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体验新版 GitCode,发现更多精彩内容 >>
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提交
c99c70cb
编写于
7月 20, 2022
作者:
L
lyq
提交者:
GitHub
7月 20, 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
[Phi] migrate sync_batch_norm to phi (#44369)
上级
b8d106e1
变更
11
展开全部
隐藏空白更改
内联
并排
Showing
11 changed file
with
1027 addition
and
186 deletion
+1027
-186
paddle/fluid/operators/inplace_abn_op.cu
paddle/fluid/operators/inplace_abn_op.cu
+83
-47
paddle/fluid/operators/sync_batch_norm_op.cu
paddle/fluid/operators/sync_batch_norm_op.cu
+0
-137
paddle/phi/api/yaml/legacy_api.yaml
paddle/phi/api/yaml/legacy_api.yaml
+10
-0
paddle/phi/api/yaml/legacy_backward.yaml
paddle/phi/api/yaml/legacy_backward.yaml
+12
-0
paddle/phi/kernels/gpu/sync_batch_norm_grad_kernel.cu
paddle/phi/kernels/gpu/sync_batch_norm_grad_kernel.cu
+75
-0
paddle/phi/kernels/gpu/sync_batch_norm_kernel.cu
paddle/phi/kernels/gpu/sync_batch_norm_kernel.cu
+190
-0
paddle/phi/kernels/gpu/sync_batch_norm_utils.h
paddle/phi/kernels/gpu/sync_batch_norm_utils.h
+493
-0
paddle/phi/kernels/sync_batch_norm_grad_kernel.h
paddle/phi/kernels/sync_batch_norm_grad_kernel.h
+45
-0
paddle/phi/kernels/sync_batch_norm_kernel.h
paddle/phi/kernels/sync_batch_norm_kernel.h
+43
-0
paddle/phi/ops/compat/sync_batch_norm_sig.cc
paddle/phi/ops/compat/sync_batch_norm_sig.cc
+67
-0
python/paddle/nn/layer/norm.py
python/paddle/nn/layer/norm.py
+9
-2
未找到文件。
paddle/fluid/operators/inplace_abn_op.cu
浏览文件 @
c99c70cb
...
...
@@ -13,17 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/inplace_abn_op.h"
#include <iostream>
#include "paddle/fluid/operators/batch_norm_op.h"
#include "paddle/fluid/operators/sync_batch_norm_op.cu.h"
#include "paddle/phi/kernels/batch_norm_grad_kernel.h"
#include "paddle/phi/kernels/batch_norm_kernel.h"
#include "paddle/phi/kernels/gpu/sync_batch_norm_utils.h"
#include "paddle/phi/kernels/sync_batch_norm_grad_kernel.h"
#include "paddle/phi/kernels/sync_batch_norm_kernel.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
InplaceABNKernel
:
public
paddle
::
operators
::
SyncBatchNormKernel
<
DeviceContext
,
T
>
{
class
InplaceABNKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
...
...
@@ -36,29 +38,49 @@ class InplaceABNKernel
GetInplaceABNActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"activation"
));
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"Mean"
);
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"Variance"
);
auto
momentum
=
ctx
.
Attr
<
float
>
(
"momentum"
);
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
auto
data_layout
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
auto
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
use_global_stats
=
ctx
.
Attr
<
bool
>
(
"use_global_stats"
);
auto
trainable_statistics
=
ctx
.
Attr
<
bool
>
(
"trainable_statistics"
);
auto
fuse_with_relu
=
ctx
.
Attr
<
bool
>
(
"fuse_with_relu"
);
auto
*
mean_out
=
ctx
.
Output
<
Tensor
>
(
"MeanOut"
);
auto
*
variance_out
=
ctx
.
Output
<
Tensor
>
(
"VarianceOut"
);
auto
*
saved_mean
=
ctx
.
Output
<
Tensor
>
(
"SavedMean"
);
auto
*
saved_variance
=
ctx
.
Output
<
Tensor
>
(
"SavedVariance"
);
auto
*
reserve_space
=
ctx
.
Output
<
Tensor
>
(
"ReserveSpace"
);
if
(
ctx
.
