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体验新版 GitCode,发现更多精彩内容 >>
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54474637
编写于
3月 30, 2019
作者:
K
Kaipeng Deng
提交者:
GitHub
3月 30, 2019
浏览文件
操作
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差异文件
Merge pull request #16057 from heavengate/softmax_axis
Add attr 'axis' for softmax
上级
63ac947e
3e352388
变更
22
隐藏空白更改
内联
并排
Showing
22 changed file
with
413 addition
and
78 deletion
+413
-78
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
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
+17
-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
+37
-0
paddle/fluid/operators/jit/more/mkl/mkl.h
paddle/fluid/operators/jit/more/mkl/mkl.h
+20
-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
+37
-4
paddle/fluid/operators/jit/test.cc
paddle/fluid/operators/jit/test.cc
+105
-19
paddle/fluid/operators/math/softmax.h
paddle/fluid/operators/math/softmax.h
+5
-4
paddle/fluid/operators/math/softmax_impl.h
paddle/fluid/operators/math/softmax_impl.h
+20
-12
paddle/fluid/operators/softmax_op.cc
paddle/fluid/operators/softmax_op.cc
+23
-4
paddle/fluid/operators/softmax_op.h
paddle/fluid/operators/softmax_op.h
+47
-11
paddle/fluid/operators/softmax_with_cross_entropy_op.h
paddle/fluid/operators/softmax_with_cross_entropy_op.h
+3
-1
paddle/fluid/operators/warpctc_cudnn_op.cu.cc
paddle/fluid/operators/warpctc_cudnn_op.cu.cc
+3
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+14
-6
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+1
-1
python/paddle/fluid/tests/unittests/test_softmax_op.py
python/paddle/fluid/tests/unittests/test_softmax_op.py
+48
-3
未找到文件。
paddle/fluid/API.spec
浏览文件 @
54474637
...
...
@@ -95,7 +95,7 @@ paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size',
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'a194fb80614023f543df3949fbd0d0b8'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name'
], varargs=None, keywords=None, defaults=(False, None)), ('document', 'f19dd380864e61134ce3814e4be0de4b
'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name'
, 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', '59b1c6bf2f0fa9dc649c85fef3a3b2ea
'))
paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', 'bbd84e855e660cd1084bb71a2fd0cdaa'))
paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', '043de7333b79ee0ac55053c14ed81625'))
paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '859b887174d06f361658f69cb7c06d95'))
...
...
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @
54474637
...
...
@@ -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 @
54474637
...
...
@@ -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
(
kStrideASum
);
ONE_CASE
(
kSoftmax
);
ONE_CASE
(
kEmbSeqPool
);
ONE_CASE
(
kSgd
);
...
...
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @
54474637
...
...
@@ -38,6 +38,8 @@ typedef enum {
kNCHW16CMulNC
,
kSeqPool
,
kSoftmax
,
kStrideASum
,
kStrideScal
,
kVAdd
,
kVAddBias
,
kVAddRelu
,
...
...
@@ -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
,
StrideASum
);
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 @
54474637
...
...
@@ -50,10 +50,15 @@ 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
)
{
// remain is the product of dimension shapes after the axis dimension
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
,
int
remain
)
{
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_strideasum
=
KernelFuncs
<
StrideASumTuple
<
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 +69,17 @@ 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
(
remain
==
1
)
{
compute_hsum
(
y
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
compute_vscal
(
&
scalar
,
y
,
y
,
n
);
}
else
{
for
(
int
j
=
0
;
j
<
remain
;
++
j
)
{
compute_strideasum
(
&
y
[
j
],
&
scalar
,
n
,
remain
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
compute_stridescal
(
&
scalar
,
&
y
[
j
],
&
y
[
j
],
n
,
remain
);
}
}
x
+=
n
;
y
+=
n
;
}
...
...
paddle/fluid/operators/jit/more/mix/mix.h
浏览文件 @
54474637
...
...
@@ -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
remain
);
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 @
54474637
...
...
@@ -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 @
54474637
...
...
