diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 1a3686ccf..450b43d43 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -172,8 +172,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         if use_quantized_matmul and not re_quantize_for_matmul:
             scale.transpose_(0,1)
             layer.weight.transpose_(0,1)
-            weight_stride = layer.weight.stride()
-            if not (weight_stride[0] == 1 and weight_stride[1] > 1):
+            if layer.weight.is_contiguous():
                 if devices.backend != "ipex":
                     layer.weight.data = layer.weight.t_().contiguous().t_()
             elif devices.backend == "ipex":
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index d404966c8..18009c27b 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -6,11 +6,8 @@ import torch
 
 
 def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    input_stride = input.stride()
-    if not (input_stride[0] > input_stride[1] and input_stride[1] == 1):
-        input = input.contiguous()
-    weight_stride = weight.stride()
-    if not (weight_stride[0] == 1 and weight_stride[1] > 1):
+    input = input.contiguous()
+    if weight.is_contiguous():
         if weight.device.type != "xpu":
             weight = weight.t().contiguous().t()
     elif weight.device.type == "xpu":