unnorm_key in model.norm_stats - ), f'Action un-norm key {unnorm_key} not found in VLA `norm_stats`!' + requested_key = getattr(cfg, 'unnorm_key', None) + if requested_key: + candidate_keys = [requested_key, f'{requested_key}_no_noops'] + else: + candidate_keys = [] + if len(available_keys) == 1: + candidate_keys.append(available_keys[0]) + # Prefer VLA-Arena/LIBERO keys when multiple stats are available. + candidate_keys.extend( + [ + 'vla_arena_l0_l', + 'vla_arena_l0_l_no_noops', + 'libero_spatial', + 'libero_spatial_no_noops', + ] + ) + candidate_keys = list(dict.fromkeys(candidate_keys)) - # Set the unnorm_key in cfg - cfg.unnorm_key = unnorm_key + for candidate in candidate_keys: + if candidate in model.norm_stats: + cfg.unnorm_key = candidate + return + + if requested_key: + raise ValueError( + f'Action un-norm key {requested_key} not found in VLA `norm_stats`. ' + f'Available keys: {available_keys}' + ) + raise ValueError( + 'Unable to auto-detect action un-norm key from model `norm_stats`. ' + f'Please set `unnorm_key` explicitly. Available keys: {available_keys}' + ) def setup_logging(cfg: GenerateConfig): @@ -679,17 +773,18 @@ def run_task( # Save replay video based on mode should_save_video = False - if cfg.save_video_mode == 'all': - should_save_video = True - elif cfg.save_video_mode == 'first_success_failure': - if success and not first_success_saved: + if cfg.save_video: + if cfg.save_video_mode == 'all': should_save_video = True - first_success_saved = True - log_message('Saving first successful episode video', log_file) - elif not success and not first_failure_saved: - should_save_video = True - first_failure_saved = True - log_message('Saving first failed episode video', log_file) + elif cfg.save_video_mode == 'first_success_failure': + if success and not first_success_saved: + should_save_video = True + first_success_saved = True + log_message('Saving first successful episode video', log_file) + elif not success and not first_failure_saved: + should_save_video = True + first_failure_saved = True + log_message('Saving first failed episode video', log_file) # For "none" mode, should_save_video remains False if should_save_video: @@ -801,9 +896,6 @@ def main(cfg: GenerateConfig | str | Path) -> float: tasks_payload: list[dict[str, object]] = [] replacements_dict = load_replacements_dict(cfg, logger) - if cfg.use_replacements: - log_message(f"Using instruction replacements with probability {cfg.replacement_probability}", log_file) - log_message(f"Loaded {len(replacements_dict)} replacement entries", log_file) for suite_name in suite_names: if suite_name not in benchmark_dict: @@ -822,6 +914,15 @@ def main(cfg: GenerateConfig | str | Path) -> float: f'Evaluating {num_tasks} tasks from the {suite_name} suite...' ) log_message(f'Task suite: {suite_name}', log_file) + if cfg.use_replacements: + log_message( + f'Using instruction replacements with probability {cfg.replacement_probability}', + log_file, + ) + log_message( + f'Loaded {len(replacements_dict)} replacement entries', + log_file, + ) total_episodes = 0 total_successes = 0 diff --git a/vla_arena/models/univla/experiments/robot/vla_arena/run_vla_arena_eval.py b/vla_arena/models/univla/experiments/robot/vla_arena/run_vla_arena_eval.py index 08643fd3..e54e8356 100644 --- a/vla_arena/models/univla/experiments/robot/vla_arena/run_vla_arena_eval.py +++ b/vla_arena/models/univla/experiments/robot/vla_arena/run_vla_arena_eval.py @@ -21,6 +21,7 @@ import json import logging import os +import re import sys from collections import deque from dataclasses import dataclass @@ -32,6 +33,7 @@ import torch.nn as nn import tqdm import wandb +from huggingface_hub import HfApi, hf_hub_download # Append current directory so that interpreter can find experiments.robot from vla_arena_utils import ( @@ -274,12 +276,84 @@ def validate_config(cfg: GenerateConfig) -> None: # assert cfg.task_suite_name in [suite.value for suite in TaskSuite], f"Invalid task suite: {cfg.task_suite_name}" +def _extract_checkpoint_step(path_like: str) -> int: + filename = Path(path_like).name + for pattern in (r'--(\d+)_checkpoint', r'step[-_=]?