@@ -78,6 +78,26 @@ 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
/
stride
,
*
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
/
stride
,
*
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 +148,16 @@ void ASum<double>(const double* x, double* res, int n) {
res
[
0
]
=
platform
::
dynload
::
cblas_dasum
(
n
,
x
,
1
);
}
template
<
>
void
StrideASum
<
float
>
(
const
float
*
x
,
float
*
res
,
int
n
,
int
stride
)
{
res
[
0
]
=
platform
::
dynload
::
cblas_sasum
(
n
/
stride
,
x
,
stride
);
}
template
<
>
void
StrideASum
<
double
>
(
const
double
*
x
,
double
*
res
,
int
n
,
int
stride
)
{
res
[
0
]
=
platform
::
dynload
::
cblas_dasum
(
n
/
stride
,
x
,
stride
);
}
// TODO(TJ): tuning me carefully on AVX, AVX2 and AVX512
template
<
>
bool
VMulKernel
<
float
>::
CanBeUsed
(
const
int
&
d
)
const
{
...
...
@@ -144,6 +174,11 @@ 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
true
;
}
template
<
>
bool
VExpKernel
<
float
>::
CanBeUsed
(
const
int
&
d
)
const
{
return
d
>
7
;
...
...
@@ -235,6 +270,7 @@ 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 +295,7 @@ 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 @
54474637
...
...
@@ -129,7 +129,14 @@ 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
StrideASum
(
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
);
// remain is the product of dimension shapes after the axis dimension
template
<
typename
T
>
void
Softmax
(
const
T
*
x
,
T
*
y
,
int
n
,
int
bs
,
int
remain
=
1
)
{
std
::
vector
<
T
>
entities
(
bs
);
for
(
int
i
=
0
;
i
<
bs
;
++
i
)
{
entities
[
i
]
=
x
[
i
*
n
];
...
...
@@ -143,9 +150,17 @@ 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
(
remain
==
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
<
remain
;
++
j
)
{
StrideASum
(
&
y
[
i
*
n
+
j
],
&
sum
,
n
,
remain
);
sum
=
static_cast
<
T
>
(
1
)
/
sum
;
StrideScal
(
&
sum
,
&
y
[
i
*
n
+
j
],
&
y
[
i
*
n
+
j
],
n
,
remain
);
}
}
}
}
...
...
@@ -193,6 +208,7 @@ 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 @
54474637
...
...
@@ -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
(
kStrideASum
)
USE_JITKERNEL_REFER
(
kSoftmax
)
USE_JITKERNEL_REFER
(
kEmbSeqPool
)
USE_JITKERNEL_REFER
(
kSgd
)
...
...
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @
54474637
...
...
@@ -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
(
StrideASum
);
REGISTER_REFER_KERNEL
(
Softmax
);
REGISTER_REFER_KERNEL
(
EmbSeqPool
);
REGISTER_REFER_KERNEL
(
Sgd
);
...
...
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @
54474637
...
...
@@ -411,19 +411,47 @@ void HSum(const T* x, T* res, int n) {
}
}
template
<
typename
T
>
void
StrideASum
(
const
T
*
x
,
T
*
res
,
int
n
,
int
stride
)
{
res
[
0
]
=
x
[
0
];
for
(
int
i
=
stride
;
i
<
n
;
i
+=
stride
)
{
res
[
0
]
+=
std
::
abs
(
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
)
{
if
(
i
%
stride
==
0
)
{
y
[
i
]
=
x
[
i
]
*
a
[
0
];
}
else
{
y
[
i
]
=
x
[
i
];
}
}
}
// y = e^(x - max(x))
// y = y / sum(y)
// remain is the product of dimension shapes after the axis dimension
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
remain
=
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
(
remain
==
1
)
{
HSum
(
y
,
&
scalar
,
n
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
VScal
(
&
scalar
,
y
,
y
,
n
);
}
else
{
for
(
int
j
=
0
;
j
<
remain
;
j
++
)
{
StrideASum
(
&
y
[
j
],
&
scalar
,
n
,
remain
);
scalar
=
static_cast
<
T
>
(
1
)
/
scalar
;
StrideScal
(
&
scalar
,
&
y
[
j
],
&
y
[
j
],
n
,
remain
);
}
}
x
+=
n
;
y
+=
n
;
}
...
...
