Enable PaddleOCR-VL

examples/offline_inference/vision_language.py

@@ -1032,6 +1032,35 @@ def run_ovis2_5(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# PaddleOCR-VL
+def run_paddleocr_vl(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+    model_name = "PaddlePaddle/PaddleOCR-VL"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=8192,
+        max_num_seqs=5,
+        mm_processor_kwargs={
+            "min_pixels": 147384,
+            "max_pixels": 2822400,
+        },
+        limit_mm_per_prompt={modality: 1},
+    )
+
+    placeholder = "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>"
+
+    prompts = [
+        f"<|begin_of_sentence|>User:{placeholder}\n{question}" for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # PaliGemma
 def run_paligemma(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1474,6 +1503,7 @@ def run_skyworkr1v(questions: list[str], modality: str) -> ModelRequestData:
     "NVLM_D": run_nvlm_d,
     "ovis": run_ovis,
     "ovis2_5": run_ovis2_5,
+    "paddleocr_vl": run_paddleocr_vl,
     "paligemma": run_paligemma,
     "paligemma2": run_paligemma2,
     "phi3_v": run_phi3v,

scripts/docs/vllm推理手册.md

@@ -39,7 +39,8 @@
     - [3.4.2 client 端请求格式样例](#342-client-端请求格式样例)
     - [3.4.3 FP8 static quant](#343-fp8-static-quant)
     - [3.4.4 FP8 dynamic quant](#344-fp8-dynamic-quant)
-    - [3.4.5 问题解答](#345-问题解答)
+    - [3.4.5 PaddleOCR-VL 模型](#345-paddleocr-vl-模型)
+    - [3.4.6 问题解答](#346-问题解答)
 
 ## 1.0 环境部署
 
@@ -959,7 +960,41 @@ PT_HPU_LAZY_MODE=1 VLLM_GRAPH_RESERVED_MEM=0.5 vllm serve \
     --mm_processor_kwargs max_pixels=1003520,min_pixels=3136
 ```
 
-#### 3.4.5 问题解答
+#### 3.4.5 PaddleOCR-VL 模型
+**启动服务**
+
+```bash
+PT_HPU_LAZY_MODE=1 vllm serve \
+    PaddlePaddle/PaddleOCR-VL \
+    --host 0.0.0.0 \
+    --port 8080 \
+    --trust-remote-code \
+    --gpu-memory-utilization 0.5 \
+    --max-model-len 16384 \
+    --served-model-name 'PaddleOCR-VL-0.9B'
+```
+
+**client 端请求格式样例**\
+PaddleOCR-VL 模型的client端依赖于PaddleOCR pipeline, 先安装必要的paddle相关库:
+
+```bash
+pip install paddlepaddle==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
+pip install paddlex==3.3.4
+pip install "paddleocr[doc-parser]"
+```
+
+然后，使用PaddleOCR CLI 命令发送请求:
+
+```bash
+paddleocr doc_parser \
+    -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png \
+    --enable_mkldnn False \
+    --vl_rec_backend vllm-server \
+    --vl_rec_server_url http://127.0.0.1:8080/v1 \
+    --save_path ./output
+```
+
+#### 3.4.6 问题解答
 
 - 如果 server 端出现获取图像音视频超时错误，可以通过设置环境变量`VLLM_IMAGE_FETCH_TIMEOUT` `VLLM_VIDEO_FETCH_TIMEOUT` `VLLM_AUDIO_FETCH_TIMEOUT` 来提高超时时间。默认为 5/30/10
 - 过大的输入图像要求更多的设备内存，可以通过设置更小的参数`--gpu-memory-utilization` （默认 0.9）来解决。例如参考脚本`openai_chat_completion_client_for_multimodal.py`中的图像分辨率最高达到 7952x5304,这会导致 server 端推理出错。可以通过设置`--gpu-memory-utilization`至 0.6~0.7 来解决。

vllm/entrypoints/chat_utils.py

@@ -549,6 +549,8 @@ def _placeholder_str(self, modality: ModalityStr,
                 return "<start_of_image>"
             if model_type == "kimi_vl":
                 return "<|media_start|>image<|media_content|><|media_pad|><|media_end|>" # noqa: E501
+            if model_type == "paddleocr_vl":
+                return None
 
             raise TypeError(f"Unknown {modality} model type: {model_type}")
         elif modality == "audio":