Revert "[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (#28971)"

vllm/lora/layers/fused_moe.py

@@ -20,24 +20,15 @@
     _get_config_dtype_str,
 )
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
-    MarlinExperts,
+    modular_marlin_fused_moe,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    TritonExperts,
+    modular_triton_fused_moe,
     try_get_optimal_moe_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
     FusedMoEModularMethod,
 )
-from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
-    UnfusedOAITritonExperts,
-)
-from vllm.model_executor.layers.fused_moe.modular_kernel import (
-    FusedMoEModularKernel,
-)
-from vllm.model_executor.layers.fused_moe.prepare_finalize import (
-    MoEPrepareAndFinalizeNoEP,
-)
 
 from .utils import _get_lora_device
 
@@ -123,23 +114,15 @@ def _inject_lora_into_fused_moe(self):
         self.base_layer.ensure_moe_quant_config_init()
         quant_config = self.base_layer.quant_method.moe_quant_config
 
-        prepare_finalize = MoEPrepareAndFinalizeNoEP()
-        m_fused_moe_fn = FusedMoEModularKernel(
-            prepare_finalize,
-            self.base_layer.quant_method.select_gemm_impl(
-                prepare_finalize, self.base_layer
-            ),
-            self.base_layer.shared_experts,
-            getattr(self.base_layer, "shared_experts_stream", None),
-        )
-        if quant_config.use_mxfp4_w4a16:
-            assert isinstance(
-                m_fused_moe_fn.fused_experts, (MarlinExperts, UnfusedOAITritonExperts)
+        m_fused_moe_fn = (
+            modular_triton_fused_moe(
+                quant_config, shared_experts=self.base_layer.shared_experts
             )
-        else:
-            assert isinstance(
-                m_fused_moe_fn.fused_experts, (MarlinExperts, TritonExperts)
+            if not quant_config.use_mxfp4_w4a16
+            else modular_marlin_fused_moe(
+                quant_config, shared_experts=self.base_layer.shared_experts
             )
+        )
 
         def fwd_decorator(layer, func):
             def wrapper(*args, **kwargs):