model: support qwen3-vl series (#10323)

Co-authored-by: ocss884 <ocss.lin@gmail.com>
Co-authored-by: cao1zhg <653506626@qq.com>
Co-authored-by: yhyang201 <yhyang201@gmail.com>
Co-authored-by: yhyang201 <47235274+yhyang201@users.noreply.github.com>
Co-authored-by: 瑀澈 <yuche.lz@alibaba-inc.com>
Co-authored-by: Mick <mickjagger19@icloud.com>
Co-authored-by: Yineng Zhang <me@zhyncs.com>

python/sglang/srt/configs/model_config.py

@@ -749,6 +749,8 @@ multimodal_model_archs = [
     "Qwen2AudioForConditionalGeneration",
     "Qwen2VLForConditionalGeneration",
     "Qwen2_5_VLForConditionalGeneration",
+    "Qwen3VLForConditionalGeneration",
+    "Qwen3VLMoeForConditionalGeneration",
     "KimiVLForConditionalGeneration",
     "InternVLChatModel",
     "InternS1ForConditionalGeneration",

python/sglang/srt/configs/qwen3_vl.py

+from typing import Optional, Union
+
+from transformers import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+
+class Qwen3VLVisionConfig(PretrainedConfig):
+    model_type = "qwen3_vl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        deepstack_visual_indexes=[8, 16, 24],
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+        self.deepstack_visual_indexes = deepstack_visual_indexes
+
+
+class Qwen3VLTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3VLTextModel`]. It is used to instantiate a
+    Qwen3-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3-VL-4B-Instruct [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen3VL model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen3VLModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+        head_dim (`int`, *optional*, defaults to 128):
+            The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 128000):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 5000000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import Qwen3VLTextModel, Qwen3VLTextConfig
+
+    >>> # Initializing a Qwen3VL style configuration
+    >>> configuration = Qwen3VLTextConfig()
+
+    >>> # Initializing a model from the Qwen3-VL-7B style configuration
+    >>> model = Qwen3VLTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_vl_text"
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=128000,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=5000000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"})
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class Qwen3VLConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3VLModel`]. It is used to instantiate a
+    Qwen3-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3-VL-4B-Instruct [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLTextConfig`):
+            The config object or dictionary of the text backbone.
+        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Qwen3VLVisionConfig`):
+            The config object or dictionary of the vision backbone.
+        image_token_id (`int`, *optional*, defaults to 151655):
+            The image token index to encode the image prompt.
+        video_token_id (`int`, *optional*, defaults to 151656):
+            The video token index to encode the image prompt.
+        vision_start_token_id (`int`, *optional*, defaults to 151652):
+            The start token index to encode the image prompt.
+        vision_end_token_id (`int`, *optional*, defaults to 151653):
+            The end token index to encode the image prompt.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie the word embeddings.
+
+    ```python
+    >>> from transformers import Qwen3VLForConditionalGeneration, Qwen3VLConfig
+
+    >>> # Initializing a Qwen3-VL style configuration
+    >>> configuration = Qwen3VLConfig()
+
+    >>> # Initializing a model from the Qwen3-VL-4B style configuration
+    >>> model = Qwen3VLForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_vl"
+    sub_configs = {
+        "vision_config": Qwen3VLVisionConfig,
+        "text_config": Qwen3VLTextConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_token_id=151655,
+        video_token_id=151656,
+        vision_start_token_id=151652,
+        vision_end_token_id=151653,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
+
+
+class Qwen3VLMoeTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3VLMoeTextModel`]. It is used to instantiate a
+    Qwen3-VL-MOE model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2MoeModel`]
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 5632):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 128000):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 5000000.0):
+            The base period of the RoPE embeddings.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        decoder_sparse_step (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer.
+        moe_intermediate_size (`int`, *optional*, defaults to 1408):
+            Intermediate size of the routed expert.
+        num_experts_per_tok (`int`, *optional*, defaults to 4):
+            Number of selected experts.
+        num_experts (`int`, *optional*, defaults to 60):
+            Number of routed experts.
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the topk probabilities.
+        mlp_only_layers (`List[int]`, *optional*, defaults to `[]`):
+            Indicate which layers use Qwen3VLMoeMLP rather than Qwen3VLMoeSparseMoeBlock
+            The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
+            If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        head_dim (`int`, *optional*):
+            The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.
+
+    ```python
+    >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig
+
+    >>> # Initializing a Qwen3VLMoe style configuration
+    >>> configuration = Qwen3VLMoeConfig()
+
+    >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration
+    >>> model = Qwen3VLMoeForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_vl_moe_text"
+    base_config_key = "text_config"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    # Default tensor parallel plan for base model `Qwen3VLMoe`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=2048,
+        intermediate_size=5632,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        hidden_act="silu",
+        max_position_embeddings=128000,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=5000000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        decoder_sparse_step=1,
+        moe_intermediate_size=1408,
+        num_experts_per_tok=4,
+        num_experts=60,
+        norm_topk_prob=True,
+        mlp_only_layers=None,
+        rope_scaling=None,
+        head_dim=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.rope_scaling = rope_scaling
+        self.head_dim = head_dim or hidden_size // num_attention_heads
+
+        rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"})
+
+        # MoE arguments
+        self.decoder_sparse_step = decoder_sparse_step
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.norm_topk_prob = norm_topk_prob
+        self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class Qwen3VLMoeVisionConfig(PretrainedConfig):
+    model_type = "qwen3_vl_moe"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        deepstack_visual_indexes=[8, 16, 24],
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+        self.deepstack_visual_indexes = deepstack_visual_indexes
+
+
+class Qwen3VLMoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3VLMoeModel`]. It is used to instantiate a
+    Qwen3-VL-MOE model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLMoeTextConfig`):
+            The config object or dictionary of the text backbone.
