diff --git a/test/unit/test_linalg.py b/test/unit/test_linalg.py
index 9bdff0b5cf..2e97ad6c9b 100644
--- a/test/unit/test_linalg.py
+++ b/test/unit/test_linalg.py
@@ -83,6 +83,15 @@ class TestLinAlg(unittest.TestCase):
       s_diag = (S.unsqueeze(-2) * Tensor.eye(2))
       reconstruction_helper([U, s_diag, V], a)
 
+  def test_svd_identity_4x4(self):
+    a = Tensor.eye(4)
+    U,S,V = a.svd()
+    assert not np.isnan(U.numpy()).any()
+    assert not np.isnan(S.numpy()).any()
+    assert not np.isnan(V.numpy()).any()
+    s_diag = (S.unsqueeze(-2) * Tensor.eye(4))
+    reconstruction_helper([U, s_diag, V], a)
+
   def test_svd_rank1(self):
     a = Tensor([[1.0, 1.0], [2.0, 2.0]]).realize()
     U, S, V = a.svd()
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 605142ed9d..21c6fbb6fc 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -3462,8 +3462,9 @@ class Tensor(OpMixin):
     #preprocess the matrix
     Q, R = (self.qr() if m >= n else self.transpose(-2, -1).qr())
     num, q_num = min(m, n), max(m, n)
-    U = R.shrink(tuple([None] * len(b_shape) + [(0, num), (0, num)]))
-    V = Tensor.eye(num, dtype=self.dtype).reshape((1,) * len(b_shape) + (num, num)).expand(b_shape + (num, num))
+    # TODO: codegen infinite loop without contiguous
+    U = R.shrink(tuple([None] * len(b_shape) + [(0, num), (0, num)])).contiguous()
+    V = Tensor.eye(num, dtype=self.dtype).reshape((1,) * len(b_shape) + (num, num)).expand(b_shape + (num, num)).contiguous()
     #prepare round robin pairing
     permute, inverse_permute = Tensor.arange(0, num, dtype=dtypes.int), Tensor.zeros(num, dtype=dtypes.int)
     permute[num//2:num] = permute[num//2:num].flip(0)