@@ -507,6 +535,9 @@ 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 +559,8 @@ DECLARE_REFER_KERNEL(GRUHtPart2);
DECLARE_REFER_KERNEL
(
HMax
);
DECLARE_REFER_KERNEL
(
HSum
);
DECLARE_REFER_KERNEL
(
StrideASum
);
// others
DECLARE_REFER_KERNEL
(
CRFDecoding
);
DECLARE_REFER_KERNEL
(
LayerNorm
);
...
...
paddle/fluid/operators/jit/test.cc
浏览文件 @
54474637
...
...
@@ -723,39 +723,122 @@ void TestKernelSoftmax() {
VLOG
(
10
)
<<
"Test JITKernel: "
<<
jit
::
to_string
(
KernelTuple
::
kernel_type
);
for
(
int
bs
:
{
1
,
2
,
10
})
{
for
(
int
n
:
TestSizes
())
{
for
(
int
m
:
{
1
,
2
,
3
})
{
// remain
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
,
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
,
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
);
}
}
}
}
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
TestKernelStrideASum
()
{
using
T
=
typename
KernelTuple
::
data_type
;
VLOG
(
10
)
<<
"Test JITKernel: "
<<
jit
::
to_string
(
KernelTuple
::
kernel_type
);
for
(
int
d
:
TestSizes
())
{
for
(
int
m
:
{
1
,
2
,
3
})
{
// stride
if
(
m
>
d
||
d
%
m
!=
0
)
{
continue
;
}
auto
ref
=
jit
::
GetReferFunc
<
KernelTuple
>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
d
);
RandomVec
<
T
>
(
d
,
x
.
data
());
T
ref_res
;
ref
(
x
.
data
(),
&
ref_res
,
d
,
m
);
auto
verifier
=
[](
const
typename
KernelTuple
::
func_type
tgt
,
const
std
::
vector
<
T
>&
x
,
const
T
ref_res
,
const
int
m
)
{
EXPECT_TRUE
(
tgt
!=
nullptr
);
T
tgt_res
;
tgt
(
x
.
data
(),
&
tgt_res
,
x
.
size
(),
m
);
ExpectEQ
<
T
>
(
&
tgt_res
,
&
ref_res
,
1
);
};
TestAllImpls
<
KernelTuple
,
PlaceType
>
(
d
,
verifier
,
x
,
ref_res
,
m
);
}
}
}
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
TestKernelStrideScal
()
{
using
T
=
typename
KernelTuple
::
data_type
;
VLOG
(
10
)
<<
"Test JITKernel: "
<<
jit
::
to_string
(
KernelTuple
::
kernel_type
);
for
(
int
d
:
TestSizes
())
{
for
(
int
m
:
{
1
,
2
,
3
})
{
// stride
if
(
m
>
d
||
d
%
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
const
T
a
=
static_cast
<
T
>
(
3
);
std
::
vector
<
T
>
x
(
d
),
yref
(
d
);
std
::
vector
<
T
>
xinp
(
d
);
// inplace test
RandomVec
<
T
>
(
d
,
x
.
data
());
std
::
copy
(
x
.
begin
(),
x
.
end
(),
xinp
.
begin
());
ref
(
x_data
,
y_data
,
n
,
bs
);
const
T
*
x_data
=
x
.
data
();
T
*
yref_data
=
yref
.
data
();
T
*
xinp_data
=
xinp
.
data
();
ref
(
xinp_data
,
xinp_data
,
n
,
bs
);
ExpectEQ
<
T
>
(
xinp_data
,
y_data
,
n
*
bs
);
// test refer code inplace
ref
(
&
a
,
x_data
,
yref_data
,
d
,
m
);
ref
(
&
a
,
xinp_data
,
xinp_data
,
d
,
m
);
ExpectEQ
<
T
>
(
xinp_data
,
yref_data
,
d
);
auto
verifier
=
[](
const
typename
KernelTuple
::
func_type
tgt
,
auto
verifier
=
[](
const
typename
KernelTuple
::
func_type
tgt
,
const
T
a
,
const
std
::
vector
<
T
>&
x
,
const
std
::
vector
<
T
>&
yref
,
int
n
,
int
bs
)
{
const
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
);
const
int
d
=
yref
.
size
();
std
::
vector
<
T
>
ytgt
(
d
);
T
*
ytgt_data
=
ytgt
.
