From fd04edef3e62c8dec19cddd76253c0fc6d56ab62 Mon Sep 17 00:00:00 2001
From: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
Date: Tue, 11 Aug 2026 15:44:26 -0400
Subject: [PATCH] [Refactor] Delete dead code in models (#51838)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
---
 vllm/lora/layers/fused_moe.py                 |  4 ----
 .../layers/fused_moe/runner/moe_runner.py     |  4 ----
 .../fused_moe/runner/moe_runner_interface.py  |  5 -----
 vllm/model_executor/models/AXK1.py            | 18 +++------------
 vllm/model_executor/models/afmoe.py           |  6 -----
 vllm/model_executor/models/deepseek_v2.py     | 18 +++------------
 vllm/model_executor/models/ernie45_moe.py     |  5 -----
 vllm/model_executor/models/exaone_moe.py      |  6 -----
 vllm/model_executor/models/glm4_moe.py        |  6 -----
 vllm/model_executor/models/hunyuan_v1.py      |  6 -----
 vllm/model_executor/models/hy_v3.py           |  5 -----
 vllm/model_executor/models/interns1_pro.py    |  6 -----
 vllm/model_executor/models/interns2_mobius.py | 15 ++++---------
 vllm/model_executor/models/laguna.py          |  6 -----
 vllm/model_executor/models/lfm2_moe.py        |  6 -----
 vllm/model_executor/models/mimo_v2.py         |  6 -----
 vllm/model_executor/models/mixtral.py         |  6 -----
 vllm/model_executor/models/nemotron_h.py      |  6 -----
 vllm/model_executor/models/openpangu.py       |  6 -----
 vllm/model_executor/models/qwen3_moe.py       | 22 +++----------------
 vllm/model_executor/models/qwen3_next.py      | 20 +++--------------
 vllm/model_executor/models/step3p5.py         | 19 +++-------------
 vllm/models/deepseek_v4/amd/model.py          | 19 +++++-----------
 vllm/models/deepseek_v4/nvidia/model.py       | 19 +++++-----------
 vllm/models/deepseek_v4/xpu/model.py          | 18 +++++----------
 25 files changed, 34 insertions(+), 223 deletions(-)

diff --git a/vllm/lora/layers/fused_moe.py b/vllm/lora/layers/fused_moe.py
index 3027fe6430..e47bc79bf4 100644
--- a/vllm/lora/layers/fused_moe.py
+++ b/vllm/lora/layers/fused_moe.py
@@ -466,10 +466,6 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
     def runner(self) -> MoERunner:
         return self.base_layer
 
-    @property
-    def is_internal_router(self) -> bool:
-        return self.base_layer.is_internal_router
-
     @classmethod
     def can_replace_layer(
         cls,
diff --git a/vllm/model_executor/layers/fused_moe/runner/moe_runner.py b/vllm/model_executor/layers/fused_moe/runner/moe_runner.py
index 92b559f95f..aaf9ba6142 100644
--- a/vllm/model_executor/layers/fused_moe/runner/moe_runner.py
+++ b/vllm/model_executor/layers/fused_moe/runner/moe_runner.py
@@ -311,10 +311,6 @@ class MoERunner(MoERunnerInterface):
     def shared_experts(self) -> SharedExperts | None:
         return self._shared_experts
 
-    @property
-    def is_internal_router(self) -> bool:
-        return self.gate is not None
-
     # TODO(bnell): Temporary hack. Get rid of this.
     def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
         self.routed_experts._replace_quant_method(quant_method)
diff --git a/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py b/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
index 199a98f4a0..d579b18156 100644
--- a/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
+++ b/vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
@@ -45,11 +45,6 @@ class MoERunnerInterface(PluggableLayer, ABC):
     def shared_experts(self) -> SharedExperts | None:
         raise NotImplementedError
 
