model: support Qwen3.5 (#18489)

Co-authored-by: 瑀澈 <yuche.lz@alibaba-inc.com>

benchmark/kernels/fused_moe_triton/common_utils.py

@@ -66,6 +66,7 @@ def get_model_config(
         "Qwen3MoeForCausalLM",
         "Qwen3NextForCausalLM",
         "Qwen3VLMoeForConditionalGeneration",
+        "Qwen3_5MoeForConditionalGeneration",
     ]:
         E = config.num_experts // ep_size
         topk = config.num_experts_per_tok

python/sglang/srt/configs/__init__.py

@@ -18,6 +18,7 @@ from sglang.srt.configs.longcat_flash import LongcatFlashConfig
 from sglang.srt.configs.nano_nemotron_vl import NemotronH_Nano_VL_V2_Config
 from sglang.srt.configs.nemotron_h import NemotronHConfig
 from sglang.srt.configs.olmo3 import Olmo3Config
+from sglang.srt.configs.qwen3_5 import Qwen3_5Config, Qwen3_5MoeConfig
 from sglang.srt.configs.qwen3_next import Qwen3NextConfig
 from sglang.srt.configs.step3_vl import (
     Step3TextConfig,
@@ -43,6 +44,8 @@ __all__ = [
     "KimiLinearConfig",
     "KimiK25Config",
     "Qwen3NextConfig",
+    "Qwen3_5Config",
+    "Qwen3_5MoeConfig",
     "DotsVLMConfig",
     "DotsOCRConfig",
     "FalconH1Config",

python/sglang/srt/configs/model_config.py

@@ -319,6 +319,13 @@ class ModelConfig:
             self.hf_config.architectures[0] = "Qwen3NextForCausalLMMTP"
             self.hf_config.num_nextn_predict_layers = 1
 
+        if is_draft_model and self.hf_config.architectures[0] in [
+            "Qwen3_5ForConditionalGeneration",
+            "Qwen3_5MoeForConditionalGeneration",
+        ]:
+            self.hf_config.architectures[0] = "Qwen3_5ForCausalLMMTP"
+            self.hf_config.num_nextn_predict_layers = 1
+
         if is_draft_model and self.hf_config.architectures[0] == "ExaoneMoEForCausalLM":
             self.hf_config.architectures[0] = "ExaoneMoEForCausalLMMTP"
             self.hf_config.num_nextn_predict_layers = 1
@@ -1193,6 +1200,8 @@ multimodal_model_archs = [
     "Qwen2_5_VLForConditionalGeneration",
     "Qwen3VLForConditionalGeneration",
     "Qwen3VLMoeForConditionalGeneration",
+    "Qwen3_5ForConditionalGeneration",
+    "Qwen3_5MoeForConditionalGeneration",
     "Qwen3OmniMoeForConditionalGeneration",
     "KimiVLForConditionalGeneration",
     "InternVLChatModel",

python/sglang/srt/configs/qwen3_5.py

@@ -0,0 +1,113 @@
+from transformers import PretrainedConfig
+
+from sglang.srt.configs.qwen3_next import Qwen3NextConfig
+from sglang.srt.configs.qwen3_vl import Qwen3VLVisionConfig
+
+
+class Qwen3_5VisionConfig(Qwen3VLVisionConfig):
+    model_type = "qwen3_5"
+    base_config_key = "vision_config"
+
+
+class Qwen3_5TextConfig(Qwen3NextConfig):
+    model_type = "qwen3_5_text"
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if self.rope_scaling is None:
+            self.rope_scaling = {}
+
+
+class Qwen3_5Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3_5Model`]. It is used to instantiate a
+    Qwen3.5 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3.5.
+
+    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 `Qwen3_5TextConfig`):
+            The config object or dictionary of the text backbone.
+        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Qwen3_5VisionConfig`):
+            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 Qwen3_5ForConditionalGeneration, Qwen3_5Config
+
+    >>> # Initializing a Qwen3.5 style configuration
+    >>> configuration = Qwen3_5Config()
+
+    >>> # Initializing a model from the Qwen3.5 style configuration
+    >>> model = Qwen3_5ForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_5"
+    sub_configs = {
+        "vision_config": Qwen3_5VisionConfig,
+        "text_config": Qwen3_5TextConfig,
+    }
+    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 Qwen3_5MoeVisionConfig(Qwen3_5VisionConfig):
+    model_type = "qwen3_5_moe"
+
+
+class Qwen3_5MoeTextConfig(Qwen3_5TextConfig):
+    model_type = "qwen3_5_moe_text"
+
+
+class Qwen3_5MoeConfig(Qwen3_5Config):
+    model_type = "qwen3_5_moe"
+    sub_configs = {
+        "vision_config": Qwen3_5MoeVisionConfig,
+        "text_config": Qwen3_5MoeTextConfig,
+    }

