Fix lr setting of AdamW when lr is an instance of LRScheduler (#28300)

* Fix lr setting of AdamW when lr is an instance of LRScheduler.
test=develop

* Fix static graph test mode in test_adamw_op.py.
test=develop

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

@@ -47,6 +47,7 @@ class TestAdamWOp(unittest.TestCase):
         assert (adam.__str__() is not None)
 
     def test_adamw_op(self):
+        paddle.enable_static()
         place = fluid.CPUPlace()
         shape = [2, 3, 8, 8]
         exe = fluid.Executor(place)
@@ -75,6 +76,7 @@ class TestAdamWOp(unittest.TestCase):
         data_np = np.random.random(shape).astype('float32')
         rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
         assert rets[0] is not None
+        paddle.disable_static()
 
     def test_adamw_op_invalid_input(self):
         paddle.disable_static()
@@ -89,6 +91,22 @@ class TestAdamWOp(unittest.TestCase):
             adam = paddle.optimizer.AdamW(
                 0.1, epsilon=-1, parameters=linear.parameters())
 
+    def test_adamw_lr_decay(self):
+        paddle.disable_static()
+        value = np.arange(26).reshape(2, 13).astype("float32")
+        a = paddle.to_tensor(value)
+        linear = paddle.nn.Linear(13, 5)
+        adam = paddle.optimizer.AdamW(
+            learning_rate=paddle.optimizer.lr.NoamDecay(
+                d_model=512, warmup_steps=4000),
+            parameters=linear.parameters(),
+            apply_decay_param_fun=lambda name: True,
+            weight_decay=0.01)
+        out = linear(a)
+        out.backward()
+        adam.step()
+        adam.clear_gradients()
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/optimizer/adamw.py

@@ -57,7 +57,7 @@ class AdamW(Adam):
             The default value is 1e-08.
         weight_decay (float|Tensor, optional): The weight decay coefficient, it can be float or Tensor. The default value is 0.01.
         apply_decay_param_fun (function|None, optional): If it is not None,
-            only tensors that makes apply_decay_param_fun(Tensor)==True
+            only tensors that makes apply_decay_param_fun(Tensor.name)==True
             will be updated. It only works when we want to specify tensors.
             Default: None.
         grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
@@ -168,7 +168,7 @@ class AdamW(Adam):
             if isinstance(self._learning_rate, float):
                 learning_rate = self._learning_rate
             else:
-                self._learning_rate()
+                learning_rate = self._learning_rate()
             with param.block.program._optimized_guard(
                 [param, grad]), framework.name_scope('weight decay'):
                 if param.name not in self._params_name: