[Feature] EPLB on Qwen3VLMoe and CompressedTensorsWNA16MoEMethod (#28849)

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -1921,9 +1921,20 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         if enable_eplb:
-            raise NotImplementedError(
+            if expert_load_view is None:
-                "EPLB not supported for `CompressedTensorsWNA16MoEMethod` yet."
+                raise ValueError("enable_eplb=True requiere expert_load_view != None")
-            )
+            if logical_to_physical_map is None:
+                raise ValueError(
+                    "enable_eplb=True requiere logical_to_physical_map != None"
+                )
+            if logical_replica_count is None:
+                raise ValueError(
+                    "enable_eplb=True requiere logical_replica_count != None"
+                )
+            if not isinstance(layer, FusedMoE):
+                raise TypeError(
+                    "EPLB is only supported when `layer` is a instance of FusedMoE."
+                )
 
         from vllm.model_executor.layers.fused_moe import fused_experts
 
@@ -1940,6 +1951,12 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             routed_scaling_factor=routed_scaling_factor,
             e_score_correction_bias=e_score_correction_bias,
             indices_type=self.topk_indices_dtype,
+            num_fused_shared_experts=getattr(layer, "num_fused_shared_experts", 0),
+            enable_eplb=enable_eplb,
+            expert_map=expert_map,
+            expert_load_view=expert_load_view,
+            logical_to_physical_map=logical_to_physical_map,
+            logical_replica_count=logical_replica_count,
         )
 
         return fused_experts(
@@ -1956,6 +1973,10 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             quant_config=self.moe_quant_config,
         )
 
+    @property
+    def supports_eplb(self) -> bool:
+        return True
+
 
 class CompressedTensorsW4A8Int8MoEMethod(CompressedTensorsMoEMethod):
     """

vllm/model_executor/models/qwen3_vl_moe.py

@@ -15,7 +15,7 @@
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#      http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -29,7 +29,9 @@ from collections.abc import Callable, Iterable
 from itertools import islice
 
 import torch
-from transformers.models.qwen3_vl_moe.configuration_qwen3_vl_moe import Qwen3VLMoeConfig
+from transformers.models.qwen3_vl_moe.configuration_qwen3_vl_moe import (
+    Qwen3VLMoeConfig,
+)
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
@@ -44,7 +46,12 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors
 
-from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
+from .interfaces import MixtureOfExperts
+from .qwen3_moe import (
+    Qwen3MoeForCausalLM,
+    Qwen3MoeModel,
+    Qwen3MoeSparseMoeBlock,
+)
 from .qwen3_vl import (
     Qwen3_VisionTransformer,
     Qwen3VLDummyInputsBuilder,
@@ -344,12 +351,56 @@ class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
         )
 
 
+class Qwen3VLMoeMixtureOfExperts(MixtureOfExperts):
+    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 layer in self.language_model.model.layers:
+            if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
+                moe = layer.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()
+
+    def set_moe_parameters(self):
+        self.expert_weights = []
+
+        self.moe_layers = []
+        example_moe = None
+        for layer in self.language_model.model.layers:
+            if hasattr(layer, "mlp") and isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
+                example_moe = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_moe is None:
+            raise RuntimeError("No Qwen3Moe layer found in the language_model.")
+
+        # Set MoE hyperparameters
+        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
+
+
 @MULTIMODAL_REGISTRY.register_processor(
     Qwen3VLMultiModalProcessor,
     info=Qwen3VLMoeProcessingInfo,
     dummy_inputs=Qwen3VLDummyInputsBuilder,
 )
-class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
+class Qwen3VLMoeForConditionalGeneration(
+    Qwen3VLForConditionalGeneration, Qwen3VLMoeMixtureOfExperts
+):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -413,3 +464,6 @@ class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
             self.deepstack_input_embeds = None
         self.visual_dim = config.vision_config.out_hidden_size
         self.multiscale_dim = self.visual_dim * self.deepstack_num_level
+
+        # Set MoE hyperparameters
+        self.set_moe_parameters()