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体验新版 GitCode,发现更多精彩内容 >>
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17833d30
编写于
4月 09, 2018
作者:
L
Luo Tao
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
fuse batch norm for conv operator without bias
上级
91004240
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
199 addition
and
0 deletion
+199
-0
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+1
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+9
-0
python/paddle/fluid/inference_transpiler.py
python/paddle/fluid/inference_transpiler.py
+174
-0
python/paddle/fluid/tests/book/test_image_classification.py
python/paddle/fluid/tests/book/test_image_classification.py
+15
-0
未找到文件。
python/paddle/fluid/__init__.py
浏览文件 @
17833d30
...
...
@@ -36,6 +36,7 @@ from distribute_transpiler import DistributeTranspiler
from
distribute_transpiler_simple
import
SimpleDistributeTranspiler
from
concurrency
import
(
Go
,
make_channel
,
channel_send
,
channel_recv
,
channel_close
,
Select
)
from
inference_transpiler
import
InferenceTranspiler
import
clip
from
memory_optimization_transpiler
import
memory_optimize
,
release_memory
import
profiler
...
...
python/paddle/fluid/framework.py
浏览文件 @
17833d30
...
...
@@ -920,6 +920,15 @@ class Block(object):
ops_in_cpp_index
+=
1
ops_in_python_index
+=
1
# sync ops inserted from c++ end
if
len
(
self
.
ops
)
!=
len
(
ops_in_cpp
)
and
start_index
==
0
and
len
(
self
.
ops
)
==
end_index
:
del
self
.
ops
[:]
for
index
in
range
(
len
(
ops_in_cpp
)):
op_desc
=
ops_in_cpp
[
index
]
op
=
Operator
(
self
,
op_desc
)
self
.
ops
.
append
(
op
)
assert
len
(
self
.
ops
)
==
len
(
ops_in_cpp
)
for
index
in
range
(
len
(
self
.
ops
)):
assert
self
.
ops
[
index
].
desc
==
ops_in_cpp
[
index
]
...
...
python/paddle/fluid/inference_transpiler.py
0 → 100644
浏览文件 @
17833d30
# 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.
import
numpy
as
np
import
os
import
shutil
from
.
import
core
class
InferenceTranspiler
:
def
transpile
(
self
,
program
,
scope
,
place
):
'''
Transpile the program to a inference program by fused batch normalization.
The batch normalization followed the convolution or fully connected layer
can be integrated with them. Doing so will give us a forward acceleration,
especially in environments like mobile or embedded.
For input X:
- Conv process: X = input * W + bias
- Batch norm process: X' = (X - mean) / std
- Scale Process: Y = a * X' + b
After fuse into one operation:
Y = (input * W + bias - mean) / std * a + b
= input * a * W / std + ((bias - mean) / std * a + b)
The operator transformation is:
- before:
- conv->batch_norm->any_other_op (bias == 0)
- conv->elementwise_add->batch_norm->any_other_op (bias != 0)
- after:
- conv->elementwise_add->any_other_op
The transpile stages are:
1. insert elementwise_add op when bias == 0, and adjust its input and output.
2. fuse the batch_norm's parameters to conv and elementwise_add operators.
3. remove batch_norm ops and its variables which are not used in any other ops.
4. remove unused variables.
:param program: program to transpile
:type program: Program
:param scope: inference scope
:type scope: Scope
:param place: inference place
:type place: Place
:return: program by fused batch normalization
:rtype: Program
'''
self
.
scope
=
scope
self
.
place
=
place
self
.
block_desc
=
program
.
get_desc
().
block
(
0
)
i
=
0
while
i
<
self
.
block_desc
.
op_size
():
current_op
=
self
.
block_desc
.
op
(
i
)
# TODO(luotao1): consider only conv2d now. fc would be delt later.
if
current_op
.
type
()
in
[
'conv2d'
]:
next_op
=
self
.
block_desc
.
op
(
i
+
1
)
# TODO(luotao1): consider only conv2d without bias now.
# If conv2d with bias, the next_op.type is elementwise_add.
if
(
next_op
.
type
()
==
'batch_norm'
):
# insert bias op
bias_op
=
self
.
_insert_bias_op
(
i
+
1
,
current_op
,
next_op
)
program
.
sync_with_cpp
()
# fuse batch_norm
self
.
_fuse_param
(
current_op
,
next_op
,
bias_op
)
# remove batch_norm_op
self
.
block_desc
.
remove_op
(
i
+
2
,
i
+
3
)
program
.
sync_with_cpp
()
i
=
i
+
1
i
=
i
+
1
self
.
_remove_unused_var
()
program
.
sync_with_cpp
()
return
program
# ====================== private transpiler functions =====================
def
_insert_bias_op
(
self
,
index
,
current_op
,
bn_op
):
'''
Construct elementwise_add operator for adding bias
and insert it into program.
