model(vlm): pixtral (#5084)

docs/supported_models/vision_language_models.md

@@ -20,6 +20,7 @@ python3 -m sglang.launch_server \
 | **Janus-Pro** (1B, 7B)      | `deepseek-ai/Janus-Pro-7B`                     | `janus-pro`       | DeepSeek’s open-source multimodal model capable of both image understanding and generation. Janus-Pro employs a decoupled architecture for separate visual encoding paths, enhancing performance in both tasks. |
 | **MiniCPM-V / MiniCPM-o**      | `openbmb/MiniCPM-V-2_6`                          | `minicpmv`           | MiniCPM-V (2.6, ~8B) supports image inputs, and MiniCPM-o adds audio/video; these multimodal LLMs are optimized for end-side deployment on mobile/edge devices. |
 | **Llama 3.2 Vision** (11B)     | `meta-llama/Llama-3.2-11B-Vision-Instruct`        | `llama_3_vision`     | Vision-enabled variant of Llama 3 (11B) that accepts image inputs for visual question answering and other multimodal tasks. |
+| **Pixtral** (12B, 124B)              | `mistral-community/pixtral-12b`                           | `mistral`     | Pixtral is a vision-language model from Mistral AI that can process both text and images. |
 | **LLaVA** (v1.5 & v1.6)        | *e.g.* `liuhaotian/llava-v1.5-13b`               | `vicuna_v1.1`        | Open vision-chat models that add an image encoder to LLaMA/Vicuna (e.g. LLaMA2 13B) for following multimodal instruction prompts. |
 | **LLaVA-NeXT** (8B, 72B)       | `lmms-lab/llava-next-72b`                        | `chatml-llava`       | Improved LLaVA models (with an 8B Llama3 version and a 72B version) offering enhanced visual instruction-following and accuracy on multimodal benchmarks. |
 | **LLaVA-OneVision**            | `lmms-lab/llava-onevision-qwen2-7b-ov`           | `chatml-llava`       | Enhanced LLaVA variant integrating Qwen as the backbone; supports multiple images (and even video frames) as inputs via an OpenAI Vision API-compatible format. |

examples/runtime/README.md

@@ -33,9 +33,10 @@ The `hidden_states` folder contains examples on how to extract hidden states usi
 * `hidden_states_engine.py`: An example how to extract hidden states using the Engine API.
 * `hidden_states_server.py`: An example how to extract hidden states using the Server API.
 
-## LLaVA-NeXT
+## Multimodal
+
+SGLang supports multimodal inputs for various model architectures. The `multimodal` folder contains examples showing how to use urls, files or encoded data to make requests to multimodal models. Examples include querying the [Llava-OneVision](multimodal/llava_onevision_server.py) model (image, multi-image, video), Llava-backed [Qwen-Llava](multimodal/qwen_llava_server.py) and [Llama3-Llava](multimodal/llama3_llava_server.py) models (image, multi-image), and Mistral AI's [Pixtral](multimodal/pixtral_server.py) (image, multi-image).
 
-SGLang support LLaVA-OneVision with single-image, multi-image and video are supported. The folder `llava_onevision` shows how to do this.
 
 ## Token In, Token Out
 

examples/runtime/multimodal/llama3_llava_server.py

@@ -6,7 +6,7 @@ Usage:
 # Endpoint Service CLI:
 python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000
 
-python3 http_llama3_llava_test.py
+python3 llama3_llava_server.py
 
 Output:
 "Friends posing for a fun photo with a life-sized teddy bear, creating a playful and memorable moment."

examples/runtime/multimodal/llava_onevision_server.py

@@ -3,7 +3,7 @@ Usage:
 
 python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8
 
-python3 http_llava_onevision_test.py
+python3 llava_onevision_server.py
 """
 