data
();
// test normal
tgt
(
x_data
,
ytgt_data
,
n
,
bs
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
n
*
bs
);
tgt
(
&
a
,
x_data
,
ytgt_data
,
d
,
m
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
d
);
// 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
);
tgt
(
&
a
,
ytgt_data
,
ytgt_data
,
d
,
m
);
ExpectEQ
<
T
>
(
ytgt_data
,
yref_data
,
d
);
};
TestAllImpls
<
KernelTuple
,
PlaceType
>
(
n
,
verifier
,
x
,
y
,
n
,
bs
);
TestAllImpls
<
KernelTuple
,
PlaceType
>
(
d
,
verifier
,
a
,
x
,
yref
,
m
);
}
}
}
...
...
@@ -912,7 +995,7 @@ TEST(JITKernel_pool, more) {
EXPECT_EQ
(
kers
.
size
(),
10UL
);
#else
#ifdef PADDLE_WITH_MKLML
EXPECT_EQ
(
kers
.
size
(),
2
1
UL
);
EXPECT_EQ
(
kers
.
size
(),
2
2
UL
);
#else
EXPECT_EQ
(
kers
.
size
(),
8UL
);
#endif
...
...
@@ -921,7 +1004,7 @@ TEST(JITKernel_pool, more) {
TEST
(
JITKernel_pool
,
refer
)
{
const
auto
&
kers
=
jit
::
ReferKernelPool
::
Instance
().
AllKernels
();
EXPECT_EQ
(
kers
.
size
(),
29
UL
);
EXPECT_EQ
(
kers
.
size
(),
31
UL
);
}
// test helper
...
...
@@ -1292,3 +1375,6 @@ TEST_CPU_KERNEL(MatMul);
TEST_CPU_KERNEL
(
Softmax
);
TEST_CPU_KERNEL
(
Sgd
);
TEST_CPU_KERNEL
(
VBroadcast
);
TEST_CPU_KERNEL
(
StrideASum
);
TEST_CPU_KERNEL
(
StrideScal
);
paddle/fluid/operators/math/softmax.h
浏览文件 @
54474637
...
...
@@ -23,15 +23,16 @@ template <typename DeviceContext, typename T, bool is_test,
typename
Enable
=
void
>
class
SoftmaxFunctor
{
public:
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
);
};
template
<
typename
DeviceContext
,
typename
T
>
class
SoftmaxGradFunctor
{
public:
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
*
y
,
const
framework
::
Tensor
*
y_grad
,
framework
::
Tensor
*
x_grad
);
void
operator
()(
const
DeviceContext
&
context
,
const
int
axis_dim
,
const
framework
::
Tensor
*
y
,
const
framework
::
Tensor
*
y_grad
,
framework
::
Tensor
*
x_grad
);
};
#ifdef PADDLE_WITH_CUDA
...
...
paddle/fluid/operators/math/softmax_impl.h
浏览文件 @
54474637
...
...
@@ -36,8 +36,8 @@ struct ValueClip {
template
<
typename
DeviceContext
,
typename
T
,
bool
is_test
,
typename
Enable
>
void
SoftmaxFunctor
<
DeviceContext
,
T
,
is_test
,
Enable
>::
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
*
X
,
framework
::
Tensor
*
Y
)
{
const
DeviceContext
&
context
,
const
int
axis_dim
,
const
framework
::
Tensor
*
X
,
framework
::
Tensor
*
Y
)
{
auto
logits
=
EigenMatrix
<
T
>::
From
(
*
X
);
auto
softmax
=
EigenMatrix
<
T
>::
From
(
*
Y
);
...
...
@@ -46,10 +46,13 @@ void SoftmaxFunctor<DeviceContext, T, is_test, Enable>::operator()(
const
int
batch_size
=
logits
.
dimension
(
kBatchDim
);
const
int
num_classes
=
logits
.
dimension
(
kClassDim
);
const
int
num_remain
=
num_classes
/
axis_dim
;
Eigen
::
DSizes
<
int
,
1
>
along_class
(
kClassDim
);
Eigen
::
DSizes
<
int
,
2
>
batch_by_one
(
batch_size
,
1
);
Eigen
::
DSizes
<
int
,
2
>
one_by_class
(
1
,
num_classes
);
Eigen
::
DSizes
<
int
,
3
>
batch_axis_remain
(
batch_size
,
axis_dim
,
num_remain
);
Eigen
::
DSizes
<
int
,
2
>
one_axis
(
1
,
axis_dim
);
auto
shifted_logits
=
(
logits
-
logits
.
maximum
(
along_class
)
...
