Add docs and clean up unused code

python/paddle/trainer/config_parser.py

@@ -3371,7 +3371,7 @@ def make_importer(config_dir, config_args):
     return Import
 
 
-default_settings = dict(
+DEFAULT_SETTING = dict(
     batch_size=None,
     mini_batch_size=None,
     algorithm='async_sgd',
@@ -3404,7 +3404,7 @@ default_settings = dict(
     adam_beta2=0.999,
     adam_epsilon=1e-8, )
 
-settings = copy.deepcopy(default_settings)
+settings = copy.deepcopy(DEFAULT_SETTING)
 
 settings_deprecated = dict(usage_ratio=1., )
 

python/paddle/trainer_config_helpers/config_parser_utils.py

@@ -15,7 +15,6 @@
 import copy
 import paddle.trainer.config_parser as config_parser
 from paddle.proto.TrainerConfig_pb2 import OptimizationConfig
-
 '''
 This file is a wrapper of formal config_parser. The main idea of this file is to
 separete different config logic into different function, such as network configuration
@@ -38,7 +37,7 @@ def parse_network_config(network_conf, config_arg_str=''):
 
 
 def parse_optimizer_config(optimizer_conf, config_arg_str=''):
-    config_parser.settings = copy.deepcopy(config_parser.default_settings)
+    config_parser.settings = copy.deepcopy(config_parser.DEFAULT_SETTING)
     optimizer_conf()
     opt_config = OptimizationConfig()
     for k, v in config_parser.settings.iteritems():

python/paddle/v2/config_base.py

@@ -14,206 +14,55 @@
 
 import collections
 import re
-from paddle.trainer_config_helpers.default_decorators import wrap_name_default
 import paddle.trainer_config_helpers as conf_helps
-from topology import Topology
-
-
-class LayerType(type):
-    def __new__(cls, name, bases, attrs):
-        method_name = attrs.get('METHOD_NAME', None)
-        if method_name is not None:
-            method = getattr(conf_helps, method_name)
-            if method.__doc__ is not None:
-                mapper = attrs.get("__map_docstr__", None)
-                if mapper is not None:
-                    attrs['__doc__'] = LayerType.__map_docstr__(
-                        mapper(method.__doc__),
-                        method_name=method_name,
-                        name=name)
-                else:
-                    attrs['__doc__'] = LayerType.__map_docstr__(
-                        method.__doc__, method_name=method_name, name=name)
-        return super(LayerType, cls).__new__(cls, name, bases, attrs)
-
-    @staticmethod
-    def __map_docstr__(doc, name, method_name):
-        assert isinstance(doc, basestring)
-
-        # replace LayerOutput to paddle.v2.config_base.Layer
-        doc = doc.replace("LayerOutput", "paddle.v2.config_base.Layer")
-
-        doc = doc.replace('ParameterAttribute',
-                          'paddle.v2.attr.ParameterAttribute')
-
-        doc = re.sub(r'ExtraLayerAttribute[^\s]?',
-                     'paddle.v2.attr.ExtraAttribute', doc)
-
-        # xxx_layer to xxx
-        doc = re.sub(r"(?P<name>[a-z]+)_layer", r"\g<name>", doc)
-
-        # XxxxActivation to paddle.v2.Activation.Xxxx
-        doc = re.sub(r"(?P<name>[A-Z][a-zA-Z]+)Activation",
-                     r"paddle.v2.Activation.\g<name>", doc)
-
-        # TODO(yuyang18): Add more rules if needed.
+
+__layer_map__ = {}
+
+
+def __map_docstr__(doc, name):
+    if doc is None:
         return doc
 
+    assert isinstance(doc, basestring)
+
+    # replace LayerOutput to paddle.v2.config_base.Layer
+    doc = doc.replace("LayerOutput", "paddle.v2.config_base.Layer")
+
+    doc = doc.replace('ParameterAttribute', 'paddle.v2.attr.ParameterAttribute')
+
+    doc = re.sub(r'ExtraLayerAttribute[^\s]?', 'paddle.v2.attr.ExtraAttribute',
+                 doc)
+
+    # xxx_layer to xxx
+    doc = re.sub(r"(?P<name>[a-z]+)_layer", r"\g<name>", doc)
+
+    # XxxxActivation to paddle.v2.Activation.Xxxx
+    doc = re.sub(r"(?P<name>[A-Z][a-zA-Z]+)Activation",
+                 r"paddle.v2.Activation.\g<name>", doc)
+
+    # xxx_evaluator to paddle.v2.evaluator.xxx
+    doc = re.sub(r"(?P<name>[a-z]+)_evaluator", r"evaluator.\g<name>", doc)
+
+    # TODO(yuyang18): Add more rules if needed.
+    return doc
+
+
+def __convert_to_v2__(f, name, module):
+    def wrapped(*args, **xargs):
+        out = f(*args, **xargs)
+        outs = out
+        if not isinstance(out, collections.Sequence):
+            outs = [out]
+        for l in outs:
+            if isinstance(l, conf_helps.LayerOutput):
+                __layer_map__[l.full_name] = l
+        return out
+
+    wrapped.__doc__ = __map_docstr__(f.__doc__, name)
+    wrapped.__name__ = name
+    wrapped.__module__ = module
+
+    return wrapped
+
 
