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体验新版 GitCode,发现更多精彩内容 >>
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ef61df30
编写于
10月 10, 2022
作者:
R
Rayman
提交者:
GitHub
10月 10, 2022
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差异文件
【Hackathon No.36】优化 lerp_grad op 在 GPU 上的计算性能 (#45946)
上级
5e0614a1
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
274 addition
and
2 deletion
+274
-2
paddle/phi/kernels/gpu/lerp_grad_kernel.cu
paddle/phi/kernels/gpu/lerp_grad_kernel.cu
+242
-1
paddle/phi/kernels/impl/broadcast_tensors_kernel_impl.h
paddle/phi/kernels/impl/broadcast_tensors_kernel_impl.h
+2
-1
python/paddle/fluid/tests/unittests/test_lerp_op.py
python/paddle/fluid/tests/unittests/test_lerp_op.py
+30
-0
未找到文件。
paddle/phi/kernels/gpu/lerp_grad_kernel.cu
浏览文件 @
ef61df30
...
...
@@ -15,8 +15,249 @@
#include "paddle/phi/kernels/lerp_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/impl/lerp_grad_kernel_impl.h"
#include "paddle/phi/kernels/broadcast_tensors_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/common_shape.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/gpu/reduce.h"
namespace
phi
{
template
<
typename
T
>
__global__
void
LerpGradKernelImpl
(
const
T
*
weight
,
const
T
*
dout
,
T
*
dx
,
T
*
dy
,
const
int
out_size
,
const
int
x_size
,
const
int
y_size
)
{
CUDA_KERNEL_LOOP_TYPE
(
idx
,
out_size
,
int64_t
)
{
T
temp_dx
=
weight
[
idx
]
*
dout
[
idx
];
if
(
dx
)
{
if
(
idx
<
x_size
)
{
dx
[
idx
]
=
dout
[
idx
]
-
temp_dx
;
}
}
if
(
dy
)
{
if
(
idx
<
y_size
)
{
dy
[
idx
]
=
temp_dx
;
}
}
}
}
template
<
typename
T
>
__global__
void
LerpGradScalarKernelImpl
(
const
T
*
weight
,
const
T
*
dout
,
T
*
dx
,
T
*
dy
,
const
int
out_size
,
const
int
x_size
,
const
int
y_size
)
{
T
weight_scalar
=
weight
[
0
];
CUDA_KERNEL_LOOP_TYPE
(
idx
,
out_size
,
int64_t
)
{
T
temp_dx
=
weight_scalar
*
dout
[
idx
];
if
(
dx
)
{
if
(
idx
<
x_size
)
{
dx
[
idx
]
=
dout
[
idx
]
-
temp_dx
;
}
}
if
(
dy
)
{
if
(
idx
<
y_size
)
{
dy
[
idx
]
=
temp_dx
;
}
}
}
}
bool
XYNeedReduce
(
const
DenseTensor
&
x
,
const
DenseTensor
&
y
,
const
DenseTensor
&
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
out_dims
=
out
.
dims
();
int
x_rank
=
x_dims
.
size
();
int
y_rank
=
y_dims
.
size
();
int
out_rank
=
out_dims
.
size
();
int
smaller_rank
=
std
::
min
(
x_rank
,
y_rank
);
if
(
std
::
max
(
x_rank
,
y_rank
)
<
out_rank
)
{
return
true
;
}
for
(
int
i
=
1
;
i
<=
smaller_rank
;
++
i
)
{
int
x_idx
=
x_rank
-
i
;
int
y_idx
=
y_rank
-
i
;
int
out_idx
=
out_rank
-
i
;
if
(
x_dims
[
x_idx
]
!=
y_dims
[
y_idx
])
{
return
true
;
}
if
(
x_dims
[
x_idx
]
==
1
&&
y_dims
[
y_idx
]
==
1
&&
out_dims
[
out_idx
]
!=
1
)
{
return
true
;
}
}
return
false
;
}
template
<
typename
T
,
typename
Context
>
void
SwitchKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
weight
,
const
DenseTensor
&
out_grad
,
const
int
x_grad_size
,
const
int
y_grad_size
,
T
*
x_grad_data
,
T
*
y_grad_data
)
{
if
(
weight
.
numel
()
==
1
)
{
// condition when weight is a scalar
const
T
*
weight_data
=
weight
.