+        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Qwen3VLMoeVisionConfig`):
+            The config object or dictionary of the vision backbone.
+        image_token_id (`int`, *optional*, defaults to 151655):
+            The image token index to encode the image prompt.
+        video_token_id (`int`, *optional*, defaults to 151656):
+            The video token index to encode the image prompt.
+        vision_start_token_id (`int`, *optional*, defaults to 151652):
+            The start token index to encode the image prompt.
+        vision_end_token_id (`int`, *optional*, defaults to 151653):
+            The end token index to encode the image prompt.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie the word embeddings.
+
+    ```python
+    >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig
+
+    >>> # Initializing a Qwen3-VL-MOE style configuration
+    >>> configuration = Qwen3VLMoeConfig()
+
+    >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration
+    >>> model = Qwen3VLMoeForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_vl_moe"
+    sub_configs = {
+        "vision_config": Qwen3VLMoeVisionConfig,
+        "text_config": Qwen3VLMoeTextConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_token_id=151655,
+        video_token_id=151656,
+        vision_start_token_id=151652,
+        vision_end_token_id=151653,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
+
+
+__all__ = [
+    "Qwen3VLMoeConfig",
+    "Qwen3VLMoeVisionConfig",
+    "Qwen3VLConfig",
+    "Qwen3VLVisionConfig",
+]

python/sglang/srt/layers/rotary_embedding.py

@@ -1187,7 +1187,7 @@ class MRotaryEmbedding(RotaryEmbedding):
 
                         time_tensor_long = time_tensor.long()
                         t_index = time_tensor_long.flatten()
-                    elif model_type == "qwen2_vl":
+                    elif model_type in ("qwen2_vl", "qwen3_vl", "qwen3_vl_moe"):
                         t_index = (
                             torch.arange(llm_grid_t)
                             .view(-1, 1)

python/sglang/srt/managers/mm_utils.py

@@ -507,6 +507,7 @@ def embed_mm_inputs(
         Modality, Callable[[List[MultimodalDataItem]], torch.Tensor]
     ] = None,
     placeholder_tokens: dict[Modality, List[int]] = None,
+    use_deepstack: bool = False,
 ) -> Optional[torch.Tensor]:
     """
     Embed multimodal inputs and integrate them with text token embeddings.
@@ -522,7 +523,7 @@ def embed_mm_inputs(
     Returns:
         Combined embedding tensor with multimodal content integrated
     """
-
+    other_info = {}
     if mm_inputs_list is None:
         return None
 
@@ -532,7 +533,7 @@ def embed_mm_inputs(
     for mm_inputs in mm_inputs_list:
         item_flatten_list += [item for item in mm_inputs.mm_items if item is not None]
 
-    embeddings, masks = [], []
+    embeddings, masks, deepstack_embeddings = [], [], []
     # 2. Get multimodal embedding separately
     # Try get mm embedding if any
     for modality in Modality.all():
@@ -578,6 +579,12 @@ def embed_mm_inputs(
                 extend_length=extend_seq_lens,
                 items_offset_list=items_offsets,
             )
+
+            if use_deepstack and embedding is not None:
+                embedding, deepstack_embedding = (
+                    multimodal_model.separate_deepstack_embeds(embedding)
+                )
+                deepstack_embeddings += [deepstack_embedding]
             embeddings += [embedding]
             masks += [mask]
 
@@ -591,13 +598,37 @@ def embed_mm_inputs(
     inputs_embeds = input_embedding(input_ids)
 
     # 4. scatter embeddings into input embedding
-    for embedding, mask in zip(embeddings, masks):
+
+    # deepstack embedding
+    if use_deepstack:
+        num_deepstack_embeddings = (
+            len(multimodal_model.deepstack_visual_indexes) if use_deepstack else 0
+        )
+        deepstack_embedding_shape = inputs_embeds.shape[:-1] + (
+            inputs_embeds.shape[-1] * num_deepstack_embeddings,
+        )
+
+        input_deepstack_embeds = torch.zeros(
+            deepstack_embedding_shape,
+            device=inputs_embeds.device,
+            dtype=inputs_embeds.dtype,
+        )
+
+        other_info["input_deepstack_embeds"] = input_deepstack_embeds
+
+    for i, embedding, mask in zip(range(len(embeddings)), embeddings, masks):
         if embedding is None or mask is None:
             continue
         # in-place update
         indices = torch.where(mask.squeeze(dim=-1))[0]
         inputs_embeds[indices] = embedding.to(inputs_embeds.device, inputs_embeds.dtype)
-    return inputs_embeds
+
+        if use_deepstack:
+            input_deepstack_embeds[indices] = deepstack_embeddings[i].to(
+                inputs_embeds.device, inputs_embeds.dtype
+            )
+
+    return inputs_embeds, other_info
 
 
 def general_mm_embed_routine(
@@ -609,6 +640,7 @@ def general_mm_embed_routine(
         Modality, Callable[[List[MultimodalDataItem]], torch.Tensor]
     ] = None,
     placeholder_tokens: Optional[dict[Modality, List[int]]] = None,
+    use_deepstack: bool = False,
     **kwargs,
 ) -> torch.Tensor:
     """
@@ -620,6 +652,7 @@ def general_mm_embed_routine(
         language_model: Base language model to use
         data_embedding_funcs: A dictionary mapping from modality type to the corresponding embedding function.
