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体验新版 GitCode,发现更多精彩内容 >>
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d760b6a5
编写于
12月 25, 2017
作者:
Q
qingqing01
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Refine the activation type getting in the LSTM operator to speed.
上级
d3c42f7d
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
102 addition
and
67 deletion
+102
-67
paddle/operators/lstm_op.h
paddle/operators/lstm_op.h
+13
-6
paddle/operators/math/detail/activation_functions.h
paddle/operators/math/detail/activation_functions.h
+22
-0
paddle/operators/math/detail/lstm_cpu_kernel.h
paddle/operators/math/detail/lstm_cpu_kernel.h
+18
-18
paddle/operators/math/detail/lstm_gpu_kernel.h
paddle/operators/math/detail/lstm_gpu_kernel.h
+12
-12
paddle/operators/math/detail/lstm_kernel.h
paddle/operators/math/detail/lstm_kernel.h
+11
-11
paddle/operators/math/lstm_compute.cc
paddle/operators/math/lstm_compute.cc
+8
-8
paddle/operators/math/lstm_compute.cu
paddle/operators/math/lstm_compute.cu
+10
-8
paddle/operators/math/lstm_compute.h
paddle/operators/math/lstm_compute.h
+8
-4
未找到文件。
paddle/operators/lstm_op.h
浏览文件 @
d760b6a5
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/operators/math/lstm_compute.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/sequence2batch.h"
#include "paddle/operators/math/detail/activation_functions.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -102,9 +103,12 @@ class LSTMKernel : public framework::OpKernel<T> {
auto
batch_starts
=
batch_gate
->
lod
()[
0
];
size_t
num_batch
=
batch_starts
.
size
()
-
1
;
auto
gate_act
=
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
);
auto
cell_act
=
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
);
auto
cand_act
=
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
);
auto
gate_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
));
auto
cell_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
));
auto
cand_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
));
for
(
size_t
n
=
0
;
n
<
num_batch
;
n
++
)
{
int
bstart
=
static_cast
<
int
>
(
batch_starts
[
n
]);
...
...
@@ -264,9 +268,12 @@ class LSTMGradKernel : public framework::OpKernel<T> {
batch_gate_g
.
mutable_data
<
T
>
(
batch_gate
->
dims
(),
ctx
.
GetPlace
());
batch_gate_g
.
set_lod
(
batch_gate
->
lod
());
auto
gate_act
=
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
);
auto
cell_act
=
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
);
auto
cand_act
=
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
);
auto
gate_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
));
auto
cell_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
));
auto
cand_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
));
auto
batch_starts
=
batch_gate
->
lod
()[
0
];
size_t
num_batch
=
batch_starts
.
size
()
-
1
;
...
...
paddle/operators/math/detail/activation_functions.h
浏览文件 @
d760b6a5
...
...
@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h>
#include "paddle/platform/hostdevice.h"
#include "paddle/platform/enforce.h"
#ifdef __AVX__
#include <immintrin.h>
...
...
@@ -29,6 +30,27 @@ namespace detail {
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
enum
ActivationType
{
kSigmoid
,
kReLU
,
kTanh
,
kIdentity
,
};
inline
ActivationType
GetActivationType
(
const
std
::
string
&
type
)
{
if
(
type
==
"sigmoid"
)
{
return
ActivationType
::
kSigmoid
;
}
else
if
(
type
==
"relu"
)
{
return
ActivationType
::
kReLU
;
}
else
if
(
type
==
"tanh"
)
{
return
ActivationType
::
kTanh
;
}
else
if
(
type
==
"identity"
)
{
return
ActivationType
::
kIdentity
;
}
PADDLE_THROW
(
"Not support type %s."
,
type
);
}
namespace
forward
{
template
<
typename
T
>
...
...
paddle/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @
d760b6a5
...
...
@@ -27,9 +27,9 @@ namespace detail {
template
<
class
T
,
class
Op
>
void
naive_lstm_forward_one_sequence
(
Op
op
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
T
r_value_in
;
T
r_value_ig
;
T
r_value_fg
;
...
...
@@ -77,9 +77,9 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template
<
class
T
,
class
Op
>
void
naive_lstm_backward_one_sequence
(
Op
op
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
T
r_value_in
;
T
r_value_ig
;
T
r_value_fg
;
...
...
@@ -150,9 +150,9 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
template
<
class
T
,
class
Op
>
void
avx_lstm_forward_one_sequence
(
Op
op
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
#ifdef __AVX__
__m256
r_value_in
;
__m256
r_value_ig
;
...
...
@@ -204,9 +204,9 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template
<
class
T
,
class
Op
>
void
avx_lstm_backward_one_sequence
(
Op
op
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
#ifdef __AVX__
__m256
r_value_in
;
__m256
r_value_ig
;
...
...
@@ -281,9 +281,9 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
template
<
class
T
,
class
Op
>
void
cpu_lstm_forward
(
Op
op
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
if
(
Op
::
avx
&&
!
(
frame_size
&
(
8
-
1
))
&&
(
std
::
is_same
<
T
,
float
>::
value
))
{
avx_lstm_forward_one_sequence
<
T
>
(
op
,
value
,
frame_size
,
active_node
,
active_gate
,
active_state
);
...
...
@@ -295,9 +295,9 @@ void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frame_size,
template
<
class
T
,
class
Op
>
void
cpu_lstm_backward
(
Op
op
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
int
frame_size
,
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
if
(
Op
::
avx
&&
!
(
frame_size
&
(
8
-
1
))
&&
(
std
::
is_same
<
T
,
float
>::
value
))
{
avx_lstm_backward_one_sequence
<
T
>
(
op
,
value
,
grad
,
frame_size
,
active_node
,
active_gate
,
active_state
);
...
...
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @
d760b6a5
...
...
@@ -31,9 +31,9 @@ namespace detail {
*/
template
<
class
T
,
class
Op
,
bool
is_batch
>
__global__
void
KeLstmForward
(
Op
op
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
int
batch_size
,
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
const
int
frame_idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
frame_idx
>=
frame_size
)
return
;
...
...
@@ -91,9 +91,9 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
template
<
class
T
,
class
Op
,
bool
is_batch
>
__global__
void
KeLstmBackward
(
Op
op
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
int
batch_size
,
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
const
int
frame_idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
frame_idx
>=
frame_size
)
return
;
...
...
@@ -185,9 +185,9 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
template
<
class
T
,
class
Op
>
void
gpu_lstm_forward
(
const
platform
::
DeviceContext
&
context
,
Op
op
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
dim3
threads
;
dim3
grid
;
if
(
batch_size
==
1
)
{
...
...
@@ -220,9 +220,9 @@ template <class T, class Op>
void
gpu_lstm_backward
(
const
platform
::
DeviceContext
&
context
,
Op
op
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
dim3
threads
;
dim3
grid
;
if
(
batch_size
==
1
)
{
...
...
paddle/operators/math/detail/lstm_kernel.h
浏览文件 @
d760b6a5
...
...
@@ -30,9 +30,9 @@ class lstm {
HOSTDEVICE
void
operator
()(
T
&
value_in
,
T
&
value_ig
,
T
&
value_fg
,
T
&
value_og
,
T
&
prev_state
,
T
&
state
,
T
&
state_atv
,
T
&
output
,
T
&
checkI
,
T
&
checkF
,
T
&
checkO
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
value_in
=
activation
(
value_in
,
active_node
);
value_ig
=
activation
(
value_ig
+
prev_state
*
checkI
,
active_gate
);
value_fg
=
activation
(
value_fg
+
prev_state
*
checkF
,
active_gate
);
...
...
@@ -53,9 +53,9 @@ class lstm {
__m256
&
prev_state
,
__m256
&
state
,
__m256
&
state_atv
,
__m256
&
output
,
__m256
&
checkI
,
__m256
&
checkF
,
__m256
&
checkO
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
value_in
=
activation
(
value_in
,
active_node
);
value_ig
=
activation
(
_mm256_add_ps
(
value_ig
,
_mm256_mul_ps
(
prev_state
,
checkI
)),
...
...
@@ -87,9 +87,9 @@ class lstm {
T
&
state_grad
,
T
&
state_atv
,
T
&
output_grad
,
T
&
checkI
,
T
&
checkF
,
T
&
checkO
,
T
&
checkIGrad
,
T
&
checkFGrad
,
T
&
checkOGrad
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
grad_og
=
activation
(
output_grad
*
state_atv
,
value_og
,
active_gate
);
state_grad
+=
activation
(
output_grad
*
value_og
,
state_atv
,
active_state
)
+
grad_og
*
checkO
;
...
...
@@ -114,8 +114,8 @@ class lstm {
__m256
&
prev_state
,
__m256
&
prev_state_grad
,
__m256
&
state
,
__m256
&
state_grad
,
__m256
&
state_atv
,
__m256
&
output_grad
,
__m256
&
checkI
,
__m256
&
checkF
,
__m256
&
checkO
,
__m256
&
checkIGrad
,
__m256
&
checkFGrad
,
__m256
&
checkOGrad
,
activation_mode_t
active_node
,
activation_mode_t
active_gate
,
activation_mode_t
active_state
)
{
__m256
&
checkFGrad
,
__m256
&
checkOGrad
,
ActivationType
active_node
,
ActivationType
active_gate
,
ActivationType
active_state
)
{
grad_og
=
activation
(
_mm256_mul_ps
(
output_grad
,
state_atv
),
value_og
,
active_gate
);
state_grad
=
_mm256_add_ps
(
activation
(
_mm256_mul_ps
(
output_grad
,
value_og
),
...
...
paddle/operators/math/lstm_compute.cc
浏览文件 @
d760b6a5
...
...
@@ -24,12 +24,12 @@ template <class T>
struct
LstmUnitFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
static
void
compute
(
const
platform
::
CPUDeviceContext
&
context
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
const
std
::
string
&
gate_act
,
const
std
::
string
&
cell_act
,
const
std
::
string
&
cand_act
)
{
const
detail
::
ActivationType
&
gate_act
,
const
detail
::
ActivationType
&
cell_act
,
const
detail
::
ActivationType
&
cand_act
)
{
for
(
int
b
=
0
;
b
<
batch_size
;
b
++
)
{
detail
::
cpu_lstm_forward
(
detail
::
forward
::
lstm
<
T
>
(),
value
,
frame_size
,
ActiveType
(
cand_act
),
ActiveType
(
gate_act
),
ActiveType
(
cell_act
));
cand_act
,
gate_act
,
cell_act
);
value
.
gate_value
+=
frame_size
*
4
;
value
.
state_value
+=
frame_size
;
value
.
state_active_value
+=
frame_size
;
...
...
@@ -46,12 +46,12 @@ struct LstmUnitGradFunctor<platform::CPUDeviceContext, T> {
static
void
compute
(
const
platform
::
CPUDeviceContext
&
context
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
const
std
::
string
&
gate_act
,
const
std
::
string
&
cell_act
,
const
std
::
string
&
cand_act
)
{
const
detail
::
ActivationType
&
gate_act
,
const
detail
::
ActivationType
&
cell_act
,
const
detail
::
ActivationType
&
cand_act
)
{
for
(
int
b
=
0
;
b
<
batch_size
;
b
++
)
{
detail
::
cpu_lstm_backward
(
detail
::
backward
::
lstm
<
T
>
(),
value
,
grad
,
frame_size
,
ActiveType
(
cand_act
),
ActiveType
(
gate_act
),
ActiveType
(
cell_act
));
frame_size
,
cand_act
,
gate_act
,
cell_act
);
value
.
gate_value
+=
frame_size
*
4
;
value
.
state_value
+=
frame_size
;
...
...
paddle/operators/math/lstm_compute.cu
浏览文件 @
d760b6a5
...
...
@@ -24,11 +24,12 @@ template <class T>
struct
LstmUnitFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
static
void
compute
(
const
platform
::
CUDADeviceContext
&
context
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
const
std
::
string
&
gate_act
,
const
std
::
string
&
cell_act
,
const
std
::
string
&
cand_act
)
{
const
detail
::
ActivationType
&
gate_act
,
const
detail
::
ActivationType
&
cell_act
,
const
detail
::
ActivationType
&
cand_act
)
{
detail
::
gpu_lstm_forward
<
T
>
(
context
,
detail
::
forward
::
lstm
<
T
>
(),
value
,
frame_size
,
batch_size
,
ActiveType
(
cand_act
)
,
ActiveType
(
gate_act
),
ActiveType
(
cell_act
)
);
frame_size
,
batch_size
,
cand_act
,
gate_act
,
cell_act
);
}
};
...
...
@@ -37,11 +38,12 @@ struct LstmUnitGradFunctor<platform::CUDADeviceContext, T> {
static
void
compute
(
const
platform
::
CUDADeviceContext
&
context
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
const
std
::
string
&
gate_act
,
const
std
::
string
&
cell_act
,
const
std
::
string
&
cand_act
)
{
const
detail
::
ActivationType
&
gate_act
,
const
detail
::
ActivationType
&
cell_act
,
const
detail
::
ActivationType
&
cand_act
)
{
detail
::
gpu_lstm_backward
(
context
,
detail
::
backward
::
lstm
<
T
>
(),
value
,
grad
,
frame_size
,
batch_size
,
ActiveType
(
cand_act
)
,
ActiveType
(
gate_act
),
ActiveType
(
cell_act
)
);
frame_size
,
batch_size
,
cand_act
,
gate_act
,
cell_act
);
}
};
...
...
paddle/operators/math/lstm_compute.h
浏览文件 @
d760b6a5
...
...
@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/platform/device_context.h"
#include "paddle/platform/enforce.h"
#include "paddle/operators/math/detail/activation_functions.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -29,6 +30,7 @@ typedef enum {
HL_ACTIVATION_END
}
activation_mode_t
;
template
<
class
T
>
struct
LstmMetaValue
{
T
*
gate_value
;
...
...
@@ -72,8 +74,9 @@ class LstmUnitFunctor {
public:
static
void
compute
(
const
DeviceContext
&
context
,
LstmMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
const
std
::
string
&
gate_act
,
const
std
::
string
&
cell_act
,
const
std
::
string
&
cand_act
);
const
detail
::
ActivationType
&
gate_act
,
const
detail
::
ActivationType
&
cell_act
,
const
detail
::
ActivationType
&
cand_act
);
};
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -81,8 +84,9 @@ class LstmUnitGradFunctor {
public:
static
void
compute
(
const
DeviceContext
&
context
,
LstmMetaValue
<
T
>
value
,
LstmMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
const
std
::
string
&
gate_act
,
const
std
::
string
&
cell_act
,
const
std
::
string
&
cand_act
);
const
detail
::
ActivationType
&
gate_act
,
const
detail
::
ActivationType
&
cell_act
,
const
detail
::
ActivationType
&
cand_act
);
};
}
// namespace math
...
...
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