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体验新版 GitCode,发现更多精彩内容 >>
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f8c279b1
编写于
3月 27, 2019
作者:
X
Xin Pan
提交者:
GitHub
3月 27, 2019
浏览文件
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差异文件
Merge pull request #16454 from panyx0718/imperative2
polish deepCF model to support real dataset
上级
fa1796a3
fd24ab47
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
137 addition
and
57 deletion
+137
-57
paddle/fluid/operators/gather.cu.h
paddle/fluid/operators/gather.cu.h
+1
-0
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+1
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+5
-2
python/paddle/fluid/imperative/base.py
python/paddle/fluid/imperative/base.py
+2
-1
python/paddle/fluid/imperative/tracer.py
python/paddle/fluid/imperative/tracer.py
+1
-1
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+2
-2
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+2
-0
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
...on/paddle/fluid/tests/unittests/test_imperative_deepcf.py
+123
-51
未找到文件。
paddle/fluid/operators/gather.cu.h
浏览文件 @
f8c279b1
...
...
@@ -64,6 +64,7 @@ void GPUGather(const platform::DeviceContext& ctx, const Tensor& src,
for
(
int
i
=
1
;
i
<
src_dims
.
size
();
++
i
)
slice_size
*=
src_dims
[
i
];
const
T
*
p_src
=
src
.
data
<
T
>
();
// why must be int?
const
int
*
p_index
=
index
.
data
<
int
>
();
T
*
p_output
=
output
->
data
<
T
>
();
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
f8c279b1
...
...
@@ -235,6 +235,7 @@ PYBIND11_MODULE(core, m) {
self
.
forward_id_
=
forward_id
;
},
py
::
return_value_policy
::
reference
)
.
def_property_readonly
(
"type"
,
&
imperative
::
OpBase
::
Type
)
.
def_property
(
"backward_id"
,
[](
const
imperative
::
OpBase
&
self
)
{
return
self
.
backward_id_
;
},
...
...
python/paddle/fluid/framework.py
浏览文件 @
f8c279b1
...
...
@@ -744,7 +744,7 @@ class Operator(object):
if
_in_imperative_mode
():
if
type
is
None
:
raise
ValueError
(
"`type` to initilized an Operator can not be None."
)
"`type` to initi
a
lized an Operator can not be None."
)
self
.
iop
=
core
.
OpBase
(
type
)
# TODO(minqiyang): remove these lines after we take apart all
...
...
@@ -906,7 +906,10 @@ class Operator(object):
@
property
def
type
(
self
):
return
self
.
desc
.
type
()
if
_in_imperative_mode
():
return
self
.
iop
.
type
else
:
return
self
.
desc
.
type
()
def
input
(
self
,
name
):
"""
...
...
python/paddle/fluid/imperative/base.py
浏览文件 @
f8c279b1
...
...
@@ -55,7 +55,8 @@ def to_variable(value, block=None, name=None):
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
name
=
name
,
shape
=
value
.
shape
,
dtype
=
value
.
dtype
)
dtype
=
value
.
dtype
,
stop_gradient
=
True
)
var
=
py_var
.
_ivar
.
value
()
tensor
=
var
.
get_tensor
()
tensor
.
set
(
value
,
framework
.
_current_expected_place
())
...
...
python/paddle/fluid/imperative/tracer.py
浏览文件 @
f8c279b1
...
...
@@ -62,7 +62,7 @@ class Tracer(core.Tracer):
if
len
(
backward_refs
)
>
0
:
op
.
iop
.
register_backward_hooks
(
release_op
)
# TODO(minqiyang): remove all inputs and outputs after sep
e
rate
# TODO(minqiyang): remove all inputs and outputs after sep
a
rate
# var and grad
op
.
backward_refs
=
defaultdict
(
list
)
for
k
,
v
in
six
.
iteritems
(
op
.
inputs
):
...
...
python/paddle/fluid/initializer.py
浏览文件 @
f8c279b1
...
...
@@ -212,7 +212,7 @@ class UniformInitializer(Initializer):
if
self
.
_seed
==
0
:
self
.
_seed
=
block
.
program
.
random_seed
# to be compatible of fp16 initalizers
# to be compatible of fp16 init
i
alizers
if
var
.
dtype
==
VarDesc
.
VarType
.
FP16
:
out_dtype
=
VarDesc
.
VarType
.
FP32
out_var
=
block
.
create_var
(
...
...
@@ -756,7 +756,7 @@ class NumpyArrayInitializer(Initializer):
values
=
[
int
(
v
)
for
v
in
self
.
_value
.
flat
]
else
:
raise
ValueError
(
"Unsupported dtype %s"
,
self
.
_value
.
dtype
)
if
self
.
_value
.
size
>
1024
*
1024
*
5
:
if
self
.
