Fix V2 API

paddle/parameter/Parameter.h

@@ -324,6 +324,7 @@ protected:
   std::vector<std::shared_ptr<IParameterUpdaterHook>> updaterHooks_;
 
 public:
+  void setSharedCount(int cnt) { sharedCount_ = cnt; }
   int getSharedCount() { return sharedCount_; }
 
   bool isSparse() { return config_.is_sparse(); }

python/paddle/trainer/config_parser.py

@@ -3371,7 +3371,7 @@ def make_importer(config_dir, config_args):
     return Import
 
 
-settings = dict(
+default_settings = dict(
     batch_size=None,
     mini_batch_size=None,
     algorithm='async_sgd',
@@ -3404,6 +3404,8 @@ settings = dict(
     adam_beta2=0.999,
     adam_epsilon=1e-8, )
 
+settings = copy.deepcopy(default_settings)
+
 settings_deprecated = dict(usage_ratio=1., )
 
 trainer_settings = dict(
@@ -3544,10 +3546,8 @@ def update_g_config():
     return g_config
 
 
-def parse_config(trainer_config, config_arg_str):
+def begin_parse(config_arg_str=''):
     '''
-    @param trainer_config: can be a string of config file name or a function name
-    with config logic
     @param config_arg_str: a string of the form var1=val1,var2=val2. It will be
     passed to config script as a dictionary CONFIG_ARGS
     '''
@@ -3555,12 +3555,23 @@ def parse_config(trainer_config, config_arg_str):
     for hook in _parse_config_hooks:
         hook()
 
-    config_args = {}
-
     logger.findCaller = find_caller
     logger.fatal = my_fatal
 
     g_config.model_config.type = "nn"
+
+    global g_current_submodel, g_root_submodel
+    g_root_submodel = g_config.model_config.sub_models.add()
+    g_root_submodel.name = 'root'
+    g_root_submodel.is_recurrent_layer_group = False
+    g_current_submodel = g_root_submodel
+
+
+def parse_config(trainer_config, config_arg_str):
+    begin_parse(config_arg_str)
+
+    config_args = {}
+
     if config_arg_str:
         config_args = dict([f.split('=') for f in config_arg_str.split(',')])
 
@@ -3573,14 +3584,6 @@ def parse_config(trainer_config, config_arg_str):
         extension_module = importlib(extension_module_name)
         g_extended_config_funcs = extension_module.get_config_funcs(g_config)
 
-    g_config.model_config.type = 'nn'
-
-    global g_current_submodel, g_root_submodel
-    g_root_submodel = g_config.model_config.sub_models.add()
-    g_root_submodel.name = 'root'
-    g_root_submodel.is_recurrent_layer_group = False
-    g_current_submodel = g_root_submodel
-
     if hasattr(trainer_config, '__call__'):
         trainer_config.func_globals.update(
             make_config_environment("", config_args))

python/paddle/trainer_config_helpers/config_parser_utils.py

@@ -12,15 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+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 
+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
  and optimizer configuration.
 '''
 
 __all__ = [
-    "parse_trainer_config", "parse_network_config", "parse_optimizer_config"
+    "parse_trainer_config", "parse_network_config", "parse_optimizer_config",
+    "reset_parser"
 ]
 
 
@@ -34,5 +38,15 @@ def parse_network_config(network_conf, config_arg_str=''):
 
 
 def parse_optimizer_config(optimizer_conf, config_arg_str=''):
-    config = config_parser.parse_config(optimizer_conf, config_arg_str)
-    return config.opt_config
+    config_parser.settings = copy.deepcopy(config_parser.default_settings)
+    optimizer_conf()
+    opt_config = OptimizationConfig()
+    for k, v in config_parser.settings.iteritems():
+        if v is None:
+            continue
+        opt_config.__setattr__(k, v)
+    return opt_config
+
+
+def reset_parser():
+    config_parser.begin_parse()

python/paddle/trainer_config_helpers/layers.py

@@ -285,6 +285,7 @@ class LayerOutput(object):
         assert size is not None
         assert LayerType.is_layer_type(layer_type)
         self.name = name
+        self.full_name = MakeLayerNameInSubmodel(name)
         self.layer_type = layer_type
         if parents is not None and type(parents) != list:
             parents = [parents]
@@ -3489,6 +3490,11 @@ def recurrent_group(step,
 
     RecurrentLayerGroupEnd(name=name)
 
+    for layer_out in layer_outs:
+        # Thee previous full_name is the name is the rnn group
+        # We need a full_name outside the rnn group
+        layer_out.full_name = MakeLayerNameInSubmodel(layer_out.name)
+
     if len(layer_outs) == 1:
         return layer_outs[0]
     else:

python/paddle/v2/evaluator.py

@@ -25,21 +25,9 @@ def initialize():
 
     for __ev_name__ in filter(lambda x: x.endswith('_evaluator'), evs.__all__):
         __ev__ = getattr(evs, __ev_name__)
-        if hasattr(__ev__, 'argspec'):
-            argspec = __ev__.argspec
-        else:
-            argspec = inspect.getargspec(__ev__)
-        parent_names = filter(lambda x: x in ['input', 'label', 'weight'],
-                              argspec.args)
-        v2_ev = __convert_to_v2__(
-            __ev_name__,
-            parent_names=parent_names,
-            is_default_name='name' in argspec.args,
-            attach_parent=True)
-
         __new_name__ = convert_to_new_name(__ev_name__)
 