...
@@ -60,11 +63,11 @@ void SoftmaxFunctor<DeviceContext, T, is_test, Enable>::operator()(
softmax
.
device
(
*
context
.
eigen_device
())
=
shifted_logits
.
exp
();
softmax
.
device
(
*
context
.
eigen_device
())
=
(
softmax
*
softmax
.
sum
(
along_class
)
softmax
.
reshape
(
batch_axis_remain
)
.
sum
(
along_class
)
.
inverse
()
.
eval
()
.
reshape
(
batch_by_one
)
.
broadcast
(
one_by_class
));
.
broadcast
(
one_axis
));
}
template
<
class
DeviceContext
>
...
...
@@ -73,8 +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
>
();
...
...
@@ -84,14 +87,16 @@ 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
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
void
SoftmaxGradFunctor
<
DeviceContext
,
T
>::
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
*
y
,
const
framework
::
Tensor
*
y_grad
,
framework
::
Tensor
*
x_grad
)
{
const
DeviceContext
&
context
,
const
int
axis_dim
,
const
framework
::
Tensor
*
y
,
const
framework
::
Tensor
*
y_grad
,
framework
::
Tensor
*
x_grad
)
{
auto
softmax
=
EigenMatrix
<
T
>::
From
(
*
y
);
auto
softmax_grad
=
EigenMatrix
<
T
>::
From
(
*
y_grad
);
auto
logits_grad
=
EigenMatrix
<
T
>::
From
(
*
x_grad
);
...
...
@@ -101,16 +106,19 @@ void SoftmaxGradFunctor<DeviceContext, T>::operator()(
const
int
batch_size
=
softmax
.
dimension
(
kBatchDim
);
const
int
num_classes
=
softmax
.
dimension
(
kClassDim
);
const
int
num_remain
=
num_classes
/
axis_dim
;
Eigen
::
DSizes
<
int
,
1
>
along_class
(
kClassDim
);
Eigen
::
DSizes
<
int
,
2
>
batch_by_one
(
batch_size
,
1
);
Eigen
::
DSizes
<
int
,
2
>
one_by_class
(
1
,
num_classes
);
Eigen
::
DSizes
<
int
,
3
>
batch_axis_remain
(
batch_size
,
axis_dim
,
num_remain
);
Eigen
::
DSizes
<
int
,
2
>
one_axis
(
1
,
axis_dim
);
auto
dot
=
(
softmax
*
softmax_grad
)
.
reshape
(
batch_axis_remain
)
.
sum
(
along_class
)
.
eval
()
.
reshape
(
batch_by_one
)
.
broadcast
(
one_by_class
);
.
broadcast
(
one_axis
);
logits_grad
.
device
(
*
context
.
eigen_device
())
=
(
softmax_grad
-
dot
)
*
softmax
;
}
...
...
paddle/fluid/operators/softmax_op.cc
浏览文件 @
54474637
...
...
@@ -39,6 +39,20 @@ class SoftmaxOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SoftmaxOp should not be null."
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
auto
rank_x
=
dim_x
.
size
();
auto
axis
=
ctx
->
Attrs
().
Get
<
int
>
(
"axis"
);
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"
);
}
...
...
@@ -80,8 +94,12 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of softmax, "
"whose
last dimension
is the input_feature_dimensions."
);
"whose
dimension :attr:`axis`
is the input_feature_dimensions."
);
AddOutput
(
"Out"
,
"The normalized values with the same shape as X."
);
AddAttr
<
int
>
(
"axis"
,
"The dimension index of Input(x) to perform softmax,"
"default -1 for last dimension"
)
.
SetDefault
(
-
1
);
AddAttr
<
bool
>
(
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
...
...
@@ -106,12 +124,13 @@ Softmax Operator.
The input of the softmax operator is a tensor of any rank. The output tensor
has the same shape as the input.
