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体验新版 GitCode,发现更多精彩内容 >>
提交
e2ff300b
编写于
1月 20, 2019
作者:
W
WangZhen
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add UT for quantization.
上级
451896fc
变更
8
显示空白变更内容
内联
并排
Showing
8 changed file
with
336 addition
and
33 deletion
+336
-33
paddle/fluid/framework/details/build_strategy.cc
paddle/fluid/framework/details/build_strategy.cc
+1
-0
paddle/fluid/pybind/ir.cc
paddle/fluid/pybind/ir.cc
+53
-2
paddle/fluid/pybind/protobuf.cc
paddle/fluid/pybind/protobuf.cc
+1
-7
python/paddle/fluid/contrib/slim/graph/graph.py
python/paddle/fluid/contrib/slim/graph/graph.py
+68
-12
python/paddle/fluid/contrib/slim/quantization/__init__.py
python/paddle/fluid/contrib/slim/quantization/__init__.py
+20
-0
python/paddle/fluid/contrib/slim/quantization/quantization_performer.py
...fluid/contrib/slim/quantization/quantization_performer.py
+57
-12
python/paddle/fluid/contrib/slim/unitest/test_quantization_performer.py
...fluid/contrib/slim/unitest/test_quantization_performer.py
+135
-0
python/setup.py.in
python/setup.py.in
+1
-0
未找到文件。
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @
e2ff300b
...
...
@@ -24,6 +24,7 @@ limitations under the License. */
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace
paddle
{
...
...
paddle/fluid/pybind/ir.cc
浏览文件 @
e2ff300b
...
...
@@ -15,7 +15,9 @@
#include "paddle/fluid/pybind/ir.h"
#include <string>
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/var_desc.h"
...
...
@@ -24,6 +26,7 @@
namespace
py
=
pybind11
;
using
paddle
::
framework
::
ir
::
Graph
;
using
paddle
::
framework
::
ir
::
Node
;
using
paddle
::
framework
::
ir
::
GraphSafeRemoveNodes
;
using
paddle
::
framework
::
OpDesc
;
using
paddle
::
framework
::
ProgramDesc
;
using
paddle
::
framework
::
VarDesc
;
...
...
@@ -32,6 +35,7 @@ using pybind11::return_value_policy;
namespace
paddle
{
namespace
pybind
{
void
BindGraph
(
py
::
module
*
m
)
{
m
->
def
(
"graph_safe_remove_nodes"
,
GraphSafeRemoveNodes
);
py
::
class_
<
Graph
,
std
::
shared_ptr
<
Graph
>>
(
*
m
,
"Graph"
,
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
...
...
@@ -43,6 +47,7 @@ void BindGraph(py::module *m) {
.
def
(
"get_double"
,
&
Graph
::
Get
<
double
>
)
.
def
(
"get_string"
,
&
Graph
::
Get
<
std
::
string
>
)
.
def
(
"get_program"
,
&
Graph
::
Get
<
ProgramDesc
>
)
.
def
(
"get_marked_nodes"
,
&
Graph
::
Get
<
std
::
unordered_set
<
const
Node
*>>
)
.
def
(
"set"
,
[](
Graph
&
self
,
const
std
::
string
&
attr_name
,
int
attr
)
{
return
self
.
Set
(
attr_name
,
new
int
(
attr
));
})
.
def
(
"set"
,
...
...
@@ -63,6 +68,12 @@ void BindGraph(py::module *m) {
const
ProgramDesc
&
attr
)
{
return
self
.
Set
(
attr_name
,
new
ProgramDesc
(
attr
));
})
.
def
(
"set"
,
[](
Graph
&
self
,
const
std
::
string
&
attr_name
,
const
std
::
unordered_set
<
const
Node
*>
&
attr
)
{
return
self
.
Set
(
attr_name
,
new
std
::
unordered_set
<
const
Node
*>
(
attr
));
})
.
def
(
"erase"
,
&
Graph
::
Erase
)
.
def
(
"nodes"
,
&
Graph
::
Nodes
,
return_value_policy
::
reference
)
.
def
(
"create_var_node"
,
...
