From 132766eba97515f2cfbd80ccfe462b709c686571 Mon Sep 17 00:00:00 2001 From: zilch Date: Fri, 16 Oct 2026 23:39:32 +0800 Subject: [PATCH] feat: support Python 3.11 for simulation and training --- .github/workflows/tests.yml | 37 +- AGENTS.md | 8 +- CONTRIBUTING.md | 18 +- README.md | 10 +- README.zh-CN.md | 10 +- .../getting_started/installation.md | 19 +- .../getting_started/installation.md | 19 +- install.sh | 135 ++- motrix_deploy_motrixsim/pyproject.toml | 2 +- motrix_deploy_mujoco/pyproject.toml | 2 +- motrix_deploy_tasks/pyproject.toml | 2 +- motrix_deploy_tasks/tests/test_cli.py | 14 +- motrix_deploy_unitree/pyproject.toml | 4 +- .../src/motrix_env_core/config/decorate.py | 2 +- motrix_env_core/tests/test_configclass.py | 44 + motrix_env_motrixsim/pyproject.toml | 2 +- motrix_env_mujoco/pyproject.toml | 2 +- motrix_envs/pyproject.toml | 2 +- motrix_rl/src/motrix_rl/numa.py | 261 ++++++ motrix_rl/tests/test_gpu_numa.py | 186 +++++ .../tests/test_numa.py | 2 +- motrix_rl_builtin/pyproject.toml | 2 +- .../fastsac/async_impl/numa.py | 187 +---- .../fastsac/async_impl/topology.py | 4 +- .../fastsac/async_impl/train.py | 2 +- .../fastsac/async_impl/worker.py | 2 +- .../tests/test_fastsac_async_multi.py | 28 +- motrix_rl_rslrl/pyproject.toml | 2 +- motrix_rl_skrl/pyproject.toml | 2 +- motrix_robots/pyproject.toml | 2 +- pyproject.toml | 5 +- uv.lock | 766 +++++++++++++----- .../fastsac-async-heterogeneous-trainer.md | 7 +- 33 files changed, 1290 insertions(+), 500 deletions(-) create mode 100644 motrix_rl/tests/test_gpu_numa.py rename {motrix_rl_builtin => motrix_rl}/tests/test_numa.py (97%) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 5a14ec56..16fc485e 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -18,13 +18,14 @@ concurrency: jobs: pytest: - name: pytest (${{ matrix.suite }}) + name: pytest (${{ matrix.suite }}, Python ${{ matrix.python }}) runs-on: ubuntu-latest timeout-minutes: 45 strategy: fail-fast: false matrix: - include: ${{ fromJSON((github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && '[{"suite":"full","selection":""}]' || '[{"suite":"fast","selection":"not slow and not integration and not numba"},{"suite":"numba","selection":"not integration and (slow or numba)"}]') }} + python: ["3.10", "3.11"] + suite: ${{ fromJSON((github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && '["full"]' || '["fast","numba"]') }} steps: - name: Resolve numba cache dir run: | @@ -39,10 +40,10 @@ jobs: uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0 with: path: ~/.cache/motrixlab - key: numba-kernel-${{ runner.os }}-${{ matrix.suite }}-${{ github.sha }} + key: numba-kernel-${{ runner.os }}-py${{ matrix.python }}-${{ matrix.suite }}-${{ github.sha }} restore-keys: | - numba-kernel-${{ runner.os }}-${{ matrix.suite }}- - numba-kernel-${{ runner.os }}- + numba-kernel-${{ runner.os }}-py${{ matrix.python }}-${{ matrix.suite }}- + numba-kernel-${{ runner.os }}-py${{ matrix.python }}- - name: Checkout repository uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: @@ -55,23 +56,37 @@ jobs: enable-cache: true - name: Set up Python - run: uv python install 3.10 + run: uv python install '${{ matrix.python }}' - name: Install workspace and test dependencies - run: sh install.sh --all + run: | + if [ '${{ matrix.python }}' = '3.10' ]; then + sh install.sh --all --python 3.10 + else + # Unitree/DDS remains outside Python 3.11 runtime support. + uv sync --frozen --python 3.11 --extra cuda --extra rslrl --extra skrl-jax --extra tbb --group test + fi + .venv/bin/python -c 'import sys; assert ".".join(map(str, sys.version_info[:2])) == "${{ matrix.python }}"' - name: Run tests - env: - PYTEST_SELECTION: ${{ matrix.selection }} run: | + selection='' + case '${{ matrix.suite }}' in + fast) selection='not slow and not integration and not numba' ;; + numba) selection='not integration and (slow or numba)' ;; + esac + set -- + if [ '${{ matrix.python }}' = '3.11' ]; then + set -- --ignore=motrix_deploy_unitree + fi .venv/bin/python -m pytest -q --strict-markers --disable-warnings \ --durations=20 --junitxml=test-results/${{ matrix.suite }}.xml \ - -m "$PYTEST_SELECTION" + -m "$selection" "$@" - name: Upload test results if: always() uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2 with: - name: pytest-${{ matrix.suite }} + name: pytest-py${{ matrix.python }}-${{ matrix.suite }} path: test-results/ if-no-files-found: warn diff --git a/AGENTS.md b/AGENTS.md index 4524723f..4175bc7c 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -117,9 +117,11 @@ Python 方法，由 manager 运行时契约定义（`wiki/design/manager/runtime ## 开发环境 -项目使用 UV 管理依赖，Python 版本要求 3.10.\*。可用的依赖组与 extras 见 `pyproject.toml`。 +项目使用 UV 管理依赖，默认 Python 3.10；不含 Unitree 硬件部署插件的仿真与训练也支持 Python 3.11。 +Unitree 硬件部署插件与包含该插件的 `--all` 安装仍仅支持 Python 3.10；Unitree 机器人模型的仿真与训练不受此限制。 +可用的依赖组与 extras 见 `pyproject.toml`。 -完整开发环境（全部 package、依赖组与 extras；`--gpu rocm` 切换 AMD wheel）： +完整开发环境（Python 3.10，全部 package、依赖组与 extras；`--gpu rocm` 切换 AMD wheel）： ```bash sh install.sh --all @@ -129,6 +131,8 @@ sh install.sh --all ```bash sh install.sh +# 不含 Unitree 硬件部署插件的 Python 3.11 运行环境： +sh install.sh --python 3.11 ``` 避免裸 `uv run` / `uv sync`：`cuda`/`rocm` extras 声明为互斥，裸命令会解析到默认 fork（PyPI torch） diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c6a8c249..30fc4c46 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -7,7 +7,8 @@ feature proposals, and Discussions for usage questions and design discussion. MotrixLab currently targets: -- Python **3.10.x** (the workspace declares `==3.10.*`). +- Python **3.10.x / 3.11.x**, with 3.10 as the default. Unitree hardware + deployment and `--all` require Python 3.10. - Linux x86_64 and Windows x86_64 for the documented CPU simulation and viewer workflows. The JAX training extra is Linux-only. - [uv](https://docs.astral.sh/uv/) for environments, dependency resolution, @@ -23,7 +24,8 @@ cd MotrixLab git lfs pull ``` -Create the complete development environment from the repository root: +Create the complete development environment (Python 3.10 only, including Unitree) +from the repository root: ```bash sh install.sh --all # --gpu rocm switches to the AMD wheels @@ -31,8 +33,16 @@ sh install.sh --all # --gpu rocm switches to the AMD wheels A runtime-only environment without the dev toolchain is available via `sh install.sh`: it auto-detects the GPU vendor (NVIDIA/AMD) for the torch wheels and `--rslrl` -selects the RSLRL backend. On Windows, run `install.ps1` in PowerShell. Avoid bare -`uv sync` / `uv run`: the `cuda`/`rocm` extras are declared conflicting, so a bare +selects the RSLRL backend. To select Python 3.11 for a non-Unitree runtime: + +```bash +sh install.sh --python 3.11 +``` + +`--all` cannot be combined with Python 3.11. On Windows, run `install.ps1` in +PowerShell. + +Avoid bare `uv sync` / `uv run`: the `cuda`/`rocm` extras are declared conflicting, so a bare command resolves the default fork (PyPI torch) and reinstalls the environment. Run commands from the activated environment (`source .venv/bin/activate`); the examples below assume it is active, or pass `--no-sync` to one-off `uv run` calls. diff --git a/README.md b/README.md index d3034835..2a5393b9 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ # MotrixLab [![GitHub License](https://img.shields.io/github/license/Motphys/MotrixLab)](LICENSE) -![Python Version](https://img.shields.io/badge/python-3.10-blue) +![Python Version](https://img.shields.io/badge/python-3.10%20%7C%203.11-blue) [![Release](https://img.shields.io/github/v/release/Motphys/MotrixLab?include_prereleases)](https://github.com/Motphys/MotrixLab/releases) [![Docs](https://img.shields.io/badge/docs-readthedocs-blue)](https://motrixlab.readthedocs.io) @@ -56,7 +56,7 @@ _Microduck locomotion policies trained with MotrixLab, rendered in MotrixRender | Requirement | Notes | | --- | --- | -| Python **3.10.x** | The workspace pins `==3.10.