[AMP] add state_dict and load_state_dict and unittest for class GradScaler

python/paddle/amp/grad_scaler.py

@@ -47,7 +47,7 @@ class GradScaler(AmpScaler):
     Examples:
 
         .. code-block:: python
-
+            
             import paddle
 
             model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True)
@@ -91,7 +91,7 @@ def scale(self, var):
         Examples:
 
             .. code-block:: python
-
+                
                 import paddle
 
                 model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True)
@@ -156,6 +156,7 @@ def is_enable(self):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -178,7 +179,8 @@ def is_use_dynamic_loss_scaling(self):
 
         Examples:
             .. code-block:: python
-
+
+                # required: gpu,xpu         
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -202,6 +204,7 @@ def get_init_loss_scaling(self):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -220,11 +223,12 @@ def set_init_loss_scaling(self, new_init_loss_scaling):
         Set the initial loss scaling factor by `new_init_loss_scaling`.
 
         Args:
-            new_init_loss_scaling(int):  The new_init_loss_scaling used to update initial loss scaling factor.
+            new_init_loss_scaling(float):  The new_init_loss_scaling used to update initial loss scaling factor.
 
         Examples:
             .. code-block:: python
-
+
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -250,6 +254,7 @@ def get_incr_ratio(self):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -273,6 +278,7 @@ def set_incr_ratio(self, new_incr_ratio):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -298,6 +304,7 @@ def get_decr_ratio(self):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -321,6 +328,7 @@ def set_decr_ratio(self, new_decr_ratio):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -346,6 +354,7 @@ def get_incr_every_n_steps(self):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -369,6 +378,7 @@ def set_incr_every_n_steps(self, new_incr_every_n_steps):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -394,6 +404,7 @@ def get_decr_every_n_nan_or_inf(self):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -417,6 +428,7 @@ def set_decr_every_n_nan_or_inf(self, new_decr_every_n_nan_or_inf):
         Examples:
             .. code-block:: python
 
+                # required: gpu,xpu
                 import paddle
                 scaler = paddle.amp.GradScaler(enable=True,
                                                init_loss_scaling=1024,
@@ -432,3 +444,59 @@ def set_decr_every_n_nan_or_inf(self, new_decr_every_n_nan_or_inf):
         """
         super(GradScaler,
               self).set_decr_every_n_nan_or_inf(new_decr_every_n_nan_or_inf)
+
+    def state_dict(self):
+        """
+        Returns the state of the scaler as a `dict`, If this instance is not enabled, returns an empty dict.
+
+        Reurns:
+            A dict of scaler includes:
+            init_loss_scaling (float, optional): The initial loss scaling factor.
+            incr_ratio(float, optional): The multiplier to use when increasing the loss scaling.
+            decr_ratio(float, optional): The less-than-one-multiplier to use when decreasing the loss scaling.
+            incr_every_n_steps(int, optional): Increases loss scaling every n consecutive steps with finite gradients.
+            decr_every_n_nan_or_inf(int, optional): Decreases loss scaling every n accumulated steps with nan or inf gradients.
+
+        Examples:
+
+            .. code-block:: python
+
+                # required: gpu,xpu
+                import paddle
+
+                scaler = paddle.amp.GradScaler(enable=True,
+                                               init_loss_scaling=1024,
+                                               incr_ratio=2.0,
+                                               decr_ratio=0.5,
+                                               incr_every_n_steps=1000,
+                                               decr_every_n_nan_or_inf=2,
+                                               use_dynamic_loss_scaling=True)
+                scaler_state = scaler.state_dict()
+        """
+        return super(GradScaler, self).state_dict()
+
+    def load_state_dict(self, state_dict):
+        """
+        Loads the scaler state.
+
+        Args:
+           state_dict(dict): scaler state.  Should be an object returned from a call to `GradScaler.state_dict()`.
+
+        Examples:
+
+            .. code-block:: python
+
+                # required: gpu,xpu
+                import paddle
+
+                scaler = paddle.amp.GradScaler(enable=True,
+                                               init_loss_scaling=1024,
+                                               incr_ratio=2.0,
+                                               decr_ratio=0.5,
+                                               incr_every_n_steps=1000,
+                                               decr_every_n_nan_or_inf=2,
+                                               use_dynamic_loss_scaling=True)
+                scaler_state = scaler.state_dict()
+                scaler.load_state_dict(scaler_state)
+        """
+        super(GradScaler, self).load_state_dict(state_dict)

