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43d30547
编写于
9月 12, 2018
作者:
T
tensor-tang
浏览文件
操作
浏览文件
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差异文件
Merge remote-tracking branch 'ups/develop' into refine/infershape
上级
8bb824bb
620392ef
变更
5
显示空白变更内容
内联
并排
Showing
5 changed file
with
193 addition
and
134 deletion
+193
-134
benchmark/fluid/models/resnet.py
benchmark/fluid/models/resnet.py
+108
-117
paddle/fluid/operators/conv_mkldnn_op.cc
paddle/fluid/operators/conv_mkldnn_op.cc
+43
-12
paddle/fluid/operators/conv_op.cc
paddle/fluid/operators/conv_op.cc
+3
-0
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+3
-2
python/paddle/fluid/transpiler/inference_transpiler.py
python/paddle/fluid/transpiler/inference_transpiler.py
+36
-3
未找到文件。
benchmark/fluid/models/resnet.py
浏览文件 @
43d30547
...
...
@@ -20,6 +20,7 @@ import functools
import
numpy
as
np
import
time
import
os
import
math
import
cProfile
,
pstats
,
StringIO
...
...
@@ -27,128 +28,120 @@ import paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.profiler
as
profiler
# from recordio_converter import imagenet_train, imagenet_test
from
imagenet_reader
import
train
,
val
train_parameters
=
{
"input_size"
:
[
3
,
224
,
224
],
"input_mean"
:
[
0.485
,
0.456
,
0.406
],
"input_std"
:
[
0.229
,
0.224
,
0.225
],
"learning_strategy"
:
{
"name"
:
"piecewise_decay"
,
"batch_size"
:
256
,
"epochs"
:
[
30
,
60
,
90
],
"steps"
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
}
}
class
ResNet
():
def
__init__
(
self
,
layers
=
50
,
is_train
=
True
):
self
.
params
=
train_parameters
self
.
layers
=
layers
self
.
is_train
=
is_train
def
net
(
self
,
input
,
class_dim
=
1000
):
layers
=
self
.
layers
supported_layers
=
[
50
,
101
,
152
]
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
,
layers
)
if
layers
==
50
:
depth
=
[
3
,
4
,
6
,
3
]
elif
layers
==
101
:
depth
=
[
3
,
4
,
23
,
3
]
elif
layers
==
152
:
depth
=
[
3
,
8
,
36
,
3
]
num_filters
=
[
64
,
128
,
256
,
512
]
conv
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
'relu'
)
conv
=
fluid
.
layers
.
pool2d
(
input
=
conv
,
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
pool_type
=
'max'
)
for
block
in
range
(
len
(
depth
)):
for
i
in
range
(
depth
[
block
]):
conv
=
self
.
bottleneck_block
(
input
=
conv
,
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
)
pool
=
fluid
.
layers
.
pool2d
(
input
=
conv
,
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
stdv
=
1.0
/
math
.
sqrt
(
pool
.
shape
[
1
]
*
1.0
)
out
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
class_dim
,
act
=
'softmax'
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
)))
return
out
def
conv_bn_layer
(
input
,
ch_out
,
def
conv_bn_layer
(
self
,
input
,
num_filters
,
filter_size
,
stride
,
padding
,
act
=
'relu'
,
is_train
=
True
):
conv1
=
fluid
.
layers
.
conv2d
(
stride
=
1
,
groups
=
1
,
act
=
None
):
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
num_filters
=
ch_out
,
stride
=
stride
,
padding
=
padding
,
padding
=
(
filter_size
-
1
)
//
2
,
groups
=
groups
,
act
=
None
,
bias_attr
=
False
)
return
fluid
.
layers
.
batch_norm
(
input
=
conv1
,
act
=
act
,
is_test
=
not
is_train
)
return
fluid
.
layers
.
batch_norm
(
input
=
conv
,
act
=
act
,
is_test
=
not
self
.
is_train
)
def
shortcut
(
input
,
ch_out
,
stride
,
is_train
=
True
):
ch_in
=
input
.
shape
[
1
]
# if args.data_format == 'NCHW' else input.shape[-1]
if
ch_in
!=
ch_out
:
return
conv_bn_layer
(
input
,
ch_out
,
1
,
stride
,
0
,
None
,
is_train
=
is_train
)
def
shortcut
(
self
,
input
,
ch_out
,
stride
):
ch_in
=
input
.
