fix english doc, unittest, and remove useless alias of 2.0 lr_scheduler (#27686)

* fix doc and unittest of 2.0 lr_scheduler

* fix doc of 2.0 lr_scheduler

* fix unittest

* fix english doc of lr_scheduler

* fix api name of lr scheduler

* fix api name of lr scheduler

python/paddle/__init__.py

@@ -237,13 +237,6 @@ from .framework import save  #DEFINE_ALIAS
 from .framework import load  #DEFINE_ALIAS
 from .framework import DataParallel  #DEFINE_ALIAS
 
-from .framework import NoamDecay  #DEFINE_ALIAS
-from .framework import PiecewiseDecay  #DEFINE_ALIAS
-from .framework import NaturalExpDecay  #DEFINE_ALIAS
-from .framework import ExponentialDecay  #DEFINE_ALIAS
-from .framework import InverseTimeDecay  #DEFINE_ALIAS
-from .framework import PolynomialDecay  #DEFINE_ALIAS
-from .framework import CosineDecay  #DEFINE_ALIAS
 from .framework import set_default_dtype  #DEFINE_ALIAS
 from .framework import get_default_dtype  #DEFINE_ALIAS
 

python/paddle/fluid/dygraph/checkpoint.py

@@ -164,7 +164,7 @@ def load_dygraph(model_path, **configs):
             state_dict = emb.state_dict()
             fluid.save_dygraph(state_dict, "paddle_dy")
 
-            scheduler = paddle.optimizer.lr_scheduler.NoamLR(	
+            scheduler = paddle.optimizer.lr.NoamDecay(	
                 d_model=0.01, warmup_steps=100, verbose=True)
             adam = paddle.optimizer.Adam(
                 learning_rate=scheduler,

python/paddle/fluid/executor.py

@@ -855,7 +855,7 @@ class Executor(object):
 
     def _run_parallel(self, program, scope, feed, fetch_list, fetch_var_name,
                       return_numpy, return_merged):
-        from paddle.optimizer.lr_scheduler import _LRScheduler
+        from paddle.optimizer.lr import LRScheduler
         exe = program._executor
         # TODO(zhenghuihuang): quantization uses Graph in CompiledProgram
         # instead of program. We will add support for checking Vars in Graph
@@ -901,7 +901,7 @@ class Executor(object):
 
         if hasattr(program._program, 'lr_sheduler'):
             lr_sheduler = program._program.lr_sheduler
-            assert isinstance(lr_sheduler, _LRScheduler), "must be _LRScheduler"
+            assert isinstance(lr_sheduler, LRScheduler), "must be LRScheduler"
             lr_value = lr_sheduler()
             lr_var = program._program.global_block().vars[lr_sheduler._var_name]
             lr_tensor = _as_lodtensor(lr_value, core.CPUPlace(), lr_var.dtype)
@@ -1238,7 +1238,7 @@ class Executor(object):
 
     def _run_program(self, program, feed, fetch_list, feed_var_name,
                      fetch_var_name, scope, return_numpy, use_program_cache):
-        from paddle.optimizer.lr_scheduler import _LRScheduler
+        from paddle.optimizer.lr import LRScheduler
         if feed is None:
             feed = {}
         elif isinstance(feed, (list, tuple)):
@@ -1296,7 +1296,7 @@ class Executor(object):
         self._feed_data(program, feed, feed_var_name, scope)
         if hasattr(program, 'lr_sheduler'):
             assert isinstance(program.lr_sheduler,
-                              _LRScheduler), "must be _LRScheduler"
+                              LRScheduler), "must be LRScheduler"
             lr_sheduler = program.lr_sheduler
             lr_value = lr_sheduler()
             lr_var = program.global_block().vars[lr_sheduler._var_name]

python/paddle/fluid/optimizer.py

@@ -70,15 +70,15 @@ class Optimizer(object):
                  grad_clip=None,
                  name=None):
         # Because of the loop import, so place it in the function body
-        from paddle.optimizer.lr_scheduler import _LRScheduler
+        from paddle.optimizer.lr import LRScheduler
         self._parameter_list = list(
             parameter_list) if parameter_list is not None else None
         self._name = name
         if framework.in_dygraph_mode():
             if not isinstance(learning_rate,
-                              (float, LearningRateDecay, _LRScheduler)):
+                              (float, LearningRateDecay, LRScheduler)):
                 raise TypeError(
-                    "learning rate should be float or _LRScheduler, got %s here"
+                    "learning rate should be float or LRScheduler, got %s here"
                     % type(learning_rate))
             if self._parameter_list is None:
                 raise AttributeError(
@@ -94,9 +94,9 @@ class Optimizer(object):
                         break
         else:
             if not isinstance(learning_rate,
-                              (float, framework.Variable, _LRScheduler)):
+                              (float, framework.Variable, LRScheduler)):
                 raise TypeError(
-                    "learning rate should be float or _LRScheduler, got %s here"
+                    "learning rate should be float or LRScheduler, got %s here"
                     % type(learning_rate))
 
         if grad_clip is not None:
@@ -147,13 +147,13 @@ class Optimizer(object):
                     state_dict = adam.state_dict()
 
         '''
-        from paddle.optimizer.lr_scheduler import _LRScheduler
+        from paddle.optimizer.lr import LRScheduler
         state_dict = {}
         for k, v in self._accumulators.items():
             for para_name, var_tmp in v.items():
                 state_dict[var_tmp.name] = var_tmp
         # global step if use lr decay
-        if isinstance(self._learning_rate, _LRScheduler):
+        if isinstance(self._learning_rate, LRScheduler):
             state_dict["LR_Scheduler"] = self._learning_rate.state_dict()
             return state_dict
         if isinstance(self._learning_rate, LearningRateDecay):
@@ -193,7 +193,7 @@ class Optimizer(object):
                 state_dict = emb.state_dict()
                 fluid.save_dygraph(state_dict, "paddle_dy")
 
-                scheduler = paddle.optimizer.lr_scheduler.NoamLR(	
+                scheduler = paddle.optimizer.lr.NoamDecay(	
                     d_model=0.01, warmup_steps=100, verbose=True)
                 adam = paddle.optimizer.Adam(
                     learning_rate=scheduler,
@@ -203,8 +203,8 @@ class Optimizer(object):
 
                 para_state_dict, opti_state_dict = fluid.load_dygraph("paddle_dy")
         '''
-        from paddle.optimizer.lr_scheduler import _LRScheduler
-        if isinstance(self._learning_rate, _LRScheduler):
+        from paddle.optimizer.lr import LRScheduler
+        if isinstance(self._learning_rate, LRScheduler):
             self._learning_rate.set_dict(state_dict["LR_Scheduler"])
 
         if isinstance(self._learning_rate, LearningRateDecay):
@@ -269,8 +269,8 @@ class Optimizer(object):
         return self._opti_name_list
 
     def _create_global_learning_rate(self):
-        from paddle.optimizer.lr_scheduler import _LRScheduler
-        if isinstance(self._learning_rate, _LRScheduler):
+        from paddle.optimizer.lr import LRScheduler
+        if isinstance(self._learning_rate, LRScheduler):
             lr_var = self._global_learning_rate()
             # only create global lr_var once
             if not isinstance(lr_var, framework.Variable):

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

@@ -455,8 +455,8 @@ class TestAdamOpV2(unittest.TestCase):
         state_dict = adam.state_dict()
         adam.set_state_dict(state_dict)
 
-        #learning_rate is _LRScheduler
-        learning_rate = paddle.optimizer.CosineAnnealingLR(
+        #learning_rate is LRScheduler
+        learning_rate = paddle.optimizer.lr.CosineAnnealingDecay(
             learning_rate=0.1, T_max=10)
         adam = paddle.optimizer.Adam(
             learning_rate=learning_rate,

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

@@ -43,14 +43,22 @@ class TestDirectory(unittest.TestCase):
             'paddle.distributed.prepare_context', 'paddle.DataParallel',
             'paddle.jit', 'paddle.jit.TracedLayer', 'paddle.jit.to_static',
             'paddle.jit.ProgramTranslator', 'paddle.jit.TranslatedLayer',
-            'paddle.jit.save', 'paddle.jit.load', 'paddle.NoamDecay',
-            'paddle.PiecewiseDecay', 'paddle.NaturalExpDecay',
-            'paddle.ExponentialDecay', 'paddle.InverseTimeDecay',
-            'paddle.PolynomialDecay', 'paddle.CosineDecay',
-            'paddle.static.Executor', 'paddle.static.global_scope',
-            'paddle.static.scope_guard', 'paddle.static.append_backward',
-            'paddle.static.gradients', 'paddle.static.BuildStrategy',
-            'paddle.static.CompiledProgram', 'paddle.static.ExecutionStrategy',
+            'paddle.jit.save', 'paddle.jit.load',
+            'paddle.optimizer.lr.LRScheduler', 'paddle.optimizer.lr.NoamDecay',
+            'paddle.optimizer.lr.PiecewiseDecay',
+            'paddle.optimizer.lr.NaturalExpDecay',
+            'paddle.optimizer.lr.ExponentialDecay',
+            'paddle.optimizer.lr.InverseTimeDecay',
+            'paddle.optimizer.lr.PolynomialDecay',
+            'paddle.optimizer.lr.CosineAnnealingDecay',
+            'paddle.optimizer.lr.MultiStepDecay',
+            'paddle.optimizer.lr.StepDecay', 'paddle.optimizer.lr.LambdaDecay',
+            'paddle.optimizer.lr.ReduceOnPlateau',
+            'paddle.optimizer.lr.LinearWarmup', 'paddle.static.Executor',
+            'paddle.static.global_scope', 'paddle.static.scope_guard',
+            'paddle.static.append_backward', 'paddle.static.gradients',
+            'paddle.static.BuildStrategy', 'paddle.static.CompiledProgram',
+            'paddle.static.ExecutionStrategy',
             'paddle.static.default_main_program',
             'paddle.static.default_startup_program', 'paddle.static.Program',
             'paddle.static.name_scope', 'paddle.static.program_guard',

python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py

@@ -23,7 +23,7 @@ import itertools
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
-from paddle.fluid.optimizer import SGDOptimizer, Adam, MomentumOptimizer, LarsMomentumOptimizer, AdagradOptimizer, AdamaxOptimizer, DpsgdOptimizer, DecayedAdagradOptimizer, AdadeltaOptimizer, RMSPropOptimizer, FtrlOptimizer, LambOptimizer
+from paddle.fluid.optimizer import MomentumOptimizer, LarsMomentumOptimizer, AdagradOptimizer, AdamaxOptimizer, DpsgdOptimizer, DecayedAdagradOptimizer, AdadeltaOptimizer, RMSPropOptimizer, FtrlOptimizer, LambOptimizer
 from paddle.fluid.optimizer import ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer
 from paddle.fluid.dygraph import Linear
 from paddle.fluid.dygraph.base import to_variable
@@ -72,8 +72,8 @@ class TestImperativeOptimizerBase(unittest.TestCase):
             place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
             ) else fluid.CPUPlace()
 
