# ============================================================================= # MinerU on ROCm 7.2.1 Docker Image # 原生 Linux + Ubuntu 24.04 + ROCm 7.2.1 + PyTorch 2.11.0 + vllm + MinerU 3.2.0 # # 构建前请根据你的 GPU 修改 ARCH 参数（默认 gfx1201 = RX 9070） # ============================================================================= FROM ubuntu:24.04 # -- 构建参数 --------------------------------------------------------------- # 改成你的 gfx 代号：gfx1201(RX 9070) gfx1200(RX 9060) gfx1100(RX 7900) gfx1101(RX 7800/7700) gfx1030(RX 6900/6800) ARG ARCH=gfx1201 ARG PYTHON_VER=3.12 ARG VENV=/opt/mineru_venv ARG TORCH_INDEX=https://download.pytorch.org/whl/rocm7.2 # -- 环境变量 --------------------------------------------------------------- ENV DEBIAN_FRONTEND=noninteractive \ PATH=/opt/rocm/bin:/opt/rocm/llvm/bin:${VENV}/bin:${PATH} \ PYTORCH_ROCM_ARCH=${ARCH} \ FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE \ MINERU_MODEL_SOURCE=huggingface \ TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 \ HSA_ENABLE_SDMA=1 \ VLLM_TARGET_DEVICE=rocm WORKDIR /opt # =========================================================================== # 阶段 1：安装 ROCm 7.2.1 # =========================================================================== RUN apt-get update && apt-get install -y --no-install-recommends \ wget curl ca-certificates gnupg software-properties-common && \ # 添加 AMD ROCm 仓库 wget -q https://repo.radeon.com/rocm/rocm.gpg.key -O - | \ gpg --dearmor | tee /etc/apt/trusted.gpg.d/rocm.gpg > /dev/null && \ echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/7.2.1 noble main' \ > /etc/apt/sources.list.d/rocm.list && \ apt-get update && \ # 安装 ROCm 基础组件 apt-get install -y --no-install-recommends \ rocminfo hip-dev miopen-hip && \ # 修复 rocminfo / rocm-device-libs 版本（替换 Ubuntu 自带旧版） apt-get install -y --allow-downgrades \ rocminfo=1.0.0.70201-38~24.04 \ rocm-device-libs=1.0.0.70201-38~24.04 && \ # 清理 apt-get clean && rm -rf /var/lib/apt/lists/* # =========================================================================== # 阶段 2：ROCm 头文件补丁（LLVM 22 兼容性修复） # 这些是 ROCm 7.2.1 在 24.04 上的已知问题，每次 apt 升级 ROCm 后需重新应用 # =========================================================================== RUN set -ex && \ # 补丁 1: hipcc/clang 符号链接（hipcc.pl 硬编码 clang-17，实际是 clang-22） ln -sf /usr/bin/hipvars.pm /usr/share/perl5/hipvars.pm && \ ln -sf /usr/bin/hipcc.pl /opt/rocm/bin/hipcc && \ ln -sf /opt/rocm/llvm/bin/clang-22 /opt/rocm/llvm/bin/clang-17 && \ ln -sf /opt/rocm/llvm/bin/clang++ /opt/rocm/llvm/bin/clang++-17 && \ # 补丁 2: __hip_internal::conditional → std::conditional find /opt/rocm/include/hip -name "*.h" \ -exec sed -i 's/__hip_internal::conditional/std::conditional/g' {} + && \ # 补丁 3: warpSize 常量（__AMDGCN_WAVEFRONT_SIZE 在 LLVM 22 未定义） find /opt/rocm/include/hip -name "amd_warp_functions.h" \ -exec sed -i 's/static constexpr int warpSize = __AMDGCN_WAVEFRONT_SIZE;/constexpr int warpSize = 32;/g' {} + && \ # 补丁 4: __activemask() → __builtin_amdgcn_read_exec() # 注意：只改 amd_warp_sync_functions.h，不要动 amd_warp_functions.h（那是定义本身） sed -i 's/__activemask()/__builtin_amdgcn_read_exec()/g' \ /opt/rocm/include/hip/amd_detail/amd_warp_sync_functions.h && \ echo "ROCm 7.2.1 header patches applied." # =========================================================================== # 阶段 3：系统依赖 + Python 3.12 # =========================================================================== RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential git ninja-build pkg-config \ python${PYTHON_VER} python${PYTHON_VER}-venv python${PYTHON_VER}-dev \ libnuma-dev libdrm2 libhwloc-dev libgl1 \ # vllm 运行时依赖 libgomp1 libopenblas0 && \ apt-get clean && rm -rf /var/lib/apt/lists/* # =========================================================================== # 阶段 4：CMake 4.0（vllm 要求 ≥ 4.0，Ubuntu 24.04 自带 3.28 不够） # =========================================================================== RUN cd /tmp && \ wget -q