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a0c37662
编写于
2月 22, 2019
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
enable sgd jitkernel refer code and test
test=develop
上级
1dad36f6
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
211 addition
and
34 deletion
+211
-34
paddle/fluid/operators/jit/gen/jitcode.h
paddle/fluid/operators/jit/gen/jitcode.h
+2
-1
paddle/fluid/operators/jit/helper.cc
paddle/fluid/operators/jit/helper.cc
+1
-0
paddle/fluid/operators/jit/helper.h
paddle/fluid/operators/jit/helper.h
+8
-0
paddle/fluid/operators/jit/kernel_base.h
paddle/fluid/operators/jit/kernel_base.h
+23
-0
paddle/fluid/operators/jit/kernel_key.cc
paddle/fluid/operators/jit/kernel_key.cc
+5
-0
paddle/fluid/operators/jit/refer/CMakeLists.txt
paddle/fluid/operators/jit/refer/CMakeLists.txt
+1
-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
-0
paddle/fluid/operators/jit/test.cc
paddle/fluid/operators/jit/test.cc
+102
-3
paddle/fluid/operators/optimizers/sgd_op.h
paddle/fluid/operators/optimizers/sgd_op.h
+35
-30
未找到文件。
paddle/fluid/operators/jit/gen/jitcode.h
浏览文件 @
a0c37662
...
...
@@ -31,7 +31,8 @@ namespace gen {
// Application Binary Interface
constexpr
Xbyak
::
Operand
::
Code
abi_param1
(
Xbyak
::
Operand
::
RDI
),
abi_param2
(
Xbyak
::
Operand
::
RSI
),
abi_param3
(
Xbyak
::
Operand
::
RDX
),
abi_param4
(
Xbyak
::
Operand
::
RCX
);
abi_param4
(
Xbyak
::
Operand
::
RCX
),
abi_param5
(
Xbyak
::
Operand
::
R8
),
abi_param6
(
Xbyak
::
Operand
::
R9
);
constexpr
Xbyak
::
Operand
::
Code
g_abi_regs
[]
=
{
Xbyak
::
Operand
::
RBX
,
Xbyak
::
Operand
::
RBP
,
Xbyak
::
Operand
::
R12
,
...
...
paddle/fluid/operators/jit/helper.cc
浏览文件 @
a0c37662
...
...
@@ -55,6 +55,7 @@ const char* to_string(KernelType kt) {
ONE_CASE
(
kHSum
);
ONE_CASE
(
kSoftmax
);
ONE_CASE
(
kEmbSeqPool
);
ONE_CASE
(
kSgd
);
default:
PADDLE_THROW
(
"Not support type: %d, or forget to add it."
,
kt
);
return
"NOT JITKernel"
;
...
...
paddle/fluid/operators/jit/helper.h
浏览文件 @
a0c37662
...
...
@@ -181,6 +181,14 @@ inline std::ostream& operator<<(std::ostream& os,
return
os
;
}
inline
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
sgd_attr_t
&
attr
)
{
os
<<
"param_height["
<<
attr
.
param_height
<<
"],param_width["
<<
attr
.
param_width
<<
"],grad_height["
<<
attr
.
grad_height
<<
"],grad_width["
<<
attr
.
grad_width
<<
"],selected_rows_size["
<<
attr
.
selected_rows_size
<<
"]"
;
return
os
;
}
inline
std
::
ostream
&
operator
<<
(
std
::
ostream
&
os
,
const
matmul_attr_t
&
attr
)
{
os
<<
"M["
<<
attr
.
m
<<
"],N["
<<
attr
.
n
<<
"],K["
<<
attr
.
k
<<
"]"
;
return
os
;
...
...
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @
a0c37662
...
...
@@ -46,6 +46,7 @@ typedef enum {
kVMul
,
kVRelu
,
kVScal
,
kSgd
,
kVSigmoid
,
kVSquare
,
kVSub
,
...
...