*` | +| Python **3.10.x / 3.11.x** | Default: 3.10 | | [uv](https://docs.astral.sh/uv/) | Python project and dependency manager — [installation guide](https://docs.astral.sh/uv/getting-started/installation/) | | [Git LFS](https://git-lfs.com) | Robot meshes, motion data, and videos are tracked by LFS | | OS | Linux x86_64 or Windows x86_64; the JAX training backend is Linux-only | @@ -72,12 +72,16 @@ git lfs pull ### 2. Install dependencies -Linux: +Linux (Python 3.10 by default): ```bash sh install.sh +# Python 3.11 for simulation/training without the Unitree hardware plugin: +sh install.sh --python 3.11 ``` +Unitree hardware deployment and `--all` require Python 3.10; simulation/training with Unitree robot models can use Python 3.11. + Windows (PowerShell): ```powershell diff --git a/README.zh-CN.md b/README.zh-CN.md index 58953340..3368c20c 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -5,7 +5,7 @@ # MotrixLab [![GitHub License](https://img.shields.io/github/license/Motphys/MotrixLab)](LICENSE) -![Python Version](https://img.shields.io/badge/python-3.10-blue) +![Python Version](https://img.shields.io/badge/python-3.10%20%7C%203.11-blue) [![Release](https://img.shields.io/github/v/release/Motphys/MotrixLab?include_prereleases)](https://github.com/Motphys/MotrixLab/releases) [![Docs](https://img.shields.io/badge/docs-readthedocs-blue)](https://motrixlab.readthedocs.io) @@ -56,7 +56,7 @@ _使用 MotrixLab 训练的 microduck 行走策略，由 MotrixRender 实时渲 | 要求 | 说明 | | --- | --- | -| Python **3.10.x** | workspace 锁定 `==3.10.*` | +| Python **3.10.x / 3.11.x** | 默认 3.10 | | [uv](https://docs.astral.sh/uv/) | Python 项目与依赖管理工具 — [安装指南](https://docs.astral.sh/uv/getting-started/installation/) | | [Git LFS](https://git-lfs.com) | 机器人网格、运动数据与视频由 LFS 管理 | | 操作系统 | Linux x86_64 或 Windows x86_64；JAX 训练后端仅支持 Linux | @@ -72,12 +72,16 @@ git lfs pull ### 2. 安装依赖 -Linux： +Linux（默认 Python 3.10）： ```bash sh install.sh +# 不含 Unitree 硬件部署插件的仿真与训练也可使用 Python 3.11： +sh install.sh --python 3.11 ``` +Unitree 硬件部署与 `--all` 仅支持 Python 3.10；Unitree 机器人模型的仿真与训练可使用 Python 3.11。 + Windows（PowerShell）： ```powershell diff --git a/docs/source/en/user_guide/getting_started/installation.md b/docs/source/en/user_guide/getting_started/installation.md index 431fef3f..b781febf 100644 --- a/docs/source/en/user_guide/getting_started/installation.md +++ b/docs/source/en/user_guide/getting_started/installation.md @@ -4,15 +4,18 @@ This document will guide you through the installation and configuration of Motri ## System Requirements -- **Python Version**: {bdg-danger-line}`3.10.*` +- **Python Version**: {bdg-danger-line}`3.10.* / 3.11.*` - This project requires a specific Python version, other versions are not currently supported: + Python 3.10 is the default. | Python Version | Support Status | | :------------: | :------------: | | ≤ 3.9 | ❌ | | 3.10 | ✅ | - | ≥ 3.11 | ❌ | + | 3.11 | ✅ | + | ≥ 3.12 | ❌ | + + Unitree hardware deployment and `--all` require Python 3.10; simulation/training with Unitree robot models can use Python 3.11. - **Package Manager**: {bdg-danger-line}`UV` @@ -46,7 +49,9 @@ cd MotrixLab Install the runtime environment from the repository root (on Windows, run `install.ps1` in PowerShell; if the execution policy blocks it, use `powershell -ExecutionPolicy Bypass -File install.ps1`): ```bash -sh install.sh +sh install.sh # Python 3.10 by default +# Python 3.11 runtime without the Unitree hardware deployment plugin: +sh install.sh --python 3.11 ``` This auto-detects your GPU vendor (NVIDIA → CUDA, AMD → ROCm) and installs the runtime workspace packages, the matching PyTorch wheels, the required SKRL Torch provider, and the built-in FastSAC algorithm. On Linux, `install.sh` also installs Intel TBB by default for Numba parallel kernels, including with `--all` and either GPU profile. Other platforms do not enable TBB by default for runtime installation. Development tools, docs, RSL-RL, SKRL JAX, and other optional extras are not installed unless requested. @@ -77,7 +82,8 @@ project (run tests, modify code, build the docs), install the full development e sh install.sh --all ``` -`--all` enables everything in one go: the dev toolchain, test dependencies, all training backends +`--all` includes the Unitree hardware deployment plugin, so it requires Python 3.10 and cannot be combined with `--python 3.11`. +It enables everything in one go: the dev toolchain, test dependencies, all training backends (SKRL Torch is required; `--skrl-jax` and `--rslrl` add the optional backends), and the docs tooling. On ROCm, CUDA-only extras such as SKRL JAX are skipped. You can also select individual additions, e.g. `sh install.sh --docs` adds only the docs toolchain. @@ -90,7 +96,8 @@ workspace packages and supported development extras. Backend flags select or add | Option | Values | Description | | ------ | ------ | ----------- | -| `--all` | — | Full development environment: everything below is enabled in one go (dev toolchain, test dependencies, all training backends, and docs tooling) | +| `--python` | `3.10`
`3.11` | Select Python; defaults to `3.10`, with `3.11` for runtime installs without the Unitree hardware deployment plugin | +| `--all` | — | Full development environment including Unitree; Python 3.10 only (dev toolchain, test dependencies, all training backends, and docs tooling) | | *(none)* | — | Runtime environment; the GPU vendor is auto-detected (NVIDIA → CUDA, AMD → ROCm; CUDA as fallback when detection is impossible) | | `--gpu` | `cuda`
`rocm` | Select the torch wheel flavor explicitly, overriding auto-detection | | `--skrl-torch` | — | Compatibility flag; SKRL Torch is already required by the root package | diff --git a/docs/source/zh_CN/user_guide/getting_started/installation.md b/docs/source/zh_CN/user_guide/getting_started/installation.md index ae08b9f8..a59d28b7 100644 --- a/docs/source/zh_CN/user_guide/getting_started/installation.md +++ b/docs/source/zh_CN/user_guide/getting_started/installation.md @@ -4,15 +4,18 @@ ## 系统要求 -- **Python 版本**：{bdg-danger-line}`3.10.*` +- **Python 版本**：{bdg-danger-line}`3.10.* / 3.11.*` - 本项目依赖特定 Python 版本，其他版本暂不受支持： + 默认使用 Python 3.10。 | Python 版本 | 支持状态 | | :---------: | :------: | | ≤ 3.9 | ❌ | | 3.10 | ✅ | - | ≥ 3.11 | ❌ | + | 3.11 | ✅ | + | ≥ 3.12 | ❌ | + + Unitree 硬件部署与 `--all` 仅支持 Python 3.10；Unitree 机器人模型的仿真与训练可使用 Python 3.11。 - **包管理器**：{bdg-danger-line}`UV` @@ -46,7 +49,9 @@ cd MotrixLab 在仓库根目录执行以下命令安装运行环境（Windows 上请在 PowerShell 中执行 `install.ps1`；若执行策略受限，改用 `powershell -ExecutionPolicy Bypass -File install.ps1`）： ```bash -sh install.sh +sh install.sh # 默认 Python 3.10 +# 不含 Unitree 硬件部署插件的 Python 3.11 运行环境： +sh install.sh --python 3.11 ``` 该命令会自动探测 GPU 厂商（NVIDIA → CUDA，AMD → ROCm），安装运行环境所需的 workspace package、对应的 @@ -99,7 +104,8 @@ source .venv/bin/activate sh install.sh --all ``` -`--all` 会把所有依赖一次性全部启用：开发工具链、测试依赖、全部训练后端 +`--all` 包含 Unitree 硬件部署插件，因此仅支持 Python 3.10，不能与 `--python 3.11` 组合。 +它会把所有依赖一次性全部启用：开发工具链、测试依赖、全部训练后端 （SKRL Torch 为必需项；`--skrl-jax` 和 `--rslrl` 添加可选后端）与文档工具。在 ROCm 下，CUDA-only extra（例如 SKRL JAX）会被跳过。也可以按需选择额外依赖，例如 `sh install.sh --docs` 只添加文档工具链。 @@ -110,7 +116,8 @@ workspace plugin；`--all` 才会安装全部 workspace package 与支持的开 | 参数 | 可选值 | 说明 | | ---- | ------ | ---- | -| `--all` | — | 完整开发环境：把下述所有依赖一次性全部启用（开发工具链、测试依赖、全部训练后端与文档工具） | +| `--python` | `3.10`
`3.11` | 选择 Python 版本；默认 `3.10`，`3.11` 仅用于不含 Unitree 硬件部署插件的运行环境 | +| `--all` | — | 完整开发环境，包含 Unitree，仅支持 Python 3.10（开发工具链、测试依赖、全部训练后端与文档工具） | | （无参数） | — | 运行环境；自动探测 GPU 厂商（NVIDIA → CUDA，AMD → ROCm，无法探测时回退 CUDA） | | `--gpu` | `cuda`
`rocm` | 指定 torch wheel 来源，覆盖自动探测 | | `--skrl-torch` | — | 兼容性参数；SKRL Torch 已由根项目作为必需依赖提供 | diff --git a/install.sh b/install.sh index b48bacbe..2058fb2b 100755 --- a/install.sh +++ b/install.sh @@ -5,9 +5,10 @@ # Project bootstrap: runtime environment (`sh install.sh`) or full # development environment (`sh install.sh --all`). # -# GPU wheel flavor (--gpu): cuda or rocm. When not given explicitly, detect -# from the loaded kernel driver (nvidia -> cuda, amdgpu -> rocm); CPU-only -# hosts fall back to cuda. +# GPU wheel flavor (--gpu): cuda, rocm or hygon. Hygon installs non-accelerator +# dependencies only, preserving the DTK stack through system-site-packages. +# Auto-detection uses bound hycu devices, then nvidia/amdgpu kernel modules; +# CPU-only hosts fall back to cuda. # # Built-in FastSAC and SKRL on Torch are always installed. --skrl-jax adds # the JAX runtime, and --rslrl adds the RSLRL plugin; multiple flags combine. @@ -24,7 +25,9 @@ usage() { echo " --all Install the full development environment (all packages, groups and extras;" echo " ROCm hosts skip CUDA-only extras such as skrl-jax)" echo " --docs Include the docs toolchain (sphinx) for building the documentation" - echo " --gpu cuda|rocm GPU wheel flavor (default: auto-detected from the kernel driver)" + echo " --gpu cuda|rocm|hygon GPU profile (default: auto-detected from the kernel driver)" + echo " Hygon skips accelerator packages; install DTK-compatible Torch yourself." + echo " --python 3.10|3.11 Python