-    @property
-    @abstractmethod
-    def is_internal_router(self) -> bool:
-        raise NotImplementedError
-
     @property
     @abstractmethod
     def _quant_method(self) -> FusedMoEMethodBase:
diff --git a/vllm/model_executor/models/AXK1.py b/vllm/model_executor/models/AXK1.py
index a95b701baf..6000e62828 100644
--- a/vllm/model_executor/models/AXK1.py
+++ b/vllm/model_executor/models/AXK1.py
@@ -108,7 +108,6 @@ class AXK1MoE(nn.Module):
         self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
 
         self.ep_group = get_ep_group().device_group
-        self.ep_rank = get_ep_group().rank_in_group
         self.ep_size = self.ep_group.size()
         assert config.n_routed_experts is not None
         assert config.num_experts_per_tok is not None
@@ -148,11 +147,6 @@ class AXK1MoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
         self.is_fusion_moe_shared_experts_enabled = (
             rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
@@ -210,15 +204,9 @@ class AXK1MoE(nn.Module):
         if self.is_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
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=hidden_states
+        )
 
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
diff --git a/vllm/model_executor/models/afmoe.py b/vllm/model_executor/models/afmoe.py
index 6ffc571674..808775e35f 100644
--- a/vllm/model_executor/models/afmoe.py
+++ b/vllm/model_executor/models/afmoe.py
@@ -71,7 +71,6 @@ class AfmoeMoE(nn.Module):
         self.route_norm = config.route_norm
 
         self.ep_group = get_ep_group().device_group
-        self.ep_rank = self.ep_group.rank()
         self.ep_size = self.ep_group.size()
         self.n_routed_experts: int = config.num_experts
         self.n_shared_experts: int = config.num_shared_experts
@@ -103,11 +102,6 @@ class AfmoeMoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.shared_experts = None
         # Shared experts
         if config.num_shared_experts > 0:
diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
index f076b4fb3f..254763706c 100644
--- a/vllm/model_executor/models/deepseek_v2.py
+++ b/vllm/model_executor/models/deepseek_v2.py
@@ -293,7 +293,6 @@ class DeepseekV2MoE(nn.Module):
         self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
 
         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: int = config.n_routed_experts
         self.n_shared_experts: int = config.n_shared_experts
@@ -329,11 +328,6 @@ class DeepseekV2MoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
         self.is_fusion_moe_shared_experts_enabled = (
             rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
@@ -409,15 +403,9 @@ class DeepseekV2MoE(nn.Module):
         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
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=hidden_states
+        )
 
         if self.is_sequence_parallel and not already_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
diff --git a/vllm/model_executor/models/ernie45_moe.py b/vllm/model_executor/models/ernie45_moe.py
index 6cc6a056fd..89f0b32c66 100644
--- a/vllm/model_executor/models/ernie45_moe.py
+++ b/vllm/model_executor/models/ernie45_moe.py
@@ -129,7 +129,6 @@ class Ernie4_5_MoeMoE(nn.Module):
 
         self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", None)
         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: int = config.moe_num_experts
         self.n_shared_experts: int = self.moe_num_shared_experts
@@ -143,10 +142,6 @@ class Ernie4_5_MoeMoE(nn.Module):
         self.n_logical_experts = self.n_routed_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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
         self.has_shared_experts = getattr(config, "moe_num_shared_experts", 0) > 0
 
         if self.tp_size > config.moe_num_experts:
diff --git a/vllm/model_executor/models/exaone_moe.py b/vllm/model_executor/models/exaone_moe.py
index 0d7da9232d..e942bf62ed 100644
--- a/vllm/model_executor/models/exaone_moe.py
+++ b/vllm/model_executor/models/exaone_moe.py
@@ -76,7 +76,6 @@ class ExaoneMoe(nn.Module):
         self.routed_scaling_factor = config.routed_scaling_factor
 
         self.ep_group = get_ep_group().device_group
-        self.ep_rank = self.ep_group.rank()
         self.ep_size = self.ep_group.size()
         self.n_routed_experts = config.num_experts
 
@@ -113,11 +112,6 @@ class ExaoneMoe(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         if getattr(config, "num_shared_experts", 0) > 0:
             intermediate_size = config.moe_intermediate_size * config.num_shared_experts
             self.shared_experts = ExaoneMoeGatedMLP(
diff --git a/vllm/model_executor/models/glm4_moe.py b/vllm/model_executor/models/glm4_moe.py
index 615df7cef3..6272db2a90 100644
--- a/vllm/model_executor/models/glm4_moe.py
+++ b/vllm/model_executor/models/glm4_moe.py
@@ -127,7 +127,6 @@ class Glm4MoE(nn.Module):
         self.routed_scaling_factor = config.routed_scaling_factor
 
