Adding support for the Muon Optimizer (#1914)

* initial commit with workong optmimizer

* update ACKNOWLEDGMENTS.md

* nits and adding it to test

* nits

* G.astype(mx.bfloat16) to G.astype(G.dtype)

* G.ndim >= 2 to assert G.ndim == 2

* remove coments

* replace with  mx.addmm

* remove comments

* format

* nits

* match muon

* fix addmm

---------

Co-authored-by: Awni Hannun <awni@apple.com>

python/tests/test_blas.py

@@ -691,6 +691,21 @@ class TestBlas(mlx_tests.MLXTestCase):
             self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
             self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
 
+        # Transposed c
+        a = mx.ones((10, 5)).T
+        b = mx.ones((5, 5))
+        out = mx.addmm(a, b, a, beta=1.5, alpha=0.5)
+        expected = 1.5 * a + 0.5 * (b @ a)
+        self.assertTrue(mx.allclose(expected, out))
+
+        # Broadcast c
+        a = mx.ones((5, 5))
+        b = mx.ones((5, 5))
+        c = mx.ones((1, 5))
+        out = mx.addmm(c, a, b, beta=1.5, alpha=0.5)
+        expected = 1.5 * c + 0.5 * (a @ b)
+        self.assertTrue(mx.allclose(expected, out))
+
     def test_addmm_grad(self):
         def make_ref_addmm(alpha, beta):
             return lambda c, a, b: alpha * (a @ b) + beta * c

python/tests/test_optimizers.py

@@ -286,6 +286,53 @@ class TestOptimizers(mlx_tests.MLXTestCase):
             self.assertEqual(xp["x"].shape, x.shape)
         self.assertEqual(optimizer.state["step"], 2)
 
+    def test_muon(self):
+        params = {
+            "first": [mx.zeros((10, 5)), mx.zeros((1,))],
+            "second": mx.zeros((3, 3)),
+            "conv": mx.zeros((16, 8, 3, 3)),
+        }
+        grads = tree_map(lambda x: mx.ones_like(x), params)
+
+        # Explicit init
+        optim = opt.Muon(learning_rate=1e-2, momentum=0.95, nesterov=True)
+        optim.init(params)
+        self.assertTrue(
+            tree_equal(
+                lambda p, s: mx.array_equal(s["v"], mx.zeros_like(p)),
+                params,
+                optim.state,
+            )
+        )
+
+        # Test update
+        updated_params = optim.apply_gradients(grads, params)
+
+        # Check that shapes are preserved
+        self.assertTrue(
+            tree_equal(
+                lambda p, u: p.shape == u.shape,
+                params,
+                updated_params,
+            )
+        )
+
+        # Check that parameters actually changed
+        self.assertFalse(
+            tree_equal(
+                lambda p, u: mx.array_equal(p, u),
+                params,
+                updated_params,
+            )
+        )
+
+        # Test with different configurations
+        optim_no_nesterov = opt.Muon(learning_rate=1e-2, momentum=0.95, nesterov=False)
+        optim_no_nesterov.apply_gradients(grads, params)
+
+        optim_no_momentum = opt.Muon(learning_rate=1e-2, momentum=0.0)
+        optim_no_momentum.apply_gradients(grads, params)
+
     def test_compiled_optimizer(self):
         model = nn.Linear(10, 10)
         x = mx.random.uniform(shape=(2, 10))