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0a9f5f17
编写于
10月 19, 2018
作者:
T
tensor-tang
提交者:
GitHub
10月 19, 2018
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差异文件
Merge pull request #13968 from tensor-tang/fix/jit/exp
Fix jit exp
上级
fcb2e810
60ff05e3
变更
4
展开全部
显示空白变更内容
内联
并排
Showing
4 changed file
with
327 addition
and
134 deletion
+327
-134
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
+3
-3
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+1
-1
paddle/fluid/operators/math/jit_kernel_exp.cc
paddle/fluid/operators/math/jit_kernel_exp.cc
+201
-60
paddle/fluid/operators/math/jit_kernel_lstm.cc
paddle/fluid/operators/math/jit_kernel_lstm.cc
+122
-70
未找到文件。
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
浏览文件 @
0a9f5f17
...
@@ -18,12 +18,12 @@ namespace paddle {
...
@@ -18,12 +18,12 @@ namespace paddle {
namespace
inference
{
namespace
inference
{
using
namespace
framework
;
// NOLINT
using
namespace
framework
;
// NOLINT
static
std
::
vector
<
float
>
result_data
;
struct
DataRecord
{
struct
DataRecord
{
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
link_step_data_all
;
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
link_step_data_all
;
std
::
vector
<
size_t
>
lod
;
std
::
vector
<
size_t
>
lod
;
std
::
vector
<
std
::
vector
<
float
>>
rnn_link_data
;
std
::
vector
<
std
::
vector
<
float
>>
rnn_link_data
;
std
::
vector
<
float
>
result_data
;
size_t
num_samples
;
// total number of samples
size_t
num_samples
;
// total number of samples
size_t
batch_iter
{
0
};
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
size_t
batch_size
{
1
};
...
@@ -57,6 +57,7 @@ struct DataRecord {
...
@@ -57,6 +57,7 @@ struct DataRecord {
std
::
ifstream
file
(
path
);
std
::
ifstream
file
(
path
);
std
::
string
line
;
std
::
string
line
;
int
num_lines
=
0
;
int
num_lines
=
0
;
result_data
.
clear
();
while
(
std
::
getline
(
file
,
line
))
{
while
(
std
::
getline
(
file
,
line
))
{
num_lines
++
;
num_lines
++
;
std
::
vector
<
std
::
string
>
data
;
std
::
vector
<
std
::
string
>
data
;
...
@@ -135,13 +136,12 @@ TEST(Analyzer_rnn2, profile) {
...
@@ -135,13 +136,12 @@ TEST(Analyzer_rnn2, profile) {
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
// the first inference result
// the first inference result
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
size_t
size
=
GetSize
(
outputs
[
0
]);
size_t
size
=
GetSize
(
outputs
[
0
]);
PADDLE_ENFORCE_GT
(
size
,
0
);
PADDLE_ENFORCE_GT
(
size
,
0
);
float
*
result
=
static_cast
<
float
*>
(
outputs
[
0
].
data
.
data
());
float
*
result
=
static_cast
<
float
*>
(
outputs
[
0
].
data
.
data
());
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
EXPECT_NEAR
(
result
[
i
],
data
.
result_data
[
i
],
1e-3
);
EXPECT_NEAR
(
result
[
i
],
result_data
[
i
],
1e-3
);
}
}
}
}
}
}
...
...
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
0a9f5f17
...
@@ -76,5 +76,5 @@ cc_test(concat_test SRCS concat_test.cc DEPS concat)
...
@@ -76,5 +76,5 @@ cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test
(
cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info
)
cc_test
(
cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info
)
cc_library
(
jit_kernel
cc_library
(
jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
DEPS cpu_info cblas
activation_functions
)
DEPS cpu_info cblas
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
paddle/fluid/operators/math/jit_kernel_exp.cc
浏览文件 @
0a9f5f17
此差异已折叠。
点击以展开。
paddle/fluid/operators/math/jit_kernel_lstm.cc
浏览文件 @
0a9f5f17
...
@@ -25,13 +25,18 @@ limitations under the License. */
...