Attr
<
bool
>
(
"use_sync_bn"
))
{
SyncBatchNormKernel
<
DeviceContext
,
T
>::
Compute
(
ctx
);
auto
&
dev_ctx
=
ctx
.
device_context
<
DeviceContext
>
();
phi
::
SyncBatchNormKernel
<
T
>
(
static_cast
<
const
typename
framework
::
ConvertToPhiContext
<
DeviceContext
>::
TYPE
&>
(
dev_ctx
),
*
x
,
*
scale
,
*
bias
,
*
mean
,
*
variance
,
momentum
,
epsilon
,
data_layout
,
is_test
,
use_global_stats
,
trainable_statistics
,
fuse_with_relu
,
y
,
mean_out
,
variance_out
,
saved_mean
,
saved_variance
,
reserve_space
);
}
else
{
// BatchNormKernel<DeviceContext, T>::Compute(ctx);
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"Mean"
);
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"Variance"
);
auto
momentum
=
ctx
.
Attr
<
float
>
(
"momentum"
);
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
auto
data_layout
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
auto
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
use_global_stats
=
ctx
.
Attr
<
bool
>
(
"use_global_stats"
);
auto
trainable_statistics
=
ctx
.
Attr
<
bool
>
(
"trainable_statistics"
);
auto
fuse_with_relu
=
ctx
.
Attr
<
bool
>
(
"fuse_with_relu"
);
auto
*
mean_out
=
ctx
.
Output
<
Tensor
>
(
"MeanOut"
);
auto
*
variance_out
=
ctx
.
Output
<
Tensor
>
(
"VarianceOut"
);
auto
*
saved_mean
=
ctx
.
Output
<
Tensor
>
(
"SavedMean"
);
auto
*
saved_variance
=
ctx
.
Output
<
Tensor
>
(
"SavedVariance"
);
auto
*
reserve_space
=
ctx
.
Output
<
Tensor
>
(
"ReserveSpace"
);
auto
&
dev_ctx
=
ctx
.
device_context
<
DeviceContext
>
();
phi
::
BatchNormKernel
<
T
>
(
static_cast
<
const
typename
framework
::
ConvertToPhiContext
<
...
...
@@ -92,8 +114,7 @@ class InplaceABNKernel
// Deriving the Gradient for the Backward Pass of Batch Normalization
// https://kevinzakka.github.io/2016/09/14/batch_normalization/
template
<
typename
DeviceContext
,
typename
T
>
class
InplaceABNGradKernel
:
public
paddle
::
operators
::
SyncBatchNormGradKernel
<
DeviceContext
,
T
>
{
class
InplaceABNGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
...
...
@@ -115,29 +136,44 @@ class InplaceABNGradKernel
InplaceABNActivation
<
DeviceContext
,
T
>
functor
;
functor
.
GradCompute
(
ctx
,
activation
,
place
,
cur_y
,
cur_y
,
cur_dy
,
cur_dy
);
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
saved_mean
=
ctx
.
Input
<
Tensor
>
(
"SavedMean"
);
auto
*
saved_variance
=
ctx
.
Input
<
Tensor
>
(
"SavedVariance"
);
auto
momentum
=
ctx
.
Attr
<
float
>
(
"momentum"
);
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
auto
data_layout
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
auto
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
use_global_stats
=
ctx
.
Attr
<
bool
>
(
"use_global_stats"
);
auto
trainable_statistics
=
ctx
.
Attr
<
bool
>
(
"trainable_statistics"
);
auto
fuse_with_relu
=
ctx
.
Attr
<
bool
>
(
"fuse_with_relu"
);
auto
*
scale_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
bias_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
auto
*
reserve_space
=
ctx
.
Input
<
Tensor
>
(
"ReserveSpace"
);
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"ReserveSpace"
);
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"ReserveSpace"
);
if
(
ctx
.
Attr
<
bool
>
(
"use_sync_bn"
))
{
SyncBatchNormGradKernel
<
DeviceContext
,
T
>::
Compute
(
ctx
);
auto
&
dev_ctx
=
ctx
.
device_context
<
DeviceContext
>
();
phi
::
SyncBatchNormGradFunctor
<
T
>
(
static_cast
<
const
typename
framework
::
ConvertToPhiContext
<
DeviceContext
>::
TYPE
&>
(
dev_ctx
),
nullptr
,
y
,
*
scale
,
*
bias
,
*
saved_mean
,
*
saved_variance
,
*
d_y
,
epsilon
,
data_layout
,
d_x
,
scale_grad
,
bias_grad
);
}
else
{
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
saved_mean
=
ctx
.
