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bbe0fdb0
编写于
6月 21, 2022
作者:
Y
Yao Zihang
提交者:
GitHub
6月 21, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix cudnn error for BatchNorm1D kernel (#43072)
上级
b2912939
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
305 addition
and
197 deletion
+305
-197
paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
+115
-79
paddle/phi/kernels/gpu/batch_norm_kernel.cu
paddle/phi/kernels/gpu/batch_norm_kernel.cu
+153
-113
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
+37
-5
未找到文件。
paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
浏览文件 @
bbe0fdb0
...
...
@@ -542,70 +542,60 @@ void BatchNormGradRawKernel(const Context &ctx,
// This branch calls CUDNN APIs
if
(
d_x
&&
d_scale
&&
d_bias
)
{
bool
called
=
false
;
#if CUDNN_VERSION_MIN(7, 4, 1)
called
=
true
;
size_t
workspace_size
=
0
;
void
*
workspace_ptr
=
nullptr
;
DenseTensor
workspace_tensor
;
auto
reserve_space_size
=
reserve_space
->
memory_size
();
// --------------- cudnn batchnorm workspace ---------------
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnGetBatchNormalizationBackwardExWorkspaceSize
(
/*handle=*/
ctx
.
cudnn_handle
(),
/*mode=*/
mode_
,
/*bnIps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*xDesc=*/
data_desc_
,
/*yDesc=*/
data_desc_
,
/*dyDesc=*/
data_desc_
,
/*dzDesc=*/
nullptr
,
/*dxDesc=*/
data_desc_
,
/*bnScaleBiasMeanVarDesc=*/
bn_param_desc_
,
/*activationDesc=*/
nullptr
,
/*sizeInBytes=*/
&
workspace_size
));
workspace_tensor
.
Resize
({
static_cast
<
int64_t
>
(
workspace_size
)});
workspace_ptr
=
static_cast
<
void
*>
(
ctx
.
template
Alloc
<
uint8_t
>(
&
workspace_tensor
));
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnBatchNormalizationBackwardEx
(
/*handle=*/
ctx
.
cudnn_handle
(),
/*mode=*/
mode_
,
/*bnOps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*alphaDataDiff=*/
CudnnDataType
<
T
>::
kOne
(),
/*betaDataDiff=*/
CudnnDataType
<
T
>::
kZero
(),
/*alphaParamDiff=*/
CudnnDataType
<
T
>::
kOne
(),
/*betaParamDiff=*/
CudnnDataType
<
T
>::
kZero
(),
/*xDesc=*/
data_desc_
,
/*xData=*/
transformed_x
.
template
data
<
T
>(),
/*yDesc=*/
nullptr
,
/*yData=*/
nullptr
,
/*dyDesc=*/
data_desc_
,
/*dyData=*/
transformed_d_y
.
template
data
<
T
>(),
/*dzDesc=*/
nullptr
,
/*dzData=*/
nullptr
,
/*dxDesc=*/
data_desc_
,
/*dxData=*/
ctx
.
template
Alloc
<
T
>(
&
transformed_d_x
),
/*dBnScaleBiasDesc=*/
bn_param_desc_
,
/*bnScaleData=*/
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
/*bnBiasData=*/
nullptr
,
/*dBnScaleData=*/
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
/*dBnBiasData=*/
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
),
/*epsilon=*/
epsilon
,
/*savedMean=*/
saved_mean_data
,
/*savedInvVariance=*/
saved_var_data
,
/*activationDesc=*/
nullptr
,
/*workspace=*/
workspace_ptr
,
/*workSpaceSizeInBytes=*/
workspace_size
,
/*reserveSpace=*/
const_cast
<
uint8_t
*>
(
reserve_space
->
template
data
<
uint8_t
>()),
/*reserveSpaceSizeInBytes=*/
reserve_space_size
));
#endif // CUDNN_VERSION_MIN(7, 4, 1)
if
(
!
called
)
{
#ifdef PADDLE_WITH_HIP
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
));
}
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenBatchNormalizationBackward(
// dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
// CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
// CudnnDataType<T>::kZero(), data_desc_,
// transformed_x.template data<T>(), data_desc_,
// transformed_d_y.template data<T>(), data_desc_,
// transformed_d_x.template mutable_data<T>(ctx.GetPlace()),
// bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
// d_scale->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace()),
// d_bias->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace()),
// epsilon, saved_mean_data, saved_var_data));
#else
// CUDNN PER_ACTIVATION mode only support small batch size
const
size_t
CUDNN_PER_ACTIVATION_THRESHOLD
=
131070
;
const
bool
use_native_kernel
=
(
x_dims
.
size
()
==
2
&&
N
>=
CUDNN_PER_ACTIVATION_THRESHOLD
);
if
(
use_native_kernel
)
{
if
(
compute_format
==
DataLayout
::
kNCHW
)
{
BNBackward
<
T
,
block
,
DataLayout
::
kNCHW
>
<<<
grid2
,
block
,
0
,
ctx
.
stream
()
>>>
(
...
