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36363292
编写于
8月 24, 2018
作者:
T
tensor-tang
提交者:
GitHub
8月 24, 2018
浏览文件
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差异文件
Merge pull request #12904 from tensor-tang/refine/jit
optimize cpu vec activations
上级
2b64a19f
7bdaf096
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
530 addition
and
48 deletion
+530
-48
paddle/fluid/operators/attention_lstm_op.cc
paddle/fluid/operators/attention_lstm_op.cc
+23
-25
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+1
-0
paddle/fluid/operators/math/cpu_vec.h
paddle/fluid/operators/math/cpu_vec.h
+285
-21
paddle/fluid/operators/math/cpu_vec_test.cc
paddle/fluid/operators/math/cpu_vec_test.cc
+202
-0
paddle/fluid/platform/CMakeLists.txt
paddle/fluid/platform/CMakeLists.txt
+1
-1
paddle/fluid/platform/cpu_info.h
paddle/fluid/platform/cpu_info.h
+1
-1
paddle/fluid/platform/init.cc
paddle/fluid/platform/init.cc
+17
-0
未找到文件。
paddle/fluid/operators/attention_lstm_op.cc
浏览文件 @
36363292
...
...
@@ -232,40 +232,28 @@ use lstm_x_t as input and compute as standard LSTM.
template
<
typename
T
>
inline
void
bias_relu
(
const
int
n
,
const
T
*
x
,
const
T
*
bias
,
T
*
y
)
{
if
(
bias
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
+
bias
[
0
];
}
math
::
vec_relu
<
T
>
(
n
,
y
,
y
);
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
>
(
n
,
x
,
y
);
math
::
vec_relu
<
T
,
platform
::
jit
::
avx
>
(
n
,
x
,
y
);
}
}
template
<
typename
DeviceContext
,
typename
T
>
inline
void
vec_softmax
(
const
math
::
BlasT
<
DeviceContext
,
T
>&
blas
,
const
int
n
,
const
T
*
x
,
T
*
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
;
}
// sub
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
-
scalar
;
}
// exp
blas
.
VEXP
(
n
,
y
,
y
);
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
];
}
// scale
blas
.
SCAL
(
n
,
static_cast
<
T
>
(
1
)
/
scalar
,
y
);
math
::
vec_scal
<
T
>
(
n
,
static_cast
<
T
>
(
1
)
/
scalar
,
y
);
// scale
}
template
<
typename
T
>
...
...
@@ -311,11 +299,21 @@ 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
});
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"
));
auto
&
act_gate_str
=
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
);
auto
&
act_cell_str
=
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
);
auto
&
act_cand_str
=
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
);
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx
))
{
math
::
VecActivations
<
T
,
platform
::
jit
::
avx
>
act_functor
;
act_gate
=
act_functor
(
act_gate_str
);
act_cell
=
act_functor
(
act_cell_str
);
act_cand
=
act_functor
(
act_cand_str
);
}
else
{
math
::
VecActivations
<
T
,
platform
::
jit
::
isa_any
>
act_functor
;
act_gate
=
act_functor
(
act_gate_str
);
act_cell
=
act_functor
(
act_cell_str
);
act_cand
=
act_functor
(
act_cand_str
);
}
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
h0_data
=
h0
?
h0
->
data
<
T
>
()
:
NULL
;
...
...
@@ -363,7 +361,7 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
fc_out_data
);
}
// 1d. softmax
vec_softmax
<
DeviceContext
,
T
>
(
blas
,
seq_len
,
fc_out_data
,
fc_out_data
);
vec_softmax
<
T
>
(
seq_len
,
fc_out_data
,
fc_out_data
);
// mul x(seq_len*M) and sum pool
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
1
,
M
,
seq_len
,
fc_out_data
,
cur_x_data
,
lstm_x_data
);
...
...
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
36363292
...
...
@@ -65,3 +65,4 @@ if(WITH_GPU)
nv_test
(
selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function
)
endif
()
cc_test
(
concat_test SRCS concat_test.cc DEPS concat
)
cc_test
(
cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info
)
paddle/fluid/operators/math/cpu_vec.h
浏览文件 @
36363292
...