(\d+)'): + match = re.search(pattern, filename) + if match: + return int(match.group(1)) + return -1 + + +def _select_best_action_decoder(candidates: list[str]) -> str: + return max( + candidates, + key=lambda candidate: ( + _extract_checkpoint_step(candidate), + Path(candidate).name, + ), + ) + + +def _resolve_action_decoder_path(action_decoder_path: str | Path) -> str: + decoder_ref = str(action_decoder_path).strip() + local_path = Path(decoder_ref).expanduser() + + if local_path.is_file(): + return str(local_path) + + if local_path.is_dir(): + local_candidates = [ + str(path) for path in local_path.rglob('*action_decoder*.pt') + ] + if not local_candidates: + raise FileNotFoundError( + f'No action decoder checkpoint found under directory: {local_path}' + ) + selected = _select_best_action_decoder(local_candidates) + logger.info('Using local action decoder checkpoint: %s', selected) + return selected + + try: + repo_files = HfApi().list_repo_files( + repo_id=decoder_ref, repo_type='model' + ) + except Exception as exc: + raise FileNotFoundError( + f'action_decoder_path is neither a local path nor a valid HF repo id: {decoder_ref}' + ) from exc + + repo_candidates = [ + filename + for filename in repo_files + if Path(filename).suffix in {'.pt', '.pth', '.bin'} + and 'action_decoder' in Path(filename).name + ] + if not repo_candidates: + raise FileNotFoundError( + f'No action decoder checkpoint file found in HF repo: {decoder_ref}' + ) + + selected = _select_best_action_decoder(repo_candidates) + logger.info( + 'Downloading action decoder checkpoint from HF repo %s: %s', + decoder_ref, + selected, + ) + return hf_hub_download(repo_id=decoder_ref, filename=selected) + + def initialize_model(cfg: GenerateConfig): """Initialize model and associated components.""" # Load action decoder + action_decoder_checkpoint = _resolve_action_decoder_path( + cfg.action_decoder_path + ) action_decoder = ActionDecoder(cfg.window_size) - action_decoder.net.load_state_dict(torch.load(cfg.action_decoder_path)) + action_decoder.net.load_state_dict( + torch.load(action_decoder_checkpoint, map_location='cpu') + ) action_decoder.eval().cuda() # Load model model = get_model_for_vla_arena(cfg) diff --git a/vla_arena/models/univla/lam_checkpoint_resolver.py b/vla_arena/models/univla/lam_checkpoint_resolver.py new file mode 100644 index 00000000..3c9615a4 --- /dev/null +++ b/vla_arena/models/univla/lam_checkpoint_resolver.py @@ -0,0 +1,196 @@ +"""Utilities for resolving and loading UniVLA LAM checkpoints.""" + +from __future__ import annotations + +import os +import re +from collections.abc import Mapping +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from huggingface_hub import HfApi, hf_hub_download + + +_CHECKPOINT_SUFFIXES = ('.ckpt', '.pt') +_STAGE_PATTERN = re.compile(r'stage[_-]?(\d+)', re.IGNORECASE) +_STEP_PATTERN = re.compile(r'step[=_-]?(\d+)', re.IGNORECASE) +_EPOCH_PATTERN = re.compile(r'epoch[=_-]?