         placeholder_tokens: Token IDs for multimodal placeholders
+        use_deepstack: Whether to use deepstack embeddings
         **kwargs: Additional arguments passed to language model
 
     Returns:
@@ -645,16 +678,20 @@ def general_mm_embed_routine(
             for i, seq_len in enumerate(forward_batch.extend_seq_lens_cpu)
             if forward_batch.mm_inputs[i] is not None
         ]
-        inputs_embeds = embed_mm_inputs(
+        inputs_embeds, other_info = embed_mm_inputs(
             mm_inputs_list=mm_inputs_list,
             extend_prefix_lens=extend_prefix_lens,
             extend_seq_lens=extend_seq_lens,
             input_ids=input_ids,
-            input_embedding=embed_tokens,
             multimodal_model=multimodal_model,
+            input_embedding=embed_tokens,
             data_embedding_func_mapping=data_embedding_funcs,
             placeholder_tokens=placeholder_tokens,
+            use_deepstack=use_deepstack,
         )
+        # add for qwen3_vl deepstack
+        if use_deepstack:
+            kwargs["input_deepstack_embeds"] = other_info["input_deepstack_embeds"]
         # once used, mm_inputs is useless, considering chunked-prefill is disabled for multimodal models
         # just being defensive here
         forward_batch.mm_inputs = None

python/sglang/srt/models/qwen3_vl.py

+# Copyright 2025 Qwen Team
+# Copyright 2025 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.
+# ==============================================================================
+"""Inference-only Qwen3-VL model compatible with HuggingFace weights."""
+import logging
+from functools import lru_cache, partial
+from typing import Callable, Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers.activations import ACT2FN
+from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
+    Qwen2_5_VisionRotaryEmbedding,
+)
+
+from sglang.srt.configs.qwen3_vl import Qwen3VLConfig, Qwen3VLVisionConfig
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen2_vl import Qwen2VLVideoInputs
+from sglang.srt.models.qwen3 import Qwen3Model
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+# === Vision Encoder === #
+
+
+class Qwen3_VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        bias: bool = True,
+        hidden_act="silu",
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.linear_fc1 = ColumnParallelLinear(
+            in_features,
+            hidden_features,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("linear_fc1", prefix),
+        )
+        self.linear_fc2 = RowParallelLinear(
+            hidden_features,
+            in_features,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("linear_fc2", prefix),
+        )
+        self.act = ACT2FN[hidden_act]
+
+    def forward(self, x: torch.Tensor):
+        x_fc1, _ = self.linear_fc1(x)
+        mlp_output, _ = self.linear_fc2(self.act(x_fc1))
+        return mlp_output
+
+
+class Qwen3VLVisionPatchEmbed(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.patch_size = config.patch_size
+        self.temporal_patch_size = config.temporal_patch_size
+        self.in_channels = config.in_channels
+        self.embed_dim = config.hidden_size
+
+        kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size]
+        self.proj = nn.Conv3d(
+            self.in_channels,
+            self.embed_dim,
+            kernel_size=kernel_size,
+            stride=kernel_size,
+            bias=True,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        target_dtype = self.proj.weight.dtype
+        hidden_states = hidden_states.view(
+            -1,
+            self.in_channels,
+            self.temporal_patch_size,
+            self.patch_size,
+            self.patch_size,
+        )
+        hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(
+            -1, self.embed_dim
+        )
+        return hidden_states
+
+
+class Qwen3_VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        intermediate_dim: int,
+        hidden_act="silu",
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        attn_implementation: Optional[str] = "sdpa",
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+
+        if attn_implementation == "sdpa":
+            softmax_in_single_precision = False
+            qkv_backend = "sdpa"
+            flatten_batch = True
+        elif attn_implementation == "flash_attention_2":
+            softmax_in_single_precision = False
+            qkv_backend = "triton_attn"
+            flatten_batch = True
+        elif attn_implementation == "eager":
+            softmax_in_single_precision = True
+            qkv_backend = "sdpa"
+            flatten_batch = True
+        elif attn_implementation == "flash_attention_3":
+            softmax_in_single_precision = False
+            qkv_backend = "fa3"
+            flatten_batch = True
+
+        self.attn = VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            use_qkv_parallel=True,
+            rotary_embed="normal",
+            proj_bias=True,
+            qkv_backend=qkv_backend,
+            softmax_in_single_precision=softmax_in_single_precision,
+            flatten_batch=flatten_batch,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+        self.mlp = Qwen3_VisionMLP(
+            dim,
+            intermediate_dim,
+            hidden_act=hidden_act,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        position_embeddings: torch.Tensor,
+    ) -> torch.Tensor:
+        hidden_states = self.norm1(x)
+        hidden_states = rearrange(hidden_states, "s b ... -> b s ...")