-class Layer(object):
-    __metaclass__ = LayerType
-
-    def __init__(self, name=None, parent_layers=None):
-        assert isinstance(parent_layers, dict)
-        self.name = name
-        self.__context__ = {}
-        self.__parent_layers__ = parent_layers
-        # some layer may have some extra parent layer
-        self.__extra_parent__ = []
-        # used for evaluator.
-        self.__children_layers__ = []
-
-    def extra_parent(self):
-        return self.__extra_parent__
-
-    def append_extra_parent(self, parent):
-        self.__extra_parent__.append(parent)
-
-    def append_child(self, layer, parent_names):
-        self.__children_layers__.append((layer, parent_names))
-
-    def to_proto(self, context):
-        """
-        function to set proto attribute
-        """
-        self.__context__ = context
-
-        # STEP: short cut if this layer is parsed before.
-        if self.context_name() in context:
-            if self.use_context_name():
-                return context[self.context_name()]
-            else:
-                return context[self.name]
-
-        # STEP: parse extra_parent that is not used by this layer but must
-        # be parsed before this layer.
-        for p in self.__extra_parent__:
-            p.to_proto(context=context)
-
-        # STEP: parse parent that is used by this layer, get the result and
-        # insert into kwargs of the next layer's to_proto_impl method.
-        kwargs = dict()
-        for layer_name in self.__parent_layers__:
-            if not isinstance(self.__parent_layers__[layer_name],
-                              collections.Sequence):
-                v1_layer = self.__parent_layers__[layer_name].to_proto(
-                    context=context)
-            else:
-                v1_layer = map(lambda x: x.to_proto(context=context),
-                               self.__parent_layers__[layer_name])
-            kwargs[layer_name] = v1_layer
-
-        # STEP: parse myself and add myself into context.
-        ret_val = self.to_proto_impl(**kwargs)
-        if self.context_name() is not None \
-                and self.context_name() not in context:
-            context[self.context_name()] = ret_val
-
-        # STEP: parse children that should be pased after this layer.
-        for layer, pnames in self.__children_layers__:
-            drop = False
-
-            # child will only be parsed if all parents are in context.
-            for pname in pnames:
-                if pname not in context:
-                    drop = True
-                    break
-            if drop:
-                continue
-            layer.to_proto(context=context)
-
-        # STEP: return v1 layer result
-        if self.context_name() is None:
-            return ret_val
-        elif self.use_context_name():
-            return context[self.context_name()]
-        else:
-            return context[self.name]
-
-    def to_proto_impl(self, **kwargs):
-        raise NotImplementedError()
-
-    def context_name(self):
-        """
-        Context name means the context which stores `to_proto_impl` result.
-        If multiple layer share same context_name, the `to_proto_impl` of them
-        will be invoked only once.
-        """
-        return self.name
-
-    def use_context_name(self):
-        return False
-
-    def calculate_size(self):
-        """
-        lazy calculate size of the layer, should be called when to_proto_impl of
-        this layer is called.
-        :return:
-        """
-        return self.__context__[self.context_name()].size
-
-    def attr(self):
-        topo = Topology(self)
-        return topo.get_layer_proto(self.name)
-
-
-def __convert_to_v2__(method_name,
-                      parent_names,
-                      is_default_name=True,
-                      attach_parent=False):
-    if is_default_name:
-        wrapper = wrap_name_default(name_prefix=method_name)
-    else:
-        wrapper = None
-
-    class V2LayerImpl(Layer):
-        METHOD_NAME = method_name
-
-        def __init__(self, **kwargs):
-            parent_layers = dict()
-            other_kwargs = dict()
-            for pname in parent_names:
-                if pname in kwargs:
-                    parent_layers[pname] = kwargs[pname]
-
-            if attach_parent:
-                pnames = [x.context_name() for x in parent_layers.values()]
-
-                for pname in parent_layers:
-                    layers = kwargs[pname]
-                    if not isinstance(layers, collections.Sequence):
-                        layers = [layers]
-
-                    for layer in layers:
-                        layer.append_child(self, pnames)
-
-            for key in kwargs.keys():
-                if key not in parent_names:
-                    other_kwargs[key] = kwargs[key]
-
-            name = kwargs.get('name', None)
-            super(V2LayerImpl, self).__init__(name, parent_layers)
-            self.__other_kwargs__ = other_kwargs
-
-        if wrapper is not None:
-            __init__ = wrapper(__init__)
-
-        def to_proto_impl(self, **kwargs):
-            args = dict()
-            for each in kwargs:
-                args[each] = kwargs[each]
-            for each in self.__other_kwargs__:
-                args[each] = self.__other_kwargs__[each]
-            return getattr(conf_helps, method_name)(**args)
-
-    return V2LayerImpl
+Layer = conf_helps.LayerOutput

