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c45cee03
编写于
6年前
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
refine infershape and forward
上级
c7c25067
4 合并请求
!13464
[Do Not Merge]Feature/merge all api
,
!13203
Fix cudnn holder bug
,
!13202
Ce debug
,
!12990
Feature fusion expand concat fc
变更
1
显示空白变更内容
内联
并排
Showing
1 changed file
with
54 addition
and
122 deletion
+54
-122
paddle/fluid/operators/fusion_seq_concat_fc_op.cc
paddle/fluid/operators/fusion_seq_concat_fc_op.cc
+54
-122
未找到文件。
paddle/fluid/operators/fusion_seq_concat_fc_op.cc
浏览文件 @
c45cee03
...
...
@@ -23,91 +23,36 @@ namespace paddle {
namespace
operators
{
void
FusionSeqConcatFCOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
)
,
"Input(X) of FusionSeqConcatFC should not be null
."
);
PADDLE_ENFORCE
_GT
(
ctx
->
Inputs
(
"X"
).
size
(),
1UL
,
"Inputs(X) of FusionSeqConcatFCOp should larger than 1
."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"FCWeight"
),
"Input(FCWeight) of FusionSeqConcatFC should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of FusionSeqConcatFC should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"FCOut"
),
"Output(FCOut) of FusionSeqConcatFC should not be null."
);
// need check fc height = all inputs width sum
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
const
int
M
=
x_dims
[
1
];
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank must be 2."
);
auto
w_dims
=
ctx
->
GetInputDim
(
"LSTMWeight"
);
const
int
D
=
w_dims
[
1
]
/
4
;
PADDLE_ENFORCE_EQ
(
w_dims
.
size
(),
2
,
"Input(LSTMWeight)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
w_dims
[
0
],
D
+
M
,
"LSTMWeight dims should be (%d + %d) * %d."
,
D
+
M
,
4
*
D
);
auto
b_dims
=
ctx
->
GetInputDim
(
"LSTMBias"
);
PADDLE_ENFORCE_EQ
(
b_dims
.
size
(),
2
,
"Input(LSTMBias)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
b_dims
[
0
],
1
,
"LSTMBias dims should be 1 x %d."
,
4
*
D
);
PADDLE_ENFORCE_EQ
(
b_dims
[
1
],
4
*
D
,
"LSTMBias dims should be 1 x %d."
,
4
*
D
);
auto
c_dims
=
ctx
->
GetInputDim
(
"C0"
);
PADDLE_ENFORCE_EQ
(
c_dims
.
size
(),
2
,
"Input(C0)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
c_dims
[
1
],
D
,
"C0 dims should be N x %d."
,
D
);
if
(
ctx
->
HasInput
(
"H0"
))
{
auto
h_dims
=
ctx
->
GetInputDim
(
"H0"
);
PADDLE_ENFORCE
(
h_dims
==
c_dims
,
"The dimension of Input(H0) and Input(C0) "
"should be the same."
);
}
auto
atten_w_dims
=
ctx
->
GetInputDim
(
"AttentionWeight"
);
PADDLE_ENFORCE_EQ
(
atten_w_dims
.
size
(),
2
,
"Input(AttentionWeight)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
atten_w_dims
[
0
],
M
+
D
,
"AttentionWeight shapes must be (%d + %d) * 1."
,
M
,
D
);
PADDLE_ENFORCE_EQ
(
atten_w_dims
[
1
],
1
,
"AttentionWeight shapes must be (%d + %d) * 1."
,
M
,
D
);
if
(
ctx
->
HasInput
(
"AttentionBias"
))
{
auto
atten_b_dims
=
ctx
->
GetInputDim
(
"AttentionBias"
);
PADDLE_ENFORCE_EQ
(
atten_b_dims
.
size
(),
2
,
"Input(AttentionBias)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
atten_b_dims
[
0
],
1
,
"AttentionBias shapes must be 1 * 1."
);
PADDLE_ENFORCE_EQ
(
atten_b_dims
[
1
],
1
,
"AttentionBias shapes must be 1 * 1."
);
}
if
(
ctx
->
HasInput
(
"AttentionScalar"
))
{
auto
dims
=
ctx
->
GetInputDim
(
"AttentionScalar"
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
"Input(AttentionScalar)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
dims
[
0
],
1
,
"AttentionScalar shapes must be 1 * 1."
);
PADDLE_ENFORCE_EQ
(
dims
[
1
],
1
,
"AttentionScalar shapes must be 1 * 1."
);
auto
ins_dims
=
ctx
->
GetInputsDim
(
"X"
);
auto
w_dims
=
ctx
->
GetInputDim
(
"FCWeight"
);
// (M0+M1+M2+..) x D
PADDLE_ENFORCE_EQ
(
w_dims
.
size
(),
2UL
,
"Input(FCWeight)'s rank must be 2."