:param index: insert location of bias_op
:type index: Int
:param current_op: current operator (conv or fc)
:type current_op: Operator
:param bn_op: batch norm operator
:type bn_op: Operator
:return: bias_op
:rtype: Operator
'''
bias_op
=
self
.
block_desc
.
insert_op
(
index
)
bias_op
.
set_type
(
"elementwise_add"
)
# The input of bias_op is current_op's output and Bias of bn_op
# The output of bias_op is bn_op's output
bias_op
.
set_input
(
"X"
,
current_op
.
output
(
"Output"
))
bias_op
.
set_input
(
"Y"
,
bn_op
.
input
(
"Bias"
))
bias_op
.
set_output
(
"Out"
,
bn_op
.
output
(
"Y"
))
bias_op
.
set_attr
(
'axis'
,
1
)
# dim_start=1
return
bias_op
def
_fuse_param
(
self
,
current_op
,
bn_op
,
bias_op
):
'''
fuse the batch_norm_op' parameters to current_op (conv or fc)
:param current_op: current operator (conv or fc)
:type current_op: Operator
:param bn_op: batch norm operator
:type bn_op: Operator
:param bias_op: elementwise_add operator for adding bias
:type bias_op: Operator
'''
def
_load_tensor
(
param_name
):
return
self
.
scope
.
find_var
(
param_name
[
0
]).
get_tensor
()
def
_load_param
(
param_name
):
return
np
.
array
(
_load_tensor
(
param_name
))
bias_bn
=
_load_param
(
bn_op
.
input
(
"Bias"
))
#Bias
scale_bn
=
_load_param
(
bn_op
.
input
(
"Scale"
))
#Scale
mean_bn
=
_load_param
(
bn_op
.
input
(
"Mean"
))
#Mean
var_bn
=
_load_param
(
bn_op
.
input
(
"Variance"
))
#Variance
# TODO(luotao1): consider only conv2d now. fc would be delt later.
current_param
=
_load_param
(
current_op
.
input
(
"Filter"
))
current_tensor
=
_load_tensor
(
current_op
.
input
(
"Filter"
))
std_bn
=
np
.
float32
(
np
.
sqrt
(
np
.
add
(
var_bn
,
1e-5
)))
tmp
=
np
.
float32
(
np
.
divide
(
scale_bn
,
std_bn
))
# add bias of batch_norm_op to conv2d
bias
=
np
.
zeros
(
bias_bn
.
shape
)
bias
=
np
.
float32
(
np
.
add
(
np
.
multiply
(
np
.
subtract
(
bias
,
mean_bn
),
tmp
),
bias_bn
))
bias_tensor
=
_load_tensor
(
bias_op
.
input
(
"Y"
))
bias_tensor
.
set
(
bias
,
self
.
place
)
# re-compute weight of conv2d
tmp
=
tmp
.
reshape
(
tmp
.
shape
[
0
],
-
1
)
dst_param
=
current_param
.
reshape
((
tmp
.
shape
[
0
],
-
1
))
dst_param
=
np
.
float32
(
np
.
multiply
(
dst_param
,
tmp
))
dst_param
=
dst_param
.
reshape
(
current_param
.
shape
)
# set the updated parameters
current_tensor
.
set
(
np
.
array
(
dst_param
),
self
.
place
)
def
_remove_unused_var
(
self
):
'''
remove unused varibles in program desc
'''
args
=
[]
for
i
in
xrange
(
0
,
self
.
block_desc
.
op_size
()):
current_op
=
self
.
block_desc
.
op
(
i
)
args
+=
current_op
.
input_arg_names
()
args
+=
current_op
.
output_arg_names
()
args
=
list
(
set
(
args
))
# unique the input and output arguments
for
var
in
self
.
block_desc
.
all_vars
():
if
var
.
name
()
not
in
args
:
self
.
block_desc
.
remove_var
(
var
.
name
())
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @
17833d30
...
...
@@ -22,6 +22,7 @@ import sys
import
numpy
import
unittest
import
os
import
numpy
as
np
def
resnet_cifar10
(
input
,
depth
=
32
):
...
...
@@ -224,6 +225,20 @@ def infer(use_cuda, save_dirname=None):
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
# Use inference_transpiler to speedup
t
=
fluid
.
InferenceTranspiler
()
inference_transpiler_program
=
t
.
transpile
(
inference_program
,
inference_scope
,
place
)
transpiler_results
=
exe
.
run
(
inference_transpiler_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
assert
len
(
results
[
0
])
==
len
(
transpiler_results
[
0
])
for
i
in
range
(
len
(
results
[
0
])):
np
.
testing
.
assert_almost_equal
(
results
[
0
][
i
],
transpiler_results
[
0
][
i
])
print
(
"infer results: "
,
results
[
0
])
...
...
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