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
.
data
<
T
>
();
const
int64_t
out_size
=
out_grad
.
numel
();
const
int64_t
weight_size
=
weight
.
numel
();
auto
gpu_config
=
phi
::
backends
::
gpu
::
GetGpuLaunchConfig1D
(
ctx
,
out_size
);
LerpGradScalarKernelImpl
<
T
><<<
gpu_config
.
GetGridSize
(),
gpu_config
.
GetBlockSize
(),
0
,
ctx
.
stream
()
>>>
(
weight_data
,
out_grad_data
,
x_grad_data
,
y_grad_data
,
out_size
,
x_grad_size
,
y_grad_size
);
}
else
{
// broadcast weight with out_grad's dimensions
const
std
::
vector
<
const
DenseTensor
*>
in_tensors
=
{
&
weight
,
&
out_grad
};
DenseTensor
b_weight
=
phi
::
EmptyLike
<
T
>
(
ctx
,
out_grad
);
DenseTensor
b_out
=
phi
::
EmptyLike
<
T
>
(
ctx
,
out_grad
);
std
::
vector
<
DenseTensor
*>
out_tensors
=
{
&
b_weight
,
&
b_out
};
phi
::
BroadcastTensorsKernel
<
T
,
Context
>
(
ctx
,
in_tensors
,
out_tensors
);
const
T
*
weight_data
=
b_weight
.
data
<
T
>
();
const
T
*
out_grad_data
=
b_out
.
data
<
T
>
();
const
int
out_size
=
out_grad
.
numel
();
const
int
weight_size
=
weight
.
numel
();
auto
gpu_config
=
phi
::
backends
::
gpu
::
GetGpuLaunchConfig1D
(
ctx
,
out_size
);
LerpGradKernelImpl
<
T
><<<
gpu_config
.
GetGridSize
(),
gpu_config
.
GetBlockSize
(),
0
,
ctx
.
stream
()
>>>
(
weight_data
,
out_grad_data
,
x_grad_data
,
y_grad_data
,
out_size
,
x_grad_size
,
y_grad_size
);
}
}
template
<
typename
T
,
typename
Context
>
void
LerpGradKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
y
,
const
DenseTensor
&
weight
,
const
DenseTensor
&
out
,
const
DenseTensor
&
out_grad
,
DenseTensor
*
x_grad
,
DenseTensor
*
y_grad
)
{
const
int
rank
=
out
.
dims
().
size
();
PADDLE_ENFORCE_GE
(
rank
,
1
,
phi
::
errors
::
InvalidArgument
(
"The number of dimensions for LerpGradOp must be "
"greater than or equal to 1, but the value received is %d."
,
rank
));
PADDLE_ENFORCE_LE
(
rank
,
6
,
phi
::
errors
::
InvalidArgument
(
"The number of dimensions for LerpGradOp must be "
"less than or equal to 6, but the value received is %d."
,
rank
));
// check if x_grad and y_grad need to be reduced
// if x has a different dimension with y or weight in the middle axis, then
// they need to be broadcast and then reduced.
bool
reduce_flag
=
XYNeedReduce
(
x
,
y
,
out
);
if
(
!
reduce_flag
)
{
int
x_grad_size
=
0
,
y_grad_size
=
0
;
T
*
x_grad_data
=
NULL
;
T
*
y_grad_data
=
NULL
;
if
(
x_grad
)
{
x_grad_data
=
ctx
.
template
Alloc
<
T
>(
x_grad
);
x_grad_size
=
x
.
numel
();
}
if
(
y_grad
)
{
y_grad_data
=
ctx
.
template
Alloc
<
T
>(
y_grad
);
y_grad_size
=
y
.
numel
();
}
SwitchKernel
<
T
,
Context
>
(
ctx
,
weight
,
out_grad
,
x_grad_size
,
y_grad_size
,
x_grad_data
,
y_grad_data
);
}
else
{
int
x_grad_size
=
0
,
y_grad_size
=
0
;
DenseTensor
b_xgrad
=
phi
::
EmptyLike
<
T
,
Context
>
(
ctx
,
out_grad
);
DenseTensor
b_ygrad
=
phi
::
EmptyLike
<
T
,
Context
>
(
ctx
,
out_grad
);
T
*
x_grad_data
=
NULL
;
T
*
y_grad_data
=
NULL
;
if
(
x_grad
)
{
x_grad_data
=
ctx
.
template
Alloc
<
T
>(
&
b_xgrad
);
x_grad_size
=
out
.
numel
();
}
if
(
y_grad
)
{
y_grad_data
=
ctx
.