 import base64

examples/runtime/multimodal/pixtral_server.py

@@ -0,0 +1,127 @@
+"""
+Usage:
+# Run a Pixtral model with SGLang:
+# HuggingFace:
+python -m sglang.launch_server --model-path mistral-community/pixtral-12b --port=30000
+# ModelScope:
+python -m sglang.launch_server --model-path AI-ModelScope/pixtral-12b --port=30000
+
+# Then test it with:
+python pixtral_server.py
+
+This script tests Pixtral model with both single and multiple images.
+"""
+
+import argparse
+import asyncio
+import json
+
+import aiohttp
+import requests
+
+IMAGE_TOKEN_SEP = "\n[IMG]"
+ROUTE = "/generate"
+
+
+async def send_request(url, data, delay=0):
+    await asyncio.sleep(delay)
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, json=data) as resp:
+            output = await resp.json()
+    return output
+
+
+async def test_concurrent(args):
+    url = f"{args.host}:{args.port}{ROUTE}"
+
+    # Single image test
+    if args.single_image:
+        prompt = f"<s>[INST]Describe this image in detail.{IMAGE_TOKEN_SEP}[/INST]"
+        image_url = "https://picsum.photos/id/237/400/300"
+        modality = ["image"]
+    # Multiple images test
+    else:
+        image_urls = [
+            "https://picsum.photos/id/237/400/300",
+            "https://picsum.photos/id/27/500/500",
+        ]
+        prompt = f"<s>[INST]How many photos are there? Describe each in a very short sentence.{IMAGE_TOKEN_SEP * len(image_urls)}[/INST]"
+        image_url = image_urls
+        modality = ["multi-images"]
+
+    response = await send_request(
+        url,
+        {
+            "text": prompt,
+            "image_data": image_url,
+            "sampling_params": {
+                "max_new_tokens": 100,
+                "temperature": 0.7,
+                "top_p": 0.9,
+            },
+            "modalities": modality,
+        },
+    )
+
+    print(f"Response: {response}")
+    if "text" in response:
+        print("\nOutput text:", response["text"])
+
+
+def test_streaming(args):
+    url = f"{args.host}:{args.port}/generate"
+
+    # Single image test
+    if args.single_image:
+        prompt = f"<s>[INST]Describe this image in detail.{IMAGE_TOKEN_SEP}[/INST]"
+        image_data = "https://picsum.photos/id/237/400/300"
+        modality = ["image"]
+    # Multiple images test
+    else:
+        image_urls = [
+            "https://picsum.photos/id/237/400/300",
+            "https://picsum.photos/id/27/500/500",
+        ]
+        prompt = f"<s>[INST]How many photos are there? Describe each in a very short sentence.{IMAGE_TOKEN_SEP * len(image_urls)}[/INST]"
+        image_data = image_urls
+        modality = ["multi-images"]
+
+    pload = {
+        "text": prompt,
+        "image_data": image_data,
+        "sampling_params": {"max_new_tokens": 100, "temperature": 0.7, "top_p": 0.9},
+        "modalities": modality,
+        "stream": True,
+    }
+
+    response = requests.post(url, json=pload, stream=True)
+
+    print("Streaming response:")
+    prev = 0
+    for chunk in response.iter_lines(decode_unicode=False):
+        chunk = chunk.decode("utf-8")
+        if chunk and chunk.startswith("data:"):
+            if chunk == "data: [DONE]":
+                break
+            data = json.loads(chunk[5:].strip("\n"))
+            output = data["text"].strip()
+            print(output[prev:], end="", flush=True)
+            prev = len(output)
+    print("\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="http://127.0.0.1")
+    parser.add_argument("--port", type=int, default=30000)
+    parser.add_argument(
+        "--single-image",
+        action="store_true",
+        help="Test with single image instead of multiple images",
+    )
+    parser.add_argument("--no-stream", action="store_true", help="Don't test streaming")
+    args = parser.parse_args()
+
+    asyncio.run(test_concurrent(args))
+    if not args.no_stream:
+        test_streaming(args)

examples/runtime/multimodal/qwen_llava_server.py

@@ -6,7 +6,7 @@ Usage:
 # Endpoint Service CLI:
 python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8
 
-python3 http_qwen_llava_test.py
+python3 qwen_llava_server.py
 
 Output:
 "Two children pose with a large teddy bear, one holding a smaller stuffed bear, in a room with an American flag and potted plants."

python/sglang/lang/chat_template.py

@@ -194,6 +194,21 @@ register_chat_template(
     )
 )
 
+# Reference: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/blob/main/chat_template.json
+register_chat_template(
+    ChatTemplate(
+        name="mistral",
+        default_system_prompt=None,
+        role_prefix_and_suffix={
+            "system": ("[SYSTEM_PROMPT] ", " [/SYSTEM_PROMPT]"),
+            "user": ("[INST] ", " [/INST]"),
+            "assistant": ("", " </s><s>"),
+        },
+        stop_str=("</s>",),
+        image_token="[IMG]",
+    )
+)
+
 register_chat_template(
     ChatTemplate(
         name="llama-3-instruct",
@@ -509,13 +524,19 @@ def match_vicuna(model_path: str):
 @register_chat_template_matching_function
 def match_llama2_chat(model_path: str):
     if re.search(
-        r"llama-2.*chat|(mistral|mixtral).*instruct|codellama.*instruct",
+        r"llama-2.*chat|codellama.*instruct",
         model_path,
         re.IGNORECASE,
     ):
         return "llama-2-chat"
 
 
+@register_chat_template_matching_function
+def match_mistral(model_path: str):
+    if re.search(r"pixtral|(mistral|mixtral).*instruct", model_path, re.IGNORECASE):
+        return "mistral"
+
+
 @register_chat_template_matching_function
 def match_llama3_instruct(model_path: str):
     if re.search(r"llama-3.*instruct", model_path, re.IGNORECASE):

python/sglang/srt/configs/model_config.py

@@ -545,6 +545,7 @@ multimodal_model_archs = [
     "Llama4ForConditionalGeneration",
     "LlavaMistralForCausalLM",
     "LlavaQwenForCausalLM",
+    "LlavaForConditionalGeneration",
     "LlavaVidForCausalLM",
     "MiniCPMO",
     "MiniCPMV",

python/sglang/srt/conversation.py

@@ -634,6 +634,20 @@ register_conv_template(
     )
 )
 
+# reference: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/blob/main/chat_template.json
+register_conv_template(
+    Conversation(
+        name="mistral",
+        system_template="[SYSTEM_PROMPT]\n{system_message}\n[/SYSTEM_PROMPT]\n\n",
+        roles=("[INST]", "[/INST]"),
+        sep_style=SeparatorStyle.LLAMA2,
+        sep=" ",
+        sep2=" </s><s>",
+        stop_str=["[INST]", "[/INST]", "[SYSTEM_PROMPT]", "[/SYSTEM_PROMPT]"],
+        image_token="[IMG]",
+    )
+)
+
 # reference: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct/blob/main/chat_template.json
 register_conv_template(
     Conversation(
@@ -880,13 +894,19 @@ def match_vicuna(model_path: str):
 @register_conv_template_matching_function
 def match_llama2_chat(model_path: str):
     if re.search(
-        r"llama-2.*chat|(mistral|mixtral).*instruct|codellama.*instruct",
+        r"llama-2.*chat|codellama.*instruct",
         model_path,
         re.IGNORECASE,
     ):
         return "llama-2"
 
