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体验新版 GitCode,发现更多精彩内容 >>
提交
93701dba
编写于
3月 20, 2019
作者:
D
dengkaipeng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add jit kernel for softmax axis. test=develop
上级
6c641827
变更
16
隐藏空白更改
内联
并排
Showing
16 changed file
with
185 addition
and
78 deletion
+185
-78
paddle/fluid/operators/jit/benchmark.cc
paddle/fluid/operators/jit/benchmark.cc
+1
-1
paddle/fluid/operators/jit/helper.cc
paddle/fluid/operators/jit/helper.cc
+2
-0
paddle/fluid/operators/jit/kernel_base.h
paddle/fluid/operators/jit/kernel_base.h
+23
-1
paddle/fluid/operators/jit/more/mix/mix.cc
paddle/fluid/operators/jit/more/mix/mix.cc
+14
-4
paddle/fluid/operators/jit/more/mix/mix.h
paddle/fluid/operators/jit/more/mix/mix.h
+1
-1
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
+1
-0
paddle/fluid/operators/jit/more/mkl/mkl.cc
paddle/fluid/operators/jit/more/mkl/mkl.cc
+35
-0
paddle/fluid/operators/jit/more/mkl/mkl.h
paddle/fluid/operators/jit/more/mkl/mkl.h
+19
-4
paddle/fluid/operators/jit/refer/CMakeLists.txt
paddle/fluid/operators/jit/refer/CMakeLists.txt
+2
-0
paddle/fluid/operators/jit/refer/refer.cc
paddle/fluid/operators/jit/refer/refer.cc
+2
-0
paddle/fluid/operators/jit/refer/refer.h
paddle/fluid/operators/jit/refer/refer.h
+32
-4
paddle/fluid/operators/jit/test.cc
paddle/fluid/operators/jit/test.cc
+36
-31
paddle/fluid/operators/math/softmax_impl.h
paddle/fluid/operators/math/softmax_impl.h
+4
-3
paddle/fluid/operators/softmax_op.cc
paddle/fluid/operators/softmax_op.cc
+12
-3
paddle/fluid/operators/softmax_op.h
paddle/fluid/operators/softmax_op.h
+0
-5
python/paddle/fluid/tests/unittests/test_softmax_op.py
python/paddle/fluid/tests/unittests/test_softmax_op.py
+1
-21
未找到文件。
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @
93701dba
...
...
@@ -386,7 +386,7 @@ void BenchKernelSoftmax() {
RandomVec
<
T
>
(
bs
*
n
,
x
.
mutable_data
<
T
>
(
PlaceType
()),
-
2.
f
,
2.
f
);
const
T
*
x_data
=
x
.
data
<
T
>
();
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
n
,
x_data
,
y_data
,
n
,
bs
);
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
n
,
x_data
,
y_data
,
n
,
bs
,
1
);
}
}
}
...
...
paddle/fluid/operators/jit/helper.cc
浏览文件 @
93701dba
...
...
@@ -34,6 +34,7 @@ const char* to_string(KernelType kt) {
ONE_CASE
(
kVAddRelu
);
ONE_CASE
(
kVSub
);
ONE_CASE
(
kVScal
);
ONE_CASE
(
kStrideScal
);
ONE_CASE
(
kVAddBias
);
ONE_CASE
(
kVRelu
);
ONE_CASE
(
kVBroadcast
);
...
...
@@ -55,6 +56,7 @@ const char* to_string(KernelType kt) {
ONE_CASE
(
kMatMul
);
ONE_CASE
(
kHMax
);
ONE_CASE
(
kHSum
);
ONE_CASE
(
kStrideSum
);
ONE_CASE
(
kSoftmax
);
ONE_CASE
(
kEmbSeqPool
);
ONE_CASE
(
kSgd
);
...
...
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @
93701dba
...
...
@@ -53,6 +53,8 @@ typedef enum {
kVSquare
,
kVSub
,
kVTanh
,
kStrideSum
,
kStrideScal
,
}
KernelType
;
typedef
enum
{
...
...
@@ -74,6 +76,14 @@ struct XYZNTuple {
template
<
typename
T
>
struct
AXYNTuple
:
public
XYZNTuple
<
T
>
{};
// a, x, y, n, stride
template
<
typename
T
>
struct
AXYNSTuple
{
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
T
*
,
T
*
,
int
,
int
);
};
// x, y, n
template
<
typename
T
>
struct
XYNTuple
{
...
