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体验新版 GitCode,发现更多精彩内容 >>
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424700ff
编写于
2月 08, 2022
作者:
N
niuliling123
提交者:
GitHub
2月 08, 2022
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电子邮件补丁
差异文件
Replace clip, bce_loss, full and full_like with elementwise (#39197)
* Replace clip, bce_loss, full and full_like with elementwise
上级
23d559dd
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
179 addition
and
50 deletion
+179
-50
paddle/fluid/operators/bce_loss_op.cu
paddle/fluid/operators/bce_loss_op.cu
+35
-35
paddle/fluid/operators/clip_op.h
paddle/fluid/operators/clip_op.h
+10
-0
paddle/fluid/platform/function_traits.h
paddle/fluid/platform/function_traits.h
+13
-4
paddle/pten/kernels/funcs/elementwise_base.h
paddle/pten/kernels/funcs/elementwise_base.h
+20
-5
paddle/pten/kernels/gpu/full_kernel.cu
paddle/pten/kernels/gpu/full_kernel.cu
+83
-1
paddle/pten/kernels/gpu/scale_kernel.cu
paddle/pten/kernels/gpu/scale_kernel.cu
+4
-5
paddle/pten/kernels/primitive/compute_primitives.h
paddle/pten/kernels/primitive/compute_primitives.h
+14
-0
未找到文件。
paddle/fluid/operators/bce_loss_op.cu
浏览文件 @
424700ff
...
...
@@ -21,40 +21,45 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
struct
BCELossGradFunctor
{
T
one
=
static_cast
<
T
>
(
1.0
f
);
T
eps
=
static_cast
<
T
>
(
1e-12
);
__device__
__forceinline__
T
operator
()(
const
T
x
,
const
T
label
,
const
T
dout
)
const
{
T
term1
=
max
((
one
-
x
)
*
x
,
eps
);
return
(
dout
*
(
x
-
label
)
/
term1
);
}
};
struct
BCELossFunctor
{
T
one
;
T
neg_100
;
template
<
typename
T
>
__global__
void
GPUBCELossForward
(
const
T
*
x_data
,
const
T
*
label_data
,
T
*
out_data
,
const
int
in_numel
)
{
CUDA_KERNEL_LOOP
(
i
,
in_numel
)
{
T
x
=
x_data
[
i
];
T
label
=
label_data
[
i
];
T
one
=
static_cast
<
T
>
(
1.
);
T
neg_100
=
static_cast
<
T
>
(
-
100.
);
HOSTDEVICE
inline
BCELossFunctor
()
{
one
=
static_cast
<
T
>
(
1.0
f
);
neg_100
=
static_cast
<
T
>
(
-
100.
);
}
HOSTDEVICE
inline
T
operator
()(
const
T
&
x
,
const
T
&
label
)
const
{
PADDLE_ENFORCE
(
(
x
>=
static_cast
<
T
>
(
0
))
&&
(
x
<=
one
),
"Input is expected to be within the interval [0, 1], but recieved %f."
,
x
);
T
term1
=
max
(
real_log
(
x
),
neg_100
);
T
term2
=
max
(
real_log
(
one
-
x
),
neg_100
);
return
(((
label
-
one
)
*
term2
)
-
(
label
*
term1
));
}
};
template
<
typename
T
>
struct
BCELossGradFunctor
{
T
one
;
T
eps
;
out_data
[
i
]
=
((
label
-
one
)
*
term2
)
-
(
label
*
term1
);
HOSTDEVICE
inline
BCELossGradFunctor
()
{
one
=
static_cast
<
T
>
(
1.0
f
);
eps
=
static_cast
<
T
>
(
1e-12
);
}
}
HOSTDEVICE
inline
T
operator
()(
const
T
&
x
,
const
T
&
label
,
const
T
&
dout
)
const
{
T
term1
=
max
((
one
-
x
)
*
x
,
eps
);
return
(
dout
*
(
x
-
label
)
/
term1
);
}
};
using
Tensor
=
framework
::
Tensor
;
template
<
typename
DeviceContext
,
typename
T
>
class
BCELossCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
...
...
