Fix optimizer reloading from checkpoint (#1329)

* fix optimizer reloading from checkpoint

* comment

python/mlx/optimizers/optimizers.py

@@ -48,8 +48,28 @@ class Optimizer:
             >>> optimizer.state.keys()
             dict_keys(['step', 'learning_rate', 'weight', 'bias'])
         """
-        self._state.update(tree_map(lambda x: {}, parameters))
+
-        tree_map(self.init_single, parameters, self._state)
+        # Iniatilize the optimizer state to match the parameter state
+        def update_state(params, state):
+            if isinstance(params, (list, tuple)):
+                state = list(state)
+                for i in range(len(state)):
+                    state[i] = update_state(params[i], state[i])
+                if len(state) != len(params):
+                    state.extend(tree_map(lambda x: {}, params[len(state) :]))
+                return type(params)(state)
+            elif isinstance(params, dict):
+                for k, v in params.items():
+                    if k not in state:
+                        state[k] = tree_map(lambda x: {}, v)
+                    else:
+                        state[k] = update_state(v, state[k])
+                return state
+            else:
+                return state
+
+        update_state(parameters, self._state)
+        tree_map(lambda p, s: s or self.init_single(p, s), parameters, self._state)
         self._initialized = True
 
     def init_single(self, parameter: mx.array, state: dict):
@@ -104,7 +124,7 @@ class Optimizer:
 
     @state.setter
     def state(self, state: dict):
-        self._initialized = True
+        self._initialized = False
         self._state = state
 
     @property

python/tests/test_optimizers.py

@@ -10,7 +10,7 @@ import mlx.nn as nn
 import mlx.optimizers as opt
 import mlx.utils
 import mlx_tests
-from mlx.utils import tree_flatten, tree_map
+from mlx.utils import tree_flatten, tree_map, tree_unflatten
 
 
 def get_all_optimizers():
@@ -206,20 +206,22 @@ class TestOptimizers(mlx_tests.MLXTestCase):
 
     def test_adafactor(self):
         x = mx.zeros((5, 5))
-        grad = mx.ones_like(x)
+        params = {"x": x}
+        grad = {"x": mx.ones_like(x)}
         optimizer = opt.Adafactor()
         for _ in range(2):
-            xp = optimizer.apply_gradients(grad, x)
+            xp = optimizer.apply_gradients(grad, params)
-            self.assertEqual(xp.dtype, x.dtype)
+            self.assertEqual(xp["x"].dtype, x.dtype)
-            self.assertEqual(xp.shape, x.shape)
+            self.assertEqual(xp["x"].shape, x.shape)
 
         x = mx.zeros((5, 5), mx.float16)
-        grad = mx.ones_like(x)
+        params = {"x": x}
+        grad = {"x": mx.ones_like(x)}
         optimizer = opt.Adafactor()
         for _ in range(2):
-            xp = optimizer.apply_gradients(grad, x)
+            xp = optimizer.apply_gradients(grad, params)
-            self.assertEqual(xp.dtype, x.dtype)
+            self.assertEqual(xp["x"].dtype, x.dtype)
-            self.assertEqual(xp.shape, x.shape)
+            self.assertEqual(xp["x"].shape, x.shape)
         self.assertEqual(optimizer.state["step"], 2)
 
     def test_compiled_optimizer(self):
@@ -420,6 +422,30 @@ class TestSchedulers(unittest.TestCase):
             "Gradients were not scaled correctly during clipping.",
         )
 
+    def test_init_from_state(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.Adam(learning_rate=3e-4)
+        optimizer.init(model.trainable_parameters())
+
+        # Flatten the state for serialization
+        state = tree_flatten(optimizer.state)
+
+        # Make a new optimizer and load the state
+        optimizer = opt.Adam(learning_rate=3e-4)
+        optimizer.state = tree_unflatten(state)
+
+        # This should work without any errors
+        grads = model.trainable_parameters()
+        optimizer.update(model, grads)
+
 
 if __name__ == "__main__":
     unittest.main()