         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: int = config.n_routed_experts
         self.n_shared_experts: int = config.n_shared_experts
@@ -160,11 +159,6 @@ class Glm4MoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         # AITER fused shared-expert (FSE) gate; mirrors the deepseek_v2.py
         # pattern (see Glm4MoE / MoERunner wiring there).
         self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
diff --git a/vllm/model_executor/models/hunyuan_v1.py b/vllm/model_executor/models/hunyuan_v1.py
index af26b39ee0..ef7c748519 100644
--- a/vllm/model_executor/models/hunyuan_v1.py
+++ b/vllm/model_executor/models/hunyuan_v1.py
@@ -372,7 +372,6 @@ class HunYuanSparseMoeBlock(nn.Module):
         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
 
@@ -408,11 +407,6 @@ class HunYuanSparseMoeBlock(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.gate = ReplicatedLinear(
             config.hidden_size,
             config.num_experts,
diff --git a/vllm/model_executor/models/hy_v3.py b/vllm/model_executor/models/hy_v3.py
index f75fbb0041..a4f4a558ec 100644
--- a/vllm/model_executor/models/hy_v3.py
+++ b/vllm/model_executor/models/hy_v3.py
@@ -134,7 +134,6 @@ class HYV3MoEFused(nn.Module):
         super().__init__()
         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
         if self.tp_size > config.num_experts:
@@ -153,10 +152,6 @@ class HYV3MoEFused(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
         self.gate = GateLinear(
             config.hidden_size,
             config.num_experts,
diff --git a/vllm/model_executor/models/interns1_pro.py b/vllm/model_executor/models/interns1_pro.py
index 935abb04ed..378177a557 100644
--- a/vllm/model_executor/models/interns1_pro.py
+++ b/vllm/model_executor/models/interns1_pro.py
@@ -144,7 +144,6 @@ class InternS1ProMoeSparseMoeBlock(nn.Module):
         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
 
@@ -165,11 +164,6 @@ class InternS1ProMoeSparseMoeBlock(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         # For custom routing function
         self.n_groups = getattr(config, "router_n_groups", -1)
 
diff --git a/vllm/model_executor/models/interns2_mobius.py b/vllm/model_executor/models/interns2_mobius.py
index 2e884002c4..e13de751e6 100644
--- a/vllm/model_executor/models/interns2_mobius.py
+++ b/vllm/model_executor/models/interns2_mobius.py
@@ -127,17 +127,10 @@ class InternS2MobiusMetaMoeBlock(nn.Module):
         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,
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=hidden_states,
+        )
 
         if self.is_sequence_parallel and not already_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
diff --git a/vllm/model_executor/models/laguna.py b/vllm/model_executor/models/laguna.py
index 3fcf03b7e6..9431f9a0ec 100644
--- a/vllm/model_executor/models/laguna.py
+++ b/vllm/model_executor/models/laguna.py
@@ -132,7 +132,6 @@ class LagunaMoE(nn.Module):
 
         self.tp_size = get_tensor_model_parallel_world_size()
         self.ep_group = get_ep_group().device_group
-        self.ep_rank = self.ep_group.rank()
         self.ep_size = self.ep_group.size()
 
         self.n_routed_experts = config.num_experts
@@ -160,11 +159,6 @@ class LagunaMoE(nn.Module):
         self.n_logical_experts = self.n_routed_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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         # Router gate
         self.gate = ReplicatedLinear(
             config.hidden_size,
diff --git a/vllm/model_executor/models/lfm2_moe.py b/vllm/model_executor/models/lfm2_moe.py
index 44bf55917e..966a458dea 100644
--- a/vllm/model_executor/models/lfm2_moe.py
+++ b/vllm/model_executor/models/lfm2_moe.py
@@ -106,7 +106,6 @@ class Lfm2MoeSparseMoeBlock(nn.Module):
         self.routed_scaling_factor = config.routed_scaling_factor
 
         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
 
@@ -126,11 +125,6 @@ class Lfm2MoeSparseMoeBlock(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.gate = ReplicatedLinear(
             config.hidden_size,
             config.num_experts,
diff --git a/vllm/model_executor/models/mimo_v2.py b/vllm/model_executor/models/mimo_v2.py
index 30ddd5eb12..8df6a4c0e8 100644
--- a/vllm/model_executor/models/mimo_v2.py
+++ b/vllm/model_executor/models/mimo_v2.py
@@ -127,7 +127,6 @@ class MiMoV2MoE(nn.Module):
         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.n_routed_experts
 
