306 lines
10 KiB
Bash
Executable File
306 lines
10 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -euo pipefail
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# =============================
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# Config（可用环境变量覆盖）
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# =============================
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REPO_URL="${REPO_URL:-https://github.com/microsoft/DeepSpeed.git}"
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REPO_BRANCH="${REPO_BRANCH:-v0.17.4}"
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REPO_DIR="${REPO_DIR:-DeepSpeed}" # 将在“当前目录/REPO_DIR”下操作
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CUDA_VERSION_PIN="${CUDA_VERSION_PIN:-11.8}" # 期望 Torch 的 CUDA tag 和 nvcc 都是 11.8
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ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-}" # 为空则自动探测
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PY_MIN="${PY_MIN:-3.8}" # 最低 Python 版本
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AIO_THREADS="${AIO_THREADS:-1}"
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# =============================
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# Utils
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# =============================
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log(){ printf "\n\033[1;36m==> %s\033[0m\n" "$*"; }
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die(){ echo -e "\n\033[1;31m[ERROR]\033[0m $*" ; exit 1; }
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need_bin(){
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command -v "$1" >/dev/null 2>&1 || die "缺少可执行程序: $1"
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}
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# =============================
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# 0. 前置检查
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# =============================
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need_bin python
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need_bin pip
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need_bin git
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if command -v mamba >/dev/null 2>&1; then
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CONDA_BIN="mamba"
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elif command -v conda >/dev/null 2>&1; then
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CONDA_BIN="conda"
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else
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die "没有找到 mamba/conda，无法安装 CUDA 工具链和构建依赖。"
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fi
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# Python/Torch 基线
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log "Python/Torch baseline"
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python - <<'PY'
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import sys, torch
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print("Python:", sys.version.split()[0])
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print("Torch:", getattr(torch, "__version__", "n/a"))
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print("Torch CUDA tag:", getattr(torch.version, "cuda", "n/a"))
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print("CUDA available:", torch.cuda.is_available())
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PY
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# 检查 Python 版本
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PY_CUR=$(python - <<'PY'
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import sys
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print(".".join(map(str, sys.version_info[:2])))
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PY
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)
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python - <<PY
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from packaging.version import Version
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assert Version("${PY_CUR}") >= Version("${PY_MIN}"), "Python 过旧: ${PY_CUR} < ${PY_MIN}"
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print("Python OK")
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PY
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# 如果安装的是 torch+cu118 以外的轮子，强制提醒
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TORCH_CUDA_TAG=$(python - <<'PY'
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import torch
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print(getattr(torch.version, "cuda", ""))
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PY
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)
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if [[ -n "${CUDA_VERSION_PIN}" && -n "${TORCH_CUDA_TAG}" && "${TORCH_CUDA_TAG}" != "${CUDA_VERSION_PIN}" ]]; then
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die "当前 PyTorch CUDA tag=${TORCH_CUDA_TAG}，与期望 ${CUDA_VERSION_PIN} 不一致。换成匹配的 torch 或修改 CUDA_VERSION_PIN。"
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fi
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# =============================
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# 1. 安装 CUDA 11.8 工具链（进 conda 环境）
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# =============================
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log "Installing CUDA ${CUDA_VERSION_PIN} toolchain into current conda env..."
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set +e
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${CONDA_BIN} install -y -c "nvidia/label/cuda-${CUDA_VERSION_PIN}.0" cuda-toolkit
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RC=$?
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set -e
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if [[ $RC -ne 0 ]]; then
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log "nvidia channel 失败，回退 conda-forge 组件安装..."