https://github.com/Kitware/CMake/releases/download/v4.0.0/cmake-4.0.0-linux-x86_64.tar.gz && \ tar -xzf cmake-4.0.0-linux-x86_64.tar.gz && \ cp -r cmake-4.0.0-linux-x86_64/bin/* /usr/local/bin/ && \ cp -r cmake-4.0.0-linux-x86_64/share/* /usr/local/share/ && \ rm -rf cmake-4.0.0-linux-x86_64* && \ cmake --version # =========================================================================== # 阶段 5：Python 虚拟环境 + PyTorch ROCm # =========================================================================== RUN python${PYTHON_VER} -m venv ${VENV} && \ ${VENV}/bin/pip install --no-cache-dir -U pip setuptools wheel && \ # 安装 PyTorch ROCm 版（锁定 2.11，≥ 2.12 在部分环境下有 rocprofiler 问题） ${VENV}/bin/pip install --no-cache-dir --pre \ torch==2.11.0+rocm7.2 \ torchvision \ pytorch-triton-rocm \ --index-url ${TORCH_INDEX} && \ # 验证 ${VENV}/bin/python -c "import torch; print('PyTorch:', torch.__version__); print('ROCm:', torch.version.hip); assert torch.version.hip is not None" # =========================================================================== # 阶段 6：ROCm 开发包（vllm 编译必需） # =========================================================================== RUN apt-get update && apt-get install -y --no-install-recommends \ hipblas-dev hiprand-dev hipsparse-dev hipsparselt-dev \ hipsolver-dev hipcub-dev rocprim-dev rocthrust-dev \ rocblas-dev rocrand-dev hipfft-dev hipblaslt && \ apt-get clean && rm -rf /var/lib/apt/lists/* # =========================================================================== # 阶段 7：amd-aiter + flash_attn # =========================================================================== RUN set -ex && \ # aiter（AMD 优化的 attention 算子） cd /opt && git clone --recursive --depth 1 https://github.com/ROCm/aiter.git && \ ${VENV}/bin/pip install --no-cache-dir -e /opt/aiter && \ # flash_attn（Triton AMD 后端，锁定已验证的 commit） cd /opt && git clone --recursive https://github.com/Dao-AILab/flash-attention.git && \ cd flash-attention && git checkout bba578d43974c1d3ba157ab597124dd0fe2ccdb4 && \ ${VENV}/bin/pip install --no-cache-dir --no-build-isolation -e /opt/flash-attention && \ # 验证 PyTorch 没被覆盖 ${VENV}/bin/python -c "import torch; v=torch.__version__; assert 'rocm' in v, f'PyTorch overwritten: {v}'; print('PyTorch OK:', v)" # =========================================================================== # 阶段 8：编译 vllm # =========================================================================== RUN set -ex && \ # setuptools 升级（PEP 639 兼容） ${VENV}/bin/pip install --no-cache-dir -U \ "setuptools>=77.0.3" setuptools_scm setuptools_rust wheel && \ # 克隆 vllm main cd /opt && git clone --depth 1 https://github.com/vllm-project/vllm.git && \ # 补丁 5：注释掉 vllm mamba 模块的 operator+ 定义（ROCm 7.2 头文件已自带） cd /opt/vllm && \ sed -i '109,121s/^/\/\/ /' csrc/mamba/mamba_ssm/selective_scan.h && \ echo "vllm mamba operator+ patch applied." && \ # cmake 配置 mkdir -p /opt/vllm_build && \ cmake -S /opt/vllm -B /opt/vllm_build -G Ninja \ -DCMAKE_BUILD_TYPE=RelWithDebInfo \ -DVLLM_TARGET_DEVICE=rocm \ -DVLLM_PYTHON_EXECUTABLE=${VENV}/bin/python \ -DHIP_ROOT_DIR=/opt/rocm \ -DROCM_PATH=/opt/rocm \ -DCMAKE_HIP_ARCHITECTURES=${ARCH} \ -DCMAKE_PREFIX_PATH="${VENV}/lib/python${PYTHON_VER}/site-packages/torch/share/cmake" && \ # ninja 编译（-j4 防 OOM，内存 > 32GB 可调高） cd /opt/vllm_build && ninja -j4 && \ # 安装 .so 到 vllm 源码目录 cp /opt/vllm_build/*.abi3.so /opt/vllm/vllm/ && \ # pip install vllm（让 pip 解析运行时依赖：xgrammar, compressed_tensors 等） cd /opt/vllm && ${VENV}/bin/pip install --no-cache-dir -e . --no-build-isolation && \ # 验证 PyTorch 没被 vllm 依赖覆盖 ${VENV}/bin/python -c "import torch; v=torch.__version__; assert 'rocm' in v, f'PyTorch overwritten by vllm deps: {v}'; print('PyTorch OK:', v)" && \ # 清理可能的 CUDA triton 残余 ${VENV}/bin/pip uninstall -y triton triton-rocm 2>/dev/null; \ ${VENV}/bin/pip install --no-cache-dir --force-reinstall \ torch==2.11.0+rocm7.2 torchvision pytorch-triton-rocm \ --index-url ${TORCH_INDEX} && \ # 最终验证 vllm 平台检测 ${VENV}/bin/python -c " from vllm.platforms import current_platform print('Platform:', type(current_platform).