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17e33738
编写于
10月 19, 2017
作者:
D
dangqingqing
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Enhance unit testing and fix bug.
上级
8bec26be
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
101 addition
and
62 deletion
+101
-62
paddle/operators/lstm_op.h
paddle/operators/lstm_op.h
+5
-12
paddle/operators/math/detail/hl_gpu_functions.h
paddle/operators/math/detail/hl_gpu_functions.h
+5
-1
paddle/operators/math/detail/lstm_gpu_kernel.h
paddle/operators/math/detail/lstm_gpu_kernel.h
+4
-2
paddle/operators/math/lstm_compute.h
paddle/operators/math/lstm_compute.h
+1
-1
python/paddle/v2/framework/tests/op_test.py
python/paddle/v2/framework/tests/op_test.py
+2
-2
python/paddle/v2/framework/tests/test_lstm_op.py
python/paddle/v2/framework/tests/test_lstm_op.py
+84
-44
未找到文件。
paddle/operators/lstm_op.h
浏览文件 @
17e33738
...
...
@@ -56,10 +56,6 @@ class LSTMKernel : public framework::OpKernel<T> {
framework
::
DDim
dims
({
in_dims
[
0
],
frame_size
});
if
(
bias
)
{
// framework::Tensor cpu_t;
// cpu_t.mutable_data<T>(in_dims, platform::CPUPlace());
// cpu_t.CopyFrom<T>(*batch_gate, platform::CPUPlace(),
// ctx.device_context());
Eigen
::
array
<
int
,
2
>
extents
({{
1
,
4
*
frame_size
}});
Eigen
::
array
<
int
,
2
>
offsets
({{
0
,
0
}});
auto
b
=
EigenMatrix
<
T
>::
From
(
*
bias
);
...
...
@@ -105,14 +101,14 @@ class LSTMKernel : public framework::OpKernel<T> {
int
cur_batch_size
=
bend
-
bstart
;
if
(
n
!=
0
)
{
int
pre_end
=
batch_lod
[
n
-
1
];
auto
pre_hidden_t
=
batch_out
.
Slice
<
T
>
(
pre_end
,
bstart
);
int
pre_h_start
=
batch_lod
[
n
-
1
];
int
pre_h_end
=
pre_h_start
+
cur_batch_size
;
auto
pre_hidden_t
=
batch_out
.
Slice
<
T
>
(
pre_h_start
,
pre_h_end
);
math
::
matmul
<
Place
,
T
>
(
ctx
.
device_context
(),
pre_hidden_t
,
false
,
*
weight
,
false
,
static_cast
<
T
>
(
1.0
),
&
gate_t
,
static_cast
<
T
>
(
0
.0
));
static_cast
<
T
>
(
1
.0
));
}
// else if : how to pass the state from
// last mini-batch will be supported later
// else if : support the initial hidden and cell
lstm_value
.
gateValue
=
gate_t
.
data
<
T
>
();
lstm_value
.
outputValue
=
out_t
.
data
<
T
>
();
...
...
@@ -132,9 +128,6 @@ class LSTMKernel : public framework::OpKernel<T> {
batch_cell
.
set_lod
(
batch_gate
->
lod
());
// restore the output cell state in LoDTensor from the batch cell
to_seq
(
ctx
.
device_context
(),
batch_cell
,
*
cell_out
);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
batch_gate
);
t
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
}
};
...
...
paddle/operators/math/detail/hl_gpu_functions.h
浏览文件 @
17e33738
...
...
@@ -30,7 +30,9 @@ __device__ static float sigmoid(const float a) {
}
__device__
static
float
tanh
(
const
float
a
)
{
return
__fdividef
(
2.0
f
,
(
1.0
f
+
__expf
(
-
2.0
f
*
a
)))
-
1.0
f
;
float
tmp
=
-
2.0
*
a
;
tmp
=
(
tmp
>
EXP_MAX_INPUT
)
?
EXP_MAX_INPUT
:
tmp
;
return
__fdividef
(
2.0
f
,
(
1.0
f
+
__expf
(
-
2.0
f
*
tmp
)))
-
1.0
f
;
}
__device__
static
float
linear
(
const
float
a
)
{
return
a
;
}
...
...
@@ -63,6 +65,8 @@ __device__ static double sigmoid(const double a) {
}
__device__
static
double
tanh
(
const
double
a
)
{
double
tmp
=
-
2.0
*
a
;
tmp
=
(
tmp
>
EXP_MAX_INPUT
)
?