The input tensor will first be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is as same as the last dimension of the input
The dimension :attr:`axis` of the input tensor will be permuted to the last.
Then the input tensor will be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is as same as the dimension :attr:`axis` of the input
tensor, and the first dimension(column length) is the product of all other
dimensions of the input tensor. For each row of the matrix, the softmax operator
squashes the K-dimensional(K is the width of the matrix, which is also the size
of the input tensor's
last dimension
) vector of arbitrary real values to a
of the input tensor's
dimension :attr:`axis`
) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1.
It computes the exponential of the given dimension and the sum of exponential
values of all the other dimensions in the K-dimensional vector input.
...
...
paddle/fluid/operators/softmax_op.h
浏览文件 @
54474637
...
...
@@ -20,6 +20,30 @@ namespace paddle {
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
static
inline
int
CanonicalAxis
(
const
int
axis
,
const
int
rank
)
{
if
(
axis
<
0
)
{
return
axis
+
rank
;
}
return
axis
;
}
static
inline
int
SizeToAxis
(
const
int
axis
,
DDim
dims
)
{
int
size
=
1
;
for
(
int
i
=
0
;
i
<
axis
;
i
++
)
{
size
*=
dims
[
i
];
}
return
size
;
}
static
inline
int
SizeFromAxis
(
const
int
axis
,
DDim
dims
)
{
int
size
=
1
;
for
(
int
i
=
axis
;
i
<
dims
.
size
();
i
++
)
{
size
*=
dims
[
i
];
}
return
size
;
}
template
<
typename
DeviceContext
,
typename
T
>
class
SoftmaxKernel
:
public
framework
::
OpKernel
<
T
>
{
...
...
@@ -27,20 +51,27 @@ class SoftmaxKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
Out
=
context
.
Output
<
Tensor
>
(
"Out"
);
const
int
rank
=
X
->
dims
().
size
();
const
int
axis
=
CanonicalAxis
(
context
.
Attr
<
int
>
(
"axis"
),
rank
);
int
axis_dim
=
X
->
dims
()[
axis
];
// allocate memory on device.
Out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
rank
=
X
->
dims
().
size
();
Tensor
X_2d
=
framework
::
ReshapeToMatrix
(
*
X
,
rank
-
1
);
Tensor
Out_2d
=
framework
::
ReshapeToMatrix
(
*
Out
,
rank
-
1
);
const
int
n
=
SizeToAxis
(
axis
,
X
->
dims
());
const
int
d
=
SizeFromAxis
(
axis
,
X
->
dims
());
Tensor
X_2d
,
Out_2d
;
X_2d
.
ShareDataWith
(
*
X
).
Resize
({
n
,
d
});
Out_2d
.
ShareDataWith
(
*
Out
).
Resize
({
n
,
d
});
#ifdef PADDLE_ON_INFERENCE
math
::
SoftmaxFunctor
<
DeviceContext
,
T
,
true
>
()(
context
.
template
device_context
<
DeviceContext
>(),
&
X_2d
,
&
Out_2d
);
context
.
template
device_context
<
DeviceContext
>(),
axis_dim
,
&
X_2d
,
&
Out_2d
);
#else
math
::
SoftmaxFunctor
<
DeviceContext
,
T
,
false
>
()(
context
.
template
device_context
<
DeviceContext
>(),
&
X_2d
,
&
Out_2d
);
context
.
template
device_context
<
DeviceContext
>(),
axis_dim
,
&
X_2d
,
&
Out_2d
);
#endif
}
};
...
...
@@ -52,18 +83,23 @@ class SoftmaxGradKernel : public framework::OpKernel<T> {
auto
*
Out
=
context
.
Input
<
Tensor
>
(
"Out"
);
auto
*
dOut
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dX
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
const
int
rank
=
dX
->
dims
().
size
();
const
int
axis
=
CanonicalAxis
(
context
.
Attr
<
int
>
(
"axis"
),
rank
);
int
axis_dim
=
dX
->
dims
()[
axis
];
// allocate memory on device.