-        globals()[__new_name__] = v2_ev
+        globals()[__new_name__] = __ev__
         globals()[__new_name__].__name__ = __new_name__
         __all__.append(__new_name__)
 

python/paddle/v2/layer.py

@@ -32,392 +32,39 @@ The primary usage shows below.
 """
 
 import collections
-import inspect
-import re
-
-import paddle.trainer_config_helpers as conf_helps
-from paddle.trainer.config_parser import \
-    RecurrentLayerGroupWithoutOutLinksBegin, RecurrentLayerGroupSetOutLink, \
-    RecurrentLayerGroupEnd, model_type
-from paddle.trainer_config_helpers.config_parser_utils import \
-    parse_network_config as __parse__
-from paddle.trainer_config_helpers.default_decorators import wrap_act_default
-from paddle.trainer_config_helpers.default_decorators import \
-    wrap_bias_attr_default
-from paddle.trainer_config_helpers.default_decorators import wrap_name_default
-from paddle.trainer_config_helpers.layers import RecurrentLayerGroupSetGenerator, Generator
-from paddle.trainer_config_helpers.layers import layer_support
-
-import activation
-import attr
-import data_type
-from config_base import Layer, __convert_to_v2__
-
-__all__ = ['parse_network', 'data']
+import copy
+import paddle.trainer_config_helpers.layers as v1_layers
+import paddle.trainer.config_parser as cp
+from paddle.proto.ModelConfig_pb2 import ModelConfig, SubModelConfig
 
+__all__ = ['data', 'parse_network']
+__layer_map__ = {}
 
-def parse_network(output_layers, extra_layers=None):
-    """
-    Parse all layers in the neural network graph and
-    then generate a ModelConfig object.
-
-    ..  note::
-
-        This function is used internally in paddle.v2 module. User should never
-        invoke this method.
-
-    :param output_layers: Output layers.
-    :type output_layers: Layer
-    :param extra_layers: Some layers in the neural network graph are not in the
-                         path of output_layers.
-    :type extra_layers: Layer
-    :return: A ModelConfig object instance.
-    :rtype: ModelConfig
-    """
-    if not isinstance(output_layers, collections.Sequence):
-        output_layers = [output_layers]
-    if extra_layers is not None and not isinstance(extra_layers,
-                                                   collections.Sequence):
-        extra_layers = [extra_layers]
+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
 
-    def __real_func__():
-        """
-        __real_func__ is the function that config_parser.parse invoked. It is
-        the plain old paddle configuration function.
-        """
-        context = dict()
-        real_output = [each.to_proto(context=context) for each in output_layers]
-        if extra_layers is not None:
-            extra_output = [
-                each.to_proto(context=context) for each in extra_layers
-            ]
-        conf_helps.outputs(real_output)
+    return wrapped
 
-    return __parse__(__real_func__)
+def __need_to_keep__(name):
+    if name in ['StaticInput', 'LayerType', 'layer_support']:
+        return False
+    return True
 
 
-"""
-Some layer may need some special config, and can not use __convert_to_v2__ to convert.
-So we also need to implement some special LayerV2.
-"""
+def __need_to_wrap__(name):
+    return name not in ['AggregateLevel', 'ExpandLevel']
 