version (default: uv's interpreter selection; --all requires 3.10)" echo " --skrl-torch Training backend: SKRL on PyTorch (default)" echo " --skrl-jax Training backend: SKRL on JAX (Linux only)" echo " --rslrl Training backend: RSL-RL on PyTorch" @@ -34,6 +37,7 @@ usage() { ALL="" GPU="" +PYTHON="" SKRL_JAX="" RSLRL="" DOCS="" @@ -48,10 +52,18 @@ while [ $# -gt 0 ]; do DOCS=1 ;; --gpu) - [ $# -ge 2 ] || { echo "error: $1 requires a value (cuda or rocm)" >&2; exit 1; } + [ $# -ge 2 ] || { echo "error: $1 requires a value (cuda, rocm or hygon)" >&2; exit 1; } GPU="$2" shift ;; + --python) + [ $# -ge 2 ] || { echo "error: $1 requires a value (3.10 or 3.11)" >&2; exit 1; } + case "$2" in + 3.10|3.11) PYTHON="$2" ;; + *) echo "error: invalid --python value: $2 (expected 3.10 or 3.11)" >&2; exit 1 ;; + esac + shift + ;; --skrl-torch) # SKRL Torch is already included in the required root dependencies. ;; @@ -77,12 +89,41 @@ while [ $# -gt 0 ]; do shift done -if [ -n "$GPU" ] && [ "$GPU" != cuda ] && [ "$GPU" != rocm ]; then - echo "error: invalid --gpu value: $GPU (expected cuda or rocm)" >&2 +if [ -n "$GPU" ] && [ "$GPU" != cuda ] && [ "$GPU" != rocm ] && [ "$GPU" != hygon ]; then + echo "error: invalid --gpu value: $GPU (expected cuda, rocm or hygon)" >&2 usage >&2 exit 1 fi +if [ -n "$ALL" ] && [ "$PYTHON" = 3.11 ]; then + echo "error: --all includes Unitree, which requires Python 3.10; use --python 3.10 or omit --all" >&2 + exit 1 +fi + +if [ -z "$GPU" ]; then + # Inspect bound PCI devices, not the CPU vendor or management-tool presence. + # sysfs is also visible in DTK containers where lsmod can miss the host driver. + for device in /sys/bus/pci/drivers/hycu/*:*; do + [ -e "$device" ] || continue + GPU=hygon + break + done + if [ "$GPU" = hygon ]; then + echo "==> Hygon DCU detected; preserving the externally managed DTK accelerator stack." + elif lsmod 2>/dev/null | grep -q '^nvidia '; then + GPU=cuda + elif lsmod 2>/dev/null | grep -q '^amdgpu '; then + GPU=rocm + else + GPU=cuda + fi +fi + +if [ "$GPU" = hygon ] && [ -n "$SKRL_JAX" ]; then + echo "error: --skrl-jax installs a CUDA JAX runtime and is not supported with --gpu hygon" >&2 + exit 1 +fi + if ! command -v uv >/dev/null 2>&1; then echo "==> uv not found, installing..." case "$(uname -s 2>/dev/null)" in @@ -110,6 +151,33 @@ if ! command -v uv >/dev/null 2>&1; then fi fi +# Keep uv's interpreter selection unchanged unless explicitly requested. Positional +# arguments preserve the option/value boundary without word splitting or eval. +set -- +if [ -n "$PYTHON" ]; then + set -- --python "$PYTHON" +elif [ -n "$ALL" ]; then + # Inspect the interpreter without creating an environment or downloading + # Python. If discovery fails, require an explicit compatible version rather + # than letting sync download an unchecked interpreter for Unitree. + if [ -n "${UV_PYTHON:-}" ]; then + DETECTED_PYTHON=$(uv python find --show-version "$UV_PYTHON" 2>/dev/null || true) + else + DETECTED_PYTHON=$(uv python find --show-version 2>/dev/null || true) + fi + case "$DETECTED_PYTHON" in + 3.10|3.10.*) ;; + "") + echo "error: cannot verify Python for --all (Unitree requires Python 3.10); use --python 3.10" >&2 + exit 1 + ;; + *) + echo "error: --all includes Unitree, which requires Python 3.10 (detected $DETECTED_PYTHON); use --python 3.10" >&2 + exit 1 + ;; + esac +fi + # Robot assets (STL/OBJ meshes, motion data) are stored via Git LFS; pull them # so training does not hit pointer files. `git lfs pull` is incremental and # skips objects already present on disk. @@ -132,16 +200,6 @@ if git rev-parse --is-inside-work-tree >/dev/null 2>&1; then fi fi -if [ -z "$GPU" ]; then - if lsmod 2>/dev/null | grep -q '^nvidia '; then - GPU=cuda - elif lsmod 2>/dev/null | grep -q '^amdgpu '; then - GPU=rocm - else - GPU=cuda - fi -fi - case "$(uname -s 2>/dev/null)" in Linux) IS_WINDOWS=""; TBB=1 ;; MINGW*|MSYS*|CYGWIN*) IS_WINDOWS=1 ;; @@ -156,13 +214,48 @@ fi TBB_EXTRA="" [ -n "$TBB" ] && TBB_EXTRA=" --extra tbb" -if [ -n "$ALL" ]; then +if [ "$GPU" = hygon ]; then + echo "==> Install DTK-compatible Torch yourself in the Python environment used by .venv." + echo "==> Torch, TorchVision, TorchAudio, Triton and NVIDIA/AMD management packages will not be installed." + # Use UV_PYTHON or the current Python rather than the workspace's default + # .python-version when no interpreter was explicitly requested. + if [ ! -d .venv ]; then + if [ -z "$PYTHON" ]; then + set -- --python "${UV_PYTHON:-$(command -v python3)}" + fi + uv venv "$@" --system-site-packages .venv + fi + # Existing environments must already expose the externally managed DTK stack. + if ! .venv/bin/python -c 'import sys; assert sys.prefix != sys.base_prefix; assert any(p.startswith(sys.base_prefix + "/lib/") and p.endswith("site-packages") for p in sys.path)'; then + echo "error: Hygon requires a .venv created with --system-site-packages from your DTK Python" >&2 + exit 1 + fi + REQUIREMENTS=$(mktemp) + trap 'rm -f "$REQUIREMENTS"' EXIT HUP INT TERM + set -- --frozen --no-hashes --no-header --no-annotate + if [ -n "$ALL" ]; then + set -- "$@" --all-packages --all-groups --extra rslrl --extra unitree --extra docs + else + set -- "$@" --no-default-groups + [ -n "$RSLRL" ] && set -- "$@" --extra rslrl + [ -n "$DOCS" ] && set -- "$@" --extra docs + fi + [ -n "$TBB" ] && set -- "$@" --extra tbb + # Prune transitive accelerator dependencies too. --no-deps below prevents + # editable workspace packages from pulling the skipped stack back in. + uv export "$@" --prune torch --prune torchvision --prune torchaudio \ + --prune triton --prune triton-rocm --prune amdsmi --prune nvidia-ml-py \ + --output-file "$REQUIREMENTS" >/dev/null + uv pip install --python .venv/bin/python --no-deps --requirements "$REQUIREMENTS" + echo "==> Dependencies installed. Before training, install/verify DTK-compatible Torch yourself." + echo " Do not use plain uv sync: it would replace the externally managed accelerator stack." +elif [ -n "$ALL" ]; then if [ "$GPU" = rocm ]; then set -x - uv sync --all-packages --all-groups --extra rocm --extra rslrl --extra unitree --extra docs$TBB_EXTRA + uv sync "$@" --all-packages --all-groups --extra rocm --extra rslrl --extra unitree --extra docs$TBB_EXTRA else set -x - uv sync --all-packages --all-groups --all-extras --no-extra rocm + uv sync "$@" --all-packages --all-groups --all-extras --no-extra rocm fi else EXTRAS="" @@ -171,5 +264,5 @@ else [ -n "$DOCS" ] && EXTRAS="$EXTRAS --extra docs" EXTRAS="$EXTRAS$TBB_EXTRA" set -x - uv sync --no-default-groups --extra "$GPU"$EXTRAS + uv sync "$@" --no-default-groups --extra "$GPU"$EXTRAS fi diff --git a/motrix_deploy_motrixsim/pyproject.toml b/motrix_deploy_motrixsim/pyproject.toml index 01d86140..1df09f29 100644 --- a/motrix_deploy_motrixsim/pyproject.toml +++ b/motrix_deploy_motrixsim/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-deploy-motrixsim" version = "0.4.0b1" description = "Native SceneCfg-backed MotrixSim deployment runtime plugin." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_deploy_mujoco/pyproject.toml b/motrix_deploy_mujoco/pyproject.toml index d0953a96..5faaf369 100644 --- a/motrix_deploy_mujoco/pyproject.toml +++ b/motrix_deploy_mujoco/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-deploy-mujoco" version = "0.4.0b1" description = "SceneCfg-backed MuJoCo plugin for the Motrix deployment runtime." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_deploy_tasks/pyproject.toml b/motrix_deploy_tasks/pyproject.toml index 2aaaea68..fbb82d83 100644 --- a/motrix_deploy_tasks/pyproject.toml +++ b/motrix_deploy_tasks/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-deploy-tasks" version = "0.4.0b1" description = "Concrete policy deployment tasks for MotrixLab." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_deploy_tasks/tests/test_cli.py b/motrix_deploy_tasks/tests/test_cli.py index 2ca108c8..603661d7 100644 --- a/motrix_deploy_tasks/tests/test_cli.py +++ b/motrix_deploy_tasks/tests/test_cli.py @@ -32,8 +32,6 @@ from motrix_deploy.contracts import RobotSpec, TensorSpec from motrix_deploy.env import assemble_deploy_scene, create_deploy_env from motrix_deploy_tasks.tasks.go2_walk import Go2WalkTaskSpec -from motrix_deploy_unitree.config import UnitreeBackendConfig, UnitreeGo2RobotCfg -from motrix_deploy_unitree.hardware import HardwareRobotCfg from motrix_env_core.config.scene import FlatTerrainCfg, HFieldTerrainCfg, SceneCfg @@ -76,6 +74,7 @@ def test_cli_help() -> None: def test_hardware_cli_help_selects_hardware_recipe() -> None: + pytest.importorskip("motrix_deploy_unitree.config") result = subprocess.run( [ sys.executable, @@ -102,6 +101,8 @@ def test_hardware_cli_help_selects_hardware_recipe() -> None: ], ) def test_installed_recipes_compose_outside_workspace(tmp_path, task, runtime_kind, backend) -> None: + if runtime_kind == "hardware": + pytest.importorskip("motrix_deploy_unitree.config") result = subprocess.run( [sys.executable, "-m", "motrix_deploy.cli", f"task={task}", "--cfg", "job"], cwd=tmp_path, @@ -244,6 +245,8 @@ def test_installed_go2_task_can_be_created_without_preimport(tmp_path: Path) -> def test_hardware_hydra_config_selects_runtime_defaults() -> None: + unitree_config = pytest.importorskip("motrix_deploy_unitree.config") + unitree_hardware = pytest.importorskip("motrix_deploy_unitree.hardware") with initialize_config_module(version_base=None, config_module="motrix_deploy.config"): cfg = compose( config_name="deploy", @@ -256,9 +259,9 @@ def test_hardware_hydra_config_selects_runtime_defaults() -> None: assert cfg.runtime.backend == "unitree" runtime = OmegaConf.to_object(cfg.runtime) - assert isinstance(runtime, UnitreeBackendConfig) - assert isinstance(runtime.robot, UnitreeGo2RobotCfg) - assert isinstance(runtime.robot, HardwareRobotCfg) + assert isinstance(runtime, unitree_config.UnitreeBackendConfig) + assert isinstance(runtime.robot, unitree_config.UnitreeGo2RobotCfg) + assert isinstance(runtime.robot, unitree_hardware.HardwareRobotCfg) assert runtime.network_interface == "enp3s0" assert cfg.duration_s is not None and cfg.duration_s > 0 assert "command" not in cfg @@ -266,6 +269,7 @@ def test_hardware_hydra_config_selects_runtime_defaults() -> None: def test_hardware_hydra_config_accepts_explicit_gain_overrides() -> None: + pytest.importorskip("motrix_deploy_unitree.config") with initialize_config_module(version_base=None, config_module="motrix_deploy.config"): cfg = compose( config_name="deploy", diff --git a/motrix_deploy_unitree/pyproject.toml b/motrix_deploy_unitree/pyproject.toml index 42cc1576..17967707 100644 --- a/motrix_deploy_unitree/pyproject.toml +++ b/motrix_deploy_unitree/pyproject.toml @@ -7,7 +7,9 @@ name = "motrix-deploy-unitree" version = "0.4.0b1" description = "Unitree SDK2 DDS hardware plugin for the Motrix deployment runtime." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +# Permit Python 3.11 workspace resolution; the optional Unitree SDK/DDS runtime +# remains unvalidated on Python 3.11. +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_env_core/src/motrix_env_core/config/decorate.py b/motrix_env_core/src/motrix_env_core/config/decorate.py index b2f3c099..903593cb 100644 --- a/motrix_env_core/src/motrix_env_core/config/decorate.py +++ b/motrix_env_core/src/motrix_env_core/config/decorate.py @@ -76,7 +76,7 @@ def _wrap_configclass(cls: type[_T], **dataclass_options: Any) -> type[_T]: continue value = cls.__dict__[name] - if isinstance(value, Field) or isinstance(value, type) or callable(value) or not _requires_copy(value): + if isinstance(value, Field) or isinstance(value, type) or not _requires_copy(value): continue setattr(cls, name, field(default_factory=_copy_factory(cls.__qualname__, name, value))) diff --git a/motrix_env_core/tests/test_configclass.py b/motrix_env_core/tests/test_configclass.py index acfd3ade..1ff6dfca 100644 --- a/motrix_env_core/tests/test_configclass.py +++ b/motrix_env_core/tests/test_configclass.py @@ -68,6 +68,50 @@ class DemoCfg: assert first.tuple_value[1] is not second.tuple_value[1] +def test_callable_dataclass_defaults_are_isolated(): + @configclass + class CallableCfg: + values: list[int] = [1] + + def __call__(self) -> int: + return sum(self.values) + + template = CallableCfg() + + @configclass + class BaseCfg: + callback: CallableCfg = template + + @configclass + class ChildCfg(BaseCfg): + pass + + first = ChildCfg() + second = ChildCfg() + first.callback.values.append(2) + + assert first.callback() == 3 + assert second.callback() == 1 + assert template.values == [1] + assert first.callback is not second.callback + assert second.callback is not template + + +def test_function_and_class_defaults_keep_identity(): + def callback() -> int: + return 1 + + @configclass + class DemoCfg: + function: object = callback + config_type: type = NestedCfg + + cfg = DemoCfg() + + assert cfg.function is callback + assert cfg.config_type is NestedCfg + + def test_ndarray_defaults_are_isolated(): @configclass class DemoCfg: diff --git a/motrix_env_motrixsim/pyproject.toml b/motrix_env_motrixsim/pyproject.toml index cefbdf3f..ca62c560 100644 --- a/motrix_env_motrixsim/pyproject.toml +++ b/motrix_env_motrixsim/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-env-motrixsim" version = "0.4.0b1" description = "MotrixSim simulation backend for the motrix-env-core environment framework." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_env_mujoco/pyproject.toml b/motrix_env_mujoco/pyproject.toml index 917d867e..f9367320 100644 --- a/motrix_env_mujoco/pyproject.toml +++ b/motrix_env_mujoco/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-env-mujoco" version = "0.4.0b1" description = "Compile-only MuJoCo scene backend for the motrix-env-core environment framework." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_envs/pyproject.toml b/motrix_envs/pyproject.toml index 18a82f40..5a63720d 100644 --- a/motrix_envs/pyproject.toml +++ b/motrix_envs/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-envs" version = "0.4.0b1" description = "Robot simulation environment library based on MotrixSim providing multi-task RL environments." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_rl/src/motrix_rl/numa.py b/motrix_rl/src/motrix_rl/numa.py index ddf2dd7e..79366008 100644 --- a/motrix_rl/src/motrix_rl/numa.py +++ b/motrix_rl/src/motrix_rl/numa.py @@ -11,12 +11,19 @@ from __future__ import annotations +import contextlib import ctypes import ctypes.util +import functools +import json import logging import os +import re +import subprocess +import sys from dataclasses import dataclass from pathlib import Path +from typing import Any, Literal logger = logging.getLogger(__name__) _NODE_SYSFS = Path("/sys/devices/system/node") @@ -210,3 +217,257 @@ def apply_placement(node: NodePlacement) -> dict[str, object]: result["memory_error"] = str(error) result["memory_policy"] = "cpu-only" if actual_cpus else "inherited" return result + + +@contextlib.contextmanager +def spawn_placement(node: int | None, role: str): + """Pre-place a spawn child's import-time allocations, then restore the parent. + + CPU affinity and memory policy survive fork+exec. Temporarily binding the + parent around ``Process.start()`` therefore places allocations made before + the child's worker entry point, including runtime imports, on the node. + """ + if node is None: + yield + return + saved_affinity = os.sched_getaffinity(0) + saved_policy = None + memory_changed = False + try: + cpus = sorted(set(numa_node_cpus(node)) & saved_affinity) + if not cpus: + raise ValueError(f"{role}: no allowed CPUs on NUMA node {node}") + apply_cpu_affinity(cpus, role) + try: + saved_policy = get_memory_policy() + except (OSError, AttributeError) as error: + logger.warning("%s: cannot snapshot memory policy; retaining inherited policy: %s", role, error) + if saved_policy is not None: + memory_changed = apply_memory_policy(node, role) + yield + finally: + try: + os.sched_setaffinity(0, saved_affinity) + finally: + if memory_changed: + try: + set_memory_policy(saved_policy) + except (OSError, AttributeError) as error: + logger.warning("%s: restoring inherited memory policy failed: %s", role, error) + + +def apply_binding(role: str, node: int | None, cpus: list[int]) -> None: + """Apply precomputed CPU affinity and best-effort node memory binding. + + With no CPUs and no node this is a no-op. Placement decisions belong to + the caller; memory binding only affects future allocations. + """ + if cpus: + apply_cpu_affinity(cpus, role) + if node is not None: + apply_memory_policy(node, role) + + +_nvml_state: tuple[Any, list[Any]] | tuple[()] | None = None # None: untried; (): unavailable + + +def _nvml() -> tuple[Any, list[Any]] | None: + """Lazily initialize NVML and return ``(pynvml, pci_bus_ids)``, or ``None``. + + Like ``system_metrics``, reuse an in-process session without initializing + CUDA. PCI bus IDs connect device indices to sysfs NUMA locality. + """ + global _nvml_state + if _nvml_state is None: + try: + import pynvml + + pynvml.nvmlInit() + bus_ids = [ + pynvml.nvmlDeviceGetPciInfo(pynvml.nvmlDeviceGetHandleByIndex(index)).busId + for index in range(pynvml.nvmlDeviceGetCount()) + ] + _nvml_state = (pynvml, bus_ids) + except Exception: # ImportError (pynvml missing) or NVML init failure (no driver/GPU) + _nvml_state = () + return _nvml_state or None + + +def _normalize_pci_bus_id(bus_id: str) -> str | None: + """Validate a PCI address and reduce a zero-padded eight-digit domain for sysfs.""" + if not isinstance(bus_id, str): + return None + match = re.fullmatch(r"([0-9a-fA-F]{4}|[0-9a-fA-F]{8}):([0-9a-fA-F]{2}):([0-9a-fA-F]{2})\.