...
@@ -637,22 +627,67 @@ void BatchNormGradRawKernel(const Context &ctx,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
));
}
}
else
{
#if CUDNN_VERSION_MIN(7, 4, 1)
size_t
workspace_size
=
0
;
void
*
workspace_ptr
=
nullptr
;
DenseTensor
workspace_tensor
;
auto
reserve_space_size
=
reserve_space
->
memory_size
();
// --------------- cudnn batchnorm workspace ---------------
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnGetBatchNormalizationBackwardExWorkspaceSize
(
/*handle=*/
ctx
.
cudnn_handle
(),
/*mode=*/
mode_
,
/*bnIps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*xDesc=*/
data_desc_
,
/*yDesc=*/
data_desc_
,
/*dyDesc=*/
data_desc_
,
/*dzDesc=*/
nullptr
,
/*dxDesc=*/
data_desc_
,
/*bnScaleBiasMeanVarDesc=*/
bn_param_desc_
,
/*activationDesc=*/
nullptr
,
/*sizeInBytes=*/
&
workspace_size
));
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenBatchNormalizationBackward(
// dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
// CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
// CudnnDataType<T>::kZero(), data_desc_,
// transformed_x.template data<T>(), data_desc_,
// transformed_d_y.template data<T>(), data_desc_,
// transformed_d_x.template mutable_data<T>(ctx.GetPlace()),
// bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
// d_scale->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace()),
// d_bias->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace()),
// epsilon, saved_mean_data, saved_var_data));
workspace_tensor
.
Resize
({
static_cast
<
int64_t
>
(
workspace_size
)});
workspace_ptr
=
static_cast
<
void
*>
(
ctx
.
template
Alloc
<
uint8_t
>(
&
workspace_tensor
));
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnBatchNormalizationBackwardEx
(
/*handle=*/
ctx
.
cudnn_handle
(),
/*mode=*/
mode_
,
/*bnOps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*alphaDataDiff=*/
CudnnDataType
<
T
>::
kOne
(),
/*betaDataDiff=*/
CudnnDataType
<
T
>::
kZero
(),
/*alphaParamDiff=*/
CudnnDataType
<
T
>::
kOne
(),
/*betaParamDiff=*/
CudnnDataType
<
T
>::
kZero
(),
/*xDesc=*/
data_desc_
,
/*xData=*/
transformed_x
.
template
data
<
T
>(),
/*yDesc=*/
nullptr
,
/*yData=*/
nullptr
,
/*dyDesc=*/
data_desc_
,
/*dyData=*/
transformed_d_y
.
template
data
<
T
>(),
/*dzDesc=*/
nullptr
,
/*dzData=*/
nullptr
,
/*dxDesc=*/
data_desc_
,
/*dxData=*/
ctx
.
template
Alloc
<
T
>(
&
transformed_d_x
),
/*dBnScaleBiasDesc=*/
bn_param_desc_
,
/*bnScaleData=*/
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
/*bnBiasData=*/
nullptr
,
/*dBnScaleData=*/
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_scale
),
/*dBnBiasData=*/
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
d_bias
),
/*epsilon=*/
epsilon
,
/*savedMean=*/
saved_mean_data
,
/*savedInvVariance=*/
saved_var_data
,
/*activationDesc=*/
nullptr
,
/*workspace=*/
workspace_ptr
,
/*workSpaceSizeInBytes=*/
workspace_size
,
/*reserveSpace=*/
const_cast
<
uint8_t
*>
(
reserve_space
->
template
data
<
uint8_t
>()),
/*reserveSpaceSizeInBytes=*/
reserve_space_size
));
#else
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnBatchNormalizationBackward
(
...
...
@@ -675,8 +710,9 @@ void BatchNormGradRawKernel(const Context &ctx,
epsilon
,
saved_mean_data
,
saved_var_data
));
#endif
#endif
// CUDNN_VERSION_MIN(7, 4, 1)
}
#endif
if
(
data_layout
==
DataLayout
::
kNHWC
&&
compute_format
==
DataLayout
::
kNCHW
)
{
...
...
paddle/phi/kernels/gpu/batch_norm_kernel.cu
浏览文件 @
bbe0fdb0
...
...