...
@@ -13,8 +13,16 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <cmath>
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
namespace
paddle
{
namespace
operators
{
...
...
@@ -22,16 +30,161 @@ namespace math {
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
#define AVX_FLOAT_BLOCK 8
#define AVX_DOUBLE_BLOCK 4
#define AVX2_FLOAT_BLOCK 8
#define AVX2_DOUBLE_BLOCK 4
#define AVX512_FLOAT_BLOCK 16
#define AVX512_DOUBLE_BLOCK 8
template
<
typename
T
>
inline
T
sigmoid
(
T
x
)
{
return
1.
/
(
1.
+
exp
(
-
x
));
inline
void
vec_exp
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
std
::
exp
(
x
[
i
]);
}
}
template
<
typename
T
>
inline
T
tanh
(
T
x
)
{
return
2.
*
sigmoid
(
2.
*
x
)
-
1.
;
inline
void
vec_scal
(
const
int
n
,
const
T
a
,
T
*
x
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
x
[
i
]
=
a
*
x
[
i
];
}
}
#ifdef PADDLE_WITH_MKLML
template
<
>
inline
void
vec_exp
<
float
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
platform
::
dynload
::
vsExp
(
n
,
x
,
y
);
}
template
<
>
inline
void
vec_exp
<
double
>
(
const
int
n
,
const
double
*
x
,
double
*
y
)
{
platform
::
dynload
::
vdExp
(
n
,
x
,
y
);
}
template
<
>
inline
void
vec_scal
<
float
>
(
const
int
n
,
const
float
a
,
float
*
x
)
{
platform
::
dynload
::
cblas_sscal
(
n
,
a
,
x
,
1
);
}
template
<
>
inline
void
vec_scal
<
double
>
(
const
int
n
,
const
double
a
,
double
*
x
)
{
platform
::
dynload
::
cblas_dscal
(
n
,
a
,
x
,
1
);
}
#endif
// MKL scal only support inplace, choose this if src and dst are not equal
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
inline
void
vec_scal
(
const
int
n
,
const
T
a
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
a
*
x
[
i
];
}
}
template
<
>
inline
void
vec_scal
<
float
,
platform
::
jit
::
avx
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
#ifdef __AVX__
constexpr
int
block
=
AVX_FLOAT_BLOCK
;
if
(
n
<
block
)
{
vec_scal
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
a
,
x
,
y
);
return
;
}
const
int
rest
=
n
%
block
;
const
int
end
=
n
-
rest
;
int
i
=
0
;
__m256
scalar
=
_mm256_set1_ps
(
a
);
__m256
tmp
;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(y + i, tmp)
for
(
i
=
0
;
i
<
end
;
i
+=
block
)
{
MOVE_ONE_STEP
;
}
#undef MOVE_ONE_STEP
if
(
rest
==
0
)
{
return
;
}
// can not continue move step if src and dst are inplace
for
(
i
=
n
-
rest
;
i
<
n
;
++
i
)
{
y
[
i
]
=
a
*
x
[
i
];
}
#else
vec_scal
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
a
,
x
,
y
);
#endif
}
template
<
>
inline
void
vec_scal
<
float
,
platform
::
jit
::
avx2
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
vec_scal
<
float
,
platform
::
jit
::
avx
>
(
n
,
a
,
x
,
y
);
}
template
<
>
inline
void
vec_scal
<
float
,
platform
::
jit
::
avx512_common
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_scal
<
float
,
platform
::
jit
::
avx2
>
(
n
,
a
,
x
,
y
);
}
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
inline
void
vec_add_bias
(
const
int
n
,
const
T
a
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
+
a
;
}
}
template
<
>
inline
void
vec_add_bias
<
float
,
platform
::
jit
::
avx
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
#ifdef __AVX__
constexpr
int
block
=
AVX_FLOAT_BLOCK
;
if
(
n
<
block
)
{
vec_add_bias
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
a
,
x
,
y
);
return
;
}
const
int
rest
=
n
%
block
;
const
int
end
=
n
-
rest
;
int
i
=
0
;
__m256
bias
=
_mm256_set1_ps
(
a
);
__m256
tmp
;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_add_ps(tmp, bias); \
_mm256_storeu_ps(y + i, tmp)
for
(
i
=
0
;
i
<
end
;
i
+=
block
)
{
MOVE_ONE_STEP
;
}
#undef MOVE_ONE_STEP
if
(
rest
==
0
)
{
return
;
}
// can not continue move step if src and dst are inplace
for
(
i
=
n
-
rest
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
+
a
;
}
#else
vec_add_bias
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
a
,
x
,
y
);
#endif
}
template
<
>
inline
void
vec_add_bias
<
float
,
platform
::
jit
::
avx2
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
vec_add_bias
<
float
,
platform
::
jit
::
avx
>
(
n
,
a
,
x
,
y
);
}
template
<
>
inline
void
vec_add_bias
<
float
,
platform
::
jit
::
avx512_common
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_add_bias
<
float
,
platform
::
jit
::
avx2
>
(
n
,
a
,
x
,
y
);
}
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
...
