[Bugfix] Fix moe weight losing all extra attrs after `process_weights_after_loading`. (#16854)

Signed-off-by: charlifu <charlifu@amd.com>

vllm/model_executor/layers/fused_moe/layer.py

@@ -113,12 +113,9 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         super().process_weights_after_loading(layer)
 
-        layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
-            layer.w13_weight.data),
-                                              requires_grad=False)
-        layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
-            layer.w2_weight.data),
-                                             requires_grad=False)
+        # Padding the weight for better performance on ROCm
+        layer.w13_weight.data = self._maybe_pad_weight(layer.w13_weight.data)
+        layer.w2_weight.data = self._maybe_pad_weight(layer.w2_weight.data)
         # Lazy import to avoid importing triton.
         from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
             is_rocm_aiter_moe_enabled, shuffle_weights)
@@ -127,10 +124,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             shuffled_w13, shuffled_w2 = shuffle_weights(
                 layer.w13_weight.data, layer.w2_weight.data)
 
-            layer.w13_weight = torch.nn.Parameter(shuffled_w13,
-                                                  requires_grad=False)
-            layer.w2_weight = torch.nn.Parameter(shuffled_w2,
-                                                 requires_grad=False)
+            layer.w13_weight.data = shuffled_w13
+            layer.w2_weight.data = shuffled_w2
 
         if current_platform.is_cpu():
             if current_platform.get_cpu_architecture() == CpuArchEnum.X86:

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -156,7 +156,7 @@ def rocm_per_tensor_w8a8_scaled_mm(*, qinput: torch.Tensor,
                                    input_2d: torch.Tensor,
                                    output_shape: List) -> torch.Tensor:
     from vllm.platforms.rocm import on_mi250_mi300
-    if envs.VLLM_ROCM_USE_SKINNY_GEMM and not on_mi250_mi300(
+    if envs.VLLM_ROCM_USE_SKINNY_GEMM and on_mi250_mi300(
     ) and qinput.shape[0] == 1 and qinput.shape[1] % 16 == 0:
         output = ops.wvSplitKQ(weight.t(), qinput, out_dtype, scale_a, scale_b,
                                current_platform.get_cu_count())