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体验新版 GitCode,发现更多精彩内容 >>
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36f08826
编写于
8月 03, 2022
作者:
Z
zhangkaihuo
提交者:
GitHub
8月 03, 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
opt bn1d backward (#44783)
上级
65f38869
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
414 addition
and
18 deletion
+414
-18
paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
+383
-17
paddle/phi/kernels/gpu/batch_norm_kernel.cu
paddle/phi/kernels/gpu/batch_norm_kernel.cu
+2
-1
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
+29
-0
未找到文件。
paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
浏览文件 @
36f08826
...
@@ -21,8 +21,10 @@
...
@@ -21,8 +21,10 @@
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/batch_norm_kernel.h"
#include "paddle/phi/kernels/batch_norm_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/norm_utils.h"
#include "paddle/phi/kernels/funcs/norm_utils.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/gpu/batch_norm_utils.h"
#include "paddle/phi/kernels/gpu/batch_norm_utils.h"
#ifdef __HIPCC__
#ifdef __HIPCC__
...
@@ -197,6 +199,7 @@ static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
...
@@ -197,6 +199,7 @@ static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
x_sum
+=
x_i
;
x_sum
+=
x_i
;
x_square_sum
+=
x_i
*
x_i
;
x_square_sum
+=
x_i
*
x_i
;
}
}
x_sum
=
BlockReduce
(
mean_storage
).
Reduce
(
x_sum
,
cub
::
Sum
());
x_sum
=
BlockReduce
(
mean_storage
).
Reduce
(
x_sum
,
cub
::
Sum
());
x_square_sum
=
x_square_sum
=
BlockReduce
(
variance_storeage
).
Reduce
(
x_square_sum
,
cub
::
Sum
());
BlockReduce
(
variance_storeage
).
Reduce
(
x_square_sum
,
cub
::
Sum
());
...
@@ -218,6 +221,7 @@ static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
...
@@ -218,6 +221,7 @@ static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
dy_i
*
(
static_cast
<
BatchNormParamType
<
T
>>
(
x
[
index
])
-
mean_val
);
dy_i
*
(
static_cast
<
BatchNormParamType
<
T
>>
(
x
[
index
])
-
mean_val
);
db_sum
+=
dy_i
;
db_sum
+=
dy_i
;
}
}
ds_sum
=
BlockReduce
(
ds_storage
).
Reduce
(
ds_sum
,
cub
::
Sum
());
ds_sum
=
BlockReduce
(
ds_storage
).
Reduce
(
ds_sum
,
cub
::
Sum
());
db_sum
=
BlockReduce
(
db_storage
).
Reduce
(
db_sum
,
cub
::
Sum
());
db_sum
=
BlockReduce
(
db_storage
).
Reduce
(
db_sum
,
cub
::
Sum
());
if
(
threadIdx
.
x
==
0
)
{
if
(
threadIdx
.
x
==
0
)
{
...
@@ -241,6 +245,263 @@ static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
...
@@ -241,6 +245,263 @@ static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
}
}
}
}
template
<
typename
T
>
__device__
__forceinline__
void
BlockReduceByVetical
(
BatchNormParamType
<
T
>
x_sum
,
BatchNormParamType
<
T
>
x_square_sum
,
BatchNormParamType
<
T
>
*
smem_sum
,
BatchNormParamType
<
T
>
*
smem_square_sum
,
BatchNormParamType
<
T
>
*
x_sum_out
,
BatchNormParamType
<
T
>
*
x_square_sum_out
)
{
int
tid
=
threadIdx
.
x
+
threadIdx
.
y
*
blockDim
.
x
;
#pragma unroll
for
(
int
offset
=
blockDim
.
y
/
2
;
offset
>
0
;
offset
>>=
1
)
{
if
(
threadIdx
.
y
<
offset
*
2
)
{
smem_sum
[
tid
]
=
x_sum
;
smem_square_sum
[
tid
]
=
x_square_sum
;
}
__syncthreads
();
if
(
threadIdx
.
y
<
offset
)
{
int
pair_tid
=
tid
+
offset
*
blockDim
.
x
;
x_sum
+=
smem_sum
[
pair_tid
];
x_square_sum
+=
smem_square_sum
[
pair_tid
];
}
}
if
(
threadIdx
.