Input
<
Tensor
>
(
"SavedMean"
);
auto
*
saved_variance
=
ctx
.
Input
<
Tensor
>
(
"SavedVariance"
);
auto
momentum
=
ctx
.
Attr
<
float
>
(
"momentum"
);
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
auto
data_layout
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
auto
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
use_global_stats
=
ctx
.
Attr
<
bool
>
(
"use_global_stats"
);
auto
trainable_statistics
=
ctx
.
Attr
<
bool
>
(
"trainable_statistics"
);
auto
fuse_with_relu
=
ctx
.
Attr
<
bool
>
(
"fuse_with_relu"
);
auto
*
scale_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
bias_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
auto
*
reserve_space
=
ctx
.
Input
<
Tensor
>
(
"ReserveSpace"
);
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"ReserveSpace"
);
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"ReserveSpace"
);
paddle
::
optional
<
Tensor
>
space_opt
;
paddle
::
optional
<
Tensor
>
mean_opt
;
paddle
::
optional
<
Tensor
>
variance_opt
;
...
...
paddle/fluid/operators/sync_batch_norm_op.cu
已删除
100644 → 0
浏览文件 @
b8d106e1
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sync_batch_norm_op.cu.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
SyncBatchNormKernel
<
platform
::
CUDADeviceContext
,
T
>
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
double
epsilon
=
static_cast
<
double
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
));
const
float
momentum
=
ctx
.
Attr
<
float
>
(
"momentum"
);
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
const
std
::
string
layout_str
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
const
DataLayout
layout
=
framework
::
StringToDataLayout
(
layout_str
);
const
bool
use_global_stats
=
ctx
.
Attr
<
bool
>
(
"use_global_stats"
);
const
bool
trainable_stats
=
ctx
.
Attr
<
bool
>
(
"trainable_statistics"
);
PADDLE_ENFORCE_EQ
(
use_global_stats
,
false
,
platform
::
errors
::
InvalidArgument
(
"sync_batch_norm doesn't support "
"to set use_global_stats True. Please use batch_norm "
"in this case."
));
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
const
auto
*
est_mean
=
ctx
.
Input
<
Tensor
>
(
"Mean"
);
const
auto
*
est_var
=
ctx
.
Input
<
Tensor
>
(
"Variance"
);
// moving mean/variance
auto
*
mean_out
=
ctx
.
Output
<
Tensor
>
(
"MeanOut"
);
auto
*
variance_out
=
ctx
.
Output
<
Tensor
>
(
"VarianceOut"
);
auto
*
saved_mean
=
ctx
.
Output
<
Tensor
>
(
"SavedMean"
);
auto
*
saved_inv_variance
=
ctx
.
Output
<
Tensor
>
(
"SavedVariance"
);
bool
test_mode
=
is_test
&&
(
!
trainable_stats
);
SyncBatchNormFunctor
<
platform
::
CUDADeviceContext
,
T
>
(
ctx
,
layout
,
x
,
y
,
est_mean
,
est_var
,
mean_out
,
variance_out
,
saved_mean
,
saved_inv_variance
,
epsilon
,
momentum
,
test_mode
,
use_global_stats
);
}
};
template
<
typename
T
>
class
SyncBatchNormGradKernel
<
platform
::
CUDADeviceContext
,
T
>
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
true
,
platform
::
errors
::
InvalidArgument
(
"It must use CUDAPlace."
));
double
epsilon
=
static_cast
<
double
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
));
const
std
::
string
layout_str
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
const
DataLayout
layout
=
framework
::
StringToDataLayout
(
layout_str
);
const
auto
*
d_y
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
// init output
auto
*
d_x
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
d_scale
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
d_bias
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
const
auto
*
saved_mean
=
ctx
.
Input
<
Tensor
>
(
"SavedMean"
);
const
auto
*
saved_inv_var
=
ctx
.
