Interns2mobius support (#51149)

Signed-off-by: Lyu Han <lvhan_028@163.com>
2026-08-06 10:39:35 +00:00
parent e07532b035
commit 22013f74ff
8 changed files with 664 additions and 3 deletions
@@ -540,6 +540,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `IsaacForConditionalGeneration` | Isaac | T + I<sup>+</sup> | `PerceptronAI/Isaac-0.1` | ✅︎ | ✅︎ |
 | `InternS1ForConditionalGeneration` | Intern-S1 | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S1`, `internlm/Intern-S1-mini`, etc. | ✅︎ | ✅︎ |
 | `InternS1ProForConditionalGeneration` | Intern-S1-Pro | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S1-Pro`, etc. | ✅︎ | ✅︎ |
+| `InternS2MobiusForConditionalGeneration` | Intern-S2-Mobius | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S2-Mobius` | ✅︎ | ✅︎ |
 | `InternS2PreviewForConditionalGeneration` | Intern-S2-Preview | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S2-Preview`, etc. | ✅︎ | ✅︎ |
 | `InternVLChatModel` | InternVL 3.5, InternVL 3.0, InternVideo 2.5, InternVL 2.5, InternVL 2.0 | T + I<sup>E+</sup> + (V<sup>E+</sup>) | `OpenGVLab/InternVL3_5-14B`, `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/InternVL2-4B`, etc. | ✅︎ | ✅︎ |
 | `InternVLForConditionalGeneration` | InternVL 3.0 (HF format) | T + I<sup>E+</sup> + V<sup>E+</sup> | `OpenGVLab/InternVL3-1B-hf`, etc. | ✅︎ | ✅︎ |
@@ -996,6 +996,11 @@ _MULTIMODAL_EXAMPLE_MODELS = {
        "internlm/Intern-S1-Pro",
        trust_remote_code=True,
    ),
+    "InternS2MobiusForConditionalGeneration": _HfExamplesInfo(
+        "internlm/Intern-S2-Mobius",
+        trust_remote_code=True,
+        is_available_online=False,
+    ),
    "InternS2PreviewForConditionalGeneration": _HfExamplesInfo(
        "internlm/Intern-S2-Preview",
        trust_remote_code=True,
@@ -1682,6 +1687,12 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
        trust_remote_code=True,
        max_model_len=4096,
    ),
+    "InternS2MobiusMTP": _HfExamplesInfo(
+        "internlm/Intern-S2-Mobius",
+        trust_remote_code=True,
+        speculative_model="internlm/Intern-S2-Mobius",
+        is_available_online=False,
+    ),
    "KimiK3MTPModel": _HfExamplesInfo(
        "moonshotai/Kimi-K3",
        speculative_model="moonshotai/Kimi-K3",
@@ -538,15 +538,28 @@ class SpeculativeConfig:
                    "architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
                }
            )
-        if hf_config.model_type == "intern_s2_preview":
+        if hf_config.model_type in ("intern_s2_preview", "interns2_mobius"):
+            is_mobius = hf_config.model_type == "interns2_mobius"
            text_config = getattr(hf_config, "text_config", None)
-            is_moe = getattr(text_config, "model_type", None) == "qwen3_5_moe_text"
+            is_moe = is_mobius or (
+                getattr(text_config, "model_type", None) == "qwen3_5_moe_text"
+            )
+            if is_mobius:
+                assert text_config is not None
+                text_config.model_type = "qwen3_5_moe_text"
+                text_config.num_experts = text_config.mtp_num_experts
+                text_config.num_experts_per_tok = text_config.mtp_num_experts_per_tok
            hf_config.model_type = "qwen3_5_mtp"
            n_predict = getattr(text_config, "mtp_num_hidden_layers", None)
+            architecture = (
+                "InternS2MobiusMTP"
+                if is_mobius
+                else ("Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP")
+            )
            hf_config.update(
                {
                    "n_predict": n_predict,
-                    "architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
+                    "architectures": [architecture],
                }
            )
        if hf_config.model_type in ("longcat_flash", "longcat_flash_ngram"):
@@ -62,6 +62,31 @@ from vllm.utils.multi_stream_utils import maybe_execute_in_parallel
 class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
    """Triton-based fused MoE expert implementation."""