 
-class DataLayerV2(Layer):
-    METHOD_NAME = 'data_layer'
-
-    def __init__(self, name, type, **kwargs):
-        assert isinstance(type, data_type.InputType)
-
-        self.type = type
-        self.__method_name__ = 'data_layer'
-        self.__kwargs__ = kwargs
-
-        super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
-
-    def to_proto_impl(self, **kwargs):
-        args = dict()
-        args['size'] = self.type.dim
-        for each in kwargs:
-            args[each] = kwargs[each]
-        for each in self.__kwargs__:
-            args[each] = self.__kwargs__[each]
-        return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
-
-    def __map_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
-
-
-class MemoryV2(Layer):
-    def __init__(self, name, extra_input=None, **kwargs):
-        """
-        Init memory object, if memory is inited inside recurrent_group step
-        function, it may depend on a boot_layer that should be initialized
-        outside recurrent_group, so we:
-            1. add RecurrentLayerInput to extra_parent of self.
-            2. add boot_layer to the extra_parent of RecurrentLayerInput.
-
-        :param extra_input: list of RecurrentLayerInput
-        :type extra_input: [RecurrentLayerInput]
-        """
-        self.name = name
-        super(MemoryV2, self).__init__(name=name, parent_layers=dict())
-        self.__kwargs__ = kwargs
-        self.__boot_layer_name__ = None
-
-        if 'boot_layer' in kwargs:
-            begin_of_current_rnn = []
-            # TODO(yuyang18): Fix inspect, it could be wrong when user invoke a
-            # function inside step.
-            st = inspect.stack()
-            for i in xrange(len(st)):
-                locs = inspect.stack()[i][0].f_locals
-                keys = locs.keys()
-                for key in keys:
-                    val = locs[key]
-                    if isinstance(val, RecurrentLayerInput):
-                        begin_of_current_rnn.append(val)
-                    elif isinstance(val, collections.Sequence):
-                        for v in val:
-                            if isinstance(v, RecurrentLayerInput):
-                                begin_of_current_rnn.append(v)
-
-                if begin_of_current_rnn:
-                    break
-            assert begin_of_current_rnn is not None
-            for extra in begin_of_current_rnn:
-                self.append_extra_parent(extra)
-                extra.append_extra_parent(kwargs['boot_layer'])
-                self.__boot_layer_name__ = kwargs['boot_layer'].name
-
-    def to_proto_impl(self, **kwargs):
-        args = dict()
-        for each in kwargs:
-            args[each] = kwargs[each]
-        for each in self.__kwargs__:
-            args[each] = self.__kwargs__[each]
-
-        if self.__boot_layer_name__ is not None:
-            args['boot_layer'] = self.__context__[self.__boot_layer_name__]
-
-        size = args.get('size', None)
-        if size is not None:
-            if callable(size):
-                real_size = size()
-            else:
-                real_size = size
-            args['size'] = real_size
-        return conf_helps.memory(name=self.name, **args)
-
-    def context_name(self):
-        return self.name + "#memory"
-
-    def use_context_name(self):
-        """
-        memory layer will have the same name with some layer
-        :return:
-        """
-        return True
-
-
-class StaticInputV2(object):
-    def __init__(self, input, is_seq=False, size=None):
-        assert isinstance(input, LayerV2)
-        self.name = input.name
-        self.input = input
-        self.is_seq = is_seq
-        self.size = size
-        # TODO(add size check)
-        # assert input.size is not None or size is not None
-
-
-class BaseGeneratedInputV2(object):
-    def __init__(self):
-        self.bos_id = None
-        self.eos_id = None
-
-    def before_real_step(self):
-        raise NotImplementedError()
-
-    def after_real_step(self, *args):
-        raise NotImplementedError()
-
-
-class GeneratedInputV2(BaseGeneratedInputV2):
-    def __init__(self, size, embedding_name, embedding_size):
-        super(GeneratedInputV2, self).__init__()
-        self.size = size
-        self.embedding_name = embedding_name
-        self.embedding_size = embedding_size
-
-    def after_real_step(self, input):
-        return max_id(input=input, name='__beam_search_predict__')
-
-    def before_real_step(self):
-        predict_id = memory(
-            name='__beam_search_predict__',
-            size=self.size,
-            boot_with_const_id=self.bos_id)
-
-        trg_emb = embedding(
-            input=predict_id,
-            size=self.embedding_size,
-            param_attr=attr.ParamAttr(name=self.embedding_name))
-        return trg_emb
-
-
-class RecurrentLayerGroupSetGeneratorV2(Layer):
-    def __init__(self, eos_name, max_length, beam_size, num_results_per_sample):
-        self.eos_name = eos_name
-        self.max_length = max_length
-        self.beam_size = beam_size
-        self.num_results_per_sample = num_results_per_sample
-        super(RecurrentLayerGroupSetGeneratorV2, self).__init__(
-            name=eos_name, parent_layers={})
-
