Add set_lr_scheduler api (#54752)

* demo1

* add test cases

* modify the usage of StepDecay

* refine

python/paddle/optimizer/optimizer.py

@@ -553,6 +553,52 @@ class Optimizer:
                     stop_gradient=True,
                 )
 
+    @framework.dygraph_only
+    def set_lr_scheduler(self, scheduler):
+        """
+        :api_attr: imperative
+
+        Set the LRScheduler of the learning rate manually in the optimizer. If the optimizer already used LRScheduler previously,
+        this API will set it be the new one.
+
+        Args:
+            scheduler (LRScheduler): the LRScheduler of learning rate
+
+        Returns:
+            None
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+                linear = paddle.nn.Linear(10, 10)
+
+                adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
+
+                # set learning rate manually by class LRScheduler
+                scheduler = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5, milestones=[2,4,6], gamma=0.8)
+                adam.set_lr_scheduler(scheduler)
+                lr = adam.get_lr()
+                print("current lr is {}".format(lr))
+                #    current lr is 0.5
+
+                # set learning rate manually by another LRScheduler
+                scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.1, step_size=5, gamma=0.6)
+                adam.set_lr_scheduler(scheduler)
+                lr = adam.get_lr()
+                print("current lr is {}".format(lr))
+                #    current lr is 0.1
+
+        """
+        from paddle.optimizer.lr import LRScheduler
+
+        if not isinstance(scheduler, LRScheduler):
+            raise TypeError(
+                "The type of 'scheduler' in optimizer.set_lr_schduler must be LRScheduler, but received %s."
+                % (type(scheduler))
+            )
+        self._learning_rate = scheduler
+
     def get_lr(self):
         """
         Get current learning rate of optimizer.

test/legacy_test/test_imperative_optimizer_v2.py

@@ -656,6 +656,42 @@ class TestOptimizerLearningRate(unittest.TestCase):
                 )
                 adam.set_lr(0.01)
 
+    def test_set_lr_scheduler(self):
+        with fluid.dygraph.guard():
+            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+
+            linear = paddle.nn.Linear(10, 10)
+
+            a = fluid.dygraph.to_variable(a)
+
+            b = linear(a)
+
+            loss = paddle.mean(b)
+
+            adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
+
+            # float to LRScheduler
+            scheduler = paddle.optimizer.lr.StepDecay(
+                learning_rate=0.2, step_size=5, gamma=0.6
+            )
+            adam.set_lr_scheduler(scheduler)
+            adam.minimize(loss)
+            lr = adam.get_lr()
+            np.testing.assert_allclose(lr, 0.2, rtol=1e-06, atol=0.0)
+
+            # LRScheduler to another LRScheduler
+            scheduler = paddle.optimizer.lr.MultiStepDecay(
+                learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8
+            )
+            adam.set_lr_scheduler(scheduler)
+            adam.minimize(loss)
+            lr = adam.get_lr()
+            np.testing.assert_allclose(lr, 0.5, rtol=1e-06, atol=0.0)
+
+            with self.assertRaises(TypeError):
+                scheduler_var = paddle.fluid.dygraph.StepDecay(0.5, step_size=3)
+                adam.set_lr_scheduler(scheduler_var)
+
 
 class TestImperativeMomentumOptimizer(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):