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c90d3556
编写于
10月 13, 2020
作者:
H
hong19860320
提交者:
GitHub
10月 13, 2020
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电子邮件补丁
差异文件
Add batch_norm and layer_norm XPU kernels (#27818)
上级
ddcd1b53
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
661 addition
and
0 deletion
+661
-0
paddle/fluid/operators/batch_norm_op_xpu.cc
paddle/fluid/operators/batch_norm_op_xpu.cc
+167
-0
paddle/fluid/operators/layer_norm_op_xpu.cc
paddle/fluid/operators/layer_norm_op_xpu.cc
+114
-0
python/paddle/fluid/tests/unittests/xpu/test_batch_norm_op_xpu.py
...addle/fluid/tests/unittests/xpu/test_batch_norm_op_xpu.py
+269
-0
python/paddle/fluid/tests/unittests/xpu/test_layer_norm_op_xpu.py
...addle/fluid/tests/unittests/xpu/test_layer_norm_op_xpu.py
+111
-0
未找到文件。
paddle/fluid/operators/batch_norm_op_xpu.cc
0 → 100644
浏览文件 @
c90d3556
/* Copyright (c) 2016 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/batch_norm_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
template
<
typename
DeviceContext
,
typename
T
>
class
BatchNormXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
const
auto
momentum
=
ctx
.
Attr
<
float
>
(
"momentum"
);
const
auto
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
const
auto
use_global_stats
=
ctx
.
Attr
<
bool
>
(
"use_global_stats"
);
const
auto
trainable_stats
=
ctx
.
Attr
<
bool
>
(
"trainable_statistics"
);
bool
test_mode
=
is_test
&&
(
!
trainable_stats
);
bool
global_stats
=
test_mode
||
use_global_stats
;
const
auto
&
data_layout_str
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
const
auto
data_layout
=
framework
::
StringToDataLayout
(
data_layout_str
);
PADDLE_ENFORCE_EQ
(
data_layout
,
DataLayout
::
kNCHW
,
platform
::
errors
::
InvalidArgument
(
"The 'data_layout' attribute must be NCHW. But "
"recevived 'data_layout' is [%s]."
,
data_layout_str
));
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
auto
&
x_dims
=
x
->
dims
();
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
4
,
platform
::
errors
::
InvalidArgument
(
"The input tensor X's dimension must equal to 4. But "
"received X's shape = [%s], X's dimension = [%d]."
,
x_dims
,
x_dims
.
size
()));
const
int
N
=
x_dims
[
0
];
const
int
C
=
x_dims
[
1
];
const
int
H
=
x_dims
[
2
];
const
int
W
=
x_dims
[
3
];
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
const
auto
*
x_data
=
x
->
data
<
T
>
();
const
auto
*
scale_data
=
scale
->
data
<
T
>
();
const
auto
*
bias_data
=
bias
->
data
<
T
>
();
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
auto
*
y_data
=
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
if
(
!
global_stats
)
{
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"
);
mean_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
variance_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
saved_mean
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
saved_variance
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
mean_out_data
=
mean_out
->
data
<
T
>
();
auto
*
variance_out_data
=
variance_out
->
data
<
T
>
();
auto
*
saved_mean_data
=
saved_mean
->
data
<
T
>
();
auto
*
saved_variance_data
=
saved_variance
->
data
<
T
>
();
int
r
=
xpu
::
batch_norm_train_forward
(
dev_ctx
.
x_context
(),
epsilon
,
momentum
,
N
,
C
,
H
,
W
,
x_data
,
y_data
,
scale_data
,
bias_data
,
mean_out_data
,
variance_out_data
,
saved_mean_data
,
saved_variance_data
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API(batch_norm_train_forward) return "
"wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
}
else
{
const
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"Mean"
);
const
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"Variance"
);
const
auto
*
mean_data
=
mean
->
data
<
T
>
();
const
auto
*
variance_data
=
variance
->
data
<
T
>
();
int
r
=
xpu
::
batch_norm_infer_forward
(
dev_ctx
.
x_context
(),
epsilon
,
N
,
C
,
H
,
W
,
x_data
,
y_data
,
scale_data
,
bias_data
,
mean_data
,
variance_data
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API(batch_norm_infer_forward) return "
"wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
BatchNormGradXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
auto
*
dy
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
saved_mean
=
ctx
.
