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f2adaf1c
编写于
10月 08, 2018
作者:
T
tensor-tang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add vrelu and lstm kernel
test=develop
上级
e6d8aca3
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
269 addition
and
75 deletion
+269
-75
paddle/fluid/operators/math/jit_kernel.cc
paddle/fluid/operators/math/jit_kernel.cc
+0
-17
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+21
-12
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+109
-0
paddle/fluid/operators/math/jit_kernel_exp.cc
paddle/fluid/operators/math/jit_kernel_exp.cc
+1
-0
paddle/fluid/operators/math/jit_kernel_lstm.cc
paddle/fluid/operators/math/jit_kernel_lstm.cc
+84
-46
paddle/fluid/operators/math/jit_kernel_test.cc
paddle/fluid/operators/math/jit_kernel_test.cc
+54
-0
未找到文件。
paddle/fluid/operators/math/jit_kernel.cc
浏览文件 @
f2adaf1c
...
...
@@ -35,23 +35,6 @@ std::shared_ptr<const Kernel> KernelPool::Get(const std::string& key) const {
return
kers_
.
at
(
key
);
}
template
<
>
std
::
shared_ptr
<
const
LSTMKernel
<
float
>>
KernelPool
::
Get
<
LSTMKernel
<
float
>
,
int
,
const
std
::
string
&
,
const
std
::
string
&
,
const
std
::
string
&>
(
int
d
,
const
std
::
string
&
act_gate
,
const
std
::
string
&
act_cand
,
const
std
::
string
&
act_cell
)
{
std
::
string
key
=
"lstmf"
+
std
::
to_string
(
d
)
+
act_gate
+
act_cand
+
act_cell
;
if
(
kers_
.
find
(
key
)
==
kers_
.
end
())
{
auto
p
=
std
::
make_shared
<
LSTMKernel
<
float
>>
(
d
,
act_gate
,
act_cand
,
act_cell
);
kers_
.
insert
({
key
,
std
::
dynamic_pointer_cast
<
Kernel
>
(
p
)});
return
p
;
}
return
std
::
dynamic_pointer_cast
<
const
LSTMKernel
<
float
>>
(
kers_
.
at
(
key
));
}
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
...
...
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @
f2adaf1c
...
...
@@ -87,36 +87,45 @@ class VAddBiasKernel : public Kernel {
};
template
<
typename
T
>
class
V
Exp
Kernel
:
public
Kernel
{
class
V
Act
Kernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
V
SigmoidKernel
:
public
Kernel
{
class
V
ReluKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
V
TanhKernel
:
public
Kernel
{
class
V
IdentityKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
LSTMKernel
:
public
Kernel
{
class
VExpKernel
:
public
VActKernel
<
T
>
{
public:
explicit
LSTMKernel
(
int
d
,
const
std
::
string
&
act_gate
,
const
std
::
string
&
act_cand
,
const
std
::
string
&
act_cell
)
;
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
}
;
void
(
*
jit_ker
)(
T
*
,
const
T
*
,
T
*
,
T
*
);
std
::
function
<
void
(
T
*
,
const
T
*
,
T
*
,
T
*
)
>
ComputeCtHt
,
ComputeCtHt_NoC0H0
;
template
<
typename
T
>
class
VSigmoidKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
private:
int
d_
,
d2_
,
d3_
;
std
::
function
<
void
(
const
int
,
const
T
*
,
T
*
)
>
act_gate_
,
act_cell_
,
act_cand_
;
template
<
typename
T
>
class
VTanhKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
LSTMKernel
:
public
Kernel
{
public:
virtual
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
)
const
=
0
;
};
}
// namespace jitkernel
...
...
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @
f2adaf1c
...
...
