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1f89249a
编写于
3月 26, 2019
作者:
X
Xin Pan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
update DeepCF model
test=develop
上级
0fff666f
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
146 addition
and
56 deletion
+146
-56
paddle/fluid/operators/gather.cu.h
paddle/fluid/operators/gather.cu.h
+1
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+1
-1
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
+12
-1
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+1
-1
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
...on/paddle/fluid/tests/unittests/test_imperative_deepcf.py
+129
-52
未找到文件。
paddle/fluid/operators/gather.cu.h
浏览文件 @
1f89249a
...
...
@@ -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
>
();
...
...
python/paddle/fluid/framework.py
浏览文件 @
1f89249a
...
...
@@ -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
...
...
python/paddle/fluid/imperative/base.py
浏览文件 @
1f89249a
...
...
@@ -55,7 +55,8 @@ def to_variable(value, block=None):
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
name
=
None
,
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 @
1f89249a
...
...
@@ -14,7 +14,9 @@
from
__future__
import
print_function
import
sys
import
six
from
six.moves
import
reduce
from
collections
import
defaultdict
from
paddle.fluid
import
core
...
...
@@ -49,7 +51,16 @@ class Tracer(core.Tracer):
def
trace_op
(
self
,
op
,
stop_gradient
=
False
):
# record op's trace id
op
.
iop
.
_trace_id
=
self
.
_trace_id
"""
all_input_stop_grads = True
for vars in op.inputs.values():
for v in vars:
sys.stderr.write('%s %s
\n
' % (v.name, v.stop_gradient))
all_input_stop_grads &= v.stop_gradient
stop_gradient = False if not stop_gradient else True
stop_gradient = all_input_stop_grads | stop_gradient
"""
backward_refs
=
self
.
trace
(
op
.
iop
,
op
.
inputs
,
op
.
outputs
,
op
.
attrs
,
framework
.
_current_expected_place
(),
stop_gradient
)
...
...
python/paddle/fluid/initializer.py
浏览文件 @
1f89249a
...
...
@@ -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/tests/unittests/test_imperative_deepcf.py
浏览文件 @
1f89249a
...
...
@@ -15,6 +15,7 @@
import
unittest
import
numpy
as
np
import
random
import
os
import
sys
import
paddle
...
...
@@ -23,16 +24,15 @@ 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
DATA_PATH
=
os
.
environ
.
get
(
'DATA_PATH'
,
''
)
BATCH_SIZE
=
int
(
os
.
environ
.
get
(
'BATCH_SIZE'
,
256
))
NUM_BATCHES
=
int
(
os
.
environ
.
get
(
'NUM_BATCHES'
,
2
))
NUM_EPOCHES
=
int
(
os
.
environ
.
get
(
'NUM_EPOCHES'
,
1
))
BATCH_SIZE
=
32
NUM_BATCHES
=
2
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 +61,9 @@ 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 +87,36 @@ 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
.
_num_users
=
num_users
self
.
_num_items
=
num_items
self
.
_rating_matrix
=
self
.
create_parameter
(
None
,
matrix
.
shape
,
matrix
.
dtype
,
is_bias
=
False
,
default_initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
matrix
))
self
.
_rating_matrix
.
_stop_gradient
=
True
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._user_emb = fluid.imperative.Embedding(self.full_name(),
# [self._num_users
, 256])
#
self._item_emb = fluid.imperative.Embedding(self.full_name(),
# [self._num_items
, 256])
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)
sys
.
stderr
.
write
(
'forward: %s
\n
'
%
users
.
_stop_gradient
)
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
)
...
...
@@ -116,40 +131,92 @@ def get_data():
user_ids
=
[]
item_ids
=
[]
labels
=
[]
matrix
=
np
.
zeros
([
100
,
1000
],
dtype
=
np
.
float32
)
NUM_USERS
=
100
NUM_ITEMS
=
1000
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
main
=
fluid
.
Program
()
main
.
random_seed
=
seed
"""
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 +226,45 @@ 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
)
sys
.
stderr
.
write
(
'matrix: %s
\n
'
%
deepcf
.
_rating_matrix
.
_numpy
())
for
e
in
range
(
NUM_EPOCHES
):
sys
.
stderr
.
write
(
'epoch %d
\n
'
%
e
)
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
()
sys
.
stderr
.
write
(
'dynamic loss: %s
\n
'
%
dy_loss
)
sys
.
stderr
.
write
(
'matrix: %s
\n
'
%
deepcf
.
_rating_matrix
.
_numpy
())
self
.
assertEqual
(
static_loss
,
dy_loss
)
...
...
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