Input
<
Tensor
>
(
"SavedMean"
);
// SavedVariance have been reverted in forward operator
const
auto
*
saved_inv_variance
=
ctx
.
Input
<
Tensor
>
(
"SavedVariance"
);
const
auto
&
data_layout_str
=
ctx
.
Attr
<
std
::
string
>
(
"data_layout"
);
const
auto
data_layout
=
framework
::
StringToDataLayout
(
data_layout_str
);
PADDLE_ENFORCE_EQ
(
data_layout
,
DataLayout
::
kNCHW
,
platform
::
errors
::
InvalidArgument
(
"The 'data_layout' attribute must be NCHW. But "
"recevived 'data_layout' is [%s]."
,
data_layout_str
));
const
auto
&
x_dims
=
x
->
dims
();
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
4
,
platform
::
errors
::
InvalidArgument
(
"The input tensor X's dimension must equal to 4. But "
"received X's shape = [%s], X's dimension = [%d]."
,
x_dims
,
x_dims
.
size
()));
const
int
N
=
x_dims
[
0
];
const
int
C
=
x_dims
[
1
];
const
int
H
=
x_dims
[
2
];
const
int
W
=
x_dims
[
3
];
const
auto
*
x_data
=
x
->
data
<
T
>
();
const
auto
*
dy_data
=
dy
->
data
<
T
>
();
const
auto
*
scale_data
=
scale
->
data
<
T
>
();
const
auto
*
saved_mean_data
=
saved_mean
->
data
<
T
>
();
const
auto
*
saved_inv_variance_data
=
saved_inv_variance
->
data
<
T
>
();
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dscale
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
dbias
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
auto
*
dx_data
=
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
dscale_data
=
dscale
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
dbias_data
=
dbias
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
int
r
=
xpu
::
batch_norm_backward
(
dev_ctx
.
x_context
(),
N
,
C
,
H
,
W
,
x_data
,
dy_data
,
scale_data
,
saved_mean_data
,
saved_inv_variance_data
,
dx_data
,
dscale_data
,
dbias_data
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API(batch_norm_infer_forward) return "
"wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
batch_norm
,
ops
::
BatchNormXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
REGISTER_OP_XPU_KERNEL
(
batch_norm_grad
,
ops
::
BatchNormGradXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
#endif // PADDLE_WITH_XPU
paddle/fluid/operators/layer_norm_op_xpu.cc
0 → 100644
浏览文件 @
c90d3556
/* Copyright (c) 2016 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/layer_norm_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
template
<
typename
DeviceContext
,
typename
T
>
class
LayerNormXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
begin_norm_axis
=
ctx
.
Attr
<
int
>
(
"begin_norm_axis"
);
const
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
auto
&
x_dims
=
x
->
dims
();
auto
matrix_dim
=
framework
::
flatten_to_2d
(
x_dims
,
begin_norm_axis
);
int
left
=
static_cast
<
int
>
(
matrix_dim
[
0
]);
int
right
=
static_cast
<
int
>
(
matrix_dim
[
1
]);
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
auto
*
mean
=
ctx
.
Output
<
Tensor
>
(
"Mean"
);
auto
*
variance
=
ctx
.
Output
<
Tensor
>
(
"Variance"
);
const
auto
*
x_data
=
x
->
data
<
T
>
();
const
auto
*
scale_data
=
(
scale
==
nullptr
?
nullptr
:
scale
->
data
<
T
>
());
const
auto
*
bias_data
=
(
bias
==
nullptr
?
nullptr
:
bias
->
data
<
T
>
());
auto
*
y_data
=
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
mean_data
=
mean
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
variance_data
=
variance
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
int
r
=
xpu
::
layer_norm
(
dev_ctx
.
x_context
(),
left
,
right
,
x_data
,
y_data
,
scale_data
,
bias_data
,
epsilon
,
mean_data
,
variance_data
,
false
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API(layer_norm) return wrong "
"value[%d], please check whether Baidu "
"Kunlun Card is properly installed."