shape
[
1
]
if
ch_in
!=
ch_out
or
stride
!=
1
:
return
self
.
conv_bn_layer
(
input
,
ch_out
,
1
,
stride
)
else
:
return
input
def
bottleneck_block
(
self
,
input
,
num_filters
,
stride
):
conv0
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
1
,
act
=
'relu'
)
conv1
=
self
.
conv_bn_layer
(
input
=
conv0
,
num_filters
=
num_filters
,
filter_size
=
3
,
stride
=
stride
,
act
=
'relu'
)
conv2
=
self
.
conv_bn_layer
(
input
=
conv1
,
num_filters
=
num_filters
*
4
,
filter_size
=
1
,
act
=
None
)
def
basicblock
(
input
,
ch_out
,
stride
,
is_train
=
True
):
short
=
shortcut
(
input
,
ch_out
,
stride
,
is_train
=
is_train
)
conv1
=
conv_bn_layer
(
input
,
ch_out
,
3
,
stride
,
1
,
is_train
=
is_train
)
conv2
=
conv_bn_layer
(
conv1
,
ch_out
,
3
,
1
,
1
,
act
=
None
,
is_train
=
is_train
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
)
def
bottleneck
(
input
,
ch_out
,
stride
,
is_train
=
True
):
short
=
shortcut
(
input
,
ch_out
*
4
,
stride
,
is_train
=
is_train
)
conv1
=
conv_bn_layer
(
input
,
ch_out
,
1
,
stride
,
0
,
is_train
=
is_train
)
conv2
=
conv_bn_layer
(
conv1
,
ch_out
,
3
,
1
,
1
,
is_train
=
is_train
)
conv3
=
conv_bn_layer
(
conv2
,
ch_out
*
4
,
1
,
1
,
0
,
act
=
None
,
is_train
=
is_train
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv3
,
act
=
'relu'
)
def
layer_warp
(
block_func
,
input
,
ch_out
,
count
,
stride
):
res_out
=
block_func
(
input
,
ch_out
,
stride
)
for
i
in
range
(
1
,
count
):
res_out
=
block_func
(
res_out
,
ch_out
,
1
)
return
res_out
def
resnet_imagenet
(
input
,
class_dim
,
depth
=
50
,
data_format
=
'NCHW'
,
is_train
=
True
):
cfg
=
{
18
:
([
2
,
2
,
2
,
1
],
basicblock
),
34
:
([
3
,
4
,
6
,
3
],
basicblock
),
50
:
([
3
,
4
,
6
,
3
],
bottleneck
),
101
:
([
3
,
4
,
23
,
3
],
bottleneck
),
152
:
([
3
,
8
,
36
,
3
],
bottleneck
)
}
stages
,
block_func
=
cfg
[
depth
]
conv1
=
conv_bn_layer
(
input
,
ch_out
=
64
,
filter_size
=
7
,
stride
=
2
,
padding
=
3
)
pool1
=
fluid
.
layers
.
pool2d
(
input
=
conv1
,
pool_type
=
'avg'
,
pool_size
=
3
,
pool_stride
=
2
)
res1
=
layer_warp
(
block_func
,
pool1
,
64
,
stages
[
0
],
1
)
res2
=
layer_warp
(
block_func
,
res1
,
128
,
stages
[
1
],
2
)
res3
=
layer_warp
(
block_func
,
res2
,
256
,
stages
[
2
],
2
)
res4
=
layer_warp
(
block_func
,
res3
,
512
,
stages
[
3
],
2
)
pool2
=
fluid
.
layers
.
pool2d
(
input
=
res4
,
pool_size
=
7
,
pool_type
=
'avg'
,
pool_stride
=
1
,
global_pooling
=
True
)
out
=
fluid
.
layers
.
fc
(
input
=
pool2
,
size
=
class_dim
,
act
=
'softmax'
)
return
out
def
resnet_cifar10
(
input
,
class_dim
,
depth
=
32
,
data_format
=
'NCHW'
):
assert
(
depth
-
2
)
%
6
==
0
n
=
(
depth
-
2
)
//
6
short
=
self
.
shortcut
(
input
,
num_filters
*
4
,
stride
)
conv1
=
conv_bn_layer
(
input
=
input
,
ch_out
=
16
,
filter_size
=
3
,
stride
=
1
,
padding
=
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
64
,
n
,
2
)
pool
=
fluid
.
layers
.