...
@@ -91,12 +102,52 @@ void BindNode(py::module *m) {
py
::
class_
<
Node
>
node
(
*
m
,
"Node"
);
node
.
def
(
"name"
,
&
Node
::
Name
)
.
def
(
"node_type"
,
&
Node
::
NodeType
)
.
def
(
"var"
,
&
Node
::
Var
)
.
def
(
"op"
,
&
Node
::
Op
)
.
def
(
"var"
,
&
Node
::
Var
,
return_value_policy
::
reference
)
.
def
(
"op"
,
&
Node
::
Op
,
return_value_policy
::
reference
)
.
def
(
"id"
,
&
Node
::
id
)
.
def
(
"is_op"
,
&
Node
::
IsOp
)
.
def
(
"is_var"
,
&
Node
::
IsVar
)
.
def
(
"is_ctrl_var"
,
&
Node
::
IsCtrlVar
)
.
def
(
"inputs_remove"
,
[](
Node
&
self
,
int
node_id
)
{
for
(
auto
it
=
self
.
inputs
.
begin
();
it
!=
self
.
inputs
.
end
();
it
++
)
{
if
((
*
it
)
->
id
()
==
node_id
)
{
self
.
inputs
.
erase
(
it
);
}
}
})
.
def
(
"inputs_remove"
,
[](
Node
&
self
,
Node
&
node
)
{
for
(
auto
it
=
self
.
inputs
.
begin
();
it
!=
self
.
inputs
.
end
();
it
++
)
{
if
(
*
it
==
&
node
)
{
self
.
inputs
.
erase
(
it
);
}
}
})
.
def
(
"inputs_append"
,
[](
Node
&
self
,
Node
&
node
)
{
self
.
inputs
.
push_back
(
&
node
);
})
.
def
(
"outputs_remove"
,
[](
Node
&
self
,
int
node_id
)
{
for
(
auto
it
=
self
.
outputs
.
begin
();
it
!=
self
.
outputs
.
end
();
it
++
)
{
if
((
*
it
)
->
id
()
==
node_id
)
{
self
.
outputs
.
erase
(
it
);
}
}
})
.
def
(
"outputs_remove"
,
[](
Node
&
self
,
Node
&
node
)
{
for
(
auto
it
=
self
.
outputs
.
begin
();
it
!=
self
.
outputs
.
end
();
it
++
)
{
if
(
*
it
==
&
node
)
{
self
.
outputs
.
erase
(
it
);
}
}
})
.
def
(
"outputs_append"
,
[](
Node
&
self
,
Node
&
node
)
{
self
.
outputs
.
push_back
(
&
node
);
})
.
def_readwrite
(
"inputs"
,
&
Node
::
inputs
)
.
def_readwrite
(
"outputs"
,
&
Node
::
outputs
);
...
...
paddle/fluid/pybind/protobuf.cc
浏览文件 @
e2ff300b
...
...
@@ -228,13 +228,7 @@ void BindBlockDesc(pybind11::module *m) {
void
BindVarDsec
(
pybind11
::
module
*
m
)
{
pybind11
::
class_
<
pd
::
VarDesc
>
var_desc
(
*
m
,
"VarDesc"
,
""
);
var_desc
.
def
(
"__init__"
,
[](
pd
::
VarDesc
&
self
,
const
pybind11
::
bytes
&
binary_str
)
{
std
::
string
str
(
binary_str
);
new
(
&
self
)
pd
::
VarDesc
(
str
);
},
pybind11
::
return_value_policy
::
reference
)
var_desc
.
def
(
pybind11
::
init
<
const
std
::
string
&>
())
.
def
(
"name"
,
&
pd
::
VarDesc
::
Name
,
pybind11
::
return_value_policy
::
reference
)
.
def
(
"set_name"
,
&
pd
::
VarDesc
::
SetName
)
.
def
(
"set_shape"
,
&
pd
::
VarDesc
::
SetShape
)
...
