Add a multi optimizer (#1916)

python/tests/test_optimizers.py

@@ -39,6 +39,7 @@ def tree_equal(fn, *args):
 
 
 optimizers_dict = get_all_optimizers()
+del optimizers_dict["MultiOptimizer"]
 
 
 class TestOptimizers(mlx_tests.MLXTestCase):
@@ -500,6 +501,30 @@ class TestSchedulers(unittest.TestCase):
         grads = model.trainable_parameters()
         optimizer.update(model, grads)
 
+    def test_multi_optimizer(self):
+        class Model(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.l1 = nn.Linear(2, 2)
+                self.drop = nn.Dropout(p=0.5)
+                self.l2 = nn.Linear(2, 2)
+                self.vals = [nn.Linear(2, 2), nn.ReLU(), nn.ReLU()]
+
+        model = Model()
+        optimizer = opt.MultiOptimizer(
+            [opt.Adam(learning_rate=0.001), opt.SGD(learning_rate=0.1)],
+            [lambda name, weight: weight.ndim > 1],
+        )
+        optimizer.init(model.trainable_parameters())
+
+        self.assertEqual(len(optimizer.state["states"]), 2)
+
+        adam_states = tree_flatten(optimizer.state["states"][0])
+        sgd_states = tree_flatten(optimizer.state["states"][1])
+        self.assertEqual((len(sgd_states) - 2) * 2, len(adam_states) - 2)
+        self.assertFalse(any("bias" in k for k, v in adam_states))
+        self.assertFalse(any("weight" in k for k, v in sgd_states))
+
 
 if __name__ == "__main__":
     unittest.main()