@@ -25,13 +25,18 @@ limitations under the License. */
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
namespace
math
{
namespace
math
{
#ifdef __AVX__
namespace
jitkernel
{
namespace
detail
{
namespace
detail
{
__m256
Exp
(
__m256
a
);
#ifdef __AVX__
}
// namespace detail
__m256
ExpAVX
(
__m256
x
);
#endif
#endif
namespace
jitkernel
{
#ifdef __AVX2__
__m256
ExpAVX2
(
__m256
x
);
#endif
}
// namespace detail
namespace
jit
=
platform
::
jit
;
namespace
jit
=
platform
::
jit
;
#ifdef __AVX__
#ifdef __AVX__
...
@@ -43,43 +48,72 @@ class AVXAct {
...
@@ -43,43 +48,72 @@ class AVXAct {
virtual
__m256
Compute
(
__m256
x
)
const
=
0
;
virtual
__m256
Compute
(
__m256
x
)
const
=
0
;
};
};
template
<
act_type
type
>
template
<
act_type
type
,
jit
::
cpu_isa_t
isa
>
class
AVXActImpl
:
public
AVXAct
{
class
AVXActImpl
:
public
AVXAct
{
public:
public:
__m256
Compute
(
__m256
x
)
const
override
{
PADDLE_THROW
(
"Unkown type!"
);
}
__m256
Compute
(
__m256
x
)
const
override
{
PADDLE_THROW
(
"Unkown type!"
);
}
};
};
template
<
>
#define AVX_SIGMOID(isa, expisa) \
__m256
AVXActImpl
<
kSigmoid
>::
Compute
(
__m256
x
)
const
{
template <> \
__m256
ones
=
_mm256_set1_ps
(
1.0
f
);
__m256 AVXActImpl<kSigmoid, isa>::Compute(__m256 x) const { \
x
=
_mm256_max_ps
(
x
,
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MIN
));
__m256 ones = _mm256_set1_ps(1.0f); \
x
=
_mm256_min_ps
(
x
,
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MAX
));
x = _mm256_max_ps(x, _mm256_set1_ps(SIGMOID_THRESHOLD_MIN)); \
x
=
_mm256_sub_ps
(
_mm256_set1_ps
(
0.0
f
),
x
);
x = _mm256_min_ps(x, _mm256_set1_ps(SIGMOID_THRESHOLD_MAX)); \
x
=
detail
::
Exp
(
x
);
x = _mm256_sub_ps(_mm256_set1_ps(0.0f), x); \
x
=
_mm256_add_ps
(
ones
,
x
);
x = expisa(x); \
return
_mm256_div_ps
(
ones
,
x
);
x = _mm256_add_ps(ones, x); \
}
return _mm256_div_ps(ones, x); \
}
template
<
>
#define AVX_TANH(isa, expisa) \
__m256
AVXActImpl
<
kTanh
>::
Compute
(
__m256
x
)
const
{
template <> \
__m256
ones
=
_mm256_set1_ps
(
1.0
f
);
__m256 AVXActImpl<kTanh, isa>::Compute(__m256 x) const { \
x
=
_mm256_mul_ps
(
_mm256_set1_ps
(
-
2.0
f
),
x
);
__m256 ones = _mm256_set1_ps(1.0f); \
x
=
_mm256_min_ps
(
x
,
_mm256_set1_ps
(
EXP_MAX_INPUT
));
x = _mm256_mul_ps(_mm256_set1_ps(-2.0f), x); \
x
=
detail
::
Exp
(
x
);
x = _mm256_min_ps(x, _mm256_set1_ps(EXP_MAX_INPUT)); \
x
=
_mm256_add_ps
(
ones
,
x
);
x = expisa(x); \
x
=
_mm256_div_ps
(
_mm256_set1_ps
(
2.0
f
),
x
);
x = _mm256_add_ps(ones, x); \
return
_mm256_sub_ps
(
x
,
ones
);
x = _mm256_div_ps(_mm256_set1_ps(2.0f), x); \
}
return _mm256_sub_ps(x, ones); \
}
template
<
>
#define AVX_RELU(isa) \
__m256
AVXActImpl
<
kRelu
>::
Compute
(
__m256
x
)
const
{
template <> \
return
_mm256_max_ps
(
x
,
_mm256_setzero_ps
());
__m256 AVXActImpl<kRelu, isa>::Compute(__m256 x) const { \
}
return _mm256_max_ps(x, _mm256_setzero_ps()); \
}
#define AVX_IDENTITY(isa) \
template <> \
__m256 AVXActImpl<kIdentity, isa>::Compute(__m256 x) const { \
return x; \