...
@@ -45,18 +198,97 @@ inline void vec_sigmoid(const int n, const T* x, T* y) {
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
const
T
max
=
SIGMOID_THRESHOLD_MAX
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
T
tmp
=
(
x
[
i
]
<
min
)
?
min
:
((
x
[
i
]
>
max
)
?
max
:
x
[
i
]);
y
[
i
]
=
1.0
/
(
1.0
+
std
::
exp
(
-
tmp
));
y
[
i
]
=
(
x
[
i
]
<
min
)
?
min
:
((
x
[
i
]
>
max
)
?
max
:
x
[
i
]);
y
[
i
]
=
static_cast
<
T
>
(
0
)
-
y
[
i
];
}
vec_exp
<
T
>
(
n
,
y
,
y
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
y
[
i
]);
}
}
template
<
>
inline
void
vec_sigmoid
<
float
,
platform
::
jit
::
avx
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
#ifdef __AVX__
constexpr
int
block
=
AVX_FLOAT_BLOCK
;
if
(
n
<
block
)
{
vec_sigmoid
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
x
,
y
);
return
;
}
const
int
rest
=
n
%
block
;
const
int
end
=
n
-
rest
;
int
i
=
0
;
__m256
max
=
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MAX
);
__m256
min
=
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MIN
);
__m256
zeros
=
_mm256_setzero_ps
();
__m256
tmp
;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, min); \
tmp = _mm256_min_ps(tmp, max); \
tmp = _mm256_sub_ps(zeros, tmp); \
_mm256_storeu_ps(y + i, tmp)
for
(
i
=
0
;
i
<
end
;
i
+=
block
)
{
MOVE_ONE_STEP
;
}
#undef MOVE_ONE_STEP
if
(
rest
!=
0
)
{
// can not continue move step since the src and dst address could be equal
const
float
xmin
=
SIGMOID_THRESHOLD_MIN
;
const
float
xmax
=
SIGMOID_THRESHOLD_MAX
;
for
(
i
=
n
-
rest
;
i
<
n
;
++
i
)
{
y
[
i
]
=
0.
f
-
((
x
[
i
]
<
xmin
)
?
xmin
:
((
x
[
i
]
>
xmax
)
?
xmax
:
x
[
i
]));
}
}
vec_exp
<
float
>
(
n
,
y
,
y
);
__m256
ones
=
_mm256_set1_ps
(
1.0
f
);
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(y + i); \
tmp = _mm256_add_ps(ones, tmp); \
tmp = _mm256_div_ps(ones, tmp); \
_mm256_storeu_ps(y + i, tmp)
for
(
i
=
0
;
i
<
end
;
i
+=
block
)
{
MOVE_ONE_STEP
;
}
#undef MOVE_ONE_STEP
if
(
rest
==
0
)
{
return
;
}
// can not continue move step
for
(
i
=
n
-
rest
;
i
<
n
;
++
i
)
{
y
[
i
]
=
1.
f
/
(
1.