 
+@register_conv_template_matching_function
+def match_mistral(model_path: str):
+    if re.search(r"pixtral|(mistral|mixtral).*instruct", model_path, re.IGNORECASE):
+        return "mistral"
+
+
 @register_conv_template_matching_function
 def match_deepseek_vl(model_path: str):
     if re.search(r"deepseek.*vl2", model_path, re.IGNORECASE):

python/sglang/srt/managers/multimodal_processors/llava.py

@@ -1,14 +1,20 @@
 import asyncio
+import importlib
 from typing import List, Optional, Union
 
 import numpy as np
+from transformers.models.auto.processing_auto import (
+    PROCESSOR_MAPPING_NAMES as HF_MAPPING_NAMES,
+)
 
+import sglang.srt.managers.multimodal_processor as sgl_mm_processor_utils
 from sglang.srt.managers.multimodal_processors.base_processor import (
     BaseMultimodalProcessor,
 )
 from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
 from sglang.srt.mm_utils import expand2square, process_anyres_image
 from sglang.srt.models.llava import (
+    LlavaForConditionalGeneration,
     LlavaLlamaForCausalLM,
     LlavaMistralForCausalLM,
     LlavaQwenForCausalLM,
@@ -133,6 +139,7 @@ class LlavaImageProcessor(BaseMultimodalProcessor):
                             img_data, aspect_ratio, grid_pinpoints
                         )
                     )
+
                 res = await asyncio.gather(*res)
                 for pixel_v, image_h, image_s in res:
                     pixel_values.append(pixel_v)
@@ -165,3 +172,42 @@ class LlavaImageProcessor(BaseMultimodalProcessor):
                 )
             ],
         }
+
+
+class LlavaMultimodalProcessor(BaseMultimodalProcessor):
+    """
+    This is a wrapper class used to identify the multimodal processor for Llava architecture models.
+    """
+
+    models = [LlavaForConditionalGeneration]
+
+    def _get_sgl_processor_cls(self, model_type: str):
+        if hf_name := HF_MAPPING_NAMES.get(model_type):
+            sgl_mm_processor_set = sgl_mm_processor_utils.PROCESSOR_MAPPING.values()
+            sgl_processor_cls = list(
+                filter(lambda p: p.__name__ == hf_name, sgl_mm_processor_set)
+            )
+            if sgl_processor_cls:
+                return sgl_processor_cls[0]
+        raise ValueError(
+            f"Cannot find corresponding multimodal processor registered in sglang for model type `{model_type}`"
+        )
+
+    def __init__(self, hf_config, server_args, _processor):
+        assert hasattr(hf_config, "vision_config")
+        assert hasattr(hf_config, "text_config")
+        self.vision_config = hf_config.vision_config
+        self.text_config = hf_config.text_config
+        self.hf_config = hf_config
+
+        if vision_type := getattr(self.vision_config, "model_type"):
+            self.inner = self._get_sgl_processor_cls(vision_type)(
+                hf_config, server_args, _processor
+            )
+        else:
+            raise ValueError(
+                f"Required `vision_config.model_type` is not found in hf_config: `{hf_config}`"
+            )
+
+    async def process_mm_data_async(self, *args, **kwargs):
+        return await self.inner.process_mm_data_async(*args, **kwargs)

python/sglang/srt/managers/multimodal_processors/pixtral.py

@@ -0,0 +1,127 @@
+import asyncio
+import math
+from typing import List, Optional, Union
+
+import numpy as np
+from transformers import PretrainedConfig
+from transformers.models.pixtral.image_processing_pixtral import (
+    _num_image_tokens as _get_pixtral_hf_num_image_tokens,
+)
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalInputs,
+)
+from sglang.srt.models.pixtral import PixtralVisionModel
+
+
+class PixtralProcessor(BaseMultimodalProcessor):
+    models = [PixtralVisionModel]
+
+    PAD_TOKEN = "<pad>"
+    IMG_BREAK_TOKEN_ID = 12
+    IMG_END_TOKEN_ID = 13
+
+    def get_patch_grid_size(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> tuple[int, int]:
+        max_width = max_height = self.image_size
+        patch_width = patch_height = self.patch_size
+
+        ratio = max(image_width / max_width, image_height / max_height)
+
+        if ratio > 1:
+            image_width = int(math.floor(image_width / ratio))
+            image_height = int(math.floor(image_height / ratio))
+
+        nrows, ncols = _get_pixtral_hf_num_image_tokens(
+            (image_height, image_width),
+            (patch_height, patch_width),
+        )
+
+        return ncols, nrows
+
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.image_token_id = getattr(
+            hf_config, "image_token_index", PixtralVisionModel.DEFAULT_IMAGE_TOKEN_ID
+        )
+        # Instantiate the patcher logic helper using the class defined above
+
+        self.vision_config = hf_config.vision_config
+        self.image_size = self.vision_config.image_size
+        self.patch_size = self.vision_config.patch_size
+        self.multimodal_tokens = MultimodalSpecialTokens(
+            image_token=_processor.image_token
+        )
+        _processor.tokenizer.add_special_tokens(
+            {
+                "pad_token": getattr(hf_config, "pad_token", self.PAD_TOKEN),
+            }
+        )
+
+    async def _resize(self, image):
+        num_w_tokens, num_h_tokens = self.get_patch_grid_size(
+            image_width=image.size[0],
+            image_height=image.size[1],
+        )
+        new_size = (num_w_tokens * self.patch_size, num_h_tokens * self.patch_size)
+        return image.resize(new_size)
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        request_obj,
+        *args,
+        **kwargs,
+    ):
+        if not image_data:
+            return None
+
+        if isinstance(image_data, str):
+            image_data = [image_data]
+
+        mm_data = self.load_mm_data(
+            prompt=input_text,
+            multimodal_tokens=self.multimodal_tokens,
+            max_req_input_len=kwargs.get("max_req_input_len", 4096),
+            image_data=image_data,
+            return_text=True,
+        )
+
+        if mm_data.images:
+            resize_tasks = [self._resize(image) for image in mm_data.images]
+            mm_data.images = await asyncio.gather(*resize_tasks)
+
+        processor_output = self.process_mm_data(
+            input_text=mm_data.input_text,
+            images=mm_data.images,
+        )
+
+        if "pixel_values" in processor_output:
+            mm_items = [
+                MultimodalDataItem(
+                    pixel_values=processor_output["pixel_values"],
+                    image_sizes=processor_output["image_sizes"],
+                    modality=Modality.IMAGE,
+                )
+            ]
+
+            input_ids = processor_output["input_ids"].view(-1).tolist()
+            processor_output.update(
+                input_ids=input_ids,
+                mm_items=mm_items,
+                # there's no im_start_id for pixtral, only im_token and im_end_token
+                im_end_id=self.IMG_END_TOKEN_ID,
+                im_token_id=self.image_token_id,
+            )
+        return processor_output

