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938717ba
编写于
12月 26, 2017
作者:
Y
Yang Yu
浏览文件
操作
浏览文件
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差异文件
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上级
2a36e8ad
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
79 addition
and
8 deletion
+79
-8
paddle/framework/executor.cc
paddle/framework/executor.cc
+8
-0
python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py
python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py
+71
-8
未找到文件。
paddle/framework/executor.cc
浏览文件 @
938717ba
...
...
@@ -66,6 +66,14 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
PADDLE_ENFORCE_LT
(
static_cast
<
size_t
>
(
block_id
),
pdesc
.
Size
());
auto
&
block
=
pdesc
.
Block
(
block_id
);
if
(
VLOG_IS_ON
(
100
))
{
std
::
ostringstream
sout
;
for
(
auto
&
name
:
scope
->
GetAllNames
(
false
))
{
sout
<<
name
<<
", "
;
}
VLOG
(
100
)
<<
"Scope has variable "
<<
sout
.
str
();
}
Scope
*
local_scope
=
scope
;
if
(
create_vars
)
{
if
(
create_local_scope
)
{
...
...
python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py
浏览文件 @
938717ba
...
...
@@ -159,6 +159,39 @@ class BaseRNN(object):
g
[
i
][
j
]
=
(
pos
-
neg
)
/
(
delta
*
2
)
return
g
def
get_numeric_gradient_of_input
(
self
,
input_name
,
delta
=
0.001
,
return_one_tensor
=
True
):
ipt
=
self
.
inputs
[
input_name
]
grad
=
[]
for
seq
in
ipt
:
seq_grad
=
[]
for
item
in
seq
:
item_grad
=
numpy
.
zeros
(
shape
=
item
.
shape
,
dtype
=
item
.
dtype
)
if
len
(
item
.
shape
)
!=
1
:
raise
ValueError
(
"Not support"
)
for
i
in
xrange
(
len
(
item
)):
o
=
item
[
i
]
item
[
i
]
+=
delta
pos
=
self
.
_exe_mean_out_
()
item
[
i
]
-=
2
*
delta
neg
=
self
.
_exe_mean_out_
()
item
[
i
]
=
o
item_grad
[
i
]
=
(
pos
-
neg
)
/
(
delta
*
2
)
seq_grad
.
append
(
item_grad
)
grad
.
append
(
seq_grad
)
if
not
return_one_tensor
:
return
grad
for
i
in
xrange
(
len
(
grad
)):
grad
[
i
]
=
numpy
.
concatenate
(
grad
[
i
])
grad
=
numpy
.
concatenate
(
grad
)
return
grad
def
_exe_mean_out_
(
self
):
outs
=
self
.
exe
()
return
numpy
.
array
([
o
.
mean
()
for
o
in
outs
.
itervalues
()]).
mean
()
...
...
@@ -191,9 +224,10 @@ class TestSimpleMul(unittest.TestCase):
# @many_times(10)
@
prog_scope
()
def
test_forward_backward
(
self
):
py
thon_impl
=
TestSimpleMul
.
SimpleMul
()
py
_rnn
=
TestSimpleMul
.
SimpleMul
()
dat
=
fluid
.
layers
.
data
(
name
=
self
.
DATA_NAME
,
shape
=
[
self
.
DATA_WIDTH
],
lod_level
=
1
)
dat
.
stop_gradient
=
False
rnn
=
fluid
.
layers
.
DynamicRNN
()
with
rnn
.
block
():
...
...
@@ -212,17 +246,26 @@ class TestSimpleMul(unittest.TestCase):
cpu
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
cpu
)
out
,
w_g
=
exe
.
run
(
feed
=
python_impl
.
to_feed
(
cpu
),
fetch_list
=
[
out
,
self
.
PARAM_NAME
+
"@GRAD"
])
out_by_python
=
python_impl
.
exe
()[
self
.
OUT_NAME
]
out
,
w_g
,
i_g
=
map
(
numpy
.
array
,
exe
.
run
(
feed
=
py_rnn
.
to_feed
(
cpu
),
fetch_list
=
[
out
,
self
.
