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model(vlm): mistral 3.1 (#5099)

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Co-authored-by: KivenChen <sleigh-queue-0y@icloud.com>
This commit is contained in:
Kiv Chen
2025-05-16 18:36:18 -07:00
committed by GitHub
parent 69748d088d
commit 64825b8395
6 changed files with 152 additions and 21 deletions
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29
docs/supported_models/vision_language_models.md Normal file
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@@ -0,0 +1,29 @@
# Vision Language Models
These models accept multi-modal inputs (e.g., images and text) and generate text output. They augment language models with visual encoders and require a specific chat template for handling vision prompts.
## Example launch Command
```shell
python3 -m sglang.launch_server \
--model-path meta-llama/Llama-3.2-11B-Vision-Instruct \ # example HF/local path
--host 0.0.0.0 \
--port 30000 \
```
## Supporting Matrixs
| Model Family (Variants) | Example HuggingFace Identifier | Chat Template | Description |
|--------------------------------|--------------------------------------------------|----------------------|----------------------------------------------------------------------------------------|
| **Qwen-VL** (Qwen2 series) | `Qwen/Qwen2.5-VL-7B-Instruct` | `qwen2-vl` | Alibaba’s vision-language extension of Qwen; for example, Qwen2.5-VL (7B and larger variants) can analyze and converse about image content. |
| **DeepSeek-VL2** | `deepseek-ai/deepseek-vl2` | `deepseek-vl2` | Vision-language variant of DeepSeek (with a dedicated image processor), enabling advanced multimodal reasoning on image and text inputs. |
| **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. |
| **Gemma 3 (Multimodal)** | `google/gemma-3-4b-it` | `gemma-it` | Gemma 3’s larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context. |
| **Kimi-VL** (A3B) | `moonshotai/Kimi-VL-A3B-Instruct` | `kimi-vl` | Kimi-VL is a multimodal model that can understand and generate text from images. |
| **Mistral-Small-3.1-24B** | `mistralai/Mistral-Small-3.1-24B-Instruct-2503` | `mistral` | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. |

1
python/sglang/srt/configs/model_config.py
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@@ -549,6 +549,7 @@ multimodal_model_archs = [
"LlavaVidForCausalLM",
"MiniCPMO",
"MiniCPMV",
"Mistral3ForConditionalGeneration",
"MultiModalityCausalLM",
"MllamaForConditionalGeneration",
"Qwen2VLForConditionalGeneration",

5
python/sglang/srt/managers/multimodal_processors/llava.py
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@@ -20,6 +20,7 @@ from sglang.srt.models.llava import (
LlavaQwenForCausalLM,
)
from sglang.srt.models.llavavid import LlavaVidForCausalLM
from sglang.srt.models.mistral import Mistral3ForConditionalGeneration
from sglang.srt.utils import load_image, logger
from sglang.utils import get_exception_traceback
@@ -176,10 +177,10 @@ class LlavaImageProcessor(BaseMultimodalProcessor):
class LlavaMultimodalProcessor(BaseMultimodalProcessor):
"""
This is a wrapper class used to identify the multimodal processor for Llava architecture models.
This is a wrapper class used to identify the multimodal processor for Llava architectures' vision model.
"""
models = [LlavaForConditionalGeneration]
models = [LlavaForConditionalGeneration, Mistral3ForConditionalGeneration]
def _get_sgl_processor_cls(self, model_type: str):
if hf_name := HF_MAPPING_NAMES.get(model_type):