python/sglang/srt/models/llava.py

@@ -15,7 +15,8 @@
 
 import math
 import re
-from typing import Iterable, List, Optional, Tuple
+from functools import lru_cache
+from typing import Dict, Iterable, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import torch
@@ -28,10 +29,18 @@ from transformers import (
     Qwen2Config,
     SiglipVisionModel,
 )
+from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
 from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
 
+# leave till last and symbol only in case circular import
+import sglang.srt.models as sgl_models
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
-from sglang.srt.managers.schedule_batch import Modality, MultimodalInputs
+from sglang.srt.managers.mm_utils import general_mm_embed_routine
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalInputs,
+)
 from sglang.srt.mm_utils import (
     get_anyres_image_grid_shape,
     unpad_image,
@@ -42,7 +51,7 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.llama import LlamaForCausalLM
 from sglang.srt.models.mistral import MistralForCausalLM
 from sglang.srt.models.qwen2 import Qwen2ForCausalLM
-from sglang.srt.utils import add_prefix, flatten_nested_list
+from sglang.srt.utils import add_prefix, flatten_nested_list, logger
 
 
 class LlavaBaseForCausalLM(nn.Module):
@@ -114,7 +123,16 @@ class LlavaBaseForCausalLM(nn.Module):
         image_inputs.image_offsets = offset_list
         return input_ids
 
-    def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
+    def encode_images(
+        self, pixel_values: Union[torch.Tensor, List[torch.Tensor]]
+    ) -> torch.Tensor:
+        """
+        encode images by vision tower and multimodal projector
+        Args:
+            pixel_values: torch.Tensor or List[torch.Tensor]: each tensor for an input image
+        Returns:
+            torch.Tensor: encoded image features from the input image; if multiple, flattened by seq_len axis
+        """
         image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
         # NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.
 
@@ -583,4 +601,229 @@ class LlavaMistralForCausalLM(LlavaBaseForCausalLM):
             )
 
