提交 0ef86cbd 编写于 作者: E emailweixu 提交者: GitHub

Merge pull request #2288 from emailweixu/fix_v2_api

Fix V2 API
......@@ -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(); }
......
......@@ -3371,7 +3371,7 @@ def make_importer(config_dir, config_args):
return Import
settings = dict(
DEFAULT_SETTING = 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_SETTING)
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))
......
......@@ -12,15 +12,18 @@
# 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 +37,15 @@ 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_SETTING)
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()
......@@ -287,6 +287,7 @@ 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]
......@@ -3491,6 +3492,11 @@ 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:
......
......@@ -14,206 +14,55 @@
import collections
import re
from paddle.trainer_config_helpers.default_decorators import wrap_name_default
import paddle.trainer_config_helpers as conf_helps
from topology import Topology
class LayerType(type):
def __new__(cls, name, bases, attrs):
method_name = attrs.get('METHOD_NAME', None)
if method_name is not None:
method = getattr(conf_helps, method_name)
if method.__doc__ is not None:
mapper = attrs.get("__map_docstr__", None)
if mapper is not None:
attrs['__doc__'] = LayerType.__map_docstr__(
mapper(method.__doc__),
method_name=method_name,
name=name)
else:
attrs['__doc__'] = LayerType.__map_docstr__(
method.__doc__, method_name=method_name, name=name)
return super(LayerType, cls).__new__(cls, name, bases, attrs)
@staticmethod
def __map_docstr__(doc, name, method_name):
assert isinstance(doc, basestring)
# replace LayerOutput to paddle.v2.config_base.Layer
doc = doc.replace("LayerOutput", "paddle.v2.config_base.Layer")
doc = doc.replace('ParameterAttribute',
'paddle.v2.attr.ParameterAttribute')
doc = re.sub(r'ExtraLayerAttribute[^\s]?',
'paddle.v2.attr.ExtraAttribute', doc)
# xxx_layer to xxx
doc = re.sub(r"(?P<name>[a-z]+)_layer", r"\g<name>", doc)
# XxxxActivation to paddle.v2.Activation.Xxxx
doc = re.sub(r"(?P<name>[A-Z][a-zA-Z]+)Activation",
r"paddle.v2.Activation.\g<name>", doc)
# TODO(yuyang18): Add more rules if needed.
__layer_map__ = {}
def __map_docstr__(doc, name):
if doc is None:
return doc
assert isinstance(doc, basestring)
# replace LayerOutput to paddle.v2.config_base.Layer
doc = doc.replace("LayerOutput", "paddle.v2.config_base.Layer")
doc = doc.replace('ParameterAttribute', 'paddle.v2.attr.ParameterAttribute')
doc = re.sub(r'ExtraLayerAttribute[^\s]?', 'paddle.v2.attr.ExtraAttribute',
doc)
# xxx_layer to xxx
doc = re.sub(r"(?P<name>[a-z]+)_layer", r"\g<name>", doc)
# XxxxActivation to paddle.v2.Activation.Xxxx
doc = re.sub(r"(?P<name>[A-Z][a-zA-Z]+)Activation",
r"paddle.v2.Activation.\g<name>", doc)
# xxx_evaluator to paddle.v2.evaluator.xxx
doc = re.sub(r"(?P<name>[a-z]+)_evaluator", r"evaluator.\g<name>", doc)
# TODO(yuyang18): Add more rules if needed.
return doc
def __convert_to_v2__(f, name, module):
def wrapped(*args, **xargs):
out = f(*args, **xargs)
outs = out
if not isinstance(out, collections.Sequence):
outs = [out]
for l in outs:
if isinstance(l, conf_helps.LayerOutput):
__layer_map__[l.full_name] = l
return out
wrapped.__doc__ = __map_docstr__(f.__doc__, name)
wrapped.__name__ = name
wrapped.__module__ = module
return wrapped
class Layer(object):
__metaclass__ = LayerType
def __init__(self, name=None, parent_layers=None):
assert isinstance(parent_layers, dict)
self.name = name
self.__context__ = {}
self.__parent_layers__ = parent_layers
# some layer may have some extra parent layer
self.__extra_parent__ = []
# used for evaluator.
self.__children_layers__ = []
def extra_parent(self):
return self.__extra_parent__
def append_extra_parent(self, parent):
self.__extra_parent__.append(parent)
def append_child(self, layer, parent_names):
self.__children_layers__.append((layer, parent_names))
def to_proto(self, context):
"""
function to set proto attribute
"""
self.__context__ = context
# STEP: short cut if this layer is parsed before.
if self.context_name() in context:
if self.use_context_name():
return context[self.context_name()]
else:
return context[self.name]
# STEP: parse extra_parent that is not used by this layer but must
# be parsed before this layer.
for p in self.__extra_parent__:
p.to_proto(context=context)
# STEP: parse parent that is used by this layer, get the result and
# insert into kwargs of the next layer's to_proto_impl method.
kwargs = dict()
for layer_name in self.__parent_layers__:
if not isinstance(self.__parent_layers__[layer_name],
collections.Sequence):
v1_layer = self.__parent_layers__[layer_name].to_proto(
context=context)
else:
v1_layer = map(lambda x: x.to_proto(context=context),
self.__parent_layers__[layer_name])
kwargs[layer_name] = v1_layer
# STEP: parse myself and add myself into context.
ret_val = self.to_proto_impl(**kwargs)
if self.context_name() is not None \
and self.context_name() not in context:
context[self.context_name()] = ret_val
# STEP: parse children that should be pased after this layer.
for layer, pnames in self.__children_layers__:
drop = False
# child will only be parsed if all parents are in context.
for pname in pnames:
if pname not in context:
drop = True
break
if drop:
continue
layer.to_proto(context=context)
# STEP: return v1 layer result
if self.context_name() is None:
return ret_val
elif self.use_context_name():
return context[self.context_name()]
else:
return context[self.name]
def to_proto_impl(self, **kwargs):
raise NotImplementedError()
def context_name(self):
"""
Context name means the context which stores `to_proto_impl` result.
If multiple layer share same context_name, the `to_proto_impl` of them
will be invoked only once.
"""
return self.name
def use_context_name(self):
return False
def calculate_size(self):
"""
lazy calculate size of the layer, should be called when to_proto_impl of
this layer is called.
:return:
"""
return self.__context__[self.context_name()].size
def attr(self):
topo = Topology(self)
return topo.get_layer_proto(self.name)
def __convert_to_v2__(method_name,
parent_names,
is_default_name=True,
attach_parent=False):
if is_default_name:
wrapper = wrap_name_default(name_prefix=method_name)
else:
wrapper = None
class V2LayerImpl(Layer):
METHOD_NAME = method_name
def __init__(self, **kwargs):
parent_layers = dict()
other_kwargs = dict()
for pname in parent_names:
if pname in kwargs:
parent_layers[pname] = kwargs[pname]
if attach_parent:
pnames = [x.context_name() for x in parent_layers.values()]
for pname in parent_layers:
layers = kwargs[pname]
if not isinstance(layers, collections.Sequence):
layers = [layers]
for layer in layers:
layer.append_child(self, pnames)
for key in kwargs.keys():
if key not in parent_names:
other_kwargs[key] = kwargs[key]
name = kwargs.get('name', None)
super(V2LayerImpl, self).__init__(name, parent_layers)
self.__other_kwargs__ = other_kwargs
if wrapper is not None:
__init__ = wrapper(__init__)
def to_proto_impl(self, **kwargs):
args = dict()
for each in kwargs:
args[each] = kwargs[each]
for each in self.__other_kwargs__:
args[each] = self.__other_kwargs__[each]
return getattr(conf_helps, method_name)(**args)
return V2LayerImpl
Layer = conf_helps.LayerOutput
......@@ -13,8 +13,8 @@
# limitations under the License.
import paddle.trainer_config_helpers.evaluators as evs
import inspect
from config_base import __convert_to_v2__
import inspect
__all__ = []
......@@ -25,21 +25,10 @@ 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__] = __convert_to_v2__(__ev__, __new_name__,
__name__)
globals()[__new_name__].__name__ = __new_name__
__all__.append(__new_name__)
......
......@@ -12,9 +12,9 @@ class Inference(object):
"""
Inference combines neural network output and parameters together
to do inference.
.. code-block:: python
inferer = Inference(output_layer=prediction, parameters=parameters)
for data_batch in batches:
print inferer.infer(data_batch)
......@@ -92,8 +92,8 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
.. code-block:: python
result = paddle.infer(output_layer=prediction,
parameters=parameters,
result = paddle.infer(output_layer=prediction,
parameters=parameters,
input=SomeData)
print result
......@@ -101,14 +101,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
.. code-block:: python
result = paddle.infer(output_layer=[prediction1, prediction2],
parameters=parameters,
result = paddle.infer(output_layer=[prediction1, prediction2],
parameters=parameters,
input=SomeData,
field=[id, value]])
print result
:param output_layer: output of the neural network that would be inferred
:type output_layer: paddle.v2.config_base.Layer or a list of
:type output_layer: paddle.v2.config_base.Layer or a list of
paddle.v2.config_base.Layer
:param parameters: parameters of the neural network.