@@ -154,11 +153,6 @@ class MiMoV2MoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         dtype = getattr(config, "moe_router_dtype", "float32")
         self.gate_dtype = str_dtype_to_torch_dtype(dtype)
         self.gate = nn.Linear(
diff --git a/vllm/model_executor/models/mixtral.py b/vllm/model_executor/models/mixtral.py
index 991729354b..f9b452f47f 100644
--- a/vllm/model_executor/models/mixtral.py
+++ b/vllm/model_executor/models/mixtral.py
@@ -92,7 +92,6 @@ class MixtralMoE(nn.Module):
         self.hidden_size = hidden_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()
 
         # Expert Parallelism Load balancing settings.
@@ -105,11 +104,6 @@ class MixtralMoE(nn.Module):
         self.n_redundant_experts = parallel_config.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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         # Gate always runs at half / full precision for now.
 
         self.gate = ReplicatedLinear(
diff --git a/vllm/model_executor/models/nemotron_h.py b/vllm/model_executor/models/nemotron_h.py
index d68a619eb2..6c7b253063 100644
--- a/vllm/model_executor/models/nemotron_h.py
+++ b/vllm/model_executor/models/nemotron_h.py
@@ -136,7 +136,6 @@ class NemotronHMoE(nn.Module):
         self.routed_scaling_factor = config.routed_scaling_factor
 
         self.ep_group = get_ep_group().device_group
-        self.ep_rank = self.ep_group.rank()
         self.ep_size = self.ep_group.size()
         self.n_routed_experts: int = config.n_routed_experts
         self.n_shared_experts: int = config.n_shared_experts
@@ -166,11 +165,6 @@ class NemotronHMoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         if config.n_shared_experts is None or config.n_shared_experts == 0:
             self.shared_experts = None
         else:
diff --git a/vllm/model_executor/models/openpangu.py b/vllm/model_executor/models/openpangu.py
index 348c0578f3..3557bdd0a5 100644
--- a/vllm/model_executor/models/openpangu.py
+++ b/vllm/model_executor/models/openpangu.py
@@ -142,7 +142,6 @@ class OpenPanguMoE(nn.Module):
 
         self.routed_scaling_factor = config.routed_scaling_factor
         self.ep_group = get_ep_group().device_group
-        self.ep_rank = self.ep_group.rank()
         self.ep_size = self.ep_group.size()
         self.n_routed_experts: int = config.n_routed_experts
         self.n_shared_experts: int = config.n_shared_experts
@@ -176,11 +175,6 @@ class OpenPanguMoE(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         if config.n_shared_experts is not None:
             intermediate_size = config.moe_intermediate_size * config.n_shared_experts
             self.shared_experts = OpenPanguMLP(
diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py
index 2c29f9cc61..41698bfd4d 100644
--- a/vllm/model_executor/models/qwen3_moe.py
+++ b/vllm/model_executor/models/qwen3_moe.py
@@ -142,7 +142,6 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
         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
 
@@ -164,11 +163,6 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.gate = ReplicatedLinear(
             config.hidden_size,
             config.num_experts,
@@ -227,19 +221,9 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
         if self.is_sequence_parallel:
             hidden_states = sequence_parallel_chunk(hidden_states)
 
-        if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the MoERunner class
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states, router_logits=hidden_states
-            )
-        else:
-            # Actually this will be dead code, since we always pass gate into
-            # MoERunner in the current implementation. But we keep this code
-            # here for clarity and future flexibility.
-            router_logits, _ = self.gate(hidden_states)
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states, router_logits=router_logits
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=hidden_states
+        )
 
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py
index b43aea8795..ffeada5444 100644
--- a/vllm/model_executor/models/qwen3_next.py
+++ b/vllm/model_executor/models/qwen3_next.py
@@ -109,7 +109,6 @@ class Qwen3NextSparseMoeBlock(nn.Module):
         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
 
@@ -130,11 +129,6 @@ class Qwen3NextSparseMoeBlock(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         self.gate = ReplicatedLinear(
             config.hidden_size,
             config.num_experts,
@@ -205,17 +199,9 @@ class Qwen3NextSparseMoeBlock(nn.Module):
         if self.is_sequence_parallel and not already_sequence_parallel:
             hidden_states = sequence_parallel_chunk(hidden_states)
 