 
-EntryClass = [LlavaLlamaForCausalLM, LlavaQwenForCausalLM, LlavaMistralForCausalLM]
+class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
+    """
+    An adaptor class to enable support for multiple mmlm such as mistral-community/pixtral-12b
+    It follows the structure of (vision_tower, multi_modal_projector, language_model)
+
+    Once a model config is loaded, text_config and vision_config will be extracted, and
+    LlavaForConditionalGeneration will load the language_model and vision_tower models
+    according to config.
+    """
+
+    MULTIMODAL_PROJECTOR_TYPE = LlavaMultiModalProjector
+
+    def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
+        if hasattr(self.vision_tower, "pad_input_ids"):
+            return self.vision_tower.pad_input_ids(input_ids, image_inputs)
+        else:
+            return super().pad_input_ids(input_ids, image_inputs)
+
+    def _get_sgl_model_cls(self, config, auto_model_type: Type[AutoModel] = AutoModel):
+        """
+        Get the SGLang model implementation class according to config.
+
+        Args:
+            config: The config object of the model.
+            auto_model_type: The type of the auto model.
+
+        Returns:
+            The SGLang model implementation class.
+        """
+        config_cls_name = config.__class__.__name__
+        arch_name_mapping = self._config_cls_name_to_arch_name_mapping(auto_model_type)
+        if arch := arch_name_mapping.get(config_cls_name):
+            if isinstance(arch, tuple):
+                arch = arch[0]
+                logger.warning(
+                    f"Multiple {auto_model_type.__name__} models found for submodule config `{config_cls_name}`, defaulting to [0]: {arch.__name__}"
+                )
+            try:
+                return sgl_models.registry.ModelRegistry.resolve_model_cls(arch)[0]
+            except Exception as e:
+                raise ValueError(
+                    f"{auto_model_type.__name__} found a corresponding model `{arch}` for config class `{config_cls_name}`, but failed to load it from SGLang ModelRegistry. \n{e}"
+                )
+        else:
+            raise ValueError(
+                f"{auto_model_type.__name__} cannot find a corresponding model for config class `{config_cls_name}`"
+            )
+
+    @lru_cache
+    def _config_cls_name_to_arch_name_mapping(
+        self, auto_model_type: Type[AutoModel]
+    ) -> Dict[str, str]:
+        mapping = {}
+        for config_cls, archs in auto_model_type._model_mapping.items():
+            if isinstance(archs, tuple):
+                mapping[config_cls.__name__] = tuple(arch.__name__ for arch in archs)
+            else:
+                mapping[config_cls.__name__] = archs.__name__
+        return mapping
+
+    def __init__(
+        self,
+        config: LlavaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        assert hasattr(config, "text_config")
+        assert hasattr(config, "vision_config")
+        self.config = config
+        self.text_config = config.text_config
+        self.vision_config = config.vision_config
+
+        if not hasattr(self.config, "vocab_size"):
+            self.config.vocab_size = self.config.text_config.vocab_size
+        if not hasattr(self.config, "image_aspect_ratio"):
+            self.config.image_aspect_ratio = "anyres"
+        if not hasattr(self.config, "image_grid_pinpoints"):
+            # from transformers.models.llava_onevision.configuration_llava_onevision import LlavaOnevisionConfig
+            # self.config.image_grid_pinpoints = LlavaOnevisionConfig().image_grid_pinpoints
+            self.config.image_grid_pinpoints = [
+                [96, 96],
+                [224, 224],
+                [384, 384],
+                [512, 512],
+                [768, 768],
+                [1024, 1024],
+            ]
+        if not hasattr(self.config, "mm_patch_merge_type"):
+            self.config.mm_patch_merge_type = "flat"
+        if not hasattr(self.config, "image_token_index"):
+            self.config.image_token_index = 10
+        if not hasattr(self.config, "projector_hidden_act"):
+            self.config.projector_hidden_act = "gelu"
+
+        self.vision_feature_layer = getattr(config, "vision_feature_layer", -1)
+        self.vision_feature_select_strategy = getattr(
+            config, "vision_feature_select_strategy", "full"
+        )
+        self.image_size = self.config.vision_config.image_size
+        self.patch_size = self.config.vision_config.patch_size
+
+        self.mm_patch_merge_type = config.mm_patch_merge_type
+        self.image_aspect_ratio = config.image_aspect_ratio
+        self.image_grid_pinpoints = config.image_grid_pinpoints
+
+        self.image_feature_len = int((self.image_size // self.patch_size) ** 2)
+
+        self.multi_modal_projector = self.MULTIMODAL_PROJECTOR_TYPE(config)
+
+        language_model_cls = self._get_sgl_model_cls(
+            config.text_config, AutoModelForCausalLM
+        )
+        vision_model_cls = self._get_sgl_model_cls(config.vision_config, AutoModel)
+        self.language_model = language_model_cls(
+            config.text_config,
+            quant_config=quant_config,
+            prefix=add_prefix("language_model", prefix),
+        )
+        self.vision_tower = vision_model_cls(
+            config.vision_config,
+            quant_config=quant_config,
+            prefix=add_prefix("vision_tower", prefix),
+        )
+
+        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
+            self.language_model.model.image_newline = nn.Parameter(
+                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
+            )
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        """Extract features from image inputs.
+
+        Args:
+            items: List of MultimodalDataItem objects containing image data
+                Note that an item can be either "image" or "multi-images"
+
+        Returns:
+            torch.Tensor: features from image inputs, concatenated
+        """
+        features = []
+        for item in items:
+            # in each item, we assume pixel_values is always batched
+            pixel_values, image_sizes = item.pixel_values, item.image_sizes
+            image_outputs = self.vision_tower(
+                pixel_values, image_sizes, output_hidden_states=True
+            )
+            selected_image_feature = image_outputs.hidden_states[
+                self.vision_feature_layer
+            ]
+
+            if self.vision_feature_select_strategy in ["default", "patch"]:
+                selected_image_feature = selected_image_feature[:, 1:]
+            elif self.vision_feature_select_strategy == "full":
+                selected_image_feature = selected_image_feature
+            else:
+                raise ValueError(
+                    f"Unexpected select feature: {self.vision_feature_select_strategy}"
+                )
+            features.append(
+                self.multi_modal_projector(selected_image_feature.squeeze(0))
+            )
+        ret = torch.cat(features, dim=0)
+        return ret
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            get_embedding=get_embedding,
+            language_model=self.language_model,
+            image_data_embedding_func=self.get_image_feature,
+            placeholder_tokens=None,  # using mm_item.pad_value
+            positions=positions,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        """Load weights for LlavaForConditionalGeneration.
+
+        Unlike the base class implementation, this one doesn't need to handle
+        weight name remapping as the weights are already properly structured with
+        'language_model' and 'vision_tower' prefixes in the safetensors files.
+        """
+        if (
+            self.vision_feature_select_strategy == "patch"
+            or self.vision_feature_select_strategy == "full"
+        ):
+            pass
+        elif self.vision_feature_select_strategy == "cls_patch":
+            self.image_feature_len += 1
+        else:
+            raise ValueError(
+                f"Unexpected select feature: {self.vision_feature_select_strategy}"
+            )
+
+        # Create dictionaries for direct parameter loading
+        params_dict = dict(self.named_parameters())
+
+        # Load weights directly without remapping
+        for name, loaded_weight in weights:
+            for part in ("language_model", "vision_tower"):
+                if name.startswith(part):
+                    name = name[len(part + ".") :]
+                    getattr(self, part).load_weights([(name, loaded_weight)])
+                    break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = [
+    LlavaLlamaForCausalLM,
+    LlavaQwenForCausalLM,
+    LlavaMistralForCausalLM,
+    LlavaForConditionalGeneration,
+]