(\d+)', re.IGNORECASE) + + +@dataclass(frozen=True) +class ResolvedLamCheckpoint: + resolved_path: str + source: str + effective_lam_path: str + selected_checkpoint: str | None = None + env_overridden: bool = False + + +def _extract_number(pattern: re.Pattern[str], text: str) -> int: + match = pattern.search(text) + return int(match.group(1)) if match else -1 + + +def _is_checkpoint_file(path: str) -> bool: + return path.lower().endswith(_CHECKPOINT_SUFFIXES) + + +def _checkpoint_score(path: str) -> tuple[int, int, int, int, str]: + stage_num = _extract_number(_STAGE_PATTERN, path) + step_num = _extract_number(_STEP_PATTERN, path) + epoch_num = _extract_number(_EPOCH_PATTERN, path) + ext_priority = 1 if path.lower().endswith('.ckpt') else 0 + return stage_num, step_num, epoch_num, ext_priority, path + + +def _choose_checkpoint(candidates: list[str], context: str) -> str: + if not candidates: + raise FileNotFoundError( + f'No checkpoint files (*.ckpt/*.pt) found in {context}. ' + 'Set `lam_ckpt_file` to specify an exact checkpoint file.' + ) + return max(candidates, key=_checkpoint_score) + + +def _looks_like_state_dict(value: Mapping[str, Any]) -> bool: + if not value or not all(isinstance(k, str) for k in value.keys()): + return False + return any(hasattr(v, 'shape') for v in value.values()) + + +def extract_lam_state_dict(ckpt_obj: Any) -> Mapping[str, Any]: + """Extract state_dict from supported checkpoint formats.""" + if not isinstance(ckpt_obj, Mapping): + raise ValueError( + 'LAM checkpoint must be a mapping object or contain a `state_dict` mapping.' + ) + + if 'state_dict' in ckpt_obj: + state_dict = ckpt_obj['state_dict'] + if not isinstance(state_dict, Mapping): + raise ValueError( + 'LAM checkpoint key `state_dict` exists but is not a mapping.' + ) + if not _looks_like_state_dict(state_dict): + raise ValueError( + 'LAM checkpoint `state_dict` does not look like model weights.' + ) + return state_dict + + if _looks_like_state_dict(ckpt_obj): + return ckpt_obj + + raise ValueError( + 'Unsupported LAM checkpoint format. Expected `{"state_dict": ...}` ' + 'or a plain state_dict mapping.' + ) + + +def resolve_lam_checkpoint( + lam_path: str, + lam_ckpt_file: str | None = None, + env_var: str = 'UNIVLA_LAM_PATH', +) -> ResolvedLamCheckpoint: + env_value = os.getenv(env_var) + has_env_override = bool(env_value and env_value.strip()) + effective_lam_path = env_value.strip() if has_env_override else lam_path + + path_candidate = Path(effective_lam_path).expanduser() + + if path_candidate.is_file(): + return ResolvedLamCheckpoint( + resolved_path=str(path_candidate), + source='local_file', + effective_lam_path=effective_lam_path, + selected_checkpoint=str(path_candidate), + env_overridden=has_env_override, + ) + + if lam_ckpt_file and Path(lam_ckpt_file).is_absolute(): + raise ValueError( + '`lam_ckpt_file` must be a relative path, not an absolute path.' + ) + + if path_candidate.is_dir(): + if lam_ckpt_file: + selected_path = path_candidate / lam_ckpt_file + if not selected_path.is_file(): + raise FileNotFoundError( + f'LAM checkpoint file not found: {selected_path}' + ) + else: + local_candidates = [ + str(path) + for path in path_candidate.rglob('*') + if path.is_file() and _is_checkpoint_file(path.name) + ] + selected_path = Path( + _choose_checkpoint( + local_candidates, + context=f'local directory `{path_candidate}`', + ) + ) + return ResolvedLamCheckpoint( + resolved_path=str(selected_path), + source='local_dir', + effective_lam_path=effective_lam_path, + selected_checkpoint=str(selected_path), + env_overridden=has_env_override, + ) + + repo_id = effective_lam_path + target_filename = lam_ckpt_file + try: + if target_filename is None: + repo_files = HfApi().list_repo_files( + repo_id=repo_id, + repo_type='model', + ) + checkpoint_files = [ + filename + for filename in repo_files + if _is_checkpoint_file(filename) + ] + target_filename = _choose_checkpoint( + checkpoint_files, context=f'HF repo `{repo_id}`' + ) + + downloaded_path = hf_hub_download( + repo_id=repo_id, + filename=target_filename, + repo_type='model', + ) + except