...
python/paddle/fluid/contrib/slim/graph/graph.py
浏览文件 @
e2ff300b
...
...
@@ -11,12 +11,14 @@
# 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
os
import
subprocess
from
....framework
import
Program
from
....framework
import
Block
from
....
import
core
__all__
=
[
'Graph'
,
'ImitationGraph'
,
'PyGraph'
]
__all__
=
[
'Graph'
,
'ImitationGraph'
,
'
IRGraph'
,
'
PyGraph'
]
class
PyGraph
(
object
):
...
...
@@ -30,17 +32,18 @@ class PyGraph(object):
self
.
graph
=
graph
def
all_parameters
(
self
):
param
s
=
[]
param
_nodes
=
set
()
for
node
in
self
.
graph
.
nodes
():
if
node
.
is_var
()
and
node
.
var
().
persistable
():
params
.
append
(
node
)
return
params
if
node
.
is_var
()
and
node
.
var
()
is
not
None
and
node
.
var
(
).
persistable
():
param_nodes
.
add
(
node
)
return
param_nodes
def
all_vars
(
self
):
return
[
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_var
()]
return
{
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_var
()}
def
all_ops
(
self
):
return
[
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_op
()]
return
{
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_op
()}
def
create_param_node
(
self
,
name
,
var_type
,
shape
,
var_dtype
):
var_desc
=
core
.
VarDesc
(
name
)
...
...
@@ -65,10 +68,16 @@ class PyGraph(object):
op_desc
.
set_type
(
op_type
)
for
attr
,
value
in
attrs
.
iteritems
():
self
.
_update_desc_attr
(
op_desc
,
attr
,
value
)
for
input_name
,
var_node
in
inputs
.
iteritems
():
op_desc
.
set_input
(
input_name
,
[
var_node
.
name
()])
for
output_name
,
var_node
in
outputs
.
iteritems
():
op_desc
.
set_output
(
output_name
,
[
var_node
.
name
()])
for
input_name
,
var_nodes
in
inputs
.
iteritems
():
if
not
isinstance
(
var_nodes
,
list
):
var_nodes
=
[
var_nodes
]
op_desc
.
set_input
(
input_name
,
[
var_node
.
name
()
for
var_node
in
var_nodes
])
for
output_name
,
var_nodes
in
outputs
.
iteritems
():
if
not
isinstance
(
var_nodes
,
list
):
var_nodes
=
[
var_nodes
]
op_desc
.
set_output
(
output_name
,
[
var_node
.
name
()
for
var_node
in
var_nodes
])
return
self
.
graph
.
create_op_node
(
op_desc
)
def
create_op_node_from_desc
(
self
,
op_desc
):
...
...
@@ -89,6 +98,49 @@ class PyGraph(object):
else
:
desc
.
_set_attr
(
name
,
val
)
def
safe_remove_nodes
(
self
,
remove_nodes
):
if
not
isinstance
(
remove_nodes
,
set
):
remove_nodes
=
set
(
remove_nodes
)
core
.
graph_safe_remove_nodes
(
self
.
graph
,
remove_nodes
)
def
draw_graph
(
self
,
save_path
,
name
,
marked_nodes
=
None
):
def
_convert_to_pdf
(
dot_file_path
):
pdf_save_path
=
os
.
path
.
splitext
(
dot_file_path
)[
0
]
+
'.pdf'
exited_code
=
subprocess
.
call
(
'dot -Tpdf '
+
dot_file_path
\
+
' -o '
+
pdf_save_path
,
shell
=
True
)
if
exited_code
!=
0
:
print
(
'The dot command is needed for creating pdf files.'
)
print
(
'The {} is saved as the dot filetype.'
.
format
(
dot_file_path
))
remove_ctr_vars
=
set
()
ops_num
=
0
for
node
in
self
.
graph
.
nodes
():
if
node
.
is_ctrl_var
():
remove_ctr_vars
.
add
(
node
)
elif
node
.
is_op
():
ops_num
+=
1
print
(
'Total ops num = {}.'