@@ -446,90 +446,81 @@ void BatchNormKernel(const Context &ctx,
paddle
::
framework
::
TensorCopy
(
x
,
ctx
.
GetPlace
(),
y
);
}
else
{
double
this_factor
=
1.
-
momentum
;
bool
called
=
false
;
#if CUDNN_VERSION_MIN(7, 4, 1)
called
=
true
;
size_t
workspace_size
=
0
;
size_t
reserve_space_size
=
0
;
void
*
reserve_space_ptr
=
nullptr
;
void
*
workspace_ptr
=
nullptr
;
DenseTensor
workspace_tensor
;
DenseTensor
reserve_space_tensor
;
// Create reserve space and workspace for batch norm.
// Create tensor for each batchnorm op, it will be used in the
// backward. Thus this tensor shouldn't be temp.
// auto *reserve_space = ctx.Output<Tensor>("ReserveSpace");
if
(
reserve_space
==
nullptr
)
{
reserve_space
=
&
reserve_space_tensor
;
}
PADDLE_ENFORCE_NOT_NULL
(
reserve_space
,
phi
::
errors
::
NotFound
(
"The argument ReserveSpace of batch_norm op is not found."
));
// --------------- cudnn batchnorm workspace ---------------
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize
(
/*handle=*/
handle
,
/*mode=*/
mode_
,
/*bnIps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*xDesc=*/
data_desc_
,
/*zDesc=*/
nullptr
,
/*yDesc=*/
data_desc_
,
/*bnScaleBiasMeanVarDesc=*/
bn_param_desc_
,
/*activationDesc=*/
nullptr
,
/*sizeInBytes=*/
&
workspace_size
));
// -------------- cudnn batchnorm reserve space --------------
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnGetBatchNormalizationTrainingExReserveSpaceSize
(
/*handle=*/
handle
,
/*mode=*/
mode_
,
/*bnOps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*activationDesc=*/
nullptr
,
/*xDesc=*/
data_desc_
,
/*sizeInBytes=*/
&
reserve_space_size
));
reserve_space
->
Resize
({
static_cast
<
int64_t
>
(
reserve_space_size
)});
reserve_space_ptr
=
static_cast
<
void
*>
(
ctx
.
template
Alloc
<
uint8_t
>(
reserve_space
));
workspace_tensor
.
Resize
({
static_cast
<
int64_t
>
(
workspace_size
)});
workspace_ptr
=
static_cast
<
void
*>
(
ctx
.
template
Alloc
<
uint8_t
>(
&
workspace_tensor
));
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnBatchNormalizationForwardTrainingEx
(
handle
,
mode_
,
CUDNN_BATCHNORM_OPS_BN
,
CudnnDataType
<
T
>::
kOne
(),
CudnnDataType
<
T
>::
kZero
(),
data_desc_
,
transformed_x
.
template
data
<
T
>(),
nullptr
,
nullptr
,
data_desc_
,
transformed_y
.
template
data
<
T
>(),
bn_param_desc_
,
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
bias
.
template
data
<
BatchNormParamType
<
T
>
>
(),
this_factor
,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
mean_out
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
variance_out
),
epsilon
,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_mean
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_variance
),
nullptr
,
workspace_ptr
,
workspace_size
,
reserve_space_ptr
,
reserve_space_size
));
#endif // CUDNN_VERSION_MIN(7, 4, 1)
if
(
!
called
)
{
#ifdef PADDLE_WITH_HIP
const
int
num
=
transformed_x
.
numel
();
const
int
block
=
256
;
const
int
num
=
transformed_x
.
numel
();
const
int
block
=
256
;
const
int
max_threads
=
ctx
.
GetMaxPhysicalThreadCount
();
const
int
max_blocks
=
std
::
max
(
max_threads
/
block
,
1
);
const
int
grid
=
std
::
min
(
C
,
max_blocks
);
if
(
compute_format
==
DataLayout
::
kNCHW
)
{
BNForwardTraining
<
T
,
block
,
DataLayout
::
kNCHW
>
<<<
grid
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_x
.
template
data
<
T
>(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
bias
.
template
data
<
BatchNormParamType
<
T
>
>
(),
C
,
N
,
H
*
W
*
D
,
epsilon
,
this_factor
,
transformed_y
.
template
data
<
T
>(),
mean_out
->
template
data
<
BatchNormParamType
<
T
>
>
(),
variance_out
->
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_mean
->
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_variance
->
template
data
<
BatchNormParamType
<
T
>
>
());
}
else
{
BNForwardTraining
<
T
,
block
,
DataLayout
::
kNHWC
>
<<<
grid
,
block
,
0
,
ctx
.