-        with fluid.dygraph.guard(place):
         try:
+            paddle.disable_static()
             paddle.manual_seed(seed)
             paddle.framework.random._manual_program_seed(seed)
             mlp = MLP()
@@ -81,6 +81,8 @@ class TestImperativeOptimizerBase(unittest.TestCase):
                 parameter_list=mlp.parameters())
         except Exception as e:
             assert str(e) == exception_message
+        finally:
+            paddle.enable_static()
 
     def _check_mlp(self, place=None):
         seed = 90
@@ -90,13 +92,12 @@ class TestImperativeOptimizerBase(unittest.TestCase):
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
 
-        with fluid.dygraph.guard(place):
+        paddle.disable_static(place)
         paddle.manual_seed(seed)
         paddle.framework.random._manual_program_seed(seed)
 
         mlp = MLP()
-            optimizer = self.get_optimizer_dygraph(
-                parameter_list=mlp.parameters())
+        optimizer = self.get_optimizer_dygraph(parameter_list=mlp.parameters())
 
         batch_py_reader = fluid.io.PyReader(capacity=1)
         batch_py_reader.decorate_sample_list_generator(
@@ -113,6 +114,7 @@ class TestImperativeOptimizerBase(unittest.TestCase):
 
             img = data[0]
             label = data[1]
+
             label.stop_gradient = True
 
             img = fluid.layers.reshape(img, shape=[batch_size, -1])
@@ -126,11 +128,19 @@ class TestImperativeOptimizerBase(unittest.TestCase):
 
             avg_loss.backward()
             optimizer.minimize(avg_loss)
+            if isinstance(optimizer._learning_rate,
+                          paddle.optimizer.lr.LRScheduler):
+                if isinstance(optimizer._learning_rate,
+                              paddle.optimizer.lr.ReduceOnPlateau):
+                    optimizer._learning_rate.step(avg_loss)
+                else:
+                    optimizer._learning_rate.step()
             mlp.clear_gradients()
             dy_param_value = {}
             for param in mlp.parameters():
                 dy_param_value[param.name] = param.numpy()
 
+        paddle.enable_static()
         with new_program_scope():
             paddle.manual_seed(seed)
             paddle.framework.random._manual_program_seed(seed)
@@ -181,6 +191,13 @@ class TestImperativeOptimizerBase(unittest.TestCase):
                               feed={"pixel": static_x_data,
                                     "label": y_data},
                               fetch_list=fetch_list)
+                if isinstance(optimizer._learning_rate,
+                              paddle.optimizer.lr.LRScheduler):
+                    if isinstance(optimizer._learning_rate,
+                                  paddle.optimizer.lr.ReduceOnPlateau):
+                        optimizer._learning_rate.step(out[0])
+                    else:
+                        optimizer._learning_rate.step()
 
                 static_param_value = {}
                 static_out = out[0]
@@ -199,17 +216,19 @@ class TestImperativeOptimizerBase(unittest.TestCase):
 class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
         bd = [3, 6, 9]
-        optimizer = SGDOptimizer(
-            learning_rate=paddle.optimizer.PiecewiseLR(
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.PiecewiseDecay(
                 boundaries=bd,
                 values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]),
-            parameter_list=parameter_list)
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
         bd = [3, 6, 9]
-        optimizer = SGDOptimizer(learning_rate=paddle.optimizer.PiecewiseLR(
-            boundaries=bd, values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.PiecewiseDecay(
+                boundaries=bd,
+                values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
         return optimizer
 
     def test_sgd(self):
@@ -218,21 +237,16 @@ class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
 
 class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
-        optimizer = SGDOptimizer(
-            learning_rate=fluid.layers.natural_exp_decay(
-                learning_rate=0.1,
-                decay_steps=10000,
-                decay_rate=0.5,
-                staircase=True),
-            parameter_list=parameter_list)
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.NaturalExpDecay(
+                learning_rate=0.5, gamma=0.9),
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
-        optimizer = SGDOptimizer(learning_rate=fluid.layers.natural_exp_decay(
-            learning_rate=0.1,
-            decay_steps=10000,
-            decay_rate=0.5,
-            staircase=True))
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.NaturalExpDecay(
+                learning_rate=0.5, gamma=0.9))
         return optimizer
 
     def test_sgd(self):
@@ -241,21 +255,16 @@ class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):
 
 class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
-        optimizer = SGDOptimizer(
-            learning_rate=fluid.layers.exponential_decay(
-                learning_rate=0.1,
-                decay_steps=10000,
-                decay_rate=0.5,
-                staircase=True),
-            parameter_list=parameter_list)
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.ExponentialDecay(
+                learning_rate=0.5, gamma=0.9),
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
-        optimizer = SGDOptimizer(learning_rate=fluid.layers.exponential_decay(
-            learning_rate=0.1,
-            decay_steps=10000,
-            decay_rate=0.5,
-            staircase=True))
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.ExponentialDecay(
+                learning_rate=0.5, gamma=0.9))
         return optimizer
 
     def test_sgd(self):
@@ -264,21 +273,16 @@ class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):
 
 class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
-        optimizer = Adam(
-            learning_rate=fluid.layers.inverse_time_decay(
-                learning_rate=0.1,
-                decay_steps=10000,
-                decay_rate=0.5,
-                staircase=True),
-            parameter_list=parameter_list)
+        optimizer = paddle.optimizer.Adam(
+            learning_rate=paddle.optimizer.lr.InverseTimeDecay(
+                learning_rate=0.5, gamma=0.9),
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
-        optimizer = Adam(learning_rate=fluid.layers.inverse_time_decay(
-            learning_rate=0.1,
-            decay_steps=10000,
-            decay_rate=0.5,
-            staircase=True))
+        optimizer = paddle.optimizer.Adam(
+            learning_rate=paddle.optimizer.lr.InverseTimeDecay(
+                learning_rate=0.5, gamma=0.9))
         return optimizer
 
     def test_adam(self):
@@ -287,15 +291,16 @@ class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):
 
 class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
-        optimizer = SGDOptimizer(
-            learning_rate=fluid.layers.polynomial_decay(
-                learning_rate=0.1, decay_steps=5, cycle=self.cycle),
-            parameter_list=parameter_list)
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.PolynomialDecay(
+                learning_rate=0.5, decay_steps=5, cycle=self.cycle),
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
-        optimizer = SGDOptimizer(learning_rate=fluid.layers.polynomial_decay(
-            learning_rate=0.1, decay_steps=5, cycle=self.cycle))
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.PolynomialDecay(
+                learning_rate=0.5, decay_steps=5, cycle=self.cycle))
         return optimizer
 
     def test_sgd_cycle(self):
@@ -307,17 +312,18 @@ class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):
         self._check_mlp()
 
 
-class TestImperativeOptimizerCosineDecay(TestImperativeOptimizerBase):
+class TestImperativeOptimizerCosineAnnealingDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
-        optimizer = SGDOptimizer(
-            learning_rate=fluid.layers.cosine_decay(
-                learning_rate=0.1, step_each_epoch=10000, epochs=120),
-            parameter_list=parameter_list)
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
+                learning_rate=0.5, T_max=5),
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
-        optimizer = SGDOptimizer(learning_rate=fluid.layers.cosine_decay(
-            learning_rate=0.1, step_each_epoch=10000, epochs=120))
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
+                learning_rate=0.5, T_max=5))
         return optimizer
 
     def test_sgd(self):
@@ -326,15 +332,110 @@ class TestImperativeOptimizerCosineDecay(TestImperativeOptimizerBase):
 
 class TestImperativeOptimizerNoamDecay(TestImperativeOptimizerBase):
     def get_optimizer_dygraph(self, parameter_list):
-        optimizer = SGDOptimizer(
-            learning_rate=fluid.layers.noam_decay(
-                d_model=512, warmup_steps=8000),
-            parameter_list=parameter_list)
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.NoamDecay(
+                d_model=0.01, warmup_steps=100, verbose=True),
+            parameters=parameter_list)
+        return optimizer
+
+    def get_optimizer(self):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.NoamDecay(
+                d_model=0.01, warmup_steps=100))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerLambdaDecay(TestImperativeOptimizerBase):
+    def get_optimizer_dygraph(self, parameter_list):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.LambdaDecay(
+                learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch),
+            parameters=parameter_list)
+        return optimizer
+
+    def get_optimizer(self):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.LambdaDecay(
+                learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerLinearWarmup(TestImperativeOptimizerBase):
+    def get_optimizer_dygraph(self, parameter_list):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.LinearWarmup(
+                learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5),
+            parameters=parameter_list)
+        return optimizer
+
+    def get_optimizer(self):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.LinearWarmup(
+                learning_rate=0.5,
+                warmup_steps=20,
+                start_lr=0,
+                end_lr=0.5,
+                verbose=True))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerMultiStepDecay(TestImperativeOptimizerBase):
+    def get_optimizer_dygraph(self, parameter_list):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.MultiStepDecay(
+                learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8),
+            parameters=parameter_list)
+        return optimizer
+
+    def get_optimizer(self):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.MultiStepDecay(
+                learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerStepLR(TestImperativeOptimizerBase):
+    def get_optimizer_dygraph(self, parameter_list):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.StepDecay(
+                learning_rate=0.5, step_size=5, gamma=0.8),
+            parameters=parameter_list)
+        return optimizer
+
+    def get_optimizer(self):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.StepDecay(
+                learning_rate=0.5, step_size=5, gamma=0.8))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerReduceOnPlateau(TestImperativeOptimizerBase):
+    def get_optimizer_dygraph(self, parameter_list):
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
+                learning_rate=0.5),
+            parameters=parameter_list)
         return optimizer
 
     def get_optimizer(self):
-        optimizer = SGDOptimizer(learning_rate=fluid.layers.noam_decay(
-            d_model=512, warmup_steps=8000))
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
+                learning_rate=0.5))
         return optimizer
 
     def test_sgd(self):
@@ -381,7 +482,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
             bd = [2, 4, 6, 8]
             value = [0.2, 0.4, 0.6, 0.8, 1.0]
 