,
r
));
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
LayerNormGradXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
begin_norm_axis
=
ctx
.
Attr
<
int
>
(
"begin_norm_axis"
);
const
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
auto
&
x_dims
=
x
->
dims
();
auto
matrix_dim
=
framework
::
flatten_to_2d
(
x_dims
,
begin_norm_axis
);
int
left
=
static_cast
<
int
>
(
matrix_dim
[
0
]);
int
right
=
static_cast
<
int
>
(
matrix_dim
[
1
]);
const
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"Mean"
);
const
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"Variance"
);
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
dy
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dscale
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
dbias
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
const
auto
*
x_data
=
x
->
data
<
T
>
();
const
auto
*
dy_data
=
dy
->
data
<
T
>
();
const
auto
*
mean_data
=
mean
->
data
<
T
>
();
const
auto
*
variance_data
=
variance
->
data
<
T
>
();
const
auto
*
scale_data
=
(
scale
==
nullptr
?
nullptr
:
scale
->
data
<
T
>
());
auto
*
dscale_data
=
(
dscale
==
nullptr
?
nullptr
:
dscale
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()));
auto
*
dbias_data
=
(
dbias
==
nullptr
?
nullptr
:
dbias
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()));
auto
*
dx_data
=
(
dx
==
nullptr
?
nullptr
:
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()));
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
int
r
=
xpu
::
layer_norm_backward
(
dev_ctx
.
x_context
(),
left
,
right
,
x_data
,
scale_data
,
variance_data
,
mean_data
,
dy_data
,
dx_data
,
dscale_data
,
dbias_data
,
epsilon
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API(layer_norm_backward) return wrong "
"value[%d], please check whether Baidu "
"Kunlun Card is properly installed."
,
r
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
layer_norm
,
ops
::
LayerNormXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
REGISTER_OP_XPU_KERNEL
(
layer_norm_grad
,
ops
::
LayerNormGradXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
#endif // PADDLE_WITH_XPU
python/paddle/fluid/tests/unittests/xpu/test_batch_norm_op_xpu.py
0 → 100644
浏览文件 @
c90d3556
# Copyright (c) 2020 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.
from
__future__
import
print_function
import
sys
sys
.
path
.
append
(
".."
)
import
unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
from
scipy.special
import
expit
,
erf
import
paddle
import
paddle.fluid
as
fluid
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
from
paddle.fluid
import
compiler
,
Program
,
program_guard
def
ref_batch_norm_infer
(
x
,
scale
,
bias
,
mean
,
variance
,
momentum
,
epsilon
,
data_layout
):
if
data_layout
==
"NCHW"
:
n
,
c
,
h
,
w
=
x
.
shape
mean_tile
=
np
.
reshape
(
mean
,
(
1
,
c
,
1
,
1
))
mean_tile
=
np
.
tile
(
mean_tile
,
(
n
,
1
,
h
,
w
))
variance_tile
=
np
.
reshape
(
variance
,
(
1
,
c
,
1
,
1
))
variance_tile
=
np
.
tile
(
variance_tile
,
(
n
,
1
,
h
,
w
))
normalized_x
=
(
x
-
mean_tile
)
/
np
.
sqrt
(
variance_tile
+
epsilon
)
scale_tile
=
np
.
reshape
(
scale
,
(
1
,
c
,
1
,
1
))
scale_tile
=
np
.
tile
(
scale_tile
,
(
n
,
1
,
h
,
w
))
bias_tile
=
np
.
reshape
(
bias
,
(
1
,
c
,
1
,
1
))
bias_tile
=
np
.
reshape
(
bias_tile
,
(
1
,
c
,
1
,
1
))
y
=
normalized_x
*
scale_tile
+
bias_tile
elif
data_layout
==
"NHWC"
:
normalized_x
=
(
x
-
mean
)
/
np
.