y
==
0
)
{
*
x_sum_out
=
x_sum
;
*
x_square_sum_out
=
x_square_sum
;
}
}
template
<
typename
T
,
int
BlockDim
>
static
__global__
void
BNBackward2DChannelLastStage1
(
const
T
*
x
,
const
int
C
,
const
int
N
,
const
int
HxW
,
const
double
epsilon
,
BatchNormParamType
<
T
>
*
block_data_ptr
,
BatchNormParamType
<
T
>
*
compute_mean
,
BatchNormParamType
<
T
>
*
compute_inv_var
,
int
*
flag_ptr
)
{
int
outer_size
=
C
;
int
inner_size
=
N
*
HxW
;
__shared__
BatchNormParamType
<
T
>
smem_sum
[
BlockDim
];
__shared__
BatchNormParamType
<
T
>
smem_square_sum
[
BlockDim
];
__shared__
BatchNormParamType
<
T
>
inv_var_val
;
__shared__
BatchNormParamType
<
T
>
mean_val
;
int
outer_loop_stride
=
gridDim
.
x
*
blockDim
.
x
;
int
inner_loop_stride
=
gridDim
.
y
*
blockDim
.
y
;
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
outer_size
;
i
+=
outer_loop_stride
)
{
BatchNormParamType
<
T
>
x_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
BatchNormParamType
<
T
>
x_square_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
for
(
int
j
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
j
<
inner_size
;
j
+=
inner_loop_stride
)
{
const
int
index
=
j
*
outer_size
+
i
;
BatchNormParamType
<
T
>
x_i
=
static_cast
<
BatchNormParamType
<
T
>>
(
x
[
index
]);
x_sum
+=
x_i
;
x_square_sum
+=
x_i
*
x_i
;
}
// vertical block sum
BlockReduceByVetical
<
T
>
(
x_sum
,
x_square_sum
,
&
smem_sum
[
0
],
&
smem_square_sum
[
0
],
&
x_sum
,
&
x_square_sum
);
if
(
gridDim
.
y
>
1
)
{
volatile
BatchNormParamType
<
T
>
*
staging_sum
=
block_data_ptr
;
volatile
BatchNormParamType
<
T
>
*
staging_square_sum
=
&
block_data_ptr
[
C
*
gridDim
.
y
];
// write block data to global memory
if
(
threadIdx
.
y
==
0
)
{
staging_sum
[
i
+
blockIdx
.
y
*
C
]
=
x_sum
;
staging_square_sum
[
i
+
blockIdx
.
y
*
C
]
=
x_square_sum
;
}
// make sure write is visible to all blocks
__threadfence
();
__syncthreads
();
__shared__
bool
is_last_block_done
;
// mark block done
if
(
threadIdx
.
x
==
0
&&
threadIdx
.
y
==
0
)
{
int
old
=
atomicAdd
(
&
flag_ptr
[
blockIdx
.
x
],
1
);
is_last_block_done
=
(
old
==
(
gridDim
.
y
-
1
));
}
__syncthreads
();
if
(
is_last_block_done
)
{
x_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
x_square_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
// thread sum
for
(
int
y
=
threadIdx
.
y
;
y
<
gridDim
.
y
;
y
+=
blockDim
.
y
)
{
x_sum
+=
staging_sum
[
i
+
y
*
C
];
x_square_sum
+=
staging_square_sum
[
i
+
y
*
C
];
}
// vertical block sum
BlockReduceByVetical
<
T
>
(
x_sum
,
x_square_sum
,
&
smem_sum
[
0
],
&
smem_square_sum
[
0
],
&
x_sum
,
&
x_square_sum
);
// final compute
if
(
threadIdx
.