44
python/sglang/srt/models/llava.py
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@@ -135,7 +135,6 @@ class LlavaBaseForCausalLM(nn.Module):
"""
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.
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:]
@@ -146,7 +145,6 @@ class LlavaBaseForCausalLM(nn.Module):
f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}"
)
image_features = self.multi_modal_projector(selected_image_feature)
return image_features
@torch.no_grad()
@@ -613,6 +611,10 @@ class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
MULTIMODAL_PROJECTOR_TYPE = LlavaMultiModalProjector
@property
def dtype(self):
return self.torch_dtype
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)
@@ -672,11 +674,17 @@ class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
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
self.text_config = self.config.text_config
self.vision_config = self.config.vision_config
self.torch_dtype = getattr(self.config, "torch_dtype")
if not getattr(self.text_config, "torch_dtype"):
self.text_config.torch_dtype = self.torch_dtype
if not getattr(self.vision_config, "torch_dtype"):
self.vision_config.torch_dtype = self.torch_dtype
if not hasattr(self.config, "vocab_size"):
self.config.vocab_size = self.config.text_config.vocab_size
self.config.vocab_size = self.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"):
@@ -697,39 +705,39 @@ class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
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_layer = getattr(self.config, "vision_feature_layer", -1)
self.vision_feature_select_strategy = getattr(
config, "vision_feature_select_strategy", "full"
self.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.image_size = self.vision_config.image_size
self.patch_size = self.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.mm_patch_merge_type = self.config.mm_patch_merge_type
self.image_aspect_ratio = self.config.image_aspect_ratio
self.image_grid_pinpoints = self.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
self.text_config, AutoModelForCausalLM
)
vision_model_cls = self._get_sgl_model_cls(config.vision_config, AutoModel)
vision_model_cls = self._get_sgl_model_cls(self.vision_config, AutoModel)
self.language_model = language_model_cls(
config.text_config,
self.text_config,
quant_config=quant_config,
prefix=add_prefix("language_model", prefix),
)
self.vision_tower = vision_model_cls(
config.vision_config,
self.vision_config,
quant_config=quant_config,
prefix=add_prefix("vision_tower", prefix),
)
if "unpad" in getattr(config, "mm_patch_merge_type", ""):
if "unpad" in getattr(self.config, "mm_patch_merge_type", ""):
self.language_model.model.image_newline = nn.Parameter(
torch.empty(config.text_config.hidden_size, dtype=torch.float16)
torch.empty(self.text_config.hidden_size, dtype=self.torch_dtype)
)
def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:

72
python/sglang/srt/models/mistral.py
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@@ -13,6 +13,12 @@
# ==============================================================================
"""Inference-only Mistral model."""
from typing import List, Union
import torch
from transformers.models.mistral3.modeling_mistral3 import Mistral3MultiModalProjector
from sglang.srt.managers.schedule_batch import MultimodalDataItem
from sglang.srt.models.llama import LlamaForCausalLM
@@ -20,4 +26,68 @@ class MistralForCausalLM(LlamaForCausalLM):
pass
EntryClass = MistralForCausalLM
class Mistral3ForConditionalGeneration:
MULTIMODAL_PROJECTOR_TYPE = Mistral3MultiModalProjector
def __init__(self, **kwargs):
# lazy load inner class
# to bypass circular import
from sglang.srt.models.llava import LlavaForConditionalGeneration
# override config: mistral's projector adds patchmerger that doesn't require padding
kwargs["config"].vision_config.pad_image_border = False
self.inner = LlavaForConditionalGeneration(**kwargs)
self.inner.multi_modal_projector = self.MULTIMODAL_PROJECTOR_TYPE(
kwargs["config"]
)
self.inner.get_image_feature = self.get_image_feature
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), image_sizes
)
)
ret = torch.cat(features, dim=0)
return ret
def __getattr__(self, name):
return getattr(self.inner, name)
def __hasattr__(self, name):
return hasattr(self.inner, name)
def __call__(self, *args, **kwargs):
return self.inner(*args, **kwargs)
EntryClass = [MistralForCausalLM, Mistral3ForConditionalGeneration]

22
test/srt/test_vision_openai_server.py
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@@ -664,6 +664,28 @@ class TestPixtralServer(TestOpenAIVisionServer):
pass
class TestMistral3_1Server(TestOpenAIVisionServer):
@classmethod
def setUpClass(cls):
cls.model = "unsloth/Mistral-Small-3.1-24B-Instruct-2503"
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",
"0.8",
],
)
cls.base_url += "/v1"
def test_video_chat_completion(self):
pass
class TestDeepseekVL2Server(TestOpenAIVisionServer):
@classmethod
def setUpClass(cls):