@@ -63,18 +68,13 @@ class BCELossCUDAKernel : public framework::OpKernel<T> {
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
labels
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
const
auto
*
x_data
=
x
->
data
<
T
>
();
auto
*
out_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x_numel
=
x
->
numel
();
auto
&
dev_ctx
=
ctx
.
cuda_device_context
();
platform
::
GpuLaunchConfig
config
=
platform
::
GetGpuLaunchConfig1D
(
dev_ctx
,
x_numel
);
GPUBCELossForward
<
T
><<<
config
.
block_per_grid
,
config
.
thread_per_block
,
0
,
dev_ctx
.
stream
()
>>>
(
x_data
,
labels
->
data
<
T
>
(),
out_data
,
x_numel
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
std
::
vector
<
const
framework
::
Tensor
*>
ins
=
{
x
,
labels
};
std
::
vector
<
framework
::
Tensor
*>
outs
=
{
out
};
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
auto
functor
=
BCELossFunctor
<
T
>
();
paddle
::
operators
::
LaunchSameDimsElementwiseCudaKernel
<
ElementwiseType
::
kBinary
,
T
,
T
>
(
dev_ctx
,
ins
,
&
outs
,
functor
);
}
};
...
...
paddle/fluid/operators/clip_op.h
浏览文件 @
424700ff
...
...
@@ -172,6 +172,15 @@ class ClipGradKernel : public framework::OpKernel<T> {
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
d_x
!=
nullptr
)
{
auto
*
x
=
context
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
#if defined(__NVCC__) || defined(__HIPCC__)
std
::
vector
<
const
framework
::
Tensor
*>
ins
=
{
d_out
,
x
};
std
::
vector
<
framework
::
Tensor
*>
outs
=
{
d_x
};
auto
functor
=
ClipGradFunctor
<
T
>
(
min
,
max
);
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
LaunchSameDimsElementwiseCudaKernel
<
ElementwiseType
::
kBinary
,
T
,
T
>
(
context
.
template
device_context
<
platform
::
CUDADeviceContext
>(),
ins
,
&
outs
,
functor
);
#else
int64_t
numel
=
d_out
->
numel
();
auto
*
d_x_data
=
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
d_out_data
=
d_out
->
data
<
T
>
();
...
...
@@ -179,6 +188,7 @@ class ClipGradKernel : public framework::OpKernel<T> {
Transform
<
DeviceContext
>
trans
;
trans
(
context
.
template
device_context
<
DeviceContext
>(),
d_out_data
,
d_out_data
+
numel
,
x_data
,
d_x_data
,
ClipGradFunctor
<
T
>
(
min
,
max
));
#endif
}
}
};
...
...
paddle/fluid/platform/function_traits.h
浏览文件 @
424700ff
...
...
@@ -18,6 +18,18 @@ limitations under the License. */
namespace
paddle
{
namespace
platform
{
template
<
int
Arity
,
typename
...
Args
>
struct
IsPointerArgs
{
static_assert
(
Arity
==
sizeof
...(
Args
),
"Arity and Args not match!"
);
static
const
bool
value
=
false
;
};
template
<
typename
...
Args
>
struct
IsPointerArgs
<
1
,
Args
...
>
{
static_assert
(
1
==
sizeof
...(
Args
),
"Arity and Args not match!"
);
static
const
bool
value
=
std
::
is_pointer
<
typename
std
::
tuple_element
<
0
,
std
::
tuple
<
Args
...
>>::
type
>::
value
;
};
// Declare a template class with a single template parameter.
template
<
typename
>
...
...
@@ -41,10 +53,7 @@ struct FunctionTraits<ReturnType (ClassType::*)(Args...)>
template
<
typename
ReturnType
,
typename
...
Args
>
struct
FunctionTraits
<
ReturnType
(
Args
...)
>
{
static
const
size_t
arity
=
sizeof
...(
Args
);
static
const
bool
has_pointer_args
=
(
arity
==
1
)
&&
(
std
::
is_pointer
<
typename
std
::
tuple_element
<
0
,
std
::
tuple
<
Args
...
>>::
type
>::
value
);
static
const
bool
has_pointer_args
=
IsPointerArgs
<
arity
,
Args
...
>::
value
;
};
}
// namespace platform
...
...
paddle/pten/kernels/funcs/elementwise_base.h
浏览文件 @
424700ff
...
...
@@ -31,6 +31,8 @@ namespace kps = pten::kps;
#endif
#define BASE_SIZE 1 // To avoid running errors when Arity == 0 in args[Arity]
namespace
pten
{
enum
ElementwiseType
{
kUnary
=
1
,
kBinary
=
2
,
kTernary
=
3
,
kAny
=
-
1
};
...