python/sglang/srt/models/pixtral.py

@@ -0,0 +1,467 @@
+# Copyright 2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""
+Using mistral-community/pixtral-12b as reference.
+"""
+
+import logging
+import math
+from typing import Iterable, List, Optional, Set, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PixtralVisionConfig, PretrainedConfig
+from transformers.models.pixtral.modeling_pixtral import PixtralRotaryEmbedding
+from transformers.models.pixtral.modeling_pixtral import (
+    generate_block_attention_mask as _get_pixtral_attention_mask,
+)
+from transformers.models.pixtral.modeling_pixtral import position_ids_in_meshgrid
+
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import MergedColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.managers.mm_utils import MultiModalityDataPaddingPatternMultimodalTokens
+from sglang.srt.managers.schedule_batch import MultimodalInputs
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+
+
+class PixtralHFMLP(nn.Module):
+    """MLP for PixtralHFVisionModel using SGLang components."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        assert config.intermediate_size is not None
+
+        # Use MergedColumnParallelLinear for gate_up_proj to handle combined weights
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=config.hidden_size,
+            output_sizes=[config.intermediate_size, config.intermediate_size],
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+
+        self.down_proj = RowParallelLinear(
+            input_size=config.intermediate_size,
+            output_size=config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up_output, _ = self.gate_up_proj(x)
+
+        # Apply SiLU activation and multiply
+        gate_up = self.act_fn(gate_up_output)
+
+        # Project back to hidden size
+        out, _ = self.down_proj(gate_up)
+        return out
+
+
+class PixtralHFTransformerBlock(nn.Module):
+    """Transformer block for PixtralHFVisionModel using SGLang components."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
+
+        # Use SGLang's VisionAttention instead of vLLM's PixtralHFAttention
+        self.attention = VisionAttention(
+            embed_dim=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            projection_size=config.hidden_size,
+            use_qkv_parallel=True,
+            quant_config=quant_config,
+            dropout=0.0,
+            use_context_forward=False,
+            softmax_in_single_precision=False,
+            flatten_batch=False,
+            prefix=f"{prefix}.attention",
+        )
+
+        self.feed_forward = PixtralHFMLP(
+            config, quant_config=quant_config, prefix=f"{prefix}.feed_forward"
+        )
+
+        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]],
+    ) -> torch.Tensor:
+        # Ensure hidden_states has the batch dimension [batch, seq_len, hidden_dim]
+        batch_size, seq_len, hidden_dim = hidden_states.shape
+
+        # Apply attention norm - normalize along the last dimension
+        attn_normalized = self.attention_norm(hidden_states.view(-1, hidden_dim)).view(
+            batch_size, seq_len, hidden_dim
+        )
+
+        # Pass through attention layer
+        attention_output = self.attention(
+            attn_normalized,
+            attention_mask=attention_mask,
+            cu_seqlens=None,
+            position_embeddings=position_embeddings,
+        )
+
+        # Apply first residual connection
+        hidden_states = hidden_states + attention_output
+
+        # Apply feed-forward norm - normalize along the last dimension
+        ffn_normalized = self.ffn_norm(hidden_states.view(-1, hidden_dim)).view(
+            batch_size, seq_len, hidden_dim
+        )
+
+        # Pass through feed-forward layer
+        # First reshape to 2D for the feed-forward network, then reshape back
+        ffn_output = self.feed_forward(ffn_normalized)
+
+        # Apply second residual connection
+        output = hidden_states + ffn_output
+
+        return output
+
+
+class PixtralHFTransformer(nn.Module):
+    """Transformer for PixtralHFVisionModel using SGLang components."""
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        num_hidden_layers = config.num_hidden_layers
+        if num_hidden_layers_override is not None:
+            num_hidden_layers = num_hidden_layers_override
+
+        self.layers = nn.ModuleList(
+            [
+                PixtralHFTransformerBlock(
+                    config=config,
+                    layer_id=layer_idx,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                )
+                for layer_idx in range(num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        return_all_hidden_states: bool = False,
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
+        """Forward pass through transformer layers.
+
+        Args:
+            x: Input tensor
+            attention_mask: Optional attention mask
+            position_embeddings: Optional position embeddings for rotary attention
+            return_all_hidden_states: Whether to return all hidden states
+
+        Returns:
+            Either the final hidden state, or a list of all hidden states if
+            return_all_hidden_states is True
+        """
+        # For HF model compatibility, always start with the input
+        hidden_states = x
+        all_hidden_states = [hidden_states] if return_all_hidden_states else None
+
+        for i, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, attention_mask, position_embeddings)
+            if return_all_hidden_states:
+                all_hidden_states.append(hidden_states)
+
+        if return_all_hidden_states:
+            return all_hidden_states
+        return hidden_states
+
+
+def resolve_visual_encoder_outputs(
+    outputs: Union[torch.Tensor, List[torch.Tensor]],
+    feature_sample_layers: Optional[List[int]],
+    post_norm: Optional[nn.Module],
+    num_hidden_layers: int,
+) -> torch.Tensor:
+    """Resolve outputs from visual encoder based on feature_sample_layers."""
+    if feature_sample_layers is None:
+        # Just use the last layer's output
+        if isinstance(outputs, list):
+            outputs = outputs[-1]
+        if post_norm is not None:
+            outputs = post_norm(outputs)
+        return outputs
+
+    # Handle the case where we want to use specific layers
+    if not isinstance(outputs, list):
+        raise ValueError(
+            "Expected outputs to be a list when feature_sample_layers is provided"
+        )
+
+    # Validate layer indices
+    for layer_idx in feature_sample_layers:
+        if layer_idx < 0 or layer_idx > num_hidden_layers:
+            raise ValueError(
+                f"Feature sample layer index {layer_idx} is out of range "
+                f"[0, {num_hidden_layers}]"
+            )
+
+    # Collect outputs from specified layers
+    selected_outputs = [outputs[layer_idx] for layer_idx in feature_sample_layers]
+
+    # Combine the outputs
+    combined_outputs = torch.cat(selected_outputs, dim=-1)
+
+    if post_norm is not None:
+        combined_outputs = post_norm(combined_outputs)
+
+    return combined_outputs
+
+
+class PixtralHFVisionModel(nn.Module):
+    """Hugging Face Pixtral Vision Model implemented using SGLang components."""
+
+    DEFAULT_IMAGE_TOKEN_ID = 10
+
+    def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
+        return self.input_padder.pad_input_tokens(input_ids, image_inputs)
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        image_token_id: int = DEFAULT_IMAGE_TOKEN_ID,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_conv = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=config.hidden_size,
+            kernel_size=config.patch_size,
+            stride=config.patch_size,
+            bias=False,
+        )
+
+        self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5)
+
+        self.transformer = PixtralHFTransformer(
+            config,
+            quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.transformer",
+        )
+
+        # Check that num_hidden_layers is valid
+        num_hidden_layers = config.num_hidden_layers
+        if len(self.transformer.layers) > config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.transformer.layers)} "
+                "layers."
+            )
+
+        # Initialize patch position embedding
+        self.image_token_id = image_token_id
+        self.patch_positional_embedding = PixtralRotaryEmbedding(config)
+        self.input_padder = MultiModalityDataPaddingPatternMultimodalTokens(
+            [self.image_token_id]
+        )
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        image_sizes: list[tuple[int, int]],
+        output_hidden_states: bool = False,
+        feature_sample_layers: Optional[list[int]] = None,
+    ) -> Union[torch.Tensor, tuple]:
+        """
+        Args:
+            pixel_values: [batch_size, C, H, W], padded if multiple images
+            image_sizes: list of (H, W) for each image in the batch
+            output_hidden_states: Whether to return all hidden states.
+            feature_sample_layers: Layer indices whose features should be
+                concatenated and used as the visual encoder output. If none
+                are provided, the last layer is used.
+
+        Returns:
+            A tuple containing:
+              - hidden_states: Final model outputs (or selected layers if feature_sample_layers given)
+              - hidden_states tuple (optional): All hidden states if output_hidden_states=True
+        """
+        # batch patch images
+        embeds_orig = self.patch_conv(
+            pixel_values.to(device=self.device, dtype=self.dtype)
+        )
+        # crop the embeddings
+        embeds_2d = [
+            embed[..., : h // self.patch_size, : w // self.patch_size]
+            for embed, (h, w) in zip(embeds_orig, image_sizes)
+        ]
+
+        # flatten to sequence
+        embeds_1d = torch.cat([p.flatten(1).T for p in embeds_2d], dim=0)
+        embeds_featurized = self.ln_pre(embeds_1d).unsqueeze(0)
+
+        # positional embeddings
+        position_ids = position_ids_in_meshgrid(
+            embeds_2d,
+            max_width=self.image_size // self.patch_size,
+        ).to(self.device)
+
+        # The original PixtralRotaryEmbedding expects 2D input but returns a tuple of tensors (cos, sin)
+        # These tensors are used by apply_rotary_pos_emb in the transformer blocks
+        position_embedding = self.patch_positional_embedding(
+            embeds_featurized, position_ids
+        )
+        attention_mask = _get_pixtral_attention_mask(
+            [p.shape[-2] * p.shape[-1] for p in embeds_2d], embeds_featurized
+        )
+
+        return_all_hidden_states = (
+            output_hidden_states or feature_sample_layers is not None
+        )
+
+        transformer_outputs = self.transformer(
+            embeds_featurized,  # add batch dimension
+            attention_mask,
+            position_embedding,
+            return_all_hidden_states=return_all_hidden_states,
+        )
+
+        # Store all hidden states if requested
+        all_hidden_states = None
+        if isinstance(transformer_outputs, list):
+            all_hidden_states = transformer_outputs
+            # Use the last layer by default if feature_sample_layers is not specified
+            if feature_sample_layers is None:
+                out = transformer_outputs[-1]
+            else:
+                # Resolve outputs based on feature sample layers
+                out = resolve_visual_encoder_outputs(
+                    transformer_outputs,
+                    feature_sample_layers,
+                    None,
+                    self.config.num_hidden_layers,
+                )
+        else:
+            out = transformer_outputs
+
+        # Format return to be compatible with HuggingFace vision models
+        if output_hidden_states:
+            return type(
+                "VisualOutput",
+                (),
+                {
+                    "last_hidden_state": out,
+                    "hidden_states": all_hidden_states,
+                },
+            )
+        else:
+            return out
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
+        """Load weights from a HuggingFace checkpoint with proper parameter mapping."""
+        params_dict = dict(self.named_parameters())
+
+        # for (param, weight, shard_id): load weight into param as param's shard_id part
+        stacked_params_mapping = [
+            (".attention.qkv_proj", ".attention.q_proj", "q"),
+            (".attention.qkv_proj", ".attention.k_proj", "k"),
+            (".attention.qkv_proj", ".attention.v_proj", "v"),
+            (".feed_forward.gate_up_proj", ".feed_forward.gate_proj", 0),
+            (".feed_forward.gate_up_proj", ".feed_forward.up_proj", 1),
+        ]
+
+        # Process each weight
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name in name:
+                    # Replace the weight name part with the combined parameter name
+                    transformed_name = name.replace(weight_name, param_name)
+                    if transformed_name in params_dict:
+                        param = params_dict[transformed_name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight, shard_id)
+                        break
+            else:
+                if ".attention.o_proj" in name:
+                    alt_name = name.replace(".attention.o_proj", ".attention.proj")
+                    if alt_name in params_dict:
+                        name = alt_name
+                if name in params_dict:
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+
+class PixtralVisionModel(PixtralHFVisionModel):
+    pass
+
+
+# Register the model classes for external access
+EntryClass = [PixtralVisionModel]

python/sglang/test/runners.py

@@ -19,7 +19,9 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
+import transformers
 from transformers import (
+    AutoConfig,
     AutoModel,
     AutoModelForCausalLM,
     AutoModelForVision2Seq,
@@ -211,7 +213,12 @@ class HFRunner:
 
