diff --git a/test/xpu/test_linalg_xpu.py b/test/xpu/test_linalg_xpu.py
index 012d30f98..206f362bf 100644
--- a/test/xpu/test_linalg_xpu.py
+++ b/test/xpu/test_linalg_xpu.py
@@ -6,7 +6,7 @@
 from torch.testing._internal.common_dtype import floating_and_complex_types_and
 from torch.testing._internal.common_cuda import tf32_on_and_off
 from torch.testing._internal.common_mkldnn import bf32_on_and_off
-from torch.testing._internal.common_quantization import _group_quantize_tensor, _dynamically_quantize_per_channel
+from torch.testing._internal.common_quantization import _dynamically_quantize_per_channel
 from torch.testing import make_tensor
 import unittest
 import itertools
@@ -174,9 +174,53 @@ def _test(m, k, n, transpose_a, transpose_b, test_equal=True):
 
 @unittest.skipIf(IS_WINDOWS, "Skipped on Windows!")
 @parametrize("m", [1])
-@parametrize("k", [1024, 2048])
-@parametrize("n", [48, 64])
+@parametrize("k", [32])
+@parametrize("n", [32])
 def _int4_mm(self, device, m, k, n):
+    def _group_quantize_tensor(w, n_bit=4, q_group_size=16):
+        assert w.dim() == 2
+        w = w.transpose(0, 1).contiguous()
+        assert q_group_size > 1
+        assert w.shape[-1] % q_group_size == 0
+
+        to_quant = w.reshape(-1, q_group_size)
+        assert torch.isnan(to_quant).sum() == 0
+
+        max_val = to_quant.amax(dim=1, keepdim=True)
+        min_val = to_quant.amin(dim=1, keepdim=True)
+        max_int = 2 ** n_bit - 1
+        min_int = 0
+        scales = (max_val - min_val).clamp(min=1e-6) / max_int
+        assert torch.isnan(scales).sum() == 0
+
+        zeros = min_val + scales * (2 ** (n_bit - 1))
+        assert torch.isnan(zeros).sum() == 0
+
+        out = to_quant.sub(min_val).div(scales).round().clamp_(min_int, max_int)
+        assert torch.isnan(out).sum() == 0
+
+        out = out.to(dtype=torch.int32).reshape(w.shape)
+        if out.device.type != 'cpu' or out.device.type != 'xpu':
+            out = (out[::, ::2] << 4 | out[::, 1::2]).to(torch.uint8)
+
+        # Scales and zeros for the same q-group should be contiguous, so we can
+        # load as a 32-bit word
+        scales = scales.view(w.shape[0], -1)
+        zeros = zeros.view(w.shape[0], -1)
+        scales_and_zeros = (
+            torch.cat(
+                [
+                    scales.reshape(scales.size(0), scales.size(1), 1),
+                    zeros.reshape(zeros.size(0), zeros.size(1), 1),
+                ],
+                2,
+            )
+        )
+
+        if out.device.type != 'xpu':
+            scales_and_zeros = scales_and_zeros.transpose(0, 1).contiguous()
+        return out, scales_and_zeros
+
     def convert_weight_to_int4pack(b):
         b_tmp, b_scales_and_zeros = _group_quantize_tensor(
             b, n_bit=4, q_group_size=q_group
@@ -231,7 +275,8 @@ def weight_int4pack_mm(a, b_int4pack, b_scales_and_zeros):
         b_scales_and_zeros = b_scales_and_zeros_bf16.to(dtype=dtype)
         ref = torch.mm(a, b)
         res = weight_int4pack_mm(a, b_int4pack, b_scales_and_zeros)
-  
+        print(ref)
+        print(res)
         mean_err = ((res - ref).abs() / ref).mean()
         self.assertTrue(mean_err < 0.05)