Merge pull request #3557 from opendatalab/dev

Dev

README.md

@@ -44,7 +44,13 @@
 
 # Changelog
 
-- 2025/09/19 2.5.1 Released
+- 2025/09/20 2.5.3 Released
+  - Dependency version range adjustment to enable Turing and earlier architecture GPUs to use vLLM acceleration for MinerU2.5 model inference.
+  - `pipeline` backend compatibility fixes for torch 2.8.0.
+  - Reduced default concurrency for vLLM async backend to lower server pressure and avoid connection closure issues caused by high load.
+  - More compatibility-related details can be found in the [announcement](https://github.com/opendatalab/MinerU/discussions/3548)
+
+- 2025/09/19 2.5.2 Released
 
   We are officially releasing MinerU2.5, currently the most powerful multimodal large model for document parsing.
   With only 1.2B parameters, MinerU2.5's accuracy on the OmniDocBench benchmark comprehensively surpasses top-tier multimodal models like Gemini 2.5 Pro, GPT-4o, and Qwen2.5-VL-72B. It also significantly outperforms leading specialized models such as dots.ocr, MonkeyOCR, and PP-StructureV3.

README_zh-CN.md

@@ -43,10 +43,15 @@
 </div>
 
 # 更新记录
+- 2025/09/20 2.5.3 发布
+  - 依赖版本范围调整，使得Turing及更早架构显卡可以使用vLLM加速推理MinerU2.5模型。
+  - `pipeline`后端对torch 2.8.0的一些兼容性修复。
+  - 降低vLLM异步后端默认的并发数，降低服务端压力以避免高压导致的链接关闭问题。
+  - 更多兼容性相关内容详见[公告](https://github.com/opendatalab/MinerU/discussions/3547)
 
-- 2025/09/19 2.5.1 发布
+- 2025/09/19 2.5.2 发布
   我们正式发布 MinerU2.5，当前最强文档解析多模态大模型。仅凭 1.2B 参数，MinerU2.5 在 OmniDocBench 文档解析评测中，精度已全面超越 Gemini2.5-Pro、GPT-4o、Qwen2.5-VL-72B等顶级多模态大模型，并显著领先于主流文档解析专用模型（如 dots.ocr, MonkeyOCR, PP-StructureV3 等）。
-  模型已发布至[HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)和[ModelScope](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)平台，欢迎大家下载使用！
+  模型已发布至[HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)和[ModelScope](https://modelscope.cn/models/opendatalab/MinerU2.5-2509-1.2B)平台，欢迎大家下载使用！
   - 核心亮点
     - 极致能效，性能SOTA: 以 1.2B 的轻量化规模，实现了超越百亿乃至千亿级模型的SOTA性能，重新定义了文档解析的能效比。
     - 先进架构，全面领先: 通过 “两阶段推理” (解耦布局分析与内容识别) 与 原生高分辨率架构 的结合，在布局分析、文本识别、公式识别、表格识别及阅读顺序五大方面均达到 SOTA 水平。

docker/china/Dockerfile

@@ -1,9 +1,16 @@
-# Use DaoCloud mirrored vllm image for China region
+# Use DaoCloud mirrored vllm image for China region for gpu with Ampere architecture and above (Compute Capability>=8.0)
+# Compute Capability version query (https://developer.nvidia.com/cuda-gpus)
 FROM docker.m.daocloud.io/vllm/vllm-openai:v0.10.1.1
 
 # Use the official vllm image
 # FROM vllm/vllm-openai:v0.10.1.1
 
+# Use DaoCloud mirrored vllm image for China region for gpu with Turing architecture and below (Compute Capability<8.0)
+# FROM docker.m.daocloud.io/vllm/vllm-openai:v0.10.2
+
+# Use the official vllm image
+# FROM vllm/vllm-openai:v0.10.2
+
 # Install libgl for opencv support & Noto fonts for Chinese characters
 RUN apt-get update && \
     apt-get install -y \

docker/global/Dockerfile

@@ -1,6 +1,10 @@
-# Use the official vllm image
+# Use the official vllm image for gpu with Ampere architecture and above (Compute Capability>=8.0)
+# Compute Capability version query (https://developer.nvidia.com/cuda-gpus)
 FROM vllm/vllm-openai:v0.10.1.1
 