...
@@ -475,6 +477,15 @@ struct ElementwisePrimitiveCaller<InT, OutT, VecSize, Functor, Arity, true> {
}
};
template
<
typename
InT
,
typename
OutT
,
int
VecSize
,
typename
Functor
>
struct
ElementwisePrimitiveCaller
<
InT
,
OutT
,
VecSize
,
Functor
,
0
,
false
>
{
__device__
inline
void
operator
()(
Functor
func
,
InT
(
*
args
)[
VecSize
],
OutT
*
result
)
{
kps
::
ElementwiseFillConst
<
InT
,
OutT
,
VecSize
,
1
,
1
,
Functor
>
(
result
,
func
);
}
};
template
<
typename
InT
,
typename
OutT
,
int
VecSize
,
typename
Functor
>
struct
ElementwisePrimitiveCaller
<
InT
,
OutT
,
VecSize
,
Functor
,
1
,
false
>
{
__device__
inline
void
operator
()(
Functor
func
,
...
...
@@ -548,12 +559,14 @@ template <typename InT,
int
VecSize
,
bool
IsBoundary
>
__device__
void
VectorizedElementwiseKernelImpl
(
const
pten
::
framework
::
Array
<
const
_ptr_
InT
*
__restrict__
,
Arity
>
&
in
,
const
pten
::
framework
::
Array
<
const
_ptr_
InT
*
__restrict__
,
Arity
+
BASE_SIZE
>
&
in
,
pten
::
framework
::
Array
<
_ptr_
OutT
*
,
NumOuts
>
outs
,
int
num
,
int
data_offset
,
Functor
func
)
{
InT
args
[
Arity
][
VecSize
];
InT
args
[
Arity
+
BASE_SIZE
][
VecSize
];
ConditionalT
<
OutT
,
NumOuts
>
result
[
VecSize
];
#pragma unroll
...
...
@@ -583,7 +596,8 @@ template <typename InT,
int
NumOuts
,
int
VecSize
>
__global__
void
VectorizedElementwiseKernel
(
pten
::
framework
::
Array
<
const
_ptr_
InT
*
__restrict__
,
Arity
>
ins
,
pten
::
framework
::
Array
<
const
_ptr_
InT
*
__restrict__
,
Arity
+
BASE_SIZE
>
ins
,
pten
::
framework
::
Array
<
_ptr_
OutT
*
,
NumOuts
>
outs
,
int
size
,
int
main_offset
,
...
...
@@ -623,8 +637,9 @@ void ElementwiseCudaKernel(const KPDevice &ctx,
const
std
::
vector
<
const
DenseTensor
*>
&
ins
,
std
::
vector
<
DenseTensor
*>
*
outs
,
Functor
func
)
{
auto
numel
=
ins
[
0
]
->
numel
();
pten
::
framework
::
Array
<
const
_ptr_
InT
*
__restrict__
,
Arity
>
ins_data
;
auto
numel
=
(
*
outs
)[
0
]
->
numel
();
pten
::
framework
::
Array
<
const
_ptr_
InT
*
__restrict__
,
Arity
+
BASE_SIZE
>
ins_data
;
pten
::
framework
::
Array
<
_ptr_
OutT
*
,
NumOuts
>
outs_data
;
for
(
int
i
=
0
;
i
<
Arity
;
++
i
)
{
...
...
paddle/pten/kernels/gpu/full_kernel.cu
浏览文件 @
424700ff
...
...
@@ -16,7 +16,89 @@ limitations under the License. */
#include "paddle/pten/backends/gpu/gpu_context.h"
#include "paddle/pten/core/kernel_registry.h"
#include "paddle/pten/kernels/impl/full_kernel_impl.h"
#include "paddle/pten/kernels/funcs/elementwise_base.h"
namespace
pten
{
template
<
typename
InT
,
typename
OutT
=
InT
>
struct
FullFuctor
{
OutT
value
;
template
<
typename
VType
>
explicit
inline
FullFuctor
(
VType
val
)
{
value
=
static_cast
<
OutT
>
(
val
);
}
__device__
__forceinline__
OutT
operator
()()
const
{
return
static_cast
<
OutT
>
(
value
);
}
};
template
<
typename
T
,
typename
ContextT
>
void
FullKernel
(
const
ContextT
&
dev_ctx
,
const
ScalarArray
&
shape
,
const
Scalar
&
val
,
DenseTensor
*
out
)
{
out
->
Resize
(
paddle
::
framework
::
make_ddim
(
shape
.