...
@@ -86,6 +96,14 @@ struct XYNTuple {
template
<
typename
T
>
struct
XRNTuple
:
public
XYNTuple
<
T
>
{};
// x, returned value, n, stride
template
<
typename
T
>
struct
XRNSTuple
{
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int
,
int
);
};
#define DECLARE_KERNELTUPLE(kernel_tuple, type) \
template <typename T> \
struct type##Tuple : public kernel_tuple<T> { \
...
...
@@ -101,6 +119,8 @@ DECLARE_KERNELTUPLE(XYZNTuple, VSub);
DECLARE_KERNELTUPLE
(
AXYNTuple
,
VScal
);
DECLARE_KERNELTUPLE
(
AXYNTuple
,
VAddBias
);
DECLARE_KERNELTUPLE
(
AXYNSTuple
,
StrideScal
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VRelu
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VIdentity
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VSquare
);
...
...
@@ -112,6 +132,8 @@ DECLARE_KERNELTUPLE(XYNTuple, VCopy);
DECLARE_KERNELTUPLE
(
XRNTuple
,
HMax
);
DECLARE_KERNELTUPLE
(
XRNTuple
,
HSum
);
DECLARE_KERNELTUPLE
(
XRNSTuple
,
StrideSum
);
typedef
struct
{
void
*
gates
;
// gates: x_ch, x_ih, x_fh, x_oh
const
void
*
ct_1
;
...
...
@@ -285,7 +307,7 @@ struct SoftmaxTuple {
static
constexpr
KernelType
kernel_type
=
kSoftmax
;
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int
,
int
);
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int
,
int
,
int
);
};
// nChw16c = nChw16c .* NC
...
...
paddle/fluid/operators/jit/more/mix/mix.cc
浏览文件 @
93701dba
...
...
@@ -50,10 +50,12 @@ void VTanh(const T* x, T* y, int n) {
compute_addbias
(
&
b
,
y
,
y
,
n
);
}
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
)
{
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
,
int
m
)
{
auto
compute_hmax
=
KernelFuncs
<
HMaxTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_hsum
=
KernelFuncs
<
HSumTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_vscal
=
KernelFuncs
<
VScalTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_stridesum
=
KernelFuncs
<
StrideSumTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_stridescal
=
KernelFuncs
<
StrideScalTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_vaddbias
=
KernelFuncs
<
VAddBiasTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
auto
compute_vexp
=
KernelFuncs
<
VExpTuple
<
T
>
,
CPUPlace
>::
Cache
().
At
(
n
);
...
...
@@ -64,9 +66,17 @@ void Softmax(const T* x, T* y, int n, int bs) {
scalar
=
static_cast
<
T
>
(
0
)
-
scalar
;
compute_vaddbias
(
&
scalar
,
x
,
y
,
n
);
// x - max
compute_vexp
(
y
,
y
,
n
);
compute_hsum
(
y
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
compute_vscal
(
&
scalar
,
y
,
y
,
n
);
if
(
m
==
1
)
{
compute_hsum
(
y
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
compute_vscal
(
&
scalar
,
y
,
y
,
n
);
}
else
{
for
(
int
j
=
0
;
j
<
m
;
++
j
)
{
compute_stridesum
(
&
y
[
j
],
&
scalar
,
n
,
m
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
compute_stridescal
(
&
scalar
,
&
y
[
j
],
&
y
[
j
],
n
,
m
);
}
}
x
+=
n
;
y
+=
n
;
}
...
...
paddle/fluid/operators/jit/more/mix/mix.h
浏览文件 @
93701dba
...
...
@@ -26,7 +26,7 @@ using T = float;
void
VSigmoid
(
const
T
*
x
,
T
*
y
,
int
n
);
void
VTanh
(
const
T
*
x
,
T
*
y
,
int
n
);
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
);
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
,
int
m
);
void
LSTMCtHt
(
lstm_t
*
step
,
const
lstm_attr_t
*
attr
);
void
LSTMC1H1
(
lstm_t
*
step
,
const
lstm_attr_t
*
attr
);
...
...
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
浏览文件 @
93701dba
...
...
