Merge remote-tracking branch 'origin/v0.15.1-dev' into v0.15.1-dev

# Conflicts:
#	vllm/model_executor/layers/fused_moe/modular_kernel.py

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -2202,6 +2202,9 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
             hidden_states, w1, w2, topk_ids
         )
         
+        if use_nn_moe:
+            N = w1.size(-1)
+
         if use_nn_moe:
             N = w1.size(-1)
 

vllm/model_executor/layers/fused_moe/layer.py

@@ -1920,13 +1920,13 @@ class FusedMoE(CustomOp):
 
                 if self.capture is not None:
                     self.capture(topk_ids)
+                    
                 final_hidden_states = self.quant_method.apply(
                         layer=self,
                         x=x,  # The type signture of this is wrong due to the hack.
                         topk_weights=topk_weights,
                         topk_ids=topk_ids,
                         use_nn_moe=self.use_nn_moe,
-                    # use_fused_gate=self.use_fused_gate,
                     )
 
             if has_separate_shared_experts:

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -1210,7 +1210,7 @@ class FusedMoEModularKernel(torch.nn.Module):
                 workspace2=workspace2,
                 expert_tokens_meta=c_expert_tokens_meta,
                 apply_router_weight_on_input=apply_router_weight_on_input,
-                use_nn_moe=use_nn_moe
+                use_nn_moe=use_nn_moe,
             )
 
         return fused_out

vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py

@@ -316,7 +316,6 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         use_nn_moe: bool | None = False,
-        use_fused_gate: bool | None = False,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         assert self.kernel is not None
         return self.kernel(

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

@@ -1248,6 +1248,7 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
         x: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
+        use_nn_moe: bool | None = False,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         from vllm.model_executor.layers.fused_moe import fused_experts
 
@@ -1263,6 +1264,7 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
             global_num_experts=layer.global_num_experts,
             expert_map=layer.expert_map,
             quant_config=self.moe_quant_config,
+            use_nn_moe=use_nn_moe,
         )