f
+
y
[
i
]);
}
#else
vec_sigmoid
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
x
,
y
);
#endif
}
template
<
>
inline
void
vec_sigmoid
<
float
,
platform
::
jit
::
avx2
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
vec_sigmoid
<
float
,
platform
::
jit
::
avx
>
(
n
,
x
,
y
);
}
template
<
>
inline
void
vec_sigmoid
<
float
,
platform
::
jit
::
avx512_common
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_sigmoid
<
float
,
platform
::
jit
::
avx2
>
(
n
,
x
,
y
);
}
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
inline
void
vec_tanh
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
tanh
<
T
>
(
x
[
i
]);
}
vec_scal
<
T
,
isa
>
(
n
,
static_cast
<
T
>
(
2
),
x
,
y
);
vec_sigmoid
<
T
,
isa
>
(
n
,
y
,
y
);
vec_scal
<
T
>
(
n
,
static_cast
<
T
>
(
2
),
y
);
vec_add_bias
<
T
,
isa
>
(
n
,
static_cast
<
T
>
(
-
1
),
y
,
y
);
}
// TODO(TJ): make relu clip
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
inline
void
vec_relu
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
...
...
@@ -64,24 +296,56 @@ inline void vec_relu(const int n, const T* x, T* y) {
}
}
template
<
>
inline
void
vec_relu
<
float
,
platform
::
jit
::
avx
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
#ifdef __AVX__
constexpr
int
block
=
AVX_FLOAT_BLOCK
;
if
(
n
<
block
*
4
)
{
vec_relu
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
x
,
y
);
return
;
}
const
int
rest
=
n
%
block
;
const
int
end
=
n
-
rest
;
int
i
=
0
;
__m256
zeros
=
_mm256_setzero_ps
();
__m256
tmp
;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + i, tmp)
for
(
i
=
0
;
i
<
end
;
i
+=
block
)
{
MOVE_ONE_STEP
;
}
if
(
rest
==
0
)
{
return
;
}
i
=
n
-
block
;
MOVE_ONE_STEP
;
#undef MOVE_ONE_STEP
#else
vec_relu
<
float
,
platform
::
jit
::
isa_any
>
(
n
,
x
,
y
);
#endif
}
template
<
>
inline
void
vec_relu
<
float
,
platform
::
jit
::
avx2
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): complete me
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
>
0
?
x
[
i
]
:
0
;
}
vec_relu
<
float
,
platform
::
jit
::
avx
>
(
n
,
x
,
y
);
}
template
<
>
inline
void
vec_relu
<
float
,
platform
::
jit
::
avx
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): complete me
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
>
0
?
x
[
i
]
:
0
;
}
inline
void
vec_relu
<
float
,
platform
::
jit
::
avx512_common
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_relu
<
float
,
platform
::
jit
::
avx2
>
(
n
,
x
,
y
);
}
// TODO(TJ): optimize double of sigmoid, tanh and relu if necessary
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
=
platform
::
jit
::
isa_any
>
class
VecActivations
{
public:
...
...
@@ -96,7 +360,7 @@ class VecActivations {
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
vec_identity
<
T
,
isa
>
;
}
PADDLE_THROW
(
"Not support type %s."
,
type
)
;
LOG
(
FATAL
)
<<
"Not support type: "
<<
type
;
}
};
...
...
paddle/fluid/operators/math/cpu_vec_test.cc
0 → 100644
浏览文件 @
36363292
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <sys/time.h>
#include <cmath>
#include <cstring>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
inline
double
GetCurrentUS
()
{
struct
timeval
time
;
gettimeofday
(
&
time
,
NULL
);
return
1e+6
*
time
.
tv_sec
+
time
.
tv_usec
;
}
constexpr
int
repeat
=
1000
;
template
<
typename
T
>
inline
T
_sigmoid
(
T
x
)
{
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
const
T
max
=
SIGMOID_THRESHOLD_MAX
;
T
tmp
=
(
x
<
min
)
?
min
:
((
x
>
max
)
?
max
:
x
);
return
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
std
::
exp
(
-
tmp
));
}
template
<
typename
T
>
inline
T
_tanh
(
T
x
)
{
return
static_cast
<
T
>
(
2
)
*
_sigmoid
<
T
>
(
static_cast
<
T
>
(
2
)
*
x
)
-
static_cast
<
T
>
(
1
);
}
template
<
typename
T
>
void
ref_sigmoid
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
_sigmoid
(
x
[
i
]);
}
}
template
<
typename
T
>
void
ref_tanh
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
_tanh
(
x
[
i
]);
}
}
template
<
typename
T
>
void
ref_relu
(
const
int
n
,
const
T
*
x
,
T
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
>
0
?