+    @staticmethod
+    def is_supported_config(
+        cls: type[mk.FusedMoEExperts],
+        moe_config: FusedMoEConfig,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+        activation_format: mk.FusedMoEActivationFormat,
+    ) -> tuple[bool, str | None]:
+        supported, reason = mk.FusedMoEExperts.is_supported_config(
+            cls,
+            moe_config,
+            weight_key,
+            activation_key,
+            activation_format,
+        )
+        if not supported or not current_platform.is_cuda_alike():
+            return supported, reason
+
+        padded_num_experts = (moe_config.num_experts + 31) // 32 * 32
+        if padded_num_experts >= 1024:
+            return False, (
+                "kernel's moe_align_block_size requires fewer than 1024 padded experts"
+            )
+        return True, None
+
    def __init__(
        self,
        moe_config: FusedMoEConfig,
@@ -34,6 +34,28 @@ class TrtLlmBf16ExpertsBase:
    monolithic interfaces.
    """

+    @staticmethod
+    def is_supported_config(
+        cls: type[mk.FusedMoEExperts],
+        moe_config: FusedMoEConfig,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+        activation_format: mk.FusedMoEActivationFormat,
+    ) -> tuple[bool, str | None]:
+        supported, reason = mk.FusedMoEExperts.is_supported_config(
+            cls,
+            moe_config,
+            weight_key,
+            activation_key,
+            activation_format,
+        )
+        if not supported or moe_config.num_experts <= 2048:
+            return supported, reason
+        return False, (
+            "FlashInfer TRTLLM routing supports at most 2048 experts, "
+            f"but got {moe_config.num_experts}"
+        )
+
    def supports_routing_replay_capture(self) -> bool:
        return True

@@ -42,6 +42,28 @@ class TrtLlmFp8ExpertsBase:
    interfaces.
    """

+    @staticmethod
+    def is_supported_config(
+        cls: type[mk.FusedMoEExperts],
+        moe_config: FusedMoEConfig,
+        weight_key: QuantKey | None,
+        activation_key: QuantKey | None,
+        activation_format: mk.FusedMoEActivationFormat,
+    ) -> tuple[bool, str | None]:
+        supported, reason = mk.FusedMoEExperts.is_supported_config(
+            cls,
+            moe_config,
+            weight_key,
+            activation_key,
+            activation_format,
+        )
+        if not supported or moe_config.num_experts <= 2048:
+            return supported, reason
+        return False, (
+            "FlashInfer TRTLLM routing supports at most 2048 experts, "
+            f"but got {moe_config.num_experts}"
+        )
+
    def __init__(
        self,
        moe_config: FusedMoEConfig,
@@ -0,0 +1,562 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Intern-S2-Mobius model."""
+
+from collections.abc import Iterable
+from itertools import islice
+
+import torch
+from torch import nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig, replace, set_current_vllm_config
+from vllm.distributed import (
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_gather,
+    tensor_model_parallel_reduce_scatter,
+)
+from vllm.model_executor.layers.fused_moe import FusedMoEFactory
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.gdn.qwen_gdn_linear_attn import (
+    QwenGatedDeltaNetAttention,
+)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.sequence import IntermediateTensors
+from vllm.tokenizers.registry import cached_tokenizer_from_config
+
+from .interfaces import EagleModelMixin
+from .interns2_preview import (
+    InternS2PreviewForConditionalGeneration,
+    InternS2PreviewProcessingInfo,
+)
+from .qwen2_moe import Qwen2MoeMLP
+from .qwen3_5 import (
+    Qwen3_5ForCausalLMBase,
+    Qwen3_5Model,
+    Qwen3_5RMSNorm,
+)
+from .qwen3_5_mtp import Qwen3_5MoeMTP
+from .qwen3_next import Qwen3NextAttention, _all_gather_hidden_and_residual
+from .qwen3_vl import (
+    Qwen3_VisionTransformer,
+    Qwen3VLDummyInputsBuilder,
+    Qwen3VLMultiModalProcessor,
+)
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    extract_layer_index,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+    sequence_parallel_chunk,
+)
+
+
+class InternS2MobiusProcessingInfo(InternS2PreviewProcessingInfo):
+    pass
+
+
+class InternS2MobiusMetaMoeBlock(nn.Module):
+    """A routed MoE bank shared by multiple decoder layers."""
