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502 lines
16 KiB
502 lines
16 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Before this new package paddle.v2.layer, users would need to use functions
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in paddle.trainer_config_helpers.layers to configure networks.
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The Old Way:
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=========
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This old way requires that the creation of a network be defined in a Python
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function, say network_config, and that this Python function being passed to
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paddle.trainer_config_helpers.parse_network_config for the creation of
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protobuf message description of this network.
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```python
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def network_config():
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img = paddle.trainer_config_helpers.data_layer(name="pixel", size=784)
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inference = paddle.trainer_config_helpers.fc_layer(
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input=img,
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size=10,
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act=paddle.trainer_config_helpers.SoftmaxActivation())
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cost = paddle.trainer_config_helpers.classification_cost(
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input=inference,
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label=paddle.trainer_config_helpers.data_layer(name="label", size=10))
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proto_desc = parse_network_config(network_config)
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```
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When parse_network_config executes network_config, those layer definition
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functions like data_layer and fc_layer would change some Python global variables,
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so that after the execution, parse_network_config could collect information from
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these global variables and generates the protobuf message.
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The New Way:
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=========
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In this PR, we define a function in paddle.v2.layer which creates a Python
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class for each layer creation function in paddle.trainer_config_helpers.layers.
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Users can use create a network as follows:
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```python
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img = paddle.v2.layer.data(name="pixel", size=784)
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inference = paddle.v2.layer.fc(input=img, size=10, act=paddle.v2.layer.Softmax())
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cost = paddle.v2.layer.classification(
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input=inference,
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label=paddle.v2.layer.data(name="label", size=10))
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parameters = paddle.v2.parameters.create(cost)
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```
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This new way doesn't require those invocations to layer definition functions
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to be in a Python function but could be anywhere.
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Also, the creation of a protobuf message is hidden in the invocation of
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paddle.v2.parameters.create, no longer exposed to users.
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"""
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import collections
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import inspect
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from config_base import Layer, __convert_to_v2__
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import paddle.trainer_config_helpers as conf_helps
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from paddle.trainer_config_helpers.config_parser_utils import \
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parse_network_config as __parse__
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from paddle.trainer_config_helpers.default_decorators import wrap_act_default
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from paddle.trainer_config_helpers.default_decorators import \
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wrap_bias_attr_default
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from paddle.trainer_config_helpers.default_decorators import wrap_name_default
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from paddle.trainer_config_helpers.layers import layer_support
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from paddle.trainer.config_parser import \
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RecurrentLayerGroupWithoutOutLinksBegin, RecurrentLayerGroupSetOutLink, \
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RecurrentLayerGroupEnd, model_type
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import activation
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import data_type
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__all__ = ['parse_network', 'data']
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__projection_names__ = filter(lambda x: x.endswith('_projection'),
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dir(conf_helps))
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__all__ += __projection_names__
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__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps))
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__all__ += __operator_names__
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def parse_network(*outputs):
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"""
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parse all output layers and then generate a model config proto.
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:param outputs:
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:return:
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"""
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def __real_func__():
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context = dict()
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real_output = [each.to_proto(context=context) for each in outputs]
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conf_helps.outputs(real_output)
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return __parse__(__real_func__)
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"""
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Some layer may need some special config, and can not use __convert_to_v2__ to convert.
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So we also need to implement some special LayerV2.
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"""
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class DataLayerV2(Layer):
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def __init__(self, name, type, **kwargs):
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assert isinstance(type, data_type.InputType)
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self.type = type
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self.__method_name__ = 'data_layer'
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self.__kwargs__ = kwargs
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super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
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def to_proto_impl(self, **kwargs):
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args = dict()
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args['size'] = self.type.dim
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__kwargs__:
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args[each] = self.__kwargs__[each]
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return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
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class WithExtraParent(Layer):
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def extra_parent(self):
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return self.__extra_parent__
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def __init__(self, name=None, parent_layers=None):
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self.__extra_parent__ = []
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super(WithExtraParent, self).__init__(
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name=name, parent_layers=parent_layers)
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def append_extra_parent(self, parent):
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self.__extra_parent__.append(parent)
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def to_proto(self, context):
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"""
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function to set proto attribute
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"""
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kwargs = dict()
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for p in self.__extra_parent__:
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p.to_proto(context=context)
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for layer_name in self.__parent_layers__:
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if not isinstance(self.__parent_layers__[layer_name],
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collections.Sequence):
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v1_layer = self.__parent_layers__[layer_name].to_proto(
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context=context)
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else:
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v1_layer = map(lambda x: x.to_proto(context=context),
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self.__parent_layers__[layer_name])
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kwargs[layer_name] = v1_layer
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if self.context_name() is None:
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return self.to_proto_impl(context=context, **kwargs)
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elif self.context_name() not in context:
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context[self.context_name()] = self.to_proto_impl(
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context=context, **kwargs)
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if self.use_context_name():
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return context[self.context_name()]
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else:
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return context[self.name]
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class MemoryV2(WithExtraParent):
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def __init__(self, name, **kwargs):
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self.name = name
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super(MemoryV2, self).__init__(name=name, parent_layers=dict())
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self.__kwargs__ = kwargs
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self.__boot_layer_name__ = None
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if 'boot_layer' in kwargs:
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begin_of_current_rnn = []
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# TODO(yuyang18): Fix inspect, it could be wrong when user invoke a
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# function inside step.