x
[
i
]
:
0
;
}
}
template
<
typename
T
>
void
RandomVec
(
const
int
n
,
T
*
a
)
{
static
unsigned
int
seed
=
100
;
std
::
mt19937
rng
(
seed
++
);
std
::
uniform_real_distribution
<
double
>
uniform_dist
(
0
,
1
);
const
T
lower
=
static_cast
<
T
>
(
-
20.
f
);
const
T
upper
=
static_cast
<
T
>
(
20.
f
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
a
[
i
]
=
static_cast
<
T
>
(
uniform_dist
(
rng
)
*
(
upper
-
lower
)
+
lower
);
}
}
template
<
typename
T
>
void
TestAndBench
(
const
int
n
,
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
tgt
,
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
ref
)
{
std
::
vector
<
T
>
x
(
n
);
std
::
vector
<
T
>
ytgt
(
n
),
yref
(
n
);
RandomVec
<
T
>
(
n
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
T
*
ytgt_data
=
ytgt
.
data
();
T
*
yref_data
=
yref
.
data
();
auto
st
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
tgt
(
n
,
x_data
,
ytgt_data
);
}
auto
mt
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
ref
(
n
,
x_data
,
yref_data
);
}
auto
et
=
GetCurrentUS
();
VLOG
(
3
)
<<
"Vec size "
<<
n
<<
": refer takes: "
<<
(
et
-
mt
)
/
repeat
<<
" us, tgt takes: "
<<
(
mt
-
st
)
/
repeat
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
EXPECT_NEAR
(
ytgt_data
[
i
],
yref_data
[
i
],
1e-3
);
}
}
TEST
(
CpuVecTest
,
sigmoid
)
{
namespace
jit
=
paddle
::
platform
::
jit
;
using
namespace
paddle
::
operators
::
math
;
// NOLINT
for
(
auto
sz
:
{
1
,
2
,
15
,
16
,
30
,
32
,
128
,
200
,
512
})
{
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
>
,
ref_sigmoid
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx
>
,
ref_sigmoid
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx2
>
,
ref_sigmoid
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx512_common
>
,
ref_sigmoid
<
float
>
);
}
TestAndBench
<
double
>
(
30
,
vec_sigmoid
<
double
>
,
ref_sigmoid
<
double
>
);
}
TEST
(
CpuVecTest
,
tanh
)
{
namespace
jit
=
paddle
::
platform
::
jit
;
using
namespace
paddle
::
operators
::
math
;
// NOLINT
for
(
auto
sz
:
{
1
,
2
,
15
,
16
,
30
,
32
,
128
,
200
,
512
})
{
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx2
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx512_common
>
,
ref_tanh
<
float
>
);
}
TestAndBench
<
double
>
(
30
,
vec_tanh
<
double
>
,
ref_tanh
<
double
>
);
}
TEST
(
CpuVecTest
,
relu
)
{
namespace
jit
=
paddle
::
platform
::
jit
;
using
namespace
paddle
::
operators
::
math
;
// NOLINT
for
(
auto
sz
:
{
1
,
2
,
15
,
16
,
30
,
32
,
128
,
200
,
512
})
{
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx2
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx512_common
>
,
ref_relu
<
float
>
);
}
TestAndBench
<
double
>
(
30
,
vec_relu
<
double
>
,
ref_relu
<
double
>
);
}
template
<
typename
T
>
void
TestInplace
(
const
int
n
,
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
tgt
,
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
ref
)
{
std
::
vector
<
T
>
x
(
n
);
std
::
vector
<
T
>
ytgt
(
n
),
yref
(
n
);
RandomVec
<
T
>
(
n
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
T
*
yref_data
=
yref
.
data
();
T
*
ytgt_data
=
ytgt
.