stream
()
>>>
(
transformed_x
.
template
data
<
T
>(),
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
bias
.
template
data
<
BatchNormParamType
<
T
>
>
(),
C
,
N
,
H
*
W
*
D
,
epsilon
,
this_factor
,
transformed_y
.
template
data
<
T
>(),
mean_out
->
template
data
<
BatchNormParamType
<
T
>
>
(),
variance_out
->
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_mean
->
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_variance
->
template
data
<
BatchNormParamType
<
T
>
>
());
}
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenBatchNormalizationForwardTraining(
// handle, mode_, const_cast<void *>(static_cast<const void *>(
// CudnnDataType<T>::kOne())),
// const_cast<void *>(
// static_cast<const void *>(CudnnDataType<T>::kZero())),
// data_desc_,
// static_cast<const void *>(transformed_x.template data<T>()),
// data_desc_,
// static_cast<void *>(
// transformed_y.template mutable_data<T>(ctx.GetPlace())),
// bn_param_desc_,
// const_cast<void *>(static_cast<const void *>(
// scale->template data<BatchNormParamType<T>>())),
// const_cast<void *>(static_cast<const void *>(
// bias->template data<BatchNormParamType<T>>())),
// this_factor,
// static_cast<void *>(
// mean_out->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace())),
// static_cast<void *>(variance_out->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace())),
// epsilon,
// static_cast<void *>(
// saved_mean->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace())),
// static_cast<void *>(saved_variance->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace()))));
#else
// CUDNN PER_ACTIVATION mode only support small batch size
const
size_t
CUDNN_PER_ACTIVATION_THRESHOLD
=
131070
;
const
bool
use_native_kernel
=
(
x_dims
.
size
()
==
2
&&
N
>=
CUDNN_PER_ACTIVATION_THRESHOLD
);
if
(
use_native_kernel
)
{
const
int
block
=
512
;
const
int
max_threads
=
ctx
.
GetMaxPhysicalThreadCount
();
const
int
max_blocks
=
std
::
max
(
max_threads
/
block
,
1
);
const
int
grid
=
std
::
min
(
C
,
max_blocks
);
...
...
@@ -566,35 +557,83 @@ void BatchNormKernel(const Context &ctx,
saved_mean
->
template
data
<
BatchNormParamType
<
T
>
>
(),
saved_variance
->
template
data
<
BatchNormParamType
<
T
>
>
());
}
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenBatchNormalizationForwardTraining(
// handle, mode_, const_cast<void *>(static_cast<const void *>(
// CudnnDataType<T>::kOne())),
// const_cast<void *>(
// static_cast<const void *>(CudnnDataType<T>::kZero())),
// data_desc_,
// static_cast<const void *>(transformed_x.template data<T>()),
// data_desc_,
// static_cast<void *>(
// transformed_y.template mutable_data<T>(ctx.GetPlace())),
// bn_param_desc_,
// const_cast<void *>(static_cast<const void *>(
// scale->template data<BatchNormParamType<T>>())),
// const_cast<void *>(static_cast<const void *>(
// bias->template data<BatchNormParamType<T>>())),
// this_factor,
// static_cast<void *>(
// mean_out->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace())),
// static_cast<void *>(variance_out->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace())),
// epsilon,
// static_cast<void *>(
// saved_mean->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace())),
// static_cast<void *>(saved_variance->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace()))));
}
else
{
#if CUDNN_VERSION_MIN(7, 4, 1)
size_t
workspace_size
=
0
;
size_t
reserve_space_size
=
0
;
void
*
reserve_space_ptr
=
nullptr
;
void
*
workspace_ptr
=
nullptr
;
DenseTensor
workspace_tensor
;
DenseTensor
reserve_space_tensor
;
// Create reserve space and workspace for batch norm.
// Create tensor for each batchnorm op, it will be used in the
// backward. Thus this tensor shouldn't be temp.
// auto *reserve_space = ctx.Output<Tensor>("ReserveSpace");
if
(
reserve_space
==
nullptr
)
{
reserve_space
=
&
reserve_space_tensor
;
}
PADDLE_ENFORCE_NOT_NULL
(
reserve_space
,
phi
::
errors
::
NotFound
(
"The argument ReserveSpace of batch_norm op is not found."