@@ -7,6 +7,7 @@ USE_JITKERNEL_MORE(kMatMul, mkl)
USE_JITKERNEL_MORE
(
kVMul, mkl
)
USE_JITKERNEL_MORE
(
kVAdd, mkl
)
USE_JITKERNEL_MORE
(
kVScal, mkl
)
USE_JITKERNEL_MORE
(
kStrideScal, mkl
)
USE_JITKERNEL_MORE
(
kVExp, mkl
)
USE_JITKERNEL_MORE
(
kVSquare, mkl
)
USE_JITKERNEL_MORE
(
kVCopy, mkl
)
...
...
paddle/fluid/operators/jit/more/mkl/mkl.cc
浏览文件 @
93701dba
...
...
@@ -78,6 +78,24 @@ void VScal<double>(const double* a, const double* x, double* y, int n) {
}
}
template
<
>
void
StrideScal
<
float
>
(
const
float
*
a
,
const
float
*
x
,
float
*
y
,
int
n
,
int
stride
)
{
if
(
x
==
y
)
{
platform
::
dynload
::
cblas_sscal
(
n
,
*
a
,
y
,
stride
);
}
else
{
refer
::
StrideScal
<
float
>
(
a
,
x
,
y
,
n
,
stride
);
}
}
template
<
>
void
StrideScal
<
double
>
(
const
double
*
a
,
const
double
*
x
,
double
*
y
,
int
n
,
int
stride
)
{
if
(
x
==
y
)
{
platform
::
dynload
::
cblas_dscal
(
n
,
*
a
,
y
,
stride
);
}
else
{
refer
::
StrideScal
<
double
>
(
a
,
x
,
y
,
n
,
stride
);
}
}
template
<
>
void
VExp
<
float
>
(
const
float
*
x
,
float
*
y
,
int
n
)
{
platform
::
dynload
::
vsExp
(
n
,
x
,
y
);
...
...
@@ -128,6 +146,16 @@ void ASum<double>(const double* x, double* res, int n) {
res
[
0
]
=
platform
::
dynload
::
cblas_dasum
(
n
,
x
,
1
);
}
template
<
>
void
StrideSum
<
float
>
(
const
float
*
x
,
float
*
res
,
int
n
,
int
stride
)
{
res
[
0
]
=
platform
::
dynload
::
cblas_sasum
(
n
,
x
,
stride
);
}
template
<
>
void
StrideSum
<
double
>
(
const
double
*
x
,
double
*
res
,
int
n
,
int
stride
)
{
res
[
0
]
=
platform
::
dynload
::
cblas_dasum
(
n
,
x
,
stride
);
}
// TODO(TJ): tuning me carefully on AVX, AVX2 and AVX512
template
<
>
bool
VMulKernel
<
float
>::
CanBeUsed
(
const
int
&
d
)
const
{
...
...
@@ -144,6 +172,11 @@ bool VScalKernel<float>::CanBeUsed(const int& d) const {
return
platform
::
MayIUse
(
platform
::
avx512f
)
&&
d
>
512
;
}
template
<
>
bool
StrideScalKernel
<
float
>::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx512f
)
&&
d
>
512
;
}
template
<
>
bool
VExpKernel
<
float
>::
CanBeUsed
(
const
int
&
d
)
const
{
return
d
>
7
;
...
...
@@ -235,6 +268,7 @@ bool SoftmaxKernel<float>::CanBeUsed(const int& d) const {
AWALYS_USE_ME_WITH_DOUBLE
(
VMul
);
AWALYS_USE_ME_WITH_DOUBLE
(
VAdd
);
AWALYS_USE_ME_WITH_DOUBLE
(
VScal
);
AWALYS_USE_ME_WITH_DOUBLE
(
StrideScal
);
AWALYS_USE_ME_WITH_DOUBLE
(
VExp
);
AWALYS_USE_ME_WITH_DOUBLE
(
VSigmoid
);
AWALYS_USE_ME_WITH_DOUBLE
(
VTanh
);
...
...
@@ -259,6 +293,7 @@ REGISTER_MKL_KERNEL(MatMul);
REGISTER_MKL_KERNEL
(
VMul
);
REGISTER_MKL_KERNEL
(
VAdd
);
REGISTER_MKL_KERNEL
(
VScal
);
REGISTER_MKL_KERNEL
(
StrideScal
);
REGISTER_MKL_KERNEL
(
VExp
);
REGISTER_MKL_KERNEL
(
VSquare
);
REGISTER_MKL_KERNEL
(
VCopy
);
...