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_text_config
+        parallel_config = vllm_config.parallel_config
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = get_ep_group().rank_in_group
+        self.ep_size = self.ep_group.size()
+        self.n_routed_experts = config.num_experts
+        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
+
+        if self.tp_size > self.n_routed_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {self.n_routed_experts}."
+            )
+
+        eplb_config = parallel_config.eplb_config
+        self.enable_eplb = parallel_config.enable_eplb
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
+        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+        self.experts = FusedMoEFactory(
+            gate=self.gate,
+            num_experts=config.num_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            renormalize=getattr(config, "norm_topk_prob", True),
+            quant_config=vllm_config.quant_config,
+            prefix=f"{prefix}.experts",
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+            is_sequence_parallel=self.is_sequence_parallel,
+            activation=config.hidden_act,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        already_sequence_parallel: bool = False,
+    ) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        if self.is_sequence_parallel and not already_sequence_parallel:
+            hidden_states = sequence_parallel_chunk(hidden_states)
+
+        if self.experts.is_internal_router:
+            final_hidden_states = self.experts(
+                hidden_states=hidden_states,
+                router_logits=hidden_states,
+            )
+        else:
+            router_logits, _ = self.gate(hidden_states)
+            final_hidden_states = self.experts(
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+            )
+
+        if self.is_sequence_parallel and not already_sequence_parallel:
+            final_hidden_states = tensor_model_parallel_all_gather(
+                final_hidden_states, 0
+            )
+            final_hidden_states = final_hidden_states[:num_tokens]
+
+        return final_hidden_states.view(orig_shape)
+
+
+class InternS2MobiusSharedExpertBlock(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_text_config
+        is_sequence_parallel = vllm_config.parallel_config.use_sequence_parallel_moe
+        self.shared_expert_gate = ReplicatedLinear(
+            config.hidden_size,
+            1,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.shared_expert_gate",
+        )
+        self.shared_expert = Qwen2MoeMLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.shared_expert_intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=vllm_config.quant_config,
+            expert_gate=self.shared_expert_gate,
+            is_sequence_parallel=is_sequence_parallel,
+            prefix=f"{prefix}.shared_expert",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.shared_expert(hidden_states)
+
+
+class InternS2MobiusDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        layer_type: str,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_text_config
+        model_config = vllm_config.model_config
+        parallel_config = vllm_config.parallel_config
+
+        self.layer_type = layer_type
+        self.layer_idx = extract_layer_index(prefix)
+        self.num_blocks = config.num_blocks
+        self.use_attn_reduce_scatter_for_moe = (
+            parallel_config.use_sequence_parallel_moe
+            and parallel_config.pipeline_parallel_size == 1
+        )
+
+        if self.layer_type == "linear_attention":
+            self.linear_attn = QwenGatedDeltaNetAttention(
+                config=config,
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.linear_attn",
+                gqa_interleaved_layout=False,
+                reduce_results=not self.use_attn_reduce_scatter_for_moe,
+            )
+        elif self.layer_type == "full_attention":
+            self.self_attn = Qwen3NextAttention(
+                config,
+                model_config=model_config,
+                cache_config=vllm_config.cache_config,
+                quant_config=vllm_config.quant_config,
+                prefix=f"{prefix}.self_attn",
+                reduce_results=not self.use_attn_reduce_scatter_for_moe,
+            )
+        else:
+            raise ValueError(f"Invalid layer_type {self.layer_type}")
+
+        self.mlp = InternS2MobiusSharedExpertBlock(
+            vllm_config=vllm_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = Qwen3_5RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = Qwen3_5RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        self.layer_scale = getattr(config, "layer_scale", False)
+        if self.layer_scale:
+            self.attn_layer_scale = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+            self.ffn_layer_scale = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+        positions: torch.Tensor,
+        meta_mlp: nn.ModuleList,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        full_num_tokens = positions.shape[-1]
+        input_is_sequence_parallel = (
+            self.use_attn_reduce_scatter_for_moe
+            and residual is not None
+            and hidden_states.shape[0] != full_num_tokens
+        )
+
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        if input_is_sequence_parallel:
+            hidden_states = tensor_model_parallel_all_gather(hidden_states, 0)