sqrt
(
variance
+
epsilon
)
y
=
normalized_x
*
scale
+
bias
else
:
raise
ValueError
(
"Unsupported data layout! Only NCHW and NHWC is supported, but received "
+
data_layout
)
return
y
def
ref_batch_norm_train
(
x
,
y_grad
,
scale
,
bias
,
mean
,
variance
,
momentum
,
epsilon
,
data_layout
):
# Forward
if
data_layout
==
"NCHW"
:
n
,
c
,
h
,
w
=
x
.
shape
x_square
=
x
*
x
x_square_sum
=
np
.
sum
(
x_square
,
(
0
,
2
,
3
))
x_sum
=
np
.
sum
(
x
,
axis
=
(
0
,
2
,
3
))
element_count
=
np
.
size
(
x
)
/
int
(
np
.
shape
(
x
)[
1
])
saved_mean
=
x_sum
/
element_count
saved_variance
=
x_square_sum
/
element_count
-
saved_mean
*
saved_mean
saved_mean_tile
=
np
.
reshape
(
saved_mean
,
(
1
,
c
,
1
,
1
))
saved_mean_tile
=
np
.
tile
(
saved_mean_tile
,
(
n
,
1
,
h
,
w
))
saved_variance_tile
=
np
.
reshape
(
saved_variance
,
(
1
,
c
,
1
,
1
))
saved_variance_tile
=
np
.
tile
(
saved_variance_tile
,
(
n
,
1
,
h
,
w
))
normalized_x
=
(
x
-
saved_mean_tile
)
/
np
.
sqrt
(
saved_variance_tile
+
epsilon
)
scale_tile
=
np
.
reshape
(
scale
,
(
1
,
c
,
1
,
1
))
scale_tile
=
np
.
tile
(
scale_tile
,
(
n
,
1
,
h
,
w
))
bias_tile
=
np
.
reshape
(
bias
,
(
1
,
c
,
1
,
1
))
bias_tile
=
np
.
reshape
(
bias_tile
,
(
1
,
c
,
1
,
1
))
y
=
normalized_x
*
scale_tile
+
bias_tile
elif
data_layout
==
"NHWC"
:
x_square
=
x
*
x
x_square_sum
=
np
.
sum
(
x_square
,
(
0
,
1
,
2
))
x_sum
=
np
.
sum
(
x
,
axis
=
(
0
,
1
,
2
))
element_count
=
np
.
size
(
x
)
/
int
(
np
.
shape
(
x
)[
-
1
])
saved_mean
=
x_sum
/
element_count
saved_variance
=
x_square_sum
/
element_count
-
saved_mean
*
saved_mean
normalized_x
=
(
x
-
saved_mean
)
/
np
.
sqrt
(
saved_variance
+
epsilon
)
y
=
normalized_x
*
scale
+
bias
else
:
raise
ValueError
(
"Unsupported data layout! Only NCHW and NHWC is supported, but received "
+
data_layout
)
mean_out
=
saved_mean
*
(
1.
-
momentum
)
+
momentum
*
mean
variance_out
=
saved_variance
*
(
1.
-
momentum
)
+
momentum
*
variance
saved_inv_std
=
1.
/
np
.
sqrt
(
saved_variance
+
epsilon
)
# Backward
# Use the following formulas to calculate gradients:
# grad_scale =
# sum(grad_y * (x - mean)) * rsqrt(variance + epsilon)
#
# grad_bias = sum(y)
#
# x_grad =
# 1/N * scale * rsqrt(variance + epsilon) * (N * grad_y - sum(grad_y) -
# (x - mean) * sum(grad_y * (x - mean)) / (variance + epsilon))
# Transfer from (N, C, H, W) to (N, H, W, C) to simplify computation
if
data_layout
==
"NCHW"
:
x
=
np
.
transpose
(
x
,
(
0
,
2
,
3
,
1
))
y_grad
=
np
.
transpose
(
y_grad
,
(
0
,
2
,
3
,
1
))
x_grad
=
scale
*
(
y_grad
-
np
.