...
paddle/fluid/operators/jit/more/mkl/mkl.h
浏览文件 @
93701dba
...
...
@@ -129,7 +129,13 @@ template <typename T>
void
ASum
(
const
T
*
x
,
T
*
res
,
int
n
);
template
<
typename
T
>
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
)
{
void
StrideSum
(
const
T
*
x
,
T
*
res
,
int
n
,
int
stride
);
template
<
typename
T
>
void
StrideScal
(
const
T
*
a
,
const
T
*
x
,
T
*
y
,
int
n
,
int
stride
);
template
<
typename
T
>
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
,
int
m
=
1
)
{
std
::
vector
<
T
>
entities
(
bs
);
for
(
int
i
=
0
;
i
<
bs
;
++
i
)
{
entities
[
i
]
=
x
[
i
*
n
];
...
...
@@ -143,9 +149,17 @@ void Softmax(const T* x, T* y, int n, int bs) {
VExp
(
y
,
y
,
n
*
bs
);
for
(
int
i
=
0
;
i
<
bs
;
++
i
)
{
T
sum
;
ASum
(
&
y
[
i
*
n
],
&
sum
,
n
);
sum
=
static_cast
<
T
>
(
1
)
/
sum
;
VScal
(
&
sum
,
&
y
[
i
*
n
],
&
y
[
i
*
n
],
n
);
if
(
m
==
1
)
{
ASum
(
&
y
[
i
*
n
],
&
sum
,
n
);
sum
=
static_cast
<
T
>
(
1
)
/
sum
;
VScal
(
&
sum
,
&
y
[
i
*
n
],
&
y
[
i
*
n
],
n
);
}
else
{
for
(
int
j
=
0
;
j
<
m
;
++
j
)
{
StrideSum
(
&
y
[
i
*
n
+
j
],
&
sum
,
n
/
m
,
m
);
sum
=
static_cast
<
T
>
(
1
)
/
sum
;
StrideScal
(
&
sum
,
&
y
[
i
*
n
+
j
],
&
y
[
i
*
n
+
j
],
n
/
m
,
m
);
}
}
}
}
...
...
@@ -193,6 +207,7 @@ DECLARE_MKL_KERNEL(VAdd);
// AXYN
DECLARE_MKL_KERNEL
(
VScal
);
DECLARE_MKL_KERNEL
(
StrideScal
);
// XYN
DECLARE_MKL_KERNEL
(
VExp
);
...
...
paddle/fluid/operators/jit/refer/CMakeLists.txt
浏览文件 @
93701dba
...
...
@@ -12,6 +12,7 @@ USE_JITKERNEL_REFER(kVAdd)
USE_JITKERNEL_REFER
(
kVAddRelu
)
USE_JITKERNEL_REFER
(
kVSub
)
USE_JITKERNEL_REFER
(
kVScal
)
USE_JITKERNEL_REFER
(
kStrideScal
)
USE_JITKERNEL_REFER
(
kVAddBias
)
USE_JITKERNEL_REFER
(
kVCopy
)
USE_JITKERNEL_REFER
(
kVRelu
)
...
...
@@ -32,6 +33,7 @@ USE_JITKERNEL_REFER(kMatMul)
USE_JITKERNEL_REFER
(
kVSquare
)
USE_JITKERNEL_REFER
(
kHSum
)
USE_JITKERNEL_REFER
(
kHMax
)
USE_JITKERNEL_REFER
(
kStrideSum
)
USE_JITKERNEL_REFER
(
kSoftmax
)
USE_JITKERNEL_REFER
(
kEmbSeqPool
)
USE_JITKERNEL_REFER
(
kSgd
)
...
...
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @
93701dba
...
...
@@ -27,6 +27,7 @@ REGISTER_REFER_KERNEL(VAddRelu);
REGISTER_REFER_KERNEL
(
VSub
);
REGISTER_REFER_KERNEL
(
VScal
);
REGISTER_REFER_KERNEL
(
StrideScal
);
REGISTER_REFER_KERNEL
(
VAddBias
);
REGISTER_REFER_KERNEL
(
VRelu
);
...
...
