hapi/model step learning rate on batch end. (#27991)

* hapi/model step learning rate on batch end. test=develop

Author: heavengate

python/paddle/hapi/model.py

@@ -453,6 +453,12 @@ def _run(self, inputs, labels=None):
             if len(name) > 0:
                 rets.insert(i, feed[name])
 
+        # step learning rate scheduler on each batch end
+        if self.model._optimizer and \
+                isinstance(self.model._optimizer._learning_rate,
+                           paddle.optimizer.lr.LRScheduler):
+            self.model._optimizer._learning_rate.step()
+
         # LoDTensor cannot be fetch as numpy directly
         rets = [np.array(v) for v in rets]
         if self.mode == 'test':
@@ -652,6 +658,13 @@ def train_batch(self, inputs, labels=None):
 
         self.model._optimizer.minimize(final_loss)
         self.model.network.clear_gradients()
+
+        # step learning rate scheduler on each batch end
+        if self.model._optimizer and \
+                isinstance(self.model._optimizer._learning_rate,
+                           paddle.optimizer.lr.LRScheduler):
+            self.model._optimizer._learning_rate.step()
+
         metrics = []
         for metric in self.model._metrics:
             metric_outs = metric.compute(*(to_list(outputs) + labels))
@@ -1461,11 +1474,6 @@ def fit(
 
                 cbks.on_end('eval', eval_logs)
 
-            # step learning rate scheduler on each epcoh end
-            if isinstance(self._optimizer._learning_rate,
-                          paddle.optimizer.lr.LRScheduler):
-                self._optimizer._learning_rate.step()
-
         cbks.on_end('train', logs)
         self._test_dataloader = None
 

python/paddle/tests/test_model.py

@@ -631,6 +631,7 @@ def make_optimizer(parameters=None):
                 parameters=parameters)
             return optimizer
 
+        # dynamic test
         device = paddle.set_device('cpu')
         fluid.enable_dygraph(device)
         net = MyModel()
@@ -643,8 +644,19 @@ def make_optimizer(parameters=None):
         dataset = MyDataset()
         model.fit(dataset, dataset, batch_size=4, epochs=10, num_workers=0)
 
+        # static test
         paddle.enable_static()
 
+        net = MyModel()
+        inputs = [InputSpec([None, 20], 'float32', 'x')]
+        labels = [InputSpec([None, 1], 'int64', 'label')]
+        optim = make_optimizer(net.parameters())
+        model = Model(net, inputs, labels)
+        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
+
+        dataset = MyDataset()
+        model.fit(dataset, dataset, batch_size=4, epochs=10, num_workers=0)
+
 
 class TestRaiseError(unittest.TestCase):
     def test_input_without_name(self):