[MoE Refactor] Rename FusedMoE.make_expert_params_mapping to...

[MoE Refactor] Rename FusedMoE.make_expert_params_mapping to fused_moe_make_expert_params_mapping (#40671)
Signed-off-by: Bill Nell <bnell@redhat.com>

vllm/model_executor/models/openpangu.py

@@ -44,7 +44,10 @@ from vllm.model_executor.layers.attention import (
     Attention,
     StaticSinkAttention,
 )
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -1149,7 +1152,7 @@ class OpenPanguModel(nn.Module):
         ]
         has_experts = hasattr(self.config, "n_routed_experts")
         if has_experts:
-            expert_merge_mapping = FusedMoE.make_expert_params_mapping(
+            expert_merge_mapping = fused_moe_make_expert_params_mapping(
                 self,
                 ckpt_gate_proj_name="gate_proj",
                 ckpt_down_proj_name="down_proj",

vllm/model_executor/models/openpangu_mtp.py

@@ -28,7 +28,9 @@ from vllm.config import VllmConfig
 
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -147,7 +149,7 @@ class OpenPanguMTP(nn.Module):
             ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
         ]
 
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/param2moe.py

@@ -32,7 +32,10 @@ from vllm.distributed import (
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -690,7 +693,7 @@ class Param2MoEModel(nn.Module):
         return loaded_params
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/phimoe.py

@@ -35,7 +35,10 @@ from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
     ReplicatedLinear,
@@ -514,7 +517,7 @@ class PhiMoEModel(nn.Module):
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",

vllm/model_executor/models/qwen2_moe.py

@@ -40,7 +40,10 @@ from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -418,7 +421,7 @@ class Qwen2MoeModel(nn.Module):
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/qwen3_5_mtp.py

@@ -12,7 +12,9 @@ from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -194,7 +196,7 @@ class Qwen3_5MultiTokenPredictor(nn.Module):
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/qwen3_moe.py

@@ -43,7 +43,10 @@ from vllm.distributed import (
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -516,7 +519,7 @@ class Qwen3MoeModel(nn.Module, EagleModelMixin):
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/qwen3_next.py

@@ -23,7 +23,10 @@ from vllm.distributed import (
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import (
     GemmaRMSNorm as Qwen3NextRMSNorm,
 )
@@ -533,7 +536,7 @@ class Qwen3NextModel(nn.Module, EagleModelMixin):
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/qwen3_next_mtp.py

@@ -11,7 +11,9 @@ from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -145,7 +147,7 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+        expert_params_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/sarvam.py

@@ -35,7 +35,10 @@ from vllm.distributed import (
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -529,7 +532,7 @@ class SarvamMLAModel(nn.Module):
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return FusedMoE.make_expert_params_mapping(
+        return fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/step3_text.py

@@ -18,7 +18,9 @@ from vllm.distributed import (
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,

vllm/model_executor/models/step3p5.py

@@ -23,7 +23,10 @@ from vllm.distributed import (
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -637,7 +640,7 @@ class Step3p5Model(nn.Module):
         ]
 
         # New per-expert format: .moe.experts.E.gate_proj.weight_packed [out, in]
-        per_expert_mapping = FusedMoE.make_expert_params_mapping(
+        per_expert_mapping = fused_moe_make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",

vllm/model_executor/models/transformers/moe.py

@@ -25,7 +25,10 @@ from vllm.config.utils import getattr_iter
 from vllm.distributed import get_dp_group, get_ep_group
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.model_executor.custom_op import PluggableLayer
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
 from vllm.model_executor.models.interfaces import MixtureOfExperts
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.platforms import current_platform
@@ -179,7 +182,7 @@ class MoEMixin(MixtureOfExperts):
         num_redundant_experts = self.parallel_config.eplb_config.num_redundant_experts
         for gate_proj, down_proj, up_proj in ckpt_names:
             expert_mapping.extend(
-                FusedMoE.make_expert_params_mapping(
+                fused_moe_make_expert_params_mapping(
                     self,
                     ckpt_gate_proj_name=gate_proj,
                     ckpt_down_proj_name=down_proj,