Merge pull request #246 from EricDinging/fix-test

Fix q-fedavg model loading error | Fix test cases

fedscale/cloud/aggregation/optimizers.py

@@ -75,11 +75,9 @@ def update_round_gradient(
                 update_weights = result["update_weight"]
                 if type(update_weights) is dict:
                     update_weights = [x for x in update_weights.values()]
+
                 weights = [
-                    torch.from_numpy(np.asarray(x, dtype=np.float32)).to(
-                        device=self.device
-                    )
-                    for x in update_weights
+                    torch.tensor(x).to(device=self.device) for x in update_weights
                 ]
                 grads = [
                     (u - v) * 1.0 / learning_rate for u, v in zip(last_model, weights)
@@ -100,8 +98,10 @@ def update_round_gradient(
                 ) + (1.0 / learning_rate) * np.float_power(loss + 1e-10, qfedq)
 
             # update global model
-            for idx, param in enumerate(target_model.parameters()):
-                param.data = last_model[idx] - Deltas[idx] / (hs + 1e-10)
+            new_state_dict = {
+                name: last_model[idx] - Deltas[idx] / (hs + 1e-10) for idx, name in enumerate(target_model.state_dict().keys())
+            }
+            target_model.load_state_dict(new_state_dict)
 
         else:
             # The default optimizer, FedAvg, has been applied in aggregator.py on the fly

fedscale/cloud/internal/model_adapter_base.py

@@ -7,11 +7,16 @@ class ModelAdapterBase(abc.ABC):
     """
     Represents an adapter that operates on a framework-specific model.
     """
+
     @abc.abstractmethod
-    def set_weights(self, weights: np.ndarray):
+    def set_weights(
+        self, weights: np.ndarray, is_aggregator=True, client_training_results=None
+    ):
         """
         Set the model's weights to the numpy weights array.
         :param weights: numpy weights array
+        :param is_aggregator: boolean indicating whether the caller is the aggregator
+        :param client_training_results: list of gradients from every clients, for q-fedavg
         """
         pass
 

fedscale/cloud/internal/tensorflow_model_adapter.py

@@ -10,13 +10,28 @@ class TensorflowModelAdapter(ModelAdapterBase):
     def __init__(self, model: tf.keras.Model):
         self.model = model
 
-    def set_weights(self, weights: List[np.ndarray]):
+    def set_weights(
+        self,
+        weights: List[np.ndarray],
+        is_aggregator=True,
+        client_training_results=None,
+    ):
+        """
+        Set the model's weights to the numpy weights array.
+        :param weights: numpy weights array
+        :param is_aggregator: boolean indicating whether the caller is the aggregator
+        :param client_training_results: list of gradients from every clients, for q-fedavg
+        """
         for i, layer in enumerate(self.model.layers):
             if layer.trainable:
                 layer.set_weights(weights[i])
 
     def get_weights(self) -> List[np.ndarray]:
-        return [np.asarray(layer.get_weights()) for layer in self.model.layers if layer.trainable]
+        return [
+            np.asarray(layer.get_weights())
+            for layer in self.model.layers
+            if layer.trainable
+        ]
 
     def get_model(self):
         return self.model

fedscale/tests/cloud/aggregation/test_aggregator.py

@@ -14,6 +14,7 @@ def __init__(self, model_wrapper):
         self.model_in_update = 1
         self.tasks_round = 3
         self.model_wrapper = model_wrapper
+        self.client_training_results = None
 
 
 def multiply_weights(weights, factor):
@@ -23,8 +24,9 @@ def multiply_weights(weights, factor):
 class TestAggregator:
     def test_update_weight_aggregation_for_keras_model(self):
         x = tf.keras.Input(shape=(2,))
-        y = tf.keras.layers.Dense(2, activation='softmax')(
-            tf.keras.layers.Dense(4, activation='softmax')(x))
+        y = tf.keras.layers.Dense(2, activation="softmax")(
+            tf.keras.layers.Dense(4, activation="softmax")(x)
+        )
         model = tf.keras.Model(x, y)
         model_adapter = TensorflowModelAdapter(model)
         aggregator = MockAggregator(model_adapter)
@@ -34,7 +36,9 @@ def test_update_weight_aggregation_for_keras_model(self):
         aggregator.update_weight_aggregation(multiply_weights(weights, 2))
         aggregator.model_in_update += 1
         aggregator.update_weight_aggregation(multiply_weights(weights, 5))
-        np.array_equal(aggregator.model_wrapper.get_weights(), multiply_weights(weights, 3))
+        np.array_equal(
+            aggregator.model_wrapper.get_weights(), multiply_weights(weights, 3)
+        )
 
     def test_update_weight_aggregation_for_torch_model(self):
         model = torch.nn.Linear(3, 2)
@@ -46,4 +50,6 @@ def test_update_weight_aggregation_for_torch_model(self):
         aggregator.update_weight_aggregation(multiply_weights(weights, 2))
         aggregator.model_in_update += 1
         aggregator.update_weight_aggregation(multiply_weights(weights, 5))
-        np.array_equal(aggregator.model_wrapper.get_weights(), multiply_weights(weights, 3))
+        np.array_equal(
+            aggregator.model_wrapper.get_weights(), multiply_weights(weights, 3)
+        )