Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
机器未来
Paddle
提交
8f051b36
P
Paddle
项目概览
机器未来
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
8f051b36
编写于
12月 25, 2018
作者:
X
xiaoli.liu@intel.com
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Enable INT8 pool OP
test=develop
上级
aba1f9b0
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
256 addition
and
11 deletion
+256
-11
paddle/fluid/operators/pool_mkldnn_op.cc
paddle/fluid/operators/pool_mkldnn_op.cc
+20
-11
python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py
...addle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py
+236
-0
未找到文件。
paddle/fluid/operators/pool_mkldnn_op.cc
浏览文件 @
8f051b36
...
...
@@ -12,6 +12,7 @@ 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/framework/data_layout_transform.h"
#include "paddle/fluid/operators/pool_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
...
...
@@ -71,7 +72,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
...
...
@@ -130,20 +130,25 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
CorrectOutputSize
(
src_tz
,
dst_tz
,
ksize
,
paddings
,
strides
,
padding_right_bottom
);
}
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
input_format
);
mkldnn
::
memory
::
data_type
dt
=
paddle
::
framework
::
ToMKLDNNDataType
(
input
->
type
());
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
dt
,
input_format
);
/* create memory descriptor for pooling without specified format
* ('any') which lets a primitive (pooling in this case) choose
* the memory format preferred for best performance
*/
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
any
);
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
dt
,
mkldnn
::
memory
::
format
::
any
);
auto
propagation
=
src_md
.
data
.
data_type
==
mkldnn_f32
?
mkldnn
::
prop_kind
::
forward_training
:
mkldnn
::
prop_kind
::
forward_scoring
;
std
::
shared_ptr
<
mkldnn
::
pooling_forward
::
primitive_desc
>
pool_pd
=
CreatePrimitiveDesc
(
src_md
,
dst_md
,
strides
,
padding_left_top
,
padding_
right_bottom
,
ksize
,
pooling_typ
e
,
mkldnn_engine
,
ceil_mode
,
is_test
);
CreatePrimitiveDesc
(
src_md
,
dst_md
,
propagation
,
strides
,
padding_
left_top
,
padding_right_bottom
,
ksiz
e
,
pooling_type
,
mkldnn_engine
,
ceil_mode
,
is_test
);
// save pool_pd into global device context to be referred in backward path
if
(
!
is_test
)
dev_ctx
.
SetBlob
(
key_pool_pd
,
pool_pd
);
...
...
@@ -203,7 +208,8 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
private:
std
::
unique_ptr
<
mkldnn
::
pooling_forward
::
primitive_desc
>
CreatePrimitiveDesc
(
const
mkldnn
::
memory
::
desc
&
src
,
const
mkldnn
::
memory
::
desc
&
dst
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding_left_top
,
const
mkldnn
::
prop_kind
&
propagation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding_left_top
,
const
std
::
vector
<
int
>&
padding_right_bot
,
const
std
::
vector
<
int
>&
kernel
,
const
std
::
string
&
pooling_type
,
const
mkldnn
::
engine
&
engine
,
bool
ceil_mode
,
bool
is_test
)
const
{
...
...
@@ -411,6 +417,9 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_KERNEL
(
pool2d
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
PoolMKLDNNOpKernel
<
float
>
);
ops
::
PoolMKLDNNOpKernel
<
float
>
,
ops
::
PoolMKLDNNOpKernel
<
int8_t
>
,
ops
::
PoolMKLDNNOpKernel
<
uint8_t
>
);
REGISTER_OP_KERNEL
(
pool2d_grad
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
PoolMKLDNNGradOpKernel
<
float
>
);
python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py
0 → 100644
浏览文件 @
8f051b36
# Copyright (c) 2018 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
from
__future__
import
division
import
unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
def
adaptive_start_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
floor
(
index
*
input_size
/
output_size
))
def
adaptive_end_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
ceil
((
index
+
1
)
*
input_size
/
output_size
))
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
))
return
out
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
field_size
=
((
r_end
-
r_start
)
*
(
c_end
-
c_start
))
\
if
(
exclusive
or
adaptive
)
else
(
ksize
[
0
]
*
ksize
[
1
])
out
[:,
:,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
))
/
field_size
return
out
class
TestPool2D_Op
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"pool2d"
self
.
use_cudnn
=
False
self
.
use_mkldnn
=
True
self
.
dtype
=
np
.
int8
self
.
init_test_case
()
self
.
init_global_pool
()
self
.
init_pool_type
()
self
.
init_ceil_mode
()
self
.
init_exclusive
()
self
.
init_adaptive
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
,
self
.
exclusive
,
self
.
adaptive
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
input
)}
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'pooling_type'
:
self
.
pool_type
,
'global_pooling'
:
self
.
global_pool
,
'use_cudnn'
:
self
.
use_cudnn
,
'use_mkldnn'
:
self
.
use_mkldnn
,
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
,
# TODO(dzhwinter) : should be fix latter
'exclusive'
:
self
.
exclusive
,
'adaptive'
:
self
.
adaptive
}
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
self
.
dtype
=
np
.
int8
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
True
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
False
def
init_exclusive
(
self
):
self
.
exclusive
=
True
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestPool2D_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
self
.
dtype
=
np
.
int8
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase2
(
TestPool2D_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
self
.
dtype
=
np
.
uint8
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase3
(
TestPool2D_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
self
.
dtype
=
np
.
int8
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
class
TestCase4
(
TestCase1
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
self
.
dtype
=
np
.
uint8
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
if
__name__
==
'__main__'
:
unittest
.
main
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录