refine code, remove beam_search.py

demo/seqToseq/api_train_v2.py

 import sys
+
 import paddle.v2 as paddle
-import paddle.v2.layers.beam_search as beam_search
 
 
 def seqToseq_net(source_dict_dim, target_dict_dim, is_generating):
@@ -106,13 +106,13 @@ def seqToseq_net(source_dict_dim, target_dict_dim, is_generating):
         # GeneratedInputs, which is initialized by a start mark, such as <s>,
         # and must be included in generation.
 
-        trg_embedding = beam_search.GeneratedInputV2(
+        trg_embedding = paddle.layer.GeneratedInputV2(
             size=target_dict_dim,
             embedding_name='_target_language_embedding',
             embedding_size=word_vector_dim)
         group_inputs.append(trg_embedding)
 
-        beam_gen = beam_search.beam_search(
+        beam_gen = paddle.layer.beam_search(
             name=decoder_group_name,
             step=gru_decoder_with_attention,
             input=group_inputs,

python/paddle/v2/config_base.py

@@ -87,19 +87,19 @@ class Layer(object):
         """
         self.__context__ = context
 
-        # 1. short cut if this layer is parsed before.
+        # 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]
 
-        # 2. parse extra_parent that is not used by this layer but must
+        # 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)
 
-        # 3. parse parent that is used by this layer, get the result and
+        # 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__:
@@ -112,13 +112,13 @@ class Layer(object):
                                self.__parent_layers__[layer_name])
             kwargs[layer_name] = v1_layer
 
-        # 4. parse myself and add myself into context.
-        ret_val = self.to_proto_impl(context=context, **kwargs)
-        if self.context_name() is not None and self.context_name(
-        ) not in context:
+        # 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
 
-        # 5. parse children that should be pased after this layer.
+        # STEP: parse children that should be pased after this layer.
         for layer, pnames in self.__children_layers__:
             drop = False
 
@@ -131,7 +131,7 @@ class Layer(object):
                 continue
             layer.to_proto(context=context)
 
-        # 6. return v1 layer result.g
+        # STEP: return v1 layer result
         if self.context_name() is None:
             return ret_val
         elif self.use_context_name():
@@ -139,7 +139,7 @@ class Layer(object):
         else:
             return context[self.name]
 
-    def to_proto_impl(self, context=None, **kwargs):
+    def to_proto_impl(self, **kwargs):
         raise NotImplementedError()
 
     def context_name(self):
@@ -203,7 +203,7 @@ def __convert_to_v2__(method_name,
         if wrapper is not None:
             __init__ = wrapper(__init__)
 
-        def to_proto_impl(self, context=None, **kwargs):
+        def to_proto_impl(self, **kwargs):
             args = dict()
             for each in kwargs:
                 args[each] = kwargs[each]

python/paddle/v2/layer.py

@@ -33,22 +33,25 @@ The primary usage shows below.
 
 import collections
 import inspect
-from config_base import Layer, __convert_to_v2__
+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
-from paddle.trainer.config_parser import \
-    RecurrentLayerGroupWithoutOutLinksBegin, RecurrentLayerGroupSetOutLink, \
-    RecurrentLayerGroupEnd, model_type
 
 import activation
-import re
+import attr
 import data_type
+from config_base import Layer, __convert_to_v2__
 
 __all__ = ['parse_network', 'data']
 
@@ -111,7 +114,7 @@ class DataLayerV2(Layer):
 
         super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
 
-    def to_proto_impl(self, context=None, **kwargs):
+    def to_proto_impl(self, **kwargs):
         args = dict()
         args['size'] = self.type.dim
         for each in kwargs:
@@ -134,6 +137,16 @@ class DataLayerV2(Layer):
 
 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
@@ -164,7 +177,7 @@ class MemoryV2(Layer):
                 extra.append_extra_parent(kwargs['boot_layer'])
                 self.__boot_layer_name__ = kwargs['boot_layer'].name
 
-    def to_proto_impl(self, context=None, **kwargs):
+    def to_proto_impl(self, **kwargs):
         args = dict()
         for each in kwargs:
             args[each] = kwargs[each]
@@ -172,7 +185,7 @@ class MemoryV2(Layer):
             args[each] = self.__kwargs__[each]
 
         if self.__boot_layer_name__ is not None:
-            args['boot_layer'] = context[self.__boot_layer_name__]
+            args['boot_layer'] = self.__context__[self.__boot_layer_name__]
 
         size = args.get('size', None)
         if size is not None:
@@ -205,6 +218,66 @@ class StaticInputV2(object):
         # 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
@@ -254,7 +327,7 @@ class MixedLayerV2(Layer):
     def __exit__(self, *args, **kwargs):
         self.finalized = True
 