([0-7])", bus_id) + if match is None: + return None + domain, bus, device, function = (int(part, 16) for part in match.groups()) + if domain > 0xFFFF or device > 0x1F: + return None + return f"{domain:04x}:{bus:02x}:{device:02x}.{function}" + + +_HIP_PCI_PROBE = r""" +import ctypes +import ctypes.util +import json +from pathlib import Path + +try: + import torch + + # Torch prepares its wheel's shared-library dependencies before HIP loads. + torch_lib = Path(torch.__file__).resolve().parent / "lib" + candidates = [] + for name in ("amdhip64", "galaxyhip"): + candidates.extend(str(path) for path in sorted(torch_lib.glob("lib" + name + ".so*"))) + for name in ("amdhip64", "galaxyhip"): + candidates.append(ctypes.util.find_library(name) or ("lib" + name + ".so")) + runtime = None + for candidate in candidates: + try: + library = ctypes.CDLL(candidate) + get_count = library.hipGetDeviceCount + get_bus_id = library.hipDeviceGetPCIBusId + runtime = library + break + except (OSError, AttributeError): + continue + if runtime is None: + raise RuntimeError("HIP runtime unavailable") + get_count.argtypes = [ctypes.POINTER(ctypes.c_int)] + get_count.restype = ctypes.c_int + get_bus_id.argtypes = [ctypes.POINTER(ctypes.c_char), ctypes.c_int, ctypes.c_int] + get_bus_id.restype = ctypes.c_int + count = ctypes.c_int() + if get_count(ctypes.byref(count)) != 0 or count.value < 0: + raise RuntimeError("HIP device enumeration failed") + bus_ids = [] + for index in range(count.value): + buffer = ctypes.create_string_buffer(64) + if get_bus_id(buffer, len(buffer), index) == 0: + bus_ids.append(buffer.value.decode("ascii")) + else: + bus_ids.append(None) + print(json.dumps(bus_ids)) +except Exception: + print("null") +""" + + +@functools.lru_cache(maxsize=8) +def _hip_pci_bus_ids( + hip_visible_devices: str | None, + rocr_visible_devices: str | None, + cuda_visible_devices: str | None, +) -> tuple[str | None, ...] | None: + """Query runtime ordinals in a child, caching each visibility mapping. + + Loading or initializing HIP in the pre-spawn parent is unsafe. The child + inherits the environment, with visibility values also explicit in the cache + key so changing an ordinal remapping cannot reuse an earlier probe. + """ + env = os.environ.copy() + for name, value in ( + ("HIP_VISIBLE_DEVICES", hip_visible_devices), + ("ROCR_VISIBLE_DEVICES", rocr_visible_devices), + ("CUDA_VISIBLE_DEVICES", cuda_visible_devices), + ): + if value is None: + env.pop(name, None) + else: + env[name] = value + try: + proc = subprocess.run( + [sys.executable, "-c", _HIP_PCI_PROBE], + capture_output=True, + text=True, + timeout=10, + check=True, + env=env, + ) + bus_ids = json.loads(proc.stdout) + except (OSError, subprocess.SubprocessError, ValueError): + return None + if not isinstance(bus_ids, list): + return None + # Keep failed entries in place: dropping one would shift all later ordinals. + return tuple(_normalize_pci_bus_id(bus_id) for bus_id in bus_ids) + + +def gpu_numa_node(device_index: int, runtime: Literal["cuda", "hip"]) -> int | None: + """NUMA node hosting a GPU for the explicitly selected runtime, or ``None``. + + HIP queries visible runtime ordinals in an isolated subprocess, never + initializing HIP in the parent. CUDA retains NVML and the NVIDIA topology + fallback. Both read sysfs ``numa_node``; -1 means unknown. The caller owns + runtime selection, so importing this module never imports Torch. CUDA uses + physical NVML indices; CUDA_VISIBLE_DEVICES ordinal remapping is not handled. + """ + if runtime not in ("cuda", "hip"): + raise ValueError(f"Unknown GPU runtime: {runtime!r}") + if device_index < 0: + return None + if runtime == "hip": + bus_ids = _hip_pci_bus_ids( + os.environ.get("HIP_VISIBLE_DEVICES"), + os.environ.get("ROCR_VISIBLE_DEVICES"), + os.environ.get("CUDA_VISIBLE_DEVICES"), + ) + else: + session = _nvml() + bus_ids = session[1] if session is not None else None + if bus_ids is None or device_index >= len(bus_ids): + return None + bus_id = _normalize_pci_bus_id(bus_ids[device_index]) + if bus_id is not None: + try: + node = int((Path("/sys/bus/pci/devices") / bus_id / "numa_node").read_text().strip()) + if node >= 0: + return node + except (OSError, ValueError): + pass + # Never use NVIDIA host ordinals to guess a HIP device's locality. + return None if runtime == "hip" else _gpu_node_from_topology(device_index) + + +@functools.lru_cache(maxsize=1) +def _gpu_cpu_affinities() -> dict[int, frozenset[int]]: + """GPU index -> CPU affinity set, parsed from ``nvidia-smi topo -m``.""" + try: + proc = subprocess.run(["nvidia-smi", "topo", "-m"], capture_output=True, text=True, timeout=10, check=True) + except (OSError, subprocess.SubprocessError): + return {} + affinities: dict[int, frozenset[int]] = {} + for line in proc.stdout.splitlines(): + cells = line.split() + if not cells or not cells[0].lstrip("\x1b[4m").startswith("GPU"): + continue + try: + index = int(cells[0].lstrip("\x1b[4m").removeprefix("GPU")) + except ValueError: + continue + # Header/data rows differ in layout; find the multi-CPU list token. + for cell in cells[1:]: + if not re.fullmatch(r"[0-9]+(?:-[0-9]+)?(?:,[0-9]+(?:-[0-9]+)?)+|[0-9]+-[0-9]+", cell): + continue + cpus = parse_cpulist(cell) + if len(cpus) > 1: + affinities[index] = frozenset(cpus) + break + return affinities + + +def _gpu_node_from_topology(device_index: int) -> int | None: + """Node whose CPU list overlaps the GPU's affinity the most.""" + affinity = _gpu_cpu_affinities().get(device_index) + if not affinity: + return None + best_node, best_overlap = None, 0 + for node in available_numa_nodes(): + try: + overlap = len(set(numa_node_cpus(node)) & affinity) + except ValueError: + continue + if overlap > best_overlap: + best_node, best_overlap = node, overlap + return best_node diff --git a/motrix_rl/tests/test_gpu_numa.py b/motrix_rl/tests/test_gpu_numa.py new file mode 100644 index 00000000..dd47c6a9 --- /dev/null +++ b/motrix_rl/tests/test_gpu_numa.py @@ -0,0 +1,186 @@ +# Copyright Motphys Technology Co., Ltd. 2025, 2026 +# SPDX-License-Identifier: Apache-2.0 + +import ctypes +import json +import subprocess +import sys +from types import SimpleNamespace + +import pytest + +from motrix_rl import numa + + +@pytest.fixture(autouse=True) +def clear_hip_cache(): + probe = numa._hip_pci_bus_ids + probe.cache_clear() + yield + probe.cache_clear() + + +@pytest.mark.parametrize( + ("raw", "expected"), + [ + ("0000:07:00.0", "0000:07:00.0"), + ("00000000:AF:00.1", "0000:af:00.1"), + ("../../etc/passwd", None), + ("bad", None), + ("0000:07:00.9", None), + ], +) +def test_normalize_pci_bus_id(raw, expected): + assert numa._normalize_pci_bus_id(raw) == expected + + +def test_hip_probe_is_isolated_and_cached(monkeypatch): + calls = [] + + def run(args, **kwargs): + calls.append((args, kwargs)) + return subprocess.CompletedProcess(args, 0, stdout=json.dumps(["00000000:07:00.0", "0000:0a:00.0"])) + + monkeypatch.setattr(numa.subprocess, "run", run) + first = numa._hip_pci_bus_ids(None, None, None) + assert first == ("0000:07:00.0", "0000:0a:00.0") + assert numa._hip_pci_bus_ids(None, None, None) == first + assert len(calls) == 1 + assert calls[0][0] == [sys.executable, "-c", numa._HIP_PCI_PROBE] + assert calls[0][1]["timeout"] > 0 + for key in ("HIP_VISIBLE_DEVICES", "ROCR_VISIBLE_DEVICES", "CUDA_VISIBLE_DEVICES"): + assert key not in calls[0][1]["env"] + numa._hip_pci_bus_ids("1,0", "0,1", "1") + assert len(calls) == 2 + assert calls[1][1]["env"]["HIP_VISIBLE_DEVICES"] == "1,0" + assert calls[1][1]["env"]["ROCR_VISIBLE_DEVICES"] == "0,1" + assert calls[1][1]["env"]["CUDA_VISIBLE_DEVICES"] == "1" + + +@pytest.mark.parametrize("output", ["garbage", "{}", "null", '["../../etc/passwd"]']) +def