mean
(
y_grad
,
axis
=
(
0
,
1
,
2
))
-
(
x
-
saved_mean
)
*
np
.
mean
(
y_grad
*
(
x
-
saved_mean
),
axis
=
(
0
,
1
,
2
))
/
(
saved_variance
+
epsilon
))
/
np
.
sqrt
(
saved_variance
+
epsilon
)
scale_grad
=
np
.
sum
(
y_grad
*
(
x
-
saved_mean
)
/
np
.
sqrt
(
saved_variance
+
epsilon
),
axis
=
(
0
,
1
,
2
))
bias_grad
=
np
.
sum
(
y_grad
,
axis
=
(
0
,
1
,
2
))
# Transfer back to N, C, H, W
if
data_layout
==
"NCHW"
:
x_grad
=
np
.
transpose
(
x_grad
,
(
0
,
3
,
1
,
2
))
x
=
np
.
transpose
(
x
,
(
0
,
3
,
1
,
2
))
y_grad
=
np
.
transpose
(
y_grad
,
(
0
,
3
,
1
,
2
))
return
y
,
mean_out
,
variance_out
,
saved_mean
,
saved_inv_std
,
x_grad
,
scale_grad
,
bias_grad
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPUBatchNormOp
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"batch_norm"
self
.
dtype
=
np
.
float32
self
.
shape
=
[
2
,
3
,
4
,
5
]
self
.
data_layout
=
"NCHW"
self
.
epsilon
=
1e-05
self
.
momentum
=
0.9
self
.
set_attrs
()
if
self
.
data_layout
==
"NHWC"
:
channel_size
=
self
.
shape
[
3
]
elif
self
.
data_layout
==
"NCHW"
:
channel_size
=
self
.
shape
[
1
]
else
:
raise
ValueError
(
"Unsupported data layout! Only NCHW and NHWC is supported, but received "
+
data_layout
)
np
.
random
.
seed
(
1024
)
self
.
x_np
=
np
.
random
.
random_sample
(
self
.
shape
).
astype
(
self
.
dtype
)
self
.
scale_np
=
np
.
random
.
random_sample
(
[
channel_size
]).
astype
(
self
.
dtype
)
self
.
bias_np
=
np
.
random
.
random_sample
(
[
channel_size
]).
astype
(
self
.
dtype
)
self
.
mean_np
=
np
.
zeros
([
channel_size
]).
astype
(
self
.
dtype
)
self
.
variance_np
=
np
.
ones
([
channel_size
]).
astype
(
self
.
dtype
)
self
.
saved_mean_np
=
np
.
zeros
([
channel_size
]).
astype
(
self
.
dtype
)
self
.
saved_variance_np
=
np
.
ones
([
channel_size
]).
astype
(
self
.
dtype
)
def
set_attrs
(
self
):
pass
def
test_infer
(
self
):
paddle
.
enable_static
()
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
x
=
paddle
.
data
(
'X'
,
self
.
x_np
.
shape
,
self
.
x_np
.
dtype
)
scale
=
paddle
.
data
(
'Scale'
,
self
.
scale_np
.
shape
,
self
.
scale_np
.
dtype
)
bias
=
paddle
.
data
(
'Bias'
,
self
.
bias_np
.
shape
,
self
.
bias_np
.
dtype
)
mean
=
paddle
.
data
(
'Mean'
,
self
.
mean_np
.
shape
,
self
.
mean_np
.
dtype
)
variance
=
paddle
.
data
(
'Variance'
,
self
.
variance_np
.
shape
,
self
.
variance_np
.
dtype
)
y
=
F
.
batch_norm
(
x
,
mean
,
variance
,
scale
,
bias
,
False
,
self
.
momentum
,
self
.
epsilon
,
self
.
data_layout
)
exe
=
paddle
.
static
.