PARAM_NAME
+
"@GRAD"
,
self
.
DATA_NAME
+
"@GRAD"
],
return_numpy
=
False
))
out_by_python
=
py_rnn
.
exe
()[
self
.
OUT_NAME
]
self
.
assertTrue
(
numpy
.
allclose
(
out
,
out_by_python
))
w_g_num
=
py
thon_impl
.
get_numeric_gradient_of_param
(
self
.
PARAM_NAME
)
w_g_num
=
py
_rnn
.
get_numeric_gradient_of_param
(
self
.
PARAM_NAME
)
self
.
assertTrue
(
numpy
.
allclose
(
w_g_num
,
w_g
,
rtol
=
0.05
))
i_g_num
=
py_rnn
.
get_numeric_gradient_of_input
(
input_name
=
self
.
DATA_NAME
)
i_g_num
=
i_g_num
.
reshape
(
i_g
.
shape
)
self
.
assertTrue
(
numpy
.
allclose
(
i_g_num
,
i_g
,
rtol
=
0.05
))
class
TestSimpleMulWithMemory
(
unittest
.
TestCase
):
DATA_WIDTH
=
32
HIDDEN_WIDTH
=
1
0
HIDDEN_WIDTH
=
2
0
DATA_NAME
=
'X'
PARAM_NAME
=
'W'
...
...
@@ -251,12 +294,14 @@ class TestSimpleMulWithMemory(unittest.TestCase):
assert
isinstance
(
Out
,
Output
)
Out
.
out
(
o
)
# @many_times(10)
@
prog_scope
()
def
test_forward_backward
(
self
):
py_rnn
=
TestSimpleMulWithMemory
.
SimpleMulWithMemory
()
data
=
fluid
.
layers
.
data
(
name
=
self
.
DATA_NAME
,
shape
=
[
self
.
DATA_WIDTH
],
lod_level
=
1
)
data
.
stop_gradient
=
False
rnn
=
fluid
.
layers
.
DynamicRNN
()
with
rnn
.
block
():
d
=
rnn
.
step_input
(
data
)
...
...
@@ -272,14 +317,32 @@ class TestSimpleMulWithMemory(unittest.TestCase):
out
=
rnn
()
last
=
fluid
.
layers
.
sequence_pool
(
input
=
out
,
pool_type
=
'last'
)
loss
=
fluid
.
layers
.
mean
(
x
=
last
)
fluid
.
backward
.
append_backward_ops
(
loss
)
cpu
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
cpu
)
last_np
,
=
exe
.
run
(
feed
=
py_rnn
.
to_feed
(
cpu
),
fetch_list
=
[
last
])
feed
=
py_rnn
.
to_feed
(
cpu
)
for
_
in
xrange
(
2
):
last_np
,
w_g
,
i_g
=
map
(
numpy
.
array
,
exe
.
run
(
feed
=
feed
,
fetch_list
=
[
last
,
self
.
PARAM_NAME
+
"@GRAD"
,
self
.
DATA_NAME
+
"@GRAD"
],
return_numpy
=
False
))
last_by_py
,
=
py_rnn
.
exe
().
values
()
self
.
assertTrue
(
numpy
.
allclose
(
last_np
,
last_by_py
))
w_g_num
=
py_rnn
.
get_numeric_gradient_of_param
(
self
.
PARAM_NAME
)
print
w_g
[
0
],
w_g_num
[
0
]
self
.
assertTrue
(
numpy
.
allclose
(
w_g_num
,
w_g
,
rtol
=
0.1
))
i_g_num
=
py_rnn
.
get_numeric_gradient_of_input
(
self
.
DATA_NAME
)
i_g_num
=
i_g_num
.
reshape
(
i_g
.
shape
)
# Since this RNN has many float add. The number could be not stable.
# rtol = 0.1
self
.
assertTrue
(
numpy
.
allclose
(
i_g_num
,
i_g
,
rtol
=
0.1
))
if
__name__
==
'__main__'
:
...
...
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