nits and adding it to test

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))