-    def to_proto_impl(self, **kwargs):
-        RecurrentLayerGroupSetGenerator(
-            Generator(
-                eos_layer_name=self.eos_name,
-                max_num_frames=self.max_length,
-                beam_size=self.beam_size,
-                num_results_per_sample=self.num_results_per_sample))
-        return self
-
-    def context_name(self):
-        return self.eos_name + ".fake"
-
-    def use_context_name(self):
-        return True
-
-
-class MixedLayerV2(Layer):
-    """
-    This class is use to support `with` grammar. If not, the following code
-    could convert mixed_layer simply.
-
-        mixed = __convert_to_v2__(
-            'mixed_layer', name_prefix='mixed', parent_names=['input'])
-    """
-
-    class AddToSealedMixedLayerExceptionV2(Exception):
-        pass
-
-    def __init__(self,
-                 size=0,
-                 input=None,
-                 name=None,
-                 act=None,
-                 bias_attr=None,
-                 layer_attr=None):
-        self.__method_name__ = 'mixed_layer'
-        self.finalized = False
-        self.__inputs__ = []
-        if input is not None:
-            self.__inputs__ = input
-
-        other_kwargs = dict()
-        other_kwargs['name'] = name
-        other_kwargs['size'] = size
-        other_kwargs['act'] = act
-        other_kwargs['bias_attr'] = bias_attr
-        other_kwargs['layer_attr'] = layer_attr
-        parent_layers = {"input": self.__inputs__}
-        super(MixedLayerV2, self).__init__(name, parent_layers)
-        self.__other_kwargs__ = other_kwargs
-
-    def __iadd__(self, other):
-        if not self.finalized:
-            self.__inputs__.append(other)
-            return self
-        else:
-            raise MixedLayerV2.AddToSealedMixedLayerExceptionV2()
-
-    def __enter__(self):
-        assert len(self.__inputs__) == 0
-        return self
-
-    def __exit__(self, *args, **kwargs):
-        self.finalized = True
-
-    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]
-        size = args.get('size', None)
-        if size is not None:
-            if callable(size):
-                real_size = size()
-            else:
-                real_size = size
-            args['size'] = real_size
-        return getattr(conf_helps, self.__method_name__)(**args)
-
-
-@wrap_name_default("mixed")
-@wrap_act_default(act=activation.Linear())
-@wrap_bias_attr_default(has_bias=False)
-@layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT)
-def mixed(size=0,
-          name=None,
-          input=None,
-          act=None,
-          bias_attr=False,
-          layer_attr=None):
-    return MixedLayerV2(size, input, name, act, bias_attr, layer_attr)
-
-
-mixed.__doc__ = conf_helps.mixed_layer.__doc__
-
-
-class RecurrentLayerInput(Layer):
-    def __init__(self, recurrent_name, index, parent_layers, reverse):
-        parents_len = len(parent_layers)
-        assert parents_len <= 1
-        if parents_len == 0:
-            self.__parents__ = []
-        else:
-            self.__parents__ = parent_layers.values()[0]
-        self.__recurrent_name__ = recurrent_name
-        self.__reverse__ = reverse
-        name = self.__parents__[
-            index].name if index >= 0 else self.context_name()
-        super(RecurrentLayerInput, self).__init__(
-            name=name, parent_layers=parent_layers)
-
-    def context_name(self):
-        return self.__recurrent_name__ + ".begin"
-
-    def to_proto_impl(self, **kwargs):
-        model_type('recurrent_nn')
-        RecurrentLayerGroupWithoutOutLinksBegin(
-            name=self.__recurrent_name__,
-            in_links=map(lambda x: x.name, self.__parents__),
-            seq_reversed=self.__reverse__)
-        return self
-
-
-class RecurrentLayerOutput(Layer):
-    def __init__(self, recurrent_name, index, parent_layers):
-        assert len(parent_layers) == 1
-        self.__parents__ = parent_layers.values()[0]
-        super(RecurrentLayerOutput, self).__init__(
-            name=self.__parents__[index].name, parent_layers=parent_layers)
-        self.__recurrent_name__ = recurrent_name
-
-    def context_name(self):
-        return self.__recurrent_name__ + ".end"
-
-    def to_proto_impl(self, **kwargs):
-        for l in self.__parents__:
-            RecurrentLayerGroupSetOutLink(l.name)
-        RecurrentLayerGroupEnd(name=self.__recurrent_name__)
-
-
-LayerV2 = Layer
-data = DataLayerV2
-data.__name__ = 'data'
-AggregateLevel = conf_helps.AggregateLevel
-ExpandLevel = conf_helps.ExpandLevel
-memory = MemoryV2
-memory.__name__ = 'memory'
-memory.__doc__ = conf_helps.memory.__doc__
-
-
-def __layer_name_mapping__(inname):
-    if inname in ['data_layer', 'memory', 'mixed_layer', 'recurrent_group']:
-        # Do Not handle these layers
-        return
-    elif inname == 'maxid_layer':
+def __convert_name__(inname):
+    if inname == 'maxid_layer':
         return 'max_id'
     elif inname.endswith('memory') or inname.endswith(
             '_seq') or inname.endswith('_sim') or inname == 'hsigmoid':
@@ -431,187 +78,202 @@ def __layer_name_mapping__(inname):
         return inname
     elif inname.endswith("_layer"):
         return inname[:-len("_layer")]
+    else:
+        return inname
 