python/paddle/v2/evaluator.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import paddle.trainer_config_helpers.evaluators as evs
-import inspect
 from config_base import __convert_to_v2__
+import inspect
 
 __all__ = []
 
@@ -27,7 +27,8 @@ def initialize():
         __ev__ = getattr(evs, __ev_name__)
         __new_name__ = convert_to_new_name(__ev_name__)
 
-        globals()[__new_name__] = __ev__
+        globals()[__new_name__] = __convert_to_v2__(__ev__, __new_name__,
+                                                    __name__)
         globals()[__new_name__].__name__ = __new_name__
         __all__.append(__new_name__)
 

python/paddle/v2/inference.py

@@ -12,9 +12,9 @@ class Inference(object):
     """
     Inference combines neural network output and parameters together
     to do inference.
-    
+
     ..  code-block:: python
-    
+
         inferer = Inference(output_layer=prediction, parameters=parameters)
         for data_batch in batches:
             print inferer.infer(data_batch)
@@ -92,8 +92,8 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
 
     ..  code-block:: python
 
-        result = paddle.infer(output_layer=prediction, 
-                              parameters=parameters, 
+        result = paddle.infer(output_layer=prediction,
+                              parameters=parameters,
                               input=SomeData)
         print result
 
@@ -101,14 +101,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
 
     ..  code-block:: python
 
-        result = paddle.infer(output_layer=[prediction1, prediction2], 
-                              parameters=parameters, 
+        result = paddle.infer(output_layer=[prediction1, prediction2],
+                              parameters=parameters,
                               input=SomeData,
                               field=[id, value]])
         print result
 
     :param output_layer: output of the neural network that would be inferred
-    :type output_layer: paddle.v2.config_base.Layer or a list of 
+    :type output_layer: paddle.v2.config_base.Layer or a list of
                         paddle.v2.config_base.Layer
     :param parameters: parameters of the neural network.
     :type parameters: paddle.v2.parameters.Parameters
@@ -117,14 +117,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
     :type input: collections.Iterable
     :param feeding: Reader dictionary. Default could generate from input
                         value.
-    :param field: The prediction field. It should in [`value`, `id`, `prob`]. 
-                  `value` and `prob` mean return the prediction probabilities, 
+    :param field: The prediction field. It should in [`value`, `id`, `prob`].
+                  `value` and `prob` mean return the prediction probabilities,
                   `id` means return the prediction labels. Default is `value`.
-                  Note that `prob` only used when output_layer is beam_search 
+                  Note that `prob` only used when output_layer is beam_search
                   or max_id.
     :type field: str
-    :return: The prediction result. If there are multiple outout_layers and fields, 
-             the return order is outout_layer1.field1, outout_layer2.field1, ..., 
+    :return: The prediction result. If there are multiple outout_layers and fields,
+             the return order is outout_layer1.field1, outout_layer2.field1, ...,
              outout_layer1.field2, outout_layer2.field2 ...
     :rtype: numpy.ndarray
     """

python/paddle/v2/layer.py

@@ -33,26 +33,14 @@ The primary usage shows below.
 
 import collections
 import copy
+import re
 import paddle.trainer_config_helpers.layers as v1_layers
 import paddle.trainer.config_parser as cp
 from paddle.proto.ModelConfig_pb2 import ModelConfig, SubModelConfig
+from config_base import __convert_to_v2__
+import config_base
 