}
#define FOR_EACH_AVX_ISA(macro_) \
macro_(jit::avx); \
macro_(jit::avx2); \
macro_(jit::avx512f)
FOR_EACH_AVX_ISA
(
AVX_RELU
);
FOR_EACH_AVX_ISA
(
AVX_IDENTITY
);
AVX_SIGMOID
(
jit
::
avx
,
detail
::
ExpAVX
);
AVX_TANH
(
jit
::
avx
,
detail
::
ExpAVX
);
#ifdef __AVX2__
AVX_SIGMOID
(
jit
::
avx2
,
detail
::
ExpAVX2
);
AVX_SIGMOID
(
jit
::
avx512f
,
detail
::
ExpAVX2
);
AVX_TANH
(
jit
::
avx2
,
detail
::
ExpAVX2
);
AVX_TANH
(
jit
::
avx512f
,
detail
::
ExpAVX2
);
#endif
#undef FOR_EACH_AVX_ISA
#undef AVX_IDENTITY
#undef AVX_RELU
#undef AVX_TANH
#undef AVX_SIGMOID
template
<
>
__m256
AVXActImpl
<
kIdentity
>::
Compute
(
__m256
x
)
const
{
return
x
;
}
#endif
#endif
template
<
typename
T
>
template
<
typename
T
>
...
@@ -119,23 +153,6 @@ class LSTMKernelImpl : public LSTMKernel<T> {
...
@@ -119,23 +153,6 @@ class LSTMKernelImpl : public LSTMKernel<T> {
act_cell_d_
=
GetActKernel
<
T
>
(
act_cell
,
d
);
act_cell_d_
=
GetActKernel
<
T
>
(
act_cell
,
d
);
vmul_d_
=
KernelPool
::
Instance
().
template
Get
<
VMulKernel
<
T
>
>
(
d
);
vmul_d_
=
KernelPool
::
Instance
().
template
Get
<
VMulKernel
<
T
>
>
(
d
);
vadd_d_
=
KernelPool
::
Instance
().
template
Get
<
VAddKernel
<
T
>
>
(
d
);
vadd_d_
=
KernelPool
::
Instance
().
template
Get
<
VAddKernel
<
T
>
>
(
d
);
#ifdef __AVX__
auto
GetAVXAct
=
[
&
](
const
std
::
string
&
type
)
->
std
::
unique_ptr
<
AVXAct
>
{
if
(
type
==
"sigmoid"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kSigmoid
>
());
}
else
if
(
type
==
"relu"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kRelu
>
());
}
else
if
(
type
==
"tanh"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kTanh
>
());
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kIdentity
>
());
}
PADDLE_THROW
(
"Not support type: %s"
,
type
);
};
avx_act_gate_
=
GetAVXAct
(
act_gate
);
avx_act_cand_
=
GetAVXAct
(
act_cand
);
avx_act_cell_
=
GetAVXAct
(
act_cell
);
#endif
}
}
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
,
const
T
*
wp_data
,
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
,
const
T
*
wp_data
,
...
@@ -176,6 +193,27 @@ class LSTMKernelImpl : public LSTMKernel<T> {
...
@@ -176,6 +193,27 @@ class LSTMKernelImpl : public LSTMKernel<T> {
};
};
#define INTRI8_FLOAT(isa) \
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
} \
template <> \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
float* gates, const float* ct_1, float* ct, float* ht, \
...
@@ -195,6 +233,20 @@ class LSTMKernelImpl : public LSTMKernel<T> {
...
@@ -195,6 +233,20 @@ class LSTMKernelImpl : public LSTMKernel<T> {
/* H_t = act_cell(C_t) * ogated */
\
/* H_t = act_cell(C_t) * ogated */
\
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/
\
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */
\
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
}
}
// TODO(TJ): optimize keq16
// TODO(TJ): optimize keq16
...
...
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