+# Use the official vllm image for gpu with Turing architecture and below (Compute Capability<8.0)
+# FROM vllm/vllm-openai:v0.10.2
+
 # Install libgl for opencv support & Noto fonts for Chinese characters
 RUN apt-get update && \
     apt-get install -y \

docs/en/quick_start/docker_deployment.md

@@ -10,7 +10,8 @@ docker build -t mineru-vllm:latest -f Dockerfile .
 ```
 
 > [!TIP]
-> The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/global/Dockerfile) uses `vllm/vllm-openai:v0.10.1.1` as the base image by default, supporting Turing/Ampere/Ada Lovelace/Hopper/Blackwell platforms.
+> The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/global/Dockerfile) uses `vllm/vllm-openai:v0.10.1.1` as the base image by default. This version of vLLM v1 engine has limited support for GPU models. 
+> If you cannot use vLLM accelerated inference on Turing and earlier architecture GPUs, you can resolve this issue by changing the base image to `vllm/vllm-openai:v0.10.2`.
 
 ## Docker Description
 

docs/zh/quick_start/docker_deployment.md

@@ -10,7 +10,8 @@ docker build -t mineru-vllm:latest -f Dockerfile .
 ```
 
 > [!TIP]
-> [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile)默认使用`vllm/vllm-openai:v0.10.1.1`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper/Blackwell平台，
+> [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile)默认使用`vllm/vllm-openai:v0.10.1.1`作为基础镜像，
+> 该版本的vLLM v1 engine对显卡型号支持有限，如您无法在Turing及更早架构的显卡上使用vLLM加速推理，可通过更改基础镜像为`vllm/vllm-openai:v0.10.2`来解决该问题。
 
 ## Docker说明
 

mineru/backend/pipeline/batch_analyze.py

@@ -116,9 +116,14 @@ class BatchAnalyze:
                 atom_model_name=AtomicModel.ImgOrientationCls,
             )
             try:
-                img_orientation_cls_model.batch_predict(table_res_list_all_page,
-                                                        det_batch_size=self.batch_ratio * OCR_DET_BASE_BATCH_SIZE,
-                                                        batch_size=TABLE_ORI_CLS_BATCH_SIZE)
+                if self.enable_ocr_det_batch:
+                    img_orientation_cls_model.batch_predict(table_res_list_all_page,
+                                                            det_batch_size=self.batch_ratio * OCR_DET_BASE_BATCH_SIZE,
+                                                            batch_size=TABLE_ORI_CLS_BATCH_SIZE)
+                else:
+                    for table_res in table_res_list_all_page:
+                        rotate_label = img_orientation_cls_model.predict(table_res['table_img'])
+                        img_orientation_cls_model.img_rotate(table_res, rotate_label)
             except Exception as e:
                 logger.warning(
                     f"Image orientation classification failed: {e}, using original image"

mineru/backend/vlm/custom_logits_processors.py

@@ -0,0 +1,41 @@
+import os
+
+from loguru import logger
+from packaging import version
+
+
+def enable_custom_logits_processors():
+    import torch
+    from vllm import __version__ as vllm_version
+
+    if not torch.cuda.is_available():
+        logger.info("CUDA not available, disabling custom_logits_processors")
+        return False
+
+    major, minor = torch.cuda.get_device_capability()
+    # 正确计算Compute Capability
+    compute_capability = f"{major}.{minor}"
+
+    # 安全地处理环境变量
+    vllm_use_v1_str = os.getenv('VLLM_USE_V1', "1")
+    if vllm_use_v1_str.isdigit():
+        vllm_use_v1 = int(vllm_use_v1_str)
+    else:
+        vllm_use_v1 = 1
+
+    if vllm_use_v1 == 0:
+        logger.info("VLLM_USE_V1 is set to 0, disabling custom_logits_processors")
+        return False
+    elif version.parse(vllm_version) < version.parse("0.10.1"):
+        logger.info(f"vllm version: {vllm_version} < 0.10.1, disable custom_logits_processors")
+        return False
+    elif version.parse(compute_capability) < version.parse("8.0"):
+        if version.parse(vllm_version) >= version.parse("0.10.2"):
+            logger.info(f"compute_capability: {compute_capability} < 8.0, but vllm version: {vllm_version} >= 0.10.2, enable custom_logits_processors")
+            return True
+        else:
+            logger.info(f"compute_capability: {compute_capability} < 8.0 and vllm version: {vllm_version} < 0.10.2, disable custom_logits_processors")
+            return False
+    else:
+        logger.info(f"compute_capability: {compute_capability} >= 8.0 and vllm version: {vllm_version} >= 0.10.1, enable custom_logits_processors")
+        return True