y
==
0
)
{
BatchNormParamType
<
T
>
compute_mean_val
=
x_sum
/
inner_size
;
BatchNormParamType
<
T
>
variance_val
=
x_square_sum
/
inner_size
-
compute_mean_val
*
compute_mean_val
;
BatchNormParamType
<
T
>
compute_inv_var_val
=
1
/
sqrt
(
variance_val
+
epsilon
);
compute_mean
[
i
]
=
compute_mean_val
;
compute_inv_var
[
i
]
=
compute_inv_var_val
;
}
}
}
}
}
template
<
typename
T
,
int
BlockDim
>
static
__global__
void
BNBackward2DChannelLastStage2
(
const
T
*
dy
,
const
T
*
x
,
const
BatchNormParamType
<
T
>
*
means
,
const
BatchNormParamType
<
T
>
*
variances
,
const
int
C
,
const
int
N
,
const
int
HxW
,
const
double
epsilon
,
BatchNormParamType
<
T
>
*
block_data_ptr
,
BatchNormParamType
<
T
>
*
dscale
,
BatchNormParamType
<
T
>
*
dbias
,
int
*
flag_ptr
)
{
int
outer_size
=
C
;
int
inner_size
=
N
*
HxW
;
__shared__
BatchNormParamType
<
T
>
smem_ds_sum
[
BlockDim
];
__shared__
BatchNormParamType
<
T
>
smem_db_sum
[
BlockDim
];
__shared__
BatchNormParamType
<
T
>
inv_var_val
;
__shared__
BatchNormParamType
<
T
>
mean_val
;
int
outer_loop_stride
=
gridDim
.
x
*
blockDim
.
x
;
int
inner_loop_stride
=
gridDim
.
y
*
blockDim
.
y
;
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
outer_size
;
i
+=
outer_loop_stride
)
{
BatchNormParamType
<
T
>
ds_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
BatchNormParamType
<
T
>
db_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
BatchNormParamType
<
T
>
mean_val
=
means
[
i
];
BatchNormParamType
<
T
>
inv_var_val
=
variances
[
i
];
for
(
int
j
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
j
<
inner_size
;
j
+=
inner_loop_stride
)
{
const
int
index
=
j
*
outer_size
+
i
;
BatchNormParamType
<
T
>
dy_i
=
static_cast
<
BatchNormParamType
<
T
>>
(
dy
[
index
]);
ds_sum
+=
dy_i
*
(
static_cast
<
BatchNormParamType
<
T
>>
(
x
[
index
])
-
mean_val
);
db_sum
+=
dy_i
;
}
// vertical block sum
BlockReduceByVetical
<
T
>
(
ds_sum
,
db_sum
,
&
smem_ds_sum
[
0
],
&
smem_db_sum
[
0
],
&
ds_sum
,
&
db_sum
);
if
(
gridDim
.
y
>
1
)
{
volatile
BatchNormParamType
<
T
>
*
staging_ds_sum
=
block_data_ptr
;
volatile
BatchNormParamType
<
T
>
*
staging_db_sum
=
&
block_data_ptr
[
C
*
gridDim
.
y
];
// write block data to global memory
if
(
threadIdx
.
y
==
0
)
{
staging_ds_sum
[
i
+
blockIdx
.
y
*
C
]
=
ds_sum
;
staging_db_sum
[
i
+
blockIdx
.
y
*
C
]
=
db_sum
;
}
// make sure write is visible to all blocks
__threadfence
();
__syncthreads
();
__shared__
bool
is_last_block_done
;
// mark block done
if
(
threadIdx
.
x
==
0
&&
threadIdx
.
y
==
0
)
{
int
old
=
atomicAdd
(
&
flag_ptr
[
blockIdx
.
x
],
1
);
is_last_block_done
=
(
old
==
(
gridDim
.
y
-
1
));
}
__syncthreads
();
if
(
is_last_block_done
)
{
ds_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
db_sum
=
static_cast
<
BatchNormParamType
<
T
>>
(
0
);
// thread sum
for
(
int
y
=
threadIdx
.
y
;
y
<
gridDim
.
y
;
y
+=
blockDim
.
y
)
{
ds_sum
+=
staging_ds_sum
[
i
+
y
*
C
];
db_sum
+=
staging_db_sum
[
i
+
y
*
C
];
}
// vertical block sum
BlockReduceByVetical
<
T
>
(
ds_sum
,
db_sum
,
&
smem_ds_sum
[
0
],
&
smem_db_sum
[
0
],
&
ds_sum
,
&
db_sum
);
// final compute
if
(
threadIdx
.
y
==
0
)
{
dscale
[
i
]
=
ds_sum
*
inv_var_val
;
dbias
[
i
]
=
db_sum
;
}
}
}
}
}
template
<
typename
T
,
int
BlockDim
>
static
__global__
void
BNBackward2DChannelLastStage3
(
const
T
*
dy
,
const
T
*
x
,
const
BatchNormParamType
<
T
>
*
scale
,
const
BatchNormParamType
<
T
>
*
dscales
,
const
BatchNormParamType
<
T
>
*
dbias
,
const
BatchNormParamType
<
T
>
*
means
,
const
BatchNormParamType
<
T
>
*
variances
,
const
int
C
,
const
int
N
,
const
int
HxW
,
const
double
epsilon
,
T
*
dx
)
{
const
int
outer_size
=
C
;
const
int
inner_size
=
N
*
HxW
;
int
outer_loop_stride
=
gridDim
.
