[Misc] Support FP8 MoE for compressed-tensors (#8588)

vllm/model_executor/layers/fused_moe/layer.py

@@ -323,10 +323,12 @@ class FusedMoE(torch.nn.Module):
                       loaded_weight: torch.Tensor, weight_name: str,
                       shard_id: str, expert_id: int) -> None:
 
-        # compressed-tensors represents weights on disk which are flipped
+        # compressed-tensors checkpoints with packed weights are stored flipped
+        # TODO (mgoin): check self.quant_method.quant_config.quant_format
+        # against known CompressionFormat enum values that have this quality
         loaded_weight = loaded_weight.t().contiguous() if (
             self.quant_method.__class__.__name__
-            == "CompressedTensorsMoEMethod") else loaded_weight
+            == "CompressedTensorsWNA16MoEMethod") else loaded_weight
 
         if shard_id not in ("w1", "w2", "w3"):
             raise ValueError(f"shard_id must be ['w1','w2','w3'] but "
@@ -353,6 +355,9 @@ class FusedMoE(torch.nn.Module):
 
         # Case input scale: input_scale loading is only supported for fp8
         if "input_scale" in weight_name:
+            # this is needed for compressed-tensors only
+            loaded_weight = loaded_weight.to(param.data.device)
+
             if param.data[expert_id] != 1 and (param.data[expert_id] -
                                                loaded_weight).abs() > 1e-5:
                 raise ValueError(