standardize random hadamard

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

Author: kylesayrs

src/compressed_tensors/transform/utils/hadamard.py

@@ -128,7 +128,7 @@ def _matmul_hadU(X, transpose=False) -> torch.Tensor:
         input = hadK.view(1, K, K).to(input) @ input
 
     # normalize
-    return input.view(X.shape) / torch.tensor(n).sqrt()
+    return input.view(X.shape)
 
 
 def _is_pow2(n: int) -> bool:

tests/test_transform/utils/test_hadamards.py

@@ -46,7 +46,7 @@ def test_packed_hadamard_compliant(had_func):
 def test_random_hadamard_matrix_compliant(size):
     had_matrix = random_hadamard_matrix(size)
     val_1 = torch.round(had_matrix @ had_matrix.T)
-    assert torch.equal(val_1, torch.eye(size))
+    assert torch.equal(val_1 / size, torch.eye(size))
 
 
 @pytest.mark.parametrize(