_value
.
size
>
1024
*
1024
*
1024
:
raise
ValueError
(
"The size of input is too big. Please consider "
"saving it to file and 'load_op' to load it"
)
op
=
block
.
_prepend_op
(
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
f8c279b1
...
...
@@ -165,6 +165,8 @@ class Optimizer(object):
name
=
self
.
_name
+
"_"
+
name
if
(
name
in
self
.
_accumulators
and
param
.
name
in
self
.
_accumulators
[
name
]):
if
framework
.
_in_imperative_mode
():
return
self
.
_accumulators
[
name
][
param
.
name
]
raise
Exception
(
"Accumulator {} already exists for parameter {}"
.
format
(
name
,
param
.
name
))
if
shape
==
None
:
...
...
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
浏览文件 @
f8c279b1
...
...
@@ -15,6 +15,7 @@
import
unittest
import
numpy
as
np
import
random
import
os
import
sys
import
paddle
...
...
@@ -23,16 +24,17 @@ import paddle.fluid.core as core
from
test_imperative_base
import
new_program_scope
from
paddle.fluid.imperative.base
import
to_variable
NUM_USERS
=
100
NUM_ITEMS
=
1000
# Can use Amusic dataset as the DeepCF describes.
DATA_PATH
=
os
.
environ
.
get
(
'DATA_PATH'
,
''
)
BATCH_SIZE
=
32
NUM_BATCHES
=
2
BATCH_SIZE
=
int
(
os
.
environ
.
get
(
'BATCH_SIZE'
,
128
))
NUM_BATCHES
=
int
(
os
.
environ
.
get
(
'NUM_BATCHES'
,
5
))
NUM_EPOCHES
=
int
(
os
.
environ
.
get
(
'NUM_EPOCHES'
,
1
))
class
MLP
(
fluid
.
imperative
.
Layer
):
class
DMF
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
name_scope
):
super
(
MLP
,
self
).
__init__
(
name_scope
)
super
(
DMF
,
self
).
__init__
(
name_scope
)
self
.
_user_latent
=
fluid
.
imperative
.
FC
(
self
.
full_name
(),
256
)
self
.
_item_latent
=
fluid
.
imperative
.
FC
(
self
.
full_name
(),
256
)
...
...
@@ -61,9 +63,9 @@ class MLP(fluid.imperative.Layer):
return
fluid
.
layers
.
elementwise_mul
(
users
,
items
)
class
DMF
(
fluid
.
imperative
.
Layer
):
class
MLP
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
name_scope
):
super
(
DMF
,
self
).
__init__
(
name_scope
)
super
(
MLP
,
self
).
__init__
(
name_scope
)
self
.
_user_latent
=
fluid
.
imperative
.
FC
(
self
.
full_name
(),
256
)
self
.
_item_latent
=
fluid
.
imperative
.
FC
(
self
.
full_name
(),
256
)
self
.
_match_layers
=
[]
...
...
@@ -87,21 +89,30 @@ class DMF(fluid.imperative.Layer):
class
DeepCF
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
name_scope
):
def
__init__
(
self
,
name_scope
,
num_users
,
num_items
,
matrix
):
super
(
DeepCF
,
self
).
__init__
(
name_scope
)
self
.
_user_emb
=
fluid
.
imperative
.
Embedding
(
self
.
full_name
(),
[
NUM_USERS
,
256
])
self
.
_item_emb
=
fluid
.
imperative
.
Embedding
(
self
.
full_name
(),
[
NUM_ITEMS
,
256
])
self
.
_num_users
=
num_users
self
.
_num_items
=
num_items
self
.
_rating_matrix
=
self
.
create_parameter
(
fluid
.
ParamAttr
(
trainable
=
False
),
matrix
.
shape
,
matrix
.
dtype
,
is_bias
=
False
,
default_initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
matrix
))
self
.
_rating_matrix
.
_stop_gradient
=
True
self
.
_mlp
=
MLP
(
self
.
full_name
())
self
.
_dmf
=
DMF
(
self
.
full_name
())
self
.
_match_fc
=
fluid
.
imperative
.
FC
(
self
.
full_name
(),
1
,
act
=
'sigmoid'
)
def
forward
(
self
,
users
,
items
):
users_emb
=
self
.
_user_emb
(
users
)
items_emb
=
self
.
_item_emb
(
items
)
# users_emb = self._user_emb(users)
# items_emb = self._item_emb(items)
users_emb
=
fluid
.
layers
.
gather
(
self
.
_rating_matrix
,
users
)
items_emb
=
fluid
.
layers
.
gather
(
fluid
.
layers
.
transpose
(
self
.
_rating_matrix
,
[
1
,
0
]),
items
)
users_emb
.
stop_gradient
=
True
items_emb
.
stop_gradient
=
True
mlp_predictive
=
self
.