x
*
blockDim
.
x
;
int
inner_loop_stride
=
gridDim
.
y
*
blockDim
.
y
;
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
outer_size
;
i
+=
outer_loop_stride
)
{
BatchNormParamType
<
T
>
mean_val
=
means
[
i
];
BatchNormParamType
<
T
>
inv_var_val
=
variances
[
i
];
BatchNormParamType
<
T
>
dscale_val
=
dscales
[
i
];
BatchNormParamType
<
T
>
dbias_val
=
dbias
[
i
];
for
(
int
j
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
j
<
inner_size
;
j
+=
inner_loop_stride
)
{
const
int
index
=
j
*
outer_size
+
i
;
dx
[
index
]
=
scale
[
i
]
*
inv_var_val
*
(
static_cast
<
BatchNormParamType
<
T
>>
(
dy
[
index
])
-
dbias_val
/
static_cast
<
BatchNormParamType
<
T
>>
(
inner_size
)
-
(
static_cast
<
BatchNormParamType
<
T
>>
(
x
[
index
])
-
mean_val
)
*
inv_var_val
*
dscale_val
/
inner_size
);
}
}
}
template
<
typename
T
,
int
BlockDim
,
phi
::
DataLayout
layout
>
template
<
typename
T
,
int
BlockDim
,
phi
::
DataLayout
layout
>
static
__global__
LAUNCH_BOUNDS
(
BlockDim
)
void
BNBackwardData
(
static
__global__
LAUNCH_BOUNDS
(
BlockDim
)
void
BNBackwardData
(
const
T
*
dy
,
const
T
*
dy
,
...
@@ -592,42 +853,147 @@ void BatchNormGradRawKernel(const Context &ctx,
...
@@ -592,42 +853,147 @@ void BatchNormGradRawKernel(const Context &ctx,
// epsilon, saved_mean_data, saved_var_data));
// epsilon, saved_mean_data, saved_var_data));
#else
#else
// CUDNN only support small batch size
// CUDNN only support small batch size
const
size_t
CUDNN_PER_ACTIVATION_THRESHOLD
=
131070
;
// const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
const
size_t
CUDNN_PER_ACTIVATION_THRESHOLD
=
10240
;
const
size_t
CUDNN_SPATIAL_THRESHOLD
=
880801
;
const
size_t
CUDNN_SPATIAL_THRESHOLD
=
880801
;
const
bool
use_native_kernel
=
const
bool
use_native_kernel
=
((
x_dims
.
size
()
==
2
&&
N
>=
CUDNN_PER_ACTIVATION_THRESHOLD
)
||
((
x_dims
.
size
()
==
2
&&
N
>=
CUDNN_PER_ACTIVATION_THRESHOLD
)
||
(
x_dims
.
size
()
==
3
&&
N
>=
CUDNN_SPATIAL_THRESHOLD
));
(
x_dims
.
size
()
==
3
&&
N
>=
CUDNN_SPATIAL_THRESHOLD
));
if
(
use_native_kernel
)
{
if
(
use_native_kernel
)
{
if
(
compute_format
==
DataLayout
::
kNCHW
)
{
if
(
x_dims
.
size
()
==
2
)
{
BNBackward
<
T
,
block
,
DataLayout
::
kNCHW
>
dim3
block
;
<<<
grid2
,
block
,
0
,
ctx
.