.
format
(
ops_num
))
self
.
safe_remove_nodes
(
remove_ctr_vars
)
if
marked_nodes
is
not
None
:
if
not
isinstance
(
marked_nodes
,
set
):
marked_nodes
=
set
(
marked_nodes
)
marked_nodes
=
marked_nodes
-
remove_ctr_vars
self
.
graph
.
set
(
'__graphviz__marked_node__'
,
marked_nodes
)
viz_dot_path
=
os
.
path
.
join
(
save_path
,
name
)
+
'.dot'
viz_pass
=
core
.
get_pass
(
'graph_viz_pass'
)
viz_pass
.
set_str
(
'graph_viz_path'
,
viz_dot_path
)
viz_pass
.
apply
(
self
.
graph
)
_convert_to_pdf
(
viz_dot_path
)
def
to_program
(
self
):
convert_pass
=
core
.
get_pass
(
'graph_to_program_pass'
)
convert_pass
.
set_program
(
'program'
,
Program
().
desc
)
convert_pass
.
apply
(
self
.
graph
)
program
=
Program
()
program
.
desc
=
convert_pass
.
get_program
(
'program'
)
return
program
class
Graph
(
object
):
"""
...
...
@@ -112,3 +164,7 @@ class ImitationGraph(Graph):
def
all_parameters
(
self
):
return
self
.
program
.
global_block
().
all_parameters
()
class
IRGraph
(
Graph
):
pass
python/paddle/fluid/contrib/slim/quantization/__init__.py
0 → 100644
浏览文件 @
e2ff300b
# Copyright (c) 2019 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
.
import
quantization_performer
from
.quantization_performer
import
*
__all__
=
quantization_performer
.
__all__
python/paddle/fluid/contrib/slim/quantization/quantization_performer.py
浏览文件 @
e2ff300b
...
...
@@ -19,6 +19,8 @@ from ....initializer import Constant
from
....
import
unique_name
from
..graph
import
PyGraph
__all__
=
[
'QuantizationPerformer'
]
class
QuantizationPerformer
(
object
):
def
__init__
(
self
,
...
...
@@ -108,19 +110,62 @@ class QuantizationPerformer(object):
graph
,
quant_var_node
,
scale_var_node
,
quant_bits
)
dequantized_vars
[
var_node
.
name
()]
=
dequant_var_node
self
.
_update_input
(
var_node
,
dequant_var_node
,
op
)
op
.
op
().
_rename_input
(
var_node
.
name
(),
dequant_var_node
.
name
())
def
_transform_backward
(
graph
,
op
):
no_dequanted_input_vars
=
True
for
var_node
in
op
.
inputs
:
if
var_node
.
name
()
in
dequantized_vars
:
dequant_var_node
=
dequantized_vars
[
var_node
.
name
()]
self
.
_update_input
(
var_node
,
dequant_var_node
,
op
)
op
.
op
().
_rename_input
(
var_node
.
name
(),
dequant_var_node
.
name
())
no_dequanted_input_vars
=
False
if
no_dequanted_input_vars
:
raise
ValueError
(
"There is no dequanted inputs for op %s."
%
(
op
.
name
()))
if
not
self
.
is_test
:
self
.
_create_global_step
(
graph
)
ops
=
graph
.
all_ops
()
# The process of _transform_forward and _transform_backward is needed in two for loops.
# The loop for transforming the forward graph:
for
op
in
ops
:
# transform the forward graph
if
op
.
name
()
in
self
.
quantizable_ops
:
_transform_forward
(
graph
,
op
)
# rename the inputs of backward op
# The loop for renaming the inputs of backward op.
for
op
in
ops
:
if
op
.
name
()
in
self
.
quantizable_grad_ops
:
_transform_backward
(
graph
,
op
)
return
self
.
need_inited_outer
def
_create_global_step
(
self
,
graph
):
if
self
.
weight_quantize_type
==
'range_abs_max'
or
\
self
.
activation_quantize_type
==
'range_abs_max'
:
counter_name
=
'@STEP_COUNTER@'
for
node
in
graph
.
all_vars
():
if
node
.
name
()
==
counter_name
:
self
.
global_step
=
node
if
self
.
global_step
is
None
:
global_step_in
=
graph
.
create_param_node
(
name
=
counter_name
,
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
shape
=
[
1
],
var_dtype
=
core
.
