[fix]: disable cutlass block scaled group gemm for EP

csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu

@@ -201,11 +201,10 @@ void run_blockwise_scaled_group_mm(
       reinterpret_cast<typename ScheduleConfig::LayoutSFB*>(
           layout_sfb.data_ptr())};
 
-  cutlass::KernelHardwareInfo hw_info;
-  hw_info.device_id = a_ptrs.get_device();
-  hw_info.sm_count =
-      cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
-          hw_info.device_id);
+  int device_id = a_ptrs.device().index();
+  static const cutlass::KernelHardwareInfo hw_info{
+      device_id, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
+                     device_id)};
 
   // Epilogue Arguments
   typename GemmKernel::EpilogueArguments epilogue_args{

vllm/entrypoints/openai/serving_score.py

@@ -216,8 +216,8 @@ async def _cross_encoding_score(
                 # cross_encoder models defaults to using pad_token.
                 tokenized_prompts = await asyncio.gather(*(
                     tokenize_async(
-                        text=t1, # type: ignore[arg-type]
-                        text_pair=t2, # type: ignore[arg-type]
+                        text=t1,  # type: ignore[arg-type]
+                        text_pair=t2,  # type: ignore[arg-type]
                         **tokenization_kwargs) for t1, t2 in input_pairs))
             else:
                 # `llm as reranker` models defaults to not using pad_token.

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -547,8 +547,10 @@ def cutlass_moe_fp4(a: torch.Tensor,
     return out.to(dtype=out_dtype)
 
 
-def _valid_cutlass_block_scaled_grouped_gemm(w1: torch.Tensor,
-                                             w2: torch.Tensor) -> bool:
+def _valid_cutlass_block_scaled_grouped_gemm(
+        w1: torch.Tensor, w2: torch.Tensor, inplace: bool, activation: str,
+        apply_router_weight_on_input: bool,
+        expert_map: Optional[torch.Tensor]) -> bool:
 
     def _valid_cutlass_block_scaled_grouped_gemm_shape(N: int, K: int):
         return N % 128 == 0 and K % 128 == 0
@@ -564,6 +566,29 @@ def _valid_cutlass_block_scaled_grouped_gemm_shape(N: int, K: int):
             "CutlassBlockScaledGroupedGemm disabled: invalid weight dtype(s).")
         return False
 
+    if expert_map is not None:
+        logger.debug(
+            "CutlassBlockScaledGroupedGemm disabled: expert_parallel is"
+            " not supported.")
+        return False
+
+    if activation != "silu":
+        logger.debug(
+            "CutlassBlockScaledGroupedGemm disabled: only activation silu is"
+            " supported.")
+        return False
+
+    if apply_router_weight_on_input:
+        logger.debug("CutlassBlockScaledGroupedGemm disabled:"
+                     " apply_router_weight_on_input is not supported.")
+        return False
+
+    if inplace:
+        logger.debug(
+            "CutlassBlockScaledGroupedGemm disabled: inplace is not supported."
+        )
+        return False
+
     return True
 
 

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1190,8 +1190,9 @@ def fused_experts(
             apply_router_weight_on_input=apply_router_weight_on_input,
         )
     elif (allow_cutlass_block_scaled_grouped_gemm and use_fp8_w8a8
-          and _valid_cutlass_block_scaled_grouped_gemm(w1, w2)):
-        assert apply_router_weight_on_input is False
+          and _valid_cutlass_block_scaled_grouped_gemm(
+              w1, w2, inplace, activation, apply_router_weight_on_input,
+              expert_map)):
         return run_cutlass_block_scaled_fused_experts(
             a=hidden_states,
             w1=w1,