EXP_MAX_INPUT
:
tmp
;
return
(
2.0
/
(
1.0
+
exp
(
-
2.0
*
a
)))
-
1.0
;
}
...
...
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @
17e33738
...
...
@@ -205,11 +205,13 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
if
(
batchSize
==
1
)
{
KeLstmForward
<
T
,
Op
,
/* isBatch= */
false
><<<
grid
,
threads
,
0
,
stream
>>>
(
op
,
value
,
frameSize
,
batchSize
,
active_node
,
active_gate
,
active_gate
);
op
,
value
,
frameSize
,
batchSize
,
active_node
,
active_gate
,
active_state
);
}
else
{
KeLstmForward
<
T
,
Op
,
/* isBatch= */
true
><<<
grid
,
threads
,
0
,
stream
>>>
(
op
,
value
,
frameSize
,
batchSize
,
active_node
,
active_gate
,
active_gate
);
op
,
value
,
frameSize
,
batchSize
,
active_node
,
active_gate
,
active_state
);
}
}
...
...
paddle/operators/math/lstm_compute.h
浏览文件 @
17e33738
...
...
@@ -60,7 +60,7 @@ inline activation_mode_t ActiveType(const std::string &type) {
return
HL_ACTIVATION_RELU
;
}
else
if
(
type
==
"tanh"
)
{
return
HL_ACTIVATION_TANH
;
}
else
if
(
type
==
"linear"
||
type
==
""
)
{
}
else
if
(
type
==
"linear"
||
type
==
"
identity"
||
type
==
"
"
)
{
return
HL_ACTIVATION_LINEAR
;
}
else
{
PADDLE_THROW
(
"Do not support activation type."
);
...
...
python/paddle/v2/framework/tests/op_test.py
浏览文件 @
17e33738
...
...
@@ -242,7 +242,7 @@ class OpTest(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
actual
,
expect
,
atol
=
atol
),
"
output name: "
+
out_name
+
" has diff."
)
"
Output ("
+
out_name
+
") has diff at "
+
str
(
place
)
)
else
:
actual
=
np
.
array
(
self
.
scope
.
find_var
(
out_name
).
get_tensor
())
expect
=
self
.
outputs
[
out_name
]
...
...
@@ -250,7 +250,7 @@ class OpTest(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
actual
,
expect
,
atol
=
atol
),
"
output name: "
+
out_name
+
" has diff."
)
"
Output ("
+
out_name
+
") has diff at "
+
str
(
place
)
)
def
check_output
(
self
,
atol
=
1e-5
):
places
=
[
core
.
CPUPlace
()]
...
...
python/paddle/v2/framework/tests/test_lstm_op.py
浏览文件 @
17e33738
...
...
@@ -28,6 +28,14 @@ def relu(x):
return
np
.
maximum
(
x
,
0
)
ACTVATION
=
{
'identity'
:
identity
,
'sigmoid'
:
sigmoid
,
'tanh'
:
tanh
,
'relu'
:
relu
}
def
lstm
(
input
,
# T x 4D
lod
,
# 1 x N
...
...
@@ -37,37 +45,45 @@ def lstm(
w_b
=
None
,
# 1 x 4D
w_c
=
None
,
# 1 x 3D
is_reverse
=
False
,
gate_act
=
None
,
cell_act
=
None
,
cand_act
=
None
):
def
_step
(
x
,
w_h
,
w_c
,
h_pre
,
c_pre
,
gate_act
,
cell_act
,
cand_act
):
act_gate
=
None
,
act_cell
=
None
,
act_cand
=
None
):
def
_step
(
x
,
w_h
,
w_c
,
h_pre
,
c_pre
,
act_gate
,
act_cell
,
act_cand
):
g
=
np
.
dot
(
h_pre
,
w_h
)
# 1 x 4D
g
=
g
+
x
g
=
np
.
reshape
(
g
,
(
1
,
g
.
size
))
c_tmp
,
g_i
,
g_f
,
g_o
=
np
.
split
(
g
,
4
,
axis
=
1
)
if
w_c
is
None
:
g_i
=
gate_act
(
g_i
)
# 1 x D
g_f
=
gate_act
(
g_f
)
# 1 x D
g_i
=
act_gate
(
g_i
)
# 1 x D
g_f
=
act_gate
(
g_f
)
# 1 x D
else
:
w_ic
,
w_fc
,
w_oc
=
np
.