python/sglang/srt/layers/logits_processor.py

@@ -104,6 +104,8 @@ class LogitsProcessorOutput:
     ## Part 5: Customized Info
     customized_info: Optional[Dict[str, List[Any]]] = None
 
+    mm_input_embeds: Optional[torch.Tensor] = None
+
 
 @dataclasses.dataclass
 class LogitsMetadata:
@@ -146,6 +148,8 @@ class LogitsMetadata:
     # Whether this batch is prefill-only (no token generation needed)
     is_prefill_only: bool = False
 
+    mm_input_embeds: Optional[torch.Tensor] = None
+
     @classmethod
     def from_forward_batch(cls, forward_batch: ForwardBatch):
         if (
@@ -196,6 +200,7 @@ class LogitsMetadata:
             global_num_tokens_for_logprob_cpu=forward_batch.global_num_tokens_for_logprob_cpu,
             global_num_tokens_for_logprob_gpu=forward_batch.global_num_tokens_for_logprob_gpu,
             dp_padding_mode=DpPaddingMode.SUM_LEN,
+            mm_input_embeds=forward_batch.mm_input_embeds,
         )
 
     def compute_dp_attention_metadata(self):
@@ -341,6 +346,7 @@ class LogitsProcessor(nn.Module):
             return LogitsProcessorOutput(
                 next_token_logits=sampled_logits,
                 hidden_states=hidden_states_to_store,
+                mm_input_embeds=logits_metadata.mm_input_embeds,
             )
 
         # Start to process input logprobs
@@ -386,6 +392,7 @@ class LogitsProcessor(nn.Module):
             input_top_logprobs_idx=logprobs_result.input_top_logprobs_idx,
             input_token_ids_logprobs_val=logprobs_result.input_token_ids_logprobs_val,
             input_token_ids_logprobs_idx=logprobs_result.input_token_ids_logprobs_idx,
+            mm_input_embeds=logits_metadata.mm_input_embeds,
         )
 
     def _get_pruned_states(
@@ -1067,6 +1074,10 @@ class LogitsProcessor(nn.Module):
             input_top_logprobs_idx=input_top_logprobs_idx,
             input_token_ids_logprobs_val=input_token_ids_logprobs_val,
             input_token_ids_logprobs_idx=input_token_ids_logprobs_idx,
+            # FIXME: These fields are not logits-related but are passed through here as a
+            # workaround since ForwardBatch is local to forward_batch_generation().
+            # They should be moved to GenerationBatchResult to keep this class clean.
+            mm_input_embeds=logits_metadata.mm_input_embeds,
         )
 
 

python/sglang/srt/layers/rotary_embedding.py

@@ -1825,7 +1825,9 @@ class MRotaryEmbedding(RotaryEmbedding):
                 **kwargs,
             )
         if (
-            model_type.startswith("qwen3_vl") or model_type.startswith("qwen3_vl_moe")
+            model_type.startswith("qwen3_vl")
+            or model_type.startswith("qwen3_vl_moe")
+            or model_type.startswith("qwen3_5")
         ) and video_grid_thw is not None:
             video_grid_thw = torch.repeat_interleave(
                 video_grid_thw, video_grid_thw[:, 0], dim=0
@@ -1925,6 +1927,8 @@ class MRotaryEmbedding(RotaryEmbedding):
                         "qwen2_vl",
                         "qwen3_vl",
                         "qwen3_vl_moe",
+                        "qwen3_5",
+                        "qwen3_5_moe",
                     ):
                         t_index = (
                             torch.arange(llm_grid_t, device=position_ids.device)