Executor
(
self
.
place
)
[
y_np
]
=
exe
.
run
(
feed
=
{
'X'
:
self
.
x_np
,
'Scale'
:
self
.
scale_np
,
'Bias'
:
self
.
bias_np
,
'Mean'
:
self
.
mean_np
,
'Variance'
:
self
.
variance_np
},
fetch_list
=
[
y
])
y_np_ref
=
ref_batch_norm_infer
(
self
.
x_np
,
self
.
scale_np
,
self
.
bias_np
,
self
.
mean_np
,
self
.
variance_np
,
self
.
momentum
,
self
.
epsilon
,
self
.
data_layout
)
self
.
assertEqual
(
np
.
allclose
(
y_np_ref
,
y_np
),
True
)
def
test_train
(
self
):
y_grad_np
=
np
.
random
.
random_sample
(
self
.
shape
).
astype
(
self
.
dtype
)
y_np
,
mean_out_np
,
variance_out_np
,
saved_mean_np
,
saved_variance_np
,
x_grad_np
,
scale_grad_np
,
bias_grad_np
=
ref_batch_norm_train
(
self
.
x_np
,
y_grad_np
,
self
.
scale_np
,
self
.
bias_np
,
self
.
mean_np
,
self
.
variance_np
,
self
.
momentum
,
self
.
epsilon
,
self
.
data_layout
)
inputs
=
{
'X'
:
self
.
x_np
,
'Scale'
:
self
.
scale_np
,
'Bias'
:
self
.
bias_np
,
'Mean'
:
self
.
mean_np
,
'Variance'
:
self
.
variance_np
,
'Y@GRAD'
:
y_grad_np
}
outputs
=
{
'Y'
:
y_np
,
'Mean'
:
mean_out_np
,
'Variance'
:
variance_out_np
,
'SavedMean'
:
saved_mean_np
,
'SavedVariance'
:
saved_variance_np
,
'X@GRAD'
:
x_grad_np
,
'Scale@GRAD'
:
scale_grad_np
,
'Bias@GRAD'
:
bias_grad_np
}
attrs
=
{
'momentum'
:
self
.
momentum
,
'epsilon'
:
self
.
epsilon
,
'is_test'
:
False
,
'data_layout'
:
self
.
data_layout
,
'use_mkldnn'
:
False
,
'fuse_with_relu'
:
False
,
'use_global_stats'
:
False
,
}
paddle
.
enable_static
()
program
=
paddle
.
static
.
Program
()
with
paddle
.
static
.
program_guard
(
program
):
block
=
program
.
global_block
()
# Set inputs, outputs and attributes to the forward op of batch_norm
input_vars
=
{}
for
var_name
in
inputs
:
arg_name
=
var_name
np_value
=
inputs
[
var_name
]
if
not
block
.
has_var
(
var_name
):
block
.
create_var
(
name
=
var_name
,
shape
=
np_value
.
shape
,
dtype
=
np_value
.
dtype
)
input_vars
[
arg_name
]
=
block
.
var
(
var_name
)
fetch_list
=
[]
output_vars
=
{}
for
var_name
in
outputs
:
arg_name
=
var_name
np_value
=
outputs
[
var_name
]
if
not
block
.
has_var
(
var_name
):
block
.
create_var
(
name
=
var_name
,
shape
=
np_value
.
shape
,
dtype
=
np_value
.
dtype
)
if
var_name
==
'Mean'
:
arg_name
=
'MeanOut'
# Share memory
if
var_name
==
'Variance'
:
arg_name
=
'VarianceOut'
# Share memory
output_vars
[
arg_name
]
=
block
.
var
(
var_name
)
fetch_list
.
append
(
var_name
)
batch_norm_op
=
block
.
append_op
(
type
=
"batch_norm"
,
inputs
=
input_vars
,
outputs
=
output_vars
,
attrs
=
attrs
)
# Generate the backward op_desc of batch_norm
grad_op_desc_list
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
batch_norm_op
.
desc
,
set
(),
[])
grad_op_desc
=
grad_op_desc_list
[
0
]
new_op_desc
=
block
.
desc
.
append_op
()
new_op_desc
.
copy_from
(
grad_op_desc
)
program
.
_sync_with_cpp
()
exe
=
paddle
.
static
.