split
(
w_c
,
3
,
axis
=
1
)
g_i
=
gate_act
(
g_i
+
w_ic
*
c_pre
)
# 1 x D
g_f
=
gate_act
(
g_f
+
w_fc
*
c_pre
)
# 1 x D
c
=
g_f
*
c_pre
+
g_i
*
cand_act
(
c_tmp
)
# 1 x D
g_i
=
act_gate
(
g_i
+
w_ic
*
c_pre
)
# 1 x D
g_f
=
act_gate
(
g_f
+
w_fc
*
c_pre
)
# 1 x D
c
=
g_f
*
c_pre
+
g_i
*
act_cand
(
c_tmp
)
# 1 x D
if
w_c
is
None
:
g_o
=
gate_act
(
g_o
)
# 1 x D
g_o
=
act_gate
(
g_o
)
# 1 x D
else
:
_
,
_
,
w_oc
=
np
.
split
(
w_c
,
3
,
axis
=
1
)
g_o
=
gate_act
(
g_o
+
w_oc
*
c
)
# 1 x D
h
=
g_o
*
cell_act
(
c
)
bg
=
np
.
concatenate
((
cand_act
(
c_tmp
),
g_i
,
g_f
,
g_o
),
axis
=
1
)
g_o
=
act_gate
(
g_o
+
w_oc
*
c
)
# 1 x D
h
=
g_o
*
act_cell
(
c
)
bg
=
np
.
concatenate
((
act_cand
(
c_tmp
),
g_i
,
g_f
,
g_o
),
axis
=
1
)
return
h
,
c
,
bg
def
_reverse
(
x
,
lod
):
y
=
np
.
zeros_like
(
x
)
for
i
in
range
(
len
(
lod
)
-
1
):
b
,
e
=
lod
[
i
],
lod
[
i
+
1
]
y
[
b
:
e
,
:]
=
np
.
flip
(
x
[
b
:
e
,
:],
0
)
return
y
offset
=
lod
[
0
]
batch_size
=
len
(
offset
)
-
1
hidden
=
[]
cell
=
[]
gate
=
[]
input
=
_reverse
(
input
,
offset
)
if
is_reverse
else
input
if
w_b
is
not
None
:
input
=
input
+
np
.
tile
(
w_b
,
(
offset
[
-
1
],
1
))
for
i
in
range
(
batch_size
):
...
...
@@ -78,8 +94,8 @@ def lstm(
c_pre
=
c0
[
i
]
# 1 x D
for
j
in
range
(
seq_len
):
# compute one step
h_pre
,
c_pre
,
g_pre
=
_step
(
x
[
j
],
w_h
,
w_c
,
h_pre
,
c_pre
,
gate_act
,
cell_act
,
cand_act
)
h_pre
,
c_pre
,
g_pre
=
_step
(
x
[
j
],
w_h
,
w_c
,
h_pre
,
c_pre
,
act_gate
,
act_cell
,
act_cand
)
hidden
.
append
(
h_pre
.
flatten
())
cell
.
append
(
c_pre
.
flatten
())
gate
.
append
(
g_pre
.
flatten
())
...
...
@@ -87,38 +103,53 @@ def lstm(
hidden
=
np
.
array
(
hidden
).
astype
(
"float64"
)
cell
=
np
.
array
(
cell
).
astype
(
"float64"
)
gate
=
np
.
array
(
gate
).
astype
(
"float64"
)
hidden
=
_reverse
(
hidden
,
offset
)
if
is_reverse
else
hidden
cell
=
_reverse
(
cell
,
offset
)
if
is_reverse
else
cell
assert
gate
.
shape
==
input
.
shape
assert
hidden
.
shape
==
(
input
.
shape
[
0
],
input
.
shape
[
1
]
/
4
)
assert
cell
.
shape
==
(
input
.
shape
[
0
],
input
.
shape
[
1
]
/
4
)
return
hidden
,
cell
,
gate
class
LstmUnitTest
(
OpTest
):
class
TestLstmOp
(
OpTest
):
def
set_data
(
self
):
D
=
4
#lod = [[0, 2, 6, 9]]
lod
=
[[
0
,
1
]]
shape
=
(
1
,
D
)
x
=
np
.
random
.
normal
(
size
=
(
1
,
4
*
D
)).
astype
(
"float64"
)
h0
=
np
.
zeros
((
4
,
D
)).