@@ -266,15 +266,124 @@ INTRI16_FLOAT(jit::avx512f);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
/* VRelu JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VReluKernelImpl
:
public
VReluKernel
<
T
>
{
public:
explicit
VReluKernelImpl
(
int
d
)
:
VReluKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
x
[
i
]
>
0
?
x
[
i
]
:
0
;
}
}
};
#define INTRI8_FLOAT(isa) \
template <> \
void VReluKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
const { \
__m256 tmp = _mm256_loadu_ps(x); \
tmp = _mm256_max_ps(tmp, _mm256_setzero_ps()); \
_mm256_storeu_ps(y, tmp); \
}
#define INTRI16_FLOAT(isa) \
template <> \
void VReluKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
const { \
__m256 zeros = _mm256_setzero_ps(); \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
tmp0 = _mm256_max_ps(tmp0, zeros); \
tmp1 = _mm256_max_ps(tmp1, zeros); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
}
#define INTRI_GT8LT16_FLOAT(isa) \
template <> \
VReluKernelImpl<float, isa, kGT8LT16>::VReluKernelImpl(int d) \
: VReluKernel<float>() { \
this->num_ = d; \
this->end_ = AVX_FLOAT_BLOCK; \
this->rest_ = d - AVX_FLOAT_BLOCK; \
} \
template <> \
void VReluKernelImpl<float, isa, kGT8LT16>::Compute(const float* x, \
float* y) const { \
__m256 zeros = _mm256_setzero_ps(); \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + this->rest_); \
tmp0 = _mm256_max_ps(tmp0, zeros); \
tmp1 = _mm256_max_ps(tmp1, zeros); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + this->rest_, tmp1); \
}
#define INTRI_GT16_FLOAT(isa) \
template <> \
VReluKernelImpl<float, isa, kGT16>::VReluKernelImpl(int d) \
: VReluKernel<float>() { \
this->num_ = d; \
this->end_ = d - d % AVX_FLOAT_BLOCK; \
this->rest_ = d - AVX_FLOAT_BLOCK; \
} \
template <> \
void VReluKernelImpl<float, isa, kGT16>::Compute(const float* x, float* y) \
const { \
__m256 zeros = _mm256_setzero_ps(); \
for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) { \
__m256 tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + i, tmp); \
} \
__m256 tmp = _mm256_loadu_ps(x + this->rest_); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + this->rest_, tmp); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx
);
INTRI_GT16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx2
);
INTRI_GT16_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
// TODO(TJ): refine avx512
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx512f
);
INTRI_GT16_FLOAT
(
jit
::
avx512f
);
#endif
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef INTRI_GT8LT16_FLOAT
#undef INTRI_GT16_FLOAT
/* An empty JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VIdentityKernelImpl
:
public
VIdentityKernel
<
T
>
{
public:
explicit
VIdentityKernelImpl
(
int
d
)
:
VIdentityKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{}
};
REGISTER_JITKERNEL
(
vmul
,
VMulKernel
);
REGISTER_JITKERNEL
(
vadd
,
VAddKernel
);
REGISTER_JITKERNEL
(
vscal
,
VScalKernel
);
REGISTER_JITKERNEL
(
vaddb
,
VAddBiasKernel
);
REGISTER_JITKERNEL
(
vrelu
,
VReluKernel
);
REGISTER_JITKERNEL
(
videntity
,
VIdentityKernel
);
}
// namespace jitkernel
}
// namespace math
...
...
paddle/fluid/operators/math/jit_kernel_exp.cc
浏览文件 @
f2adaf1c
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <cmath> // for exp
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#ifdef PADDLE_WITH_MKLML
...
...
paddle/fluid/operators/math/jit_kernel_lstm.cc
浏览文件 @
f2adaf1c
...
...
@@ -13,9 +13,13 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <functional>
#include <string>
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#include "paddle/fluid/platform/enforce.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace
paddle
{
namespace
operators
{
...
...