test_invalid_hip_probe_output_is_unknown(monkeypatch, output): + monkeypatch.setattr( + numa.subprocess, "run", lambda args, **kwargs: subprocess.CompletedProcess(args, 0, stdout=output) + ) + result = numa._hip_pci_bus_ids(None, None, None) + assert result is None or all(item is None for item in result) + + +def test_hip_probe_preserves_failed_ordinals(monkeypatch): + monkeypatch.setattr( + numa.subprocess, + "run", + lambda args, **kwargs: subprocess.CompletedProcess(args, 0, stdout='[null, "0000:0a:00.0"]'), + ) + assert numa._hip_pci_bus_ids(None, None, None) == (None, "0000:0a:00.0") + + +@pytest.mark.parametrize( + "error", + [OSError("missing runtime"), subprocess.TimeoutExpired("probe", 10), subprocess.CalledProcessError(1, "probe")], +) +def test_hip_probe_failure_is_unknown(monkeypatch, error): + def fail(*args, **kwargs): + raise error + + monkeypatch.setattr(numa.subprocess, "run", fail) + assert numa._hip_pci_bus_ids(None, None, None) is None + + +@pytest.mark.parametrize("library_name", ["libamdhip64.so.6", "libgalaxyhip.so.1"]) +def test_child_probe_prefers_torch_wheel_versioned_library(monkeypatch, tmp_path, capsys, library_name): + torch_dir = tmp_path / "torch" + library = torch_dir / "lib" / library_name + library.parent.mkdir(parents=True) + library.touch() + monkeypatch.setitem(sys.modules, "torch", SimpleNamespace(__file__=str(torch_dir / "__init__.py"))) + loaded = [] + + class GetCount: + def __call__(self, pointer): + pointer._obj.value = 2 + return 0 + + class GetBusId: + def __call__(self, buffer, size, index): + if index == 0: + return 1 + buffer.value = b"0000:0a:00.0" + return 0 + + def load(path): + assert "torch" in sys.modules + loaded.append(path) + return SimpleNamespace(hipGetDeviceCount=GetCount(), hipDeviceGetPCIBusId=GetBusId()) + + monkeypatch.setattr(ctypes, "CDLL", load) + monkeypatch.setattr(ctypes.util, "find_library", lambda name: "system-" + name) + exec(numa._HIP_PCI_PROBE, {}) + assert loaded == [str(library)] + assert json.loads(capsys.readouterr().out) == [None, "0000:0a:00.0"] + + +@pytest.mark.parametrize("failure", ["load", "symbols", "enumeration"]) +def test_child_probe_reports_runtime_failures(monkeypatch, tmp_path, capsys, failure): + monkeypatch.setitem(sys.modules, "torch", SimpleNamespace(__file__=str(tmp_path / "__init__.py"))) + monkeypatch.setattr(ctypes.util, "find_library", lambda name: None) + + class GetCount: + def __call__(self, pointer): + return 1 + + def load(path): + if failure == "load": + raise OSError("unavailable") + if failure == "symbols": + return SimpleNamespace() + return SimpleNamespace(hipGetDeviceCount=GetCount(), hipDeviceGetPCIBusId=GetCount()) + + monkeypatch.setattr(ctypes, "CDLL", load) + exec(numa._HIP_PCI_PROBE, {}) + assert json.loads(capsys.readouterr().out) is None + + +def test_hip_ordinal_reads_sysfs_without_nvidia_fallback(monkeypatch, tmp_path): + monkeypatch.setattr(numa, "_nvml", lambda: pytest.fail("HIP must not use NVML physical ordinals")) + monkeypatch.setattr(numa, "_gpu_node_from_topology", lambda index: pytest.fail("HIP must not use NVIDIA topology")) + monkeypatch.setenv("HIP_VISIBLE_DEVICES", "1,0") + seen = [] + + def probe(*visibility): + seen.append(visibility) + return ("0000:0a:00.0", "0000:07:00.0") + + monkeypatch.setattr(numa, "_hip_pci_bus_ids", probe) + node = tmp_path / "0000:0a:00.0" / "numa_node" + node.parent.mkdir() + node.write_text("3\n") + monkeypatch.setattr(numa, "Path", lambda value: tmp_path) + assert numa.gpu_numa_node(0, runtime="hip") == 3 + assert seen[0][0] == "1,0" + node.write_text("-1\n") + assert numa.gpu_numa_node(0, runtime="hip") is None + node.unlink() + assert numa.gpu_numa_node(0, runtime="hip") is None + assert numa.gpu_numa_node(9, runtime="hip") is None + assert numa.gpu_numa_node(-1, runtime="hip") is None + monkeypatch.setattr(numa, "_hip_pci_bus_ids", lambda *args: None) + assert numa.gpu_numa_node(0, runtime="hip") is None + + +def test_nvidia_path_preserved(monkeypatch, tmp_path): + monkeypatch.setattr(numa, "_nvml", lambda: (object(), ["0000:07:00.0"])) + monkeypatch.setattr(numa, "_hip_pci_bus_ids", lambda *args: pytest.fail("NVIDIA must not probe HIP")) + node = tmp_path / "0000:07:00.0" / "numa_node" + node.parent.mkdir() + node.write_text("2") + monkeypatch.setattr(numa, "Path", lambda value: tmp_path) + assert numa.gpu_numa_node(0, runtime="cuda") == 2 + node.unlink() + monkeypatch.setattr(numa, "_gpu_node_from_topology", lambda index: 4) + assert numa.gpu_numa_node(0, runtime="cuda") == 4 + + +def test_gpu_runtime_must_be_explicit_and_supported(): + with pytest.raises(ValueError, match="Unknown GPU runtime"): + numa.gpu_numa_node(0, runtime="unknown") diff --git a/motrix_rl_builtin/tests/test_numa.py b/motrix_rl/tests/test_numa.py similarity index 97% rename from motrix_rl_builtin/tests/test_numa.py rename to motrix_rl/tests/test_numa.py index 2feb5b1f..fbc8a029 100644 --- a/motrix_rl_builtin/tests/test_numa.py +++ b/motrix_rl/tests/test_numa.py @@ -5,9 +5,9 @@ import sys import pytest -from motrix_rl_builtin.fastsac.async_impl.numa import spawn_placement from motrix_rl import numa +from motrix_rl.numa import spawn_placement def test_shared_module_import_does_not_initialize_runtime(): diff --git a/motrix_rl_builtin/pyproject.toml b/motrix_rl_builtin/pyproject.toml index 6a7f3434..641797e4 100644 --- a/motrix_rl_builtin/pyproject.toml +++ b/motrix_rl_builtin/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-rl-builtin" version = "0.4.0b1" description = "MotrixLab built-in reinforcement learning algorithms." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/numa.py b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/numa.py index df994632..1f11eded 100644 --- a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/numa.py +++ b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/numa.py @@ -1,44 +1,10 @@ # Copyright Motphys Technology Co., Ltd. 2025, 2026 # SPDX-License-Identifier: Apache-2.0 -"""Best-effort NUMA binding for multi-collector async FastSAC training. - -Each collector on a multi-NUMA-node server should run its env, staging buffers -and pinned host allocations with node-local memory, and the scheduler should not -migrate it across nodes. WHICH node each worker gets is decided by -``async_impl/topology.py`` (learners on their GPU's PCIe-local node, collectors -on their owning learner's node); this module applies the decided placement -(the ``numactl --cpunodebind= --membind=`` equivalent) from inside the -worker process: - -* CPU affinity via ``os.sched_setaffinity`` (portable stdlib); -* memory policy via libnuma's checked ``set_mempolicy`` (the same call ``numactl`` uses), - loaded with :mod:`ctypes` — future allocations of the calling process come - from the bound node. - -Everything is best-effort: when libnuma is unavailable or the node is unknown -the binding logs a warning and continues with OS default placement, so single -NUMA machines and containers keep working unchanged. - -Binding must happen at process start, before the env and any staging buffer is -allocated — memory binding only affects future allocations. -""" +"""FastSAC collector CPU slicing policy for topology-resolved CPU sets.""" from __future__ import annotations -import contextlib -import functools -import logging -import os -import re -import subprocess -from pathlib import Path -from typing import Any - -from motrix_rl import numa as system_numa - -logger = logging.getLogger(__name__) - def select_collector_cpus( base: list[int], @@ -67,154 +33,3 @@ def select_collector_cpus( f"(base set has {len(base)} CPUs)" ) return base[start:end] - - -@contextlib.contextmanager -def spawn_placement(node: int | None, role: str): - """Place a spawn-started child on ``node`` by pre-placing its parent. - - A ``spawn`` child re-imports torch / the simulator / glibc arenas before - the worker entry function runs, so a bind executed inside the child only - affects allocations made after those imports — simulator thread pools and - malloc arenas created at import time land on a random node and physics - stepping pays cross-node access forever. Affinity and memory policy both - survive fork+exec, so briefly switching the *parent* onto ``node`` around - ``Process.start()`` makes the child's import-time allocations node-local; - the worker's own :func:`apply_binding` call then re-affirms the same - binding (a no-op refinement). - """ - if node is None: - yield - return - saved_affinity = os.sched_getaffinity(0) - saved_policy = None - memory_changed = False - try: - cpus = sorted(set(system_numa.numa_node_cpus(node)) & saved_affinity) - if not cpus: - raise ValueError(f"{role}: no allowed CPUs on NUMA node {node}") - system_numa.apply_cpu_affinity(cpus, role) - try: - saved_policy = system_numa.get_memory_policy() - except (OSError, AttributeError) as error: - logger.warning("%s: cannot snapshot memory policy; retaining inherited policy: %s", role, error) - if saved_policy is not None: - memory_changed = system_numa.apply_memory_policy(node, role) - yield - finally: - try: - os.sched_setaffinity(0, saved_affinity) - finally: - if memory_changed: - try: - system_numa.set_memory_policy(saved_policy) - except (OSError, AttributeError) as error: - logger.warning("%s: restoring inherited memory policy failed: %s", role, error) - - -def apply_binding(role: str, node: int | None, cpus: list[int]) -> None: - """Apply a topology-decided binding: CPU affinity plus node memory policy. - - ``cpus`` comes pre-computed from the topology resolution (the worker - never decides a binding itself). Both steps are best-effort; with no - CPUs and no node this is a no-op. - """ - if cpus: - system_numa.apply_cpu_affinity(cpus, role) - if node is not None: - system_numa.apply_memory_policy(node, role) - - -_nvml_state: tuple[Any, list[Any]] | tuple[()] | None = None # None: untried; (): unavailable - - -def _nvml() -> tuple[Any, list[Any]] | None: - """Lazily initialize NVML and return ``(pynvml, pci_bus_ids)``, or ``None``. - - Same in-process NVML session pattern as ``system_metrics``: microsecond - queries instead of an ``nvidia-smi`` subprocess spawn, and still no CUDA - context. NVML maps a device index to its PCI bus id, from which the sysfs - ``numa_node`` file gives the hosting NUMA node. - """ - global _nvml_state - if _nvml_state is None: - try: - import pynvml - - pynvml.nvmlInit() - bus_ids = [ - pynvml.nvmlDeviceGetPciInfo(pynvml.nvmlDeviceGetHandleByIndex(index)).busId - for index in range(pynvml.nvmlDeviceGetCount()) - ] - _nvml_state = (pynvml, bus_ids) - except Exception: # ImportError (pynvml missing) or NVML init failure (no driver/GPU) - _nvml_state = () - return _nvml_state or None - - -def gpu_numa_node(device_index: int) -> int | None: - """NUMA node hosting a CUDA device; None when unknown. - - Primary source: NVML (pynvml, same session pattern as ``system_metrics``) - for the index -> PCI bus id mapping and sysfs ``numa_node`` for the node - lookup — no CUDA context is created, so the pre-spawn parent can call this - freely. Fallback (containers often do not expose the PCI ``numa_node`` - sysfs attribute): the GPU's ``CPU Affinity`` column from - ``nvidia-smi topo -m``, matched against each node's ``cpulist``. A sysfs - value of -1 (unknown / single-node host) maps to None (no binding). - """ - session = _nvml() - if session is None or device_index >= len(session[1]): - return None - try: - node = int((Path("/sys/bus/pci/devices") / session[1][device_index].lower() / "numa_node").read_text().strip()) - if node >= 0: - return node - except (OSError, ValueError): - pass - return _gpu_node_from_topology(device_index) - - -@functools.lru_cache(maxsize=1) -def _gpu_cpu_affinities() -> dict[int, frozenset[int]]: - """GPU index -> CPU affinity set, parsed from ``nvidia-smi topo -m``.""" - try: - proc = subprocess.run(["nvidia-smi", "topo", "-m"], capture_output=True, text=True, timeout=10, check=True) - except (OSError, subprocess.SubprocessError): - return {} - affinities: dict[int, frozenset[int]] = {} - for line in proc.stdout.splitlines(): - cells = line.split() - if not cells or not cells[0].lstrip("\x1b[4m").startswith("GPU"): - continue - try: - index = int(cells[0].lstrip("\x1b[4m").removeprefix("GPU")) - except ValueError: - continue - # The header and data rows do not share a column layout (ANSI escapes, - # padded diagonal cells, differing column counts), so positional - # indexing is unreliable: take the token that IS a multi-cpu list. - for cell in cells[1:]: - if not re.fullmatch(r"[0-9]+(?:-[0-9]+)?(?:,[0-9]+(?:-[0-9]+)?)+|[0-9]+-[0-9]+", cell): - continue - cpus = system_numa.parse_cpulist(cell) - if len(cpus) > 1: - affinities[index] = frozenset(cpus) - break - return affinities - - -def _gpu_node_from_topology(device_index: int) -> int | None: - """Node whose CPU list overlaps the GPU's affinity the most.""" - affinity = _gpu_cpu_affinities().get(device_index) - if not affinity: - return None - best_node, best_overlap = None, 0 - for node in system_numa.available_numa_nodes(): - try: - overlap = len(set(system_numa.numa_node_cpus(node)) & affinity) - except ValueError: - continue - if overlap > best_overlap: - best_node, best_overlap = node, overlap - return best_node diff --git a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/topology.py b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/topology.py index 0775f0b0..b41dd0ce 100644 --- a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/topology.py +++ b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/topology.py @@ -23,7 +23,7 @@ already optimal there. The binding itself (affinity + libnuma memory policy, spawn-time -pre-placement) lives in :mod:`motrix_rl_builtin.fastsac.async_impl.numa`, the ring +pre-placement) lives in :mod:`motrix_rl.numa`, the ring runtime objects in :mod:`motrix_rl_builtin.fastsac.async_impl.transport`; this module only computes the decisions. """ @@ -123,7 +123,7 @@ def _learner_node(device: torch.device, multi_node: bool) -> int | None: if device.type != "cuda": return None index = device.index if device.index is not None else 0 - return numa.gpu_numa_node(index) + return system_numa.gpu_numa_node(index, runtime="hip" if torch.version.hip else "cuda") def resolve_collector_inference_devices( diff --git a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/train.py b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/train.py index cde4afe3..8ca0474d 100644 --- a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/train.py +++ b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/train.py @@ -32,6 +32,7 @@ from motrix_rl.console import TrainingPanelStats, emit_training_panel, open_training_live from motrix_rl.contracts import TrainerBase, TrainerContext from motrix_rl.device import resolve_default_device +from motrix_rl.numa import spawn_placement from motrix_rl.runs import annotate_run from motrix_rl.system_metrics import ( CpuLoadSampler, @@ -43,7 +44,6 @@ ) from motrix_rl.training_logger import TrainingLogger from motrix_rl_builtin.fastsac.agent import FastSacAgent -from motrix_rl_builtin.fastsac.async_impl.numa import spawn_placement from motrix_rl_builtin.fastsac.async_impl.panels import BootPanel from motrix_rl_builtin.fastsac.async_impl.stats import aggregate_collector_stats, nest_timing_path, timing_mean from motrix_rl_builtin.fastsac.async_impl.topology import resolve_trainer_topology diff --git a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/worker.py b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/worker.py index 1774fabb..e818ed62 100644 --- a/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/worker.py +++ b/motrix_rl_builtin/src/motrix_rl_builtin/fastsac/async_impl/worker.py @@ -39,10 +39,10 @@ from motrix_env_motrixsim.torch_env import TorchEnv from motrix_rl import checkpoints from motrix_rl.device import resolve_default_device +from motrix_rl.numa import apply_binding from motrix_rl_builtin.fastsac.agent import FastSacAgent from motrix_rl_builtin.fastsac.async_impl.collector import Collector from motrix_rl_builtin.fastsac.async_impl.learner import CollectorEndpoint, Learner -from motrix_rl_builtin.fastsac.async_impl.numa import apply_binding from motrix_rl_builtin.fastsac.async_impl.stats import timing_mean from motrix_rl_builtin.fastsac.async_impl.transport import ( Control, diff --git a/motrix_rl_builtin/tests/test_fastsac_async_multi.py b/motrix_rl_builtin/tests/test_fastsac_async_multi.py index 22260cc2..12202697 100644 --- a/motrix_rl_builtin/tests/test_fastsac_async_multi.py +++ b/motrix_rl_builtin/tests/test_fastsac_async_multi.py @@ -243,10 +243,8 @@ def test_select_collector_cpus_chunks_and_shares() -> None: def test_topology_pairs_collectors_with_owning_learner(monkeypatch) -> None: - from motrix_rl_builtin.fastsac.async_impl import numa - monkeypatch.setattr(system_numa, "available_numa_nodes", lambda: [0, 1]) - monkeypatch.setattr(numa, "gpu_numa_node", lambda index: {0: 