astype
(
"float64"
)
c0
=
np
.
zeros
((
4
,
D
)).
astype
(
"float64"
)
w
=
np
.
random
.
normal
(
size
=
(
D
,
4
*
D
)).
astype
(
"float64"
)
b
=
np
.
random
.
normal
(
size
=
(
1
,
7
*
D
)).
astype
(
"float64"
)
w_b
=
b
[:,
0
:
4
*
D
]
w_c
=
b
[:,
4
*
D
:]
#h, c, g = lstm(x, lod, h0, c0, w, w_b, w_c, False, sigmoid, tanh, tanh)
h
,
c
,
g
=
lstm
(
x
,
lod
,
h0
,
c0
,
w
,
w_b
,
w_c
,
False
,
identity
,
identity
,
identity
)
self
.
lod
=
[[
0
,
2
,
6
,
9
]]
self
.
D
=
64
self
.
sort_idx
=
[
2
,
6
,
0
,
3
,
7
,
1
,
4
,
8
,
5
]
self
.
act_gate
=
"sigmoid"
self
.
act_cell
=
"tanh"
self
.
act_cand
=
"tanh"
self
.
is_reverse
=
False
def
setUp
(
self
):
self
.
set_data
()
self
.
op_type
=
"lstm"
T
=
self
.
lod
[
0
][
-
1
]
N
=
len
(
self
.
lod
[
0
])
-
1
x
=
np
.
random
.
normal
(
size
=
(
T
,
4
*
self
.
D
)).
astype
(
"float64"
)
h0
=
np
.
zeros
((
N
,
self
.
D
)).
astype
(
"float64"
)
c0
=
np
.
zeros
((
N
,
self
.
D
)).
astype
(
"float64"
)
w
=
np
.
random
.
normal
(
size
=
(
self
.
D
,
4
*
self
.
D
)).
astype
(
"float64"
)
b
=
np
.
random
.
normal
(
size
=
(
1
,
7
*
self
.
D
)).
astype
(
"float64"
)
w_b
=
b
[:,
0
:
4
*
self
.
D
]
w_c
=
b
[:,
4
*
self
.
D
:]
h
,
c
,
g
=
lstm
(
x
,
self
.
lod
,
h0
,
c0
,
w
,
w_b
,
w_c
,
self
.
is_reverse
,
ACTVATION
[
self
.
act_gate
],
ACTVATION
[
self
.
act_cell
],
ACTVATION
[
self
.
act_cand
])
g_sort
=
np
.
zeros_like
(
x
)
#idx = [2,6,0,3,7,1,4,8,5]
#for i, j in enumerate(idx):
# g_sort[i, :] = g[j, :]
for
i
,
j
in
enumerate
(
self
.
sort_idx
):
g_sort
[
i
,
:]
=
g
[
j
,
:]
self
.
inputs
=
{
'Input'
:
(
x
,
lod
),
'Input'
:
(
x
,
self
.
lod
),
'H0'
:
h0
,
'C0'
:
c0
,
'Weight'
:
w
,
...
...
@@ -127,19 +158,28 @@ class LstmUnitTest(OpTest):
self
.
outputs
=
{
'Hidden'
:
h
,
'Cell'
:
c
,
'BatchGate'
:
g_sort
}
self
.
attrs
=
{
'usePeepholes'
:
True
,
'isReverse'
:
Fal
se
,
'gateActivation'
:
'
linear
'
,
'cellActivation'
:
'
linear
'
,
'candidateActivation'
:
'
linear
'
'isReverse'
:
self
.
is_rever
se
,
'gateActivation'
:
'
sigmoid
'
,
'cellActivation'
:
'
tanh
'
,
'candidateActivation'
:
'
tanh
'
}
def
setUp
(
self
):
self
.
set_data
()
self
.
op_type
=
"lstm"
def
test_check_output
(
self
):
self
.
check_output
()
class
TestLstmOpRerverse
(
TestLstmOp
):
def
set_data
(
self
):
self
.
lod
=
[[
0
,
2
,
6
,
9
]]
self
.
D
=
64
self
.
sort_idx
=
[
2
,
6
,
0
,
3
,
7
,
1
,
4
,
8
,
5
]
self
.
act_gate
=
"sigmoid"
self
.
act_cell
=
"tanh"
self
.
act_cand
=
"tanh"
self
.
is_reverse
=
True
if
__name__
==
"__main__"
:
unittest
.
main
()
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