data
();
std
::
memcpy
(
yref_data
,
x_data
,
sizeof
(
T
)
*
n
);
std
::
memcpy
(
ytgt_data
,
x_data
,
sizeof
(
T
)
*
n
);
ref
(
n
,
yref_data
,
yref_data
);
tgt
(
n
,
ytgt_data
,
ytgt_data
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
EXPECT_NEAR
(
ytgt_data
[
i
],
yref_data
[
i
],
1e-3
);
}
}
TEST
(
CpuVecTest
,
inplace_sigmoid
)
{
namespace
jit
=
paddle
::
platform
::
jit
;
using
namespace
paddle
::
operators
::
math
;
// NOLINT
for
(
auto
sz
:
{
1
,
2
,
15
,
16
,
30
,
32
,
128
,
200
,
512
})
{
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
>
,
ref_sigmoid
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx
>
,
ref_sigmoid
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx2
>
,
ref_sigmoid
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx512_common
>
,
ref_sigmoid
<
float
>
);
}
TestInplace
<
double
>
(
30
,
vec_sigmoid
<
double
>
,
ref_sigmoid
<
double
>
);
}
TEST
(
CpuVecTest
,
inplace_tanh
)
{
namespace
jit
=
paddle
::
platform
::
jit
;
using
namespace
paddle
::
operators
::
math
;
// NOLINT
for
(
auto
sz
:
{
1
,
2
,
15
,
16
,
30
,
32
,
128
,
200
,
512
})
{
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx2
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx512_common
>
,
ref_tanh
<
float
>
);
}
TestInplace
<
double
>
(
30
,
vec_tanh
<
double
>
,
ref_tanh
<
double
>
);
}
TEST
(
CpuVecTest
,
inplace_relu
)
{
namespace
jit
=
paddle
::
platform
::
jit
;
using
namespace
paddle
::
operators
::
math
;
// NOLINT
for
(
auto
sz
:
{
1
,
2
,
15
,
16
,
30
,
32
,
128
,
200
,
512
})
{
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx2
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx512_common
>
,
ref_relu
<
float
>
);
}
TestInplace
<
double
>
(
30
,
vec_relu
<
double
>
,
ref_relu
<
double
>
);
}
paddle/fluid/platform/CMakeLists.txt
浏览文件 @
36363292
...
...
@@ -50,7 +50,7 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library
(
device_context SRCS device_context.cc init.cc DEPS malloc
place eigen3 stringpiece cpu_helper framework_proto
${
GPU_CTX_DEPS
}
${
MKLDNN_CTX_DEPS
}
)
place eigen3 stringpiece cpu_helper
cpu_info
framework_proto
${
GPU_CTX_DEPS
}
${
MKLDNN_CTX_DEPS
}
)
nv_test
(
device_context_test SRCS device_context_test.cu DEPS device_context gpu_info
)
cc_test
(
init_test SRCS init_test.cc DEPS device_context
)
...
...
paddle/fluid/platform/cpu_info.h
浏览文件 @
36363292
...
...
@@ -51,7 +51,7 @@ typedef enum {
}
cpu_isa_t
;
// Instruction set architecture
// May I use some instruction
inline
bool
MayIUse
(
const
cpu_isa_t
cpu_isa
);
bool
MayIUse
(
const
cpu_isa_t
cpu_isa
);
}
// namespace jit
...
...
paddle/fluid/platform/init.cc
浏览文件 @
36363292
...
...
@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
...
...
@@ -120,6 +121,22 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
#ifndef PADDLE_WITH_MKLDNN
platform
::
SetNumThreads
(
FLAGS_paddle_num_threads
);
#endif
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx512_common
))
{
#ifndef __AVX512F__
LOG
(
WARNING
)
<<
"AVX512F is available, Please re-compile on local machine"
;
#endif
}
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx2
))
{
#ifndef __AVX2__
LOG
(
WARNING
)
<<
"AVX2 is available, Please re-compile on local machine"
;
#endif
}
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx
))
{
#ifndef __AVX__
LOG
(
WARNING
)
<<
"AVX is available, Please re-compile on local machine"
;
#endif
}
}
void
InitGLOG
(
const
std
::
string
&
prog_name
)
{
...
...
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