-        if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the MoERunner class
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states, router_logits=hidden_states
-            )
-        else:
-            # router_logits: (num_tokens, n_experts)
-            router_logits, _ = self.gate(hidden_states)
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states, router_logits=router_logits
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=hidden_states
+        )
 
         if self.is_sequence_parallel and not already_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
diff --git a/vllm/model_executor/models/step3p5.py b/vllm/model_executor/models/step3p5.py
index 0a699ad241..5c6cee77d7 100644
--- a/vllm/model_executor/models/step3p5.py
+++ b/vllm/model_executor/models/step3p5.py
@@ -303,7 +303,6 @@ class FusedMoEBlock(nn.Module):
         self.layer_idx = extract_layer_index(prefix)
 
         self.ep_size = get_ep_group().device_group.size()
-        self.ep_rank = get_ep_group().device_group.rank()
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         parallel_config = vllm_config.parallel_config
@@ -316,11 +315,6 @@ class FusedMoEBlock(nn.Module):
         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.physical_expert_start = self.ep_rank * self.n_local_physical_experts
-        self.physical_expert_end = (
-            self.physical_expert_start + self.n_local_physical_experts
-        )
-
         if self.tp_size > config.moe_num_experts:
             raise ValueError(
                 f"Tensor parallel size {self.tp_size} is greater than "
@@ -399,16 +393,9 @@ class FusedMoEBlock(nn.Module):
         num_tokens, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
 
-        if self.experts.is_internal_router:
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states, router_logits=hidden_states
-            )
-        else:
-            # TODO(bnell): this gate could be moved into the MoERunner?
-            router_logits, _ = self.gate(hidden_states)
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states, router_logits=router_logits
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=hidden_states
+        )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
diff --git a/vllm/models/deepseek_v4/amd/model.py b/vllm/models/deepseek_v4/amd/model.py
index d7fd88c799..5888ae563c 100644
--- a/vllm/models/deepseek_v4/amd/model.py
+++ b/vllm/models/deepseek_v4/amd/model.py
@@ -307,20 +307,11 @@ class DeepseekV4MoE(nn.Module):
             raise ValueError("DeepSeek V4 hash MoE routing requires input_ids.")
 
         org_shape = hidden_states.shape
-        if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the MoERunner class
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=hidden_states,
-                input_ids=input_ids,
-            )
-        else:
-            router_logits, _ = self.gate(hidden_states)
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=router_logits,
-                input_ids=input_ids,
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=hidden_states,
+            input_ids=input_ids,
+        )
 
         return final_hidden_states.view(org_shape)
 
diff --git a/vllm/models/deepseek_v4/nvidia/model.py b/vllm/models/deepseek_v4/nvidia/model.py
index c0d29d0b42..e102b6ddd3 100644
--- a/vllm/models/deepseek_v4/nvidia/model.py
+++ b/vllm/models/deepseek_v4/nvidia/model.py
@@ -746,20 +746,11 @@ class DeepseekV4MoE(nn.Module):
         self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None
     ) -> torch.Tensor:
         org_shape = hidden_states.shape
-        if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the MoERunner class
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=hidden_states,
-                input_ids=input_ids,
-            )
-        else:
-            router_logits, _ = self.gate(hidden_states)
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=router_logits,
-                input_ids=input_ids,
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=hidden_states,
+            input_ids=input_ids,
+        )
 
         return final_hidden_states.view(org_shape)
 
diff --git a/vllm/models/deepseek_v4/xpu/model.py b/vllm/models/deepseek_v4/xpu/model.py
index 0c10404d10..2c8aabaaf2 100644
--- a/vllm/models/deepseek_v4/xpu/model.py
+++ b/vllm/models/deepseek_v4/xpu/model.py
@@ -792,19 +792,11 @@ class DeepseekV4MoE(nn.Module):
         self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None
     ) -> torch.Tensor:
         org_shape = hidden_states.shape
-        if self.experts.is_internal_router:
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=hidden_states,
-                input_ids=input_ids,
-            )
-        else:
-            router_logits, _ = self.gate(hidden_states)
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=router_logits,
-                input_ids=input_ids,
-            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=hidden_states,
+            input_ids=input_ids,
+        )
 
         return final_hidden_states.view(org_shape)