);
const
int
D
=
w_dims
[
1
];
int
sum
=
ins_dims
[
0
][
1
];
for
(
size_t
i
=
1
;
i
<
ins_dims
.
size
();
++
i
)
{
sum
+=
ins_dims
[
i
][
1
];
}
if
(
ctx
->
HasInput
(
"AttentionScalarBias"
))
{
auto
dims
=
ctx
->
GetInputDim
(
"AttentionScalarBias"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"AttentionScalar"
),
"AttentionScalar should not be null when have AttentionScalarBias."
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
"Input(AttentionScalarBias)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
dims
[
0
],
1
,
"AttentionScalarBias shapes must be 1 * 1."
);
PADDLE_ENFORCE_EQ
(
dims
[
1
],
1
,
"AttentionScalarBias shapes must be 1 * 1."
);
PADDLE_ENFORCE_EQ
(
sum
,
w_dims
[
0
],
"FC height should be sum of all inputs width."
);
if
(
ctx
->
HasInput
(
"FCBias"
))
{
auto
b_dims
=
ctx
->
GetInputDim
(
"FCBias"
);
PADDLE_ENFORCE_EQ
(
b_dims
.
size
(),
2
,
"Input(FCBias)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
b_dims
[
0
],
1
,
"FCBias shapes must be 1 * %d."
,
D
);
PADDLE_ENFORCE_EQ
(
b_dims
[
1
],
D
,
"FCBias shapes must be 1 * %d."
,
D
);
}
framework
::
DDim
out_dims
({
x_dims
[
0
],
D
});
ctx
->
SetOutputDim
(
"Hidden"
,
out_dims
);
ctx
->
SetOutputDim
(
"Cell"
,
out_dims
);
ctx
->
SetOutputDim
(
"AttentionedX"
,
{
x_dims
[
0
],
1
});
ctx
->
SetOutputDim
(
"LSTMX"
,
{
1
,
M
});
ctx
->
SetOutputDim
(
"LSTMOUT"
,
{
1
,
4
*
D
});
// AttentionFCOut should be reshape as (maxseqlen,1) in runtime
ctx
->
ShareLoD
(
"X"
,
"Hidden"
);
ctx
->
ShareLoD
(
"X"
,
"Cell"
);
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
ctx
->
SetOutputDim
(
"Out"
,
{
ins_dims
[
0
][
0
],
D
});
// fcout should be reshape when run since can not get lod in infershape
// explicit share the ref lod
ctx
->
ShareLoD
(
"X"
,
"Out"
,
0
);
}
framework
::
OpKernelType
FusionSeqConcatFCOp
::
GetExpectedKernelType
(
...
...
@@ -154,46 +99,46 @@ The concat axis should be 1.
)DOC"
);
}
// y[i] = (x[i] + bias[0]) > 0 ? (x[i] + bias[0]) : 0;
template
<
typename
T
>
inline
void
bias_relu
(
const
int
n
,
const
T
*
x
,
const
T
*
bias
,
T
*
y
)
{
if
(
bias
)
{
math
::
vec_add_bias
<
T
,
platform
::
jit
::
avx
>
(
n
,
*
bias
,
x
,
y
);
math
::
vec_relu
<
T
,
platform
::
jit
::
avx
>
(
n
,
y
,
y
);
}
else
{
math
::
vec_relu
<
T
,
platform
::
jit
::
avx
>
(
n
,
x
,
y
);
}
}
template
<
typename
T
>
inline
void
vec_softmax
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
T
scalar
=
x
[
0
];
// max
for
(
int
i
=
1
;
i
<
n
;
++
i
)
{
scalar
=
scalar
<
x
[
i
]
?
x
[
i
]
:
scalar
;
}
math
::
vec_add_bias
<
T
,
platform
::
jit
::
avx
>
(
n
,
-
scalar
,
x
,
y
);
// sub
math
::
vec_exp
<
T
>
(
n
,
y
,
y
);
// exp
// sum
scalar
=
T
(
0
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
scalar
+=
y
[
i
];
}
math
::
vec_scal
<
T
>
(
n
,
static_cast
<
T
>
(
1
)
/
scalar
,
y
);
// scale
}
template
<
typename
T
>
class
FusionSeqConcatFCKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
DeviceContext
=
paddle
::
platform
::
CPUDeviceContext
;
auto
*
ins
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
ins
=
ctx
.
Multi
Input
<
LoDTensor
>
(
"X"
);
auto
*
w
=
ctx
.
Input
<
Tensor
>
(
"FCWeight"
);
auto
*
b
=
ctx
.
Input
<
Tensor
>
(
"FCBias"
);
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
auto
*
fc_out
=
ctx
.
