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
"""
......
此差异已折叠。
......@@ -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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