-    def to_proto_impl(self, context=None, **kwargs):
+    def to_proto_impl(self, **kwargs):
         args = dict()
         for each in kwargs:
             args[each] = kwargs[each]
@@ -300,7 +373,7 @@ class RecurrentLayerInput(Layer):
     def context_name(self):
         return self.__recurrent_name__ + ".begin"
 
-    def to_proto_impl(self, context=None, **kwargs):
+    def to_proto_impl(self, **kwargs):
         model_type('recurrent_nn')
         RecurrentLayerGroupWithoutOutLinksBegin(
             name=self.__recurrent_name__,
@@ -319,7 +392,7 @@ class RecurrentLayerOutput(Layer):
     def context_name(self):
         return self.__recurrent_name__ + ".end"
 
-    def to_proto_impl(self, context=None, **kwargs):
+    def to_proto_impl(self, **kwargs):
         for l in self.__parents__:
             RecurrentLayerGroupSetOutLink(l.name)
         RecurrentLayerGroupEnd(name=self.__recurrent_name__)
@@ -418,8 +491,7 @@ def recurrent_group(step, input, name=None):
                     size=static_input.input.calculate_size,
                     act=activation.Identity()) as mix:
                 mix += identity_projection(input=mem)
-            mem.append_child(layer=mix, parent_names=[mem.context_name()])
-            rnn_input.insert(input.index(static_input), mem)
+            rnn_input.insert(input.index(static_input), mix)
         return step(*rnn_input)
 
     actual_output = __real_step__(*actual_input)
@@ -440,6 +512,74 @@ def recurrent_group(step, input, name=None):
         return retv
 
 
+@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)
+
+    assert generated_input_index != -1
+
+    gipt = input[generated_input_index]
+    assert isinstance(gipt, BaseGeneratedInputV2)
+
+    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)
+
+        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))
+
+        eos_layer = eos(input=predict, eos_id=eos_id, name=eos_name)
+        predict.append_child(layer=eos_layer, parent_names=[predict.name])
+
+        return predict
+
+    # 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)
+
+    return tmp
+
+
 __projection_names__ = filter(lambda x: x.endswith('_projection'),
                               dir(conf_helps))
 

python/paddle/v2/layers/__init__.py

-import beam_search

python/paddle/v2/layers/beam_search.py

-import paddle.v2 as paddle
-from paddle.v2.config_base import Layer
-from paddle.trainer_config_helpers.default_decorators import wrap_name_default
-from paddle.trainer_config_helpers.layers import RecurrentLayerGroupSetGenerator, Generator
-
-
-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 paddle.layer.max_id(input=input, name='__beam_search_predict__')
-
-    def before_real_step(self):
-        predict_id = paddle.layer.memory(
-            name='__beam_search_predict__',
-            size=self.size,
-            boot_with_const_id=self.bos_id)
-
-        trg_emb = paddle.layer.embedding(
-            input=predict_id,
-            size=self.embedding_size,
-            param_attr=paddle.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, context=None, **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
-
-
-@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, paddle.layer.StaticInputV2) or isinstance(
-            input, BaseGeneratedInputV2):
-        input = [input]
-
-    generated_input_index = -1
-
-    real_input = []
-    for i, each_input in enumerate(input):
-        assert isinstance(each_input, paddle.layer.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)
-
-    assert generated_input_index != -1
-
-    gipt = input[generated_input_index]
-    assert isinstance(gipt, BaseGeneratedInputV2)
-
-    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)
-
-        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))
-
-        eos = paddle.layer.eos(input=predict, eos_id=eos_id, name=eos_name)
-        predict.append_child(layer=eos, parent_names=[predict.name])
-
-        return predict
-
-    # tmp = paddle.layer.recurrent_group(
-    #     step=__real_step__,
-    #     input=real_input,
-    #     reverse=False,
-    #     name=name,
-    #     is_generating=True)
-    tmp = paddle.layer.recurrent_group(
-        step=__real_step__, input=real_input, name=name)
-
-    return tmp

python/setup.py.in

@@ -8,8 +8,7 @@ packages=['paddle',
           'paddle.v2',
           'paddle.v2.dataset',
           'paddle.v2.reader',
-          'paddle.v2.plot',
-          'paddle.v2.layers']
+          'paddle.v2.plot']
 
 setup(name='paddle',
       version='${PADDLE_VERSION}',