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体验新版 GitCode,发现更多精彩内容 >>
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提交
a1c281f0
编写于
1月 19, 2018
作者:
Y
Yibing Liu
提交者:
GitHub
1月 19, 2018
浏览文件
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差异文件
Merge pull request #7603 from kuke/simplify_erase
Enhance GPU kernel of sequence erase op
上级
41b83884
8809d43a
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
66 addition
and
48 deletion
+66
-48
paddle/operators/sequence_erase_op.cc
paddle/operators/sequence_erase_op.cc
+2
-1
paddle/operators/sequence_erase_op.cu
paddle/operators/sequence_erase_op.cu
+33
-46
python/paddle/v2/fluid/tests/test_sequence_erase_op.py
python/paddle/v2/fluid/tests/test_sequence_erase_op.py
+31
-1
未找到文件。
paddle/operators/sequence_erase_op.cc
浏览文件 @
a1c281f0
...
...
@@ -86,4 +86,5 @@ REGISTER_OP_WITHOUT_GRADIENT(sequence_erase, ops::SequenceEraseOp,
ops
::
SequenceEraseOpMaker
);
REGISTER_OP_CPU_KERNEL
(
sequence_erase
,
ops
::
SequenceEraseKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int32_t
>
);
ops
::
SequenceEraseKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int32_t
>
,
ops
::
SequenceEraseKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/operators/sequence_erase_op.cu
浏览文件 @
a1c281f0
...
...
@@ -23,27 +23,22 @@ using platform::PADDLE_CUDA_NUM_THREADS;
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
>
__global__
void
LabelErasedIdx
(
const
T
*
in_dat
,
const
int
in_len
,
const
T
*
tokens
,
const
in
t
tokens_len
,
in
t
*
num_erased
)
{
__global__
void
LabelErasedIdx
(
const
T
*
in_dat
,
const
int
64_t
in_len
,
const
int
*
tokens
,
const
size_
t
tokens_len
,
size_
t
*
num_erased
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
in_len
)
{
int
erased
=
0
;
for
(
int
i
=
0
;
i
<
tokens_len
;
++
i
)
{
for
(
size_t
i
=
0
;
i
<
tokens_len
;
++
i
)
{
if
(
in_dat
[
index
]
==
tokens
[
i
])
{
erased
=
1
;
num_erased
[
index
+
1
]
=
1
;
break
;
}
}
num_erased
[
index
+
1
]
=
erased
;
if
(
index
==
0
)
{
num_erased
[
0
]
=
0
;
}
}
}
template
<
typename
T
>
__global__
void
GetOutLod
(
const
T
*
num_erased
,
const
int
*
in_lod
,
const
int
lod_len
,
int
*
out_lod0
)
{
__global__
void
GetOutLod
(
const
size_t
*
num_erased
,
const
size_t
*
in_lod
,
const
size_t
lod_len
,
size_t
*
out_lod0
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
lod_len
)
{
out_lod0
[
index
]
=
in_lod
[
index
]
-
num_erased
[
in_lod
[
index
]];
...
...
@@ -51,11 +46,11 @@ __global__ void GetOutLod(const T* num_erased, const int* in_lod,
}
template
<
typename
T
>
__global__
void
SetOutput
(
const
T
*
in_dat
,
const
int
in_len
,
const
in
t
*
num_erased
,
T
*
out_dat
)
{
__global__
void
SetOutput
(
const
T
*
in_dat
,
const
int
64_t
in_len
,
const
size_
t
*
num_erased
,
T
*
out_dat
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
in_len
)
{
if
(
in_dat
[
index
]
!=
in_dat
[
index
+
1
])
{
if
(
num_erased
[
index
]
==
num_erased
[
index
+
1
])
{
out_dat
[
index
-
num_erased
[
index
]]
=
in_dat
[
index
];
}
}
...
...
@@ -72,53 +67,44 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ
(
lod
.
size
(),
1UL
,
"Only support one level sequence now."
);
PADDLE_ENFORCE_EQ
(
lod
[
0
].
back
(),
(
size_t
)
in
->
numel
(),
"The actual size mismatches with the LoD information."
);
auto
tokens
=
ctx
.
Attr
<
std
::
vector
<
T
>>
(
"tokens"
);
auto
tokens_len
=
tokens
.
size
();
auto
tokens
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"tokens"
);
auto
in_len
=
in
->
numel
();
auto
in_dat
=
in
->
data
<
T
>
();
auto
lod0
=
lod
[
0
];
thrust
::
host_vector
<
T
>
host_tokens
(
tokens_len
);
for
(
size_t
i
=
0
;
i
<
tokens
.
size
();
++
i
)
{
host_tokens
[
i
]
=
tokens
[
i
];
}
thrust
::
device_vector
<
T
>
dev_tokens
=
host_tokens
;
thrust
::
device_vector
<
int
>
num_erased
(
in_len
+
1
);
T
*
dev_tokens_ptr
=
thrust
::
raw_pointer_cast
(
dev_tokens
.
data
());
int
*
num_erased_ptr
=
thrust
::
raw_pointer_cast
(
num_erased
.
data
());
// Copy tokens to GPU
thrust
::
device_vector
<
int
>
dev_tokens
(
tokens
.
begin
(),
tokens
.
end
());
int
*
dev_tokens_ptr
=
thrust
::
raw_pointer_cast
(
dev_tokens
.
data
());
// Count number of elements to be erased
thrust
::
device_vector
<
size_t
>
num_erased
(
in_len
+
1
,
0
);
size_t
*
num_erased_ptr
=
thrust
::
raw_pointer_cast
(
num_erased
.