-            scheduler = paddle.optimizer.PiecewiseLR(bd, value)
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value)
             adam = paddle.optimizer.Adam(
                 scheduler, parameters=linear.parameters())
 
@@ -396,7 +497,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
                 self.assertTrue(np.allclose(lr, ret[i], rtol=1e-06, atol=0.0))
                 scheduler.step()
 
-    def test_lr_decay_natural_exp(self):
+    def test_lr_scheduler_natural_exp(self):
         with fluid.dygraph.guard():
             a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
 
@@ -407,8 +508,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
             loss = fluid.layers.reduce_mean(b)
             base_lr = 1.0
 
-            scheduler = paddle.optimizer.NaturalExpLR(1.0, gamma=0.5)
-            print("scheduler.last_lr", scheduler.last_lr)
+            scheduler = paddle.optimizer.lr.NaturalExpDecay(1.0, gamma=0.5)
             adam = paddle.optimizer.Adam(
                 scheduler, parameters=linear.parameters())
 
@@ -453,7 +553,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
 
             with self.assertRaises(RuntimeError):
                 adam = paddle.optimizer.Adam(
-                    paddle.optimizer.NaturalExpLR(
+                    paddle.optimizer.lr.NaturalExpDecay(
                         learning_rate=0.1, gamma=0.5),
                     parameters=linear.parameters())
                 adam.set_lr(0.01)
@@ -695,9 +795,9 @@ class TestImperativeOptimizerList(unittest.TestCase):
             linear_1 = Linear(10, 10)
             linear_2 = Linear(10, 10)
 
-            sgd = SGDOptimizer(
-                1.0,
-                parameter_list=itertools.chain(linear_1.parameters(),
+            sgd = paddle.optimizer.SGD(1.0,
+                                       parameters=itertools.chain(
+                                           linear_1.parameters(),
                                            linear_2.parameters()))
 
             in_np = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")

python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py

@@ -239,7 +239,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
-            scheduler = paddle.optimizer.PiecewiseLR(
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(
                 boundaries=bd, values=lr_arr)
             adam = Adam(
                 learning_rate=scheduler, parameters=ptb_model.parameters())
@@ -328,7 +328,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
-            scheduler = paddle.optimizer.PiecewiseLR(
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(
                 boundaries=bd, values=lr_arr)
             adam = Adam(
                 learning_rate=scheduler, parameters=ptb_model.parameters())
@@ -436,7 +436,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
-            scheduler = paddle.optimizer.PiecewiseLR(
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(
                 boundaries=bd, values=lr_arr)
             adam = Adam(
                 learning_rate=scheduler, parameters=ptb_model.parameters())
@@ -544,7 +544,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
-            scheduler = paddle.optimizer.PiecewiseLR(
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(
                 boundaries=bd, values=lr_arr)
             adam = Adam(
                 learning_rate=scheduler, parameters=ptb_model.parameters())
@@ -829,7 +829,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
             ) else fluid.CUDAPlace(0)
-            scheduler = paddle.optimizer.PiecewiseLR(
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(
                 boundaries=bd, values=lr_arr)
             adam = Adam(
                 learning_rate=scheduler,

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

@@ -56,22 +56,22 @@ def reduce_lr_on_plateau(decay_rate, threshold, cooldown, patience, m, n, loss,
     return var_list[1]
 
 
-class TestReduceLROnPlateauDecay(object):
+class TestReduceOnPlateauDecay(object):
     def test_ReduceLR(self):
         # the decay rate must be less than 1.0
         with self.assertRaises(ValueError):
-            paddle.optimizer.ReduceLROnPlateau(learning_rate=1.0, factor=2.0)
+            paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, factor=2.0)
         # the mode must be "min" or "max"
         with self.assertRaises(ValueError):
-            paddle.optimizer.ReduceLROnPlateau(learning_rate=1.0, mode="test")
+            paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, mode="test")
         # the threshold_mode must be "rel" or "abs"
         with self.assertRaises(ValueError):
-            paddle.optimizer.ReduceLROnPlateau(
+            paddle.optimizer.lr.ReduceOnPlateau(
                 learning_rate=1.0, threshold_mode="test")
         with self.assertRaises(TypeError):
-            paddle.optimizer.ReduceLROnPlateau(learning_rate="test")
+            paddle.optimizer.lr.ReduceOnPlateau(learning_rate="test")
         with self.assertRaises(TypeError):
-            paddle.optimizer.ReduceLROnPlateau(learning_rate=0.5).step("test")
+            paddle.optimizer.lr.ReduceOnPlateau(learning_rate=0.5).step("test")
 
         places = [paddle.CPUPlace()]
         if core.is_compiled_with_cuda():
@@ -114,7 +114,7 @@ class TestReduceLROnPlateauDecay(object):
                 [1], 1, 'float32', persistable=True)
             paddle.increment(x)
             loss = paddle.sin(x)
-            scheduler = paddle.optimizer.ReduceLROnPlateau(**kwargs)
+            scheduler = paddle.optimizer.lr.ReduceOnPlateau(**kwargs)
             adam = paddle.optimizer.Adam(learning_rate=scheduler)
             adam.minimize(loss)
             lr_var = adam._global_learning_rate()
@@ -158,7 +158,7 @@ class TestReduceLROnPlateauDecay(object):
         var_list = [best, current_lr, cooldown_counter, num_bad_epochs]
 
         linear = paddle.nn.Linear(10, 10)
-        scheduler = paddle.optimizer.ReduceLROnPlateau(**kwargs)
+        scheduler = paddle.optimizer.lr.ReduceOnPlateau(**kwargs)
         adam = paddle.optimizer.Adam(
             learning_rate=scheduler, parameters=linear.parameters())
 
@@ -180,7 +180,7 @@ class TestReduceLROnPlateauDecay(object):
                 loss, var_list)
             self.assertEqual(current_lr, expected_lr)
         state_dict = adam.state_dict()
-        scheduler1 = paddle.optimizer.ReduceLROnPlateau(**kwargs)
+        scheduler1 = paddle.optimizer.lr.ReduceOnPlateau(**kwargs)
         adam1 = paddle.optimizer.Adam(
             learning_rate=scheduler1, parameters=linear.parameters())
         adam1.set_state_dict(state_dict)
@@ -420,7 +420,7 @@ class TestLRScheduler(unittest.TestCase):
                 adam.clear_grad()
             current_lr = adam.get_lr()
             expected_lr = python_func(epoch, **kwarg)
-            if paddle_api.__name__ != "CosineAnnealingLR":
+            if paddle_api.__name__ != "CosineAnnealingDecay":
                 self.assertEqual(current_lr, expected_lr)
                 scheduler.step()
             else:
@@ -429,74 +429,75 @@ class TestLRScheduler(unittest.TestCase):
 
     def test_scheduler(self):
         with self.assertRaises(NotImplementedError):
-            paddle.optimizer.lr_scheduler._LRScheduler().step()
+            paddle.optimizer.lr.LRScheduler().step()
         with self.assertRaises(TypeError):
-            paddle.optimizer.MultiStepLR(
+            paddle.optimizer.lr.MultiStepDecay(
                 learning_rate="test", milestones=[1, 2, 3])
         with self.assertRaises(TypeError):
-            paddle.optimizer.MultiStepLR(learning_rate=0.5, milestones='test')
+            paddle.optimizer.lr.MultiStepDecay(
+                learning_rate=0.5, milestones='test')
         with self.assertRaises(ValueError):
-            paddle.optimizer.MultiStepLR(
+            paddle.optimizer.lr.MultiStepDecay(
                 learning_rate=0.5, milestones=[3, 2, 1])
         with self.assertRaises(ValueError):
-            paddle.optimizer.MultiStepLR(
+            paddle.optimizer.lr.MultiStepDecay(
                 learning_rate=0.5, milestones=[1, 2, 3], gamma=2)
 
