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Fix/sequence pool #5229

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merged 6 commits into from
Oct 31, 2017
Merged

Fix/sequence pool #5229

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15c4e8c
"modify layers.py"
dzhwinter Oct 30, 2017
fa3232d
"fix pool interface"
dzhwinter Oct 30, 2017
2f1785c
"add export type to layers"
dzhwinter Oct 31, 2017
62b1900
"fix based on comment"
dzhwinter Oct 31, 2017
b480459
Merge branch 'develop' into fix/sequence_pool
dzhwinter Oct 31, 2017
f807fff
Merge branch 'develop' into fix/sequence_pool
dzhwinter Oct 31, 2017
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195 changes: 66 additions & 129 deletions python/paddle/v2/framework/layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

__all__ = [
'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat',
'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool'
'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'sums'
]


Expand Down Expand Up @@ -150,7 +150,7 @@ def func(**kwargs):
outputs[name] = [helper.create_tmp_variable(dtype=dtype)]
helper.append_op(
type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs)
return helper.append_activation(out)
return out

func.__name__ = op_type
globals()[op_type] = func
Expand All @@ -160,21 +160,9 @@ def func(**kwargs):

_create_op_func_('mean')
_create_op_func_('mul')
_create_op_func_('elementwise_add')
_create_op_func_('dropout')
_create_op_func_('reshape')


def cast(x, data_type, program=None):
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=data_type)
helper.append_op(
type='cast',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'in_data_type': x.data_type,
'out_data_type': out.data_type})
return out
# _create_op_func_('cos_sim')


def cast(x, data_type, program=None):
Expand Down Expand Up @@ -202,6 +190,30 @@ def concat(input, axis, program=None, init_program=None):
return out


def sums(input, program=None, init_program=None):
helper = LayerHelper('sum', **locals())
if not isinstance(input, list) and not isinstance(input, tuple):
input = [input]
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There is no need to do the converting here. Operator's constructor will do this.

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Done.

out = helper.create_tmp_variable(dtype=input[0].data_type)
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LayerHelper has a member function: input_dtype. It will check whether all the inputs are the same type and then return the type. I think using it here is a better choice.

def input_dtype(self, input_param_name='input'):
        inputs = self.multiple_input(input_param_name)
        dtype = None
        for each in inputs:
            if dtype is None:
                dtype = each.data_type
            elif dtype != each.data_type:
                raise ValueError("Data Type mismatch")
        return dtype

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done.

helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
return out


def cos_sim(X, Y, program=None, init_program=None):
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.data_type)
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Use LayerHelper::input_dtype(). See my above comments.

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Done.

xnorm = helper.create_tmp_variable(dtype=X.data_type)
ynorm = helper.create_tmp_variable(dtype=X.data_type)
helper.append_op(
type='cos_sim',
inputs={'X': [X],
'Y': [Y]},
outputs={'Out': [out],
'XNorm': [xnorm],
'YNorm': [ynorm]})
return out, xnorm, ynorm


def cross_entropy(input, label, **kwargs):
helper = LayerHelper('cross_entropy', **kwargs)
out = helper.create_tmp_variable(dtype=input.data_type)
Expand Down Expand Up @@ -232,11 +244,27 @@ def square_error_cost(input, label, **kwargs):
return square_out


def square_error_cost(input, label, **kwargs):
helper = LayerHelper('square_error_cost', **kwargs)
minus_out = helper.create_tmp_variable(dtype=input.data_type)
helper.append_op(
type='elementwise_sub',
inputs={'X': [input],
'Y': [label]},
outputs={'Out': [minus_out]})

square_out = helper.create_tmp_variable(dtype=input.data_type)
helper.append_op(
type='pow',
inputs={'X': [minus_out]},
outputs={'Y': [square_out]},
attrs={'factor': 2.0})
return square_out


def sequence_conv(input,
num_filters,
name=None,
filter_size=3,
act=None,
stride=1,
padding=None,
bias_attr=None,
Expand All @@ -250,7 +278,7 @@ def sequence_conv(input,
helper = LayerHelper('sequence_conv', **locals())
dtype = helper.input_dtype()

filter_shape = [num_filters, filter_size]
filter_shape = [num_filters * filter_size, filter_size]
filter = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
pre_bias = helper.create_tmp_variable(dtype)
Expand All @@ -259,17 +287,15 @@ def sequence_conv(input,
type='sequence_conv',
inputs={
'X': [input],
'Filter': filter,
'Filter': [filter],
},
outputs={"Out": pre_bias},
attrs={
'context_stride': stride,
'context_start': 0,
'context_length': filter_size
})

pre_act = helper.append_bias_op(pre_bias)
return helper.append_activation(pre_act)
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Why remove activation？

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I'm not sure sequence conv need a non-linear activation. So I remove it.

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@JiayiFeng JiayiFeng Oct 31, 2017 •
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I think we shall retain it. Users can set act as None to disable it.

return pre_bias


def conv2d(input,
Expand Down Expand Up @@ -311,8 +337,8 @@ def conv2d(input,
helper.append_op(
type='conv2d',
inputs={
'Input': input,
'Filter': filter,
'Input': [input],
'Filter': [filter],
},
outputs={"Output": pre_bias},
attrs={'strides': stride,
Expand All @@ -324,31 +350,32 @@ def conv2d(input,
return helper.append_activation(pre_act)


def sequence_pool(input,
pool_size,
pool_type,
pool_stride=1,
pool_padding=0,
global_pooling=False,
program=None,
init_program=None):
def sequence_pool(input, pool_type, program=None, init_program=None):
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes

ENUM_POOL_TYPE = set(["max", "avg", "sqrt", "last", "first"])
if pool_type not in ENUM_POOL_TYPE:
ENUM_POOL_TYPE = dict({
"AVERAGE": 0,
"SUM": 1,
"SQRT": 2,
"MAX": 3,
"LAST": 4,
"FIRST": 5
})
if pool_type.upper() not in ENUM_POOL_TYPE:
raise ValueError("Unknown pool_type: '%s'. It can only be %s.",
str(pool_type), " ".join(ENUM_POOL_TYPE))
str(pool_type), " ".join(ENUM_POOL_TYPE.keys()))

helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)

# FIXME(dzh): strategy
helper.append_op(
type="sequence_pool",
inputs={"X": [input]},
outputs={"Out": pool_out},
attrs={"strategy": pool_type})
outputs={"Out": [pool_out]},
attrs={"strategy": ENUM_POOL_TYPE[pool_type.upper()]})
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What's this change for?

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"max", "sqrt" are keywords of Python, I cannot bear our keyword overloading anymore.....

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@JiayiFeng JiayiFeng Oct 31, 2017 •
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I am wondering why ENUM_POOL_TYPE becomes a map.

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@dzhwinter dzhwinter Oct 31, 2017 •
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Because attribute of "strategy" needs a fixed enum index, not a string.

    AddAttr<int>(
        "strategy",
        "(int, default AVERAGE) the pooling strategy of SequencePoolOp.")
        .SetDefault(AVERAGE)
        .InEnum({AVERAGE, SUM, SQRT, MAX, LAST, FIRST});

And this attribute will be definitely replaced with another PR, and the sequence_conv attributes, etc. Those tons of code will be rewritten after I finished the book chapter 5.


return pool_out

Expand Down Expand Up @@ -378,8 +405,8 @@ def pool2d(input,

helper.append_op(
type="pool2d",
inputs={"X": input},
outputs={"Out": pool_out},
inputs={"X": [input]},
outputs={"Out": [pool_out]},
attrs={
"poolingType": pool_type,
"ksize": pool_size,
Expand All @@ -391,96 +418,6 @@ def pool2d(input,
return pool_out


def batch_norm(input,
act=None,
is_test=False,
momentum=0.9,
epsilon=1e05,
param_attr=None,
bias_attr=None,
data_layout='NCHW',
program=None,
init_program=None):
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()

input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
else:
if data_layout == 'NHWC':
channel_num = input_shape[-1]
else:
raise ValueError("unsupported data layout:" + data_layout)

def get_init_attr(value):
if not isinstance(value, float):
raise ValueError("attr value should be a float")
return {'type': 'fill_constant', 'value': value}

def prepend_init_op(var, init_attr):
assert isinstance(var, Variable)
op_type = init_attr['type']
init_attr['shape'] = var.shape
init_attr['data_type'] = int(var.data_type)
op = var.block.prepend_op(
type=op_type, inputs=None, outputs={'Out': [var]}, attrs=init_attr)
return op

def create_persistable_var(dtype, shape, init_attr=None):
name = unique_name(".".join([helper.name, "xxxx"]))
var = init_program.global_block().create_var(
dtype=dtype, shape=shape, name=name, persistable=True)
if 'init_attr' is not None:
prepend_init_op(var, init_attr)
return program.global_block().create_var(
name=name, dtype=dtype, shape=shape, persistable=True)

param_shape = [channel_num]

# create parameter
scale = helper.create_parameter(
attr=helper.param_attr, shape=param_shape, dtype=dtype)
bias = helper.create_parameter(
attr=helper.param_attr, shape=param_shape, dtype=dtype)

# create input
mean = create_persistable_var(dtype, param_shape, get_init_attr(0.0))
variance = create_persistable_var(dtype, param_shape, get_init_attr(1.0))

# create output
# mean and mean_out share the same memory
mean_out = mean
# variance and variance out share the same memory
variance_out = variance
saved_mean = helper.create_tmp_variable(dtype)
saved_variance = helper.create_tmp_variable(dtype)

batch_norm_out = helper.create_tmp_variable(dtype)

helper.append_op(
type="batch_norm",
inputs={
"X": input,
"Scale": scale,
"Bias": bias,
"Mean": mean,
"Variance": variance
},
outputs={
"Y": batch_norm_out,
"MeanOut": mean_out,
"VarianceOut": variance_out,
"SavedMean": saved_mean,
"SavedVariance": saved_variance
},
attrs={"momentum": momentum,
"epsilon": epsilon,
"is_test": is_test})

return helper.append_activation(batch_norm_out)


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Why remove batch_norm layer?

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That's a mistake remove caused by the conflict. It has been fixed.

class BlockGuard(object):
"""
BlockGuard used to create sub-block in program by using Python `with`
Expand Down
6 changes: 1 addition & 5 deletions python/paddle/v2/framework/nets.py
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Expand Up @@ -103,22 +103,18 @@ def sequence_conv_pool(input,
filter_size,
pool_size,
pool_stride,
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pool_size and pool_stride are also useless. Please remove them.

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Done.

act,
program=None,
init_program=None):
conv_out = layers.sequence_conv(
input=input,
num_filters=num_filters,
filter_size=filter_size,
act=act,
program=program,
init_program=init_program)

pool_out = layers.sequence_pool(
input=conv_out,
pool_size=pool_size,
pool_type='max',
pool_stride=pool_stride,
pool_type='sum',
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I think max is a little more normal than sum, so I think it's fine to keep the default as max.

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Fixed.
Find a frequent usage -- embedding and then pooling in Sum is equivalent to the FC layer.

program=program,
init_program=init_program)
return pool_out