@@ -51,6 +52,7 @@ REGISTER_REFER_KERNEL(SeqPool);
REGISTER_REFER_KERNEL
(
MatMul
);
REGISTER_REFER_KERNEL
(
HMax
);
REGISTER_REFER_KERNEL
(
HSum
);
REGISTER_REFER_KERNEL
(
StrideSum
);
REGISTER_REFER_KERNEL
(
Softmax
);
REGISTER_REFER_KERNEL
(
EmbSeqPool
);
REGISTER_REFER_KERNEL
(
Sgd
);
...
...
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @
93701dba
...
...
@@ -411,19 +411,42 @@ void HSum(const T* x, T* res, int n) {
}
}
template
<
typename
T
>
void
StrideSum
(
const
T
*
x
,
T
*
res
,
int
n
,
int
stride
)
{
res
[
0
]
=
x
[
0
];
for
(
int
i
=
stride
;
i
<
n
;
i
+=
stride
)
{
res
[
0
]
+=
x
[
i
];
}
}
template
<
typename
T
>
void
StrideScal
(
const
T
*
a
,
const
T
*
x
,
T
*
y
,
int
n
,
int
stride
)
{
for
(
int
i
=
0
;
i
<
n
;
i
+=
stride
)
{
y
[
i
]
=
x
[
i
]
*
a
[
0
];
}
}
// y = e^(x - max(x))
// y = y / sum(y)
template
<
typename
T
>
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
=
1
)
{
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
=
1
,
int
m
=
1
)
{
for
(
int
i
=
0
;
i
<
bs
;
++
i
)
{
T
scalar
;
HMax
(
x
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
0
)
-
scalar
;
VAddBias
(
&
scalar
,
x
,
y
,
n
);
// x - max
VExp
(
y
,
y
,
n
);
HSum
(
y
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
VScal
(
&
scalar
,
y
,
y
,
n
);
if
(
m
==
1
)
{
HSum
(
y
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
VScal
(
&
scalar
,
y
,
y
,
n
);
}
else
{
for
(
int
j
=
0
;
j
<
m
;
j
++
)
{
StrideSum
(
&
y
[
j
],
&
scalar
,
n
,
m
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
StrideScal
(
&
scalar
,
&
y
[
j
],
&
y
[
j
],
n
,
m
);
}
}
x
+=
n
;
y
+=
n
;
}
...
...
@@ -507,6 +530,9 @@ DECLARE_REFER_KERNEL(VSub);
DECLARE_REFER_KERNEL
(
VScal
);
DECLARE_REFER_KERNEL
(
VAddBias
);
// const T* a, const T* x, T* y, int n, int stride
DECLARE_REFER_KERNEL
(
StrideScal
);
// const T* x, T* y, int n
DECLARE_REFER_KERNEL
(
VRelu
);
DECLARE_REFER_KERNEL
(
VIdentity
);
...
...
@@ -528,6 +554,8 @@ DECLARE_REFER_KERNEL(GRUHtPart2);
DECLARE_REFER_KERNEL
(
HMax
);
DECLARE_REFER_KERNEL
(
HSum
);
DECLARE_REFER_KERNEL
(
StrideSum
);
// others
DECLARE_REFER_KERNEL
(
CRFDecoding
);
DECLARE_REFER_KERNEL
(
LayerNorm
);
...
...
paddle/fluid/operators/jit/test.cc
浏览文件 @
93701dba
...
...
@@ -723,39 +723,44 @@ void TestKernelSoftmax() {
VLOG
(
10
)
<<
"Test JITKernel: "
<<
jit
::
to_string
(
KernelTuple
::
kernel_type
);
for
(
int
bs
:
{
1
,
2
,
10
})
{
for
(
int
n
:
TestSizes
())
{
auto
ref
=
jit
::
GetReferFunc
<
KernelTuple
>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
bs
*
n
),
y
(
bs
*
n
);
RandomVec
<
T
>
(
bs
*
n
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
T
*
y_data
=
y
.
data
();
for
(
int
m
:
{
1
,
2
})
{
if
(
m
>
n
||
n
%
m
!=
0
)
{
continue
;
}
auto
ref
=
jit
::
GetReferFunc
<
KernelTuple
>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
bs
*
n
),
y
(
bs
*
n
);
RandomVec
<
T
>
(
bs
*
n
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
T
*
y_data
=
y
.