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${CONDA_BIN} install -y -c conda-forge \
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cuda-nvcc="${CUDA_VERSION_PIN}" cuda-version="${CUDA_VERSION_PIN}" \
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cuda-cudart-dev="${CUDA_VERSION_PIN}" libnvjitlink="${CUDA_VERSION_PIN}" \
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libcublas-dev="${CUDA_VERSION_PIN}" libcufft-dev="${CUDA_VERSION_PIN}" \
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libcurand-dev="${CUDA_VERSION_PIN}" libcusolver-dev="${CUDA_VERSION_PIN}" libcusparse-dev="${CUDA_VERSION_PIN}" \
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cuda-profiler-api="${CUDA_VERSION_PIN}"
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fi
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# =============================
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# 2. 设置 CUDA 环境变量（指向当前 conda 前缀）
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# =============================
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if [[ -z "${CONDA_PREFIX:-}" ]]; then
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die "CONDA_PREFIX 未设置。请在目标 conda/mamba 环境里执行本脚本。"
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fi
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export CUDA_HOME="${CONDA_PREFIX}"
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export CUDA_PATH="${CUDA_HOME}"
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export TORCH_CUDA_HOME="${CUDA_HOME}"
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export CUDACXX="${CUDA_HOME}/bin/nvcc"
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export PATH="${CUDA_HOME}/bin:${PATH}"
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export LD_LIBRARY_PATH="${CUDA_HOME}/lib:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH:-}"
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hash -r
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log "nvcc version check"
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need_bin nvcc
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nvcc_out=$(nvcc --version | tail -n1 || true)
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echo "$nvcc_out"
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echo "$nvcc_out" | grep -q "${CUDA_VERSION_PIN}" || die "nvcc 版本与期望 ${CUDA_VERSION_PIN} 不匹配。"
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# =============================
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# 3. 选择/推断 TORCH_CUDA_ARCH_LIST
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# =============================
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if [[ -z "${ARCH_LIST}" ]]; then
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# 尝试用 torch 探测
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ARCH_LIST=$(python - <<'PY'
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import torch
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if torch.cuda.is_available():
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cc=set()
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for i in range(torch.cuda.device_count()):
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cc.add("{}.{}".format(*torch.cuda.get_device_capability(i)))
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print(";".join(sorted(cc)))
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else:
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print("")
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PY
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)
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# 无法探测就默认 8.6（3090）
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[[ -z "${ARCH_LIST}" ]] && ARCH_LIST="8.6"
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fi
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export TORCH_CUDA_ARCH_LIST="${ARCH_LIST}"
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log "TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}"
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# =============================
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# 4. 构建依赖
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# =============================
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log "Build deps"
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${CONDA_BIN} install -y -c conda-forge cmake ninja pybind11 libaio git
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pip install -U pip setuptools wheel
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# =============================
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# 5. 获取/刷新 DeepSpeed 源码（在当前目录）
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# =============================
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log "Clone/refresh DeepSpeed repo"
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WORKDIR="$(pwd)"
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REPO_PATH="${WORKDIR}/${REPO_DIR}"
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if git -C "${REPO_PATH}" rev-parse --is-inside-work-tree >/dev/null 2>&1; then
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# 已存在且是 git 仓库：校验 remote 和分支，强制到目标分支
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cur_remote=$(git -C "${REPO_PATH}" remote get-url origin || echo "")
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if [[ "${cur_remote}" != "${REPO_URL}" ]]; then
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log "remote 不匹配，重建仓库"
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rm -rf "${REPO_PATH}"
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git clone --branch "${REPO_BRANCH}" --depth 1 --recurse-submodules --shallow-submodules "${REPO_URL}" "${REPO_PATH}"
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else
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log "更新已有仓库到 ${REPO_BRANCH}"
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git -C "${REPO_PATH}" fetch --depth 1 origin "${REPO_BRANCH}"
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git -C "${REPO_PATH}" checkout -f "${REPO_BRANCH}"
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git -C "${REPO_PATH}" reset --hard "origin/${REPO_BRANCH}"
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git -C "${REPO_PATH}" clean -fdx
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# 同步并更新子模块到正确提交
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git -C "${REPO_PATH}" submodule sync --recursive
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git -C "${REPO_PATH}" submodule update --init --recursive --depth 1
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fi
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else
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# 不存在或不是仓库：重拉（连带子模块）
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rm -rf "${REPO_PATH}"
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git clone --branch "${REPO_BRANCH}" --depth 1 --recurse-submodules --shallow-submodules "${REPO_URL}" "${REPO_PATH}"
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fi
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# 再保险：确保子模块已到位
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git -C "${REPO_PATH}" submodule sync --recursive
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git -C "${REPO_PATH}" submodule update --init --recursive --depth 1
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# 安装 ds-kernels（DeepSpeed Inference CUTLASS 依赖）
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DK_DIR=""
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# 从 .gitmodules 找子模块路径（不同版本命名可能是 ds-kernels 或 dskernels）
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if [[ -f "${REPO_PATH}/.gitmodules" ]]; then
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DK_DIR=$(git -C "${REPO_PATH}" config --file .gitmodules --get-regexp path | awk '/ds[-_]?kernels/{print $2; exit}' || true)
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fi
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if [[ -n "${DK_DIR}" && -f "${REPO_PATH}/${DK_DIR}/setup.py" ]]; then
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log "Install ds-kernels from submodule: ${DK_DIR}"
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pip install -v "${REPO_PATH}/${DK_DIR}"
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else
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log "Submodule ds-kernels 不存在或未包含构建脚本，直接从仓库安装"