@@ -173,6 +174,28 @@ struct EmbSeqPoolTuples {
const
emb_seq_pool_attr_t
*
);
};
typedef
struct
sgd_attr_s
{
int64_t
param_height
,
param_width
;
int64_t
grad_height
,
grad_width
;
int64_t
selected_rows_size
;
sgd_attr_s
()
=
default
;
explicit
sgd_attr_s
(
int64_t
param_h
,
int64_t
param_w
,
int64_t
grad_h
,
int64_t
grad_w
,
int64_t
selected_rows_sz
)
:
param_height
(
param_h
),
param_width
(
param_w
),
grad_height
(
grad_h
),
grad_width
(
grad_w
),
selected_rows_size
(
selected_rows_sz
)
{}
}
sgd_attr_t
;
template
<
typename
T
>
struct
SgdTuples
{
typedef
T
data_type
;
typedef
sgd_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
T
*
,
const
T
*
,
const
int64_t
*
,
T
*
,
const
sgd_attr_t
*
);
};
typedef
struct
matmul_attr_s
{
int
m
,
n
,
k
;
void
*
packed_weight
{
nullptr
};
...
...
paddle/fluid/operators/jit/kernel_key.cc
浏览文件 @
a0c37662
...
...
@@ -61,6 +61,11 @@ size_t JitCodeKey<emb_seq_pool_attr_t>(const emb_seq_pool_attr_t& attr) {
return
attr
.
table_width
;
}
template
<
>
size_t
JitCodeKey
<
sgd_attr_t
>
(
const
sgd_attr_t
&
attr
)
{
return
attr
.
grad_width
;
}
}
// namespace jit
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/jit/refer/CMakeLists.txt
浏览文件 @
a0c37662
...
...
@@ -33,3 +33,4 @@ USE_JITKERNEL_REFER(kHSum)
USE_JITKERNEL_REFER
(
kHMax
)
USE_JITKERNEL_REFER
(
kSoftmax
)
USE_JITKERNEL_REFER
(
kEmbSeqPool
)
USE_JITKERNEL_REFER
(
kSgd
)
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @
a0c37662
...
...
@@ -59,4 +59,6 @@ REGISTER_REFER_KERNEL(kSoftmax, Softmax);
REGISTER_REFER_KERNEL
(
kEmbSeqPool
,
EmbSeqPool
);
REGISTER_REFER_KERNEL
(
kSgd
,
Sgd
);
#undef REGISTER_REFER_KERNEL
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @
a0c37662
...
...
@@ -446,6 +446,36 @@ void EmbSeqPool(const T* table, const int64_t* idx, T* out,
}
}
// SGD algorithm:
// lr is pointor of learning rate scalar
// param is an input matrix with (param_h, param_w)
// grad is an input matrix with (grad_h, grad_w), here grad_w == param_w
// selected_rows is a vectot<int64_t> with size selected_rows_size( <= grad_h )
// out is an output matrix with (param_h, param_w)
//
// support both regular and sparse grad
// regular SGD: out[:] = param[:] - lr[0] * grad[:];
// sparse SGD: out[rows[i]][:] = param[rows[i]][:] - lr[0] * grad[i][:]
//
// Note: when use sparse SGD, and if out != param,
// the out rows which are not selected have not beed changed, which maybe empty
template
<
typename
T
>
void
Sgd
(
const
T
*
lr
,
const
T
*
param
,
const
T
*
grad
,
const
int64_t
*
rows
,
T
*
out
,
const
sgd_attr_t
*
attr
)
{
PADDLE_ENFORCE_EQ
(
attr
->
param_width
,
attr
->
grad_width
);
PADDLE_ENFORCE_LE
(
attr
->
selected_rows_size
,
attr
->
grad_height
);
for
(
int64_t
i
=
0
;
i
<
attr
->
selected_rows_size
;
++
i
)
{
auto
h_idx
=
rows
[
i
];
PADDLE_ENFORCE_LT
(
h_idx
,
attr
->
param_height
);
PADDLE_ENFORCE_GE
(
h_idx
,
0
);
for
(
int64_t
j
=
0
;
j
<
attr
->
grad_width
;
++
j
)
{
out
[
h_idx
*
attr
->
grad_width
+
j
]
=
param
[
h_idx
*
attr
->
grad_width
+
j
]
-
lr
[
0
]
*
grad
[
i
*
attr
->
grad_width
+
j
];
}
}
}
#define DECLARE_REFER_KERNEL(name, tuples) \
template <typename T> \
class name##Kernel : public ReferKernel<tuples<T>> { \
...
...
@@ -496,6 +526,8 @@ DECLARE_REFER_KERNEL(Softmax, SoftmaxTuples);
DECLARE_REFER_KERNEL
(
EmbSeqPool
,
EmbSeqPoolTuples
);
DECLARE_REFER_KERNEL
(
Sgd
,
SgdTuples
);
#undef DECLARE_REFER_KERNEL
}
// namespace refer
...
