[Bugfix] Fix marlin moe fallback logic for llama4 (#18042)

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

tests/weight_loading/models-large.txt

@@ -4,4 +4,5 @@ compressed-tensors, nm-testing/Mixtral-8x7B-Instruct-v0.1-W8A16-quantized, main
 compressed-tensors, nm-testing/test-w4a16-mixtral-actorder-group, main
 gptq_marlin, TheBloke/Mixtral-8x7B-v0.1-GPTQ, main
 gptq_marlin, TheBloke/Mixtral-8x7B-v0.1-GPTQ, gptq-8bit-128g-actorder_True
-awq_marlin, casperhansen/deepseek-coder-v2-instruct-awq, main
+awq_marlin, casperhansen/deepseek-coder-v2-instruct-awq, main
\ No newline at end of file
+compressed-tensors, RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16, main
\ No newline at end of file

vllm/model_executor/layers/fused_moe/layer.py

@@ -480,6 +480,7 @@ class FusedMoE(torch.nn.Module):
         self.custom_routing_function = custom_routing_function
         self.scoring_func = scoring_func
         self.e_score_correction_bias = e_score_correction_bias
+        self.apply_router_weight_on_input = apply_router_weight_on_input
         self.activation = activation
 
         if self.scoring_func != "softmax" and not self.use_grouped_topk:
@@ -498,7 +499,6 @@ class FusedMoE(torch.nn.Module):
             self.quant_method = quant_config.get_quant_method(self, prefix)
         assert self.quant_method is not None
 
-        self.apply_router_weight_on_input = apply_router_weight_on_input
         moe_quant_params = {
             "num_experts": self.local_num_experts,
             "hidden_size": hidden_size,

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -171,13 +171,19 @@ def check_moe_marlin_supports_layer(layer: LinearBase, group_size: int) \
                                     -> bool:
     hidden_size = layer.hidden_size
     intermediate_size_per_partition = layer.intermediate_size_per_partition
+    # apply_router_weight_on_input is not supported for moe marlin
+    supports_router_weight = not layer.apply_router_weight_on_input
+    # moe marlin requires the activation to be silu
+    supports_activation = layer.activation == "silu"
 
     # gate-up: (n, k) = (intermediate_size_per_partition * 2, hidden_size)
     # down: (n, k) = (hidden_size, intermediate_size_per_partition)
     # moe marlin requires n % 128 == 0 and k % 64 == 0
-    return hidden_size % 128 == 0 and \
-        intermediate_size_per_partition % max(64, group_size) == 0 and \
-        group_size in [-1, 32, 64, 128]
+    supports_shape = hidden_size % 128 == 0 and \
+        intermediate_size_per_partition % max(64, group_size) == 0
+    supports_group_size = group_size in [-1, 32, 64, 128]
+    return supports_shape and supports_group_size and \
+        supports_router_weight and supports_activation
 
 
 def marlin_make_workspace(output_size_per_partition: int,