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体验新版 GitCode,发现更多精彩内容 >>
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1c01d1cc
编写于
3月 25, 2022
作者:
zhouweiwei2014
提交者:
GitHub
3月 25, 2022
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
change CUDA implementation of dropout OP (#40874)
上级
236a3bc5
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
183 addition
and
46 deletion
+183
-46
paddle/fluid/operators/dropout_impl.cu.h
paddle/fluid/operators/dropout_impl.cu.h
+126
-46
python/paddle/fluid/tests/unittests/test_dropout_op.py
python/paddle/fluid/tests/unittests/test_dropout_op.py
+57
-0
未找到文件。
paddle/fluid/operators/dropout_impl.cu.h
浏览文件 @
1c01d1cc
...
...
@@ -37,8 +37,12 @@ limitations under the License. */
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/kernels/funcs/distribution_helper.h"
#include "paddle/phi/kernels/funcs/functors.h"
DECLARE_bool
(
use_curand
);
namespace
paddle
{
namespace
operators
{
template
<
typename
T1
,
typename
T2
=
T1
,
typename
OutT
=
T1
>
struct
DstMaskGenerator
{
const
float
dropout_prob_
;
...
...
@@ -71,13 +75,45 @@ struct DstMaskGenerator {
}
};
template
<
typename
T1
,
typename
T2
=
T1
,
typename
OutT
=
T1
>
struct
DstMaskFunctor
{
const
float
retain_prob_
;
const
bool
is_upscale_in_train_
;
using
MT
=
typename
details
::
MPTypeTrait
<
T1
>::
Type
;
MT
factor
;
HOSTDEVICE
inline
DstMaskFunctor
(
const
float
retain_prob
,
const
bool
is_upscale_in_train
)
:
retain_prob_
(
retain_prob
),
is_upscale_in_train_
(
is_upscale_in_train
)
{
factor
=
static_cast
<
MT
>
(
1.0
f
/
retain_prob_
);
}
HOSTDEVICE
inline
void
operator
()(
OutT
*
dst
,
const
T1
*
src_val
,
const
T2
*
rand
,
int
num
)
const
{
static
constexpr
int
kCount
=
phi
::
funcs
::
uniform_distribution
<
T2
>::
kReturnsCount
;
// 0 ~ kCount -1 is dist , kCount ~ 2 * kCount - 1 is mask
#pragma unroll
for
(
int
i
=
0
;
i
<
kCount
;
i
++
)
{
if
(
rand
[
i
]
<
retain_prob_
)
{
dst
[
i
]
=
is_upscale_in_train_
?
static_cast
<
T1
>
(
static_cast
<
MT
>
(
src_val
[
i
])
*
factor
)
:
static_cast
<
T1
>
(
src_val
[
i
]);
dst
[
i
+
kCount
]
=
static_cast
<
T1
>
(
1
);
}
else
{
dst
[
i
]
=
static_cast
<
T1
>
(
0
);
dst
[
i
+
kCount
]
=
dst
[
i
];
}
}
}
};
template
<
typename
T
,
typename
MaskType
>
__global__
void
VectorizedRandomGenerator
(
const
size_t
n
,
uint64_t
seed
,
const
float
dropout_prob
,
const
T
*
src
,
MaskType
*
mask
,
T
*
dst
,
bool
is_upscale_in_train
,
uint64_t
increment
,
size_t
main_offset
)
{
size_t
main_offset
,
bool
use_curand
)
{
size_t
idx
=
static_cast
<
size_t
>
(
BLOCK_ID_X
*
BLOCK_NUM_X
);
static
constexpr
int
kCount
=
phi
::
funcs
::
uniform_distribution
<
float
>::
kReturnsCount
;
...
...
