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8f8a02fd
编写于
11月 23, 2020
作者:
L
LielinJiang
提交者:
GitHub
11月 23, 2020
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Optimize conv performance (#28766)
* optimize conv performance
上级
00e55ded
变更
2
显示空白变更内容
内联
并排
Showing
2 changed file
with
113 addition
and
113 deletion
+113
-113
python/paddle/nn/functional/conv.py
python/paddle/nn/functional/conv.py
+70
-89
python/paddle/nn/layer/conv.py
python/paddle/nn/layer/conv.py
+43
-24
未找到文件。
python/paddle/nn/functional/conv.py
浏览文件 @
8f8a02fd
...
@@ -95,6 +95,68 @@ def _update_padding_nd(padding, channel_last, num_dims):
...
@@ -95,6 +95,68 @@ def _update_padding_nd(padding, channel_last, num_dims):
return
padding
,
padding_algorithm
return
padding
,
padding_algorithm
def
_conv_nd
(
x
,
weight
,
bias
=
None
,
stride
=
1
,
padding
=
0
,
padding_algorithm
=
None
,
dilation
=
1
,
groups
=
1
,
data_format
=
"NCHW"
,
channel_dim
=
1
,
op_type
=
"conv2d"
,
use_cudnn
=
True
,
use_mkldnn
=
False
,
name
=
None
):
if
in_dygraph_mode
():
attrs
=
(
'strides'
,
stride
,
'paddings'
,
padding
,
'dilations'
,
dilation
,
'groups'
,
groups
,
'use_cudnn'
,
use_cudnn
,
'use_mkldnn'
,
use_mkldnn
,
'fuse_relu_before_depthwise_conv'
,
False
,
"padding_algorithm"
,
padding_algorithm
,
"data_format"
,
data_format
)
pre_bias
=
getattr
(
core
.
ops
,
op_type
)(
x
,
weight
,
*
attrs
)
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
pre_bias
,
bias
,
axis
=
channel_dim
)
else
:
out
=
pre_bias
else
:
inputs
=
{
'Input'
:
[
x
],
'Filter'
:
[
weight
]}
attrs
=
{
'strides'
:
stride
,
'paddings'
:
padding
,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
use_mkldnn
,
'fuse_relu_before_depthwise_conv'
:
False
,
"padding_algorithm"
:
padding_algorithm
,
"data_format"
:
data_format
}
check_variable_and_dtype
(
x
,
'x'
,
[
'float16'
,
'float32'
,
'float64'
],
op_type
)
helper
=
LayerHelper
(
op_type
,
**
locals
())
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
pre_bias
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Output"
:
[
pre_bias
]}
helper
.
append_op
(
type
=
op_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
if
bias
is
not
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
)
helper
.
append_op
(
type
=
'elementwise_add'
,
inputs
=
{
'X'
:
[
pre_bias
],
'Y'
:
[
bias
]},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'axis'
:
channel_dim
,
'use_mkldnn'
:
use_mkldnn
})
else
:
out
=
pre_bias
return
out
def
conv1d
(
x
,
def
conv1d
(
x
,
weight
,
weight
,
bias
=
None
,
bias
=
None
,
...
@@ -472,12 +534,13 @@ def conv2d(x,
...
@@ -472,12 +534,13 @@ def conv2d(x,
"received: the number of filters is {}, the shape of weight is {}"
"received: the number of filters is {}, the shape of weight is {}"
", the groups is {}"
.
format
(
num_filters
,
weight
.
shape
,
groups
))
", the groups is {}"
.
format
(
num_filters
,
weight
.
shape
,
groups
))
# use_cudnn = True if core.is_compiled_with_cuda() else False
cudnn_version
=
get_cudnn_version
()
cudnn_version
=
get_cudnn_version
()
use_cudnn
=
True
if
(
core
.
is_compiled_with_cuda
()
and
use_cudnn
=
True
if
(
core
.
is_compiled_with_cuda
()
and
cudnn_version
is
not
None
)
else
False
cudnn_version
is
not
None
)
else
False
use_mkldnn
=
core
.
globals
()[
"FLAGS_use_mkldnn"
]
# update attrs
# update attrs
padding
,
padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
2
)
padding
,
padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
2
)
stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
...