VarDesc
.
VarType
.
INT64
)
self
.
need_inited_outer
[
global_step_in
.
var
()]
=
\
Constant
(
value
=
0
,
force_cpu
=
True
)
global_step_out
=
graph
.
create_var_node_from_desc
(
global_step_in
.
var
())
increment_op
=
graph
.
create_op_node
(
op_type
=
'increment'
,
attrs
=
{
'step'
:
1.0
},
inputs
=
{
'X'
:
global_step_in
},
outputs
=
{
'Out'
:
global_step_out
})
self
.
_link_to
(
global_step_in
,
increment_op
)
self
.
_link_to
(
increment_op
,
global_step_out
)
self
.
global_step
=
global_step_out
def
_insert_quant_op
(
self
,
graph
,
var_node
,
quant_bits
,
quant_type
):
"""
Insert fake_quantize_op in the graph.
...
...
@@ -128,7 +173,7 @@ class QuantizationPerformer(object):
if
quant_type
==
'abs_max'
:
return
self
.
_insert_quant_abs_max_op
(
graph
,
var_node
,
quant_bits
)
elif
quant_type
==
'range_abs_max'
:
return
self
.
_inser_quant_range_abs_max_op
(
graph
,
var_node
,
return
self
.
_inser
t
_quant_range_abs_max_op
(
graph
,
var_node
,
quant_bits
)
def
_insert_quant_abs_max_op
(
self
,
graph
,
var_node
,
quant_bits
):
...
...
@@ -237,14 +282,14 @@ class QuantizationPerformer(object):
return
dequant_var_node
def
_update_input
(
self
,
old_input_node
,
new_input_node
,
op_node
):
old_input_node
.
outputs
.
remove
(
op_node
)
op_node
.
inputs
.
remove
(
old_input_node
)
new_input_node
.
outputs
.
append
(
op_node
)
op_node
.
inputs
.
append
(
new_input_node
)
def
_link_to
(
node_in
,
node_out
):
node_in
.
outputs
.
append
(
node_out
)
node_out
.
inputs
.
append
(
node_in
)
old_input_node
.
outputs
_
remove
(
op_node
)
op_node
.
inputs
_
remove
(
old_input_node
)
new_input_node
.
outputs
_
append
(
op_node
)
op_node
.
inputs
_
append
(
new_input_node
)
def
_link_to
(
self
,
node_in
,
node_out
):
node_in
.
outputs
_
append
(
node_out
)
node_out
.
inputs
_
append
(
node_in
)
def
_quantized_var_name
(
self
,
var_name
):
"""
...
...
python/paddle/fluid/contrib/slim/unitest/test_quantization_performer.py
0 → 100644
浏览文件 @
e2ff300b
# 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
unittest
import
random
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
six
from
paddle.fluid.framework
import
Program
from
paddle.fluid.contrib.slim.quantization
import
QuantizationPerformer
from
paddle.fluid.contrib.slim.graph
import
PyGraph
from
paddle.fluid
import
core
def
linear_fc
(
num
):
data
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
1
,
32
,
32
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
hidden
=
data
for
_
in
six
.
moves
.
xrange
(
num
):
hidden
=
fluid
.
layers
.
fc
(
hidden
,
size
=
128
,
act
=
'relu'
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
hidden
,
label
=
label
)
loss
=
fluid
.
layers
.
mean
(
loss
)
return
loss
def
residual_block
(
num
):
def
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
act
=
'relu'
,
bias_attr
=
False
):
tmp
=
fluid
.
layers
.