python/sglang/srt/managers/mm_utils.py

@@ -1121,6 +1121,7 @@ def general_mm_embed_routine(
                             if isinstance(feature, torch.Tensor) and feature.is_cuda:
                                 mm_item.feature = feature.to("cpu", non_blocking=True)
             forward_batch.mm_inputs = None
+            forward_batch.mm_input_embeds = input_embeds
         else:
             input_embeds = embed_tokens(input_ids)
         # Copy to pre-allocated buffer if available (for CUDA graph address stability)

python/sglang/srt/model_executor/forward_batch_info.py

@@ -350,6 +350,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
     # Speculative decoding
     spec_info: Optional[SpecInput] = None
     spec_algorithm: SpeculativeAlgorithm = None
+    mm_input_embeds: Optional[torch.Tensor] = None
     capture_hidden_mode: CaptureHiddenMode = None
 
     # For padding

python/sglang/srt/model_executor/model_runner.py

@@ -38,6 +38,8 @@ from sglang.srt.configs import (
     Lfm2Config,
     NemotronH_Nano_VL_V2_Config,
     NemotronHConfig,
+    Qwen3_5Config,
+    Qwen3_5MoeConfig,
     Qwen3NextConfig,
 )
 from sglang.srt.configs.device_config import DeviceConfig
@@ -1548,8 +1550,15 @@ class ModelRunner(ModelRunnerKVCacheMixin):
 
     @property
     def hybrid_gdn_config(self):
-        config = self.model_config.hf_config
+        config = self.model_config.hf_config.get_text_config()
-        if isinstance(config, Qwen3NextConfig | JetNemotronConfig | JetVLMConfig):
+        if isinstance(
+            config,
+            Qwen3NextConfig
+            | Qwen3_5Config
+            | Qwen3_5MoeConfig
+            | JetNemotronConfig
+            | JetVLMConfig,
+        ):
             return config
         return None
 
@@ -2532,7 +2541,9 @@ class ModelRunner(ModelRunnerKVCacheMixin):
     def model_is_mrope(self) -> bool:
         """Detect if the model has "mrope" rope_scaling type.
         mrope requires keep "rope_deltas" between prompt and decoding phases."""
-        rope_scaling = getattr(self.model_config.hf_text_config, "rope_scaling", {})
+        rope_scaling = getattr(
+            self.model_config.hf_text_config, "rope_parameters", None
+        ) or getattr(self.model_config.hf_text_config, "rope_scaling", {})
         if rope_scaling is None:
             return False
         is_mrope_enabled = "mrope_section" in rope_scaling