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pip install -v "git+https://github.com/microsoft/ds-kernels.git"
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fi
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# 基本健检
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test -f "${REPO_PATH}/pyproject.toml" -o -f "${REPO_PATH}/setup.py" || die "DeepSpeed 源码目录缺少构建文件。"
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# =============================
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# 6. 编译安装 DeepSpeed
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# =============================
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log "Build & install DeepSpeed"
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cd "${REPO_PATH}"
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pip uninstall -y deepspeed >/dev/null 2>&1 || true
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rm -rf build
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# —— 编译前置（强烈建议）——
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python - <<'PY'
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import sys, subprocess
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subprocess.check_call([sys.executable, "-m", "pip", "install", "-U", "pip", "setuptools", "wheel", "ninja"])
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PY
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# 并行编译提速（按 CPU 核心数调）
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export MAX_JOBS=${MAX_JOBS:-$(nproc)}
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# —— DeepSpeed 功能开关 ——
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# 编译核心 CUDA/C++ 扩展（必须）
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export DS_BUILD_OPS=1
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# 训练常用加速内核
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export DS_BUILD_TRANSFORMER=1
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export DS_BUILD_SPARSE_ATTN=1
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export DS_BUILD_FLASH_ATTN=1
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# 常用优化器（强烈建议开）
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export DS_BUILD_FUSED_ADAM=1
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export DS_BUILD_CPU_ADAM=1
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# 可选：异步 IO（Linux 才生效；Windows 不支持 AIO/GDS）
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export DS_BUILD_AIO=1
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export DS_BUILD_INFERENCE=0
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export DS_BUILD_CUTLASS=0
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# 安装（带详细日志）
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pip install -v .
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# =============================
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# 7. 验证安装
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# =============================
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log "Verify DeepSpeed installation"
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python -m deepspeed.env_report || die "deepspeed.env_report 失败"
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python - <<'PY'
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import deepspeed, torch
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from deepspeed.ops.adam import FusedAdam
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print("DeepSpeed:", deepspeed.__version__)
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print("Torch:", torch.__version__, "CUDA tag:", torch.version.cuda)
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print("GPU count:", torch.cuda.device_count())
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if torch.cuda.is_available():
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print("GPU0:", torch.cuda.get_device_name(0))
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print("FusedAdam import OK")
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PY
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# =============================
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# 8. 最小单卡训练验证（HF Trainer + ZeRO-2）
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# =============================
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log "Prepare and run minimal single-GPU training"
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cd "${WORKDIR}"
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cat > ds_config_stage2_single.json <<JSON
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{
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"train_batch_size": 8,
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"train_micro_batch_size_per_gpu": 1,
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"gradient_accumulation_steps": 8,
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"zero_optimization": { "stage": 2, "overlap_comm": true, "contiguous_gradients": true },
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"fp16": { "enabled": true },
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"aio": {
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"block_size": 1048576,
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"queue_depth": 16,
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"thread_count": ${AIO_THREADS},
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"single_submit": false,
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"overlap_events": true,
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"verbose": false
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},
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"gradient_clipping": 1.0,
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"steps_per_print": 1000,
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"wall_clock_breakdown": false
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}
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JSON
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cat > train_single_gpu_min.py <<'PY'
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from datasets import Dataset
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from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
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import torch
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model_name = "sshleifer/tiny-gpt2"
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tok = AutoTokenizer.from_pretrained(model_name)
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if tok.pad_token is None: tok.pad_token = tok.eos_token
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texts = ["hello world", "deepspeed single gpu", "trainer test", "fast check"] * 200
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ds = Dataset.from_dict({"text": texts}).map(lambda e: tok(e["text"], truncation=True, max_length=128))
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collator = DataCollatorForLanguageModeling(tok, mlm=False)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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args = TrainingArguments(
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output_dir="out-ds-single",
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per_device_train_batch_size=1,
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gradient_accumulation_steps=8,
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learning_rate=5e-4,
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num_train_epochs=1,
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logging_steps=10,
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save_steps=0,
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fp16=True,
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deepspeed="ds_config_stage2_single.json"
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)
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Trainer(model=model, args=args, tokenizer=tok, train_dataset=ds, data_collator=collator).train()
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print("OK: single-GPU training finished.")
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PY
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CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-0} python train_single_gpu_min.py
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log "DONE: DeepSpeed built with CUDA ${CUDA_VERSION_PIN} and single-GPU test passed"
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