_mlp
(
users_emb
,
items_emb
)
dmf_predictive
=
self
.
_dmf
(
users_emb
,
items_emb
)
...
...
@@ -116,27 +127,79 @@ def get_data():
user_ids
=
[]
item_ids
=
[]
labels
=
[]
NUM_USERS
=
100
NUM_ITEMS
=
1000
matrix
=
np
.
zeros
([
NUM_USERS
,
NUM_ITEMS
],
dtype
=
np
.
float32
)
for
uid
in
range
(
NUM_USERS
):
for
iid
in
range
(
NUM_ITEMS
):
# 10% positive
label
=
float
(
random
.
randint
(
1
,
10
)
==
1
)
label
=
float
(
random
.
randint
(
1
,
6
)
==
1
)
user_ids
.
append
(
uid
)
item_ids
.
append
(
iid
)
labels
.
append
(
label
)
indices
=
np
.
arange
(
NUM_USERS
*
NUM_ITEMS
)
matrix
[
uid
,
iid
]
=
label
indices
=
np
.
arange
(
len
(
user_ids
))
np
.
random
.
shuffle
(
indices
)
users_np
=
np
.
array
(
user_ids
,
dtype
=
np
.
int32
)[
indices
]
items_np
=
np
.
array
(
item_ids
,
dtype
=
np
.
int32
)[
indices
]
labels_np
=
np
.
array
(
labels
,
dtype
=
np
.
float32
)[
indices
]
return
np
.
expand_dims
(
users_np
,
-
1
),
\
np
.
expand_dims
(
items_np
,
-
1
),
\
np
.
expand_dims
(
labels_np
,
-
1
),
NUM_USERS
,
NUM_ITEMS
,
matrix
def
load_data
(
DATA_PATH
):
sys
.
stderr
.
write
(
'loading from %s
\n
'
%
DATA_PATH
)
likes
=
dict
()
num_users
=
-
1
num_items
=
-
1
with
open
(
DATA_PATH
,
'r'
)
as
f
:
for
l
in
f
.
readlines
():
uid
,
iid
,
rating
=
[
int
(
v
)
for
v
in
l
.
split
(
'
\t
'
)]
num_users
=
max
(
num_users
,
uid
+
1
)
num_items
=
max
(
num_items
,
iid
+
1
)
if
float
(
rating
)
>
0.0
:
likes
[(
uid
,
iid
)]
=
1.0
user_ids
=
[]
item_ids
=
[]
labels
=
[]
matrix
=
np
.
zeros
([
num_users
,
num_items
],
dtype
=
np
.
float32
)
for
uid
,
iid
in
likes
.
keys
():
user_ids
.
append
(
uid
)
item_ids
.
append
(
iid
)
labels
.
append
(
1.0
)
matrix
[
uid
,
iid
]
=
1.0
negative
=
0
while
negative
<
3
:
nuid
=
random
.
randint
(
0
,
num_users
-
1
)
niid
=
random
.
randint
(
0
,
num_items
-
1
)
if
(
nuid
,
niid
)
not
in
likes
:
negative
+=
1
user_ids
.
append
(
nuid
)
item_ids
.
append
(
niid
)
labels
.
append
(
0.0
)
indices
=
np
.
arange
(
len
(
user_ids
))
np
.
random
.
shuffle
(
indices
)
users_np
=
np
.
array
(
user_ids
,
dtype
=
np
.
int
64
)[
indices
]
items_np
=
np
.
array
(
item_ids
,
dtype
=
np
.
int
64
)[
indices
]
users_np
=
np
.
array
(
user_ids
,
dtype
=
np
.
int
32
)[
indices
]
items_np
=
np
.
array
(
item_ids
,
dtype
=
np
.
int
32
)[
indices
]
labels_np
=
np
.
array
(
labels
,
dtype
=
np
.
float32
)[
indices
]
return
np
.
expand_dims
(
users_np
,
-
1
),
\
np
.
expand_dims
(
items_np
,
-
1
),
\
np
.
expand_dims
(
labels_np
,
-
1
)
np
.
expand_dims
(
labels_np
,
-
1
)
,
num_users
,
num_items
,
matrix
class
TestImperativeDeepCF
(
unittest
.
TestCase
):
def
test_
gan_float32
(
self
):
def
test_
deefcf
(
self
):
seed
=
90
users_np
,
items_np
,
labels_np
=
get_data
()
if
DATA_PATH
:
(
users_np
,
items_np
,
labels_np
,
num_users
,
num_items
,
matrix
)
=
load_data
(
DATA_PATH
)
else
:
(
users_np
,
items_np
,
labels_np
,
num_users
,
num_items
,
matrix
)
=
get_data
()
startup
=
fluid
.
Program
()
startup
.
random_seed
=
seed
...
...