python/paddle/fluid/dygraph/amp/loss_scaler.py

@@ -357,3 +357,55 @@ def set_decr_every_n_nan_or_inf(self, new_decr_every_n_nan_or_inf):
             new_decr_every_n_nan_or_inf(int):  The new_decr_every_n_nan_or_inf used to update the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
         """
         self._decr_every_n_nan_or_inf = new_decr_every_n_nan_or_inf
+
+    def state_dict(self):
+        """
+        Returns the state of the scaler as a `dict`, If this instance is not enabled, returns an empty dict.
+
+        Reurns:
+            A dict of scaler includes:
+            scale (tensor): The loss scaling factor.
+            incr_ratio(float): The multiplier to use when increasing the loss scaling.
+            decr_ratio(float): The less-than-one-multiplier to use when decreasing the loss scaling.
+            incr_every_n_steps(int): Increases loss scaling every n consecutive steps with finite gradients.
+            decr_every_n_nan_or_inf(int): Decreases loss scaling every n accumulated steps with nan or inf gradients.
+            incr_count(int): The number of recent consecutive unskipped steps.
+            decr_count(int): The number of recent consecutive skipped steps.
+            use_dynamic_loss_scaling(bool): Whether to use dynamic loss scaling. If False, fixed loss_scaling is used. If True, the loss scaling is updated dynamicly. Default is True.
+        """
+        return {
+            "scale": self._scale.numpy(),
+            "incr_ratio": self._incr_ratio,
+            "decr_ratio": self._decr_ratio,
+            "incr_every_n_steps": self._incr_every_n_steps,
+            "decr_every_n_nan_or_inf": self._decr_every_n_nan_or_inf,
+            "incr_count": self._incr_count,
+            "decr_count": self._decr_count,
+            "use_dynamic_loss_scaling": self._use_dynamic_loss_scaling
+        } if self._enable else {}
+
+    def load_state_dict(self, state_dict):
+        """
+        Loads the scaler state.
+
+        Args:
+           state_dict(dict): scaler state.  Should be an object returned from a call to `AmpScaler.state_dict()`.
+        """
+        if not self._enable:
+            return
+
+        if len(state_dict) == 0:
+            raise RuntimeError(
+                "The input state dict is empty, possibly because it was saved "
+                "from a disabled instance of GradScaler.")
+
+        self._init_loss_scaling = state_dict["scale"][0]
+        self._scale = to_variable(
+            np.array([self._init_loss_scaling]).astype(np.float32))
+        self._incr_ratio = state_dict["incr_ratio"]
+        self._decr_ratio = state_dict["decr_ratio"]
+        self._incr_every_n_steps = state_dict["incr_every_n_steps"]
+        self._decr_every_n_nan_or_inf = state_dict["decr_every_n_nan_or_inf"]
+        self._incr_count = state_dict["incr_count"]
+        self._decr_count = state_dict["decr_count"]
+        self._use_dynamic_loss_scaling = state_dict["use_dynamic_loss_scaling"]

python/paddle/fluid/tests/unittests/test_imperative_auto_mixed_precision.py

@@ -237,6 +237,37 @@ def test_get_and_set(self):
             scaler.set_init_loss_scaling(100)
             self.assertEqual(scaler.get_init_loss_scaling() == 100, True)
 
+    def test_state_dict_and_load_state_dict(self):
+        with fluid.dygraph.guard():
+            scaler1 = paddle.amp.GradScaler(
+                enable=True,
+                init_loss_scaling=14,
+                incr_ratio=233.0,
+                decr_ratio=0.523,
+                incr_every_n_steps=1090,
+                decr_every_n_nan_or_inf=20,
+                use_dynamic_loss_scaling=True)
+            scaler_state = scaler1.state_dict()
+            scaler2 = paddle.amp.GradScaler(enable=True)
+            scaler2.load_state_dict(scaler_state)
+            self.assertEqual(scaler2.get_init_loss_scaling() == 14, True)
+            self.assertEqual(scaler2.get_incr_ratio() == 233.0, True)
+            self.assertEqual(scaler2.get_decr_ratio() == 0.523, True)
+            self.assertEqual(scaler2.get_incr_every_n_steps() == 1090, True)
+            self.assertEqual(scaler2.get_decr_every_n_nan_or_inf() == 20, True)
+
+            scaler3 = paddle.amp.GradScaler(enable=False)
+            scaler3.load_state_dict(scaler_state)
+            self.assertEqual(scaler3.is_enable() == False, True)
+
+    def test_state_dict_and_load_state_dict_error(self):
+        def test_error():
+            state_empty = {}
+            scaler = paddle.amp.GradScaler(enable=True)
+            scaler.load_state_dict(state_empty)
+
+        self.assertRaises(RuntimeError, test_error)
+
 
 def reader_decorator(reader):
     def __reader__():
@@ -248,6 +279,114 @@ def __reader__():
     return __reader__
 