python/sglang/srt/models/qwen3_5_mtp.py

@@ -0,0 +1,415 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Inference-only Qwen3_5 MTP model."""
+import logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from sglang.srt.layers.layernorm import GemmaRMSNorm
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen3_5 import Qwen3_5AttentionDecoderLayer
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+
+class Qwen3_5MultiTokenPredictor(nn.Module):
+    def __init__(self, config: PretrainedConfig, quant_config=None, prefix: str = ""):
+        super().__init__()
+
+        self.config = config
+
+        self.vocab_size = config.vocab_size
+
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = getattr(config, "mtp_num_hidden_layers", 1)
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+        )
+
+        self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
+
+        config.full_attention_interval = 1
+        self.layers = torch.nn.ModuleList(
+            [
+                Qwen3_5AttentionDecoderLayer(
+                    config,
+                    idx,
+                    quant_config,
+                    prefix=add_prefix(f"layers.{idx}", prefix),
+                )
+                for idx in range(self.num_mtp_layers)
+            ]
+        )
+
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.pre_fc_norm_hidden = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.pre_fc_norm_embedding = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: torch.Tensor,
+        input_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        # if get_pp_group().is_first_rank:
+        assert input_embeds is None
+        input_embeds = forward_batch.mm_input_embeds
+        if (
+            forward_batch.forward_mode.is_extend()
+            and forward_batch.contains_mm_inputs()
+            and not forward_batch.forward_mode.is_draft_extend()
+        ):
+            assert input_embeds is not None
+            input_embeds = torch.cat(
+                [input_embeds[:-1], self.embed_tokens(input_ids[-1].unsqueeze(0))]
+            )
+
+        if input_embeds is None:
+            input_embeds = self.embed_tokens(input_ids)
+
+        hidden_states = forward_batch.spec_info.hidden_states
+
+        # Some idle batch has 0 batch size. GemmaRMSNorm.forward would fail due to bs=0.
+        if not forward_batch.forward_mode.is_idle():
+            input_embeds = self.pre_fc_norm_embedding(input_embeds)
+            hidden_states = self.pre_fc_norm_hidden(hidden_states)
+        hidden_states = torch.cat([input_embeds, hidden_states], dim=-1)
+
+        hidden_states = self.fc(hidden_states)
+        residual = None
+
+        if self.num_mtp_layers == 1:
+            hidden_states, residual = self.layers[0](
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+                forward_batch=forward_batch,
+            )
+        else:
+            raise ("not implementation for other mtp layers[self.num_mtp_layers > 1]")
+
+        if not get_pp_group().is_last_rank:
+            # For pipeline parallel, return intermediate tensors
+            return hidden_states
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Qwen3_5ForCausalLMMTP(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config=None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.is_multimodal = hasattr(config, "text_config")
+        if self.is_multimodal:
+            config = config.text_config
+
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.quant_config = quant_config
+        self.pp_group = get_pp_group()
+
+        self.model = Qwen3_5MultiTokenPredictor(
+            config, quant_config, prefix=add_prefix("mtp", prefix)
+        )
+
+        if get_pp_group().is_last_rank:
+            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),
+                )
+        else:
+            # For pipeline parallel, create a placeholder layer
+            self.lm_head = nn.Linear(1, 1, bias=False)
+
+        self.logits_processor = LogitsProcessor(config)
+
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        del self.model.embed_tokens.weight
+        if not self.config.tie_word_embeddings:
+            del self.lm_head.weight
+
+        self.model.embed_tokens.weight = embed
+        self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        hidden_states = self.model(
+            input_ids,
+            positions,
+            forward_batch,
+            input_embeds,
+        )
+
+        if not get_pp_group().is_last_rank:
+            # For pipeline parallel, return intermediate results
+            return hidden_states
+
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(
+        self, weights: Iterable[Tuple[str, torch.Tensor]], is_mtp: bool = False
+    ):
+        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", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        # Params for MoE experts (non-fused/fused)
+        num_experts = getattr(self.config, "num_experts", None)
+        if num_experts is not None:
+            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=num_experts,
+            )
+        else:
+            expert_params_mapping = []
+
+        # 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",
+        )
+
+        # fused experts: experts.w13_weight / experts.w2_weight
+        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"),
+        ]
+
+        def load_fused_expert_weights(
+            name: str,
+            params_dict: dict,
+            loaded_weight: torch.Tensor,
+            shard_id: str,
+            num_experts: int,
+        ):
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            # Let EP MoE layer handle expert_ids that do not belong to local moe rank
+            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,
+                )
+            return True
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            # Only process MTP branch weights
+            if "mtp" not in name:
+                continue
+
+            # Some checkpoints use model.language_model.mtp.* prefix
+            if "language_model" in name:
+                name = name.replace(r"model.language_model.", r"model.")
+
+            if name.startswith("mtp."):
+                # Remove the mtp. prefix for processing
+                name = name.replace("mtp.", "model.")
+
+            if ".self_attn." in name:
+                name = name.replace(".self_attn", "")
+
+            # 1) Process stacked parameters (q_proj/k_proj/v_proj & gate_proj/up_proj)
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Check if this is a fused expert weight
+                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-matching weights
+                if weight_name not in name:
+                    continue
+
+                # Skip MoE experts.* here, handled separately below
+                if "mlp.experts" in name:
+                    continue
+
+                name_mapped = name.replace(weight_name, param_name)
+
+                # Skip loading extra parameters for GPTQ/modelopt models.
+                if (
+                    name_mapped.endswith(ignore_suffixes)
+                    and name_mapped not in params_dict
+                ):
+                    continue
+
+                if name_mapped not in params_dict:
+                    continue
+
+                param = params_dict[name_mapped]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                name = name_mapped
+                break
+            else:
+                # 2) Process MoE expert weights (including fused experts)
+                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
+
+                    is_expert_weight = True
+                    name_mapped = name.replace(weight_name, param_name)
+
+                    # Fused experts: single checkpoint weight contains multiple experts
+                    if is_fused_expert and num_experts is not None:
+                        if "experts.gate_up_proj" in name:
+                            # gate_up_proj fused: split into w1 / w3
+                            loaded_w1, loaded_w3 = loaded_weight.chunk(2, dim=-2)
+                            load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_w1,
+                                "w1",
+                                num_experts,
+                            )
+                            load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_w3,
+                                "w3",
+                                num_experts,
+                            )
+                        else:
+                            # down_proj fused: distribute entire weight
+                            load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight,
+                                shard_id,
+                                num_experts,
+                            )
+                    else:
+                        # Non-fused expert, load by expert_id/shard
+                        if (
+                            name_mapped.endswith(ignore_suffixes)
+                            and name_mapped not in params_dict
+                        ):
+                            continue
+                        if name_mapped not in params_dict:
+                            break
+                        param = params_dict[name_mapped]
+                        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:
+                    # Skip expert weight if not handled by current rank
+                    if is_expert_weight:
+                        continue
+
+                    # 3) Regular non-stacked / non-expert parameters, use default loader
+                    if name.endswith(ignore_suffixes) and name not in params_dict:
+                        continue
+
+                    if name in params_dict:
+                        param = params_dict[name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+                    else:
+                        logger.warning_once(
+                            f"Parameter {name} not found in params_dict, skip loading"
+                        )
+
+            loaded_params.add(name)
+        return loaded_params
+
+
+EntryClass = [Qwen3_5ForCausalLMMTP]

