Implement Expotential NatureExp Inversetime and Polynomal Decay

python/paddle/fluid/imperative/learning_rate_scheduler.py

@@ -16,7 +16,9 @@ from __future__ import print_function
 
 from .. import unique_name
 
-__all__ = ['PiecewiseDecay']
+__all__ = [
+    'PiecewiseDecay', 'NaturalExpDecay', 'ExponentialDecay', 'InverseTimeDecay'
+]
 
 
 class LearningRateDecay(object):
@@ -65,3 +67,117 @@ class PiecewiseDecay(LearningRateDecay):
             if self.step_num < self.boundaries[i]:
                 return self.vars[i]
         return self.vars[len(self.values) - 1]
+
+
+class NaturalExpDecay(LearningRateDecay):
+    def __init__(self,
+                 learning_rate,
+                 decay_steps,
+                 decay_rate,
+                 staircase=False,
+                 begin=0,
+                 step=1,
+                 dtype='float32'):
+        super(NaturalExpDecay, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+        self.decay_steps = decay_steps
+        self.decay_rate = decay_rate
+        self.staircase = staircase
+
+    def step(self):
+        from .. import layers
+        div_res = self.create_lr_var(self.step_num / self.decay_steps)
+        if self.staircase:
+            div_res = layers.floor(div_res)
+        decayed_lr = self.learning_rate * layers.exp(-1 * self.decay_rate *
+                                                     div_res)
+
+        return decayed_lr
+
+
+class ExponentialDecay(LearningRateDecay):
+    def __init__(self,
+                 learning_rate,
+                 decay_steps,
+                 decay_rate,
+                 staircase=False,
+                 begin=0,
+                 step=1,
+                 dtype='float32'):
+        super(ExponentialDecay, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+        self.decay_steps = decay_steps
+        self.decay_rate = decay_rate
+        self.staircase = staircase
+
+    def step(self):
+        from .. import layers
+        div_res = self.create_lr_var(self.step_num / self.decay_steps)
+        if self.staircase:
+            div_res = layers.floor(div_res)
+
+        decayed_lr = self.learning_rate * (self.decay_rate**div_res)
+
+        return decayed_lr
+
+
+class InverseTimeDecay(LearningRateDecay):
+    def __init__(self,
+                 learning_rate,
+                 decay_steps,
+                 decay_rate,
+                 staircase=False,
+                 begin=0,
+                 step=1,
+                 dtype='float32'):
+        super(InverseTimeDecay, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+        self.decay_steps = decay_steps
+        self.decay_rate = decay_rate
+        self.staircase = staircase
+
+    def step(self):
+        from .. import layers
+        div_res = self.create_lr_var(self.step_num / self.decay_steps)
+        if self.staircase:
+            div_res = layers.floor(div_res)
+
+        decayed_lr = self.learning_rate / (1 + self.decay_rate * div_res)
+
+        return decayed_lr
+
+
+class PolynomialDecay(LearningRateDecay):
+    def __init__(self,
+                 learning_rate,
+                 decay_steps,
+                 end_learning_rate=0.0001,
+                 power=1.0,
+                 cycle=False,
+                 begin=0,
+                 step=1,
+                 dtype='float32'):
+        super(PolynomialDecay, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+        self.decay_steps = decay_steps
+        self.end_learning_rate = end_learning_rate
+        self.power = power
+        self.cycle = cycle
+
+    def step(self):
+        from .. import layers
+        if self.cycle:
+            div_res = layers.ceil(
+                self.create_lr_var(self.step_num / self.decay_steps))
+            zero_var = 0.0
+            one_var = 1.0
+
+            if float(self.step_num) == zero_var:
+                div_res = one_var
+            decay_steps = self.decay_steps * div_res
+        else:
+            global_step = global_step if global_step < self.decay_steps else self.decay_steps
+
+            decayed_lr = (self.learning_rate - self.end_learning_rate) * \
+                ((1 - global_step / self.decay_steps) ** self.power) + self.end_learning_rate
+        return self.create_lr_var(decayed_lr)

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -115,6 +115,11 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
 
     """
     with default_main_program()._lr_schedule_guard():
+        if imperative_base.enabled():
+            decay = imperate_lr.ExponentialDecay(learning_rate, decay_steps,
+                                                 decay_rate, staircase)
+            return decay
+        else:
             global_step = _decay_step_counter()
 
             div_res = global_step / decay_steps
@@ -144,6 +149,11 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
         The decayed learning rate
     """
     with default_main_program()._lr_schedule_guard():
+        if imperative_base.enabled():
+            decay = imperate_lr.NaturalExpDecay(learning_rate, decay_steps,
+                                                decay_rate, staircase)
+            return decay
+        else:
             global_step = _decay_step_counter()
 