 
-def __layer_name_mapping_parent_names__(inname):
-    all_args = getattr(conf_helps, inname).argspec.args
-    return filter(
-        lambda x: x in ['input1', 'input2', 'label', 'input', 'a', 'b',
-                        'expand_as',
-                        'weights', 'vectors', 'weight', 'score', 'left',
-                        'right', 'output_mem'],
-        all_args)
-
-
-def __convert_layer__(_new_name_, _old_name_, _parent_names_):
-    global __all__
-    __all__.append(_new_name_)
-    globals()[new_name] = __convert_to_v2__(_old_name_, _parent_names_)
-    globals()[new_name].__name__ = new_name
-
-
-for each_layer_name in dir(conf_helps):
-    new_name = __layer_name_mapping__(each_layer_name)
-    if new_name is not None:
-        parent_names = __layer_name_mapping_parent_names__(each_layer_name)
-        assert len(parent_names) != 0, each_layer_name
-        __convert_layer__(new_name, each_layer_name, parent_names)
-
-del parent_names
-del new_name
-del each_layer_name
-
-
-@wrap_name_default()
-def recurrent_group(step, input, reverse=False, name=None):
-    if not isinstance(input, collections.Sequence):
-        input = [input]
-
-    non_static_inputs = filter(lambda x: not isinstance(x, StaticInputV2),
-                               input)
-    actual_input = [
-        RecurrentLayerInput(
-            recurrent_name=name,
-            index=i,
-            parent_layers={'recurrent_inputs': non_static_inputs},
-            reverse=reverse) for i in xrange(len(non_static_inputs))
-    ]
-
-    extra_input = None
-    if len(non_static_inputs) == 0:
-        extra_input = RecurrentLayerInput(
-            recurrent_name=name, index=-1, parent_layers={}, reverse=reverse)
-
-    def __real_step__(*args):
-        rnn_input = list(args)
-        static_inputs = filter(lambda x: isinstance(x, StaticInputV2), input)
-        for static_input in static_inputs:
-            mem_name = "__%s_memory__" % static_input.input.name
-            mem = memory(
-                name=mem_name,
-                extra_input=extra_input,
-                is_seq=static_input.is_seq,
-                size=static_input.input.calculate_size,
-                boot_layer=static_input.input)
-            with mixed(
-                    name=mem_name,
-                    size=static_input.input.calculate_size,
-                    act=activation.Identity()) as mix:
-                mix += identity_projection(input=mem)
-            rnn_input.insert(input.index(static_input), mix)
-        return step(*rnn_input)
-
-    actual_output = __real_step__(*actual_input)
-
-    if not isinstance(actual_output, collections.Sequence):
-        actual_output = [actual_output]
-
-    retv = [
-        RecurrentLayerOutput(
-            recurrent_name=name,
-            index=i,
-            parent_layers={'recurrent_outputs': actual_output})
-        for i in xrange(len(actual_output))
-    ]
-    if len(retv) == 1:
-        return retv[0]
+for name in v1_layers.__all__:
+    obj = getattr(v1_layers, name)
+    if not __need_to_keep__(name):
+        continue
+    new_name = __convert_name__(name)
+    if callable(obj) and __need_to_wrap__(name):
+        globals()[new_name] = __wrap__(obj)
     else:
-        return retv
-
-
-recurrent_group.__doc__ = conf_helps.recurrent_group.__doc__
-
-
-@wrap_name_default()
-def beam_search(step,
-                input,
-                bos_id,
-                eos_id,
-                beam_size,
-                max_length=500,
-                name=None,
-                num_results_per_sample=None):
-    if num_results_per_sample is None:
-        num_results_per_sample = beam_size
-    assert num_results_per_sample <= beam_size
-    # logger.warning("num_results_per_sample should be less than beam_size")
-
-    if isinstance(input, StaticInputV2) or isinstance(input,
-                                                      BaseGeneratedInputV2):
-        input = [input]
-
-    generated_input_index = -1
-
-    real_input = []
-    for i, each_input in enumerate(input):
-        assert isinstance(each_input, StaticInputV2) or isinstance(
-            each_input, BaseGeneratedInputV2)
-        if isinstance(each_input, BaseGeneratedInputV2):
-            assert generated_input_index == -1
-            generated_input_index = i
-        else:
-            real_input.append(each_input)
+        globals()[new_name] = obj
+    __all__.append(new_name)
+
+
+def __data_layer__(name, type, **kwargs):
+    l = v1_layers.data_layer(name, type.dim, **kwargs)
+    l.data_type = type
+    return l
 