__name__) print('is_rocm:', current_platform.is_rocm()) print('device_type:', current_platform.device_type) assert current_platform.is_rocm(), 'vllm ROCm detection failed!' print('vllm OK') " && \ # 清理构建目录（减小镜像体积，约 3-5GB） rm -rf /opt/vllm_build # =========================================================================== # 阶段 9：安装 MinerU + RDNA 适配补丁 # =========================================================================== RUN set -ex && \ ${VENV}/bin/pip install --no-cache-dir 'mineru[core]' && \ # 验证 PyTorch 没被覆盖 ${VENV}/bin/python -c "import torch; v=torch.__version__; assert 'rocm' in v, f'PyTorch overwritten: {v}'; print('PyTorch OK:', v)" && \ # 定位 mineru infer 目录 MINERU_INFER_DIR=$(${VENV}/bin/python -c "import mineru.model.utils.tools.infer; import os; print(os.path.dirname(mineru.model.utils.tools.infer.__file__))") && \ echo "MinerU infer dir: ${MINERU_INFER_DIR}" && \ # --- Patch A: predict_rec.py imgW 对齐到 32 --- ${VENV}/bin/python -c " import re f = '${MINERU_INFER_DIR}/predict_rec.py' c = open(f).read() # 在 imgW = max(min(... 之后插入 imgW = math.ceil(imgW / 32) * 32 old = '(imgW = max\(min\(imgW, self\.limited_max_width\), self\.limited_min_width\)\n)' new = r'\1 imgW = math.ceil(imgW / 32) * 32\n' c2 = re.sub(old, new, c) if c2 == c: # 尝试找已经插入过的情况 if 'math.ceil(imgW / 32)' not in c: raise RuntimeError('Patch A: cannot find imgW line in predict_rec.py') else: print('Patch A: already applied') else: open(f, 'w').write(c2) print('Patch A: imgW 32-align inserted') " && \ # --- Patch B: predict_rec.py 批次填充 --- ${VENV}/bin/python -c " f = '${MINERU_INFER_DIR}/predict_rec.py' c = open(f).read() # 在 norm_img_batch = np.concatenate(norm_img_batch) 前插入 padding 逻辑 old = '( {8}norm_img_batch = np\.concatenate\(norm_img_batch\))' new = ''' actual_batch_size = len(norm_img_batch) if actual_batch_size < batch_num: pad_size = batch_num - actual_batch_size pad_img = np.zeros_like(norm_img_batch[0]) for _ in range(pad_size): norm_img_batch.append(pad_img) \\1''' import re c2 = re.sub(old, new, c) if c2 == c: if 'actual_batch_size' not in c: raise RuntimeError('Patch B: cannot find norm_img_batch concatenation') else: print('Patch B: already applied') else: open(f, 'w').write(c2) print('Patch B: batch padding inserted') # 修改 range(len(rec_result)) → range(actual_batch_size) c3 = open(f).read() c4 = re.sub(r'for rno in range\(len\(rec_result\)\):', ' for rno in range(actual_batch_size):', c3) open(f, 'w').write(c4) " && \ # --- Patch C: predict_det.py contiguous 检查 --- ${VENV}/bin/python -c " f = '${MINERU_INFER_DIR}/predict_det.py' c = open(f).read() old = '( {8}inp = inp\.to\(self\.device\)\n)' new = r'\1 if not inp.is_contiguous():\n inp = inp.contiguous()\n' import re c2 = re.sub(old, new, c) if c2 == c: if 'is_contiguous' not in c: raise RuntimeError('Patch C: cannot find inp.to(device) line') else: print('Patch C: already applied') else: open(f, 'w').write(c2) print('Patch C: contiguous check inserted') " && \ echo "All MinerU RDNA patches applied." # =========================================================================== # 阶段 10：MIOpen 预热脚本 # =========================================================================== COPY scripts/cache_warmer.py /opt/cache_warmer.py # =========================================================================== # 阶段 11：入口与最终设置 # =========================================================================== RUN echo 'source /opt/mineru_venv/bin/activate' >> /etc/bash.bashrc && \ echo "MinerU Docker image built successfully." && \ ${VENV}/bin/python -c " import torch, vllm, mineru print('='*50) print('MinerU ROCm Docker Image Ready') print(f' PyTorch : {torch.__version__}') print(f' ROCm : {torch.version.hip}') print(f' vllm : {vllm.__version__}') print(f' MinerU : {mineru.__version__}') print(f' Arch : ${ARCH}') print('='*50) " # 容器入口：默认 bash，用户可 override ENTRYPOINT ["/bin/bash", "-c"] CMD ["bash"]