pool2d
(
input
=
res3
,
pool_size
=
8
,
pool_type
=
'avg'
,
pool_stride
=
1
)
out
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
class_dim
,
act
=
'softmax'
)
return
out
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
)
def
_model_reader_dshape_classdim
(
args
,
is_train
):
model
=
resnet_cifar10
model
=
None
reader
=
None
if
args
.
data_set
==
"cifar10"
:
class_dim
=
10
if
args
.
data_format
==
'NCHW'
:
dshape
=
[
3
,
32
,
32
]
else
:
dshape
=
[
32
,
32
,
3
]
model
=
resnet_cifar10
if
is_train
:
reader
=
paddle
.
dataset
.
cifar
.
train10
()
else
:
reader
=
paddle
.
dataset
.
cifar
.
test10
()
elif
args
.
data_set
==
"flowers"
:
if
args
.
data_set
==
"flowers"
:
class_dim
=
102
if
args
.
data_format
==
'NCHW'
:
dshape
=
[
3
,
224
,
224
]
else
:
dshape
=
[
224
,
224
,
3
]
model
=
resnet_imagenet
if
is_train
:
reader
=
paddle
.
dataset
.
flowers
.
train
()
else
:
...
...
@@ -159,7 +152,6 @@ def _model_reader_dshape_classdim(args, is_train):
dshape
=
[
3
,
224
,
224
]
else
:
dshape
=
[
224
,
224
,
3
]
model
=
resnet_imagenet
if
not
args
.
data_path
:
raise
Exception
(
"Must specify --data_path when training with imagenet"
)
...
...
@@ -173,12 +165,11 @@ def _model_reader_dshape_classdim(args, is_train):
reader
=
train
(
xmap
=
False
)
else
:
reader
=
val
(
xmap
=
False
)
return
model
,
reader
,
dshape
,
class_dim
return
reader
,
dshape
,
class_dim
def
get_model
(
args
,
is_train
,
main_prog
,
startup_prog
):
model
,
reader
,
dshape
,
class_dim
=
_model_reader_dshape_classdim
(
args
,
is_train
)
reader
,
dshape
,
class_dim
=
_model_reader_dshape_classdim
(
args
,
is_train
)
pyreader
=
None
trainer_count
=
int
(
os
.
getenv
(
"PADDLE_TRAINERS"
))
...
...
@@ -198,7 +189,8 @@ def get_model(args, is_train, main_prog, startup_prog):
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
predict
=
model
(
input
,
class_dim
,
is_train
=
is_train
)
model
=
ResNet
(
is_train
=
is_train
)
predict
=
model
.
net
(
input
,
class_dim
=
class_dim
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
...
...
@@ -216,15 +208,14 @@ def get_model(args, is_train, main_prog, startup_prog):
total_images
=
1281167
/
trainer_count
step
=
int
(
total_images
/
args
.
batch_size
+
1
)
epochs
=
[
30
,
60
,
80
,
90
]
epochs
=
[
30
,
60
,
90
]
bd
=
[
step
*
e
for
e
in
epochs
]
base_lr
=
args
.
learning_rate
lr
=
[]
lr
=
[
base_lr
*
(
0.1
**
i
)
for
i
in
range
(
len
(
bd
)
+
1
)]
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
base_lr
,
#learning_rate=fluid.layers.piecewise_decay(
# boundaries=bd, values=lr),
learning_rate
=
fluid
.
layers
.
piecewise_decay
(
boundaries
=
bd
,
values
=
lr
),
momentum
=
0.9
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
1e-4
))
optimizer
.
minimize
(
avg_cost
)
...
...
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @
43d30547
...
...
@@ -130,12 +130,13 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireWeightsMemoryFromPrimitive
(
const
std
::
shared_ptr
<
mkldnn
::
memory
>
user_weights_memory_p
,
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
)
{
// NOLINT
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
// NOLINT
bool
is_persistent
=
false
)
{
auto
user_weights_pd
=
user_weights_memory_p
->
get_primitive_desc
();
auto
weights_pd
=
conv_pd_
->
weights_primitive_desc
();
return
this
->
AcquireMemory
(
weights_pd
,
user_weights_pd
,
user_weights_memory_p
,
"@weights_mem_p"
,
pipeline
);
pipeline
,
is_persistent
);
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireBiasMemoryFromPrimitive
(
...
...
@@ -266,6 +267,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
...
...
@@ -296,6 +299,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
bool
fuse_relu
=
ctx
.