...
paddle/fluid/operators/jit/test.cc
浏览文件 @
a0c37662
...
...
@@ -12,6 +12,7 @@ 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 <algorithm>
#include <random>
#include <string>
#include <vector>
...
...
@@ -36,13 +37,13 @@ void RandomVec(const int n, T* a, const T lower = static_cast<T>(-20.f),
}
template
<
typename
T
>
void
ExpectEQ
(
const
T
*
target
,
const
T
*
refer
,
in
t
n
)
{
void
ExpectEQ
(
const
T
*
target
,
const
T
*
refer
,
size_
t
n
)
{
if
(
std
::
is_floating_point
<
T
>::
value
)
{
for
(
in
t
i
=
0
;
i
<
n
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
n
;
++
i
)
{
EXPECT_NEAR
(
target
[
i
],
refer
[
i
],
FLAGS_acc
);
}
}
else
{
for
(
in
t
i
=
0
;
i
<
n
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
n
;
++
i
)
{
EXPECT_EQ
(
target
[
i
],
refer
[
i
]);
}
}
...
...
@@ -296,6 +297,45 @@ struct TestFuncWithRefer<jit::EmbSeqPoolTuples<T>, std::vector<T>,
}
};
template
<
typename
T
>
struct
TestFuncWithRefer
<
jit
::
SgdTuples
<
T
>
,
T
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
int64_t
>
,
std
::
vector
<
T
>
,
typename
jit
::
SgdTuples
<
T
>::
attr_type
>
{
void
operator
()(
const
typename
jit
::
SgdTuples
<
T
>::
func_type
tgt
,
const
T
lr
,
const
std
::
vector
<
T
>&
param
,
const
std
::
vector
<
T
>&
grad
,
const
std
::
vector
<
int64_t
>&
rows
,
const
std
::
vector
<
T
>&
oref
,
const
typename
jit
::
SgdTuples
<
T
>::
attr_type
&
attr
)
{
EXPECT_TRUE
(
tgt
!=
nullptr
);
EXPECT_EQ
(
param
.
size
(),
static_cast
<
size_t
>
(
attr
.
param_height
*
attr
.
param_width
));
EXPECT_EQ
(
grad
.
size
(),
static_cast
<
size_t
>
(
attr
.
grad_height
*
attr
.
grad_width
));
EXPECT_EQ
(
rows
.
size
(),
static_cast
<
size_t
>
(
attr
.
selected_rows_size
));
EXPECT_EQ
(
param
.
size
(),
oref
.
size
());
const
T
*
param_data
=
param
.
data
();
const
T
*
grad_data
=
grad
.
data
();
const
int64_t
*
rows_data
=
rows
.
data
();
const
T
*
oref_data
=
oref
.
data
();
std
::
vector
<
T
>
out
(
oref
.
size
());
T
*
o_data
=
out
.
data
();
tgt
(
&
lr
,
param_data
,
grad_data
,
rows_data
,
o_data
,
&
attr
);
// only the selected rows should be equal
for
(
size_t
i
=
0
;
i
<
rows
.
size
();
++
i
)
{
ExpectEQ
<
T
>
(
o_data
+
rows
[
i
]
*
attr
.
grad_width
,
oref_data
+
rows
[
i
]
*
attr
.
grad_width
,
attr
.
grad_width
);
}
// inplace
std
::
copy
(
param
.
begin
(),
param
.
end
(),
out
.
begin
());
tgt
(
&
lr
,
o_data
,
grad_data
,
rows_data
,
o_data
,
&
attr
);
for
(
size_t
i
=
0
;
i
<
rows
.
size
();
++
i
)
{
ExpectEQ
<
T
>
(
o_data
+
rows
[
i
]
*
attr
.
grad_width
,
oref_data
+
rows
[
i
]
*
attr
.
grad_width
,
attr
.
grad_width
);
}
}
};
template
<
typename
T
>
struct
TestFuncWithRefer
<
jit
::
MatMulTuples
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>
,
...
...