-        func_api_kwargs = [(noam_lr, paddle.optimizer.NoamLR, {
+        func_api_kwargs = [(noam_lr, paddle.optimizer.lr.NoamDecay, {
             "d_model": 0.01,
             "warmup_steps": 100,
             "verbose": False
-        }), (piecewise_lr, paddle.optimizer.PiecewiseLR, {
+        }), (piecewise_lr, paddle.optimizer.lr.PiecewiseDecay, {
             "boundaries": [3, 6, 9, 15, 20],
             "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
             "verbose": False
-        }), (natural_exp_lr, paddle.optimizer.NaturalExpLR, {
+        }), (natural_exp_lr, paddle.optimizer.lr.NaturalExpDecay, {
             "learning_rate": 0.5,
             "gamma": 0.1,
             "verbose": True
-        }), (inverse_time_lr, paddle.optimizer.InverseTimeLR, {
+        }), (inverse_time_lr, paddle.optimizer.lr.InverseTimeDecay, {
             "learning_rate": 0.5,
             "gamma": 0.1,
             "verbose": False
-        }), (polynomial_lr, paddle.optimizer.PolynomialLR, {
+        }), (polynomial_lr, paddle.optimizer.lr.PolynomialDecay, {
             "learning_rate": 0.5,
             "decay_steps": 20,
             "end_lr": 0,
             "power": 1.0,
             "cycle": False,
             "verbose": True
-        }), (polynomial_lr, paddle.optimizer.PolynomialLR, {
+        }), (polynomial_lr, paddle.optimizer.lr.PolynomialDecay, {
             "learning_rate": 0.5,
             "decay_steps": 20,
             "end_lr": 0,
             "power": 1.0,
             "cycle": True,
             "verbose": False
-        }), (linear_warmup_lr, paddle.optimizer.LinearLrWarmup, {
+        }), (linear_warmup_lr, paddle.optimizer.lr.LinearWarmup, {
             'learning_rate': 0.5,
             'warmup_steps': 20,
             'start_lr': 0,
             'end_lr': 0.5,
             "verbose": True
-        }), (exponential_lr, paddle.optimizer.ExponentialLR, {
+        }), (exponential_lr, paddle.optimizer.lr.ExponentialDecay, {
             "learning_rate": 0.5,
             "gamma": 0.9,
             "verbose": False
-        }), (multi_step_lr, paddle.optimizer.MultiStepLR, {
+        }), (multi_step_lr, paddle.optimizer.lr.MultiStepDecay, {
             "learning_rate": 0.5,
             "milestones": [3, 6, 9, 15, 20],
             "gamma": 0.8,
             "verbose": True
-        }), (step_lr, paddle.optimizer.StepLR, {
+        }), (step_lr, paddle.optimizer.lr.StepDecay, {
             "learning_rate": 0.5,
             "step_size": 2,
             "gamma": 0.8,
             "verbose": False
-        }), (lambda_lr, paddle.optimizer.LambdaLR, {
+        }), (lambda_lr, paddle.optimizer.lr.LambdaDecay, {
             "learning_rate": 0.5,
             "lr_lambda": lambda x: 0.95**x,
             "verbose": True
-        }), (cosine_annealing_lr, paddle.optimizer.CosineAnnealingLR, {
+        }), (cosine_annealing_lr, paddle.optimizer.lr.CosineAnnealingDecay, {
             "learning_rate": 0.5,
             "T_max": 10,
             "verbose": False

python/paddle/framework/__init__.py

@@ -24,11 +24,6 @@ __all__ += [
     'DataParallel'
 ]
 
-__all__ += [
-    'NoamDecay', 'PiecewiseDecay', 'NaturalExpDecay', 'ExponentialDecay',
-    'InverseTimeDecay', 'PolynomialDecay', 'CosineDecay'
-]
-
 from . import random
 from .random import manual_seed
 from .framework import get_default_dtype
@@ -51,11 +46,3 @@ from ..fluid.dygraph.base import grad  #DEFINE_ALIAS
 from .io import save
 from .io import load
 from ..fluid.dygraph.parallel import DataParallel  #DEFINE_ALIAS
-
-from ..fluid.dygraph.learning_rate_scheduler import NoamDecay  #DEFINE_ALIAS
-from ..fluid.dygraph.learning_rate_scheduler import PiecewiseDecay  #DEFINE_ALIAS
-from ..fluid.dygraph.learning_rate_scheduler import NaturalExpDecay  #DEFINE_ALIAS
-from ..fluid.dygraph.learning_rate_scheduler import ExponentialDecay  #DEFINE_ALIAS
-from ..fluid.dygraph.learning_rate_scheduler import InverseTimeDecay  #DEFINE_ALIAS
-from ..fluid.dygraph.learning_rate_scheduler import PolynomialDecay  #DEFINE_ALIAS
-from ..fluid.dygraph.learning_rate_scheduler import CosineDecay  #DEFINE_ALIAS

python/paddle/framework/io.py

@@ -228,7 +228,7 @@ def save(obj, path):
             layer_state_dict = emb.state_dict()
             paddle.save(layer_state_dict, "emb.pdparams")
 
-            scheduler = paddle.optimizer.lr_scheduler.NoamLR(	
+            scheduler = paddle.optimizer.lr.NoamDecay(	
                 d_model=0.01, warmup_steps=100, verbose=True)
             adam = paddle.optimizer.Adam(
                 learning_rate=scheduler,
@@ -320,7 +320,7 @@ def load(path, **configs):
             layer_state_dict = emb.state_dict()
             paddle.save(layer_state_dict, "emb.pdparams")
 
-            scheduler = paddle.optimizer.lr_scheduler.NoamLR(	
+            scheduler = paddle.optimizer.lr.NoamDecay(	
                 d_model=0.01, warmup_steps=100, verbose=True)
             adam = paddle.optimizer.Adam(
                 learning_rate=scheduler,

python/paddle/nn/__init__.py

@@ -121,13 +121,6 @@ from .layer.conv import ConvTranspose3d  #DEFINE_ALIAS
 # from .layer.conv import TreeConv        #DEFINE_ALIAS
 # from .layer.conv import Conv1D        #DEFINE_ALIAS
 from .layer.extension import RowConv  #DEFINE_ALIAS
-# from .layer.learning_rate import CosineDecay        #DEFINE_ALIAS
-# from .layer.learning_rate import ExponentialDecay        #DEFINE_ALIAS
-# from .layer.learning_rate import InverseTimeDecay        #DEFINE_ALIAS
-# from .layer.learning_rate import NaturalExpDecay        #DEFINE_ALIAS
-# from .layer.learning_rate import NoamDecay        #DEFINE_ALIAS
-# from .layer.learning_rate import PiecewiseDecay        #DEFINE_ALIAS
-# from .layer.learning_rate import PolynomialDecay        #DEFINE_ALIAS
 from .layer.common import Linear
 # from .layer.loss import NCELoss        #DEFINE_ALIAS
 from .layer.loss import BCEWithLogitsLoss  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -95,14 +95,6 @@ from .extension import target_assign  #DEFINE_ALIAS
 from .extension import temporal_shift  #DEFINE_ALIAS
 from .extension import warpctc  #DEFINE_ALIAS
 from .extension import diag_embed  #DEFINE_ALIAS
-from .learning_rate import cosine_decay  #DEFINE_ALIAS
-from .learning_rate import exponential_decay  #DEFINE_ALIAS
-from .learning_rate import inverse_time_decay  #DEFINE_ALIAS
-from .learning_rate import natural_exp_decay  #DEFINE_ALIAS
-from .learning_rate import noam_decay  #DEFINE_ALIAS
-from .learning_rate import piecewise_decay  #DEFINE_ALIAS
-from .learning_rate import polynomial_decay  #DEFINE_ALIAS
-from .learning_rate import linear_lr_warmup  #DEFINE_ALIAS
 # from .lod import sequence_concat        #DEFINE_ALIAS
 # from .lod import sequence_conv        #DEFINE_ALIAS
 # from .lod import sequence_enumerate        #DEFINE_ALIAS

python/paddle/nn/functional/learning_rate.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TODO: define learning rate decay  
-from ...fluid.layers import cosine_decay  #DEFINE_ALIAS
-from ...fluid.layers import exponential_decay  #DEFINE_ALIAS
-from ...fluid.layers import inverse_time_decay  #DEFINE_ALIAS
-from ...fluid.layers import natural_exp_decay  #DEFINE_ALIAS
-from ...fluid.layers import noam_decay  #DEFINE_ALIAS
-from ...fluid.layers import piecewise_decay  #DEFINE_ALIAS
-from ...fluid.layers import polynomial_decay  #DEFINE_ALIAS
-from ...fluid.layers import linear_lr_warmup  #DEFINE_ALIAS
-
-__all__ = [
-    'cosine_decay', 'exponential_decay', 'inverse_time_decay',
-    'natural_exp_decay', 'noam_decay', 'piecewise_decay', 'polynomial_decay',
-    'linear_lr_warmup'
-]

python/paddle/nn/layer/__init__.py

@@ -86,13 +86,6 @@ from .conv import ConvTranspose3d  #DEFINE_ALIAS
 # from .conv import TreeConv        #DEFINE_ALIAS
 # from .conv import Conv1D        #DEFINE_ALIAS
 from .extension import RowConv  #DEFINE_ALIAS
-# from .learning_rate import CosineDecay        #DEFINE_ALIAS
-# from .learning_rate import ExponentialDecay        #DEFINE_ALIAS
-# from .learning_rate import InverseTimeDecay        #DEFINE_ALIAS
-# from .learning_rate import NaturalExpDecay        #DEFINE_ALIAS
-# from .learning_rate import NoamDecay        #DEFINE_ALIAS
-# from .learning_rate import PiecewiseDecay        #DEFINE_ALIAS
-# from .learning_rate import PolynomialDecay        #DEFINE_ALIAS
 # from .loss import NCELoss        #DEFINE_ALIAS
 from .loss import BCEWithLogitsLoss  #DEFINE_ALIAS
 from .loss import CrossEntropyLoss  #DEFINE_ALIAS

python/paddle/nn/layer/learning_rate.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TODO: define learning rate decay  
-
-__all__ = [
-    #       'CosineDecay',
-    #       'ExponentialDecay',
-    #       'InverseTimeDecay',
-    #       'NaturalExpDecay',
-    #       'NoamDecay',
-    #       'PiecewiseDecay',
-    #       'PolynomialDecay'
-]

python/paddle/optimizer/__init__.py

@@ -16,10 +16,7 @@ __all__ = [
     'Adadelta', 'AdadeltaOptimizer', 'Adagrad', 'AdagradOptimizer', 'Adam',
     'Adamax', 'AdamW', 'DecayedAdagrad', 'DecayedAdagradOptimizer', 'Dpsgd',
     'DpsgdOptimizer', 'Ftrl', 'FtrlOptimizer', 'Momentum', 'MomentumOptimizer',
-    'RMSProp', 'SGD', 'SGDOptimizer', 'Optimizer', '_LRScheduler', 'NoamLR',
-    'PiecewiseLR', 'NaturalExpLR', 'InverseTimeLR', 'PolynomialLR',
-    'LinearLrWarmup', 'ExponentialLR', 'MultiStepLR', 'StepLR', 'LambdaLR',
-    'ReduceLROnPlateau', 'CosineAnnealingLR'
+    'RMSProp', 'SGD', 'SGDOptimizer', 'Optimizer'
 ]
 
 
@@ -36,6 +33,4 @@ from .adadelta import Adadelta
 from .sgd import SGD
 from .momentum import Momentum
 
-from . import lr_scheduler
-from .lr_scheduler import _LRScheduler, NoamLR, PiecewiseLR, NaturalExpLR, InverseTimeLR, PolynomialLR, \
-            LinearLrWarmup, ExponentialLR, MultiStepLR, StepLR, LambdaLR, ReduceLROnPlateau, CosineAnnealingLR
+from . import lr

python/paddle/optimizer/adam.py

@@ -48,8 +48,8 @@ class Adam(Optimizer):
     Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
 
     Args:
-        learning_rate (float|_LRScheduler, optional): The learning rate used to update ``Parameter``.
-            It can be a float value or a _LRScheduler. The default value is 0.001.
+        learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
+            It can be a float value or a LRScheduler. The default value is 0.001.
         beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.
             It should be a float number or a Tensor with shape [1] and data type as float32.
             The default value is 0.9.

python/paddle/optimizer/adamax.py

@@ -47,8 +47,8 @@ class Adamax(Optimizer):
     it is added here for numerical stability to prevent the division by 0 error.
 