@@ -145,11 +208,11 @@ class TestImperativeDeepCF(unittest.TestCase):
scope
=
fluid
.
core
.
Scope
()
with
new_program_scope
(
main
=
main
,
startup
=
startup
,
scope
=
scope
):
users
=
fluid
.
layers
.
data
(
'users'
,
[
1
],
dtype
=
'int
64
'
)
items
=
fluid
.
layers
.
data
(
'items'
,
[
1
],
dtype
=
'int
64
'
)
users
=
fluid
.
layers
.
data
(
'users'
,
[
1
],
dtype
=
'int
32
'
)
items
=
fluid
.
layers
.
data
(
'items'
,
[
1
],
dtype
=
'int
32
'
)
labels
=
fluid
.
layers
.
data
(
'labels'
,
[
1
],
dtype
=
'float32'
)
deepcf
=
DeepCF
(
'deepcf'
)
deepcf
=
DeepCF
(
'deepcf'
,
num_users
,
num_items
,
matrix
)
prediction
=
deepcf
(
users
,
items
)
loss
=
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
log_loss
(
prediction
,
labels
))
...
...
@@ -159,35 +222,44 @@ class TestImperativeDeepCF(unittest.TestCase):
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
exe
.
run
(
startup
)
for
slice
in
range
(
0
,
BATCH_SIZE
*
NUM_BATCHES
,
BATCH_SIZE
):
static_loss
=
exe
.
run
(
main
,
feed
=
{
users
.
name
:
users_np
[
slice
:
slice
+
BATCH_SIZE
],
items
.
name
:
items_np
[
slice
:
slice
+
BATCH_SIZE
],
labels
.
name
:
labels_np
[
slice
:
slice
+
BATCH_SIZE
]
},
fetch_list
=
[
loss
])[
0
]
sys
.
stderr
.
write
(
'static loss %s
\n
'
%
static_loss
)
for
e
in
range
(
NUM_EPOCHES
):
sys
.
stderr
.
write
(
'epoch %d
\n
'
%
e
)
for
slice
in
range
(
0
,
BATCH_SIZE
*
NUM_BATCHES
,
BATCH_SIZE
):
if
slice
+
BATCH_SIZE
>=
users_np
.
shape
[
0
]:
break
static_loss
=
exe
.
run
(
main
,
feed
=
{
users
.
name
:
users_np
[
slice
:
slice
+
BATCH_SIZE
],
items
.
name
:
items_np
[
slice
:
slice
+
BATCH_SIZE
],
labels
.
name
:
labels_np
[
slice
:
slice
+
BATCH_SIZE
]
},
fetch_list
=
[
loss
])[
0
]
sys
.
stderr
.
write
(
'static loss %s
\n
'
%
static_loss
)
with
fluid
.
imperative
.
guard
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
deepcf
=
DeepCF
(
'deepcf'
)
for
slice
in
range
(
0
,
BATCH_SIZE
*
NUM_BATCHES
,
BATCH_SIZE
):
prediction
=
deepcf
(
to_variable
(
users_np
[
slice
:
slice
+
BATCH_SIZE
]),
to_variable
(
items_np
[
slice
:
slice
+
BATCH_SIZE
]))
loss
=
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
log_loss
(
prediction
,
to_variable
(
labels_np
[
slice
:
slice
+
BATCH_SIZE
])))
loss
.
_backward
()
adam
=
fluid
.
optimizer
.
AdamOptimizer
(
0.01
)
adam
.
minimize
(
loss
)
deepcf
.
clear_gradients
()
dy_loss
=
loss
.
_numpy
()
deepcf
=
DeepCF
(
'deepcf'
,
num_users
,
num_items
,
matrix
)
adam
=
fluid
.
optimizer
.
AdamOptimizer
(
0.01
)
for
e
in
range
(
NUM_EPOCHES
):
sys
.
stderr
.
write
(
'epoch %d
\n
'
%
e
)
for
slice
in
range
(
0
,
BATCH_SIZE
*
NUM_BATCHES
,
BATCH_SIZE
):
if
slice
+
BATCH_SIZE
>=
users_np
.
shape
[
0
]:
break
prediction
=
deepcf
(
to_variable
(
users_np
[
slice
:
slice
+
BATCH_SIZE
]),
to_variable
(
items_np
[
slice
:
slice
+
BATCH_SIZE
]))
loss
=
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
log_loss
(
prediction
,
to_variable
(
labels_np
[
slice
:
slice
+
BATCH_SIZE
])))
loss
.
_backward
()
adam
.
minimize
(
loss
)
deepcf
.
clear_gradients
()
dy_loss
=
loss
.
_numpy
()
sys
.
stderr
.
write
(
'dynamic loss: %s %s
\n
'
%
(
slice
,
dy_loss
))
self
.
assertEqual
(
static_loss
,
dy_loss
)
...
...
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