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
rank
=
Out
->
dims
().
size
();
Tensor
Out_2d
=
framework
::
ReshapeToMatrix
(
*
Out
,
rank
-
1
);
Tensor
dOut_2d
=
framework
::
ReshapeToMatrix
(
*
dOut
,
rank
-
1
);
Tensor
dX_2d
=
framework
::
ReshapeToMatrix
(
*
dX
,
rank
-
1
);
const
int
n
=
SizeToAxis
(
axis
,
dX
->
dims
());
const
int
d
=
SizeFromAxis
(
axis
,
dX
->
dims
());
Tensor
dX_2d
,
Out_2d
,
dOut_2d
;
dX_2d
.
ShareDataWith
(
*
dX
).
Resize
({
n
,
d
});
Out_2d
.
ShareDataWith
(
*
Out
).
Resize
({
n
,
d
});
dOut_2d
.
ShareDataWith
(
*
dOut
).
Resize
({
n
,
d
});
math
::
SoftmaxGradFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
&
Out_2d
,
&
d
Out_2d
,
&
dX_2d
);
context
.
template
device_context
<
DeviceContext
>(),
axis_dim
,
&
Out_2d
,
&
d
Out_2d
,
&
d
X_2d
);
}
};
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.h
浏览文件 @
54474637
...
...
@@ -40,10 +40,12 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
axis_dim
=
logits
->
dims
()[
logits
->
dims
().
size
()
-
1
];
auto
&
dev_ctx
=
context
.
template
device_context
<
platform
::
CPUDeviceContext
>();
math
::
SoftmaxFunctor
<
platform
::
CPUDeviceContext
,
T
,
false
>
()(
dev_ctx
,
logits
,
softmax
);
dev_ctx
,
axis_dim
,
logits
,
softmax
);
math
::
CrossEntropyFunctor
<
platform
::
CPUDeviceContext
,
T
>
()(
dev_ctx
,
loss
,
softmax
,
labels
,
context
.
Attr
<
bool
>
(
"soft_label"
),
context
.
Attr
<
int
>
(
"ignore_index"
));
...
...
paddle/fluid/operators/warpctc_cudnn_op.cu.cc
浏览文件 @
54474637
...
...
@@ -67,9 +67,11 @@ class CudnnCTCKernel : public framework::OpKernel<T> {
softmax_logits
.
mutable_data
<
T
>
(
logits
->
dims
(),
ctx
.
GetPlace
());
softmax_logits
.
set_lod
(
logits_lod
);
int
rank
=
logits
->
dims
().
size
();
int
axis_dim
=
logits
->
dims
()[
rank
-
1
];
Tensor
in_2d
=
framework
::
ReshapeToMatrix
(
*
logits
,
rank
-
1
);
Tensor
out_2d
=
framework
::
ReshapeToMatrix
(
softmax_logits
,
rank
-
1
);
math
::
SoftmaxFunctor
<
DeviceContext
,
T
,
false
>
()(
dev_ctx
,
&
in_2d
,
&
out_2d
);
math
::
SoftmaxFunctor
<
DeviceContext
,
T
,
false
>
()(
dev_ctx
,
axis_dim
,
&
in_2d
,
&
out_2d
);
// ctc needs sequences data stored in transposed padding format
// logits and grad using padding data of layout 'TNC'
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
54474637
...
...
@@ -1820,17 +1820,18 @@ def sequence_softmax(input, use_cudnn=False, name=None):
return
softmax_out
def
softmax
(
input
,
use_cudnn
=
False
,
name
=
None
):
def
softmax
(
input
,
use_cudnn
=
False
,
name
=
None
,
axis
=-
1
):
"""
The input of the softmax operator is a tensor of any rank. The output tensor
has the same shape as the input.
The input tensor will first be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is as same as the last dimension of the input
The dimension :attr:`axis` of the input tensor will be permuted to the last.
Then the input tensor will be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is the same as the dimension :attr:`axis` of the input
tensor, and the first dimension(column length) is the product of all other
dimensions of the input tensor. For each row of the matrix, the softmax operator
squashes the K-dimensional(K is the width of the matrix, which is also the size
of the input tensor's
last dimension
) vector of arbitrary real values to a
of the input tensor's
dimension :attr:`axis`
) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1.
It computes the exponential of the given dimension and the sum of exponential
...
...