 
+class TestGradScalerStateDict(unittest.TestCase):
+    def train_resnet(self,
+                     enable_amp=True,
+                     use_data_loader=True,
+                     use_save_load=True):
+        seed = 90
+
+        EPOCH_NUM = 4  # 设置外层循环次数
+
+        batch_size = train_parameters["batch_size"]
+        batch_num = 1
+
+        paddle.seed(seed)
+        paddle.framework.random._manual_program_seed(seed)
+
+        resnet = ResNet(use_cudnn=True)
+        optimizer = optimizer_setting(
+            train_parameters, parameter_list=resnet.parameters())
+        np.random.seed(seed)
+        train_reader = paddle.batch(
+            paddle.dataset.flowers.train(use_xmap=False), batch_size=batch_size)
+
+        dy_param_init_value = {}
+        for param in resnet.parameters():
+            dy_param_init_value[param.name] = param.numpy()
+
+        program = None
+        scaler = paddle.amp.GradScaler(
+            enable=enable_amp, init_loss_scaling=2.**10)
+
+        if use_data_loader:
+            train_reader = paddle.batch(
+                reader_decorator(paddle.dataset.flowers.train(use_xmap=False)),
+                batch_size=batch_size,
+                drop_last=True)
+            train_loader = fluid.io.DataLoader.from_generator(
+                capacity=4,
+                use_double_buffer=True,
+                iterable=True,
+                return_list=True)
+            train_loader.set_sample_list_generator(train_reader)
+            train_reader = train_loader
+
+        for epoch_id in range(EPOCH_NUM):
+            for batch_id, data in enumerate(train_reader()):
+                if batch_id >= batch_num:
+                    break
+                if use_data_loader:
+                    img, label = data
+                else:
+                    dy_x_data = np.array(
+                        [x[0].reshape(3, 224, 224)
+                         for x in data]).astype('float32')
+                    if len(np.array([x[1] for x in data]).astype(
+                            'int64')) != batch_size:
+                        continue
+                    y_data = np.array(
+                        [x[1] for x in data]).astype('int64').reshape(-1, 1)
+
+                    img = paddle.to_tensor(dy_x_data)
+                    label = paddle.to_tensor(y_data)
+                label.stop_gradient = True
+
+                with paddle.amp.auto_cast(enable=enable_amp):
+                    out = resnet(img)
+
+                loss = paddle.nn.functional.cross_entropy(
+                    input=out, label=label)
+                avg_loss = paddle.mean(x=loss)
+
+                dy_out = avg_loss.numpy()
+
+                scaled_loss = scaler.scale(avg_loss)
+                scaled_loss.backward()
+
+                scaler.minimize(optimizer, scaled_loss)
+
+                dy_grad_value = {}
+                for param in resnet.parameters():
+                    if param.trainable:
+                        np_array = np.array(param._grad_ivar().value()
+                                            .get_tensor())
+                        dy_grad_value[param.name + fluid.core.grad_var_suffix(
+                        )] = np_array
+
+                resnet.clear_gradients()
+
+                dy_param_value = {}
+                for param in resnet.parameters():
+                    dy_param_value[param.name] = param.numpy()
+            if use_save_load and epoch_id == 2:
+                paddle.save(scaler.state_dict(), 'ResNet_model.pdparams')
+                dict_load = paddle.load('ResNet_model.pdparams')
+                scaler.load_state_dict(dict_load)
+        return dy_out, dy_param_value, dy_grad_value
+
+    def test_with_state_dict(self):
+        with fluid.dygraph.guard():
+            out_use_state_dict = self.train_resnet(
+                enable_amp=True, use_data_loader=True, use_save_load=True)
+            out_no_state_dict = self.train_resnet(
+                enable_amp=True, use_data_loader=True, use_save_load=False)
+        print('save_load:', out_use_state_dict[0], out_no_state_dict[0])
+        self.assertTrue(
+            np.allclose(
+                out_use_state_dict[0], out_no_state_dict[0], atol=1.e-2))
+
+
 class TestResnet2(unittest.TestCase):
     """
     Use paddle-2.0 API