@@ -97,37 +133,78 @@ __global__ void VectorizedRandomGenerator(const size_t n, uint64_t seed,
using
Rand
=
phi
::
funcs
::
uniform_distribution
<
float
>
;
using
Cast
=
kps
::
IdentityFunctor
<
T
>
;
int
deal_size
=
BLOCK_NUM_X
*
kCount
;
auto
dst_functor
=
DstMaskGenerator
<
T
,
float
>
(
dropout_prob
,
is_upscale_in_train
);
size_t
fix
=
idx
*
kCount
;
for
(;
fix
<
main_offset
;
fix
+=
stride
)
{
kps
::
ReadData
<
T
,
kCount
,
1
,
1
,
false
>
(
&
dst_mask
[
0
],
src
+
fix
,
deal_size
);
kps
::
ElementwiseRandom
<
SType
,
float
,
kCount
,
1
,
Rand
>
(
&
rands
[
0
],
Rand
(),
&
state
);
// dst
kps
::
OperatorTernary
<
T
,
float
,
T
,
DstMaskGenerator
<
T
,
float
>>
(
&
dst_mask
[
0
],
&
dst_mask
[
0
],
&
rands
[
0
],
dst_functor
,
kCount
);
kps
::
WriteData
<
T
,
kCount
,
1
,
1
,
false
>
(
dst
+
fix
,
&
dst_mask
[
0
],
deal_size
);
// mask
kps
::
ElementwiseUnary
<
T
,
MaskType
,
kCount
,
1
,
1
,
Cast
>
(
&
mask_result
[
0
],
&
dst_mask
[
kCount
],
Cast
());
kps
::
WriteData
<
MaskType
,
kCount
,
1
,
1
,
false
>
(
mask
+
fix
,
&
mask_result
[
0
],
deal_size
);
}
int
remainder
=
n
-
fix
;
if
(
remainder
>
0
)
{
kps
::
ReadData
<
T
,
kCount
,
1
,
1
,
true
>
(
&
dst_mask
[
0
],
src
+
fix
,
remainder
);
kps
::
ElementwiseRandom
<
SType
,
float
,
kCount
,
1
,
Rand
>
(
&
rands
[
0
],
Rand
(),
&
state
);
// dst
kps
::
OperatorTernary
<
T
,
float
,
T
,
DstMaskGenerator
<
T
,
float
>>
(
&
dst_mask
[
0
],
&
dst_mask
[
0
],
&
rands
[
0
],
dst_functor
,
kCount
);
kps
::
WriteData
<
T
,
kCount
,
1
,
1
,
true
>
(
dst
+
fix
,
&
dst_mask
[
0
],
remainder
);
// mask
kps
::
ElementwiseUnary
<
T
,
MaskType
,
kCount
,
1
,
1
,
Cast
>
(
&
mask_result
[
0
],
&
dst_mask
[
kCount
],
Cast
());
kps
::
WriteData
<
MaskType
,
kCount
,
1
,
1
,
true
>
(
mask
+
fix
,
&
mask_result
[
0
],
remainder
);
if
(
use_curand
)
{
auto
dst_functor
=
DstMaskFunctor
<
T
,
float
>
(
1.0
f
-
dropout_prob
,
is_upscale_in_train
);
for
(;
fix
<
main_offset
;
fix
+=
stride
)
{
kps
::
ReadData
<
T
,
kCount
,
1
,
1
,
false
>
(
&
dst_mask
[
0
],
src
+
fix
,
deal_size
);
kps
::
ElementwiseRandom
<
SType
,
float
,
kCount
,
1
,
Rand
>
(
&
rands
[
0
],
Rand
(),
&
state
);
// dst
kps
::
OperatorTernary
<
T
,
float
,
T
,
DstMaskFunctor
<
T
,
float
>>
(
&
dst_mask
[
0
],
&
dst_mask
[
0
],
&
rands
[
0
],
dst_functor
,
kCount
);
kps
::
WriteData
<
T
,
kCount
,
1
,
1
,
false
>
(
dst
+
fix
,
&
dst_mask
[
0
],
deal_size
);
// mask
kps
::
ElementwiseUnary
<
T
,
MaskType
,
kCount
,
1
,
1
,
Cast
>
(
&
mask_result
[
0
],
&
dst_mask
[
kCount
],
Cast
());
kps
::
WriteData
<
MaskType
,
kCount
,
1
,
1
,
false
>
(
mask
+
fix
,
&
mask_result
[
0
],
deal_size
);
if
(
fix
>
idx
*
kCount
+
1
)
{
__syncthreads
();
}
}
int
remainder
=
n
-
fix
;
if
(
remainder
>
0
)
{
kps
::
ReadData
<
T
,
kCount
,
1
,
1
,
true
>
(
&
dst_mask
[
0
],
src
+
fix
,
remainder
);
kps
::
ElementwiseRandom
<
SType
,
float
,
kCount
,
1
,
Rand
>
(
&
rands
[
0
],
Rand
(),
&
state
);
// dst
kps
::
OperatorTernary
<
T
,
float
,
T
,
DstMaskFunctor
<
T
,
float
>>
(
&
dst_mask
[
0
],
&
dst_mask
[
0
],
&
rands
[
0
],
dst_functor
,
kCount
);
kps
::
WriteData
<
T
,
kCount
,
1
,
1
,
true
>
(
dst
+
fix
,
&
dst_mask
[
0
],
remainder
);
// mask
kps
::
ElementwiseUnary
<
T
,
MaskType
,
kCount
,
1
,
1
,
Cast
>
(
&
mask_result
[
0
],
&
dst_mask
[
