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体验新版 GitCode,发现更多精彩内容 >>
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5ca0bb9a
编写于
8月 23, 2018
作者:
T
tensor-tang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
support more activation type and remove some comments
上级
dd938d0b
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
63 addition
and
29 deletion
+63
-29
paddle/fluid/operators/attention_lstm_op.cc
paddle/fluid/operators/attention_lstm_op.cc
+29
-28
paddle/fluid/operators/math/cpu_vec.h
paddle/fluid/operators/math/cpu_vec.h
+25
-1
python/paddle/fluid/tests/unittests/test_attention_lstm_op.py
...on/paddle/fluid/tests/unittests/test_attention_lstm_op.py
+9
-0
未找到文件。
paddle/fluid/operators/attention_lstm_op.cc
浏览文件 @
5ca0bb9a
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/attention_lstm_op.h"
#include <sys/time.h>
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
...
...
@@ -192,24 +193,23 @@ void AttentionLSTMOpMaker::Make() {
"(Tensor) the output of LSTM X(1*(D+M))* weight((D+M)*4D) for each step."
"Shape is (1 x 4D), where M is the x frame size"
)
.
AsIntermediate
();
// TODO(TJ): InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr
<
std
::
string
>
(
"gate_activation"
,
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default."
)
.
SetDefault
(
"sigmoid"
)
.
InEnum
({
"sigmoid"
});
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddAttr
<
std
::
string
>
(
"cell_activation"
,
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"
tanh
"
});
.
InEnum
({
"
sigmoid"
,
"tanh"
,
"relu"
,
"identity
"
});
AddAttr
<
std
::
string
>
(
"candidate_activation"
,
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"
tanh
"
});
.
InEnum
({
"
sigmoid"
,
"tanh"
,
"relu"
,
"identity
"
});
AddComment
(
R"DOC(
Attention Long-Short Term Memory (LSTM) Operator.
...
...
@@ -273,22 +273,23 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
DeviceContext
=
paddle
::
platform
::
CPUDeviceContext
;
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
// T x M
auto
*
h0
=
ctx
.
Input
<
Tensor
>
(
"H0"
);
// N x D
auto
*
c0
=
ctx
.
Input
<
Tensor
>
(
"C0"
);
// N x D
auto
*
atten_w
=
ctx
.
Input
<
Tensor
>
(
"AttentionWeight"
);
// (M+D) x 1
auto
*
atten_b
=
ctx
.
Input
<
Tensor
>
(
"AttentionBias"
);
// 1x1
auto
*
atten_scalar
=
ctx
.
Input
<
Tensor
>
(
"AttentionScalar"
);
// 1x1
auto
*
atten_scalar_bias
=
ctx
.
Input
<
Tensor
>
(
"AttentionScalarBias"
);
// 1x1
auto
*
lstm_w
=
ctx
.
Input
<
Tensor
>
(
"LSTMWeight"
);
// (D+M) x D*4
auto
*
lstm_b
=
ctx
.
Input
<
Tensor
>
(
"LSTMBias"
);
// 1 x D*4
auto
*
hidden_out
=
ctx
.
Output
<
LoDTensor
>
(
"Hidden"
);
// TxD
auto
*
cell_out
=
ctx
.
Output
<
LoDTensor
>
(
"Cell"
);
// TxD
auto
*
atted_x
=
ctx
.
Output
<
Tensor
>
(
"AttentionedX"
);
// T x 1
auto
*
fc_out
=
ctx
.
Output
<
Tensor
>
(
"AttentionFCOut"
);
// max_seq_len x 1
auto
*
lstm_x
=
ctx
.
Output
<
Tensor
>
(
"LSTMX"
);
// 1 x M
auto
*
lstm_out
=
ctx
.
Output
<
Tensor
>
(
"LSTMOUT"
);
// 1 x 4D
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
h0
=
ctx
.
Input
<
Tensor
>
(
"H0"
);
auto
*
c0
=
ctx
.
Input
<
Tensor
>
(
"C0"
);
auto
*
atten_w
=
ctx
.
Input
<
Tensor
>
(
"AttentionWeight"
);
auto
*
atten_b
=
ctx
.
Input
<
Tensor
>
(
"AttentionBias"
);
auto
*
atten_scalar
=
ctx
.
Input
<
Tensor
>
(
"AttentionScalar"
);
auto
*
atten_scalar_bias
=
ctx
.
Input
<
Tensor
>
(
"AttentionScalarBias"
);
auto
*
lstm_w
=
ctx
.
Input
<
Tensor
>
(
"LSTMWeight"
);
auto
*
lstm_b
=
ctx
.