+            hidden_states = hidden_states[:full_num_tokens]
+
+        if self.layer_type == "linear_attention":
+            hidden_states = self.linear_attn(hidden_states=hidden_states)
+        else:
+            hidden_states = self.self_attn(
+                hidden_states=hidden_states,
+                positions=positions,
+            )
+
+        if self.layer_scale:
+            scale = self.attn_layer_scale.to(hidden_states.dtype)
+            hidden_states = hidden_states * (scale[0] + 1)
+
+        if self.use_attn_reduce_scatter_for_moe:
+            tp_world_size = get_tensor_model_parallel_world_size()
+            sp_pad = (-hidden_states.shape[0]) % tp_world_size
+            hidden_states = torch.nn.functional.pad(hidden_states, (0, 0, 0, sp_pad))
+            hidden_states = tensor_model_parallel_reduce_scatter(hidden_states, 0)
+            if not input_is_sequence_parallel:
+                residual = sequence_parallel_chunk(residual)
+
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        block_idx = self.layer_idx % self.num_blocks
+        routed_output = meta_mlp[block_idx](
+            hidden_states,
+            already_sequence_parallel=self.use_attn_reduce_scatter_for_moe,
+        )
+        hidden_states = routed_output + self.mlp(hidden_states)
+
+        if self.layer_scale:
+            scale = self.ffn_layer_scale.to(hidden_states.dtype)
+            hidden_states = hidden_states * (scale[0] + 1)
+
+        return hidden_states, residual
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    }
+)
+class InternS2MobiusModel(nn.Module, EagleModelMixin):
+    hf_to_vllm_mapper = Qwen3_5Model.hf_to_vllm_mapper
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        nn.Module.__init__(self)
+
+        config = vllm_config.model_config.hf_text_config
+        self.config = config
+        self.quant_config = vllm_config.quant_config
+        self.num_redundant_experts = (
+            vllm_config.parallel_config.eplb_config.num_redundant_experts
+        )
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+        )
+        self.meta_mlp = nn.ModuleList(
+            InternS2MobiusMetaMoeBlock(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.meta_mlp.{idx}",
+            )
+            for idx in range(config.num_blocks)
+        )
+
+        def get_layer(prefix: str) -> InternS2MobiusDecoderLayer:
+            return InternS2MobiusDecoderLayer(
+                vllm_config,
+                layer_type=config.layer_types[extract_layer_index(prefix)],
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            get_layer,
+            prefix=f"{prefix}.layers",
+        )
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.aux_hidden_state_layers: tuple[int, ...] = ()
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
+        if get_pp_group().is_first_rank:
+            hidden_states = (
+                inputs_embeds
+                if inputs_embeds is not None
+                else self.embed_input_ids(input_ids)
+            )
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        full_num_tokens = positions.shape[-1]
+        aux_hidden_states = self._maybe_add_hidden_state([], 0, hidden_states, residual)
+        for layer_idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer),
+            start=self.start_layer,
+        ):
+            if (
+                hidden_states.shape[0] != full_num_tokens
+                and not layer.use_attn_reduce_scatter_for_moe
+            ):
+                hidden_states, residual = _all_gather_hidden_and_residual(
+                    hidden_states,
+                    residual,
+                    full_num_tokens,
+                    self.config.hidden_size,
+                )
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+                meta_mlp=self.meta_mlp,
+            )
+            if (layer_idx + 1) in self.aux_hidden_state_layers and hidden_states.shape[
+                0
+            ] != full_num_tokens:
+                hidden_states, residual = _all_gather_hidden_and_residual(
+                    hidden_states,
+                    residual,
+                    full_num_tokens,
+                    self.config.hidden_size,
+                )
+            self._maybe_add_hidden_state(
+                aux_hidden_states,
+                layer_idx + 1,
+                hidden_states,
+                residual,
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+        if hidden_states.shape[0] != full_num_tokens:
+            hidden_states, residual = _all_gather_hidden_and_residual(
+                hidden_states,
+                residual,
+                full_num_tokens,
+                self.config.hidden_size,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        if aux_hidden_states:
+            return hidden_states, aux_hidden_states
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+
+class InternS2MobiusForCausalLM(Qwen3_5ForCausalLMBase):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        config = vllm_config.model_config.hf_text_config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.quant_config = vllm_config.quant_config
+        self.scheduler_config = vllm_config.scheduler_config
+
+        if vllm_config.cache_config.mamba_cache_mode == "all":
+            raise NotImplementedError(
+                "Intern-S2-Mobius does not support 'all' prefix caching; "
+                "use '--mamba-cache-mode=align' instead."