-    assert generated_input_index != -1
+data = __wrap__(__data_layer__)
 
-    gipt = input[generated_input_index]
-    assert isinstance(gipt, BaseGeneratedInputV2)
+LayerV2 = v1_layers.LayerOutput
 
-    gipt.bos_id = bos_id
-    gipt.eos_id = eos_id
 
-    def __real_step__(*args):
-        eos_name = "__%s_eos_layer__" % name
-        generator = RecurrentLayerGroupSetGeneratorV2(
-            eos_name, max_length, beam_size, num_results_per_sample)
+def __get_used_layers__(output_layers, extra_layers=None):
+    layer_names = set()
+    parents = {}
+    def add_parent(child, parent):
+        if child in parents:
+            parents[child].append(parent)
+        else:
+            parents[child] = [parent]
+
+    def add_additional_parents():
+        for sub_model in cp.g_config.model_config.sub_models:
+            if sub_model.name == 'root':
+                continue
+            for link in sub_model.in_links:
+                add_parent(link.link_name, link.layer_name)
+                add_parent(sub_model.name, link.layer_name)
+            for link in sub_model.out_links:
+                add_parent(link.link_name, link.layer_name)
+                add_parent(link.link_name, sub_model.name)
+            for mem in sub_model.memories:
+                if mem.boot_layer_name:
+                    add_parent(mem.layer_name, mem.boot_layer_name)
+                add_parent(mem.link_name, mem.layer_name)
+
+    def dfs_travel(layer_name):
+        if layer_name in layer_names:
+            return
+        layer_names.add(layer_name)
+        layer = cp.g_layer_map[layer_name]
+
+        for inp in layer.inputs:
+            dfs_travel(inp.input_layer_name)
+        if layer.name in parents:
+            for p in parents[layer.name]:
+                dfs_travel(p)
+
+    add_additional_parents()
+
+    for layer in output_layers:
+        dfs_travel(layer.full_name)
+
+    return layer_names
+
+
+def __get_used_parameters__(layer_names):
+    parameter_names = set()
+    for name in layer_names:
+        l = cp.g_layer_map[name]
+        for inp in l.inputs:
+            if inp.input_parameter_name:
+                parameter_names.add(inp.input_parameter_name)
+        if l.bias_parameter_name:
+            parameter_names.add(l.bias_parameter_name)
+    return parameter_names
+
+
+def __get_used_submodels__(layer_names):
+    submodel_names = set()
+    for submodel in cp.g_config.model_config.sub_models:
+        if submodel.name in layer_names:
+            submodel_names.add(submodel.name)
+    return submodel_names
+
+
+def __get_used_evaluators__(layer_names):
+    evaluator_names = set()
+    for e in cp.g_config.model_config.evaluators:
+        used = True
+        for name in e.input_layers:
+            if name not in layer_names:
+                used = False
+                break
+        if used:
+            evaluator_names.add(e.name)
+    return evaluator_names
+
+
+def __trim_submodel__(old_submodel,
+                      layer_names,
+                      input_layer_names,
+                      output_layer_names,
+                      evaluator_names):
+
+    submodel = SubModelConfig()
+    submodel.name = old_submodel.name
+    submodel.layer_names.extend(filter(lambda x: x in layer_names,
+                                       old_submodel.layer_names))
+    submodel.input_layer_names.extend(filter(lambda x: x in input_layer_names,
+                                             submodel.layer_names))
+    submodel.output_layer_names.extend(filter(lambda x: x in output_layer_names,
+                                              submodel.layer_names))
+    submodel.evaluator_names.extend(filter(lambda x: x in evaluator_names,
+                                           old_submodel.evaluator_names))
+
+    submodel.is_recurrent_layer_group = old_submodel.is_recurrent_layer_group
+    submodel.reversed = old_submodel.reversed
+
+    submodel.memories.extend(filter(lambda x: x.link_name in layer_names,
+                                    old_submodel.memories))
+    target_inlinkid = (old_submodel.target_inlinkid
+                       if old_submodel.HasField('target_inlinkid') else -1)
+    in_links = []
+    for i, link in enumerate(old_submodel.in_links):
+        if link.link_name in layer_names or i == target_inlinkid:
+            in_links.append(link)
+            if i == target_inlinkid:
+                target_inlinkid = len(in_links) - 1
+    submodel.in_links.extend(in_links)
+
+    submodel.out_links.extend(filter(lambda x: x.link_name in layer_names,
+                                     old_submodel.out_links))
+    if old_submodel.HasField('generator'):
+        submodel.generator.CopyFrom(old_submodel.generator)
+
+    if old_submodel.HasField('target_inlinkid'):
+        submodel.target_inlinkid = target_inlinkid
+    return submodel
 
-        args = list(args)
-        before_step_layer = gipt.before_real_step()
-        before_step_layer.append_child(
-            layer=generator, parent_names=[before_step_layer.name])
-        args.insert(generated_input_index, before_step_layer)
 
-        predict = gipt.after_real_step(step(*args))
+def parse_network(output_layers, extra_layers=None):
+    if not isinstance(output_layers, collections.Sequence):
+        output_layers = [output_layers]
+    if extra_layers is not None and not isinstance(extra_layers,
+                                                   collections.Sequence):
+        extra_layers = [extra_layers]
+    else:
+        extra_layers = []
 
-        eos_layer = eos(input=predict, eos_id=eos_id, name=eos_name)
-        predict.append_child(layer=eos_layer, parent_names=[predict.name])
+    layer_names = __get_used_layers__(output_layers + extra_layers)
+    submodel_names = __get_used_submodels__(layer_names)
+    submodel_names.add('root')
+    parameter_names = __get_used_parameters__(layer_names)
+    evaluator_names = __get_used_evaluators__(layer_names)
+    input_layer_names = set()
+    output_layer_names = set()
 
-        return predict
+    model_config = ModelConfig()
+    model_config.type = cp.g_config.model_config.type
+    for l in cp.g_config.model_config.layers:
+        if l.name not in layer_names:
+            continue
+        model_config.layers.extend([l])
+        if l.type == 'data':
+            model_config.input_layer_names.append(l.name)
+            input_layer_names.add(l.name)
 
-    # tmp = paddle.layer.recurrent_group(
-    #     step=__real_step__,
-    #     input=real_input,
-    #     reverse=False,
-    #     name=name,
-    #     is_generating=True)
-    tmp = recurrent_group(step=__real_step__, input=real_input, name=name)
+    for p in cp.g_config.model_config.parameters:
+        if p.name in parameter_names:
+            model_config.parameters.extend([p])
 