0, 1: 1}.get(index)) + monkeypatch.setattr(system_numa, "gpu_numa_node", lambda index, runtime: {0: 0, 1: 1}.get(index)) monkeypatch.setattr("torch.cuda.device_count", lambda: 2) cuda = lambda n: torch.device("cuda", n) # noqa: E731 opts = SimpleNamespace(transition_ipc="auto", weight_ipc="auto", weight_ipc_min_bytes=0) @@ -286,11 +284,9 @@ def test_topology_chunks_cpus_by_node_local_ordinal(monkeypatch) -> None: collectors past their own node's CPUs (asymmetric bindings, or a "leaves no CPUs" abort once the offset exceeds the node's list). """ - from motrix_rl_builtin.fastsac.async_impl import numa - node_cpus = {0: list(range(8)), 1: list(range(100, 108))} monkeypatch.setattr(system_numa, "available_numa_nodes", lambda: [0, 1]) - monkeypatch.setattr(numa, "gpu_numa_node", lambda index: {0: 0, 1: 1}.get(index)) + monkeypatch.setattr(system_numa, "gpu_numa_node", lambda index, runtime: {0: 0, 1: 1}.get(index)) monkeypatch.setattr(system_numa, "numa_node_cpus", lambda node: node_cpus[node]) monkeypatch.setattr("torch.cuda.device_count", lambda: 2) cuda = lambda n: torch.device("cuda", n) # noqa: E731 @@ -319,8 +315,6 @@ def test_topology_chunks_cpus_by_node_local_ordinal(monkeypatch) -> None: def test_topology_unbound_without_gpu_locality(monkeypatch) -> None: - from motrix_rl_builtin.fastsac.async_impl import numa - cuda = torch.device("cuda", 0) cpu = torch.device("cpu") opts = SimpleNamespace(transition_ipc="auto", weight_ipc="auto", weight_ipc_min_bytes=0) @@ -338,17 +332,31 @@ def test_topology_unbound_without_gpu_locality(monkeypatch) -> None: assert [collector.numa_node for collector in topo.collectors] == [None, None] # multi-node host, unknown GPU locality: no binding anywhere - monkeypatch.setattr(numa, "gpu_numa_node", lambda index: None) + monkeypatch.setattr(system_numa, "gpu_numa_node", lambda index, runtime: None) topo = resolve_trainer_topology(2, 2, 1, None, "cuda", cuda, opts, actor_param_numel=0) assert [learner.numa_node for learner in topo.learners] == [None] assert [collector.numa_node for collector in topo.collectors] == [None, None] # index-less cuda means device 0 - monkeypatch.setattr(numa, "gpu_numa_node", lambda index: {0: 0}.get(index)) + monkeypatch.setattr(system_numa, "gpu_numa_node", lambda index, runtime: {0: 0}.get(index)) topo = resolve_trainer_topology(2, 2, 1, None, "cuda", torch.device("cuda"), opts, actor_param_numel=0) assert [learner.numa_node for learner in topo.learners] == [0] +@pytest.mark.parametrize(("hip_version", "runtime"), [(None, "cuda"), ("6.3", "hip")]) +def test_topology_passes_explicit_gpu_runtime(monkeypatch, hip_version, runtime) -> None: + from motrix_rl_builtin.fastsac.async_impl.topology import _learner_node + + seen = [] + monkeypatch.setattr(torch.version, "hip", hip_version) + monkeypatch.setattr(system_numa, "gpu_numa_node", lambda index, runtime: seen.append((index, runtime)) or 2) + assert _learner_node(torch.device("cuda:1"), multi_node=True) == 2 + assert seen == [(1, runtime)] + assert _learner_node(torch.device("cpu"), multi_node=True) is None + assert _learner_node(torch.device("cuda:1"), multi_node=False) is None + assert seen == [(1, runtime)] + + def test_topology_rejects_bad_counts() -> None: opts = SimpleNamespace(transition_ipc="auto", weight_ipc="auto", weight_ipc_min_bytes=0) with pytest.raises(ValueError, match="divide evenly"): diff --git a/motrix_rl_rslrl/pyproject.toml b/motrix_rl_rslrl/pyproject.toml index 6249ec9e..9ed1f506 100644 --- a/motrix_rl_rslrl/pyproject.toml +++ b/motrix_rl_rslrl/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-rl-rslrl" version = "0.4.0b1" description = "RSLRL plugin for MotrixLab reinforcement learning." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_rl_skrl/pyproject.toml b/motrix_rl_skrl/pyproject.toml index 4dff6446..7adb5e3e 100644 --- a/motrix_rl_skrl/pyproject.toml +++ b/motrix_rl_skrl/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-rl-skrl" version = "0.4.0b1" description = "SKRL plugin for MotrixLab reinforcement learning." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = [ diff --git a/motrix_robots/pyproject.toml b/motrix_robots/pyproject.toml index d0acb104..8614faa9 100644 --- a/motrix_robots/pyproject.toml +++ b/motrix_robots/pyproject.toml @@ -7,7 +7,7 @@ name = "motrix-robots" version = "0.4.0b1" description = "Simulation robot configurations and model assets for MotrixLab." authors = [{ name = "Motphys", email = "developers@motphys.com" }] -requires-python = "==3.10.*" +requires-python = ">=3.10,<3.12" readme = "README.md" license = "Apache-2.0" dependencies = ["motrix-env-core==0.4.0b1"] diff --git a/pyproject.toml b/pyproject.toml index 00fed928..c91d1868 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -71,7 +71,8 @@ dev = [ test = [ { include-group = "dev" }, "hydra-core>=1.3,<1.4", - "motrix-deploy-unitree==0.4.0b1", + # Keep the existing 3.10 hardware test environment; 3.11 tests omit Unitree. + "motrix-deploy-unitree==0.4.0b1; python_version < '3.11'", ] [tool.uv.workspace] @@ -93,6 +94,8 @@ members = [ ] [tool.uv] +# Resolve macOS separately so GPU-extra forks retain the PyPI Torch fallback. +environments = ["sys_platform == 'darwin'", "sys_platform != 'darwin'"] package = false prerelease = "if-necessary-or-explicit" # `cuda` and `rocm` resolve torch from different indexes and can never be diff --git a/uv.lock b/uv.lock index 70b37250..e0fcc9fe 100644 --- a/uv.lock +++ b/uv.lock @@ -1,15 +1,25 @@ version = 1 revision = 3 -requires-python = "==3.10.*" +requires-python = ">=3.10, <3.12" resolution-markers = [ - "platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm'", - "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform != 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')", - "platform_machine == 'aarch64' and 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TensorBoard。面板/系统采样由未绑定的父进程执行，CPU 视图是全机（所有 core）而非某个 node；多 collector 时额外输出 `async/policy_lag_collector{i}`、`async/ring_fill_collector{i}`，单 collector 的标量键保持不变。 - **resume 语义**：多 collector 下 resume 与单 collector 相同——learner 从 checkpoint 恢复网络与优化器，所有 collector 重新 reset env 后从 checkpointed iteration 继续采样；ring / 在途 transition 不跨进程恢复。 -**NUMA 绑定**（拓扑决策在 `async_impl/topology.py`，绑定原语在 `async_impl/numa.py`，等价 `numactl --cpunodebind= --membind=`，best-effort，**自动分配**）： +**NUMA 绑定**（FastSAC 拓扑决策在 `async_impl/topology.py`，collector CPU 分片策略在 `async_impl/numa.py`；通用节点发现、GPU locality 查询、spawn 预绑定与绑定原语统一在 `motrix_rl.numa`，等价 `numactl --cpunodebind= --membind=`，best-effort，**自动分配**）： -- **分配策略**（`resolve_worker_topology`）：learner 绑到其 GPU 的 PCIe 本地 node（经 NVML/pynvml 查 PCI bus id + sysfs `numa_node`，与 system_metrics 同款进程内会话，不创建 CUDA context）；每个 collector 跟随其归属 learner 的 node——collector/learner 对绝不跨 NUMA 边界，transition ring、pinned staging 与权重快照全部 node 本地。单 node 宿主机 / CPU learner / GPU 拓扑未知时不绑定（OS 默认放置）。实测依据：双路双卡机器上整树 node 绑定较不绑 +58%（first-touch 内存页跨 node 是主要开销），collector 与 learner 同 node 的局部性收益远大于带宽减半的损失。 +- **GPU 查询分派**：调用方按训练 runtime 显式传入 `cuda` 或 `hip`，不根据管理库是否可用猜测硬件。CUDA 经 NVML/pynvml 查 PCI bus id，再读 sysfs `numa_node`，必要时以 `nvidia-smi topo -m` 的 CPU affinity 兜底；HIP 在独立子进程加载 Torch/HIP runtime，按继承的可见性映射查询逻辑设备 PCI 地址，再读同一 sysfs 属性，不在父进程初始化 HIP，也不回退到 NVIDIA 的物理设备序号。查询失败返回未知。CUDA 路径沿用 NVML 物理设备编号，尚不解析 `CUDA_VISIBLE_DEVICES` 重映射；AMD SMI 的物理设备枚举也不能直接视为 HIP 可见逻辑序号。 +- **分配策略**（`resolve_worker_topology`）：learner 绑到其 GPU 的 PCIe 本地 node（由上述通用 GPU locality 查询提供）；每个 collector 跟随其归属 learner 的 node——collector/learner 对绝不跨 NUMA 边界，transition ring、pinned staging 与权重快照全部 node 本地。单 node 宿主机 / CPU learner / GPU 拓扑未知时不绑定（OS 默认放置）。实测依据：双路双卡机器上整树 node 绑定较不绑 +58%（first-touch 内存页跨 node 是主要开销），collector 与 learner 同 node 的局部性收益远大于带宽减半的损失。 - CPU affinity：`os.sched_setaffinity` 绑到 node 的 CPU 列表（sysfs `node{X}/cpulist`）；配置 `cpus_per_collector` 时进一步切成不重叠的连续分片，避免 collector 之间抢核。 -- 内存策略：libnuma 的 `set_membind`（ctypes 加载，numactl 同款调用）把该进程**后续**分配绑到本地 node——因此绑定发生在 worker 进程的第一行（父进程在 spawn 前预绑，保证 import 期分配也 node 本地），先于 env / staging / pinned buffer 的任何分配。libnuma 不可用或 node 未知时告警并沿用 OS 默认放置，单 NUMA 机器与容器行为不变。 +- 内存策略：经 libnuma 调用并检查返回值的 `set_mempolicy(MPOL_BIND)`（ctypes 加载）把该进程**后续**分配绑到本地 node——因此绑定发生在 worker 进程的第一行（父进程在 spawn 前预绑，保证 import 期分配也 node 本地），先于 env / staging / pinned buffer 的任何分配。libnuma 不可用或 node 未知时告警并沿用 OS 默认放置，单 NUMA 机器与容器行为不变。 **collector 推理设备**：多 collector 改变 CPU/CUDA 推理的权衡（N 个 CUDA collector 与 learner 产生 N 倍 H2D/D2H burst 争用）。首版保持默认 `collector_inference_device="cuda"`（与单 collector 一致），以实测吞吐决定多 collector 场景默认值是否调整。