template
Alloc
<
T
>(
&
b_ygrad
);
y_grad_size
=
out
.
numel
();
}
SwitchKernel
<
T
,
Context
>
(
ctx
,
weight
,
out_grad
,
x_grad_size
,
y_grad_size
,
x_grad_data
,
y_grad_data
);
if
(
x_grad
)
{
std
::
vector
<
int
>
reduce_axis_x
=
funcs
::
GetReduceDim
(
x_grad
->
dims
(),
b_xgrad
.
dims
(),
-
1
);
if
(
!
reduce_axis_x
.
empty
())
{
phi
::
funcs
::
ReduceKernel
<
T
,
T
,
kps
::
AddFunctor
,
kps
::
IdentityFunctor
<
T
>>
(
ctx
,
b_xgrad
,
x_grad
,
kps
::
IdentityFunctor
<
T
>
(),
reduce_axis_x
);
}
else
{
x_grad
->
ShareDataWith
(
b_xgrad
);
}
}
if
(
y_grad
)
{
std
::
vector
<
int
>
reduce_axis_y
=
funcs
::
GetReduceDim
(
y_grad
->
dims
(),
b_ygrad
.
dims
(),
-
1
);
if
(
!
reduce_axis_y
.
empty
())
{
phi
::
funcs
::
ReduceKernel
<
T
,
T
,
kps
::
AddFunctor
,
kps
::
IdentityFunctor
<
T
>>
(
ctx
,
b_ygrad
,
y_grad
,
kps
::
IdentityFunctor
<
T
>
(),
reduce_axis_y
);
}
else
{
y_grad
->
ShareDataWith
(
b_ygrad
);
}
}
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
lerp_grad
,
GPU
,
ALL_LAYOUT
,
phi
::
LerpGradKernel
,
float
,
double
)
{}
paddle/phi/kernels/impl/broadcast_tensors_kernel_impl.h
浏览文件 @
ef61df30
...
...
@@ -106,10 +106,11 @@ void BroadcastTensorsKernel(const Context& ctx,
SWITCH_OUT_RANK_CASE
(
3
)
SWITCH_OUT_RANK_CASE
(
4
)
SWITCH_OUT_RANK_CASE
(
5
)
SWITCH_OUT_RANK_CASE
(
6
)
default:
{
PADDLE_THROW
(
paddle
::
platform
::
errors
::
InvalidArgument
(
"Target tensor rank out of range"
"Maximum supported rank for broadcast is:
5
"
));
"Maximum supported rank for broadcast is:
6
"
));
}
}
}
...
...
python/paddle/fluid/tests/unittests/test_lerp_op.py
浏览文件 @
ef61df30
...
...
@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
...
...
@@ -78,6 +80,34 @@ class TestLerpWithDim6(TestLerp):
self
.
shape
=
[
2
,
1
,
2
,
5
,
1
,
5
]
class
TestLerpBroadXY
(
TestLerp
):
def
setUp
(
self
):
self
.
op_type
=
"lerp"
self
.
python_api
=
paddle
.
lerp
self
.
init_dtype
()
self
.
init_shape
()
x
=
np
.
arange
(
1.
,
201.
).
astype
(
self
.
dtype
).
reshape
([
2
,
1
,
2
,
50
])
y
=
np
.
full
(
200
,
10.
).
astype
(
self
.
dtype
).
reshape
([
2
,
2
,
1
,
50
])
w
=
np
.
asarray
([
0.5
]).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
,
'Weight'
:
w
}
self
.
outputs
=
{
'Out'
:
x
+
w
*
(
y
-
x
)}
class
TestLerpBroadWToXY
(
TestLerp
):
def
setUp
(
self
):
self
.
op_type
=
"lerp"
self
.
python_api
=
paddle
.
lerp
self
.
init_dtype
()
self
.
init_shape
()
x
=
np
.
full
(
600
,
2.5
).
astype
(
self
.
dtype
).
reshape
([
50
,
2
,
2
,
3
])
y
=
np
.
full
(
600
,
1.
).
astype
(
self
.
dtype
).
reshape
([
50
,
2
,
2
,
3
])
w
=
np
.
random
.
random
([
3
]).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
,
'Weight'
:
w
}
self
.
outputs
=
{
'Out'
:
x
+
w
*
(
y
-
x
)}
class
TestLerpAPI
(
unittest
.
TestCase
):
def
init_dtype
(
self
):
...
...
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