+        attn = self.attn(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+            position_embeddings=position_embeddings,
+        )
+        attn = rearrange(attn, "b s ... -> s b ...")
+        x = x + attn
+        norm2 = self.norm2(x)
+        mlp = self.mlp(norm2)
+        x = x + mlp
+        return x
+
+
+class Qwen3_VisionPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        context_dim: int,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        spatial_merge_size: int = 2,
+        use_postshuffle_norm: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+
+        self.use_postshuffle_norm = use_postshuffle_norm
+
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm = norm_layer(
+            self.hidden_size if use_postshuffle_norm else context_dim
+        )
+        self.linear_fc1 = ColumnParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=add_prefix("linear_fc1", prefix),
+        )
+        self.act_fn = nn.GELU()
+        self.linear_fc2 = RowParallelLinear(
+            self.hidden_size,
+            dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=add_prefix("linear_fc2", prefix),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.use_postshuffle_norm:
+            x = self.norm(x.view(-1, self.hidden_size))
+        else:
+            x = self.norm(x).view(-1, self.hidden_size)
+
+        x_parallel, _ = self.linear_fc1(x)
+        x_parallel = self.act_fn(x_parallel)
+        out, _ = self.linear_fc2(x_parallel)
+        return out
+
+
+class Qwen3_VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Qwen3VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+        self.num_position_embeddings = vision_config.num_position_embeddings
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.spatial_merge_unit = self.spatial_merge_size**2
+        self.temporal_patch_size = vision_config.temporal_patch_size
+        self.deepstack_visual_indexes = vision_config.deepstack_visual_indexes
+        self.patch_embed = Qwen3VLVisionPatchEmbed(config=vision_config)
+        self.pos_embed = nn.Embedding(self.num_position_embeddings, self.hidden_size)
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [
+                Qwen3_VisionBlock(
+                    dim=self.hidden_size,
+                    num_heads=self.num_heads,
+                    intermediate_dim=vision_config.intermediate_size,
+                    hidden_act=vision_config.hidden_act,
+                    norm_layer=norm_layer,
+                    attn_implementation="flash_attention_3",
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"blocks.{layer_idx}", prefix),
+                )
+                for layer_idx in range(vision_config.depth)
+            ]
+        )
+        self.merger = Qwen3_VisionPatchMerger(
+            dim=vision_config.out_hidden_size,
+            context_dim=self.hidden_size,
+            norm_layer=norm_layer,
+            spatial_merge_size=self.spatial_merge_size,
+            quant_config=quant_config,
+            prefix=add_prefix("merger", prefix),
+        )
+
+        self.deepstack_merger_list = nn.ModuleList(
+            [
+                Qwen3_VisionPatchMerger(
+                    dim=vision_config.out_hidden_size,
+                    context_dim=self.hidden_size,
+                    spatial_merge_size=self.spatial_merge_size,
+                    use_postshuffle_norm=True,
+                    norm_layer=norm_layer,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"deepstack_merger_list.{layer_idx}", prefix),
+                )
+                for layer_idx in range(len(self.deepstack_visual_indexes))
+            ]
+        )
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rot_pos_emb(self, grid_thw):
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def fast_pos_embed_interpolate(self, grid_thw):
+        num_grid_per_side = int(self.num_position_embeddings**0.5)
+
+        idx_list = [[] for _ in range(4)]
+        weight_list = [[] for _ in range(4)]
+
+        # TODO: use torch instand of np
+        for t, h, w in grid_thw:
+            h_idxs = np.linspace(0, num_grid_per_side - 1, h)
+            w_idxs = np.linspace(0, num_grid_per_side - 1, w)
+
+            h_idxs_floor = h_idxs.astype(int)
+            w_idxs_floor = w_idxs.astype(int)
+            h_idxs_ceil = (h_idxs.astype(int) + 1).clip(max=num_grid_per_side - 1)
+            w_idxs_ceil = (w_idxs.astype(int) + 1).clip(max=num_grid_per_side - 1)
+
+            dh = h_idxs - h_idxs_floor
+            dw = w_idxs - w_idxs_floor
+
+            idx_list[0].extend(
+                ((h_idxs_floor * num_grid_per_side)[None].T + w_idxs_floor[None])
+                .flatten()
+                .tolist()
+                * t
+            )
+            idx_list[1].extend(
+                ((h_idxs_floor * num_grid_per_side)[None].T + w_idxs_ceil[None])
+                .flatten()
+                .tolist()
+                * t
+            )
+            idx_list[2].extend(
+                ((h_idxs_ceil * num_grid_per_side)[None].T + w_idxs_floor[None])
+                .flatten()
+                .tolist()
+                * t
+            )
+            idx_list[3].extend(
+                ((h_idxs_ceil * num_grid_per_side)[None].T + w_idxs_ceil[None])
+                .flatten()
+                .tolist()
+                * t
+            )
+
+            weight_list[0].extend(
+                ((1 - dh)[None].T * (1 - dw)[None]).flatten().tolist() * t
+            )
+            weight_list[1].extend(((1 - dh)[None].T * dw[None]).flatten().tolist() * t)
+            weight_list[2].extend((dh[None].T * (1 - dw)[None]).flatten().tolist() * t)
+            weight_list[3].extend((dh[None].T * dw[None]).flatten().tolist() * t)
+
+        device = self.pos_embed.weight.device
+        dtype = self.pos_embed.weight.dtype
+
+        p0 = (
+            self.pos_embed(torch.tensor(idx_list[0], dtype=torch.long, device=device))