python/sglang/srt/models/qwen3_next.py

@@ -617,7 +617,10 @@ class Qwen3HybridAttentionDecoderLayer(nn.Module):
         self.scaling = self.head_dim**-0.5
         self.rope_theta = getattr(config, "rope_theta", 10000)
         self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
-        self.rope_scaling = getattr(config, "rope_scaling", None)
+        if "rope_parameters" in config:
+            self.rope_scaling = getattr(config, "rope_parameters", None)
+        else:
+            self.rope_scaling = getattr(config, "rope_scaling", None)
         self.partial_rotary_factor = config.partial_rotary_factor
         self.layer_id = layer_id
 

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

@@ -7,6 +7,7 @@ from typing import List, Union
 import numpy as np
 import torch
 import torchvision
+from decord import VideoReader
 from PIL import Image
 from torchvision.transforms import InterpolationMode
 
@@ -15,6 +16,10 @@ from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
 from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
 from sglang.srt.models.qwen2_5_vl import Qwen2_5_VLForConditionalGeneration
 from sglang.srt.models.qwen2_vl import Qwen2VLForConditionalGeneration
+from sglang.srt.models.qwen3_5 import (
+    Qwen3_5ForConditionalGeneration,
+    Qwen3_5MoeForConditionalGeneration,
+)
 from sglang.srt.models.qwen3_omni_moe import Qwen3OmniMoeForConditionalGeneration
 from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration
 from sglang.srt.models.qwen3_vl_moe import Qwen3VLMoeForConditionalGeneration
@@ -148,6 +153,9 @@ async def preprocess_video(
     image_factor: int = IMAGE_FACTOR,
     video_config: dict = {},
 ) -> torch.Tensor:
+    # preprocessed video
+    if not isinstance(vr, VideoReader):
+        return vr
     entry_time = time.perf_counter()
 
     total_frames, video_fps = len(vr), vr.get_avg_fps()
@@ -226,6 +234,8 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
         Qwen2_5_VLForConditionalGeneration,
         Qwen3VLForConditionalGeneration,
         Qwen3VLMoeForConditionalGeneration,
+        Qwen3_5ForConditionalGeneration,
+        Qwen3_5MoeForConditionalGeneration,
         Qwen3OmniMoeForConditionalGeneration,
     ]
 
@@ -326,7 +336,12 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
         preprocess_time = time.perf_counter()
 