stream
()
>>>
(
dim3
grid
;
const
int
block_size
=
512
;
const
int
MAX_GRID_SIZE
=
128
;
const
int
WARP_SIZE
=
32
;
// init intermediate storage
DenseTensor
block_data_tensor
;
DenseTensor
flag_tensor
;
DenseTensor
compute_mean_tensor
=
phi
::
Empty
<
BatchNormParamType
<
T
>
,
Context
>
(
ctx
,
{
C
});
DenseTensor
compute_inv_var_tensor
=
phi
::
Empty
<
BatchNormParamType
<
T
>
,
Context
>
(
ctx
,
{
C
});
BatchNormParamType
<
T
>
*
block_data_ptr
=
nullptr
;
int
*
flag_ptr
=
nullptr
;
int
block_x
=
std
::
min
(
phi
::
funcs
::
details
::
GetLastPow2
(
C
),
WARP_SIZE
);
int
block_y
=
std
::
min
(
phi
::
funcs
::
details
::
GetLastPow2
(
N
*
H
*
W
*
D
/
16
),
block_size
/
block_x
);
if
(
block_x
*
block_y
!=
block_size
)
{
block_x
=
std
::
min
(
phi
::
funcs
::
details
::
GetLastPow2
(
C
),
block_size
/
block_y
);
}
int
grid_x
=
(
C
+
block_x
-
1
)
/
block_x
;
int
grid_y
=
std
::
min
((
N
*
H
*
W
*
D
+
block_y
*
16
-
1
)
/
(
block_y
*
16
),
MAX_GRID_SIZE
);
block
.
x
=
block_x
;
block
.
y
=
block_y
;
grid
.
x
=
grid_x
;
grid
.
y
=
grid_y
;
if
(
grid
.
y
>
1
)
{
block_data_tensor
=
phi
::
Empty
<
BatchNormParamType
<
T
>
,
Context
>
(
ctx
,
{
2
*
C
*
grid
.
y
});
flag_tensor
=
phi
::
Empty
<
int
,
Context
>
(
ctx
,
{
grid
.
x
});
block_data_ptr
=
block_data_tensor
.
data
<
BatchNormParamType
<
T
>>
();
flag_ptr
=
flag_tensor
.
data
<
int
>
();
funcs
::
SetConstant
<
Context
,
int
>
set_zero
;
set_zero
(
ctx
,
&
flag_tensor
,
static_cast
<
int
>
(
0
));
}
// 1. reduce_sum(x) => mean, inv_var
auto
*
mean_ptr
=
saved_mean_data
==
nullptr
?
compute_mean_tensor
.
data
<
BatchNormParamType
<
T
>>
()
:
saved_mean_data
;
auto
*
variance_ptr
=
saved_var_data
==
nullptr
?
compute_inv_var_tensor
.
data
<
BatchNormParamType
<
T
>>
()
:
saved_var_data
;
if
(
saved_mean_data
==
nullptr
)
{
BNBackward2DChannelLastStage1
<
T
,
block_size
>
<<<
grid
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_x
.
template
data
<
T
>(),
C
,
N
,
H
*
W
*
D
,
epsilon
,
block_data_ptr
,
compute_mean_tensor
.
data
<
BatchNormParamType
<
T
>>
(),
compute_inv_var_tensor
.
data
<
BatchNormParamType
<
T
>>
(),
flag_ptr
);
}
// 2. reduce_sum(x, dy, mean) => dscale, dbias
BNBackward2DChannelLastStage2
<
T
,
block_size
>
<<<
grid
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_d_y
.
template
data
<
T
>(),
transformed_d_y
.
template
data
<
T
>(),
transformed_x
.
template
data
<
T
>(),
transformed_x
.
template
data
<
T
>(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
mean_ptr
,
saved_mean_data
,
variance_ptr
,
saved_var_data
,
C
,
C
,
N
,
N
,
H
*
W
*
D
,
H
*
W
*
D
,
epsilon
,
epsilon
,
transformed_d_x
.
template
data
<
T
>()
,
block_data_ptr
,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
));
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
),
}
else
{
flag_ptr
);
BNBackward
<
T
,
block
,
DataLayout
::
kNHWC
>
<<<
grid2
,
block
,
0
,
ctx
.
stream
()
>>>
(
// 3. elementwise_mul(scale, mean, inv_var, dy, dscale, dbias) => dx
BNBackward2DChannelLastStage3
<
T
,
block_size
>
<<<
grid
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_d_y
.
template
data
<
T
>(),
transformed_d_y
.
template
data
<
T
>(),
transformed_x
.
template
data
<
T
>(),
transformed_x
.
template
data
<
T
>(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_mean_data
,
d_scale
->
data
<
BatchNormParamType
<
T
>>
(),
saved_var_data
,
d_bias
->
data
<
BatchNormParamType
<
T
>>
(),
mean_ptr
,
variance_ptr
,
C
,
C
,
N
,
N
,
H
*
W
*
D
,
H
*
W
*
D
,
epsilon
,
epsilon
,
transformed_d_x
.