@@ -489,56 +552,9 @@ def conv2d(x,
...
@@ -489,56 +552,9 @@ def conv2d(x,
l_type
=
'depthwise_conv2d'
l_type
=
'depthwise_conv2d'
use_cudnn
=
False
use_cudnn
=
False
inputs
=
{
'Input'
:
[
x
],
'Filter'
:
[
weight
]}
return
_conv_nd
(
x
,
weight
,
bias
,
stride
,
padding
,
padding_algorithm
,
attrs
=
{
dilation
,
groups
,
data_format
,
channel_dim
,
l_type
,
'strides'
:
stride
,
use_cudnn
,
use_mkldnn
,
name
)
'paddings'
:
padding
,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
False
,
'fuse_relu_before_depthwise_conv'
:
False
,
"padding_algorithm"
:
padding_algorithm
,
"data_format"
:
data_format
}
if
in_dygraph_mode
():
attrs
=
(
'strides'
,
stride
,
'paddings'
,
padding
,
'dilations'
,
dilation
,
'groups'
,
groups
,
'use_cudnn'
,
use_cudnn
,
'use_mkldnn'
,
False
,
'fuse_relu_before_depthwise_conv'
,
False
,
"padding_algorithm"
,
padding_algorithm
,
"data_format"
,
data_format
)
pre_bias
=
getattr
(
core
.
ops
,
l_type
)(
x
,
weight
,
*
attrs
)
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
pre_bias
,
bias
,
axis
=
channel_dim
)
else
:
out
=
pre_bias
else
:
inputs
=
{
'Input'
:
[
x
],
'Filter'
:
[
weight
]}
attrs
=
{
'strides'
:
stride
,
'paddings'
:
padding
,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
False
,
'fuse_relu_before_depthwise_conv'
:
False
,
"padding_algorithm"
:
padding_algorithm
,
"data_format"
:
data_format
}
check_variable_and_dtype
(
x
,
'x'
,
[
'float16'
,
'float32'
,
'float64'
],
'conv2d'
)
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
pre_bias
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Output"
:
[
pre_bias
]}
helper
.
append_op
(
type
=
l_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
pre_bias
,
bias
,
axis
=
channel_dim
)
else
:
out
=
pre_bias
return
out
def
conv1d_transpose
(
x
,
def
conv1d_transpose
(
x
,
...
@@ -1201,44 +1217,9 @@ def conv3d(x,
...
@@ -1201,44 +1217,9 @@ def conv3d(x,
dilation
=
utils
.
convert_to_list
(
dilation
,
3
,
'dilation'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
3
,
'dilation'
)
op_type
=
"conv3d"
op_type
=
"conv3d"
if
in_dygraph_mode
():
return
_conv_nd
(
x
,
weight
,
bias
,
stride
,
padding
,
padding_algorithm
,
attrs
=
(
'strides'
,
stride
,
'paddings'
,
padding
,
'dilations'
,
dilation
,
dilation
,
groups
,
data_format
,
channel_dim
,
op_type
,
'groups'
,
groups
,
'use_cudnn'
,
use_cudnn
,
'use_mkldnn'
,
False
,
use_cudnn
,
False
,
name
)
"padding_algorithm"
,
padding_algorithm
,
"data_format"
,
data_format
)
pre_bias
=
getattr
(
core
.
ops
,
op_type
)(
x
,
weight
,
*
attrs
)
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
pre_bias
,
bias
,
axis
=
channel_dim
)
else
:
out
=
pre_bias
else
:
inputs
=
{
'Input'
:
[
x
],
'Filter'
:
[
weight
]}
attrs
=
{
'strides'
:
stride
,
'paddings'
:
padding
,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
False
,
"padding_algorithm"
:
padding_algorithm
,
"data_format"
:
data_format
}
helper
=
LayerHelper
(
op_type
,
**
locals
())
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
check_variable_and_dtype
(
x
,
'x'
,
[
'float16'
,
'float32'
,
'float64'
],
'conv3d'
)
pre_bias
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Output"
:
[
pre_bias
]}
helper
.
append_op
(
type
=
op_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
if
bias
is
not
None
:
out
=
nn
.
elementwise_add
(
pre_bias
,
bias
,
axis
=
channel_dim
)
else
:
out
=
pre_bias
return
out
def
conv3d_transpose
(
x
,
def
conv3d_transpose
(
x
,
...