GetData
()));
int
numel
=
out
->
numel
();
out
->
mutable_data
<
T
>
(
dev_ctx
.
GetPlace
());
if
(
numel
>
0
)
{
// in transformer model the numel of outpout will be zero.
std
::
vector
<
const
DenseTensor
*>
inputs
=
{};
std
::
vector
<
DenseTensor
*>
outputs
=
{
out
};
// This function has no input, so the inputs.size() == 0. Use kUnary, but
// the data will not be loaded in the kernel because the number of
// parameters in the operator is 0
pten
::
funcs
::
LaunchSameDimsElementwiseCudaKernel
<
ElementwiseType
::
kUnary
,
T
,
T
>
(
dev_ctx
,
inputs
,
&
outputs
,
FullFuctor
<
T
>
(
val
.
to
<
T
>
()));
}
}
template
<
typename
T
,
typename
ContextT
>
void
FullLikeKernel
(
const
ContextT
&
dev_ctx
,
const
Scalar
&
val
,
DenseTensor
*
out
)
{
auto
value
=
val
.
to
<
float
>
();
using
CommonType
=
typename
std
::
common_type
<
float
,
typename
std
::
conditional
<
std
::
is_same
<
T
,
paddle
::
platform
::
float16
>::
value
,
float
,
T
>::
type
>::
type
;
auto
common_type_value
=
static_cast
<
CommonType
>
(
value
);
PADDLE_ENFORCE_EQ
(
(
common_type_value
>=
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
lowest
()))
&&
(
common_type_value
<=
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
max
())),
true
,
paddle
::
platform
::
errors
::
InvalidArgument
(
"The filled value is out of range for target type, "
"current kernel type is %s, the range should between %f "
"and %f, but now value is %f."
,
typeid
(
T
).
name
(),
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
lowest
()),
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
max
()),
static_cast
<
float
>
(
value
)));
std
::
vector
<
const
DenseTensor
*>
inputs
=
{};
std
::
vector
<
DenseTensor
*>
outputs
=
{
out
};
out
->
mutable_data
<
T
>
(
dev_ctx
.
GetPlace
());
// This function has no input, so the inputs.size() == 0. Use kUnary, but the
// data will not be loaded in the kernel because the number of parameters in
// the operator is 0
int
numel
=
out
->
numel
();
if
(
numel
>
0
)
{
pten
::
funcs
::
LaunchSameDimsElementwiseCudaKernel
<
ElementwiseType
::
kUnary
,
T
,
T
>
(
dev_ctx
,
inputs
,
&
outputs
,
FullFuctor
<
T
>
(
value
));
}
}
}
// namespace pten
PT_REGISTER_KERNEL
(
full
,
GPU
,
...
...
paddle/pten/kernels/gpu/scale_kernel.cu
浏览文件 @
424700ff
...
...
@@ -28,11 +28,10 @@ struct ScaleFunctor {
InT
scale
;
bool
bias_after_scale
;
ScaleFunctor
(
InT
scale_data
,
InT
bias_data
,
bool
is_bias_after_sacle
)
{
scale
=
scale_data
;
bias
=
bias_data
;
bias_after_scale
=
is_bias_after_sacle
;
}
ScaleFunctor
(
InT
scale_data
,
InT
bias_data
,
bool
is_bias_after_sacle
)
:
bias
(
bias_data
),
scale
(
scale_data
),
bias_after_scale
(
is_bias_after_sacle
)
{}
__device__
__forceinline__
InT
operator
()(
const
InT
x
)
const
{
if
(
bias_after_scale
)
{
...
...
paddle/pten/kernels/primitive/compute_primitives.h
浏览文件 @
424700ff
...
...
@@ -414,5 +414,19 @@ __device__ __forceinline__ void Reduce(T* out,
}
}
template
<
typename
InT
,
typename
OutT
,
int
NX
,
int
NY
,
int
BlockSize
,
class
OpFunc
>
__device__
__forceinline__
void
ElementwiseFillConst
(
OutT
*
out
,
OpFunc
compute
)
{
#pragma unroll
for
(
int
idx
=
0
;
idx
<
NX
*
NY
;
idx
++
)
{
out
[
idx
]
=
static_cast
<
OutT
>
(
compute
());
}
}
}
// namespace kps
}
// namespace pten
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