Add LRS wrapper to optimizer

python/paddle/trainer_config_helpers/optimizers.py

@@ -16,12 +16,15 @@
     default_gradient_clipping_threshold, default_momentum
 
 from .default_decorators import wrap_param_default
+import collections
+import cStringIO
 
 __all__ = [
     'Optimizer', 'BaseSGDOptimizer', 'MomentumOptimizer', 'AdamaxOptimizer',
     'AdamOptimizer', 'AdaGradOptimizer', 'RMSPropOptimizer',
     'DecayedAdaGradOptimizer', 'AdaDeltaOptimizer', 'BaseRegularization',
-    'L2Regularization', 'settings', 'ModelAverage'
+    'L2Regularization', 'settings', 'ModelAverage', 'PolyLRS', 'ConstantLRS',
+    'ExpLRS', 'DiscreteExpLRS', 'LinearLRS', 'ManualLRS', 'PassManualLRS'
 ]
 
 
@@ -351,15 +354,141 @@ def __extends__(dict1, dict2):
     return dict1
 
 
+class BaseLRS(Optimizer):
+    def __init__(self, a, b, scheduler_name):
+        self.__a__ = float(a)
+        self.__b__ = float(b)
+        self.__scheduler_name__ = scheduler_name
+
+    def to_setting_kwargs(self):
+        return {
+            'learning_rate_schedule': self.__scheduler_name__,
+            'learning_rate_decay_a': self.__a__,
+            'learning_rate_decay_b': self.__b__
+        }
+
+
+class PolyLRS(BaseLRS):
+    """
+    Poly Learning Rate Scheduler.
+
+    lr = learning_rate * pow(1 + a * num_samples_processed, -b)
+    """
+
+    def __init__(self, a, b):
+        super(PolyLRS, self).__init__(a=a, b=b, scheduler_name='poly')
+
+
+class ConstantLRS(Optimizer):
+    """
+    Constant Learning Rate Scheduler. Learning rate will not be changed.
+    """
+
+    def to_setting_kwargs(self):
+        return {'learning_rate_schedule': 'constant'}
+
+
+class ExpLRS(BaseLRS):
+    """
+    Exp Learning Rate Scheduler.
+
+    lr = learning_rate * pow(a, num_samples_processed/b)
+    """
+
+    def __init__(self, a, b):
+        super(ExpLRS, self).__init__(a=a, b=b, scheduler_name='exp')
+
+
+class DiscreteExpLRS(BaseLRS):
+    """
+    Discrete Exp Learning Rate Scheduler.
+
+    lr = learning_rate * pow(a, floor(num_samples_processed / b))
+    """
+
+    def __init__(self, a, b):
+        super(DiscreteExpLRS, self).__init__(a=a, b=b, scheduler_name='discexp')
+
+
+class LinearLRS(BaseLRS):
+    """
+    Linear Learning Rate Scheduler.
+
+    lr = max(learning_rate - a, b)
+    """
+
+    def __init__(self, a, b):
+        super(LinearLRS, self).__init__(a=a, b=b, scheduler_name='linear')
+
+
+class ManualLRS(Optimizer):
+    """
+    specify learning rate through explicit pass all learning_rates.
+
+    :param learning_rates: list of learning rates. Each item contains two field.
+                           First is a int value, as segmentation. Second is the
+                           learning rate.
+
+                           The real learning rate is:
+
+                           if seg_{i-1} <= numSamples <= seg_i,
+                                return lr_{i}
+
+    :type learning_rates: list of list. Each element should be (int, float)
+    """
+
+    def __init__(self, learning_rates):
+        assert isinstance(learning_rates, collections.Sequence)
+        with cStringIO.StringIO() as buf:
+            for i, each in enumerate(learning_rates):
+                assert isinstance(each, collections.Sequence)
+                assert len(each) == 2
+                buf.write("{0}:{1:.5f}".format(int(each[0]), float(each[1])))
+                if i + 1 != len(learning_rates):  # not at end
+                    buf.write(",")
+            self.__args__ = buf.getvalue()
+
+    def to_setting_kwargs(self):
+        return {
+            'learning_rate_schedule': 'manual',
+            'learning_rate_args': self.__args__
+        }
+
+
+class PassManualLRS(ManualLRS):
+    """
+    Pass Manual Learning Rate Scheduler.
+
+    Basically same as manual learning rate scheduler, except pass manual LRS use
+    pass number as segment number.
+
+    The real learning rate is:
+
+    if seg_{i-1} <= pass_id <= seg_i:
+        return lr_{i}
+    """
+
+    def __init__(self, learning_rates):
+        super(PassManualLRS, self).__init__(learning_rates=learning_rates)
+
+    def to_setting_kwargs(self):
+        return {
+            'learning_rate_schedule': 'pass_manual',
+            'learning_rate_args': self.__args__
+        }
+
+
 @wrap_param_default(
     ['learning_method'], default_factory=lambda _: MomentumOptimizer())
 @wrap_param_default(
     ['regularization'], default_factory=lambda _: BaseRegularization())
+@wrap_param_default(
+    ['learning_rate_args'], default_factory=lambda _: ConstantLRS())
 def settings(batch_size,
              learning_rate=1e-3,
              learning_rate_decay_a=0.,
              learning_rate_decay_b=0.,
-             learning_rate_schedule='poly',
+             learning_rate_schedule=None,
              learning_rate_args='',
              learning_method=None,
              regularization=None,
@@ -396,6 +525,19 @@ def settings(batch_size,
                                         value larger than some value, will be
                                         clipped.
     :type gradient_clipping_threshold: float
+
+    :param learning_rate_schedule: A Learning Rate Scheduler object or basestr.
+                                   It is recommend to pass a LRS object.
+                                   If you set learning_rate_schedule as basestr,
+                                   you should manually set learning_rate_decay_a
+                                   learning_rate_decay_b and learning_rate_args.
+
+                                   Check LRS.to_setting_kwargs to figure out
+                                   how to set these arguments.
+    :type learning_rate_schedule: basestring|Optimizer
+    :param learning_rate_decay_a: See learning_rate_schedule.
+    :param learning_rate_decay_b: See learning_rate_schedule.
+    :param learning_rate_args: See learning_rate_schedule.
     """
     if isinstance(regularization, BaseRegularization):
         regularization = [regularization]
@@ -406,15 +548,24 @@ def settings(batch_size,
     else:
         algorithm = 'owlqn'
 
-    args = [
-        'batch_size', 'learning_rate', 'learning_rate_decay_a',
-        'learning_rate_decay_b', 'learning_rate_schedule', 'learning_rate_args'
-    ]
+    args = ['batch_size', 'learning_rate']
     kwargs = dict()
     kwargs['algorithm'] = algorithm
+
     for arg in args:
         kwargs[arg] = locals()[arg]
 
+    if isinstance(learning_rate_schedule, Optimizer):
+        kwargs = __extends__(kwargs, learning_rate_schedule.to_setting_kwargs())
+    elif isinstance(learning_rate_schedule, basestring):
+        for arg in [
+                'learning_rate_decay_a', 'learning_rate_decay_b',
+                'learning_rate_schedule', 'learning_rate_args'
+        ]:
+            kwargs[arg] = locals()[arg]
+    else:
+        raise RuntimeWarning("Unexcepted branch")
+
     kwargs = __extends__(kwargs, learning_method.to_setting_kwargs())
     learning_method.extra_settings()