model: support dots.vlm1 model (#8778)

Co-authored-by: weishi <bushou@xiaohongshu.com>
Co-authored-by: Ezra-Yu <1105212286@qq.com>
Co-authored-by: Jianfei Wang <905787410@qq.com>
Co-authored-by: qianwu <wangjianfei@xiaohongshu.com>

python/sglang/srt/configs/__init__.py

@@ -1,6 +1,7 @@
 from sglang.srt.configs.chatglm import ChatGLMConfig
 from sglang.srt.configs.dbrx import DbrxConfig
 from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
+from sglang.srt.configs.dots_vlm import DotsVLMConfig
 from sglang.srt.configs.exaone import ExaoneConfig
 from sglang.srt.configs.janus_pro import MultiModalityConfig
 from sglang.srt.configs.kimi_vl import KimiVLConfig
@@ -26,4 +27,5 @@ __all__ = [
     "Step3TextConfig",
     "Step3VisionEncoderConfig",
     "Qwen3NextConfig",
+    "DotsVLMConfig",
 ]

python/sglang/srt/configs/dots_vlm.py

@@ -0,0 +1,139 @@
+from typing import Any, List, Optional, Union
+
+from transformers import AutoProcessor, LlamaTokenizerFast, PretrainedConfig
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessingKwargs, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+try:
+    from transformers import Qwen2_5_VLProcessor
+except ImportError:
+    raise ImportError(
+        "Qwen2_5_VLProcessor can not be found. Please upgrade your transformers version."
+    )
+
+from sglang.srt.configs.deepseekvl2 import DeepseekV2Config
+
+
+class DotsVisionConfig(PretrainedConfig):
+    model_type: str = "dots_vit"
+
+    def __init__(
+        self,
+        embed_dim: int = 1536,  # vision encoder embed size
+        hidden_size: int = 1536,  # after merger hidden size
+        intermediate_size: int = 4224,
+        num_hidden_layers: int = 42,
+        num_attention_heads: int = 12,
+        num_channels: int = 3,
+        patch_size: int = 14,
+        spatial_merge_size: int = 2,
+        temporal_patch_size: int = 1,
+        rms_norm_eps: float = 1e-5,
+        use_bias: bool = False,
+        attn_implementation="flash_attention_2",  # "eager","sdpa","flash_attention_2"
+        initializer_range=0.02,
+        init_merger_std=0.02,
+        is_causal=False,  # ve causal forward
+        post_norm=True,
+        gradient_checkpointing=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.rms_norm_eps = rms_norm_eps
+        self.use_bias = use_bias
+        self.attn_implementation = attn_implementation
+        self.initializer_range = initializer_range
+        self.init_merger_std = init_merger_std
+        self.is_causal = is_causal
+        self.post_norm = post_norm
+        self.gradient_checkpointing = gradient_checkpointing
+
+
+class DotsVLMConfig(PretrainedConfig):
+    model_type = "dots_vlm"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        vision_config = kwargs.get("vision_config", {})
+        self.im_span_id = kwargs.get("image_token_id", 128815)
+        self.video_span_id = kwargs.get("video_token_id", 128836)
+        self.vision_config = DotsVisionConfig(**vision_config)
+        self.language_config = DeepseekV2Config(**kwargs)
+        self.architectures = ["DotsVLMForCausalLM"]
+
+
+class DotsVLMProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+    }
+
+
+class DotsVLMProcessor(Qwen2_5_VLProcessor):
+    r"""
+    Constructs a DotsVLM processor which derives from Qwen2_5_VLProcessor, but overrides the image and video token ids.
+    Besides, its tokenizer is a LlamaTokenizerFast instead of Qwen2TokenizerFast.
+    [`DotsVLMProcessor`] offers all the functionalities of [`DotsVisionConfig`] and [`LlamaTokenizerFast`]. See the
+    [`~DotsVLMProcessor.__call__`] and [`~DotsVLMProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`LlamaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+
+    valid_kwargs = ["chat_template"]
+
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+
+    def __init__(
+        self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
+    ):
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+        self.image_token = (
+            "<|imgpad|>"
+            if not hasattr(tokenizer, "image_token")
+            else tokenizer.image_token
+        )
+        self.video_token = (
+            "<|video_pad|>"
+            if not hasattr(tokenizer, "video_token")
+            else tokenizer.video_token
+        )
+        self.img_token = (
+            "<|img|>" if not hasattr(tokenizer, "img_token") else tokenizer.img_token
+        )
+        self.endofimg_token = (
+            "<|endofimg|>"
+            if not hasattr(tokenizer, "endofimg_token")
+            else tokenizer.endofimg_token
+        )
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.encode(self.image_token)[0]
+        )
+        self.video_token_id = (
+            tokenizer.video_token_id
+            if getattr(tokenizer, "video_token_id", None)
+            else tokenizer.encode(self.video_token)[0]
+        )
+
+
+AutoProcessor.register(DotsVLMConfig, DotsVLMProcessor)

python/sglang/srt/configs/model_config.py

@@ -216,6 +216,7 @@ class ModelConfig:
             or "DeepseekV3ForCausalLMNextN" in self.hf_config.architectures
             or "LongcatFlashForCausalLM" in self.hf_config.architectures
             or "LongcatFlashForCausalLMNextN" in self.hf_config.architectures
+            or "DotsVLMForCausalLM" in self.hf_config.architectures
         ):
             self.head_dim = 256
             self.attention_arch = AttentionArch.MLA
@@ -734,6 +735,7 @@ multimodal_model_archs = [
     "Phi4MMForCausalLM",
     "VILAForConditionalGeneration",
     "Step3VLForConditionalGeneration",
+    "DotsVLMForCausalLM",
 ]