-    return tmp
+    for layer in output_layers:
+        model_config.output_layer_names.append(layer.full_name)
+        output_layer_names.add(layer.full_name)
 
+    for e in cp.g_config.model_config.evaluators:
+        if e.name in evaluator_names:
+            model_config.evaluators.extend([e])
 
-beam_search.__doc__ = conf_helps.beam_search.__doc__
+    for s in cp.g_config.model_config.sub_models:
+        if s.name in submodel_names:
+            s = __trim_submodel__(
+                s, layer_names, input_layer_names, output_layer_names,
+                evaluator_names)
+            model_config.sub_models.extend([s])
 
-__projection_names__ = filter(lambda x: x.endswith('_projection'),
-                              dir(conf_helps))
+    return model_config
 
-__all__ += __projection_names__
 
-__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps))
-__all__ += __operator_names__
+def get_layer(name):
+    return __layer_map__.get(name)
 
-# convert projection
-for prj in __projection_names__:
-    globals()[prj] = __convert_to_v2__(
-        prj, parent_names=['input'], is_default_name=False)
-    globals()[prj].__name__ = prj
 
-# convert operator
-operator_list = [
-    # [V1_method_name, parent_names],
-    ['dotmul_operator', ['a', 'b']],
-    ['conv_operator', ['img', 'filter']]
-]
-for op in operator_list:
-    globals()[op[0]] = __convert_to_v2__(
-        op[0], parent_names=op[1], is_default_name=False)
-    globals()[op[0]].__name__ = op[0]
+cp.begin_parse()

python/paddle/v2/networks.py

@@ -24,20 +24,7 @@ def __initialize__():
         if each_subnetwork in ['inputs', 'outputs']:
             continue
         func = getattr(conf_nw, each_subnetwork)
-        if hasattr(func, 'argspec'):
-            argspec = func.argspec
-        else:
-            argspec = inspect.getargspec(func)
-        if each_subnetwork == 'simple_attention':
-            parents = ['encoded_sequence', 'encoded_proj', 'decoder_state']
-        else:
-            parents = filter(lambda x: x.startswith('input'), argspec.args)
-        assert len(parents) != 0, each_subnetwork
-        v2_subnet = __convert_to_v2__(
-            each_subnetwork,
-            parent_names=parents,
-            is_default_name='name' in argspec.args)
-        globals()[each_subnetwork] = v2_subnet
+        globals()[each_subnetwork] = func
         globals()[each_subnetwork].__name__ = each_subnetwork
         global __all__
         __all__.append(each_subnetwork)

python/paddle/v2/tests/test_layer.py

@@ -173,9 +173,9 @@ class OtherLayerTest(unittest.TestCase):
 
 class ProjOpTest(unittest.TestCase):
     def test_projection(self):
-        input = layer.data(name='data', type=data_type.dense_vector(784))
+        input = layer.data(name='data2', type=data_type.dense_vector(784))
         word = layer.data(
-            name='word', type=data_type.integer_value_sequence(10000))
+            name='word2', type=data_type.integer_value_sequence(10000))
         fc0 = layer.fc(input=input, size=100, act=activation.Sigmoid())
         fc1 = layer.fc(input=input, size=200, act=activation.Sigmoid())
         mixed0 = layer.mixed(
@@ -204,8 +204,8 @@ class ProjOpTest(unittest.TestCase):
             dotmul1 += dotmul
 
         context = layer.context_projection(input=fc0, context_len=5)
-        context0 = layer.mixed(size=100, input=context)
-        with layer.mixed(size=100) as context1:
+        context0 = layer.mixed(size=500, input=context)
+        with layer.mixed(size=500) as context1:
             context1 += context
 
         conv = layer.conv_projection(
@@ -231,8 +231,8 @@ class ProjOpTest(unittest.TestCase):
         print layer.parse_network(conv1)
 
     def test_operator(self):
-        ipt0 = layer.data(name='data', type=data_type.dense_vector(784))
-        ipt1 = layer.data(name='word', type=data_type.dense_vector(128))
+        ipt0 = layer.data(name='data1', type=data_type.dense_vector(784))
+        ipt1 = layer.data(name='word1', type=data_type.dense_vector(128))
         fc0 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
         fc1 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
 