FileNotFoundError: + raise + except Exception as err: + target = ( + f'{repo_id}/{target_filename}' + if target_filename is not None + else repo_id + ) + raise RuntimeError( + f'Failed to download LAM checkpoint `{target}` from Hugging Face Hub. ' + 'Please check repo/file names, run `hf auth login` if needed, ' + 'or use a local checkpoint path.' + ) from err + + return ResolvedLamCheckpoint( + resolved_path=downloaded_path, + source='hf_repo', + effective_lam_path=repo_id, + selected_checkpoint=target_filename, + env_overridden=has_env_override, + ) + + +def resolve_lam_checkpoint_path( + lam_path: str, + lam_ckpt_file: str | None = None, + env_var: str = 'UNIVLA_LAM_PATH', +) -> str: + return resolve_lam_checkpoint( + lam_path=lam_path, + lam_ckpt_file=lam_ckpt_file, + env_var=env_var, + ).resolved_path diff --git a/vla_arena/models/univla/latent_action_model/config/lam-stage-1.yaml b/vla_arena/models/univla/latent_action_model/config/lam-stage-1.yaml index 12cb40e3..03f86b81 100644 --- a/vla_arena/models/univla/latent_action_model/config/lam-stage-1.yaml +++ b/vla_arena/models/univla/latent_action_model/config/lam-stage-1.yaml @@ -24,7 +24,7 @@ model: data: data_root: /path/to/your/rlds_data_collection - data_mix: omni_magic_soup_plus_plus # Manip. + Navi. + Human + data_mix: VLA_Arena_L0_L_rlds batch_size: 64 resolution: 224 num_frames: 16 # TODO diff --git a/vla_arena/models/univla/latent_action_model/config/lam-stage-2.yaml b/vla_arena/models/univla/latent_action_model/config/lam-stage-2.yaml index 2216d311..2b3df3d3 100644 --- a/vla_arena/models/univla/latent_action_model/config/lam-stage-2.yaml +++ b/vla_arena/models/univla/latent_action_model/config/lam-stage-2.yaml @@ -25,7 +25,7 @@ model: data: data_root: /path/to/your/rlds_data_collection - data_mix: omni_magic_soup_plus_plus # Manip. + Navi. + Human + data_mix: VLA_Arena_L0_L_rlds batch_size: 64 resolution: 224 num_frames: 16 # TODO diff --git a/vla_arena/models/univla/trainer.py b/vla_arena/models/univla/trainer.py index 0f9d5195..f2559164 100644 --- a/vla_arena/models/univla/trainer.py +++ b/vla_arena/models/univla/trainer.py @@ -41,6 +41,10 @@ AutoProcessor, BitsAndBytesConfig, ) +from vla_arena.models.univla.lam_checkpoint_resolver import ( + extract_lam_state_dict, + resolve_lam_checkpoint, +) from vla_arena.models.univla.prismatic.extern.hf.configuration_prismatic import ( OpenVLAConfig, @@ -174,7 +178,8 @@ def action_decoder_forward(self, batch, vla_output): class FinetuneConfig: # fmt: off vla_path: str = '/path/to/your/pretrained-univla-7b' # Path to your local UniVLA path - lam_path: str = 'latent_action_model/logs/task_centric_lam_stage2/epoch=0-step=200000.ckpt' + lam_path: str = 'latent_action_model/logs/task_centric_lam_stage2/epoch=0-step=200000.ckpt' # Local file/dir or HF repo id + lam_ckpt_file: str | None = None # Optional relative checkpoint file within local dir / HF repo # Directory Paths data_root_dir: Path = Path('/your/path/to/rlds') # Path to Open-X dataset directory dataset_name: str = 'vla_arena' # Name of fine-tuning dataset (e.g., `droid_wipe`) @@ -212,7 +217,7 @@ class FinetuneConfig: # Tracking Parameters wandb_project: str = 'fientune-VLA-ARENA' # Name of W&B project to log to (use default!) - wandb_entity: str = 'jiahao-li' # Name of entity to log under + wandb_entity: str = 'your-wandb-entity' # Name of entity to log under run_id_note: str | None = None # Extra note for logging, Weights & Biases @@ -375,7 +380,29 @@ def main(config: FinetuneConfig | str | Path) -> None: dropout=0.0, ) - lam_ckpt = torch.load(cfg.lam_path)['state_dict'] + resolved_lam = resolve_lam_checkpoint( + lam_path=cfg.lam_path, + lam_ckpt_file=cfg.lam_ckpt_file, + env_var='UNIVLA_LAM_PATH', + ) + if