mineru/backend/vlm/vlm_analyze.py

@@ -4,6 +4,7 @@ import time
 
 from loguru import logger
 
+from .custom_logits_processors import enable_custom_logits_processors
 from .model_output_to_middle_json import result_to_middle_json
 from ...data.data_reader_writer import DataWriter
 from mineru.utils.pdf_image_tools import load_images_from_pdf
@@ -88,7 +89,6 @@ class ModelSingleton:
                 elif backend == "vllm-engine":
                     try:
                         import vllm
-                        vllm_version = vllm.__version__
                         from mineru_vl_utils import MinerULogitsProcessor
                     except ImportError:
                         raise ImportError("Please install vllm to use the vllm-engine backend.")
@@ -96,7 +96,7 @@ class ModelSingleton:
                         kwargs["gpu_memory_utilization"] = 0.5
                     if "model" not in kwargs:
                         kwargs["model"] = model_path
-                    if version.parse(vllm_version) >= version.parse("0.10.1") and "logits_processors" not in kwargs:
+                    if enable_custom_logits_processors() and ("logits_processors" not in kwargs):
                         kwargs["logits_processors"] = [MinerULogitsProcessor]
                     # 使用kwargs为 vllm初始化参数
                     vllm_llm = vllm.LLM(**kwargs)
@@ -104,7 +104,6 @@ class ModelSingleton:
                     try:
                         from vllm.engine.arg_utils import AsyncEngineArgs
                         from vllm.v1.engine.async_llm import AsyncLLM
-                        from vllm import __version__ as vllm_version
                         from mineru_vl_utils import MinerULogitsProcessor
                     except ImportError:
                         raise ImportError("Please install vllm to use the vllm-async-engine backend.")
@@ -112,7 +111,7 @@ class ModelSingleton:
                         kwargs["gpu_memory_utilization"] = 0.5
                     if "model" not in kwargs:
                         kwargs["model"] = model_path
-                    if version.parse(vllm_version) >= version.parse("0.10.1") and "logits_processors" not in kwargs:
+                    if enable_custom_logits_processors() and ("logits_processors" not in kwargs):
                         kwargs["logits_processors"] = [MinerULogitsProcessor]
                     # 使用kwargs为 vllm初始化参数
                     vllm_async_llm = AsyncLLM.from_engine_args(AsyncEngineArgs(**kwargs))

mineru/backend/vlm/vlm_middle_json_mkcontent.py

@@ -54,7 +54,7 @@ def mk_blocks_to_markdown(para_blocks, make_mode, formula_enable, table_enable,
         elif para_type == BlockType.LIST:
             for block in para_block['blocks']:
                 item_text = merge_para_with_text(block, formula_enable=formula_enable, img_buket_path=img_buket_path)
-                para_text += f"{item_text}\n"
+                para_text += f"{item_text}  \n"
         elif para_type == BlockType.TITLE:
             title_level = get_title_level(para_block)
             para_text = f'{"#" * title_level} {merge_para_with_text(para_block)}'