kCount
],
Cast
());
kps
::
WriteData
<
MaskType
,
kCount
,
1
,
1
,
true
>
(
mask
+
fix
,
&
mask_result
[
0
],
remainder
);
__syncthreads
();
}
}
else
{
auto
dst_functor
=
DstMaskGenerator
<
T
,
float
>
(
dropout_prob
,
is_upscale_in_train
);
for
(;
fix
<
main_offset
;
fix
+=
stride
)
{
kps
::
ReadData
<
T
,
kCount
,
1
,
1
,
false
>
(
&
dst_mask
[
0
],
src
+
fix
,
deal_size
);
kps
::
ElementwiseRandom
<
SType
,
float
,
kCount
,
1
,
Rand
>
(
&
rands
[
0
],
Rand
(),
&
state
);
// dst
kps
::
OperatorTernary
<
T
,
float
,
T
,
DstMaskGenerator
<
T
,
float
>>
(
&
dst_mask
[
0
],
&
dst_mask
[
0
],
&
rands
[
0
],
dst_functor
,
kCount
);
kps
::
WriteData
<
T
,
kCount
,
1
,
1
,
false
>
(
dst
+
fix
,
&
dst_mask
[
0
],
deal_size
);
// mask
kps
::
ElementwiseUnary
<
T
,
MaskType
,
kCount
,
1
,
1
,
Cast
>
(
&
mask_result
[
0
],
&
dst_mask
[
kCount
],
Cast
());
kps
::
WriteData
<
MaskType
,
kCount
,
1
,
1
,
false
>
(
mask
+
fix
,
&
mask_result
[
0
],
deal_size
);
}
int
remainder
=
n
-
fix
;
if
(
remainder
>
0
)
{
kps
::
ReadData
<
T
,
kCount
,
1
,
1
,
true
>
(
&
dst_mask
[
0
],
src
+
fix
,
remainder
);
kps
::
ElementwiseRandom
<
SType
,
float
,
kCount
,
1
,
Rand
>
(
&
rands
[
0
],
Rand
(),
&
state
);
// dst
kps
::
OperatorTernary
<
T
,
float
,
T
,
DstMaskGenerator
<
T
,
float
>>
(
&
dst_mask
[
0
],
&
dst_mask
[
0
],
&
rands
[
0
],
dst_functor
,
kCount
);
kps
::
WriteData
<
T
,
kCount
,
1
,
1
,
true
>
(
dst
+
fix
,
&
dst_mask
[
0
],
remainder
);
// mask
kps
::
ElementwiseUnary
<
T
,
MaskType
,
kCount
,
1
,
1
,
Cast
>
(
&
mask_result
[
0
],
&
dst_mask
[
kCount
],
Cast
());
kps
::
WriteData
<
MaskType
,
kCount
,
1
,
1
,
true
>
(
mask
+
fix
,
&
mask_result
[
0
],
remainder
);
}
}
}
...
...
@@ -164,31 +241,34 @@ void DropoutFwGPUKernelDriver(const phi::GPUContext& dev_ctx, bool is_test,
return
;
}
// increment is used to set the args(offset) of curand_init, which defines
// offset in subsequence.
// The detail:
// https://docs.nvidia.com/cuda/curand/device-api-overview.html
// Increment should be at least the number of curand() random numbers used
// in each thread to avoid the random number generated this time being the
// same as the previous calls.
uint64_t
seed_data
;
uint64_t
increment
;
// VectorizedRandomGenerator use curand_uniform4, so we only support
// kVecSize is 4;
// VectorizedRandomGenerator use curand_uniform4, so kVecSize is 4;
constexpr
int
kVecSize
=
phi
::
funcs
::
uniform_distribution
<
float
>::
kReturnsCount
;
auto
gpu_config
=
phi
::
backends
::
gpu
::
GetGpuLaunchConfig1D
(
dev_ctx
,
x_numel
,
kVecSize
);
size_t
grid_size
=
gpu_config
.
GetGridSize
();
size_t
block_size
=
gpu_config
.
GetBlockSize
();
if
(
FLAGS_use_curand
)
{
int64_t
device_id
=
dev_ctx
.
GetPlace
().
GetDeviceId
();
const
auto
&
prop
=
platform
::
GetDeviceProperties
(
device_id
);
size_t
max_grid_size
=
prop
.
maxThreadsPerMultiProcessor
*
prop
.
multiProcessorCount
/
block_size
;
grid_size
=
std
::
min
(
grid_size
,
max_grid_size
);
}
auto
offset
=
((
x_numel
-
1
)
/
(
g
pu_config
.
GetThreadNum
()
*
kVecSize
)
+
1
)
*
kVecSize
;
((
x_numel
-
1
)
/
(
g
rid_size
*
block_size
*
kVecSize
)
+
1
)
*
kVecSize
;
GetSeedDataAndIncrement
(
dev_ctx
,
seed
,
is_fix_seed
,
seed_val
,
offset
,
&
seed_data
,
&
increment
);
size_t
main_offset
=
size
/
(
gpu_config
.