+            * torch.tensor(weight_list[0], dtype=dtype, device=device)[:, None]
+        )
+        p1 = (
+            self.pos_embed(torch.tensor(idx_list[1], dtype=torch.long, device=device))
+            * torch.tensor(weight_list[1], dtype=dtype, device=device)[:, None]
+        )
+        p2 = (
+            self.pos_embed(torch.tensor(idx_list[2], dtype=torch.long, device=device))
+            * torch.tensor(weight_list[2], dtype=dtype, device=device)[:, None]
+        )
+        p3 = (
+            self.pos_embed(torch.tensor(idx_list[3], dtype=torch.long, device=device))
+            * torch.tensor(weight_list[3], dtype=dtype, device=device)[:, None]
+        )
+
+        patch_pos_embeds = p0 + p1 + p2 + p3
+        patch_pos_embeds = patch_pos_embeds.split([t * h * w for t, h, w in grid_thw])
+        patch_pos_embeds_permute = []
+        m_size = self.spatial_merge_size
+        for pos_embed, (t, h, w) in zip(patch_pos_embeds, grid_thw):
+            pos_embed = (
+                pos_embed.view(t, h // m_size, m_size, w // m_size, m_size, -1)
+                .permute(0, 1, 3, 2, 4, 5)
+                .flatten(0, 4)
+            )
+            patch_pos_embeds_permute.append(pos_embed)
+        patch_pos_embeds = torch.cat(patch_pos_embeds_permute)
+        return patch_pos_embeds
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+
+        pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
+        x = x + pos_embeds
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        seq_len, _ = x.size()
+        rotary_pos_emb = rotary_pos_emb.to(x.device)
+
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        position_embeddings = (emb.cos(), emb.sin())
+
+        # compute cu_seqlens
+        cu_seqlens = torch.cat(
+            [
+                torch.tensor([0], device=grid_thw.device),
+                (grid_thw[:, 0] * grid_thw[:, 1] * grid_thw[:, 2]).cumsum(dim=0),
+            ]
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+        # max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        x = x.unsqueeze(1)
+
+        deepstack_feature_lists = []
+        num_deepstack_captured = 0
+        for layer_num, blk in enumerate(self.blocks):
+            x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings)
+            if layer_num in self.deepstack_visual_indexes:
+                deepstack_feature = self.deepstack_merger_list[num_deepstack_captured](
+                    x
+                )
+                deepstack_feature_lists.append(deepstack_feature)
+                num_deepstack_captured += 1
+        x = self.merger(x)
+        hidden_states = torch.cat(
+            [x] + deepstack_feature_lists, dim=1
+        )  # [seq_len, hidden_size * (1 + depth_of_deepstack)]
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("attn.qkv.", "attn.q.", "q"),
+            ("attn.qkv.", "attn.k.", "k"),
+            ("attn.qkv.", "attn.v.", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Qwen3LLMModel(Qwen3Model):
+
+    def __init__(
+        self,
+        *,
+        config: Qwen3VLConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__(config=config, quant_config=quant_config, prefix=prefix)
+        if not self.pp_group.is_first_rank:
+            assert self.start_layer >= len(
+                config.vision_config.deepstack_visual_indexes
+            ), "start_layer should be greater than or equal to len(deepstack_visual_indexes)"
+
+        self.hidden_size = config.hidden_size
+        self.deepstack_embed_to_decoder_layer = range(
+            len(config.vision_config.deepstack_visual_indexes)
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+        input_deepstack_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                hidden_states = self.embed_tokens(input_ids)
+            else:
+                hidden_states = input_embeds
+            residual = None
+        else:
+            assert pp_proxy_tensors is not None
+            hidden_states = pp_proxy_tensors["hidden_states"]
+            residual = pp_proxy_tensors["residual"]
+
+        aux_hidden_states = []
+        for layer_idx, layer in enumerate(
+            self.layers[self.start_layer : self.end_layer]
+        ):
+            layer_idx = layer_idx + self.start_layer
+            if layer_idx in self.layers_to_capture:
+                aux_hidden_states.append(
+                    hidden_states + residual if residual is not None else hidden_states
+                )
+
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+                residual,
+            )
+
+            # process deepstack
+            if (
+                input_deepstack_embeds is not None
+                and layer_idx in self.deepstack_embed_to_decoder_layer
+            ):
+                sep = self.hidden_size * layer_idx
+                hidden_states = (
+                    hidden_states
+                    + input_deepstack_embeds[:, sep : sep + self.hidden_size]
+                )
+
+        if not self.pp_group.is_last_rank:
+            return PPProxyTensors(
+                {
+                    "hidden_states": hidden_states,
+                    "residual": residual,
+                }
+            )
+        else:
+            if hidden_states.shape[0] != 0:
+                if residual is None:
+                    hidden_states = self.norm(hidden_states)
+                else:
+                    hidden_states, _ = self.norm(hidden_states, residual)
+
+        if len(aux_hidden_states) == 0:
+            return hidden_states
+
+        return hidden_states, aux_hidden_states
+
+
+class Qwen3VLForConditionalGeneration(nn.Module):
+    def __init__(
+        self,
+        config: Qwen3VLConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.visual = Qwen3_VisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+            # NOTE: Qwen3-VL vision encoder currently supports BitsAndBytes 4-bit quantization.