@@ -704,6 +744,60 @@ void TestEmbSeqPoolKernel() {
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
TestSgdKernel
()
{
VLOG
(
10
)
<<
"===== Test JITKernel "
<<
jit
::
to_string
(
KT
);
const
T
lr
=
0.1
;
auto
UnDuplicatedRandomVec
=
[](
int
n
,
const
int64_t
lower
,
const
int64_t
upper
)
->
std
::
vector
<
int64_t
>
{
PADDLE_ENFORCE_LE
(
static_cast
<
size_t
>
(
upper
-
lower
),
n
-
1
);
PADDLE_ENFORCE_GT
(
n
,
0
);
std
::
vector
<
int64_t
>
all
,
out
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
all
.
push_back
(
i
);
}
std
::
random_shuffle
(
all
.
begin
(),
all
.
end
());
out
.
insert
(
out
.
begin
(),
all
.
begin
(),
all
.
begin
()
+
n
);
return
out
;
};
for
(
int
param_h
:
{
1
,
10
})
{
for
(
int
grad_w
:
TestSizes
())
{
std
::
vector
<
T
>
param
(
param_h
*
grad_w
);
std
::
vector
<
T
>
param_out
(
param_h
*
grad_w
);
RandomVec
<
T
>
(
param_h
*
grad_w
,
param
.
data
(),
-
2.
f
,
2.
f
);
const
T
*
param_data
=
param
.
data
();
T
*
out_data
=
param_out
.
data
();
for
(
int
rows_size
=
1
;
rows_size
<=
param_h
;
++
rows_size
)
{
std
::
vector
<
T
>
grad
(
rows_size
*
grad_w
);
std
::
vector
<
int64_t
>
rows
=
UnDuplicatedRandomVec
(
rows_size
,
0
,
rows_size
-
1
);
RandomVec
<
T
>
(
rows_size
*
grad_w
,
grad
.
data
(),
-
2.
f
,
2.
f
);
const
int64_t
*
rows_data
=
rows
.
data
();
const
T
*
grad_data
=
grad
.
data
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SgdTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
jit
::
sgd_attr_t
attr
(
param_h
,
grad_w
,
rows_size
,
grad_w
,
rows_size
);
ref
(
&
lr
,
param_data
,
grad_data
,
rows_data
,
out_data
,
&
attr
);
// inplace test
std
::
vector
<
T
>
inp
(
param
.
size
());
std
::
copy
(
param
.
begin
(),
param
.
end
(),
inp
.
begin
());
T
*
inp_data
=
inp
.
data
();
ref
(
&
lr
,
inp_data
,
grad_data
,
rows_data
,
inp_data
,
&
attr
);
// only the selected rows should be equal
for
(
int
i
=
0
;
i
<
rows_size
;
++
i
)
{
ExpectEQ
<
T
>
(
inp_data
+
rows
[
i
]
*
grad_w
,
out_data
+
rows
[
i
]
*
grad_w
,
grad_w
);
}
TestAllImpls
<
KT
,
jit
::
SgdTuples
<
T
>
,
PlaceType
,
T
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
int64_t
>
,
std
::
vector
<
T
>>
(
attr
,
lr
,
param
,
grad
,
rows
,
param_out
,
attr
);
}
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
TestNCHW16CMulNCKernel
()
{
VLOG
(
10
)
<<
"===== Test JITKernel "
<<
jit
::
to_string
(
KT
);
...
...
@@ -943,6 +1037,11 @@ TEST(JITKernel, kEmbSeqPool) {
TestEmbSeqPoolKernel
<
jit
::
kEmbSeqPool
,
double
,
CPUPlace
>
();
}
TEST
(
JITKernel
,
kSgd
)
{
TestSgdKernel
<
jit
::
kSgd
,
float
,
CPUPlace
>
();
TestSgdKernel
<
jit
::
kSgd
,
double
,
CPUPlace
>
();
}
TEST
(
JITKernel
,
kNCHW16CMulNC
)
{
TestNCHW16CMulNCKernel
<
jit
::
kNCHW16CMulNC
,
float
,
CPUPlace
>
();
TestNCHW16CMulNCKernel
<
jit
::
kNCHW16CMulNC
,
double
,
CPUPlace
>
();
...
...
paddle/fluid/operators/optimizers/sgd_op.h
浏览文件 @
a0c37662
...
...