Input
<
Tensor
>
(
"SavedVariance"
);
SyncBatchNormGradFunctor
<
platform
::
CUDADeviceContext
,
T
>
(
ctx
,
layout
,
scale
,
bias
,
d_x
,
d_y
,
d_scale
,
d_bias
,
saved_mean
,
saved_inv_var
,
epsilon
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
#ifdef PADDLE_WITH_HIP
// MIOPEN do not support double
REGISTER_OP_CUDA_KERNEL
(
sync_batch_norm
,
ops
::
SyncBatchNormKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
SyncBatchNormKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
REGISTER_OP_CUDA_KERNEL
(
sync_batch_norm_grad
,
ops
::
SyncBatchNormGradKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
SyncBatchNormGradKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
#else
REGISTER_OP_CUDA_KERNEL
(
sync_batch_norm
,
ops
::
SyncBatchNormKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
SyncBatchNormKernel
<
plat
::
CUDADeviceContext
,
double
>
,
ops
::
SyncBatchNormKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
REGISTER_OP_CUDA_KERNEL
(
sync_batch_norm_grad
,
ops
::
SyncBatchNormGradKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
SyncBatchNormGradKernel
<
plat
::
CUDADeviceContext
,
double
>
,
ops
::
SyncBatchNormGradKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
#endif
// clang-format on
paddle/phi/api/yaml/legacy_api.yaml
浏览文件 @
c99c70cb
...
...
@@ -2075,6 +2075,16 @@
func
:
swish
backward
:
swish_grad
# sync_batch_norm
-
api
:
sync_batch_norm
args
:
(Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
output
:
Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
infer_meta
:
func
:
BatchNormInferMeta
kernel
:
func
:
sync_batch_norm
backward
:
sync_batch_norm_grad
# take_along_axis
-
api
:
take_along_axis
args
:
(Tensor x, Tensor index, int axis)
...
...
paddle/phi/api/yaml/legacy_backward.yaml
浏览文件 @
c99c70cb
...
...
@@ -2085,6 +2085,18 @@
func
:
swish_grad
inplace
:
(out_grad -> x_grad)
-
backward_api
:
sync_batch_norm_grad
forward
:
sync_batch_norm (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
args
:
(Tensor x, Tensor scale, Tensor bias, Tensor mean_out, Tensor variance_out, Tensor saved_mean, Tensor saved_variance, Tensor reserve_space, Tensor out_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
output
:
Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
infer_meta
:
func
:
GeneralTernaryGradInferMeta
param
:
[
x
,
scale
,
bias
]
kernel
:
func
:
sync_batch_norm_grad
data_type
:
out_grad
optional
:
mean_out, variance_out, reserve_space
-
backward_api
:
take_along_axis_grad
forward
:
take_along_axis (Tensor x, Tensor index, int axis) -> Tensor(out)
args
:
(Tensor x, Tensor index, Tensor out_grad, int axis)
...
...
paddle/phi/kernels/gpu/sync_batch_norm_grad_kernel.cu
0 → 100644
浏览文件 @
c99c70cb
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/sync_batch_norm_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/sync_batch_norm_utils.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
SyncBatchNormGradKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
scale
,
const
DenseTensor
&
bias
,
const
paddle
::
optional
<
DenseTensor
>&
mean
,
const
paddle
::
optional
<
DenseTensor
>&
variance
,
const
DenseTensor
&
saved_mean
,
const
DenseTensor
&
saved_variance
,
const
paddle
::
optional
<
DenseTensor
>&
reserve_space
,
const
DenseTensor
&
y_grad
,
float
momentum
,
float
epsilon_f
,
const
std
::
string
&
data_layout_str
,
bool
is_test
,
bool
use_global_stats
,
bool
trainable_statistics
,
bool
fuse_with_relu
,
DenseTensor
*
x_grad
,
DenseTensor
*
scale_grad
,
DenseTensor
*
bias_grad
)
{
SyncBatchNormGradFunctor
<
T
,
Context
>
(
ctx
,
&
x
,
nullptr
,
scale
,
bias
,
saved_mean
,
saved_variance
,
y_grad
,
epsilon_f
,
data_layout_str
,
x_grad
,
scale_grad
,
bias_grad
);
}
}
// namespace phi
#ifdef PADDLE_WITH_HIP
PD_REGISTER_KERNEL
(
sync_batch_norm_grad
,
GPU
,
ALL_LAYOUT
,
phi
::
SyncBatchNormGradKernel
,
float
,
phi
::
dtype
::
float16
)
{}
#else
PD_REGISTER_KERNEL
(
sync_batch_norm_grad
,
GPU
,
ALL_LAYOUT
,
phi
::
SyncBatchNormGradKernel
,
float
,
double
,
phi
::
dtype
::
float16
)
{}
#endif
paddle/phi/kernels/gpu/sync_batch_norm_kernel.cu
0 → 100644
浏览文件 @
c99c70cb
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/sync_batch_norm_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/sync_batch_norm_utils.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
SyncBatchNormKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
scale
,
const
DenseTensor
&
bias
,
const
DenseTensor
&
mean
,
const
DenseTensor
&
variance
,
float
momentum
,
float
epsilon_f
,
const
std
::
string
&
data_layout_str
,
bool
is_test
,
bool
use_global_stats
,
bool
trainable_statistics
,
bool
fuse_with_relu
,
DenseTensor
*
y
,
DenseTensor
*
mean_out
,
DenseTensor
*
variance_out
,
DenseTensor
*
saved_mean
,
DenseTensor
*
saved_variance
,
DenseTensor
*
reserve_space
)
{
PADDLE_ENFORCE_EQ
(
use_global_stats
,
false
,
phi
::
errors
::
InvalidArgument
(
"sync_batch_norm doesn't support "
"to set use_global_stats True. Please use batch_norm "
"in this case."