GetBlockSize
()
*
kVecSize
)
*
(
gpu_config
.
GetBlockSize
()
*
kVecSize
);
VectorizedRandomGenerator
<
T
,
uint8_t
><<<
gpu_config
.
GetGridSize
(),
gpu_config
.
GetBlockSize
()
,
0
,
stream
>>>
(
size_t
main_offset
=
size
/
(
block_size
*
kVecSize
)
*
(
block_size
*
kVecSize
);
VectorizedRandomGenerator
<
T
,
uint8_t
><<<
grid_size
,
block_size
,
0
,
stream
>>>
(
size
,
seed_data
,
dropout_prob
,
x_data
,
mask_data
,
y_data
,
upscale_in_train
,
increment
,
main_offset
);
upscale_in_train
,
increment
,
main_offset
,
FLAGS_use_curand
);
}
else
{
if
(
upscale_in_train
)
{
// todo: can y share with data with x directly?
...
...
python/paddle/fluid/tests/unittests/test_dropout_op.py
浏览文件 @
1c01d1cc
...
...
@@ -22,6 +22,7 @@ import paddle
import
paddle.static
as
static
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
import
os
class
TestDropoutOp
(
OpTest
):
...
...
@@ -992,6 +993,62 @@ class TestDropoutBackward(unittest.TestCase):
),
self
.
cal_grad_upscale_train
(
mask
.
numpy
(),
prob
)))
class
TestRandomValue
(
unittest
.
TestCase
):
def
test_fixed_random_number
(
self
):
# Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
if
not
paddle
.
is_compiled_with_cuda
():
return
# Different GPU generate different random value. Only test V100 here.
if
not
"V100"
in
paddle
.
device
.
cuda
.
get_device_name
():
return
if
os
.
getenv
(
"FLAGS_use_curand"
,
None
)
in
(
'0'
,
'False'
,
None
):
return
print
(
"Test Fixed Random number on V100 GPU------>"
)
paddle
.
disable_static
()
paddle
.
set_device
(
'gpu'
)
paddle
.
seed
(
100
)
x
=
paddle
.
rand
([
32
,
1024
,
1024
],
dtype
=
'float32'
)
out
=
paddle
.
nn
.
functional
.
dropout
(
x
,
0.25
).
numpy
()
index0
,
index1
,
index2
=
np
.
nonzero
(
out
)
self
.
assertEqual
(
np
.
sum
(
index0
),
390094540
)
self
.
assertEqual
(
np
.
sum
(
index1
),
12871475125
)
self
.
assertEqual
(
np
.
sum
(
index2
),
12872777397
)
self
.
assertEqual
(
np
.
sum
(
out
),
16778744.0
)
expect
=
[
0.6914956
,
0.5294584
,
0.19032137
,
0.6996228
,
0.3338527
,
0.8442094
,
0.96965003
,
1.1726775
,
0.
,
0.28037727
]
self
.
assertTrue
(
np
.
allclose
(
out
[
10
,
100
,
500
:
510
],
expect
))
x
=
paddle
.
rand
([
32
,
1024
,
1024
],
dtype
=
'float64'
)
out
=
paddle
.
nn
.
functional
.
dropout
(
x
).
numpy
()
index0
,
index1
,
index2
=
np
.
nonzero
(
out
)
self
.
assertEqual
(
np
.
sum
(
index0
),
260065137
)
self
.
assertEqual
(
np
.
sum
(
index1
),
8582636095
)
self
.
assertEqual
(
np
.
sum
(
index2
),
8582219962
)
self
.
assertEqual
(
np
.
sum
(
out
),
16778396.563660286
)
expect
=
[
1.28587354
,
0.15563703
,
0.
,
0.28799703
,
0.
,
0.
,
0.
,
0.54964
,
0.51355682
,
0.33818988
]
self
.
assertTrue
(
np
.
allclose
(
out
[
20
,
100
,
500
:
510
],
expect
))
x
=
paddle
.
ones
([
32
,
1024
,
1024
],
dtype
=
'float16'
)
out
=
paddle
.
nn
.
functional
.
dropout
(
x
,
0.75
).
numpy
()
index0
,
index1
,
index2
=
np
.
nonzero
(
out
)
self
.
assertEqual
(
np
.
sum
(
index0
),
130086900
)
self
.
assertEqual
(
np
.
sum
(
index1
),
4291190105
)
self
.
assertEqual
(
np
.
sum
(
index2
),
4292243807
)
expect
=
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
,
0.
,
0.
,
4.
,
4.
]
self
.
assertTrue
(
np
.
allclose
(
out
[
0
,
100
,
500
:
510
],
expect
))
paddle
.
enable_static
()
if
__name__
==
'__main__'
:
paddle
.
enable_static
()
unittest
.
main
()
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