+            )
+
+        nn.Module.__init__(self)
+        self.config = config
+        self.model = InternS2MobiusModel(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+
+        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=self.quant_config,
+                    prefix=maybe_prefix(prefix, "lm_head"),
+                )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+
+class InternS2MobiusMTP(Qwen3_5MoeMTP):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        speculative_config = vllm_config.speculative_config
+        if speculative_config is None or speculative_config.draft_model_config is None:
+            raise ValueError(
+                "Intern-S2-Mobius MTP requires a draft model configuration."
+            )
+
+        draft_vllm_config = replace(
+            vllm_config,
+            model_config=speculative_config.draft_model_config,
+        )
+        with set_current_vllm_config(draft_vllm_config, prefix=prefix):
+            super().__init__(vllm_config=draft_vllm_config, prefix=prefix)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen3VLMultiModalProcessor,
+    info=InternS2MobiusProcessingInfo,
+    dummy_inputs=Qwen3VLDummyInputsBuilder,
+)
+class InternS2MobiusForConditionalGeneration(InternS2PreviewForConditionalGeneration):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model") -> None:
+        nn.Module.__init__(self)
+
+        config = vllm_config.model_config.hf_config
+        if getattr(config, "image_token_index", None) is None:
+            config.image_token_index = config.image_token_id
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.model_config = vllm_config.model_config
+        self.multimodal_config = multimodal_config
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
+        self.is_multimodal_pruning_enabled = (
+            multimodal_config.is_multimodal_pruning_enabled()
+        )
+        self.video_pruning_rate = multimodal_config.video_pruning_rate
+        self._tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
+
+        self.use_deepstack = False
+
+        with self._mark_tower_model(vllm_config, {"image", "video"}):
+            self.visual = Qwen3_VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+
+        with self._mark_language_model(vllm_config):
+            self.language_model = InternS2MobiusForCausalLM(
+                vllm_config=vllm_config,
+                prefix=maybe_prefix(prefix, "language_model"),
+            )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+        self.set_moe_parameters()
+
+    def set_moe_parameters(self) -> None:
+        meta_mlp = self.language_model.model.meta_mlp
+        if not meta_mlp:
+            raise RuntimeError("Intern-S2-Mobius requires at least one MoE bank.")
+
+        self.moe_layers = [moe.experts for moe in meta_mlp]
+        example_moe = meta_mlp[0]
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0
+        self.num_logical_experts = example_moe.n_logical_experts
+        self.num_physical_experts = example_moe.n_physical_experts
+        self.num_local_physical_experts = example_moe.n_local_physical_experts
+        self.num_routed_experts = example_moe.n_routed_experts
+        self.num_redundant_experts = example_moe.n_redundant_experts
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for moe in self.language_model.model.meta_mlp:
+            moe.n_local_physical_experts = num_local_physical_experts
+            moe.n_physical_experts = num_physical_experts
+            moe.n_redundant_experts = self.num_redundant_experts
+            moe.experts.update_expert_map()
@@ -447,6 +447,10 @@ _MULTIMODAL_MODELS = {
        "interns1_pro",
        "InternS1ProForConditionalGeneration",
    ),
+    "InternS2MobiusForConditionalGeneration": (
+        "interns2_mobius",
+        "InternS2MobiusForConditionalGeneration",
+    ),
    "InternS2PreviewForConditionalGeneration": (
        "interns2_preview",
        "InternS2PreviewForConditionalGeneration",
@@ -661,6 +665,7 @@ _SPECULATIVE_DECODING_MODELS = {
    "Step3p5MTP": ("step3p5_mtp", "Step3p5MTP"),
    "Qwen3_5MTP": ("qwen3_5_mtp", "Qwen3_5MTP"),
    "Qwen3_5MoeMTP": ("qwen3_5_mtp", "Qwen3_5MoeMTP"),
+    "InternS2MobiusMTP": ("interns2_mobius", "InternS2MobiusMTP"),
    "HYV3MTPModel": ("hy_v3_mtp", "HYV3MTP"),
    "KimiK3MTPModel": ("vllm.models.kimi_k3", "KimiK3MTP"),
    # Temporarily disabled.