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st = inspect.stack()
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for i in xrange(len(st)):
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locs = inspect.stack()[i][0].f_locals
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keys = locs.keys()
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for key in keys:
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val = locs[key]
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if isinstance(val, RecurrentLayerInput):
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begin_of_current_rnn.append(val)
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elif isinstance(val, collections.Sequence):
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for v in val:
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if isinstance(v, RecurrentLayerInput):
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begin_of_current_rnn.append(v)
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if begin_of_current_rnn:
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break
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assert begin_of_current_rnn is not None
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for extra in begin_of_current_rnn:
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self.append_extra_parent(extra)
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assert isinstance(extra, WithExtraParent)
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extra.append_extra_parent(kwargs['boot_layer'])
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self.__boot_layer_name__ = kwargs['boot_layer'].name
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def to_proto_impl(self, context, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__kwargs__:
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args[each] = self.__kwargs__[each]
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if self.__boot_layer_name__ is not None:
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args['boot_layer'] = context[self.__boot_layer_name__]
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size = args.get('size', None)
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if size is not None:
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if callable(size):
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real_size = size()
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else:
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real_size = size
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args['size'] = real_size
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return conf_helps.memory(name=self.name, **args)
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def context_name(self):
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return self.name + "#memory"
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def use_context_name(self):
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"""
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memory layer will have the same name with some layer
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:return:
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"""
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return True
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class LayerOutputV2(Layer):
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"""
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LayerOutputV2 is used to store the result of LayerOutput in v1 api.
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It will not store it's parents because layer_output has been parsed already.
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"""
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def __init__(self, layer_output):
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assert isinstance(layer_output, conf_helps.LayerOutput)
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self.layer_output = layer_output
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super(LayerOutputV2, self).__init__(
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name=layer_output.name, parent_layers=dict())
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def to_proto_impl(self):
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return self.layer_output
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class StaticInputV2(object):
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def __init__(self, input, is_seq=False, size=None):
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assert isinstance(input, LayerV2)
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self.name = input.name
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self.input = input
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self.is_seq = is_seq
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self.size = size
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# TODO(qiaolongfei): add size
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# assert input.size is not None or size is not None
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class MixedLayerV2(Layer):
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"""
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This class is use to support `with` grammar. If not, the following code
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could convert mixed_layer simply.
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mixed = __convert_to_v2__(
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'mixed_layer', name_prefix='mixed', parent_names=['input'])
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"""
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class AddToSealedMixedLayerExceptionV2(Exception):
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pass
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def __init__(self,
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size=0,
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input=None,
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name=None,
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act=None,
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bias_attr=None,
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layer_attr=None):
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self.__method_name__ = 'mixed_layer'
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self.finalized = False
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self.__inputs__ = []
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if input is not None:
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self.__inputs__ = input
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other_kwargs = dict()
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other_kwargs['name'] = name
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other_kwargs['size'] = size
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other_kwargs['act'] = act
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other_kwargs['bias_attr'] = bias_attr
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other_kwargs['layer_attr'] = layer_attr
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parent_layers = {"input": self.__inputs__}
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super(MixedLayerV2, self).__init__(name, parent_layers)
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self.__other_kwargs__ = other_kwargs
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def __iadd__(self, other):
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if not self.finalized:
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self.__inputs__.append(other)
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return self
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else:
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raise MixedLayerV2.AddToSealedMixedLayerExceptionV2()
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def __enter__(self):
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assert len(self.__inputs__) == 0
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return self
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def __exit__(self, *args, **kwargs):
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self.finalized = True
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def to_proto_impl(self, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__other_kwargs__:
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args[each] = self.__other_kwargs__[each]
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size = args.get('size', None)
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if size is not None:
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if callable(size):
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real_size = size()
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else:
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real_size = size
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args['size'] = real_size
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return getattr(conf_helps, self.__method_name__)(**args)
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@wrap_name_default("mixed")
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@wrap_act_default(act=activation.Linear())
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@wrap_bias_attr_default(has_bias=False)
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@layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT)
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def mixed(size=0,
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name=None,
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input=None,
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act=None,
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bias_attr=False,
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layer_attr=None):
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return MixedLayerV2(size, input, name, act, bias_attr, layer_attr)
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class RecurrentLayerInput(WithExtraParent):
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def __init__(self, recurrent_name, index, parent_layers):
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assert len(parent_layers) == 1
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self.__parents__ = parent_layers.values()[0]
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super(RecurrentLayerInput, self).__init__(