...
python/paddle/nn/layer/conv.py
浏览文件 @
8f8a02fd
...
@@ -25,6 +25,8 @@ __all__ = [
...
@@ -25,6 +25,8 @@ __all__ = [
import
numpy
as
np
import
numpy
as
np
from
...fluid
import
core
from
...device
import
get_cudnn_version
from
...fluid.dygraph
import
layers
from
...fluid.dygraph
import
layers
from
...fluid.initializer
import
Normal
from
...fluid.initializer
import
Normal
from
..
import
functional
as
F
from
..
import
functional
as
F
...
@@ -83,6 +85,13 @@ class _ConvNd(layers.Layer):
...
@@ -83,6 +85,13 @@ class _ConvNd(layers.Layer):
"when padding_mode in ['reflect', 'replicate', 'circular'], type of padding must be int"
"when padding_mode in ['reflect', 'replicate', 'circular'], type of padding must be int"
)
)
channel_last
=
(
data_format
==
"NHWC"
)
or
(
data_format
==
"NDHWC"
)
or
(
data_format
==
"NLC"
)
if
channel_last
:
self
.
_channel_dim
=
len
(
data_format
)
-
1
else
:
self
.
_channel_dim
=
1
self
.
_stride
=
utils
.
convert_to_list
(
stride
,
dims
,
'stride'
)
self
.
_stride
=
utils
.
convert_to_list
(
stride
,
dims
,
'stride'
)
self
.
_dilation
=
utils
.
convert_to_list
(
dilation
,
dims
,
'dilation'
)
self
.
_dilation
=
utils
.
convert_to_list
(
dilation
,
dims
,
'dilation'
)
self
.
_kernel_size
=
utils
.
convert_to_list
(
kernel_size
,
dims
,
self
.
_kernel_size
=
utils
.
convert_to_list
(
kernel_size
,
dims
,
...
@@ -90,10 +99,15 @@ class _ConvNd(layers.Layer):
...
@@ -90,10 +99,15 @@ class _ConvNd(layers.Layer):
self
.
_padding
=
padding
self
.
_padding
=
padding
self
.
_padding_mode
=
padding_mode
self
.
_padding_mode
=
padding_mode
self
.
output_padding
=
output_padding
self
.
output_padding
=
output_padding
if
dims
!=
1
:
self
.
_padding
,
self
.
_padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
dims
)
if
transposed
:
if
transposed
:
filter_shape
=
[
self
.
_in_channels
,
out_channels
//
groups
filter_shape
=
[
self
.
_in_channels
,
out_channels
//
groups
]
+
self
.
_kernel_size
]
+
self
.
_kernel_size
self
.
_padding
,
self
.
_padding_algorithm
=
_update_padding_nd
(
padding
,
channel_last
,
dims
)
else
:
else
:
if
in_channels
%
groups
!=
0
:
if
in_channels
%
groups
!=
0
:
raise
ValueError
(
"in_channels must be divisible by groups."
)
raise
ValueError
(
"in_channels must be divisible by groups."
)
...
@@ -104,6 +118,8 @@ class _ConvNd(layers.Layer):
...
@@ -104,6 +118,8 @@ class _ConvNd(layers.Layer):
self
.
_reversed_padding_repeated_twice
=
_reverse_repeat_list
(
self
.
_reversed_padding_repeated_twice
=
_reverse_repeat_list
(
_paired_padding
,
2
)
_paired_padding
,
2
)
self
.
_padding
,
_
=
_update_padding_nd
(
0
,
channel_last
,
dims
)
filter_shape
=
[
out_channels
,
in_channels
//
groups
filter_shape
=
[
out_channels
,
in_channels
//
groups
]
+
self
.
_kernel_size
]
+
self
.
_kernel_size
...
@@ -112,6 +128,17 @@ class _ConvNd(layers.Layer):
...