conv2d
(
input
=
input
,
filter_size
=
filter_size
,
num_filters
=
ch_out
,
stride
=
stride
,
padding
=
padding
,
act
=
None
,
bias_attr
=
bias_attr
)
return
fluid
.
layers
.
batch_norm
(
input
=
tmp
,
act
=
act
)
data
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
1
,
32
,
32
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
hidden
=
data
for
_
in
six
.
moves
.
xrange
(
num
):
conv
=
conv_bn_layer
(
hidden
,
16
,
3
,
1
,
1
,
act
=
None
,
bias_attr
=
True
)
short
=
conv_bn_layer
(
hidden
,
16
,
1
,
1
,
0
,
act
=
None
)
hidden
=
fluid
.
layers
.
elementwise_add
(
x
=
conv
,
y
=
short
,
act
=
'relu'
)
fc
=
fluid
.
layers
.
fc
(
input
=
hidden
,
size
=
10
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
fc
,
label
=
label
)
loss
=
fluid
.
layers
.
mean
(
loss
)
return
loss
class
TestQuantizationPerformer
(
unittest
.
TestCase
):
def
setUp
(
self
):
# since quant_op and dequant_op is not ready, use cos and sin for test
self
.
weight_quant_op_type
=
'fake_quantize_abs_max'
self
.
dequant_op_type
=
'fake_dequantize_max_abs'
self
.
quantizable_op_and_inputs
=
{
'conv2d'
:
[
'Input'
,
'Filter'
],
'depthwise_conv2d'
:
[
'Input'
,
'Filter'
],
'mul'
:
[
'X'
,
'Y'
]
}
self
.
quantizable_op_grad_and_inputs
=
{
'conv2d_grad'
:
[
'Input'
,
'Filter'
],
'depthwise_conv2d_grad'
:
[
'Input'
,
'Filter'
],
'mul_grad'
:
[
'X'
,
'Y'
]
}
def
linear_fc_quant
(
self
,
quant_type
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
linear_fc
(
3
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
graph
=
PyGraph
(
core
.
Graph
(
main
.
desc
))
performer
=
QuantizationPerformer
(
activation_quantize_type
=
quant_type
)
performer
.
quantize_transform
(
graph
,
False
)
marked_nodes
=
set
()
for
op
in
graph
.
all_ops
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
graph
.
draw_graph
(
'.'
,
'quantize_fc_'
+
quant_type
,
marked_nodes
)
def
test_linear_fc_quant_abs_max
(
self
):
self
.
act_quant_op_type
=
'fake_quantize_abs_max'
self
.
linear_fc_quant
(
'abs_max'
)
def
test_linear_fc_quant_range_abs_max
(
self
):
self
.
act_quant_op_type
=
'fake_quantize_range_abs_max'
self
.
linear_fc_quant
(
'range_abs_max'
)
def
residual_block_quant
(
self
,
quant_type
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
residual_block
(
2
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
graph
=
PyGraph
(
core
.
Graph
(
main
.
desc
))
performer
=
QuantizationPerformer
(
activation_quantize_type
=
quant_type
)
performer
.
quantize_transform
(
graph
,
False
)
marked_nodes
=
set
()
for
op
in
graph
.
all_ops
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
graph
.
draw_graph
(
'.'
,
'quantize_residual_'
+
quant_type
,
marked_nodes
)
def
test_residual_block_abs_max
(
self
):
self
.
act_quant_op_type
=
'fake_quantize_abs_max'
self
.
residual_block_quant
(
'abs_max'
)
def
test_residual_block_range_abs_max
(
self
):
self
.
act_quant_op_type
=
'fake_quantize_range_abs_max'
self
.
residual_block_quant
(
'range_abs_max'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/setup.py.in
浏览文件 @
e2ff300b
...
...
@@ -113,6 +113,7 @@ packages=['paddle',
'paddle.fluid.contrib.slim.core',
'paddle.fluid.contrib.slim.graph',
'paddle.fluid.contrib.slim.prune',
'paddle.fluid.contrib.slim.quantization',
'paddle.fluid.contrib.utils',
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details']
...
...
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