         # Load the model and tokenizer
         if self.model_type == "generation":
-            self.base_model = AutoModelForCausalLM.from_pretrained(
+            config = AutoConfig.from_pretrained(model_path)
+            if model_archs := getattr(config, "architectures"):
+                model_cls = getattr(transformers, model_archs[0])
+            else:
+                model_cls = AutoModelForCausalLM
+            self.base_model = model_cls.from_pretrained(
                 model_path,
                 torch_dtype=torch_dtype,
                 trust_remote_code=self.trust_remote_code,

test/srt/models/test_generation_models.py

@@ -14,14 +14,15 @@
 """
 Usage:
 
-To test a specific model:
+To test a specific model locally:
-1. Add it to ALL_OTHER_MODELS
+1. Add it to ALL_MODELS, for example, `ModelCase("Qwen/Qwen2-1.5B")`
-2. Run `ONLY_RUN=Qwen/Qwen2-1.5B python3 -m unittest test_generation_models.TestGenerationModels.test_others`
+2. Run `ONLY_RUN=Qwen/Qwen2-1.5B python3 -m unittest test_generation_models.TestGenerationModels`
 """
 
 import dataclasses
 import multiprocessing as mp
 import os
+import random
 import unittest
 from typing import List
 
@@ -53,8 +54,9 @@ CI_MODELS = [
     ModelCase("google/gemma-2-2b"),
 ]
 
-# All other models that do not run on the CI
+# the complete set of models to test sglang's generation model
-ALL_OTHER_MODELS = [
+ALL_MODELS = [
+    *CI_MODELS,
     ModelCase("Qwen/Qwen2-1.5B"),
     ModelCase("Qwen/Qwen2.5-14B-Instruct"),
     ModelCase("HuggingFaceTB/SmolLM-135M-Instruct", skip_long_prompt=True),
@@ -63,7 +65,7 @@ ALL_OTHER_MODELS = [
         "THUDM/glm-4-9b-chat", tp_size=2, trust_remote_code=True, skip_long_prompt=True
     ),
     ModelCase("openai-community/gpt2"),
-    ModelCase("microsoft/Phi-3-small-8k-instruct"),
+    ModelCase("microsoft/Phi-3-small-8k-instruct", trust_remote_code=True),
     ModelCase("allenai/OLMo-2-1124-7B-Instruct", skip_long_prompt=True),
     ModelCase("ibm-granite/granite-3.0-2b-instruct", skip_long_prompt=True),
 ]
@@ -117,9 +119,30 @@ class TestGenerationModels(CustomTestCase):
             debug_text=f"model_path={model_path} prompts={prompts}",
         )
 
+    @unittest.skipIf(not is_in_ci(), "Local test should run all models")
     def test_ci_models(self):
         for model_case in CI_MODELS:
             for torch_dtype in TORCH_DTYPES:
+                prompts = DEFAULT_PROMPTS
+
+                # Skip long prompts for models that do not have a long context
+                if model_case.skip_long_prompt:
+                    prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]
+
+                # Assert the logits and output strs are close
+                self.assert_close_logits_and_output_strs(
+                    prompts, model_case, torch_dtype
+                )
+
+    @unittest.skipIf(is_in_ci(), "CI only runs selected models for simplicity")
+    def test_all_models(self):
+        for model_case in ALL_MODELS:
+            for torch_dtype in TORCH_DTYPES:
+                if (
+                    "ONLY_RUN" in os.environ
+                    and os.environ["ONLY_RUN"] != model_case.model_path
+                ):
+                    continue
 
                 # Skip long prompts for models that do not have a long context
                 prompts = DEFAULT_PROMPTS
@@ -131,26 +154,6 @@ class TestGenerationModels(CustomTestCase):
                     prompts, model_case, torch_dtype
                 )
 
-    def test_others(self):
-        if is_in_ci():
-            return
-
-        for model_case in ALL_OTHER_MODELS:
-            # Only run a specified model
-            if (
-                "ONLY_RUN" in os.environ
-                and os.environ["ONLY_RUN"] != model_case.model_path
-            ):
-                continue
-
-            # Skip long prompts for models that do not have a long context
-            prompts = DEFAULT_PROMPTS
-            if model_case.skip_long_prompt:
-                prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]
-
-            # Assert the logits and output strs are close
-            self.assert_close_logits_and_output_strs(prompts, model_case, torch.float16)
-
 
 if __name__ == "__main__":
     unittest.main()

test/srt/test_vision_openai_server.py

@@ -642,6 +642,28 @@ class TestMinicpmoServer(TestOpenAIVisionServer):
         self._test_audio_ambient_completion()
 
 
+class TestPixtralServer(TestOpenAIVisionServer):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "mistral-community/pixtral-12b"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.api_key = "sk-123456"
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--trust-remote-code",
+                "--mem-fraction-static",
+                "0.73",
+            ],
+        )
+        cls.base_url += "/v1"
+
+    def test_video_chat_completion(self):
+        pass
+
+
 class TestDeepseekVL2Server(TestOpenAIVisionServer):
     @classmethod
     def setUpClass(cls):