fix empty channelwise scales for compressed tensors

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -54,7 +54,12 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
         output_size_per_partition = sum(output_partition_sizes)
 
         # If group_size is -1, we are in channelwise case.
-        group_size = input_size if self.group_size == -1 else self.group_size
+        channelwise = (self.group_size == -1)   
+        group_size = input_size if channelwise else self.group_size
+        row_parallel = (input_size != input_size_per_partition)
+        # In the case of channelwise quantization, we need to replicate the
+        # scales across all gpus.
+        partition_scales = (row_parallel and not channelwise)
 
         verify_marlin_supports_shape(
             output_size_per_partition=output_size_per_partition,
@@ -65,9 +70,8 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
         weight_scale_dim = None
         scales_and_zp_size = input_size // group_size
 
-        if (input_size != input_size_per_partition
-                and self.group_size is not None):
-            weight_scale_dim = 1
+        if partition_scales:
+            assert input_size_per_partition % group_size == 0
             scales_and_zp_size = input_size_per_partition // group_size
 
         weight = Parameter(