mineru/model/ori_cls/paddle_ori_cls.py

@@ -255,25 +255,28 @@ class PaddleOrientationClsModel:
                     results = self.sess.run(None, {"x": x})
                     for img_info, res in zip(rotated_imgs, results[0]):
                         label = self.labels[np.argmax(res)]
-                        if label == "270":
-                            img_info["table_img"] = cv2.rotate(
-                                np.asarray(img_info["table_img"]),
-                                cv2.ROTATE_90_CLOCKWISE,
-                            )
-                            img_info["wired_table_img"] = cv2.rotate(
-                                np.asarray(img_info["wired_table_img"]),
-                                cv2.ROTATE_90_CLOCKWISE,
-                            )
-                        elif label == "90":
-                            img_info["table_img"] = cv2.rotate(
-                                np.asarray(img_info["table_img"]),
-                                cv2.ROTATE_90_COUNTERCLOCKWISE,
-                            )
-                            img_info["wired_table_img"] = cv2.rotate(
-                                np.asarray(img_info["wired_table_img"]),
-                                cv2.ROTATE_90_COUNTERCLOCKWISE,
-                            )
-                        else:
-                            # 180度和0度不做处理
-                            pass
+                        self.img_rotate(img_info, label)
                         pbar.update(1)
+
+    def img_rotate(self, img_info, label):
+        if label == "270":
+            img_info["table_img"] = cv2.rotate(
+                np.asarray(img_info["table_img"]),
+                cv2.ROTATE_90_CLOCKWISE,
+            )
+            img_info["wired_table_img"] = cv2.rotate(
+                np.asarray(img_info["wired_table_img"]),
+                cv2.ROTATE_90_CLOCKWISE,
+            )
+        elif label == "90":
+            img_info["table_img"] = cv2.rotate(
+                np.asarray(img_info["table_img"]),
+                cv2.ROTATE_90_COUNTERCLOCKWISE,
+            )
+            img_info["wired_table_img"] = cv2.rotate(
+                np.asarray(img_info["wired_table_img"]),
+                cv2.ROTATE_90_COUNTERCLOCKWISE,
+            )
+        else:
+            # 180度和0度不做处理
+            pass

mineru/model/vlm_vllm_model/server.py

@@ -1,10 +1,9 @@
 import sys
 
+from mineru.backend.vlm.custom_logits_processors import enable_custom_logits_processors
 from mineru.utils.models_download_utils import auto_download_and_get_model_root_path
 
 from vllm.entrypoints.cli.main import main as vllm_main
-from vllm import __version__ as vllm_version
-from packaging import version
 
 
 def main():
@@ -37,6 +36,8 @@ def main():
         for index in sorted(model_arg_indices, reverse=True):
             args.pop(index)
 
+    custom_logits_processors = enable_custom_logits_processors()
+
     # 添加默认参数
     if not has_port_arg:
         args.extend(["--port", "30000"])
@@ -44,7 +45,7 @@ def main():
         args.extend(["--gpu-memory-utilization", "0.5"])
     if not model_path:
         model_path = auto_download_and_get_model_root_path("/", "vlm")
-    if not has_logits_processors_arg and version.parse(vllm_version) >= version.parse("0.10.1"):
+    if (not has_logits_processors_arg) and custom_logits_processors:
         args.extend(["--logits-processors", "mineru_vl_utils:MinerULogitsProcessor"])
 
     # 重构参数，将模型路径作为位置参数

mineru/version.py

@@ -1 +1 @@
-__version__ = "2.5.0"
+__version__ = "2.5.2"

pyproject.toml

@@ -39,7 +39,7 @@ dependencies = [
     "openai>=1.70.0,<2",
     "beautifulsoup4>=4.13.5,<5",
     "magika>=0.6.2,<0.7.0",
-    "mineru-vl-utils>=0.1.7,<1",
+    "mineru-vl-utils>=0.1.8,<1",
 ]
 
 [project.optional-dependencies]
@@ -51,12 +51,12 @@ test = [
     "fuzzywuzzy"
 ]
 vlm = [
-    "torch>=2.6.0,<2.8.0",
+    "torch>=2.6.0,<3",
     "transformers>=4.51.1,<5.0.0",
     "accelerate>=1.5.1",
 ]
 vllm = [
-    "vllm==0.10.1.1",
+    "vllm>=0.10.1.1,<0.11",
 ]
 pipeline = [
     "matplotlib>=3.10,<4",
@@ -68,7 +68,7 @@ pipeline = [
     "shapely>=2.0.7,<3",
     "pyclipper>=1.3.0,<2",
     "omegaconf>=2.3.0,<3",
-    "torch>=2.6.0,<2.8.0",
+    "torch>=2.6.0,<3",
     "torchvision",
     "transformers>=4.49.0,!=4.51.0,<5.0.0",
     "onnxruntime>1.17.0",