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+11
-93
@@ -165,17 +165,16 @@ RUN set -ex && \
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torch==2.11.0+rocm7.2 torchvision pytorch-triton-rocm \
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--index-url ${TORCH_INDEX} && \
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# 最终验证 vllm 平台检测
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${VENV}/bin/python -c "
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from vllm.platforms import current_platform
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print('Platform:', type(current_platform).__name__)
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print('is_rocm:', current_platform.is_rocm())
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print('device_type:', current_platform.device_type)
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assert current_platform.is_rocm(), 'vllm ROCm detection failed!'
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print('vllm OK')
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" && \
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${VENV}/bin/python -c "from vllm.platforms import current_platform; print('Platform:', type(current_platform).__name__); print('is_rocm:', current_platform.is_rocm()); print('device_type:', current_platform.device_type); assert current_platform.is_rocm(), 'vllm ROCm detection failed!'; print('vllm OK')" && \
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# 清理构建目录（减小镜像体积，约 3-5GB）
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rm -rf /opt/vllm_build
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# ===========================================================================
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# 阶段 8.5：复制辅助脚本（必须在 MinerU 安装前就位）
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# ===========================================================================
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COPY scripts/apply_mineru_patches.py /opt/apply_mineru_patches.py
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COPY scripts/cache_warmer.py /opt/cache_warmer.py
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# ===========================================================================
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# 阶段 9：安装 MinerU + RDNA 适配补丁
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# ===========================================================================
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@@ -183,96 +182,15 @@ RUN set -ex && \
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${VENV}/bin/pip install --no-cache-dir 'mineru[core]' && \
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# 验证 PyTorch 没被覆盖
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${VENV}/bin/python -c "import torch; v=torch.__version__; assert 'rocm' in v, f'PyTorch overwritten: {v}'; print('PyTorch OK:', v)" && \
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# 定位 mineru infer 目录
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MINERU_INFER_DIR=$(${VENV}/bin/python -c "import mineru.model.utils.tools.infer; import os; print(os.path.dirname(mineru.model.utils.tools.infer.__file__))") && \
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echo "MinerU infer dir: ${MINERU_INFER_DIR}" && \
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# --- Patch A: predict_rec.py imgW 对齐到 32 ---
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${VENV}/bin/python -c "
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import re
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f = '${MINERU_INFER_DIR}/predict_rec.py'
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c = open(f).read()
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# 在 imgW = max(min(... 之后插入 imgW = math.ceil(imgW / 32) * 32
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old = '(imgW = max\(min\(imgW, self\.limited_max_width\), self\.limited_min_width\)\n)'
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new = r'\1 imgW = math.ceil(imgW / 32) * 32\n'
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c2 = re.sub(old, new, c)
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if c2 == c:
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# 尝试找已经插入过的情况
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if 'math.ceil(imgW / 32)' not in c:
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raise RuntimeError('Patch A: cannot find imgW line in predict_rec.py')
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else:
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print('Patch A: already applied')
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else:
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open(f, 'w').write(c2)
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print('Patch A: imgW 32-align inserted')
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" && \
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# --- Patch B: predict_rec.py 批次填充 ---
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${VENV}/bin/python -c "
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f = '${MINERU_INFER_DIR}/predict_rec.py'
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c = open(f).read()
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# 在 norm_img_batch = np.concatenate(norm_img_batch) 前插入 padding 逻辑
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old = '( {8}norm_img_batch = np\.concatenate\(norm_img_batch\))'
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new = ''' actual_batch_size = len(norm_img_batch)
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if actual_batch_size < batch_num:
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pad_size = batch_num - actual_batch_size
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pad_img = np.zeros_like(norm_img_batch[0])
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for _ in range(pad_size):
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norm_img_batch.append(pad_img)
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\\1'''
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import re
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c2 = re.sub(old, new, c)
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if c2 == c:
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if 'actual_batch_size' not in c:
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raise RuntimeError('Patch B: cannot find norm_img_batch concatenation')
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else:
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print('Patch B: already applied')
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else:
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open(f, 'w').write(c2)
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print('Patch B: batch padding inserted')
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# 修改 range(len(rec_result)) → range(actual_batch_size)
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c3 = open(f).read()
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c4 = re.sub(r'for rno in range\(len\(rec_result\)\):', ' for rno in range(actual_batch_size):', c3)
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open(f, 'w').write(c4)
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" && \
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# --- Patch C: predict_det.py contiguous 检查 ---
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${VENV}/bin/python -c "
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f = '${MINERU_INFER_DIR}/predict_det.py'
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c = open(f).read()
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old = '( {8}inp = inp\.to\(self\.device\)\n)'
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new = r'\1 if not inp.is_contiguous():\n inp = inp.contiguous()\n'
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import re
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c2 = re.sub(old, new, c)
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if c2 == c:
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if 'is_contiguous' not in c:
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raise RuntimeError('Patch C: cannot find inp.to(device) line')
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else:
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print('Patch C: already applied')
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else:
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open(f, 'w').write(c2)
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print('Patch C: contiguous check inserted')
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" && \
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echo "All MinerU RDNA patches applied."