@@ -261,7 +261,7 @@ class ProjOpTest(unittest.TestCase):
 
 class NetworkTests(unittest.TestCase):
     def test_vgg(self):
-        img = layer.data(name='pixel', type=data_type.dense_vector(784))
+        img = layer.data(name='pixel1', type=data_type.dense_vector(784))
         vgg_out = networks.small_vgg(
             input_image=img, num_channels=1, num_classes=2)
         print layer.parse_network(vgg_out)
@@ -269,12 +269,12 @@ class NetworkTests(unittest.TestCase):
 
 class EvaluatorTest(unittest.TestCase):
     def test_evaluator(self):
-        img = layer.data(name='pixel', type=data_type.dense_vector(784))
+        img = layer.data(name='pixel2', type=data_type.dense_vector(784))
         output = layer.fc(input=img,
                           size=10,
                           act=activation.Softmax(),
                           name='fc_here')
-        lbl = layer.data(name='label', type=data_type.integer_value(10))
+        lbl = layer.data(name='label2', type=data_type.integer_value(10))
         cost = layer.cross_entropy_cost(input=output, label=lbl)
 
         evaluator.classification_error(input=output, label=lbl)

python/paddle/v2/tests/test_rnn_layer.py

@@ -20,6 +20,8 @@ import paddle.v2.data_type as data_type
 import paddle.v2.layer as layer
 from paddle.trainer_config_helpers.config_parser_utils import \
     parse_network_config as parse_network
+from paddle.trainer_config_helpers.config_parser_utils import \
+    reset_parser
 
 
 class RNNTest(unittest.TestCase):
@@ -29,6 +31,7 @@ class RNNTest(unittest.TestCase):
         hidden_dim = 8
 
         def parse_old_rnn():
+            reset_parser()
             def step(y):
                 mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
                 out = conf_helps.fc_layer(
@@ -48,6 +51,7 @@ class RNNTest(unittest.TestCase):
             return str(parse_network(test))
 
         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],
@@ -68,6 +72,7 @@ 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
@@ -75,6 +80,7 @@ class RNNTest(unittest.TestCase):
         label_dim = 3
 
         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)
@@ -114,6 +120,7 @@ class RNNTest(unittest.TestCase):
             return str(parse_network(test))
 
         def parse_new_rnn():
+            reset_parser()
             data = layer.data(
                 name="word", type=data_type.dense_vector(dict_dim))
             label = layer.data(

python/paddle/v2/tests/test_topology.py

@@ -46,8 +46,8 @@ class TestTopology(unittest.TestCase):
         self.assertEqual(label_data_type[1].dim, 10)
 
     def test_get_layer(self):
-        pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
-        label = layer.data(name='label', type=data_type.integer_value(10))
+        pixel = layer.data(name='pixel2', type=data_type.dense_vector(784))
+        label = layer.data(name='label2', type=data_type.integer_value(10))
         hidden = layer.fc(input=pixel,
                           size=100,
                           act=conf_helps.SigmoidActivation())
@@ -56,14 +56,14 @@ class TestTopology(unittest.TestCase):
                              act=conf_helps.SoftmaxActivation())
         cost = layer.classification_cost(input=inference, label=label)
         topo = topology.Topology(cost)
-        pixel_layer = topo.get_layer("pixel")
-        label_layer = topo.get_layer("label")
+        pixel_layer = topo.get_layer("pixel2")
+        label_layer = topo.get_layer("label2")
         self.assertEqual(pixel_layer, pixel)
         self.assertEqual(label_layer, label)
 
     def test_parse(self):
-        pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
-        label = layer.data(name='label', type=data_type.integer_value(10))
+        pixel = layer.data(name='pixel3', type=data_type.dense_vector(784))
+        label = layer.data(name='label3', type=data_type.integer_value(10))
         hidden = layer.fc(input=pixel,
                           size=100,
                           act=conf_helps.SigmoidActivation())

python/paddle/v2/topology.py

@@ -15,7 +15,7 @@
 import collections
 
 from paddle.proto.ModelConfig_pb2 import ModelConfig
-
+import paddle.trainer_config_helpers as conf_helps
 import layer as v2_layer
 
 __all__ = ['Topology']
@@ -94,31 +94,18 @@ class Topology(object):
         :param name:
         :return:
         """
-        result_layer = [None]
-
-        def __impl__(l):
-            if l.name == name:
-                result_layer[0] = l
-                return True  # break
-            return False
-
-        __bfs_travel__(__impl__, *self.layers)
-        if result_layer[0] is None:
-            raise ValueError("No such layer %s" % name)
-        return result_layer[0]
+        return v2_layer.get_layer(name)
 
     def data_layers(self):
         """
         get all data layer
         :return:
         """
-        data_layers = dict()
-
-        def __impl__(l):
-            if isinstance(l, v2_layer.DataLayerV2):
-                data_layers[l.name] = l
-
-        __bfs_travel__(__impl__, *self.layers)
+        data_layers = {}
+        for layer in self.proto().layers:
+            l = v2_layer.get_layer(layer.name)
+            if l and l.layer_type == conf_helps.LayerType.DATA:
+                data_layers[layer.name] = l
         return data_layers
 
     def data_type(self):
@@ -127,7 +114,7 @@ class Topology(object):
         [('image', dense_vector(768)), ('label', integer_value(10))]
         """
         data_layers = self.data_layers()
-        return [(nm, data_layers[nm].type)
+        return [(nm, data_layers[nm].data_type)
                 for nm in self.proto().input_layer_names]
 
     def get_layer_proto(self, name):