@@ -24,51 +28,85 @@ namespace jitkernel {
namespace
jit
=
platform
::
jit
;
template
<
>
LSTMKernel
<
float
>::
LSTMKernel
(
int
d
,
const
std
::
string
&
act_gate_str
,
const
std
::
string
&
act_cand_str
,
const
std
::
string
&
act_cell_str
)
:
Kernel
(),
d_
(
d
)
{
d2_
=
d
*
2
;
d3_
=
d
*
3
;
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx512f
))
{
math
::
VecActivations
<
float
,
platform
::
jit
::
avx512f
>
act_functor
;
act_gate_
=
act_functor
(
act_gate_str
);
act_cell_
=
act_functor
(
act_cell_str
);
act_cand_
=
act_functor
(
act_cand_str
);
}
else
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx2
))
{
math
::
VecActivations
<
float
,
platform
::
jit
::
avx2
>
act_functor
;
act_gate_
=
act_functor
(
act_gate_str
);
act_cell_
=
act_functor
(
act_cell_str
);
act_cand_
=
act_functor
(
act_cand_str
);
}
else
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx
))
{
math
::
VecActivations
<
float
,
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
);
// ComputeCtHt = [&](float*gates,const float*ct_1,float*ct, float*ht) {
// // gates: W_ch, W_ih, W_fh, W_oh
// act_gate(d3_, gates + d_, gates + d_);
// /* C_t = C_t-1 * fgated + cand_gated * igated */
// act_cand(d_, gates, gates);
// blas.VMUL(d_, gates, gates + d_, gates + d_);
// blas.VMUL(d_, ct_1, gates + d2_, gates + d2_);
// blas.VADD(d_, gates + d_, gates + d2_, ct);
// /* H_t = act_cell(C_t) * ogated */
// act_cell(d_, ct, gates + d2_);
// blas.VMUL(d_, gates + d2_, gates + d3_, ht)
// GET_Ct(ct_1, gates, ct);
// GET_Ht(ct, gates, ht);
// };
}
else
{
math
::
VecActivations
<
float
,
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
);
/* LSTM JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
LSTMKernelImpl
:
public
LSTMKernel
<
T
>
{
public:
explicit
LSTMKernelImpl
(
int
d
,
const
std
::
string
&
act_gate
,
const
std
::
string
&
act_cand
,
const
std
::
string
&
act_cell
)
:
LSTMKernel
<
T
>
()
{
d_
=
d
;
d2_
=
d
*
2
;
d3_
=
d
*
3
;
auto
GetActKernel
=
[
&
](
const
std
::
string
&
type
,
int
n
)
->
std
::
shared_ptr
<
const
VActKernel
<
T
>>
{
if
(
type
==
"sigmoid"
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VSigmoidKernel
<
T
>
>
(
n
));
}
else
if
(
type
==
"relu"
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VReluKernel
<
T
>
>
(
n
));
}
else
if
(
type
==
"tanh"
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VTanhKernel
<
T
>
>
(
n
));
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VIdentityKernel
<
T
>
>
(
n
));
}
PADDLE_THROW
(
"Not support type: %s"
,
type
);
};
act_gate_3d_
=
GetActKernel
(
act_gate
,
d
*
3
);
act_cand_d_
=
GetActKernel
(
act_cand
,
d
);
act_cell_d_
=
GetActKernel
(
act_cell
,
d
);
vmul_d_
=
KernelPool
::
Instance
().
template
Get
<
VMulKernel
<
T
>
>
(
d
);
vadd_d_
=
KernelPool
::
Instance
().