));
double
epsilon
=
epsilon_f
;
const
bool
trainable_stats
=
trainable_statistics
;
const
DataLayout
layout
=
paddle
::
framework
::
StringToDataLayout
(
data_layout_str
);
bool
test_mode
=
is_test
&&
(
!
trainable_statistics
);
const
auto
&
x_dims
=
x
.
dims
();
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
phi
::
errors
::
InvalidArgument
(
"The Input dim size should be larger than 1."
));
PADDLE_ENFORCE_LE
(
x_dims
.
size
(),
5
,
phi
::
errors
::
InvalidArgument
(
"The Input dim size should be less than 6."
));
int
N
,
C
,
H
,
W
,
D
;
funcs
::
ExtractNCWHD
(
x_dims
,
layout
,
&
N
,
&
C
,
&
H
,
&
W
,
&
D
);
int
x_numel
=
x
.
numel
();
const
T
*
x_d
=
x
.
template
data
<
T
>();
const
auto
*
s_d
=
scale
.
template
data
<
BatchNormParamType
<
T
>
>
();
const
auto
*
b_d
=
bias
.
template
data
<
BatchNormParamType
<
T
>
>
();
T
*
y_d
=
ctx
.
template
Alloc
<
T
>(
y
);
const
BatchNormParamType
<
T
>
*
mean_data
=
nullptr
;
const
BatchNormParamType
<
T
>
*
var_data
=
nullptr
;
auto
stream
=
ctx
.
stream
();
const
int
block
=
512
;
int
max_threads
=
ctx
.
GetMaxPhysicalThreadCount
();
paddle
::
memory
::
AllocationPtr
alloc_ptr
{
nullptr
};
if
(
test_mode
)
{
mean_data
=
mean
.
template
data
<
BatchNormParamType
<
T
>
>
();
var_data
=
variance
.
template
data
<
BatchNormParamType
<
T
>
>
();
}
else
{
// x, x^2, 1, here 1 is used to calc device num
// device num also can be got from platform::DeviceContextPool
const
int
bytes
=
(
C
*
2
+
1
)
*
sizeof
(
BatchNormParamType
<
T
>
);
alloc_ptr
=
paddle
::
memory
::
Alloc
(
ctx
,
bytes
);
auto
*
stats
=
reinterpret_cast
<
BatchNormParamType
<
T
>
*>
(
alloc_ptr
->
ptr
());
const
int
threads
=
256
;
int
grid
=
std
::
min
(
C
,
(
max_threads
+
threads
-
1
)
/
threads
);
if
(
layout
==
paddle
::
framework
::
DataLayout
::
kNCHW
)
{
KeLocalStats
<
T
,
threads
,
paddle
::
framework
::
DataLayout
::
kNCHW
>
<<<
grid
,
threads
,
0
,
stream
>>>
(
x_d
,
N
,
H
*
W
*
D
,
C
,
stats
);
}
else
{
KeLocalStats
<
T
,
threads
,
paddle
::
framework
::
DataLayout
::
kNHWC
>
<<<
grid
,
threads
,
0
,
stream
>>>
(
x_d
,
N
,
H
*
W
*
D
,
C
,
stats
);
}
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
auto
*
comm
=
ctx
.