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name=self.__parents__[index].name, parent_layers=parent_layers)
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self.__recurrent_name__ = recurrent_name
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def context_name(self):
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return self.__recurrent_name__ + ".begin"
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def to_proto_impl(self, context, **kwargs):
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model_type('recurrent_nn')
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RecurrentLayerGroupWithoutOutLinksBegin(
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name=self.__recurrent_name__,
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in_links=map(lambda x: x.name, self.__parents__))
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return self
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class RecurrentLayerOutput(Layer):
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def __init__(self, recurrent_name, index, parent_layers):
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assert len(parent_layers) == 1
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self.__parents__ = parent_layers.values()[0]
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super(RecurrentLayerOutput, self).__init__(
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name=self.__parents__[index].name, parent_layers=parent_layers)
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self.__recurrent_name__ = recurrent_name
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def context_name(self):
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return self.__recurrent_name__ + ".end"
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def to_proto_impl(self, **kwargs):
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for l in self.__parents__:
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RecurrentLayerGroupSetOutLink(l.name)
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RecurrentLayerGroupEnd(name=self.__recurrent_name__)
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LayerV2 = Layer
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data = DataLayerV2
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AggregateLevel = conf_helps.layers.AggregateLevel
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ExpandLevel = conf_helps.layers.ExpandLevel
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memory = MemoryV2
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def __layer_name_mapping__(inname):
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if inname in ['data_layer', 'memory', 'mixed_layer', 'recurrent_group']:
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# Do Not handle these layers
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return
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elif inname == 'maxid_layer':
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return 'max_id'
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elif inname.endswith('memory') or inname.endswith(
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'_seq') or inname.endswith('_sim') or inname == 'hsigmoid':
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return inname
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elif inname in [
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'cross_entropy', 'multi_binary_label_cross_entropy',
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'cross_entropy_with_selfnorm'
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]:
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return inname + "_cost"
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elif inname.endswith('_cost'):
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return inname
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elif inname.endswith("_layer"):
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return inname[:-len("_layer")]
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def __layer_name_mapping_parent_names__(inname):
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all_args = getattr(conf_helps, inname).argspec.args
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return filter(
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lambda x: x in ['input1', 'input2', 'label', 'input', 'a', 'b',
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'expand_as',
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'weights', 'vectors', 'weight', 'score', 'left',
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'right', 'output_mem'],
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all_args)
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def __convert_layer__(_new_name_, _old_name_, _parent_names_):
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global __all__
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__all__.append(_new_name_)
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globals()[new_name] = __convert_to_v2__(_old_name_, _parent_names_)
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for each_layer_name in dir(conf_helps):
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new_name = __layer_name_mapping__(each_layer_name)
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if new_name is not None:
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parent_names = __layer_name_mapping_parent_names__(each_layer_name)
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assert len(parent_names) != 0, each_layer_name
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__convert_layer__(new_name, each_layer_name, parent_names)
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del parent_names
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del new_name
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del each_layer_name
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# convert projection
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for prj in __projection_names__:
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globals()[prj] = __convert_to_v2__(
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prj, parent_names=['input'], is_default_name=False)
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# convert operator
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operator_list = [
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# [V1_method_name, parent_names],
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['dotmul_operator', ['a', 'b']],
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['conv_operator', ['img', 'filter']]
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]
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for op in operator_list:
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globals()[op[0]] = __convert_to_v2__(
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op[0], parent_names=op[1], is_default_name=False)
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@wrap_name_default()
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def recurrent_group(step, input, name=None):
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if not isinstance(input, collections.Sequence):
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input = [input]
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non_static_inputs = filter(lambda x: not isinstance(x, StaticInputV2),
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input)
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actual_input = [
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RecurrentLayerInput(
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recurrent_name=name,
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index=i,
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parent_layers={'recurrent_inputs': non_static_inputs})
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for i in xrange(len(non_static_inputs))
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]
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def __real_step__(*args):
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rnn_input = list(args)
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static_inputs = filter(lambda x: isinstance(x, StaticInputV2), input)
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for static_input in static_inputs:
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mem_name = "__%s_memory__" % static_input.input.name
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mem = memory(
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name=mem_name,
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is_seq=static_input.is_seq,
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size=static_input.input.calculate_size,
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boot_layer=static_input.input)
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with mixed(
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name=mem_name,
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size=static_input.input.calculate_size,
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act=activation.Identity()) as mix:
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mix += identity_projection(input=mem)
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rnn_input.insert(input.index(static_input), mix)
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return step(*rnn_input)
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actual_output = __real_step__(*actual_input)
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if not isinstance(actual_output, collections.Sequence):
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actual_output = [actual_output]
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retv = [
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RecurrentLayerOutput(
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recurrent_name=name,
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index=i,
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parent_layers={'recurrent_outputs': actual_output})
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for i in xrange(len(actual_output))
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]
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if len(retv) == 1:
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return retv[0]
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else:
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return retv
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