Support Llama 4 for fused_marlin_moe (#20457)

Signed-off-by: mgoin <mgoin64@gmail.com>

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -24,6 +24,7 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
                      topk_weights: torch.Tensor,
                      topk_ids: torch.Tensor,
                      quant_type_id: int,
+                     apply_router_weight_on_input: bool = False,
                      global_num_experts: int = -1,
                      expert_map: Optional[torch.Tensor] = None,
                      global_scale1: Optional[torch.Tensor] = None,
@@ -149,7 +150,7 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
         topk_weights,
         moe_block_size=block_size_m,
         top_k=topk,
-        mul_topk_weights=False,
+        mul_topk_weights=apply_router_weight_on_input,
         is_ep=expert_map is not None,
         b_q_type=quant_type,
         size_m=M,
@@ -182,7 +183,7 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
         topk_weights,
         moe_block_size=block_size_m,
         top_k=1,
-        mul_topk_weights=True,
+        mul_topk_weights=not apply_router_weight_on_input,
         is_ep=expert_map is not None,
         b_q_type=quant_type,
         size_m=M * topk,
@@ -208,6 +209,7 @@ def fused_marlin_moe_fake(hidden_states: torch.Tensor,
                           topk_weights: torch.Tensor,
                           topk_ids: torch.Tensor,
                           quant_type_id: int,
+                          apply_router_weight_on_input: bool = False,
                           global_num_experts: int = -1,
                           global_scale1: Optional[torch.Tensor] = None,
                           global_scale2: Optional[torch.Tensor] = None,

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -493,11 +493,6 @@ class AWQMoEMethod(FusedMoEMethodBase):
 
         assert activation == "silu", "Only SiLU activation is supported."
 
-        if apply_router_weight_on_input:
-            raise NotImplementedError(
-                "Apply router weight on input is not supported for"
-                "fused Marlin MoE method.")
-
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
@@ -520,6 +515,7 @@ class AWQMoEMethod(FusedMoEMethodBase):
             topk_weights,
             topk_ids,
             quant_type_id=self.quant_type.id,
+            apply_router_weight_on_input=apply_router_weight_on_input,
             global_num_experts=global_num_experts,
             expert_map=expert_map,
             w1_zeros=layer.w13_qzeros,

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

@@ -322,6 +322,7 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
                 global_scale1=layer.w13_weight_scale_2,
                 global_scale2=layer.w2_weight_scale_2,
                 quant_type_id=scalar_types.float4_e2m1f.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
                 global_num_experts=global_num_experts,
                 expert_map=expert_map)
 
@@ -669,8 +670,6 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
         if self.use_marlin:
             assert activation == "silu", (
                 f"{activation} not supported for Marlin MoE.")
-            assert not apply_router_weight_on_input, (
-                "Apply router weight on input not supported for Marlin MoE.")
             return torch.ops.vllm.fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -681,6 +680,7 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                 topk_weights,
                 topk_ids,
                 quant_type_id=scalar_types.float8_e4m3fn.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
                 global_num_experts=global_num_experts,
                 expert_map=expert_map)
 
@@ -1356,8 +1356,6 @@ class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
 
         assert activation == "silu", (
             f"{activation} not supported for Marlin MoE.")
-        assert not apply_router_weight_on_input, (
-            "Apply router weight on input not supported for Marlin MoE.")
 
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
@@ -1381,6 +1379,7 @@ class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
             topk_weights,
             topk_ids,
             quant_type_id=self.quant_type.id,
+            apply_router_weight_on_input=apply_router_weight_on_input,
             global_num_experts=global_num_experts,
             expert_map=expert_map,
             g_idx1=layer.w13_weight_g_idx,

vllm/model_executor/layers/quantization/fp8.py

@@ -889,8 +889,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         elif self.use_marlin:
             assert activation == "silu", (
                 f"{activation} not supported for Marlin MoE.")
-            assert not apply_router_weight_on_input, (
-                "Apply router weight on input not supported for Marlin MoE.")
             return torch.ops.vllm.fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -901,6 +899,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                 topk_weights,
                 topk_ids,
                 quant_type_id=scalar_types.float8_e4m3fn.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
                 global_num_experts=global_num_experts,
                 expert_map=expert_map)
         else:

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -645,10 +645,6 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
                 "EPLB not supported for `GPTQMarlinMoEMethod` yet.")
 
         assert activation == "silu", "Only SiLU activation is supported."
-        if apply_router_weight_on_input:
-            raise NotImplementedError(
-                "Apply router weight on input is not supported for "
-                "fused Marlin MoE method.")
 
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
@@ -672,6 +668,7 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
             topk_weights,
             topk_ids,
             quant_type_id=self.quant_type.id,
+            apply_router_weight_on_input=apply_router_weight_on_input,
             global_num_experts=global_num_experts,
             expert_map=expert_map,
             g_idx1=layer.w13_g_idx,

vllm/model_executor/layers/quantization/modelopt.py

@@ -700,6 +700,7 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
                 global_scale1=layer.w13_weight_scale_2,
                 global_scale2=layer.w2_weight_scale_2,
                 quant_type_id=scalar_types.float4_e2m1f.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
                 global_num_experts=global_num_experts,
                 expert_map=expert_map)