Attr
<
bool
>
(
"fuse_relu"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
// TODO(pzelazko-intel) add support for group convolution and dilation
...
...
@@ -348,11 +352,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bias_tz
=
paddle
::
framework
::
vectorize2int
(
bias
->
dims
());
auto
bias_md
=
platform
::
MKLDNNMemDesc
(
bias_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
memory
::
format
::
x
);
conv_pd
=
ConvFwdPrimitiveDesc
(
src_md
,
weights_md
,
bias_md
,
dst_md
,
strides
,
paddings
,
mkldnn_engine
);
conv_pd
=
ConvFwdPrimitiveDesc
(
src_md
,
weights_md
,
bias_md
,
dst_md
,
strides
,
paddings
,
mkldnn_engine
,
fuse_relu
);
}
else
{
conv_pd
=
ConvFwdPrimitiveDesc
(
src_md
,
weights_md
,
dst_md
,
strides
,
paddings
,
mkldnn_engine
);
paddings
,
mkldnn_engine
,
fuse_relu
);
}
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx
.
SetBlob
(
key_conv_pd
,
conv_pd
);
...
...
@@ -371,7 +376,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto
src_memory_p
=
handler
.
AcquireSrcMemoryFromPrimitive
(
user_src_memory_p
,
pipeline
);
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryFromPrimitive
(
user_weights_memory_p
,
pipeline
);
user_weights_memory_p
,
pipeline
,
is_test
);
auto
dst_memory_p
=
handler
.
AcquireDstMemoryFromPrimitive
(
to_void_cast
<
T
>
(
output_data
));
...
...
@@ -402,11 +407,26 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
}
private:
mkldnn
::
primitive_attr
AddRelu
()
const
{
// Fusion with ReLU layer is executed through the PostOps feature. Create a
// PostOps object and configure it to execute an eltwise relu operation.
mkldnn
::
primitive_attr
conv_attr
;
constexpr
float
scale
=
1.0
f
;
constexpr
float
negative_slope
=
0.0
f
;
constexpr
float
placeholder
=
0.0
f
;
mkldnn
::
post_ops
post_operations
;
post_operations
.
append_eltwise
(
scale
,
mkldnn
::
algorithm
::
eltwise_relu
,
negative_slope
,
placeholder
);
conv_attr
.
set_post_ops
(
post_operations
);
return
conv_attr
;
}
std
::
unique_ptr
<
mkldnn
::
convolution_forward
::
primitive_desc
>
ConvFwdPrimitiveDesc
(
const
memory
::
desc
&
src
,
const
memory
::
desc
&
weights
,
const
memory
::
desc
&
dst
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
mkldnn
::
engine
&
engine
)
const
{
const
mkldnn
::
engine
&
engine
,
const
bool
fuse_relu
)
const
{
memory
::
dims
stride_dims
=
{
strides
[
0
],
strides
[
1
]};
memory
::
dims
padding_dims
=
{
paddings
[
0
],
paddings
[
1
]};
...
...
@@ -415,8 +435,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
dst
,
stride_dims
,
padding_dims
,
padding_dims
,
mkldnn
::
padding_kind
::
zero
);
auto
p_conv_pd
=
new
mkldnn
::
convolution_forward
::
primitive_desc
(
conv_desc
,
engine
);
mkldnn
::
primitive_attr
conv_attr
;
if
(
fuse_relu
)
{
conv_attr
=
AddRelu
();
}
auto
p_conv_pd
=
new
mkldnn
::
convolution_forward
::
primitive_desc
(
conv_desc
,
conv_attr
,
engine
);
return
std
::
unique_ptr
<
mkldnn
::
convolution_forward
::
primitive_desc
>
(
p_conv_pd
);
...
...
@@ -427,7 +452,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const
memory
::
desc
&
bias
,
const
memory
::
desc
&
dst
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
mkldnn
::
engine
&
engine
)
const
{
const
mkldnn
::
engine
&
engine
,
const
bool
fuse_relu
)
const
{
memory
::
dims
stride_dims
=
{
strides
[
0
],
strides
[
1
]};
memory
::
dims
padding_dims
=
{
paddings
[
0
],
paddings
[
1
]};
...
...