     Args:
-        learning_rate (float|_LRScheduler, optional): The learning rate used to update ``Parameter``.
-            It can be a float value or a _LRScheduler. The default value is 0.001.
+        learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
+            It can be a float value or a LRScheduler. The default value is 0.001.
         beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
             The default value is 0.9.
         beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.

python/paddle/optimizer/adamw.py

@@ -42,8 +42,8 @@ class AdamW(Adam):
 
 
     Args:
-        learning_rate (float|_LRScheduler, optional): The learning rate used to update ``Parameter``.
-            It can be a float value or a _LRScheduler. The default value is 0.001.
+        learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
+            It can be a float value or a LRScheduler. The default value is 0.001.
 	parameters (list, optional): List of ``Tensor`` names to update to minimize ``loss``. \
 	    This parameter is required in dygraph mode. \
 	    The default value is None in static mode, at this time all parameters will be updated.

python/paddle/optimizer/lr_scheduler.py → python/paddle/optimizer/lr.py

@@ -18,18 +18,62 @@ import warnings
 from paddle import Tensor
 
 __all__ = [
-    'NoamLR', 'PiecewiseLR', 'NaturalExpLR', 'InverseTimeLR', 'PolynomialLR',
-    'LinearLrWarmup', 'ExponentialLR', 'MultiStepLR', 'StepLR', 'LambdaLR',
-    'ReduceLROnPlateau', 'CosineAnnealingLR'
+    'LRScheduler', 'NoamDecay', 'PiecewiseDecay', 'NaturalExpDecay',
+    'InverseTimeDecay', 'PolynomialDecay', 'LinearWarmup', 'ExponentialDecay',
+    'MultiStepDecay', 'StepDecay', 'LambdaDecay', 'ReduceOnPlateau',
+    'CosineAnnealingDecay'
 ]
 
 
-class _LRScheduler(object):
-    """LRScheduler Base class.
+class LRScheduler(object):
+    """
+
+    LRScheduler Base class. Define the common interface of a learning rate scheduler.
+
+    User can import it by ``form paddle.optimizer.lr import LRScheduler`` ,
+
+    then overload it for your subclass and have a custom implementation of ``get_lr()`` .
+
+    Otherwise, an ``NotImplementedError`` exception will be thrown.
+
+    Args:
+        learning_rate (float): The initial learning rate. It is a python float number.
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+        verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
+
+    Returns:
+        instance to schedule learning rate.
+
+    Examples:
+        Here is an example of a simple ``StepDecay`` implementation. 
+        
+        .. code-block:: python
+            
+            import paddle
+            form paddle.optimizer.lr import LRScheduler
+
+            class StepDecay(LRScheduler):
+                def __init__(self,
+                            learning_rate,
+                            step_size,
+                            gamma=0.1,
+                            last_epoch=-1,
+                            verbose=False):
+                    if not isinstance(step_size, int):
+                        raise TypeError(
+                            "The type of 'step_size' must be 'int', but received %s." %
+                            type(step_size))
+                    if gamma >= 1.0:
+                        raise ValueError('gamma should be < 1.0.')
+
+                    self.step_size = step_size
+                    self.gamma = gamma
+                    super(StepDecay, self).__init__(learning_rate, last_epoch, verbose)
+
+                def get_lr(self):
+                    i = self.last_epoch // self.step_size
+                    return self.base_lr * (self.gamma**i)
 
-    Define the common interface of an LRScheduler.
-    User can 'form paddle.optimizer.lr_scheduler import _LRScheduler'
-    And inherit from it to have a custom implementation of get_lr().
     """
 
     def __init__(self, learning_rate=0.1, last_epoch=-1, verbose=False):
@@ -47,14 +91,15 @@ class _LRScheduler(object):
 
     def __call__(self):
         """ 
-        Return last computed learning rate on current epoch.
+        Return lastest computed learning rate on current epoch.
         """
         return self.last_lr
 
     def step(self, epoch=None):
         """
-        'step' should be called after 'minimize' . It will update the learning rate in optimizer according to 'epoch'.  
-        The new learning rate will take effect on next epoch.
+
+        ``step`` should be called after ``optimizer.step`` . It will update the learning rate in optimizer according to current ``epoch`` .  
+        The new learning rate will take effect on next ``optimizer.step`` .
 
         Args:
             epoch (int, None): specify current epoch. Default: None. Auto-increment from last_epoch=-1.
@@ -62,8 +107,6 @@ class _LRScheduler(object):
         Returns:
             None
 
-        Examples:
-            Please refer to the example of current _LRScheduler. 
         """
         if epoch is None:
             self.last_epoch += 1
@@ -81,11 +124,12 @@ class _LRScheduler(object):
 
     def state_dict(self):
         """
+
         Returns the state of the scheduler as a :class:`dict`.
 
-        It is a subset of self.__dict__ .
+        It is a subset of ``self.__dict__`` .
         """
-        self._state_keys()
+        self.state_keys()
         state_dict = {}
         for key in self.keys:
             if key not in self.__dict__:
@@ -101,19 +145,26 @@ class _LRScheduler(object):
 
         return state_dict
 
-    # For those subclass who overload _LRScheduler, "last_epoch, last_lr" will be saved by default.
+    # For those subclass who overload LRScheduler, "last_epoch, last_lr" will be saved by default.
     # (Note): you can change it for your subclass.
-    def _state_keys(self):
+    def state_keys(self):
         """
-        set the keys in self.__dict__ that are needed to be saved.
+
+        For those subclass who overload ``LRScheduler`` (Base Class). Acquiescently, "last_epoch, last_lr" will be saved by ``self.keys = ['last_epoch', 'last_lr']`` .
+
+        ``last_epoch`` is the current epoch num, and ``last_lr`` is the current learning rate.
+
+        If you want to change the default behavior, you should have a custom implementation of ``_state_keys()`` to redefine ``self.keys`` .
+
         """
         self.keys = ['last_epoch', 'last_lr']
 
     def set_state_dict(self, state_dict):
         """
+
         Loads the schedulers state.
         """
-        self._state_keys()
+        self.state_keys()
         for key in self.keys:
             if key in state_dict:
                 self.__dict__[key] = state_dict[key]
@@ -130,14 +181,20 @@ class _LRScheduler(object):
     set_dict = set_state_dict
 
     def get_lr(self):
+        """
+        
+        For those subclass who overload ``LRScheduler`` (Base Class), User should have a custom implementation of ``get_lr()`` .
+
+        Otherwise, an ``NotImplementedError`` exception will be thrown.
+        """
         # calculate by python float
         raise NotImplementedError
 
 
-class NoamLR(_LRScheduler):
+class NoamDecay(LRScheduler):
     """
 
-    Applies Noam Lear to the initial learning rate. 
+    Applies Noam Decay to the initial learning rate. 
 
     The algorithm can be described as following.
 
@@ -156,7 +213,7 @@ class NoamLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``NoamLR`` instance to schedule learning rate.
+        ``NoamDecay`` instance to schedule learning rate.
 
     Examples:
         .. code-block:: python
@@ -164,23 +221,21 @@ class NoamLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.NoamLR(d_model=0.01, warmup_steps=100, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.NoamDecay(d_model=0.01, warmup_steps=100, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -189,7 +244,7 @@ class NoamLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.NoamLR(d_model=0.01, warmup_steps=100, verbose=True)
+                scheduler = paddle.optimizer.lr.NoamDecay(d_model=0.01, warmup_steps=100, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -216,7 +271,7 @@ class NoamLR(_LRScheduler):
                  verbose=False):
         self.d_model = d_model
         self.warmup_steps = warmup_steps
-        super(NoamLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(NoamDecay, self).__init__(learning_rate, last_epoch, verbose)
 
     def get_lr(self):
         if self.last_epoch == 0:
@@ -227,7 +282,7 @@ class NoamLR(_LRScheduler):
         return self.base_lr * (self.d_model**-0.5) * min(a, b)
 
 
-class PiecewiseLR(_LRScheduler):
+class PiecewiseDecay(LRScheduler):
     """
 
     Piecewise learning rate scheduler.
@@ -253,7 +308,7 @@ class PiecewiseLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``PiecewiseLR`` instance to schedule learning rate.
+        ``PiecewiseDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -262,23 +317,21 @@ class PiecewiseLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.PiecewiseLR(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -287,7 +340,7 @@ class PiecewiseLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.PiecewiseLR(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
+                scheduler = paddle.optimizer.lr.PiecewiseDecay(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -308,7 +361,7 @@ class PiecewiseLR(_LRScheduler):
     def __init__(self, boundaries, values, last_epoch=-1, verbose=False):
         self.boundaries = boundaries
         self.values = values
-        super(PiecewiseLR, self).__init__(
+        super(PiecewiseDecay, self).__init__(
             last_epoch=last_epoch, verbose=verbose)
 
     def get_lr(self):
@@ -319,7 +372,7 @@ class PiecewiseLR(_LRScheduler):
         return self.values[len(self.values) - 1]
 
 
-class NaturalExpLR(_LRScheduler):
+class NaturalExpDecay(LRScheduler):
     """
 
     Applies natural exponential decay to the initial learning rate.
@@ -328,7 +381,7 @@ class NaturalExpLR(_LRScheduler):
 