+            # Other quantization methods (e.g., GPTQ, AWQ) are untested and may not be supported.
+            quant_config=quant_config,
+            prefix=add_prefix("visual", prefix),
+        )
+
+        self.model = Qwen3LLMModel(
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=add_prefix("lm_head", prefix),
+            )
+        self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        # like {8:0, 16:1, 24:2}, which stands for the captured deepstack features on
+        # 8, 16, 24 layer will be merged to 0, 1, 2 layer of decoder output hidden_states
+
+        # deepstack
+        self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes
+        self.num_deepstack_embeddings = len(self.deepstack_visual_indexes)
+
+    @property
+    def use_deepstack(self) -> bool:
+        return hasattr(self, "deepstack_visual_indexes")
+
+    def separate_deepstack_embeds(self, embedding):
+        assert (
+            embedding.shape[-1] % (1 + self.num_deepstack_embeddings) == 0
+        ), f"hidden_state of {embedding.shape} should be divisible by ({1 + self.num_deepstack_embeddings})"
+
+        separate_index = self.config.hidden_size
+        input_embeds = embedding[:, :separate_index]
+        input_deepstack_embeds = embedding[:, separate_index:]
+        return input_embeds, input_deepstack_embeds
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        pattern = MultiModalityDataPaddingPatternMultimodalTokens()
+        return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # in qwen-vl, last dim is the same
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
+            self.visual.dtype
+        )
+        image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
+        assert pixel_values.dim() == 2, pixel_values.dim()
+        assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+        return image_embeds
+
+    def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # in qwen-vl, last dim is the same
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
+            self.visual.dtype
+        )
+        video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
+        assert pixel_values.dim() == 2, pixel_values.dim()
+        assert video_grid_thw.dim() == 2, video_grid_thw.dim()
+        video_embeds = self.visual(pixel_values, grid_thw=video_grid_thw)
+        return video_embeds
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        """Run forward pass for Qwen3-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+                (Use input_metadata.mrope_positions to replace it)
+        """
+        if self.is_mrope_enabled:
+            positions = forward_batch.mrope_positions
+
+        if not (
+            forward_batch.forward_mode.is_decode()
+            or not forward_batch.contains_image_inputs()
+        ):
+            if self.is_mrope_enabled:
+                assert positions.ndim == 2 and positions.size(0) == 3, (
+                    "multimodal section rotary embedding requires "
+                    f"(3, seq_len) positions, but got {positions.size()}"
+                )
+
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.model,
+            multimodal_model=self,
+            positions=positions,
+            use_deepstack=self.use_deepstack,
+        )
+
+        if not get_embedding:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch
+            )
+        else:
+            return self.pooler(hidden_states, forward_batch)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            ("gate_up_proj", "up_proj", 1),
+            ("gate_up_proj", "gate_proj", 0),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "language_model" in name:
+                name = name.replace(r"model.language_model.", r"model.")
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "visual" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if "visual" in name:
+                    # adapt to VisionAttention
+                    name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+                    name = name.replace(r"model.visual.", r"visual.")
+
+                try:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                except KeyError:
+                    print(params_dict.keys())
+                    raise
+
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = Qwen3VLForConditionalGeneration

python/sglang/srt/models/qwen3_vl_moe.py

+# Copyright 2025 Qwen Team
+# Copyright 2025 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.
+# ==============================================================================
+"""Inference-only Qwen3-VL model compatible with HuggingFace weights."""