 __all__ = ['data', 'parse_network']
-__layer_map__ = {}
-
-
-def __wrap__(f):
-    def wrapped(*args, **xargs):
-        out = f(*args, **xargs)
-        outs = out
-        if not isinstance(out, collections.Sequence):
-            outs = [out]
-        for l in outs:
-            if isinstance(l, v1_layers.LayerOutput):
-                __layer_map__[l.full_name] = l
-        return out
-
-    return wrapped
 
 
 def __need_to_keep__(name):
@@ -90,7 +78,7 @@ for name in v1_layers.__all__:
         continue
     new_name = __convert_name__(name)
     if callable(obj) and __need_to_wrap__(name):
-        globals()[new_name] = __wrap__(obj)
+        globals()[new_name] = __convert_to_v2__(obj, new_name, __name__)
     else:
         globals()[new_name] = obj
     __all__.append(new_name)
@@ -102,9 +90,21 @@ def __data_layer__(name, type, **kwargs):
     return l
 
 
-data = __wrap__(__data_layer__)
+def __map_data_docstr__(doc):
+    doc = re.sub(r'(data = [^\)]+)\).*',
+                 "data = paddle.layer.data(name=\"input\", "
+                 "type=paddle.data_type.dense_vector(1000))", doc)
+
+    doc = re.sub(r':param size:.*', ':param type: Data type of this data layer',
+                 doc)
+    doc = re.sub(r':type size:.*', ":type size: paddle.v2.data_type.InputType",
+                 doc)
+    return doc
+
+
+__data_layer__.__doc__ = __map_data_docstr__(v1_layers.data_layer.__doc__)
 
-LayerV2 = v1_layers.LayerOutput
+data = __convert_to_v2__(__data_layer__, 'name', __name__)
 
 
 def __get_used_layers__(output_layers, extra_layers=None):
@@ -273,7 +273,7 @@ def parse_network(output_layers, extra_layers=None):
 
 
 def get_layer(name):
-    return __layer_map__.get(name)
+    return config_base.__layer_map__.get(name)
 
 
 cp.begin_parse()

python/paddle/v2/tests/test_rnn_layer.py

@@ -32,6 +32,7 @@ class RNNTest(unittest.TestCase):
 
         def parse_old_rnn():
             reset_parser()
+
             def step(y):
                 mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
                 out = conf_helps.fc_layer(
@@ -52,6 +53,7 @@ class RNNTest(unittest.TestCase):
 
         def parse_new_rnn():
             reset_parser()
+
             def new_step(y):
                 mem = layer.memory(name="rnn_state", size=hidden_dim)
                 out = layer.fc(input=[y, mem],
@@ -72,7 +74,6 @@ class RNNTest(unittest.TestCase):
                                     parse_new_rnn().splitlines(1))
         print ''.join(diff)
 
-
     def test_sequence_rnn_multi_input(self):
         dict_dim = 10
         word_dim = 8
@@ -81,6 +82,7 @@ class RNNTest(unittest.TestCase):
 
         def parse_old_rnn():
             reset_parser()
+
             def test():
                 data = conf_helps.data_layer(name="word", size=dict_dim)
                 label = conf_helps.data_layer(name="label", size=label_dim)

python/paddle/v2/topology.py

@@ -17,34 +17,11 @@ import collections
 from paddle.proto.ModelConfig_pb2 import ModelConfig
 import paddle.trainer_config_helpers as conf_helps
 import layer as v2_layer
+import config_base
 
 __all__ = ['Topology']
 
 
-def __flatten__(lis):
-    """
-    Given a list, possibly nested to any level, return it flattened.
-    """
-    new_lis = []
-    for item in lis:
-        if isinstance(item, collections.Sequence):
-            new_lis.extend(__flatten__(item))
-        else:
-            new_lis.append(item)
-    return new_lis
-
-
-def __bfs_travel__(callback, *layers):
-    layers = __flatten__(layers)
-    for each_layer in layers:
-        __break__ = callback(each_layer)
-        if __break__:
-            return
-        __layers__ = each_layer.__parent_layers__.values() + \
-                     each_layer.extra_parent()
-        __bfs_travel__(callback, *__layers__)
-
-
 class Topology(object):
     """
     Topology is used to store the information about all layers
@@ -125,5 +102,5 @@ class Topology(object):
 
 
 def __check_layer_type__(layer):
-    if not isinstance(layer, v2_layer.LayerV2):
-        raise ValueError('layer should have type paddle.layer.Layer')
+    if not isinstance(layer, config_base.Layer):
+        raise ValueError('layer should have type paddle.v2.config_base.Layer')