     .. math::
 
-        new\_learning\_rate = learning\_rate * e^{- gama * epoch}
+        new\_learning\_rate = learning\_rate * e^{- gamma * epoch}
 
     Args:
         learning_rate (float): The initial learning rate. It is a python float number.
@@ -337,7 +390,7 @@ class NaturalExpLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``NaturalExpLR`` instance to schedule learning rate.
+        ``NaturalExpDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -346,23 +399,21 @@ class NaturalExpLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.NaturalExpLR(learning_rate=0.5, gamma=0.1, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.5, gamma=0.1, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -371,7 +422,7 @@ class NaturalExpLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.NaturalExpLR(learning_rate=0.5, gamma=0.1, verbose=True)
+                scheduler = paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.5, gamma=0.1, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -391,13 +442,14 @@ class NaturalExpLR(_LRScheduler):
 
     def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
         self.gamma = gamma
-        super(NaturalExpLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(NaturalExpDecay, self).__init__(learning_rate, last_epoch,
+                                              verbose)
 
     def get_lr(self):
         return self.base_lr * math.exp(-1 * self.gamma * self.last_epoch)
 
 
-class InverseTimeLR(_LRScheduler):
+class InverseTimeDecay(LRScheduler):
     """
 
     Applies inverse time decay to the initial learning rate.
@@ -416,7 +468,7 @@ class InverseTimeLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``InverseTimeLR`` instance to schedule learning rate.
+        ``InverseTimeDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -425,23 +477,21 @@ class InverseTimeLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.InverseTimeLR(learning_rate=0.5, gamma=0.1, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.InverseTimeDecay(learning_rate=0.5, gamma=0.1, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -450,7 +500,7 @@ class InverseTimeLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.InverseTimeLR(learning_rate=0.5, gamma=0.1, verbose=True)
+                scheduler = paddle.optimizer.lr.InverseTimeDecay(learning_rate=0.5, gamma=0.1, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -471,13 +521,14 @@ class InverseTimeLR(_LRScheduler):
 
     def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
         self.gamma = gamma
-        super(InverseTimeLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(InverseTimeDecay, self).__init__(learning_rate, last_epoch,
+                                               verbose)
 
     def get_lr(self):
         return self.base_lr / (1 + self.gamma * self.last_epoch)
 
 
-class PolynomialLR(_LRScheduler):
+class PolynomialDecay(LRScheduler):
     """
 
     Applies polynomial decay to the initial learning rate.
@@ -512,7 +563,7 @@ class PolynomialLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``PolynomialLR`` instance to schedule learning rate.
+        ``PolynomialDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -521,23 +572,21 @@ class PolynomialLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.PolynomialLR(learning_rate=0.5, decay_steps=20, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.PolynomialDecay(learning_rate=0.5, decay_steps=20, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -546,7 +595,7 @@ class PolynomialLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.PolynomialLR(learning_rate=0.5, decay_steps=20, verbose=True)
+                scheduler = paddle.optimizer.lr.PolynomialDecay(learning_rate=0.5, decay_steps=20, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -576,7 +625,8 @@ class PolynomialLR(_LRScheduler):
         self.end_lr = end_lr
         self.power = power
         self.cycle = cycle
-        super(PolynomialLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(PolynomialDecay, self).__init__(learning_rate, last_epoch,
+                                              verbose)
 
     def get_lr(self):
         tmp_epoch_num = self.last_epoch
@@ -596,7 +646,7 @@ class PolynomialLR(_LRScheduler):
              )**self.power) + self.end_lr
 
 
-class LinearLrWarmup(_LRScheduler):
+class LinearWarmup(LRScheduler):
     """
 
     Linear learning rate warm up strategy. Update the learning rate preliminarily before the normal learning rate scheduler.
@@ -604,22 +654,22 @@ class LinearLrWarmup(_LRScheduler):
     
     When epoch < warmup_steps, learning rate is updated as:
     
-    .. code-block:: text
+    .. math::
     
-            lr = start_lr + (end_lr - start_lr) * (epoch / warmup_steps)
+            lr = start\_lr + (end\_lr - start\_lr) * \\frac{epoch}{warmup\_steps}
     
     where start_lr is the initial learning rate, and end_lr is the final learning rate;
     
     When epoch >= warmup_steps, learning rate is updated as:
     
-    .. code-block:: text
+    .. math::
     
             lr = learning_rate
     
-    where lr is float or any subclass of ``_LRScheduler`` .
+    where ``learning_rate`` is float or any subclass of ``LRScheduler`` .
 
     Args:
-        learning_rate (float|_LRScheduler): The learning rate after warm-up. It is a python float number or any subclass of ``_LRScheduler`` .
+        learning_rate (float|LRScheduler): The learning rate after warm-up. It is a python float number or any subclass of ``LRScheduler`` .
         warmup_steps (int): total steps of warm up.
         start_lr (float): Initial learning rate of warm up.
         end_lr (float): Final learning rate of warm up.
@@ -627,7 +677,7 @@ class LinearLrWarmup(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``LinearLrWarmup`` instance to schedule learning rate.
+        ``LinearWarmup`` instance to schedule learning rate.
 
     Examples:
         
@@ -636,24 +686,22 @@ class LinearLrWarmup(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.LinearLrWarmup(
+            scheduler = paddle.optimizer.lr.LinearWarmup(
                     learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -662,7 +710,7 @@ class LinearLrWarmup(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.LinearLrWarmup(
+                scheduler = paddle.optimizer.lr.LinearWarmup(
                     learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
@@ -689,10 +737,10 @@ class LinearLrWarmup(_LRScheduler):
                  last_epoch=-1,
                  verbose=False):
         type_check = isinstance(learning_rate, float) or isinstance(
-            learning_rate, int) or isinstance(learning_rate, _LRScheduler)
+            learning_rate, int) or isinstance(learning_rate, LRScheduler)
         if not type_check:
             raise TypeError(
-                "the type of learning_rate should be [int, float or _LRScheduler], the current type is {}".
+                "the type of learning_rate should be [int, float or LRScheduler], the current type is {}".
                 format(learning_rate))
         self.learning_rate = learning_rate
         self.warmup_steps = warmup_steps
@@ -700,24 +748,24 @@ class LinearLrWarmup(_LRScheduler):
         self.end_lr = end_lr
         assert end_lr > start_lr, "end_lr {} must be greater than start_lr {}".format(
             end_lr, start_lr)
-        super(LinearLrWarmup, self).__init__(start_lr, last_epoch, verbose)
+        super(LinearWarmup, self).__init__(start_lr, last_epoch, verbose)
 
     def get_lr(self):
         if self.last_epoch < self.warmup_steps:
             return (self.end_lr - self.start_lr) * float(
                 self.last_epoch) / float(self.warmup_steps) + self.start_lr
         else:
-            if isinstance(self.learning_rate, _LRScheduler):
+            if isinstance(self.learning_rate, LRScheduler):
                 self.learning_rate.step()
                 return self.learning_rate()
 
             return self.learning_rate
 
 
-class ExponentialLR(_LRScheduler):
+class ExponentialDecay(LRScheduler):
     """
 
-    Update learning rate by 'gamma' each epoch.
+    Update learning rate by `gamma` each epoch.
 
     The algorithm can be described as following.
     
@@ -733,7 +781,7 @@ class ExponentialLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``ExponentialLR`` instance to schedule learning rate.
+        ``ExponentialDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -742,23 +790,21 @@ class ExponentialLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.ExponentialLR(learning_rate=0.5, gamma=0.9, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.ExponentialDecay(learning_rate=0.5, gamma=0.9, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -767,7 +813,7 @@ class ExponentialLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.ExponentialLR(learning_rate=0.5, gamma=0.9, verbose=True)
+                scheduler = paddle.optimizer.lr.ExponentialDecay(learning_rate=0.5, gamma=0.9, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -787,15 +833,16 @@ class ExponentialLR(_LRScheduler):
 
     def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
         self.gamma = gamma
-        super(ExponentialLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(ExponentialDecay, self).__init__(learning_rate, last_epoch,
+                                               verbose)
 
     def get_lr(self):
         return self.base_lr * (self.gamma**self.last_epoch)
 
 
-class MultiStepLR(_LRScheduler):
+class MultiStepDecay(LRScheduler):
     """
-    Update the learning rate by ``gama`` once ``epoch`` reaches one of the milestones.
+    Update the learning rate by ``gamma`` once ``epoch`` reaches one of the milestones.
 
     The algorithm can be described as the code below. 
 
@@ -821,7 +868,7 @@ class MultiStepLR(_LRScheduler):
         
 
     Returns:
-        ``MultiStepLR`` instance to schedule learning rate.
+        ``MultiStepDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -830,23 +877,21 @@ class MultiStepLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.MultiStepLR(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -855,7 +900,7 @@ class MultiStepLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.MultiStepLR(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
+                scheduler = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -894,7 +939,7 @@ class MultiStepLR(_LRScheduler):
 
         self.milestones = milestones
         self.gamma = gamma
-        super(MultiStepLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(MultiStepDecay, self).__init__(learning_rate, last_epoch, verbose)
 
     def get_lr(self):
         for i in range(len(self.milestones)):
@@ -903,7 +948,7 @@ class MultiStepLR(_LRScheduler):
         return self.base_lr * (self.gamma**len(self.milestones))
 
 
-class StepLR(_LRScheduler):
+class StepDecay(LRScheduler):
     """
     Update the learning rate of ``optimizer`` by ``gamma`` every ``step_size`` number of epoch.
 
@@ -929,7 +974,7 @@ class StepLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``StepLR`` instance to schedule learning rate.
+        ``StepDecay`` instance to schedule learning rate.
 
 
     Examples:
@@ -939,23 +984,21 @@ class StepLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.StepLR(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -964,7 +1007,7 @@ class StepLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.StepLR(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
+                scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -997,14 +1040,14 @@ class StepLR(_LRScheduler):
 
         self.step_size = step_size
         self.gamma = gamma
-        super(StepLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(StepDecay, self).__init__(learning_rate, last_epoch, verbose)
 
     def get_lr(self):
         i = self.last_epoch // self.step_size
         return self.base_lr * (self.gamma**i)
 
 
-class LambdaLR(_LRScheduler):
+class LambdaDecay(LRScheduler):
     """
     Sets the learning rate of ``optimizer`` by function ``lr_lambda`` . ``lr_lambda`` is funciton which receives ``epoch`` .
 