             div_res = global_step / decay_steps
@@ -190,6 +200,11 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
           sgd_optimizer.minimize(avg_cost)
     """
     with default_main_program()._lr_schedule_guard():
+        if imperative_base.enabled():
+            decay = imperate_lr.InverseTimeDecay(learning_rate, decay_steps,
+                                                 decay_rate, staircase)
+            return decay
+        else:
             global_step = _decay_step_counter()
 
             div_res = global_step / decay_steps
@@ -230,6 +245,11 @@ def polynomial_decay(learning_rate,
         Variable: The decayed learning rate
     """
     with default_main_program()._lr_schedule_guard():
+        if imperative_base.enabled():
+            decay = imperate_lr.PolynomialDecay(learning_rate, decay_steps,
+                                                end_learning_rate, power, cycle)
+            return decay
+        else:
             global_step = _decay_step_counter()
 
             if cycle:
@@ -246,7 +266,8 @@ def polynomial_decay(learning_rate,
             else:
                 decay_steps_var = tensor.fill_constant(
                     shape=[1], dtype='float32', value=float(decay_steps))
-            global_step = nn.elementwise_min(x=global_step, y=decay_steps_var)
+                global_step = nn.elementwise_min(
+                    x=global_step, y=decay_steps_var)
 
             decayed_lr = (learning_rate - end_learning_rate) * \
                 ((1 - global_step / decay_steps) ** power) + end_learning_rate

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

@@ -22,7 +22,7 @@ import six
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
-from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.optimizer import SGDOptimizer, Adam
 from paddle.fluid.imperative.nn import FC
 from paddle.fluid.imperative.base import to_variable
 from test_imperative_base import new_program_scope
@@ -46,14 +46,9 @@ class TestImperativeOptimizerBase(unittest.TestCase):
         self.batch_num = 10
 
     def get_optimizer(self):
-        bd = [3, 6, 9]
-        self.optimizer = SGDOptimizer(
-            learning_rate=fluid.layers.piecewise_decay(
-                boundaries=bd,
-                values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
-        return self.optimizer
+        raise NotImplementedError()
 
-    def test_optimizer_float32(self):
+    def _check_mlp(self):
         seed = 90
         with fluid.imperative.guard():
             fluid.default_startup_program().random_seed = seed
@@ -83,16 +78,14 @@ class TestImperativeOptimizerBase(unittest.TestCase):
                 dy_out = avg_loss._numpy()
 
                 if batch_id == 0:
-                    for param in fluid.default_main_program().global_block(
-                    ).all_parameters():
+                    for param in mlp.parameters():
                         dy_param_init_value[param.name] = param._numpy()
 
                 avg_loss._backward()
                 optimizer.minimize(avg_loss)
                 mlp.clear_gradients()
                 dy_param_value = {}
-                for param in fluid.default_main_program().global_block(
-                ).all_parameters():
+                for param in mlp.parameters():
                     dy_param_value[param.name] = param._numpy()
 
         with new_program_scope():
@@ -102,7 +95,7 @@ class TestImperativeOptimizerBase(unittest.TestCase):
             exe = fluid.Executor(fluid.CPUPlace(
             ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
 
-            mnist = MLP('mlp')
+            mlp = MLP('mlp')
             optimizer = self.get_optimizer()
             train_reader = paddle.batch(
                 paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
@@ -110,14 +103,14 @@ class TestImperativeOptimizerBase(unittest.TestCase):
             img = fluid.layers.data(
                 name='pixel', shape=[1, 28, 28], dtype='float32')
             label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-            cost = mnist(img)
+            cost = mlp(img)
             avg_loss = fluid.layers.reduce_mean(cost)
             optimizer.minimize(avg_loss)
 
             # initialize params and fetch them
             static_param_init_value = {}
             static_param_name_list = []
-            for param in mnist.parameters():
+            for param in mlp.parameters():
                 static_param_name_list.append(param.name)
 
             out = exe.run(fluid.default_startup_program(),
@@ -156,5 +149,70 @@ class TestImperativeOptimizerBase(unittest.TestCase):
             self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-5))
 
 
+class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
+    def get_optimizer(self):
+        bd = [3, 6, 9]
+        optimizer = SGDOptimizer(learning_rate=fluid.layers.piecewise_decay(
+            boundaries=bd, values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):
+    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))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):
+    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))
+        return optimizer
+
+    def test_sgd(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):
+    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))
+        return optimizer
+
+    def test_adam(self):
+        self._check_mlp()
+
+
+class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):
+    def get_optimizer(self):
+        optimizer = SGDOptimizer(learning_rate=fluid.layers.polynomial_decay(
+            learning_rate=0.1, decay_steps=5, cycle=self.cycle))
+        return optimizer
+
+    def test_sgd_cycle(self):
+        self.cycle = True
+        self._check_mlp()
+
+    def test_sgd(self):
+        self.cycle = False
+        self._check_mlp()
+
+
 if __name__ == '__main__':
     unittest.main()