Executor
(
self
.
place
)
outs
=
exe
.
run
(
program
,
feed
=
inputs
,
fetch_list
=
fetch_list
)
for
id
,
name
in
enumerate
(
fetch_list
):
self
.
assertEqual
(
np
.
allclose
(
outputs
[
name
],
outs
[
id
],
atol
=
1e-4
),
True
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_layer_norm_op_xpu.py
0 → 100644
浏览文件 @
c90d3556
# Copyright (c) 2020 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.
import
paddle
import
numpy
as
np
import
sys
import
unittest
from
functools
import
reduce
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
from
operator
import
mul
paddle
.
enable_static
()
def
ref_layer_norm
(
x
,
scale
,
bias
,
epsilon
,
begin_norm_axis
=
1
):
x_shape
=
x
.
shape
left
=
reduce
(
mul
,
x_shape
[
0
:
begin_norm_axis
],
1
)
right
=
reduce
(
mul
,
x_shape
[
begin_norm_axis
:
len
(
x_shape
)],
1
)
x
.
shape
=
[
left
,
right
]
mean
=
np
.
mean
(
x
,
axis
=
1
)
variance
=
np
.
var
(
x
,
axis
=
1
)
+
epsilon
y
=
np
.
divide
((
x
-
mean
.
reshape
([
left
,
1
])),
(
np
.
sqrt
(
variance
)).
reshape
([
left
,
1
]))
if
scale
is
not
None
:
y
=
scale
.
reshape
([
1
,
right
])
*
y
if
bias
is
not
None
:
y
=
y
+
bias
.
reshape
([
1
,
right
])
x
.
shape
,
y
.
shape
=
x_shape
,
x_shape
return
y
,
mean
,
variance
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPULayerNormOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"layer_norm"
self
.
dtype
=
np
.
float32
self
.
shape
=
[
2
,
3
,
4
,
5
]
self
.
epsilon
=
1e-05
self
.
begin_norm_axis
=
1
self
.
set_attrs
()
right
=
reduce
(
mul
,
self
.
shape
[
self
.
begin_norm_axis
:
len
(
self
.
shape
)],
1
)
np
.
random
.
seed
(
10
)
x_np
=
np
.
random
.
uniform
(
0.1
,
1
,
self
.
shape
).
astype
(
self
.
dtype
)
scale_np
=
np
.
random
.
uniform
(
0.1
,
1
,
[
right
]).
astype
(
self
.
dtype
)
bias_np
=
np
.
random
.
uniform
(
0.1
,
1
,
[
right
]).
astype
(
self
.
dtype
)
ref_y_np
,
ref_mean_np
,
ref_variance_np
=
ref_layer_norm
(
x_np
,
scale_np
,
bias_np
,
self
.
epsilon
,
self
.
begin_norm_axis
)
self
.
inputs
=
{
'X'
:
x_np
,
'Scale'
:
scale_np
,
'Bias'
:
bias_np
}
self
.
outputs
=
{
'Y'
:
ref_y_np
,
'Mean'
:
ref_mean_np
,
'Variance'
:
ref_variance_np
}
self
.
attrs
=
{
'begin_norm_axis'
:
self
.
begin_norm_axis
,
'use_xpu'
:
True
}
def
set_attrs
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output_with_place
(
paddle
.
XPUPlace
(
0
),
atol
=
1e-4
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
paddle
.
XPUPlace
(
0
),
[
'X'
],
'Y'
,
max_relative_error
=
0.02
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPULayerNormOpAxis2
(
TestXPULayerNormOp
):
def
set_attrs
(
self
):
self
.
begin_norm_axis
=
2
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPULayerNormOpAxis3
(
TestXPULayerNormOp
):
def
set_attrs
(
self
):
self
.
begin_norm_axis
=
3
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPULayerNormOp2D
(
TestXPULayerNormOp
):
def
set_attrs
(
self
):
self
.
shape
=
[
10
,
12
]
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPULayerNormOp3D
(
TestXPULayerNormOp
):
def
set_attrs
(
self
):
self
.
shape
=
[
4
,
5
,
6
]
if
__name__
==
"__main__"
:
unittest
.
main
()
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