[MoE Refactor] Rename FusedMoE.make_expert_params_mapping to fused_moe_make_expert_params_mapping

vllm/model_executor/layers/fused_moe/__init__.py

@@ -19,6 +19,7 @@
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE,
     FusedMoeWeightScaleSupported,
+    fused_moe_make_expert_params_mapping,
 )
 from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEActivationFormat,
@@ -65,6 +66,7 @@ def get_config() -> dict[str, Any] | None:
     "RoutingMethodType",
     "activation_without_mul",
     "apply_moe_activation",
+    "fused_moe_make_expert_params_mapping",
     "override_config",
     "get_config",
 ]

vllm/model_executor/layers/fused_moe/layer.py

@@ -1618,6 +1618,25 @@ def extra_repr(self) -> str:
         return s
 
 
+# This is a temporary forwarding method which will be removed/modified layer.
+def fused_moe_make_expert_params_mapping(
+    model: torch.nn.Module,
+    ckpt_gate_proj_name: str,
+    ckpt_down_proj_name: str,
+    ckpt_up_proj_name: str,
+    num_experts: int,
+    num_redundant_experts: int = 0,
+) -> list[tuple[str, str, int, str]]:
+    return FusedMoE.make_expert_params_mapping(
+        model,
+        ckpt_gate_proj_name,
+        ckpt_down_proj_name,
+        ckpt_up_proj_name,
+        num_experts,
+        num_redundant_experts,
+    )
+
+
 # Mark the FusedMoE weight_loader as supporting MoE-specific parameters
 # to avoid expensive runtime reflection in model loading code
 FusedMoE.weight_loader.supports_moe_loading = True  # type: ignore[attr-defined]

vllm/model_executor/models/AXK1.py

@@ -42,7 +42,10 @@
 )
 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 RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -916,7 +919,7 @@ def compute_logits(
     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",
@@ -950,7 +953,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # 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/afmoe.py

@@ -18,7 +18,10 @@
 )
 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 RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -479,7 +482,7 @@ def make_empty_intermediate_tensors(
     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/arctic.py

@@ -18,7 +18,10 @@
 )
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.fused_moe import (
+    fused_experts,
+    fused_topk,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,

vllm/model_executor/models/aria.py

@@ -14,7 +14,9 @@
 from vllm.distributed import get_tensor_model_parallel_rank
 from vllm.inputs import MultiModalDataDict
 from vllm.model_executor.layers.activation import get_act_fn
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+)
 from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig

vllm/model_executor/models/bailing_moe.py

@@ -41,7 +41,10 @@
 )
 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,
@@ -461,7 +464,7 @@ def forward(
         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/bailing_moe_linear.py

@@ -21,7 +21,10 @@
     RMSNormGated,
     layernorm_fn,
 )
-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,
@@ -990,7 +993,7 @@ def forward(
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         """Get expert parameter mapping for MoE layers."""
-        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/dbrx.py

@@ -15,7 +15,9 @@
     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,
+)
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
     ReplicatedLinear,

vllm/model_executor/models/deepseek_eagle.py

@@ -8,7 +8,9 @@
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
-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 (
@@ -105,7 +107,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # 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/deepseek_mtp.py

@@ -11,7 +11,9 @@
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 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.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -252,7 +254,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         ]
         stacked_params_mapping.extend(indexer_fused_mapping)
 
-        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/deepseek_v2.py

@@ -51,6 +51,7 @@
     FusedMoE,
     GateLinear,
     RoutingMethodType,
+    fused_moe_make_expert_params_mapping,
 )
 from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -1432,7 +1433,7 @@ def compute_logits(
     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",
@@ -1474,7 +1475,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # 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/dots1.py

@@ -40,7 +40,10 @@
 )
 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,
@@ -413,7 +416,7 @@ def forward(
         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/ernie45_moe.py

@@ -42,7 +42,10 @@
 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,
@@ -485,7 +488,7 @@ def forward(
     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/ernie45_vl_moe.py

@@ -36,7 +36,10 @@
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.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 (
     QKVParallelLinear,
@@ -649,7 +652,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
         # 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/exaone_moe.py

@@ -30,7 +30,10 @@
     get_pp_group,
     get_tensor_model_parallel_world_size,
 )
-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 ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -326,7 +329,7 @@ def forward(
     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/flex_olmo.py

@@ -20,7 +20,9 @@
 from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
-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 ReplicatedLinear
 from vllm.model_executor.models.olmoe import OlmoeAttention, OlmoeForCausalLM

vllm/model_executor/models/gemma4.py

@@ -37,7 +37,10 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import GeluAndMul
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    GateLinear,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,

vllm/model_executor/models/glm4_moe.py

@@ -42,7 +42,10 @@
 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,
@@ -466,7 +469,7 @@ def forward(
     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",