distributed_state.is_main_process: + print( + f'Resolved LAM checkpoint source={resolved_lam.source}, ' + f'path={resolved_lam.resolved_path}' + ) + if resolved_lam.source == 'hf_repo': + print( + f'LAM HF repo={resolved_lam.effective_lam_path}, ' + f'selected={resolved_lam.selected_checkpoint}' + ) + if resolved_lam.env_overridden: + print( + 'LAM path was overridden by environment variable ' + '`UNIVLA_LAM_PATH`.' + ) + + ckpt_obj = torch.load(resolved_lam.resolved_path, map_location='cpu') + lam_ckpt = extract_lam_state_dict(ckpt_obj) new_ckpt = {} for key in lam_ckpt.keys(): new_ckpt[key.replace('lam.', '')] = lam_ckpt[key] diff --git a/vla_arena/models/univla/vla-scripts/finetune_vla_arena.py b/vla_arena/models/univla/vla-scripts/finetune_vla_arena.py index 3a8da5d8..b33b1012 100644 --- a/vla_arena/models/univla/vla-scripts/finetune_vla_arena.py +++ b/vla_arena/models/univla/vla-scripts/finetune_vla_arena.py @@ -212,7 +212,7 @@ class FinetuneConfig: # Tracking Parameters wandb_project: str = 'fientune-VLA-ARENA' # Name of W&B project to log to (use default!) - wandb_entity: str = 'jiahao-li' # Name of entity to log under + wandb_entity: str = 'your-wandb-entity' # Name of entity to log under run_id_note: str | None = None # Extra note for logging, Weights & Biases diff --git a/vla_arena/vla_arena/assets/stable_hope_objects/milk/milk.xml b/vla_arena/vla_arena/assets/stable_hope_objects/milk/milk.xml index 83566ffd..843a3324 100644 --- a/vla_arena/vla_arena/assets/stable_hope_objects/milk/milk.xml +++ b/vla_arena/vla_arena/assets/stable_hope_objects/milk/milk.xml @@ -18,55 +18,48 @@ - - + - - - - - - - - - + + + diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/black_bowl/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/black_bowl/.DS_Store deleted file mode 100644 index 997737b2..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/black_bowl/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/ceramic_plate/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/ceramic_plate/.DS_Store deleted file mode 100644 index 007513fe..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/ceramic_plate/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/coffee_machine/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/coffee_machine/.DS_Store deleted file mode 100644 index 37646967..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/coffee_machine/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/new_plate/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/new_plate/.DS_Store deleted file mode 100644 index 913964df..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/new_plate/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/pan/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/pan/.DS_Store deleted file mode 100644 index dfecba68..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/pan/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/porcelain_plate/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/porcelain_plate/.DS_Store deleted file mode 100644 index 0fc39dbc..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/porcelain_plate/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/assets/stable_scanned_objects/teapot/.DS_Store b/vla_arena/vla_arena/assets/stable_scanned_objects/teapot/.DS_Store deleted file mode 100644 index 5ae888de..00000000 Binary files a/vla_arena/vla_arena/assets/stable_scanned_objects/teapot/.DS_Store and /dev/null differ diff --git a/vla_arena/vla_arena/envs/__init__.py b/vla_arena/vla_arena/envs/__init__.py index 2306d040..9eba029f 100644 --- a/vla_arena/vla_arena/envs/__init__.py +++ b/vla_arena/vla_arena/envs/__init__.py @@ -19,5 +19,5 @@ from .robots