nccl_comm
();
if
(
comm
)
{
int
dtype
=
paddle
::
platform
::
ToNCCLDataType
(
paddle
::
framework
::
TransToProtoVarType
(
mean_out
->
dtype
()));
// In-place operation
PADDLE_ENFORCE_GPU_SUCCESS
(
paddle
::
platform
::
dynload
::
ncclAllReduce
(
stats
,
stats
,
2
*
C
+
1
,
static_cast
<
ncclDataType_t
>
(
dtype
),
ncclSum
,
comm
,
stream
));
}
#endif
auto
*
est_mean_data
=
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
mean_out
);
auto
*
est_var_data
=
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
variance_out
);
auto
*
sv_mean_data
=
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_mean
);
auto
*
sv_inv_var_data
=
ctx
.
template
Alloc
<
BatchNormParamType
<
T
>
>
(
saved_variance
);
// Note, Input('Mean')/Input('Variance') share variable with
// Output('MeanOut')/Output('VarianceOut')
KeSyncAndMovingStats
<
T
>
<<<
(
C
+
block
-
1
)
/
block
,
block
,
0
,
stream
>>>
(
stats
,
stats
+
C
,
stats
+
2
*
C
,
C
,
momentum
,
epsilon
,
sv_mean_data
,
sv_inv_var_data
,
est_mean_data
,
est_var_data
);
mean_data
=
sv_mean_data
;
var_data
=
stats
+
C
;
}
int
grid2
=
(
std
::
min
(
x_numel
,
max_threads
)
+
block
-
1
)
/
block
;
if
(
layout
==
paddle
::
framework
::
DataLayout
::
kNCHW
)
{
KeNormAffine
<
T
,
paddle
::
framework
::
DataLayout
::
kNCHW
>
<<<
grid2
,
block
,
0
,
stream
>>>
(
x_d
,
s_d
,
b_d
,
mean_data
,
var_data
,
epsilon
,
C
,
H
*
W
*
D
,
x_numel
,
y_d
);
}
else
{
KeNormAffine
<
T
,
paddle
::
framework
::
DataLayout
::
kNHWC
>
<<<
grid2
,
block
,
0
,
stream
>>>
(
x_d
,
s_d
,
b_d
,
mean_data
,
var_data
,
epsilon
,
C
,
H
*
W
*
D
,
x_numel
,
y_d
);
}
}
}
// namespace phi
#ifdef PADDLE_WITH_HIP
PD_REGISTER_KERNEL
(
sync_batch_norm
,
GPU
,
ALL_LAYOUT
,
phi
::
SyncBatchNormKernel
,
float
,
phi
::
dtype
::
float16
)
{}
#else
PD_REGISTER_KERNEL
(
sync_batch_norm
,
GPU
,
ALL_LAYOUT
,
phi
::
SyncBatchNormKernel
,
float
,
double
,
phi
::
dtype
::
float16
)
{}
#endif
paddle/
fluid/operators/sync_batch_norm_op.cu
.h
→
paddle/
phi/kernels/gpu/sync_batch_norm_utils
.h
浏览文件 @
c99c70cb
此差异已折叠。
点击以展开。
paddle/phi/kernels/sync_batch_norm_grad_kernel.h
0 → 100644
浏览文件 @
c99c70cb
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include "paddle/phi/core/dense_tensor.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
SyncBatchNormGradKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
scale
,
const
DenseTensor
&
bias
,
const
paddle
::
optional
<
DenseTensor
>&
mean
,
const
paddle
::
optional
<
DenseTensor
>&
variance
,
const
DenseTensor
&
saved_mean
,
const
DenseTensor
&
saved_variance
,
const
paddle
::
optional
<
DenseTensor
>&
reserve_space
,
const
DenseTensor
&
y_grad
,
float
momentum
,
float
epsilon
,
const
std
::
string
&
data_layout
,
bool
is_test
,
bool
use_global_stats
,
bool
trainable_statistics
,
bool
fuse_with_relu
,
DenseTensor
*
x_grad
,
DenseTensor
*
scale_grad
,
DenseTensor
*
bias_grad
);
}
// namespace phi
paddle/phi/kernels/sync_batch_norm_kernel.h
0 → 100644
浏览文件 @
c99c70cb
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include "paddle/phi/core/dense_tensor.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
SyncBatchNormKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
scale
,
const
DenseTensor
&
bias
,
const
DenseTensor
&
mean
,
const
DenseTensor
&
variance
,
float
momentum
,
float
epsilon
,
const
std
::
string
&
data_layout
,
bool
is_test
,
bool
use_global_stats
,
bool
trainable_statistics
,
bool
fuse_with_relu
,
DenseTensor
*
y
,
DenseTensor
*
mean_out
,
DenseTensor
*
variance_out
,
DenseTensor
*
saved_mean
,
DenseTensor
*
saved_variance
,
DenseTensor
*
reserve_space
);
}
// namespace phi
paddle/phi/ops/compat/sync_batch_norm_sig.cc
0 → 100644
浏览文件 @
c99c70cb
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/core/compat/op_utils.h"
namespace
phi
{
KernelSignature
SyncBatchNormOpArgumentMapping
(
const
ArgumentMappingContext
&
ctx
)
{
return
KernelSignature
(
"sync_batch_norm"
,
{
"X"
,
"Scale"
,
"Bias"
,
"Mean"
,
"Variance"
},
{
"momentum"
,
"epsilon"
,
"data_layout"
,
"is_test"
,
"use_global_stats"
,
"trainable_statistics"
,
"fuse_with_relu"
},
{
"Y"
,
"MeanOut"
,
"VarianceOut"
,
"SavedMean"
,
"SavedVariance"
,
"ReserveSpace"
});
}
KernelSignature
SyncBatchNormGradOpArgumentMapping
(
const
ArgumentMappingContext
&
ctx
)
{
return
KernelSignature
(
"sync_batch_norm_grad"
,
{
"X"
,
"Scale"
,
"Bias"
,
"Mean"
,
"Variance"
,
"SavedMean"
,
"SavedVariance"
,
"ReserveSpace"
,
"Y@GRAD"
,
},
{
"momentum"
,
"epsilon"
,
"data_layout"
,
"is_test"
,
"use_global_stats"
,
"trainable_statistics"
,
"fuse_with_relu"
},
{
"X@GRAD"
,
"Scale@GRAD"
,
"Bias@GRAD"
});
}
}
// namespace phi
PD_REGISTER_ARG_MAPPING_FN
(
sync_batch_norm
,
phi
::
SyncBatchNormOpArgumentMapping
);
PD_REGISTER_ARG_MAPPING_FN
(
sync_batch_norm_grad
,
phi
::
SyncBatchNormGradOpArgumentMapping
);
python/paddle/nn/layer/norm.py
浏览文件 @
c99c70cb
...
...
@@ -49,6 +49,7 @@ from .. import functional as F
from
paddle
import
_C_ops
from
..
import
Layer
from
paddle
import
in_dynamic_mode
from
paddle.fluid.framework
import
in_dygraph_mode
__all__
=
[]
...
...
@@ -1100,7 +1101,14 @@ class SyncBatchNorm(_BatchNormBase):
### train mode: use mini-batch stats, eval mode: use global stats
### use_global_stats only support False in sync_batch_norm
if
in_dynamic_mode
():
if
in_dygraph_mode
():
sync_batch_norm_out
,
_
,
_
,
_
,
_
,
_
=
_C_ops
.
final_state_sync_batch_norm
(
x
,
self
.
weight
,
self
.
bias
,
self
.
_mean
,
self
.
_variance
,
self
.
_momentum
,
self
.
_epsilon
,
self
.
_data_format
,
not
self
.
training
,
False
,
False
,
False
)
return
sync_batch_norm_out
elif
in_dynamic_mode
():
attrs
=
(
"momentum"
,
self
.
_momentum
,
"epsilon"
,
self
.
_epsilon
,
"is_test"
,
not
self
.
training
,
"data_layout"
,
self
.
_data_format
,
"use_mkldnn"
,
False
,
"fuse_with_relu"
,
...
...
@@ -1109,7 +1117,6 @@ class SyncBatchNorm(_BatchNormBase):
sync_batch_norm_out
,
_
,
_
,
_
,
_
,
_
=
_C_ops
.
sync_batch_norm
(
x
,
self
.
weight
,
self
.
bias
,
self
.
_mean
,
self
.
_variance
,
mean_out
,
variance_out
,
*
attrs
)
return
sync_batch_norm_out
check_variable_and_dtype
(
x
,
'input'
,
[
'float16'
,
'float32'
,
'float64'
],
...
...
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