:type parameters: paddle.v2.parameters.Parameters
......@@ -117,14 +117,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
:type input: collections.Iterable
:param feeding: Reader dictionary. Default could generate from input
value.
:param field: The prediction field. It should in [`value`, `id`, `prob`].
`value` and `prob` mean return the prediction probabilities,
:param field: The prediction field. It should in [`value`, `id`, `prob`].
`value` and `prob` mean return the prediction probabilities,
`id` means return the prediction labels. Default is `value`.
Note that `prob` only used when output_layer is beam_search
Note that `prob` only used when output_layer is beam_search
or max_id.
:type field: str
:return: The prediction result. If there are multiple outout_layers and fields,
the return order is outout_layer1.field1, outout_layer2.field1, ...,
:return: The prediction result. If there are multiple outout_layers and fields,
the return order is outout_layer1.field1, outout_layer2.field1, ...,
outout_layer1.field2, outout_layer2.field2 ...
:rtype: numpy.ndarray
"""
......
......@@ -32,392 +32,29 @@ The primary usage shows below.
"""
import collections
import inspect
import copy
import re
import paddle.trainer_config_helpers.layers as v1_layers
import paddle.trainer.config_parser as cp
from paddle.proto.ModelConfig_pb2 import ModelConfig, SubModelConfig
from config_base import __convert_to_v2__
import config_base
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
__all__ = ['data', 'parse_network']
import activation
import attr
import data_type
from config_base import Layer, __convert_to_v2__
__all__ = ['parse_network', 'data']
def __need_to_keep__(name):
if name in ['StaticInput', 'LayerType', 'layer_support']:
return False
return True
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 __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 __parse__(__real_func__)
def __need_to_wrap__(name):
return name not in ['AggregateLevel', 'ExpandLevel']
"""
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.
"""
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 +68,212 @@ 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] = __convert_to_v2__(obj, new_name, __name__)
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
def __map_data_docstr__(doc):
doc = re.sub(r'(data = [^\)]+)\).*',
"data = paddle.layer.data(name=\"input\", "
"type=paddle.data_type.dense_vector(1000))", doc)
doc = re.sub(r':param size:.*', ':param type: Data type of this data layer',
doc)
doc = re.sub(r':type size:.*', ":type size: paddle.v2.data_type.InputType",
doc)
return doc
__data_layer__.__doc__ = __map_data_docstr__(v1_layers.data_layer.__doc__)
assert generated_input_index != -1
data = __convert_to_v2__(__data_layer__, 'name', __name__)
gipt = input[generated_input_index]
assert isinstance(gipt, BaseGeneratedInputV2)
gipt.bos_id = bos_id
gipt.eos_id = eos_id
def __get_used_layers__(output_layers, extra_layers=None):
layer_names = set()
parents = {}
def __real_step__(*args):
eos_name = "__%s_eos_layer__" % name
generator = RecurrentLayerGroupSetGeneratorV2(
eos_name, max_length, beam_size, num_results_per_sample)
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 config_base.__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()
......@@ -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)
......
......@@ -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)
......
......@@ -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 +31,8 @@ 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 +52,8 @@ 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],
......@@ -75,6 +81,8 @@ 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 +122,7 @@ 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(
......
......@@ -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())
......
......@@ -15,36 +15,13 @@
import collections
from paddle.proto.ModelConfig_pb2 import ModelConfig
import paddle.trainer_config_helpers as conf_helps
import layer as v2_layer
import config_base
__all__ = ['Topology']
def __flatten__(lis):
"""
Given a list, possibly nested to any level, return it flattened.
"""
new_lis = []
for item in lis:
if isinstance(item, collections.Sequence):
new_lis.extend(__flatten__(item))
else:
new_lis.append(item)
return new_lis
def __bfs_travel__(callback, *layers):
layers = __flatten__(layers)
for each_layer in layers:
__break__ = callback(each_layer)
if __break__:
return
__layers__ = each_layer.__parent_layers__.values() + \
each_layer.extra_parent()
__bfs_travel__(callback, *__layers__)
class Topology(object):
"""
Topology is used to store the information about all layers
......@@ -94,31 +71,18 @@ 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 +91,7 @@ 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):
......@@ -138,5 +102,5 @@ class Topology(object):
def __check_layer_type__(layer):
if not isinstance(layer, v2_layer.LayerV2):
raise ValueError('layer should have type paddle.layer.Layer')
if not isinstance(layer, config_base.Layer):
raise ValueError('layer should have type paddle.v2.config_base.Layer')
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