[fix] added support for vlm in offline inference (#3548)

examples/runtime/engine/offline_batch_inference_vlm.py

@@ -0,0 +1,67 @@
+"""
+Usage:
+python offline_batch_inference_vlm.py --model-path Qwen/Qwen2-VL-7B-Instruct --chat-template=qwen2-vl
+"""
+
+import argparse
+import dataclasses
+
+from transformers import AutoProcessor
+
+import sglang as sgl
+from sglang.srt.openai_api.adapter import v1_chat_generate_request
+from sglang.srt.openai_api.protocol import ChatCompletionRequest
+from sglang.srt.server_args import ServerArgs
+
+
+def main(
+    server_args: ServerArgs,
+):
+    # Create an LLM.
+    vlm = sgl.Engine(**dataclasses.asdict(server_args))
+
+    # prepare prompts.
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What’s in this image?"},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true",
+                    },
+                },
+            ],
+        }
+    ]
+    chat_request = ChatCompletionRequest(
+        messages=messages,
+        model=server_args.model_path,
+        temperature=0.8,
+        top_p=0.95,
+    )
+    gen_request, _ = v1_chat_generate_request(
+        [chat_request],
+        vlm.tokenizer_manager,
+    )
+
+    outputs = vlm.generate(
+        input_ids=gen_request.input_ids,
+        image_data=gen_request.image_data,
+        sampling_params=gen_request.sampling_params,
+    )
+
+    print("===============================")
+    print(f"Prompt: {messages[0]['content'][0]['text']}")
+    print(f"Generated text: {outputs['text']}")
+
+
+# The __main__ condition is necessary here because we use "spawn" to create subprocesses
+# Spawn starts a fresh program every time, if there is no __main__, it will run into infinite loop to keep spawning processes from sgl.Engine
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    args = parser.parse_args()
+    server_args = ServerArgs.from_cli_args(args)
+    main(server_args)

python/sglang/srt/entrypoints/engine.py

@@ -115,6 +115,9 @@ class Engine:
         sampling_params: Optional[Union[List[Dict], Dict]] = None,
         # The token ids for text; one can either specify text or input_ids.
         input_ids: Optional[Union[List[List[int]], List[int]]] = None,
+        # The image input. It can be a file name, a url, or base64 encoded string.
+        # See also python/sglang/srt/utils.py:load_image.
+        image_data: Optional[Union[List[str], str]] = None,
         return_logprob: Optional[Union[List[bool], bool]] = False,
         logprob_start_len: Optional[Union[List[int], int]] = None,
         top_logprobs_num: Optional[Union[List[int], int]] = None,
@@ -126,14 +129,20 @@ class Engine:
         The arguments of this function is the same as `sglang/srt/managers/io_struct.py::GenerateReqInput`.
         Please refer to `GenerateReqInput` for the documentation.
         """
+        modalities_list = []
+        if image_data is not None:
+            modalities_list.append("image")
+
         obj = GenerateReqInput(
             text=prompt,
             input_ids=input_ids,
             sampling_params=sampling_params,
+            image_data=image_data,
             return_logprob=return_logprob,
             logprob_start_len=logprob_start_len,
             top_logprobs_num=top_logprobs_num,
             lora_path=lora_path,
+            modalities=modalities_list,
             custom_logit_processor=custom_logit_processor,
             stream=stream,
         )
@@ -162,6 +171,9 @@ class Engine:
         sampling_params: Optional[Union[List[Dict], Dict]] = None,
         # The token ids for text; one can either specify text or input_ids.
         input_ids: Optional[Union[List[List[int]], List[int]]] = None,
+        # The image input. It can be a file name, a url, or base64 encoded string.
+        # See also python/sglang/srt/utils.py:load_image.
+        image_data: Optional[Union[List[str], str]] = None,
         return_logprob: Optional[Union[List[bool], bool]] = False,
         logprob_start_len: Optional[Union[List[int], int]] = None,
         top_logprobs_num: Optional[Union[List[int], int]] = None,
@@ -177,6 +189,7 @@ class Engine:
             text=prompt,
             input_ids=input_ids,
             sampling_params=sampling_params,
+            image_data=image_data,
             return_logprob=return_logprob,
             logprob_start_len=logprob_start_len,
             top_logprobs_num=top_logprobs_num,