feat: Add orthogonal initializer and corresponding tests

ACKNOWLEDGMENTS.md

@@ -7,7 +7,7 @@ with a short description of your contribution(s) below. For example:
 
 MLX was developed with contributions from the following individuals:
 
-- Nripesh Niketan: Added `softsign`, `softmax`, `hardswish`, `logsoftmax` activation functions. Added `dropout3d` ops. Added `LogicalAnd` and `LogicalOR` ops. Added `clip_grad_norm` along with `tree_reduce`. Added `cross`.
+- Nripesh Niketan: Added `softsign`, `softmax`, `hardswish`, `logsoftmax` activation functions. Added `dropout3d` ops. Added `LogicalAnd` and `LogicalOR` ops. Added `clip_grad_norm` along with `tree_reduce`. Added `cross`. Added `orthogonal` initializer.
 - Juarez Bochi: Fixed bug in cross attention.
 - Justin Deschenaux: Sine, Cosine, arange, randint, truncated normal, bernoulli, lion optimizer, Dropout2d, linear and logistic regression python example.
 - Diogo Da Cruz: Added `tri`, `tril`, `triu`, `tensordot`, `inner`, `outer`, `tile`, `StreamContext`, `stream`, safetensors support, `einsum`, and `einsum_path`.

python/mlx/nn/init.py

@@ -395,3 +395,47 @@ def initializer(a: mx.array) -> mx.array:
         return a
 
     return initializer
+
+
+def orthogonal(
+    gain: float = 1.0, dtype: mx.Dtype = mx.float32
+) -> Callable[[mx.array], mx.array]:
+    r"""An initializer that returns an orthogonal matrix.
+
+    Args:
+        gain (float, optional): Scaling factor for the orthogonal matrix.
+            Default: ``1.0``.
+        dtype (Dtype, optional): Data type of the array. Default: ``float32``.
+
+    Returns:
+        Callable[[array], array]: An initializer that returns
+        an orthogonal matrix with the same shape as the input.
+    """
+
+    def initializer(a: mx.array) -> mx.array:
+        if a.ndim != 2:
+            raise ValueError(
+                f"Orthogonal initialization requires a 2D array but got"
+                " a {a.ndim}D array."
+            )
+
+        rows, cols = a.shape
+        n = max(rows, cols)
+
+        rmat = mx.random.normal(shape=(n, n))
+
+        # Perform QR decomposition on CPU
+        q, r = mx.linalg.qr(rmat, stream=mx.cpu)
+
+        # Adjust the sign of Q using the diagonal of R
+        d = mx.diag(r)
+        q = q * mx.sign(d)
+
+        # Slice Q to the desired shape
+        q = q[:rows, :cols]
+
+        # Scale Q by gain
+        q = q * gain
+        return q.astype(dtype)
+
+    return initializer

python/tests/test_init.py

@@ -106,6 +106,34 @@ def test_sparse(self):
             with self.assertRaises(ValueError):
                 result = initializer(mx.zeros((1,)))
 
+    def test_orthogonal(self):
+        initializer = init.orthogonal(gain=1.0, dtype=mx.float32)
+
+        # Test with a square matrix
+        shape = (4, 4)
+        result = initializer(mx.zeros(shape, dtype=mx.float32))
+        self.assertEqual(result.shape, shape)
+        self.assertEqual(result.dtype, mx.float32)
+
+        I = result @ result.T
+        eye = mx.eye(shape[0], dtype=mx.float32)
+        self.assertTrue(
+            mx.allclose(I, eye, atol=1e-5), "Orthogonal init failed on a square matrix."
+        )
+
+        # Test with a rectangular matrix: more rows than cols
+        shape = (6, 4)
+        result = initializer(mx.zeros(shape, dtype=mx.float32))
+        self.assertEqual(result.shape, shape)
+        self.assertEqual(result.dtype, mx.float32)
+
+        I = result.T @ result
+        eye = mx.eye(shape[1], dtype=mx.float32)
+        self.assertTrue(
+            mx.allclose(I, eye, atol=1e-5),
+            "Orthogonal init failed on a rectangular matrix.",
+        )
+
 
 if __name__ == "__main__":
     unittest.main()