@@ -1015,9 +1058,9 @@ class LambdaLR(_LRScheduler):
         learning_rate = 0.5        # init learning_rate
         lr_lambda = lambda epoch: 0.95 ** epoch
 
-        learning_rate = 0.5        # epoch 0
-        learning_rate = 0.475      # epoch 1
-        learning_rate = 0.45125    # epoch 2
+        learning_rate = 0.5        # epoch 0, 0.5*0.95**0
+        learning_rate = 0.475      # epoch 1, 0.5*0.95**1
+        learning_rate = 0.45125    # epoch 2, 0.5*0.95**2
 
     Args:
         learning_rate (float): The initial learning rate. It is a python float number.
@@ -1026,7 +1069,7 @@ class LambdaLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
     
     Returns:
-        ``LambdaLR`` instance to schedule learning rate.
+        ``LambdaDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -1035,23 +1078,21 @@ class LambdaLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.LambdaLR(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.LambdaDecay(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -1060,7 +1101,7 @@ class LambdaLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.LambdaLR(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
+                scheduler = paddle.optimizer.lr.LambdaDecay(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -1082,17 +1123,17 @@ class LambdaLR(_LRScheduler):
     def __init__(self, learning_rate, lr_lambda, last_epoch=-1, verbose=False):
         if not callable(lr_lambda):
             raise TypeError(
-                "The type of 'lr_lambda' in 'LambdaLR' must be 'function', but received %s."
+                "The type of 'lr_lambda' in 'LambdaDecay' must be 'function', but received %s."
                 % type(lr_lambda))
 
         self.lr_lambda = lr_lambda
-        super(LambdaLR, self).__init__(learning_rate, last_epoch, verbose)
+        super(LambdaDecay, self).__init__(learning_rate, last_epoch, verbose)
 
     def get_lr(self):
         return self.base_lr * self.lr_lambda(self.last_epoch)
 
 
-class ReduceLROnPlateau(_LRScheduler):
+class ReduceOnPlateau(LRScheduler):
     """
     Reduce learning rate when ``metrics`` has stopped descending. Models often benefit from reducing the learning rate 
     by 2 to 10 times once model performance has no longer improvement.
@@ -1126,7 +1167,7 @@ class ReduceLROnPlateau(_LRScheduler):
 
     
     Returns:
-        ``ReduceLROnPlateau`` instance to schedule learning rate.
+        ``ReduceOnPlateau`` instance to schedule learning rate.
 
 
     Examples:
@@ -1135,23 +1176,21 @@ class ReduceLROnPlateau(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.ReduceLROnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step(loss)
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -1160,7 +1199,7 @@ class ReduceLROnPlateau(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.ReduceLROnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
+                scheduler = paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -1207,7 +1246,7 @@ class ReduceLROnPlateau(_LRScheduler):
         self.threshold_mode = threshold_mode
         if not isinstance(learning_rate, (float, int)):
             raise TypeError(
-                "The type of 'learning_rate' in 'ReduceLROnPlateau' must be 'float', but received %s."
+                "The type of 'learning_rate' in 'ReduceOnPlateau' must be 'float', but received %s."
                 % type(learning_rate))
 
         self.verbose = verbose
@@ -1230,7 +1269,7 @@ class ReduceLROnPlateau(_LRScheduler):
         self._var_name = None
 
     # "cooldown_counter / best / num_bad_epochs / last_epoch / last_lr" will be stored.
-    def _state_keys(self):
+    def state_keys(self):
         self.keys = [
             'cooldown_counter', 'best', 'num_bad_epochs', 'last_epoch',
             'last_lr'
@@ -1238,7 +1277,7 @@ class ReduceLROnPlateau(_LRScheduler):
 
     def step(self, metrics, epoch=None):
         """
-        step should be called after 'minimize' . It will update the learning rate in optimizer according to ``metrics`` .  
+        step should be called after `optimizer.step()` . It will update the learning rate in optimizer according to ``metrics`` .  
         The new learning rate will take effect on next epoch.
 
         Args:
@@ -1251,14 +1290,14 @@ class ReduceLROnPlateau(_LRScheduler):
             None
         
         Examples:
-            Please refer to the example of current _LRScheduler.
+            Please refer to the example of current LRScheduler.
         """
         if epoch is None:
             self.last_epoch = self.last_epoch + 1
         else:
             self.last_epoch = epoch
 
-        # loss must be 1-D Tensor with shape [1]
+        # loss must be float, numpy.ndarray or 1-D Tensor with shape [1]
         if isinstance(metrics, (Tensor, numpy.ndarray)):
             assert len(metrics.shape) == 1 and metrics.shape[0] == 1, "the metrics.shape " \
                 "should be (1L,), but the current metrics.shape is {}. Maybe that "  \
@@ -1290,7 +1329,6 @@ class ReduceLROnPlateau(_LRScheduler):
                             self.last_lr))
 
     def _is_better(self, current, best):
-        print("mode", self.mode, 'threshold_mode', self.threshold_mode)
         if self.mode == 'min' and self.threshold_mode == 'rel':
             return current < best - best * self.threshold
 
@@ -1304,31 +1342,23 @@ class ReduceLROnPlateau(_LRScheduler):
             return current > best + self.threshold
 
 
-class CosineAnnealingLR(_LRScheduler):
+class CosineAnnealingDecay(LRScheduler):
     """
 
     Set the learning rate using a cosine annealing schedule, where :math:`\eta_{max}` is set to 
     the initial learning_rate. :math:`T_{cur}` is the number of epochs since the last restart in 
-    SGDR:
-
-        \begin{aligned}
-            \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
-            + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
-            & T_{cur} \neq (2k+1)T_{max}; \\
-            \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
-            \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
-            & T_{cur} = (2k+1)T_{max}.
-        \end{aligned}
+    SGDR.
 
     The algorithm can be described as following.
 
     .. math::
-        \begin{aligned}
-            \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
-            + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
-            & T_{cur} \neq (2k+1)T_{max}; \\
-            \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
-            \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
+
+        \\begin{aligned}
+            \eta_t & = \eta_{min} + \\frac{1}{2}(\eta_{max} - \eta_{min})\left(1
+            + \cos\left(\\frac{T_{cur}}{T_{max}}\pi\\right)\\right),
+            & T_{cur} \\neq (2k+1)T_{max}; \\
+            \eta_{t+1} & = \eta_{t} + \\frac{1}{2}(\eta_{max} - \eta_{min})
+            \left(1 - \cos\left(\\frac{1}{T_{max}}\pi\\right)\\right),
             & T_{cur} = (2k+1)T_{max}.
         \end{aligned}
     
@@ -1343,7 +1373,7 @@ class CosineAnnealingLR(_LRScheduler):
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
     Returns:
-        ``CosineAnnealingLR`` instance to schedule learning rate.
+        ``CosineAnnealingDecay`` instance to schedule learning rate.
 
     Examples:
         
@@ -1352,23 +1382,21 @@ class CosineAnnealingLR(_LRScheduler):
             import paddle
             import numpy as np
 
-            # train on default dygraph mode
-            paddle.disable_static()
-            x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
+            # train on default dynamic graph mode
             linear = paddle.nn.Linear(10, 10)
-            scheduler = paddle.optimizer.lr_scheduler.CosineAnnealingLR(learning_rate=0.5, T_max=10, verbose=True)
-            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
+            scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.5, T_max=10, verbose=True)
+            sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
             for epoch in range(20):
                 for batch_id in range(2):
-                    x = paddle.to_tensor(x)
+                    x = paddle.uniform([10, 10])
                     out = linear(x)
                     loss = paddle.reduce_mean(out)
                     loss.backward()
-                    sgd.minimize(loss)
-                    linear.clear_gradients()
+                    sgd.step()
+                    sgd.clear_gradients()
                 scheduler.step()
 
-            # train on static mode
+            # train on static graph mode
             paddle.enable_static()
             main_prog = paddle.static.Program()
             start_prog = paddle.static.Program()
@@ -1377,7 +1405,7 @@ class CosineAnnealingLR(_LRScheduler):
                 y = paddle.static.data(name='y', shape=[None, 4, 5])
                 z = paddle.static.nn.fc(x, 100)
                 loss = paddle.mean(z)
-                scheduler = paddle.optimizer.lr_scheduler.CosineAnnealingLR(learning_rate=0.5, T_max=10, verbose=True)
+                scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.5, T_max=10, verbose=True)
                 sgd = paddle.optimizer.SGD(learning_rate=scheduler)
                 sgd.minimize(loss)
 
@@ -1403,15 +1431,15 @@ class CosineAnnealingLR(_LRScheduler):
                  verbose=False):
         if not isinstance(T_max, int):
             raise TypeError(
-                "The type of 'T_max' in 'CosineAnnealingLR' must be 'int', but received %s."
+                "The type of 'T_max' in 'CosineAnnealingDecay' must be 'int', but received %s."
                 % type(T_max))
         if not isinstance(eta_min, (float, int)):
             raise TypeError(
-                "The type of 'eta_min' in 'CosineAnnealingLR' must be 'float, int', but received %s."
+                "The type of 'eta_min' in 'CosineAnnealingDecay' must be 'float, int', but received %s."
                 % type(eta_min))
         self.T_max = T_max
         self.eta_min = float(eta_min)
-        super(CosineAnnealingLR, self).__init__(learning_rate, last_epoch,
+        super(CosineAnnealingDecay, self).__init__(learning_rate, last_epoch,
                                                    verbose)
 
     def get_lr(self):

python/paddle/optimizer/optimizer.py

@@ -41,7 +41,7 @@ from paddle.fluid.layers import tensor
 from functools import reduce
 from ..fluid.wrapped_decorator import signature_safe_contextmanager
 from .. import compat as cpt
-from .lr_scheduler import _LRScheduler
+from .lr import LRScheduler
 
 __all__ = ['Optimizer']
 
@@ -54,8 +54,8 @@ class Optimizer(object):
     but need to use one of it's implementation.
 