Input
<
Tensor
>
(
"LSTMBias"
);
auto
*
hidden_out
=
ctx
.
Output
<
LoDTensor
>
(
"Hidden"
);
auto
*
cell_out
=
ctx
.
Output
<
LoDTensor
>
(
"Cell"
);
auto
*
atted_x
=
ctx
.
Output
<
Tensor
>
(
"AttentionedX"
);
auto
*
fc_out
=
ctx
.
Output
<
Tensor
>
(
"AttentionFCOut"
);
auto
*
lstm_x
=
ctx
.
Output
<
Tensor
>
(
"LSTMX"
);
auto
*
lstm_out
=
ctx
.
Output
<
Tensor
>
(
"LSTMOUT"
);
// some shape should be reshape here since infershape can not get lod info
auto
x_lod
=
x
->
lod
();
...
...
@@ -310,11 +311,11 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ
(
c0
->
dims
()[
0
],
N
,
"C0 dims should be %d x %d."
,
N
,
D
);
fc_out
->
Resize
({
max_seq_len
,
1
});
// TODO(TJ): act functor init here
// if (platform::jit::MayIUse(platform::jit::avx2)) {
// } else if (platform::jit::MayIUse(platform::jit::avx)) {
// } else {
// }
math
::
VecActivations
<
T
>
act_functor
;
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
act_gate
,
act_cell
,
act_cand
;
act_gate
=
act_functor
(
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
));
act_cell
=
act_functor
(
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
));
act_cand
=
act_functor
(
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
));
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
h0_data
=
h0
?
h0
->
data
<
T
>
()
:
NULL
;
...
...
@@ -381,9 +382,9 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
blas
.
VADD
(
D4
,
lstm_b_data
,
lstm_out_data
,
lstm_out_data
);
// gate act: sigmoid
math
::
vec_sigmoid
(
D3
,
lstm_out_data
,
lstm_out_data
);
act_gate
(
D3
,
lstm_out_data
,
lstm_out_data
);
// candicate act: tanh
math
::
vec_tanh
(
D
,
lstm_out_data
+
D3
,
lstm_out_data
+
D3
);
act_cand
(
D
,
lstm_out_data
+
D3
,
lstm_out_data
+
D3
);
// a = forget * prev_cell
blas
.
VMUL
(
D
,
lstm_out_data
,
prev_cell_data
,
lstm_out_data
);
...
...
@@ -395,7 +396,7 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
blas
.
VADD
(
D
,
lstm_out_data
,
lstm_out_data
+
D
,
cur_cell_out_data
);
// state act tanh(cell_out) * output_gate
math
::
vec_tanh
(
D
,
cur_cell_out_data
,
lstm_out_data
);
act_cell
(
D
,
cur_cell_out_data
,
lstm_out_data
);
blas
.
VMUL
(
D
,
lstm_out_data
,
lstm_out_data
+
D2
,
cur_hidden_out_data
);
prev_hidden_data
=
cur_hidden_out_data
;
...
...
paddle/fluid/operators/math/cpu_vec.h
浏览文件 @
5ca0bb9a
...
...
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
namespace
paddle
{
...
...
@@ -34,6 +34,12 @@ inline T tanh(T x) {
return
2.
*
sigmoid
(
2.
*
x
)
-
1.
;
}
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
inline
void
vec_identity
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
// do nothing
return
;
}
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
inline
void
vec_sigmoid
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
...
...
@@ -76,6 +82,24 @@ inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
}
}
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
class
VecActivations
{
public:
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
operator
()(
const
std
::
string
&
type
)
{
if
(
type
==
"sigmoid"
)
{
return
vec_sigmoid
<
T
,
isa
>
;
}
else
if
(
type
==
"relu"
)
{
return
vec_relu
<
T
,
isa
>
;
}
else
if
(
type
==
"tanh"
)
{
return
vec_tanh
<
T
,
isa
>
;
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
vec_identity
<
T
,
isa
>
;
}
PADDLE_THROW
(
"Not support type %s."
,
type
);
}
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/test_attention_lstm_op.py
浏览文件 @
5ca0bb9a
...
...
@@ -160,6 +160,15 @@ class TestAttentionOpNonInit(TestAttentionLSTMOp):
self
.
has_initial_hidden
=
False
class
TestAttentionOpAct
(
TestAttentionLSTMOp
):
def
set_conf
(
self
):
self
.
M
=
3
self
.
D
=
2
self
.
act_gate
=
'relu'
self
.
act_cell
=
'tanh'
self
.
act_cand
=
'sigmoid'
class
TestAttentionOpMD1
(
TestAttentionLSTMOp
):
def
set_conf
(
self
):
self
.
M
=
36
...
...
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