@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/jit/kernels.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -32,53 +33,57 @@ class SGDOpKernel : public framework::OpKernel<T> {
if
(
param_var
->
IsType
<
framework
::
LoDTensor
>
())
{
const
auto
*
param
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
);
auto
*
param_out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"ParamOut"
);
// Actually, all tensors are LoDTensor except SelectedRows.
if
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
())
{
param_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
*
grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Grad"
);
auto
p
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
param
);
auto
g
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
grad
);
auto
o
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
param_out
);
auto
*
lr
=
learning_rate
->
data
<
T
>
();
o
=
p
-
lr
[
0
]
*
g
;
auto
sz
=
param_out
->
numel
();
PADDLE_ENFORCE_EQ
(
param
->
numel
(),
sz
);
PADDLE_ENFORCE_EQ
(
grad
->
numel
(),
sz
);
jit
::
sgd_attr_t
attr
(
1
,
sz
,
1
,
sz
,
1
);
const
T
*
lr
=
learning_rate
->
data
<
T
>
();
const
T
*
param_data
=
param
->
data
<
T
>
();
const
T
*
grad_data
=
grad
->
data
<
T
>
();
int64_t
rows_idx
=
0
;
T
*
out_data
=
param_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
sgd
=
jit
::
Get
<
jit
::
kSgd
,
jit
::
SgdTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
);
sgd
(
lr
,
param_data
,
grad_data
,
&
rows_idx
,
out_data
,
&
attr
);
}
else
if
(
grad_var
->
IsType
<
framework
::
SelectedRows
>
())
{
// TODO(qijun): In Sparse SGD operator, in-place update is enforced.
// This manual optimization brings difficulty to track data dependency.
// It's better to find a more elegant solution.
PADDLE_ENFORCE_EQ
(
param
,
param_out
);
const
auto
*
grad
=
ctx
.
Input
<
framework
::
SelectedRows
>
(
"Grad"
);
auto
&
grad_rows
=
grad
->
rows
();
// for distributed training, a sparse var may be empty,
// just skip updating.
if
(
grad
->
rows
()
.
size
()
==
0
)
{
if
(
grad
_rows
.
size
()
==
0
)
{
return
;
}
auto
grad_height
=
grad
->
height
();
auto
out_dims
=
param_out
->
dims
();
PADDLE_ENFORCE_EQ
(
grad_height
,
out_dims
[
0
]);
PADDLE_ENFORCE_EQ
(
grad
->
height
(),
out_dims
[
0
]);
auto
&
grad_value
=
grad
->
value
();
auto
&
grad_rows
=
grad
->
rows
();
size_t
grad_row_numel
=
grad_value
.
numel
()
/
grad_rows
.
size
();
PADDLE_ENFORCE_EQ
(
static_cast
<
int64_t
>
(
grad_row_numel
),
param_out
->
numel
()
/
grad_height
);
auto
*
grad_data
=
grad_value
.
data
<
T
>
()
;
a
uto
*
out_data
=
param_out
->
data
<
T
>
()
;
a
uto
*
lr
=
learning_rate
->
data
<
T
>
()
;
for
(
size_t
i
=
0
;
i
<
grad_rows
.
size
();
i
++
)
{
PADDLE_ENFORCE
(
grad_rows
[
i
]
<
grad_height
,
"Input rows index should less than height"
);
for
(
size_t
j
=
0
;
j
<
grad_row_numel
;
j
++
)
{
out_data
[
grad_rows
[
i
]
*
grad_row_numel
+
j
]
-=
lr
[
0
]
*
grad_data
[
i
*
grad_row_numel
+
j
];
}
}
const
T
*
param_data
=
param
->
data
<
T
>
();
const
T
*
grad_data
=
grad_value
.
data
<
T
>
();
const
T
*
lr
=
learning_rate
->
data
<
T
>
();
const
int64_t
*
rows_data
=
grad_rows
.
data
();
T
*
out_data
=
param_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()
);
jit
::
sgd_attr_t
attr
;
a
ttr
.
param_height
=
out_dims
[
0
]
;
a
ttr
.
param_width
=
param_out
->
numel
()
/
attr
.
param_height
;
attr
.
grad_height
=
grad_rows
.
size
();
// note: it is not grad->height()
attr
.
grad_width
=
grad_value
.
numel
()
/
attr
.
grad_height
;
attr
.
selected_rows_size
=
grad_rows
.
size
(
);
PADDLE_ENFORCE_EQ
(
attr
.
grad_width
,
attr
.
param_width
);
auto
sgd
=
jit
::
Get
<
jit
::
kSgd
,
jit
::
SgdTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
);
sgd
(
lr
,
param_data
,
grad_data
,
rows_data
,
out_data
,
&
attr
);
}
else
{
PADDLE_THROW
(
"Unsupported Variable Type of Grad"
);
}
...
...
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