data
();
std
::
vector
<
T
>
xinp
(
x
.
size
());
// inplace test
std
::
copy
(
x
.
begin
(),
x
.
end
(),
xinp
.
begin
());
ref
(
x_data
,
y_data
,
n
,
bs
);
T
*
xinp_data
=
xinp
.
data
();
ref
(
xinp_data
,
xinp_data
,
n
,
bs
);
ExpectEQ
<
T
>
(
xinp_data
,
y_data
,
n
*
bs
);
std
::
vector
<
T
>
xinp
(
x
.
size
());
// inplace test
std
::
copy
(
x
.
begin
(),
x
.
end
(),
xinp
.
begin
());
ref
(
x_data
,
y_data
,
n
,
bs
,
m
);
T
*
xinp_data
=
xinp
.
data
();
ref
(
xinp_data
,
xinp_data
,
n
,
bs
,
m
);
ExpectEQ
<
T
>
(
xinp_data
,
y_data
,
n
*
bs
);
auto
verifier
=
[](
const
typename
KernelTuple
::
func_type
tgt
,
const
std
::
vector
<
T
>&
x
,
const
std
::
vector
<
T
>&
yref
,
int
n
,
int
bs
)
{
EXPECT_TRUE
(
tgt
!=
nullptr
);
EXPECT_EQ
(
yref
.
size
(),
x
.
size
());
EXPECT_EQ
(
x
.
size
(),
static_cast
<
size_t
>
(
n
*
bs
));
const
T
*
x_data
=
x
.
data
();
const
T
*
yref_data
=
yref
.
data
();
std
::
vector
<
T
>
ytgt
(
n
*
bs
);
T
*
ytgt_data
=
ytgt
.
data
();
// test normal
tgt
(
x_data
,
ytgt_data
,
n
,
bs
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
n
*
bs
);
// test inplace x
std
::
copy
(
x
.
begin
(),
x
.
end
(),
ytgt
.
begin
());
tgt
(
ytgt_data
,
ytgt_data
,
n
,
bs
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
n
*
bs
);
};
TestAllImpls
<
KernelTuple
,
PlaceType
>
(
n
,
verifier
,
x
,
y
,
n
,
bs
);
auto
verifier
=
[](
const
typename
KernelTuple
::
func_type
tgt
,
const
std
::
vector
<
T
>&
x
,
const
std
::
vector
<
T
>&
yref
,
int
n
,
int
bs
,
int
m
)
{
EXPECT_TRUE
(
tgt
!=
nullptr
);
EXPECT_EQ
(
yref
.
size
(),
x
.
size
());
EXPECT_EQ
(
x
.
size
(),
static_cast
<
size_t
>
(
n
*
bs
));
const
T
*
x_data
=
x
.
data
();
const
T
*
yref_data
=
yref
.
data
();
std
::
vector
<
T
>
ytgt
(
n
*
bs
);
T
*
ytgt_data
=
ytgt
.
data
();
// test normal
tgt
(
x_data
,
ytgt_data
,
n
,
bs
,
m
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
n
*
bs
);
// test inplace x
std
::
copy
(
x
.
begin
(),
x
.
end
(),
ytgt
.
begin
());
tgt
(
ytgt_data
,
ytgt_data
,
n
,
bs
,
m
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
n
*
bs
);
};
TestAllImpls
<
KernelTuple
,
PlaceType
>
(
n
,
verifier
,
x
,
y
,
n
,
bs
,
m
);
}
}
}
}
...
...
paddle/fluid/operators/math/softmax_impl.h
浏览文件 @
93701dba
...
...
@@ -76,8 +76,8 @@ using enable_if_CPU = typename std::enable_if<
template
<
typename
DeviceContext
>
class
SoftmaxFunctor
<
DeviceContext
,
float
,
true
,
enable_if_CPU
<
DeviceContext
>>
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
*
X
,
framework
::
Tensor
*
Y
)
{
void
operator
()(
const
DeviceContext
&
context
,
const
int
axis_dim
,
const
framework
::
Tensor
*
X
,
framework
::
Tensor
*
Y
)
{
auto
in_dims
=
X
->
dims
();
const
float
*
in_data
=
X
->
data
<
float
>
();
float
*
out_data
=
Y
->
data
<
float
>
();
...
...