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# 应用 MinerU RDNA 适配补丁
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${VENV}/bin/python /opt/apply_mineru_patches.py
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# ===========================================================================
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# 阶段 10：MIOpen 预热脚本
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# ===========================================================================
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COPY scripts/cache_warmer.py /opt/cache_warmer.py
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# ===========================================================================
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# 阶段 11：入口与最终设置
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# 阶段 10：入口与最终验证
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# ===========================================================================
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RUN echo 'source /opt/mineru_venv/bin/activate' >> /etc/bash.bashrc && \
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echo "MinerU Docker image built successfully." && \
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${VENV}/bin/python -c "
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import torch, vllm, mineru
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print('='*50)
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print('MinerU ROCm Docker Image Ready')
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print(f' PyTorch : {torch.__version__}')
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print(f' ROCm : {torch.version.hip}')
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print(f' vllm : {vllm.__version__}')
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print(f' MinerU : {mineru.__version__}')
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print(f' Arch : ${ARCH}')
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print('='*50)
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"
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${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)"
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# 容器入口：默认 bash，用户可 override
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ENTRYPOINT ["/bin/bash", "-c"]
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@@ -0,0 +1,88 @@
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#!/usr/bin/env python3
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"""MinerU RDNA 适配补丁 — 一键应用脚本"""
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import re
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import os
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import sys
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def find_infer_dir():
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import mineru.model.utils.tools.infer
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return os.path.dirname(mineru.model.utils.tools.infer.__file__)
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def patch_a_predict_rec_imgw(infer_dir):
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"""predict_rec.py: imgW 对齐到 32"""
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f = os.path.join(infer_dir, 'predict_rec.py')
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c = open(f).read()
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old = r'(imgW = max\(min\(imgW, self\.limited_max_width\), self\.limited_min_width\)\n)'
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new = r'\1 imgW = math.ceil(imgW / 32) * 32\n'
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c2 = re.sub(old, new, c)
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if c2 == c:
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if 'math.ceil(imgW / 32)' not in c:
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raise RuntimeError('Patch A: cannot find imgW line')
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print('Patch A: already applied')
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else:
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open(f, 'w').write(c2)
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print('Patch A: imgW 32-align inserted')
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def patch_b_predict_rec_batch(infer_dir):
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"""predict_rec.py: 批次填充"""
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f = os.path.join(infer_dir, 'predict_rec.py')
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c = open(f).read()
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old = r'( norm_img_batch = np\.concatenate\(norm_img_batch\))'
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new = (
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' actual_batch_size = len(norm_img_batch)\n'
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' if actual_batch_size < batch_num:\n'
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' pad_size = batch_num - actual_batch_size\n'
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' pad_img = np.zeros_like(norm_img_batch[0])\n'
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' for _ in range(pad_size):\n'
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' norm_img_batch.append(pad_img)\n'
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r'\1'
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)
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c2 = re.sub(old, new, c)
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if c2 == c:
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if 'actual_batch_size' not in c:
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raise RuntimeError('Patch B: cannot find norm_img_batch concatenation')
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print('Patch B: already applied')
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else:
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open(f, 'w').write(c2)
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print('Patch B: batch padding inserted')
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# 修改 range(len(rec_result)) → range(actual_batch_size)
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c3 = open(f).read()
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c4 = re.sub(
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r'for rno in range\(len\(rec_result\)\):',
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' for rno in range(actual_batch_size):',
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c3
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)
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open(f, 'w').write(c4)
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def patch_c_predict_det_contiguous(infer_dir):
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"""predict_det.py: contiguous 检查"""
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f = os.path.join(infer_dir, 'predict_det.py')
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c = open(f).read()
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old = r'( inp = inp\.to\(self\.device\)\n)'
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new = r'\1 if not inp.is_contiguous():\n inp = inp.contiguous()\n'
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c2 = re.sub(old, new, c)
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if c2 == c:
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if 'is_contiguous' not in c:
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raise RuntimeError('Patch C: cannot find inp.to(device) line')
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print('Patch C: already applied')
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else:
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open(f, 'w').write(c2)
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print('Patch C: contiguous check inserted')
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def main():
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infer_dir = find_infer_dir()
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print(f'MinerU infer dir: {infer_dir}')
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patch_a_predict_rec_imgw(infer_dir)
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patch_b_predict_rec_batch(infer_dir)
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patch_c_predict_det_contiguous(infer_dir)
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print('All MinerU RDNA patches applied.')
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if __name__ == '__main__':
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main()
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