Output
<
Tensor
>
(
"FCOUT"
);
auto
*
ref_in
=
ins
[
0
];
auto
ref_lod
=
ref_in
->
lod
();
auto
in1_lod
=
ins
[
1
]
->
lod
();
auto
ref_dims
=
ref_in
->
dims
();
// T x M0
auto
in1_dims
=
ins
[
1
]
->
dims
();
// N x M1
auto
w_dims
=
w
->
dims
();
const
int
N
=
ref_lod
[
0
].
size
()
-
1
;
const
int
total_T
=
ref_dims
[
0
];
const
int
M0
=
ref_dims
[
1
];
const
int
M1
=
in1_dims
[
1
];
const
int
D
=
w_dims
[
1
];
// some check and fcout should be reshape here
// since infershape can not get lod info
PADDLE_ENFORCE_EQ
(
ref_lod
.
size
(),
1UL
,
"Only support input lod size is 1."
);
PADDLE_ENFORCE_EQ
(
in1_lod
.
size
(),
1UL
,
"Only support input lod size is 1."
);
PADDLE_ENFORCE_EQ
(
in1_lod
[
0
].
size
()
-
1
,
N
,
"Batch size of all inputs should be equal."
);
PADDLE_ENFORCE_EQ
(
in1_lod
[
0
][
N
],
N
,
"Seq_length of other inputs should be 1."
);
PADDLE_ENFORCE_EQ
(
in1_dims
[
0
],
N
,
"input height should be batch size."
);
for
(
size_t
i
=
2
;
i
<
ins
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
ins
[
i
]
->
dims
()[
0
],
N
,
"All other inputs height should be equal"
);
PADDLE_ENFORCE_EQ
(
ins
[
i
]
->
lod
(),
in1_lod
,
"All other inputs should have same lod"
);
}
fc_out
->
Resize
({
N
,
D
});
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
fc_act
;
auto
&
fc_act_str
=
ctx
.
Attr
<
std
::
string
>
(
"fc_activation"
);
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx
))
{
...
...
@@ -204,19 +149,7 @@ class FusionSeqConcatFCKernel : public framework::OpKernel<T> {
fc_act
=
act_functor
(
fc_act_str
);
}
PADDLE_ENFORCE_GT
(
ins
.
size
(),
1
,
"Input(X)'s size must larger than 1."
);
auto
*
ref_in
=
ins
[
0
];
auto
ref_in_lod
=
ref_in
->
lod
();
const
int
N
=
ref_in_lod
[
0
].
size
()
-
1
;
auto
ref_in_dims
=
ref_in
->
dims
();
// T x M0
auto
w_dims
=
w
->
dims
();
// (M0+M1+M2+..) x D
const
int
total_T
=
ref_in_dims
[
0
];
const
int
M0
=
ref_in_dims
[
1
];
const
int
M1
=
ins
[
1
]
->
dims
()[
1
];
const
int
D
=
w_dims
[
1
];
const
T
*
ref_in_data
=
ref_in
->
data
<
T
>
();
// size should be check at infershape
const
T
*
ref_in_data
=
ref_in
->
data
<
T
>
();
const
T
*
in1_data
=
ins
[
1
]
->
data
<
T
>
();
const
T
*
w_data
=
w
->
data
<
T
>
();
T
*
out_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
...
...
@@ -226,11 +159,10 @@ class FusionSeqConcatFCKernel : public framework::OpKernel<T> {
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
total_T
,
D
,
M0
,
ref_in_data
,
w_data
,
out_data
,
b
?
b
->
data
<
T
>
()
:
NULL
);
w_data
=
w_data
+
M0
*
D
;
// first one use write on
blas
.
MatMul
(
N
,
D
,
M1
,
in1_data
,
w_data
,
fc_out_data
);
w_data
=
w_data
+
M1
*
D
;
for
(
in
t
i
=
2
;
i
<
ins
.
size
();
++
i
)
{
for
(
size_
t
i
=
2
;
i
<
ins
.
size
();
++
i
)
{
// add on
const
T
*
in_data
=
ins
[
i
]
->
data
<
T
>
();
const
int
K
=
ins
[
i
]
->
dims
()[
1
];
...
...
@@ -240,7 +172,7 @@ class FusionSeqConcatFCKernel : public framework::OpKernel<T> {
}
for
(
int
i
=
0
;
i
<
N
;
++
i
)
{
int
seq_len
=
ref_
in_lod
[
0
][
i
+
1
]
-
ref_in
_lod
[
0
][
i
];
int
seq_len
=
ref_
lod
[
0
][
i
+
1
]
-
ref
_lod
[
0
][
i
];
T
*
src
=
fc_out_data
+
i
*
D
;
for
(
int
step
=
0
;
step
<
seq_len
;
++
step
)
{
blas
.
VADD
(
D
,
out_data
,
src
,
out_data
);
...
...
@@ -248,7 +180,7 @@ class FusionSeqConcatFCKernel : public framework::OpKernel<T> {
}
}
fc_act
(
out_dims
[
0
]
*
out_dims
[
1
]
,
out_data
,
out_data
);
fc_act
(
total_T
*
D
,
out_data
,
out_data
);
}
};
...
...
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