import * from .env_wrapper import * -from .env_wrapper import OffScreenRenderEnv, SegmentationRenderEnv +from .env_wrapper import OffScreenRenderEnv from .venv import DummyVectorEnv, SubprocVectorEnv diff --git a/vla_arena/vla_arena/envs/env_wrapper.py b/vla_arena/vla_arena/envs/env_wrapper.py index e62bfb3b..6971f991 100644 --- a/vla_arena/vla_arena/envs/env_wrapper.py +++ b/vla_arena/vla_arena/envs/env_wrapper.py @@ -14,7 +14,6 @@ import os -import cv2 import matplotlib.cm as cm import numpy as np import robosuite as suite @@ -203,111 +202,6 @@ def __init__( ) -class SegmentationRenderEnv(OffScreenRenderEnv): - """ - This wrapper will additionally generate the segmentation mask of objects, - which is useful for comparing attention. - """ - - def __init__( - self, - camera_segmentations='instance', - camera_heights=128, - camera_widths=128, - **kwargs, - ): - assert camera_segmentations is not None - kwargs['camera_segmentations'] = camera_segmentations - kwargs['camera_heights'] = camera_heights - kwargs['camera_widths'] = camera_widths - self.segmentation_id_mapping = {} - self.instance_to_id = {} - self.segmentation_robot_id = None - super().__init__(**kwargs) - - def step(self, action): - return self.env.step(action) - - def reset(self): - obs = self.env.reset() - self.segmentation_id_mapping = {} - - for i, instance_name in enumerate( - list(self.env.model.instances_to_ids.keys()) - ): - if instance_name == 'Panda0': - self.segmentation_robot_id = i - - for i, instance_name in enumerate( - list(self.env.model.instances_to_ids.keys()) - ): - if instance_name not in [ - 'Panda0', - 'RethinkMount0', - 'PandaGripper0', - ]: - self.segmentation_id_mapping[i] = instance_name - - self.instance_to_id = { - v: k + 1 for k, v in self.segmentation_id_mapping.items() - } - return obs - - def get_segmentation_instances(self, segmentation_image): - # get all instances' segmentation separately - seg_img_dict = {} - segmentation_image[segmentation_image > self.segmentation_robot_id] = ( - self.segmentation_robot_id + 1 - ) - seg_img_dict['robot'] = segmentation_image * ( - segmentation_image == self.segmentation_robot_id + 1 - ) - - for seg_id, instance_name in self.segmentation_id_mapping.items(): - seg_img_dict[instance_name] = segmentation_image * ( - segmentation_image == seg_id + 1 - ) - return seg_img_dict - - def get_segmentation_of_interest(self, segmentation_image): - # get the combined segmentation of obj of interest - # 1 for obj_of_interest - # -1.0 for robot - # 0 for other things - ret_seg = np.zeros_like(segmentation_image) - for obj in self.obj_of_interest: - ret_seg[segmentation_image == self.instance_to_id[obj]] = 1.0 - # ret_seg[segmentation_image == self.segmentation_robot_id+1] = -1.0 - ret_seg[segmentation_image == 0] = -1.0 - return ret_seg - - def segmentation_to_rgb(self, seg_im, random_colors=False): - """ - Helper function to visualize segmentations as RGB frames. - NOTE: assumes that geom IDs go up to 255 at most - if not, - multiple geoms might be assigned to the same color. - """ - # ensure all values lie within [0, 255] - seg_im = np.mod(seg_im, 256) - - if random_colors: - colors = randomize_colors(N=256, bright=True) - return (255.0 * colors[seg_im]).astype(np.uint8) - # deterministic shuffling of values to map each geom ID to a random int in [0, 255] - rstate = np.random.RandomState(seed=2) - inds = np.arange(256) - rstate.shuffle(inds) - seg_img = ( - np.array(255.0 * cm.rainbow(inds[seg_im], 10)) - .astype(np.uint8)[..., :3] - .astype(np.uint8) - .squeeze(-2) - ) - print(seg_img.shape) - cv2.imshow('Seg Image', seg_img[::-1]) - cv2.waitKey(1) - # use @inds to map each geom ID to a color - return seg_img class DemoRenderEnv(ControlEnv):