@@ -1852,6 +1853,9 @@ def softmax(input, use_cudnn=False, name=None):
False by default. Default: False
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
axis (int): The index of dimension to perform softmax calculations, it should
be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of
input variable. Default: -1.
Returns:
Variable: output of softmax
...
...
@@ -1861,7 +1865,10 @@ def softmax(input, use_cudnn=False, name=None):
.. code-block:: python
fc = fluid.layers.fc(input=x, size=10)
softmax = fluid.layers.softmax(input=fc)
# perform softmax in the second dimension
softmax = fluid.layers.softmax(input=fc, axis=1)
# perform softmax in the last dimension
softmax = fluid.layers.softmax(input=fc, axis=-1)
"""
helper
=
LayerHelper
(
'softmax'
,
**
locals
())
...
...
@@ -1871,7 +1878,8 @@ def softmax(input, use_cudnn=False, name=None):
type
=
"softmax"
,
inputs
=
{
"X"
:
input
},
outputs
=
{
"Out"
:
softmax_out
},
attrs
=
{
"use_cudnn"
:
use_cudnn
})
attrs
=
{
"axis"
:
axis
,
"use_cudnn"
:
use_cudnn
})
return
softmax_out
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
54474637
...
...
@@ -845,7 +845,7 @@ class TestBook(unittest.TestCase):
with
program_guard
(
program
):
data
=
layers
.
data
(
name
=
'data'
,
shape
=
[
10
],
dtype
=
'float32'
)
hid
=
layers
.
fc
(
input
=
data
,
size
=
20
)
self
.
assertIsNotNone
(
layers
.
softmax
(
hid
))
self
.
assertIsNotNone
(
layers
.
softmax
(
hid
,
axis
=
1
))
print
(
str
(
program
))
def
test_space_to_depth
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @
54474637
...
...
@@ -31,6 +31,9 @@ class TestSoftmaxOp(OpTest):
def
get_x_shape
(
self
):
return
[
10
,
10
]
def
get_axis
(
self
):
return
-
1
def
setUp
(
self
):
self
.
op_type
=
"softmax"
self
.
use_cudnn
=
False
...
...
@@ -38,15 +41,15 @@ class TestSoftmaxOp(OpTest):
self
.
dtype
=
np
.
float32
self
.
init_kernel_type
()
self
.
shape
=
self
.
get_x_shape
()
self
.
axis
=
self
.
get_axis
()
x
=
np
.
random
.
uniform
(
0.1
,
1
,
self
.
shape
).
astype
(
self
.
dtype
)
out
=
np
.
apply_along_axis
(
stable_softmax
,
1
,
x
.
reshape
([
-
1
,
self
.
shape
[
-
1
]]))
out
=
out
.
reshape
(
self
.
shape
)
out
=
np
.
apply_along_axis
(
stable_softmax
,
self
.
axis
,
x
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
x
)}
self
.
outputs
=
{
'Out'
:
out
}
self
.
attrs
=
{
'axis'
:
self
.
axis
,
'use_cudnn'
:
self
.
use_cudnn
,
'use_mkldnn'
:
self
.
use_mkldnn
}
...
...
@@ -76,6 +79,38 @@ class TestSoftmaxOp2(TestSoftmaxOp):
return
[
2
,
3
,
4
,
5
]
class
TestSoftmaxOp3
(
TestSoftmaxOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
0
class
TestSoftmaxOp4
(
TestSoftmaxOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
1
class
TestSoftmaxOp5
(
TestSoftmaxOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
2
class
TestSoftmaxOp5
(
TestSoftmaxOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
3
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
"core is not compiled with CUDA"
)
class
TestSoftmaxCUDNNOp
(
TestSoftmaxOp
):
...
...
@@ -90,6 +125,16 @@ class TestSoftmaxCUDNNOp2(TestSoftmaxCUDNNOp):
return
[
2
,
3
,
4
,
5
]
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
"core is not compiled with CUDA"
)
class
TestSoftmaxCUDNNOp5
(
TestSoftmaxCUDNNOp
):
def
get_x_shape
(
self
):
return
[
2
,
3
,
4
,
5
]
def
get_axis
(
self
):
return
3
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_cuda
(),
"core is not compiled with CUDA"
)
class
TestSoftmaxFP16Op
(
TestSoftmaxOp
):
...
...
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