template
data
<
T
>(),
transformed_d_x
.
template
data
<
T
>());
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
));
}
else
{
if
(
compute_format
==
DataLayout
::
kNCHW
)
{
BNBackward
<
T
,
block
,
DataLayout
::
kNCHW
>
<<<
grid2
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_d_y
.
template
data
<
T
>(),
transformed_x
.
template
data
<
T
>(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_mean_data
,
saved_var_data
,
C
,
N
,
H
*
W
*
D
,
epsilon
,
transformed_d_x
.
template
data
<
T
>(),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
));
}
else
{
BNBackward
<
T
,
block
,
DataLayout
::
kNHWC
>
<<<
grid2
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_d_y
.
template
data
<
T
>(),
transformed_x
.
template
data
<
T
>(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_mean_data
,
saved_var_data
,
C
,
N
,
H
*
W
*
D
,
epsilon
,
transformed_d_x
.
template
data
<
T
>(),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
));
}
}
}
}
else
{
}
else
{
#if CUDNN_VERSION_MIN(7, 4, 1)
#if CUDNN_VERSION_MIN(7, 4, 1)
...
...
paddle/phi/kernels/gpu/batch_norm_kernel.cu
浏览文件 @
36f08826
...
@@ -908,7 +908,8 @@ void BatchNormKernel(const Context &ctx,
...
@@ -908,7 +908,8 @@ void BatchNormKernel(const Context &ctx,
// static_cast<void *>(saved_variance->template mutable_data<
// static_cast<void *>(saved_variance->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace()))));
// BatchNormParamType<T>>(ctx.GetPlace()))));
#else
#else
const
size_t
CUDNN_PER_ACTIVATION_THRESHOLD
=
131070
;
// const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
const
size_t
CUDNN_PER_ACTIVATION_THRESHOLD
=
10240
;
const
size_t
CUDNN_SPATIAL_THRESHOLD
=
880801
;
const
size_t
CUDNN_SPATIAL_THRESHOLD
=
880801
;
const
bool
use_native_kernel
=
const
bool
use_native_kernel
=
((
x_dims
.
size
()
==
2
&&
N
>=
CUDNN_PER_ACTIVATION_THRESHOLD
)
||
((
x_dims
.
size
()
==
2
&&
N
>=
CUDNN_PER_ACTIVATION_THRESHOLD
)
||
...
...
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
浏览文件 @
36f08826
...
@@ -323,6 +323,35 @@ class TestBatchNormChannelLast(unittest.TestCase):
...
@@ -323,6 +323,35 @@ class TestBatchNormChannelLast(unittest.TestCase):
else
:
else
:
self
.
assertEqual
(
np
.
allclose
(
y1
.
numpy
(),
y2
.
numpy
()),
True
)
self
.
assertEqual
(
np
.
allclose
(
y1
.
numpy
(),
y2
.
numpy
()),
True
)
def
test_1d_opt
(
self
):
with
fluid
.
dygraph
.
guard
():
batch_size
=
13700
channels
=
16
shape
=
(
batch_size
,
channels
)
x
=
paddle
.
randn
(
shape
)
x_4d
=
x
.
reshape
((
batch_size
,
channels
,
1
,
1
))
x
.
stop_gradient
=
False
x_4d
.
stop_gradient
=
False
bn1d
=
paddle
.
nn
.
BatchNorm1D
(
channels
)
bn2d
=
paddle
.
nn
.
BatchNorm2D
(
channels
)
y
=
bn1d
(
x
)
y2
=
bn2d
(
x_4d
)
y
.
backward
()
y2
.
backward
()
assert
np
.
allclose
(
y
.
numpy
().
flatten
(),
y2
.
numpy
().
flatten
(),
atol
=
1e-5
,
rtol
=
1e-5
)
assert
np
.
allclose
(
bn1d
.
weight
.
grad
.
numpy
().
flatten
(),
bn2d
.
weight
.
grad
.
numpy
().
flatten
(),
atol
=
1e-5
,
rtol
=
1e-5
)
class
TestBatchNormUseGlobalStats
(
unittest
.
TestCase
):
class
TestBatchNormUseGlobalStats
(
unittest
.
TestCase
):
...
...
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