+import logging
+from functools import lru_cache, partial
+from typing import Callable, Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import BatchFeature
+from transformers.activations import ACT2FN
+from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
+    Qwen2_5_VisionRotaryEmbedding,
+)
+
+from sglang.srt.configs.qwen3_vl import Qwen3VLMoeConfig, Qwen3VLMoeVisionConfig
+from sglang.srt.distributed import (
+    get_moe_expert_parallel_world_size,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+)
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.utils import get_layer_id
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import (
+    MultimodalDataItem,
+    MultimodalInputs,
+    global_server_args_dict,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
+from sglang.srt.models.qwen3_vl import (
+    Qwen3_VisionTransformer,
+    Qwen3VLForConditionalGeneration,
+)
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Qwen3MoeLLMModel(Qwen3MoeModel):
+    def __init__(
+        self,
+        *,
+        config: Qwen3VLMoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__(config=config, quant_config=quant_config, prefix=prefix)
+
+        self.hidden_size = config.hidden_size
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.embed_tokens
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # in qwen-vl, last dim is the same
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
+            self.visual.dtype
+        )
+        image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
+        assert pixel_values.dim() == 2, pixel_values.dim()
+        assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+        return image_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+        input_deepstack_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                hidden_states = self.embed_tokens(input_ids)
+            else:
+                hidden_states = input_embeds
+            residual = None
+        else:
+            assert pp_proxy_tensors is not None
+            hidden_states = pp_proxy_tensors["hidden_states"]
+            residual = pp_proxy_tensors["residual"]
+
+        aux_hidden_states = []
+        for layer_idx, layer in enumerate(
+            self.layers[self.start_layer : self.end_layer]
+        ):
+            layer_idx = layer_idx + self.start_layer
+            if layer_idx in self.layers_to_capture:
+                aux_hidden_states.append(
+                    hidden_states + residual if residual is not None else hidden_states
+                )
+
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+                residual,
+            )
+
+            # process deepstack
+            if input_deepstack_embeds is not None and layer_idx in range(3):
+                sep = self.hidden_size * layer_idx
+                hidden_states = (
+                    hidden_states
+                    + input_deepstack_embeds[:, sep : sep + self.hidden_size]
+                )
+
+        if not self.pp_group.is_last_rank:
+            return PPProxyTensors(
+                {
+                    "hidden_states": hidden_states,
+                    "residual": residual,
+                }
+            )
+        else:
+            if hidden_states.shape[0] != 0:
+                if residual is None:
+                    hidden_states = self.norm(hidden_states)
+                else:
+                    hidden_states, _ = self.norm(hidden_states, residual)
+
+        if len(aux_hidden_states) == 0:
+            return hidden_states
+
+        return hidden_states, aux_hidden_states
+
+
+class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
+    def __init__(
+        self,
+        *,
+        config: Qwen3VLMoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super(Qwen3VLForConditionalGeneration, self).__init__()
+        self.config = config
+
+        self.visual = Qwen3_VisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+            # NOTE: Qwen3-VL vision encoder currently supports BitsAndBytes 4-bit quantization.
+            # Other quantization methods (e.g., GPTQ, AWQ) are untested and may not be supported.
+            quant_config=quant_config,
+            prefix=add_prefix("visual", prefix),
+        )
+
+        self.model = Qwen3MoeLLMModel(
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=add_prefix("lm_head", prefix),
+            )
+        self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+        # deepstack
+        self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes
+        self.num_deepstack_embeddings = len(self.deepstack_visual_indexes)
+
+    @property
+    def use_deepstack(self) -> bool:
+        return hasattr(self, "deepstack_visual_indexes")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        """Run forward pass for Qwen3-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+                (Use input_metadata.mrope_positions to replace it)
+        """
+        if self.is_mrope_enabled:
+            positions = forward_batch.mrope_positions
+
+        if not (
+            forward_batch.forward_mode.is_decode()
+            or not forward_batch.contains_image_inputs()
+        ):
+            if self.is_mrope_enabled:
+                assert positions.ndim == 2 and positions.size(0) == 3, (
+                    "multimodal section rotary embedding requires "
+                    f"(3, seq_len) positions, but got {positions.size()}"
+                )
+
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.model,
+            multimodal_model=self,
+            positions=positions,
+            use_deepstack=self.use_deepstack,
+        )
+
+        if not get_embedding:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch
+            )
+        else:
+            return self.pooler(hidden_states, forward_batch)
+
+    def load_fused_expert_weights(
+        self,
+        name: str,
+        params_dict: dict,
+        loaded_weight: torch.Tensor,
+        shard_id: str,
+        num_experts: int,
+    ):
+        param = params_dict[name]
+        # weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
+        weight_loader = param.weight_loader
+        ep_rank = get_tensor_model_parallel_rank()
+        ep_size = get_moe_expert_parallel_world_size()
+        if ep_size == 1:
+            for expert_id in range(num_experts):
+                curr_expert_weight = loaded_weight[expert_id]
+                weight_loader(
+                    param,
+                    curr_expert_weight,
+                    name,
+                    shard_id,
+                    expert_id,
+                )
+        else:
+            experts_per_ep = num_experts // ep_size
+            start_expert = ep_rank * experts_per_ep
+            end_expert = (
+                (ep_rank + 1) * experts_per_ep
+                if ep_rank != ep_size - 1
+                else num_experts
+            )
+
+            for idx, expert_id in enumerate(range(start_expert, end_expert)):
+                curr_expert_weight = loaded_weight[expert_id]
+                weight_loader(
+                    param,
+                    curr_expert_weight,
+                    name,
+                    shard_id,
+                    idx,
+                )
+        return True
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            ("gate_up_proj", "up_proj", 1),
+            ("gate_up_proj", "gate_proj", 0),
+        ]
+
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
+        # Skip loading extra parameters for GPTQ/modelopt models.
+        ignore_suffixes = (
+            ".bias",
+            "_bias",
+            ".k_scale",
+            "_k_scale",
+            ".v_scale",
+            "_v_scale",
+            ".weight_scale",
+            "_weight_scale",
+            ".input_scale",
+            "_input_scale",
+        )
+
+        is_fused_expert = False
+        fused_expert_params_mapping = [
+            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
+            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+        ]
+
+        num_experts = self.config.num_experts
+
+        # Cache params_dict to avoid repeated expensive traversal of model parameters
+        if not hasattr(self, "_cached_params_dict"):
+            self._cached_params_dict = dict(self.named_parameters())
+        params_dict = self._cached_params_dict
+        for name, loaded_weight in weights:
+            if "language_model" in name:
+                name = name.replace(r"model.language_model.", r"model.")
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if "experts.gate_up_proj" in name or "experts.down_proj" in name:
+                    is_fused_expert = True
+                    expert_params_mapping = fused_expert_params_mapping
+
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                if "visual" in name:
+                    continue
+
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra parameters for GPTQ/modelopt models.
+                if name.endswith(ignore_suffixes) and name not in params_dict:
+                    continue
+                # [TODO] Skip layers that are on other devices (check if sglang has a similar function)
+                # if is_pp_missing_parameter(name, self):
+                #     continue
+
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Track if this is an expert weight to enable early skipping
+                is_expert_weight = False
+
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    if "visual" in name:
+                        continue
+                    # Anyway, this is an expert weight and should not be
+                    # attempted to load as other weights later
+                    is_expert_weight = True
+                    name_mapped = name.replace(weight_name, param_name)
+                    if is_fused_expert:
+                        loaded_weight = loaded_weight.transpose(-1, -2)  # no bias
+                        if "experts.gate_up_proj" in name:
+                            loaded_weight = loaded_weight.chunk(2, dim=-2)
+                            self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight[0],
+                                "w1",
+                                num_experts,
+                            )
+                            self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight[1],
+                                "w3",
+                                num_experts,
+                            )
+                        else:
+                            self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight,
+                                shard_id,
+                                num_experts,
+                            )
+                    else:
+                        # Skip loading extra parameters for GPTQ/modelopt models.
+                        if (
+                            name_mapped.endswith(ignore_suffixes)
+                            and name_mapped not in params_dict
+                        ):
+                            continue
+                        param = params_dict[name_mapped]
+                        # We should ask the weight loader to return success or
+                        # not here since otherwise we may skip experts with
+                        # # other available replicas.
+                        weight_loader = param.weight_loader
+                        weight_loader(
+                            param,
+                            loaded_weight,
+                            name_mapped,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                        )
+                    name = name_mapped
+                    break
+                else:
+                    if is_expert_weight:
+                        # This is an expert weight but not mapped to this rank, skip all remaining processing
+                        continue
+                    if "visual" in name:
+                        # adapt to VisionAttention
+                        name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+                        name = name.replace(r"model.visual.", r"visual.")
+
+                    # Skip loading extra parameters for GPTQ/modelopt models.
+                    if name.endswith(ignore_suffixes) and name not in params_dict:
+                        continue
+
+                    if name in params_dict.keys():
+                        param = params_dict[name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+                    else:
+                        logger.warning(f"Parameter {name} not found in params_dict")
+
+        # TODO mimic deepseek
+        # Lazy initialization of expert weights cache to avoid slowing down load_weights
+        # if not hasattr(self, "routed_experts_weights_of_layer"):
+        #     self.routed_experts_weights_of_layer = {
+        #         layer_id: self.model.layers[layer_id].mlp.get_moe_weights()
+        #         for layer_id in range(self.start_layer, self.end_layer)
+        #         if isinstance(self.model.layers[layer_id].mlp, Qwen3MoeSparseMoeBlock)
+        #     }
+
+
+EntryClass = Qwen3VLMoeForConditionalGeneration

python/sglang/srt/multimodal/processors/qwen_vl.py

@@ -12,6 +12,8 @@ from torchvision.transforms import InterpolationMode
 from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
 from sglang.srt.models.qwen2_5_vl import Qwen2_5_VLForConditionalGeneration
 from sglang.srt.models.qwen2_vl import Qwen2VLForConditionalGeneration
+from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration
+from sglang.srt.models.qwen3_vl_moe import Qwen3VLMoeForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
@@ -209,7 +211,12 @@ async def preprocess_video(
 
 # Compatible with Qwen2VL and Qwen2_5VL
 class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
-    models = [Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration]
+    models = [
+        Qwen2VLForConditionalGeneration,
+        Qwen2_5_VLForConditionalGeneration,
+        Qwen3VLForConditionalGeneration,
+        Qwen3VLMoeForConditionalGeneration,
+    ]
 
     def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _processor, *args, **kwargs)