     Args:
-        learning_rate (float|_LRScheduler): The learning rate used to update ``Parameter``.
-            It can be a float value or any subclass of ``_LRScheduler`` .
+        learning_rate (float|LRScheduler): The learning rate used to update ``Parameter``.
+            It can be a float value or any subclass of ``LRScheduler`` .
         parameters (list, optional): List of ``Tensor`` names to update to minimize ``loss``. \
             This parameter is required in dygraph mode. \
             The default value is None in static mode, at this time all parameters will be updated.
@@ -82,12 +82,8 @@ class Optimizer(object):
 
             #Take the subclass adam as an example
             import paddle
-            import numpy as np
-
-            paddle.disable_static()
-            inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
             linear = paddle.nn.Linear(10, 10)
-            inp = paddle.to_tensor(inp)
+            inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
             out = linear(inp)
             loss = paddle.mean(out)
             adam = paddle.optimizer.Adam(learning_rate=0.1,
@@ -121,9 +117,9 @@ class Optimizer(object):
                             "The weight_decay[%s] in Optimizer will not take effect, and it will only be applied to other Parameters!"
                             % weight_decay.__str__())
                         break
-        if not isinstance(learning_rate, (float, _LRScheduler)):
+        if not isinstance(learning_rate, (float, LRScheduler)):
             raise TypeError(
-                "learning rate should be float or _LRScheduler, got %s here" %
+                "learning rate should be float or LRScheduler, got %s here" %
                 type(learning_rate))
         if grad_clip is not None:
             if not isinstance(grad_clip, GradientClipBase):
@@ -156,7 +152,7 @@ class Optimizer(object):
     @framework.dygraph_only
     def state_dict(self):
         '''
-        Get state dict information from optimizer. It contain all the tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If _LRScheduler have been used, global_step will be include in state dict.
+        Get state dict information from optimizer. It contain all the tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LRScheduler have been used, global_step will be include in state dict.
         If the optimizer never be called(minimize function), the state_dict is empty.
 
         Args: 
@@ -169,7 +165,6 @@ class Optimizer(object):
             .. code-block:: python
 
                 import paddle
-                paddle.disable_static()
                 emb = paddle.nn.Embedding(10, 10)
 
                 adam = paddle.optimizer.Adam(0.001, parameters=emb.parameters())
@@ -181,14 +176,14 @@ class Optimizer(object):
             for para_name, var_tmp in v.items():
                 state_dict[var_tmp.name] = var_tmp
         # global step if use lr decay
-        if isinstance(self._learning_rate, _LRScheduler):
+        if isinstance(self._learning_rate, LRScheduler):
             state_dict["LR_Scheduler"] = self._learning_rate.state_dict()
         return state_dict
 
     @framework.dygraph_only
     def set_state_dict(self, state_dict):
         '''
-        Load optimizer state dict. For Adam optimizer, contains beta1, beta2, momentum etc. If _LRScheduler have been used, global_step will be changed.
+        Load optimizer state dict. For Adam optimizer, contains beta1, beta2, momentum etc. If LRScheduler have been used, global_step will be changed.
 
         Args: 
             state_dict(dict) : Dict contains all the Tensor needed by optimizer
@@ -199,26 +194,28 @@ class Optimizer(object):
             .. code-block:: python
 
                 import paddle
-                paddle.disable_static()
+
                 emb = paddle.nn.Embedding(10, 10)
 
-                state_dict = emb.state_dict()
-                paddle.framework.save(state_dict, "paddle_dy")
+                layer_state_dict = emb.state_dict()
+                paddle.save(layer_state_dict, "emb.pdparams")
 
-                adam = paddle.optimizer.Adam(learning_rate=paddle.optimizer.NoamLR( 100, 10000), 
+                scheduler = paddle.optimizer.lr.NoamDecay(	
+                    d_model=0.01, warmup_steps=100, verbose=True)
+                adam = paddle.optimizer.Adam(
+                    learning_rate=scheduler,
                     parameters=emb.parameters())
-                state_dict = adam.state_dict()
-                paddle.framework.save(state_dict, "paddle_dy")
-
-                para_state_dict, opti_state_dict = paddle.framework.load( "paddle_dy")
+                opt_state_dict = adam.state_dict()
+                paddle.save(opt_state_dict, "adam.pdopt")
 
+                opti_state_dict = paddle.load("adam.pdopt")
                 adam.set_state_dict(opti_state_dict)
 
         '''
-        if isinstance(self._learning_rate, _LRScheduler):
+        if isinstance(self._learning_rate, LRScheduler):
             self._learning_rate.set_dict(state_dict["LR_Scheduler"])
 
-        if isinstance(self._learning_rate, _LRScheduler):
+        if isinstance(self._learning_rate, LRScheduler):
             self._learning_rate.set_state_dict(state_dict["LR_Scheduler"])
 
         self._accumulators_holder = state_dict
@@ -256,7 +253,7 @@ class Optimizer(object):
         return self._opti_name_list
 
     def _create_global_learning_rate(self):
-        if isinstance(self._learning_rate, _LRScheduler):
+        if isinstance(self._learning_rate, LRScheduler):
             lr_var = self._global_learning_rate()
             # only create global lr_var once
             if not isinstance(lr_var, framework.Variable):
@@ -299,7 +296,7 @@ class Optimizer(object):
         """
         :api_attr: imperative
         
-        Set the value of the learning rate manually in the optimizer. If the optimizer use _LRScheduler,
+        Set the value of the learning rate manually in the optimizer. If the optimizer use LRScheduler,
         this API cannot be invoked, because it will lead to conflict.
 
         Args:
@@ -312,7 +309,6 @@ class Optimizer(object):
             .. code-block:: python
 
                 import paddle
-                paddle.disable_static()
                 linear = paddle.nn.Linear(10, 10)
 
                 adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
@@ -335,9 +331,9 @@ class Optimizer(object):
             raise TypeError(
                 "The type of 'value' in optimizer.set_lr must be float, but received %s."
                 % (type(value)))
-        if isinstance(self._learning_rate, _LRScheduler):
+        if isinstance(self._learning_rate, LRScheduler):
             raise RuntimeError(
-                "optimizer's learning rate can't be _LRScheduler when invoke this API, because this will lead to conflict."
+                "optimizer's learning rate can't be LRScheduler when invoke this API, because this will lead to conflict."
             )
         self._learning_rate = float(value)
         current_lr = self._global_learning_rate()
@@ -358,7 +354,7 @@ class Optimizer(object):
         """
         :api_attr: imperative
         
-        Get current step learning rate. The return value is all the same When _LRScheduler is not used,
+        Get current step learning rate. The return value is all the same When LRScheduler is not used,
         otherwise return the current step learning rate.
 
 
@@ -370,15 +366,13 @@ class Optimizer(object):
 
                 import numpy as np
                 import paddle
-                # example1: _LRScheduler is not used, return value is all the same
-                paddle.disable_static()
+                # example1: LRScheduler is not used, return value is all the same
                 emb = paddle.nn.Embedding(10, 10)
                 adam = paddle.optimizer.Adam(0.001, parameters = emb.parameters())
                 lr = adam.get_lr()
                 print(lr) # 0.001
 
-                # example2: PiecewiseLR is used, return the step learning rate
-                paddle.disable_static()
+                # example2: PiecewiseDecay is used, return the scheduled learning rate
                 inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
                 linear = paddle.nn.Linear(10, 10)
                 inp = paddle.to_tensor(inp)
@@ -387,7 +381,7 @@ class Optimizer(object):
                 
                 bd = [2, 4, 6, 8]
                 value = [0.2, 0.4, 0.6, 0.8, 1.0]
-                scheduler = paddle.optimizer.PiecewiseLR(bd, value, 0)
+                scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value, 0)
                 adam = paddle.optimizer.Adam(scheduler,
                                        parameters=linear.parameters())
 
@@ -656,7 +650,6 @@ class Optimizer(object):
 
                 import paddle
                 import numpy as np
-                paddle.disable_static()
                 value = np.arange(26).reshape(2, 13).astype("float32")
                 a = paddle.to_tensor(value)
                 linear = paddle.nn.Linear(13, 5)
@@ -727,7 +720,6 @@ class Optimizer(object):
                 import paddle
                 import numpy as np
 
-                paddle.disable_static()
                 inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
                 linear = paddle.nn.Linear(10, 10)
                 inp = paddle.to_tensor(inp)
@@ -805,7 +797,7 @@ class Optimizer(object):
 
                 import numpy as np
                 import paddle
-                paddle.disable_static()
+
                 value = np.arange(26).reshape(2, 13).astype("float32")
                 a = paddle.to_tensor(value)
                 linear = paddle.nn.Linear(13, 5)
@@ -854,13 +846,9 @@ class Optimizer(object):
             .. code-block:: python
  
                 import paddle
-                import numpy as np
-
-                paddle.disable_static()
-                inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
                 linear = paddle.nn.Linear(10, 10)
-                inp = paddle.to_tensor(inp)
-                out = linear(inp)
+                input = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
+                out = linear(input)
                 loss = paddle.mean(out)
 
                 beta1 = paddle.to_tensor([0.9], dtype="float32")
@@ -903,7 +891,7 @@ class Optimizer(object):
 
                 import paddle
                 import numpy as np
-                paddle.disable_static()
+
                 value = np.arange(26).reshape(2, 13).astype("float32")
                 a = paddle.to_tensor(value)
                 linear = paddle.nn.Linear(13, 5)

python/paddle/optimizer/rmsprop.py

@@ -69,8 +69,8 @@ class RMSProp(Optimizer):
 
 
     Parameters:
-        learning_rate (float|_LRScheduler): The learning rate used to update ``Parameter``.
-            It can be a float value or a _LRScheduler.
+        learning_rate (float|LRScheduler): The learning rate used to update ``Parameter``.
+            It can be a float value or a LRScheduler.
         rho(float): rho is :math: `\\rho` in equation, default is 0.95.
         epsilon(float): :math: `\\epsilon` in equation is smoothing term to
             avoid division by zero, default is 1e-6.