@@ -87,7 +87,8 @@ class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
auto
compute_softmax
=
jit
::
KernelFuncs
<
jit
::
SoftmaxTuple
<
float
>
,
platform
::
CPUPlace
>::
Cache
()
.
At
(
in_dims
[
kClassDim
]);
compute_softmax
(
in_data
,
out_data
,
in_dims
[
kClassDim
],
in_dims
[
kBatchDim
]);
compute_softmax
(
in_data
,
out_data
,
in_dims
[
kClassDim
],
in_dims
[
kBatchDim
],
in_dims
[
kClassDim
]
/
axis_dim
);
}
};
...
...
paddle/fluid/operators/softmax_op.cc
浏览文件 @
93701dba
...
...
@@ -42,9 +42,18 @@ class SoftmaxOp : public framework::OperatorWithKernel {
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
auto
rank_x
=
dim_x
.
size
();
auto
axis
=
ctx
->
Attrs
().
Get
<
int
>
(
"axis"
);
PADDLE_ENFORCE
(
axis
>=
-
1
&&
axis
<
rank_x
,
"Attr(axis) value should larger equal then -1"
"and less then the rank of Input(X)"
);
PADDLE_ENFORCE
(
axis
>=
-
rank_x
&&
axis
<
rank_x
,
"Attr(axis) value should be in range [-R, R-1], "
"R is the rank of Input(X)."
);
auto
use_cudnn
=
ctx
->
Attrs
().
Get
<
bool
>
(
"use_cudnn"
);
auto
use_mkldnn
=
ctx
->
Attrs
().
Get
<
bool
>
(
"use_mkldnn"
);
if
(
axis
!=
rank_x
-
1
&&
axis
!=
-
1
)
{
PADDLE_ENFORCE
(
!
use_cudnn
,
"CUDNN kernel only support axis as -1."
);
PADDLE_ENFORCE
(
!
use_mkldnn
,
"MKLDNN kernel only support axis as -1."
);
}
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
...
...
paddle/fluid/operators/softmax_op.h
浏览文件 @
93701dba
...
...
@@ -63,8 +63,6 @@ class SoftmaxKernel : public framework::OpKernel<T> {
Tensor
X_2d
,
Out_2d
;
X_2d
.
ShareDataWith
(
*
X
).
Resize
({
n
,
d
});
Out_2d
.
ShareDataWith
(
*
Out
).
Resize
({
n
,
d
});
// Tensor X_2d = framework::ReshapeToMatrix(*X, axis - 1);
// Tensor Out_2d = framework::ReshapeToMatrix(*Out, axis - 1);
#ifdef PADDLE_ON_INFERENCE
math
::
SoftmaxFunctor
<
DeviceContext
,
T
,
true
>
()(
...
...
@@ -96,9 +94,6 @@ class SoftmaxGradKernel : public framework::OpKernel<T> {
dX_2d
.
ShareDataWith
(
*
dX
).
Resize
({
n
,
d
});
Out_2d
.
ShareDataWith
(
*
Out
).
Resize
({
n
,
d
});
dOut_2d
.
ShareDataWith
(
*
dOut
).
Resize
({
n
,
d
});
// Tensor Out_2d = framework::ReshapeToMatrix(*Out, axis - 1);
// Tensor dOut_2d = framework::ReshapeToMatrix(*dOut, axis - 1);
// Tensor dX_2d = framework::ReshapeToMatrix(*dX, axis - 1);
math
::
SoftmaxGradFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
axis_dim
,
&
Out_2d
,
&
dOut_2d
,
...
...
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @
93701dba
...
...
@@ -125,26 +125,6 @@ class TestSoftmaxCUDNNOp2(TestSoftmaxCUDNNOp):
return
[
2
,
3
,
4
,
5
]
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
"core is not compiled with CUDA"
)
class
TestSoftmaxCUDNNOp3
(
TestSoftmaxCUDNNOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
0
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
"core is not compiled with CUDA"
)
class
TestSoftmaxCUDNNOp4
(
TestSoftmaxCUDNNOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
1
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
"core is not compiled with CUDA"
)
class
TestSoftmaxCUDNNOp5
(
TestSoftmaxCUDNNOp
):
...
...
@@ -152,7 +132,7 @@ class TestSoftmaxCUDNNOp5(TestSoftmaxCUDNNOp):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
2
return
3
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
...
...
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