data
());
auto
stream
=
ctx
.
cuda_device_context
().
stream
();
LabelErasedIdx
<<<
(
in_len
-
1
)
/
PADDLE_CUDA_NUM_THREADS
+
1
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
in_dat
,
in_len
,
dev_tokens_ptr
,
tokens
_len
,
num_erased_ptr
);
in_dat
,
in_len
,
dev_tokens_ptr
,
tokens
.
size
()
,
num_erased_ptr
);
thrust
::
inclusive_scan
(
num_erased
.
begin
()
+
1
,
num_erased
.
end
(),
num_erased
.
begin
()
+
1
);
// Calc LoD
// Copy LoD to GPU
auto
lod0
=
lod
[
0
];
auto
lod_len
=
lod0
.
size
();
thrust
::
host_vector
<
int
>
host_lod
(
lod_len
);
for
(
size_t
i
=
0
;
i
<
lod_len
;
++
i
)
{
host_lod
[
i
]
=
lod0
[
i
];
}
thrust
::
device_vector
<
int
>
dev_in_lod
=
host_lod
;
thrust
::
device_vector
<
int
>
dev_out_lod
(
lod_len
);
int
*
dev_in_lod_ptr
=
thrust
::
raw_pointer_cast
(
dev_in_lod
.
data
());
int
*
dev_out_lod_ptr
=
thrust
::
raw_pointer_cast
(
dev_out_lod
.
data
());
thrust
::
device_vector
<
size_t
>
dev_in_lod
=
lod0
;
size_t
*
dev_in_lod_ptr
=
thrust
::
raw_pointer_cast
(
dev_in_lod
.
data
());
// Calc output LoD
thrust
::
device_vector
<
size_t
>
dev_out_lod
(
lod_len
);
size_t
*
dev_out_lod_ptr
=
thrust
::
raw_pointer_cast
(
dev_out_lod
.
data
());
GetOutLod
<<<
(
lod_len
-
1
)
/
PADDLE_CUDA_NUM_THREADS
+
1
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
num_erased_ptr
,
dev_in_lod_ptr
,
lod_len
,
dev_out_lod_ptr
);
thrust
::
host_vector
<
int
>
host_out_lod
=
dev_out_lod
;
std
::
vector
<
int
>
out_lod0
(
lod_len
,
0
);
for
(
size_t
i
=
0
;
i
<
lod_len
;
i
++
)
{
out_lod0
[
i
]
=
host_out_lod
[
i
];
}
// Set LoD for output
thrust
::
host_vector
<
size_t
>
out_lod0
=
dev_out_lod
;
framework
::
LoD
out_lod
;
out_lod
.
push_back
(
out_lod0
);
out
->
set_lod
(
out_lod
);
// Set output
out
->
Resize
({
out_lod0
.
back
(
),
1
});
out
->
Resize
({
static_cast
<
int64_t
>
(
out_lod0
.
back
()
),
1
});
auto
out_dat
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
SetOutput
<<<
(
in_len
-
1
)
/
PADDLE_CUDA_NUM_THREADS
+
1
,
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
in_dat
,
in_len
,
...
...
@@ -130,4 +116,5 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
}
// namespace paddle
REGISTER_OP_CUDA_KERNEL
(
sequence_erase
,
paddle
::
operators
::
SequenceEraseOpCUDAKernel
<
int32_t
>
);
paddle
::
operators
::
SequenceEraseOpCUDAKernel
<
int32_t
>
,
paddle
::
operators
::
SequenceEraseOpCUDAKernel
<
int64_t
>
);
python/paddle/v2/fluid/tests/test_sequence_erase_op.py
浏览文件 @
a1c281f0
...
...
@@ -29,7 +29,7 @@ def sequence_erase(in_seq, lod0, tokens):
return
np
.
array
(
out_seq
).
astype
(
"int32"
),
new_lod0
class
TestSequenceEraseOp
(
OpTest
):
class
TestSequenceEraseOp
Int32
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sequence_erase"
in_seq
=
np
.
random
.
randint
(
0
,
10
,
(
30
,
1
)).
astype
(
"int32"
)
...
...
@@ -44,5 +44,35 @@ class TestSequenceEraseOp(OpTest):
self
.
check_output
()
class
TestSequenceEraseOpInt64
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sequence_erase"
in_seq
=
np
.
random
.
randint
(
0
,
10
,
(
30
,
1
)).
astype
(
"int64"
)
lod
=
[[
0
,
9
,
13
,
24
,
30
]]
tokens
=
[
2
,
3
,
5
]
out_seq
,
new_lod0
=
sequence_erase
(
in_seq
,
lod
[
0
],
tokens
)
self
.
attrs
=
{
'tokens'
:
tokens
}
self
.
inputs
=
{
'X'
:
(
in_seq
,
lod
)}
self
.
outputs
=
{
'Out'
:
(
out_seq
,
[
new_lod0
])}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestSequenceEraseOpEmpty
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sequence_erase"
in_seq
=
np
.
random
.
randint
(
0
,
10
,
(
30
,
1
)).
astype
(
"int32"
)
lod
=
[[
0
,
9
,
13
,
24
,
30
]]
tokens
=
[]
out_seq
,
new_lod0
=
sequence_erase
(
in_seq
,
lod
[
0
],
tokens
)
self
.
attrs
=
{
'tokens'
:
tokens
}
self
.
inputs
=
{
'X'
:
(
in_seq
,
lod
)}
self
.
outputs
=
{
'Out'
:
(
out_seq
,
[
new_lod0
])}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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