template
Get
<
VAddKernel
<
T
>
>
(
d
);
}
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
)
const
override
{
// gates: W_ch, W_ih, W_fh, W_oh
act_gate_3d_
->
Compute
(
gates
+
d_
,
gates
+
d_
);
/* C_t = C_t-1 * fgated + cand_gated * igated */
act_cand_d_
->
Compute
(
gates
,
gates
);
vmul_d_
->
Compute
(
gates
,
gates
+
d_
,
gates
+
d_
);
vmul_d_
->
Compute
(
ct_1
,
gates
+
d2_
,
gates
+
d2_
);
vadd_d_
->
Compute
(
gates
+
d_
,
gates
+
d2_
,
ct
);
/* H_t = act_cell(C_t) * ogated */
act_cell_d_
->
Compute
(
ct
,
gates
+
d2_
);
vmul_d_
->
Compute
(
gates
+
d2_
,
gates
+
d3_
,
ht
);
}
}
private:
int
d_
,
d2_
,
d3_
;
std
::
shared_ptr
<
const
VActKernel
<
T
>>
act_gate_3d_
,
act_cand_d_
,
act_cell_d_
;
std
::
shared_ptr
<
const
VMulKernel
<
T
>>
vmul_d_
;
std
::
shared_ptr
<
const
VAddKernel
<
T
>>
vadd_d_
;
};
#define JITKERNEL_DECLARE_LSTM(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const ker_class<ker_dtype>> \
KernelPool::Get<ker_class<ker_dtype>, int, const std::string&, \
const std::string&, const std::string&>( \
int d, const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell)
#define JITKERNEL_KEY_LSTM(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_cand + act_cell
#define JITKERNEL_NEW_LSTM_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(d, act_gate, act_cand, \
act_cell))
REGISTER_JITKERNEL_ARGS
(
lstm
,
LSTMKernel
,
JITKERNEL_DECLARE_LSTM
,
JITKERNEL_KEY_LSTM
,
JITKERNEL_NEW_LSTM_IMPL
);
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
}
// namespace jitkernel
}
// namespace math
...
...
paddle/fluid/operators/math/jit_kernel_test.cc
浏览文件 @
f2adaf1c
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <sys/time.h>
#include <cmath> // for exp
#include <cstring> // for memcpy
#include <string>
#include <vector>
...
...
@@ -48,6 +49,59 @@ void RandomVec(const int n, T* a, const T lower = static_cast<T>(-20.f),
}
}
void
vrelu_ref
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
>
0.
f
?
x
[
i
]
:
0.
f
;
}
}
#if defined __AVX__ || defined __AVX2__
void
vrelu_intri8
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
__m256
tmp
=
_mm256_loadu_ps
(
x
);
tmp
=
_mm256_max_ps
(
tmp
,
_mm256_setzero_ps
());
_mm256_storeu_ps
(
y
,
tmp
);
}
#endif
TEST
(
JitKernel
,
vrelu
)
{
namespace
jit
=
paddle
::
operators
::
math
::
jitkernel
;
for
(
int
d
:
{
7
,
8
,
15
,
16
,
30
,
256
,
512
})
{
std
::
vector
<
float
>
x
(
d
);
std
::
vector
<
float
>
zref
(
d
),
ztgt
(
d
);
RandomVec
<
float
>
(
d
,
x
.
data
(),
-
10.
f
,
1.
f
);
const
auto
&
ker
=
jit
::
KernelPool
::
Instance
().
template
Get
<
jit
::
VReluKernel
<
float
>
>
(
d
);
const
float
*
x_data
=
x
.
data
();
float
*
ztgt_data
=
ztgt
.
data
();
float
*
zref_data
=
zref
.
data
();
auto
trefs
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vrelu_ref
(
d
,
x_data
,
zref_data
);
}
auto
trefe
=
GetCurrentUS
();
#if defined __AVX__ || defined __AVX2__
if
(
d
==
8
)
{
auto
si0
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
vrelu_intri8
(
d
,
x_data
,
zref_data
);
}
auto
si1
=
GetCurrentUS
();
VLOG
(
3
)
<<
"Vec size 8 intr takes: "
<<
(
si1
-
si0
)
/
repeat
;
}
#endif
auto
ttgts
=
GetCurrentUS
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
ker
->
Compute
(
x_data
,
ztgt_data
);
}
auto
ttgte
=
GetCurrentUS
();
VLOG
(
3
)
<<
"Vec size "
<<
d
<<
": refer takes: "
<<
(
trefe
-
trefs
)
/
repeat
<<
" us, tgt takes: "
<<
(
ttgte
-
ttgts
)
/
repeat
;
for
(
int
i
=
0
;
i
<
d
;
++
i
)
{
EXPECT_NEAR
(
ztgt_data
[
i
],
zref_data
[
i
],
1e-3
);
}
}
}
void
vaddbias_ref
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
y
[
i
]
=
x
[
i
]
+
a
;
...
...
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