@@ -436,8 +462,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bias
,
dst
,
stride_dims
,
padding_dims
,
padding_dims
,
mkldnn
::
padding_kind
::
zero
);
auto
p_conv_pd
=
new
mkldnn
::
convolution_forward
::
primitive_desc
(
conv_desc
,
engine
);
mkldnn
::
primitive_attr
conv_attr
;
if
(
fuse_relu
)
{
conv_attr
=
AddRelu
();
}
auto
p_conv_pd
=
new
mkldnn
::
convolution_forward
::
primitive_desc
(
conv_desc
,
conv_attr
,
engine
);
return
std
::
unique_ptr
<
mkldnn
::
convolution_forward
::
primitive_desc
>
(
p_conv_pd
);
...
...
paddle/fluid/operators/conv_op.cc
浏览文件 @
43d30547
...
...
@@ -109,6 +109,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
}
void
Conv2DOpMaker
::
Make
()
{
AddAttr
<
bool
>
(
"is_test"
,
""
).
SetDefault
(
false
);
AddInput
(
"Input"
,
"(Tensor) The input tensor of convolution operator. "
...
...
@@ -161,6 +162,8 @@ void Conv2DOpMaker::Make() {
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"fuse_relu"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"data_format"
,
"(string, default NCHW) Only used in "
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
43d30547
...
...
@@ -192,7 +192,8 @@ class MKLDNNHandler {
mkldnn
::
memory
::
primitive_desc
&
user_mpd
,
// NOLINT
const
std
::
shared_ptr
<
mkldnn
::
memory
>
user_memory_p
,
const
std
::
string
&
suffix
,
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
)
{
// NOLINT
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
,
// NOLINT
bool
is_persistent
=
false
)
{
// create reorder primitive if the input format is not the preferred one
auto
local_key
=
key_
+
suffix
;
auto
key_reorder_p
=
key_
+
suffix
+
"reorder_p"
;
...
...
@@ -213,7 +214,7 @@ class MKLDNNHandler {
pipeline
.
push_back
(
*
reorder_p
);
}
dev_ctx_
.
SetBlob
(
local_key
,
target_memory_p
);
}
else
{
}
else
if
(
!
is_persistent
)
{
// Make reorder if needed
auto
reorder_p
=
std
::
static_pointer_cast
<
mkldnn
::
reorder
>
(
dev_ctx_
.
GetBlob
(
key_reorder_p
));
...
...
python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @
43d30547
...
...
@@ -60,12 +60,46 @@ class InferenceTranspiler(object):
if
not
isinstance
(
scope
,
core
.
Scope
):
raise
TypeError
(
"scope should be as Scope type or None"
)
use_mkldnn
=
bool
(
os
.
getenv
(
"FLAGS_use_mkldnn"
,
False
))
self
.
_fuse_batch_norm
(
program
,
place
,
scope
)
if
use_mkldnn
:
self
.
_fuse_relu_mkldnn
(
program
)
self
.
_fuse_conv_bias_mkldnn
(
program
)
self
.
_fuse_conv_relu_mkldnn
(
program
)
self
.
_fuse_bn_relu_mkldnn
(
program
)
def
_fuse_conv_relu_mkldnn
(
self
,
program
):
'''
Transpile the program by fused relu activation for MKLDNN program.
Relu activation following convolution OP can be fused by adding
'fuse_relu' attribute to convolution OP.
The result of fuse is:
- before:
- conv->relu->any_other_op
- after:
- conv->any_other_op
:param program: program to transpile
:type program: Program
'''
self
.
block
=
program
.
block
(
0
)
i
=
0
while
i
<
len
(
self
.
block
.
ops
):
current_op
=
self
.
block
.
ops
[
i
]
if
current_op
.
type
in
[
'conv2d'
]:
next_op
=
self
.
block
.
ops
[
i
+
1
]
if
next_op
.
type
==
'relu'
:
# modify conv OP to include relu
current_op
.
set_attr
(
"fuse_relu"
,
True
)
# remove conv OP
self
.
block
.
_remove_op
(
i
+
1
)
i
=
i
+
1
def
_fuse_relu_mkldnn
(
self
,
program
):
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program
=
program
.
clone
()
def
_fuse_bn_relu_mkldnn
(
self
,
program
):
'''
Transpile the program by fused relu activation for MKLDNN program.
...
...
@@ -160,7 +194,6 @@ class InferenceTranspiler(object):
self
.
block
.
_remove_op
(
i
+
1
)
# Remove old conv
self
.
block
.
_remove_op
(
i
+
1
)
# Remove elementwise_add
i
=
i
+
1
i
=
i
+
1
self
.
_remove_unused_var
()
# TODO(luotao): use clone() method to flush the program.desc in force,
...
...
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