@@ -112,6 +128,17 @@ class _ConvNd(layers.Layer):
self
.
bias
=
self
.
create_parameter
(
self
.
bias
=
self
.
create_parameter
(
attr
=
self
.
_bias_attr
,
shape
=
[
self
.
_out_channels
],
is_bias
=
True
)
attr
=
self
.
_bias_attr
,
shape
=
[
self
.
_out_channels
],
is_bias
=
True
)
cudnn_version
=
get_cudnn_version
()
self
.
_use_cudnn
=
True
if
(
core
.
is_compiled_with_cuda
()
and
cudnn_version
is
not
None
)
else
False
self
.
_op_type
=
"conv"
+
str
(
dims
)
+
'd'
if
dims
==
2
and
(
in_channels
==
groups
and
in_channels
!=
1
and
out_channels
%
in_channels
==
0
):
self
.
op_type
=
'depthwise_conv2d'
self
.
_use_cudnn
=
False
class
Conv1D
(
_ConvNd
):
class
Conv1D
(
_ConvNd
):
"""
"""
...
@@ -581,24 +608,20 @@ class Conv2D(_ConvNd):
...
@@ -581,24 +608,20 @@ class Conv2D(_ConvNd):
self
.
_reversed_padding_repeated_twice
,
self
.
_reversed_padding_repeated_twice
,
mode
=
self
.
_padding_mode
,
mode
=
self
.
_padding_mode
,
data_format
=
self
.
_data_format
)
data_format
=
self
.
_data_format
)
return
F
.
conv2d
(
x
,
self
.
weight
,
bias
=
self
.
bias
,
stride
=
self
.
_stride
,
dilation
=
self
.
_dilation
,
groups
=
self
.
_groups
,
data_format
=
self
.
_data_format
)
out
=
F
.
conv
2
d
(
out
=
F
.
conv
.
_conv_n
d
(
x
,
x
,
self
.
weight
,
self
.
weight
,
bias
=
self
.
bias
,
bias
=
self
.
bias
,
padding
=
self
.
_padding
,
stride
=
self
.
_stride
,
stride
=
self
.
_stride
,
padding
=
self
.
_padding
,
padding_algorithm
=
self
.
_padding_algorithm
,
dilation
=
self
.
_dilation
,
dilation
=
self
.
_dilation
,
groups
=
self
.
_groups
,
groups
=
self
.
_groups
,
data_format
=
self
.
_data_format
)
data_format
=
self
.
_data_format
,
channel_dim
=
self
.
_channel_dim
,
op_type
=
self
.
_op_type
,
use_cudnn
=
self
.
_use_cudnn
)
return
out
return
out
...
@@ -902,24 +925,20 @@ class Conv3D(_ConvNd):
...
@@ -902,24 +925,20 @@ class Conv3D(_ConvNd):
self
.
_reversed_padding_repeated_twice
,
self
.
_reversed_padding_repeated_twice
,
mode
=
self
.
_padding_mode
,
mode
=
self
.
_padding_mode
,
data_format
=
self
.
_data_format
)
data_format
=
self
.
_data_format
)
return
F
.
conv3d
(
x
,
self
.
weight
,
bias
=
self
.
bias
,
stride
=
self
.
_stride
,
dilation
=
self
.
_dilation
,
groups
=
self
.
_groups
,
data_format
=
self
.
_data_format
)
out
=
F
.
conv
3
d
(
out
=
F
.
conv
.
_conv_n
d
(
x
,
x
,
self
.
weight
,
self
.
weight
,
bias
=
self
.
bias
,
bias
=
self
.
bias
,
padding
=
self
.
_padding
,
stride
=
self
.
_stride
,
stride
=
self
.
_stride
,
padding
=
self
.
_padding
,
padding_algorithm
=
self
.
_padding_algorithm
,
dilation
=
self
.
_dilation
,
dilation
=
self
.
_dilation
,
groups
=
self
.
_groups
,
groups
=
self
.
_groups
,
data_format
=
self
.
_data_format
)
data_format
=
self
.
_data_format
,
channel_dim
=
self
.
_channel_dim
,
op_type
=
self
.
_op_type
,
use_cudnn
=
self
.
_use_cudnn
)
return
out
return
out
...
...
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