));
// --------------- cudnn batchnorm workspace ---------------
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize
(
/*handle=*/
handle
,
/*mode=*/
mode_
,
/*bnIps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*xDesc=*/
data_desc_
,
/*zDesc=*/
nullptr
,
/*yDesc=*/
data_desc_
,
/*bnScaleBiasMeanVarDesc=*/
bn_param_desc_
,
/*activationDesc=*/
nullptr
,
/*sizeInBytes=*/
&
workspace_size
));
// -------------- cudnn batchnorm reserve space --------------
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnGetBatchNormalizationTrainingExReserveSpaceSize
(
/*handle=*/
handle
,
/*mode=*/
mode_
,
/*bnOps=*/
CUDNN_BATCHNORM_OPS_BN
,
/*activationDesc=*/
nullptr
,
/*xDesc=*/
data_desc_
,
/*sizeInBytes=*/
&
reserve_space_size
));
reserve_space
->
Resize
({
static_cast
<
int64_t
>
(
reserve_space_size
)});
reserve_space_ptr
=
static_cast
<
void
*>
(
ctx
.
template
Alloc
<
uint8_t
>(
reserve_space
));
workspace_tensor
.
Resize
({
static_cast
<
int64_t
>
(
workspace_size
)});
workspace_ptr
=
static_cast
<
void
*>
(
ctx
.
template
Alloc
<
uint8_t
>(
&
workspace_tensor
));
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnBatchNormalizationForwardTrainingEx
(
handle
,
mode_
,
CUDNN_BATCHNORM_OPS_BN
,
CudnnDataType
<
T
>::
kOne
(),
CudnnDataType
<
T
>::
kZero
(),
data_desc_
,
transformed_x
.
template
data
<
T
>(),
nullptr
,
nullptr
,
data_desc_
,
transformed_y
.
template
data
<
T
>(),
bn_param_desc_
,
scale
.
template
data
<
BatchNormParamType
<
T
>
>
(),
bias
.
template
data
<
BatchNormParamType
<
T
>
>
(),
this_factor
,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
mean_out
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
variance_out
),
epsilon
,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_mean
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_variance
),
nullptr
,
workspace_ptr
,
workspace_size
,
reserve_space_ptr
,
reserve_space_size
));
#else
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
cudnnBatchNormalizationForwardTraining
(
...
...
@@ -615,8 +654,9 @@ void BatchNormKernel(const Context &ctx,
epsilon
,
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_mean
),
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_variance
)));
#endif
#endif
// CUDNN_VERSION_MIN(7, 4, 1)
}
#endif
}
}
...
...
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
浏览文件 @
bbe0fdb0
...
...
@@ -110,11 +110,43 @@ class TestBatchNorm(unittest.TestCase):
y
.
backward
()
return
y
.
numpy
(),
x1
.
gradient
()
x
=
np
.
random
.
randn
(
*
shape
).
astype
(
"float32"
)
y1
,
g1
=
compute_v1
(
x
)
y2
,
g2
=
compute_v2
(
x
)
self
.
assertTrue
(
np
.
allclose
(
g1
,
g2
))
self
.
assertTrue
(
np
.
allclose
(
y1
,
y2
))
x
=
np
.
random
.
randn
(
*
shape
).
astype
(
"float32"
)
y1
,
g1
=
compute_v1
(
x
)
y2
,
g2
=
compute_v2
(
x
)
self
.
assertTrue
(
np
.
allclose
(
g1
,
g2
))
self
.
assertTrue
(
np
.
allclose
(
y1
,
y2
))
def
test_eager_api_1d
(
self
):
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
p
in
places
:
shape
=
[
200000
,
4
]
def
compute_v1
(
x
):
with
fluid
.
dygraph
.
guard
(
p
):
bn
=
fluid
.
dygraph
.
BatchNorm
(
shape
[
1
])
x1
=
paddle
.
to_tensor
(
x
)
x1
.
stop_gradient
=
False
y
=
bn
(
x1
)
y
.
backward
()
return
y
.
numpy
(),
x1
.
gradient
()
def
compute_v2
(
x
):
with
fluid
.
dygraph
.
guard
(
p
):
with
_test_eager_guard
():
bn
=
paddle
.
nn
.
BatchNorm1D
(
shape
[
1
])
x1
=
paddle
.
to_tensor
(
x
)
x1
.
stop_gradient
=
False
y
=
bn
(
x1
)
y
.
backward
()
return
y
.
numpy
(),
x1
.
gradient
()
x
=
np
.
random
.
randn
(
*
shape
).
astype
(
"float32"
)
y1
,
g1
=
compute_v1
(
x
)
y2
,
g2
=
compute_v2
(
x
)
self
.
assertTrue
(
np
.
allclose
(
g1
,
g2
))
self
.
assertTrue
(
np
.
allclose
(
y1
,
y2
))
def
test_dygraph
(
self
):
places
=
[
fluid
.
CPUPlace
()]
...
...
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