         # NOTE: for qwen3-vl, video_meta need to be passed in, since do_sample_frames is already done in preprocess_video
-        if self.hf_config.model_type in ("qwen3_vl", "qwen3_vl_moe"):
+        if self.hf_config.model_type in (
+            "qwen3_vl",
+            "qwen3_vl_moe",
+            "qwen3_5",
+            "qwen3_5_moe",
+        ):
             mm_items, input_ids, ret = self.process_and_combine_mm_data(
                 base_output,
                 self.mm_tokens,

python/sglang/srt/server_args.py

@@ -1558,7 +1558,11 @@ class ServerArgs:
                         "Use flashinfer_trtllm as MoE runner backend on sm100 for "
                         f"{model_arch}"
                     )
-        elif model_arch in ["Qwen3NextForCausalLM"]:
+        elif model_arch in [
+            "Qwen3NextForCausalLM",
+            "Qwen3_5MoeForConditionalGeneration",
+            "Qwen3_5ForConditionalGeneration",
+        ]:
             if is_sm100_supported():
                 quant_method = get_quantization_config(hf_config)
                 if self.quantization is None and quant_method is not None:
@@ -1573,7 +1577,7 @@ class ServerArgs:
                 ):
                     self.moe_runner_backend = "flashinfer_trtllm"
                     logger.info(
-                        "Use flashinfer_trtllm as MoE runner backend on sm100 for Qwen3NextForCausalLM"
+                        f"Use flashinfer_trtllm as MoE runner backend on sm100 for {model_arch}"
                     )
             self._handle_mamba_radix_cache(
                 model_arch=model_arch,

python/sglang/srt/speculative/eagle_worker.py

@@ -291,7 +291,11 @@ class EAGLEWorker(TpModelWorker):
                 self.draft_model_runner.tp_group
             ), speculative_moe_backend_context(), speculative_moe_a2a_backend_context():
                 self.forward_draft_extend(
-                    batch, logits_output.hidden_states, next_token_ids, seq_lens_cpu
+                    batch,
+                    logits_output.hidden_states,
+                    next_token_ids,
+                    seq_lens_cpu,
+                    logits_output.mm_input_embeds,
                 )
             return GenerationBatchResult(
                 logits_output=logits_output,
@@ -856,6 +860,7 @@ class EAGLEWorker(TpModelWorker):
         hidden_states: torch.Tensor,
         next_token_ids: torch.Tensor,
         seq_lens_cpu: Optional[torch.Tensor],
+        mm_input_embeds: Optional[torch.Tensor] = None,
     ):
         """Run draft model extend. This API modifies the states of the batch.
 
@@ -880,6 +885,8 @@ class EAGLEWorker(TpModelWorker):
             model_worker_batch, self.draft_model_runner
         )
         forward_batch.return_logprob = False
+        if mm_input_embeds is not None:
+            forward_batch.mm_input_embeds = mm_input_embeds
         logits_output = self.draft_model_runner.forward(forward_batch).logits_output
         if self.enable_nan_detection:
             detect_nan(logits_output)

python/sglang/srt/utils/common.py

@@ -1000,6 +1000,8 @@ def load_video(video_file: Union[str, bytes], use_gpu: bool = True):
                 tmp_file.write(video_bytes)
                 tmp_file.close()
                 vr = VideoReader(tmp_file.name, ctx=ctx)
+        elif isinstance(video_file, (list, tuple, torch.Tensor, np.ndarray)):
+            vr = video_file
         else:
             raise ValueError(f"Unsupported video input type: {type(video_file)}")
 

python/sglang/srt/utils/hf_transformers_utils.py

@@ -62,6 +62,8 @@ from sglang.srt.configs import (
     NemotronH_Nano_VL_V2_Config,
     NemotronHConfig,
     Olmo3Config,
+    Qwen3_5Config,
+    Qwen3_5MoeConfig,
     Qwen3NextConfig,
     Step3p5Config,
     Step3VLConfig,
@@ -93,6 +95,8 @@ _CONFIG_REGISTRY: List[Type[PretrainedConfig]] = [
     NemotronH_Nano_VL_V2_Config,
     NemotronHConfig,
     DeepseekVLV2Config,
+    Qwen3_5Config,
+    Qwen3_5MoeConfig,
     JetNemotronConfig,
     JetVLMConfig,
     KimiK25Config,