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体验新版 GitCode,发现更多精彩内容 >>
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提交
e00c7a2e
编写于
2月 26, 2019
作者:
Z
Zhen Wang
提交者:
GitHub
2月 26, 2019
浏览文件
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差异文件
Merge pull request #15830 from wzzju/add_ir_node_encapsulation
add IrNode&IrVarNode&IrOpNode. test=develop
上级
a4b4ecd8
54893145
变更
5
展开全部
隐藏空白更改
内联
并排
Showing
5 changed file
with
549 addition
and
145 deletion
+549
-145
paddle/fluid/pybind/ir.cc
paddle/fluid/pybind/ir.cc
+7
-6
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
...ddle/fluid/contrib/slim/quantization/quantization_pass.py
+68
-55
python/paddle/fluid/contrib/slim/tests/test_graph.py
python/paddle/fluid/contrib/slim/tests/test_graph.py
+3
-3
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
...paddle/fluid/contrib/slim/tests/test_quantization_pass.py
+26
-31
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+445
-50
未找到文件。
paddle/fluid/pybind/ir.cc
浏览文件 @
e00c7a2e
...
...
@@ -14,6 +14,7 @@
#include "paddle/fluid/pybind/ir.h"
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
...
...
@@ -116,7 +117,7 @@ void BindNode(py::module *m) {
.
def
(
"is_var"
,
&
Node
::
IsVar
)
.
def
(
"is_ctrl_var"
,
&
Node
::
IsCtrlVar
)
.
def
(
"clear_inputs"
,
[](
Node
&
self
)
{
self
.
inputs
.
clear
();
})
.
def
(
"
inputs_remove
"
,
.
def
(
"
remove_input
"
,
[](
Node
&
self
,
int
node_id
)
{
auto
pos
=
std
::
find_if
(
self
.
inputs
.
begin
(),
self
.
inputs
.
end
(),
...
...
@@ -125,7 +126,7 @@ void BindNode(py::module *m) {
self
.
inputs
.
erase
(
pos
);
}
})
.
def
(
"
inputs_remove
"
,
.
def
(
"
remove_input
"
,
[](
Node
&
self
,
Node
&
node
)
{
auto
pos
=
std
::
find
(
self
.
inputs
.
begin
(),
self
.
inputs
.
end
(),
&
node
);
...
...
@@ -133,10 +134,10 @@ void BindNode(py::module *m) {
self
.
inputs
.
erase
(
pos
);
}
})
.
def
(
"
inputs_append
"
,
.
def
(
"
append_input
"
,
[](
Node
&
self
,
Node
&
node
)
{
self
.
inputs
.
push_back
(
&
node
);
})
.
def
(
"clear_outputs"
,
[](
Node
&
self
)
{
self
.
outputs
.
clear
();
})
.
def
(
"
outputs_remove
"
,
.
def
(
"
remove_output
"
,
[](
Node
&
self
,
int
node_id
)
{
auto
pos
=
std
::
find_if
(
self
.
outputs
.
begin
(),
self
.
outputs
.
end
(),
...
...
@@ -145,7 +146,7 @@ void BindNode(py::module *m) {
self
.
outputs
.
erase
(
pos
);
}
})
.
def
(
"
outputs_remove
"
,
.
def
(
"
remove_output
"
,
[](
Node
&
self
,
Node
&
node
)
{
auto
pos
=
std
::
find
(
self
.
outputs
.
begin
(),
self
.
outputs
.
end
(),
&
node
);
...
...
@@ -153,7 +154,7 @@ void BindNode(py::module *m) {
self
.
outputs
.
erase
(
pos
);
}
})
.
def
(
"
outputs_append
"
,
.
def
(
"
append_output
"
,
[](
Node
&
self
,
Node
&
node
)
{
self
.
outputs
.
push_back
(
&
node
);
})
.
def_readwrite
(
"inputs"
,
&
Node
::
inputs
)
.
def_readwrite
(
"outputs"
,
&
Node
::
outputs
);
...
...
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @
e00c7a2e
...
...
@@ -17,7 +17,9 @@ import numpy as np
import
six
from
.....
import
compat
as
cpt
from
....
import
core
from
....
import
Executor
from
....framework
import
IrGraph
from
....framework
import
IrNode
from
....framework
import
Program
from
....initializer
import
Constant
from
....
import
unique_name
...
...
@@ -31,7 +33,7 @@ __all__ = [
class
QuantizationTransformPass
(
object
):
def
__init__
(
self
,
scope
=
None
,
p
rogram_ex
e
=
None
,
p
lac
e
=
None
,
weight_bits
=
8
,
activation_bits
=
8
,
activation_quantize_type
=
'abs_max'
,
...
...
@@ -45,7 +47,7 @@ class QuantizationTransformPass(object):
scope(fluid.Scope): When activation use 'range_abs_max' as the quantize
type, this pass will create some new parameters. The scope is used to
initialize these new parameters.
p
rogram_exe(fluid.Executor): program_ex
e is used to initialize new
p
lace(fluid.CPUPlace|fluid.CUDAPlace): plac
e is used to initialize new
parameters described above.
weight_bits (int): quantization bit number for weights,
the bias is not quantized.
...
...
@@ -71,13 +73,13 @@ class QuantizationTransformPass(object):
from paddle.fluid import core
graph = IrGraph(core.Graph(program.desc), for_test=False)
exe = fluid.Executor(fluid.CPUPlace()
)
place = fluid.CPUPlace(
)
transform_pass = QuantizationTransformPass(fluid.global_scope(),
ex
e)
plac
e)
transform_pass.apply(graph)
"""
self
.
_scope
=
scope
self
.
_p
rogram_exe
=
program_ex
e
self
.
_p
lace
=
plac
e
self
.
_weight_bits
=
weight_bits
self
.
_activation_bits
=
activation_bits
...
...
@@ -118,7 +120,7 @@ class QuantizationTransformPass(object):
self
.
_is_test
=
graph
.
is_test
()
# marked the variable which has been dequantized.
dequantized_vars
=
collections
.
OrderedDict
()
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
var
s
()]
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
node
s
()]
def
_transform_forward
(
graph
,
op
):
for
var_node
in
op
.
inputs
:
...
...
@@ -149,7 +151,7 @@ class QuantizationTransformPass(object):
if
not
self
.
_is_test
:
self
.
_create_global_step
(
graph
)
ops
=
graph
.
all_ops
()
ops
=
graph
.
all_op
_node
s
()
# 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
:
...
...
@@ -163,8 +165,8 @@ class QuantizationTransformPass(object):
if
len
(
self
.
_need_initialized
)
>
0
:
assert
self
.
_scope
is
not
None
,
\
'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
assert
self
.
_p
rogram_ex
e
is
not
None
,
\
'The p
rogram_ex
e cannot be set None when activation_quantize_type equals to range_abs_max.'
assert
self
.
_p
lac
e
is
not
None
,
\
'The p
lac
e cannot be set None when activation_quantize_type equals to range_abs_max.'
init_program
=
Program
()
for
var_desc
,
initializer
in
six
.
iteritems
(
self
.
_need_initialized
):
var
=
init_program
.
global_block
().
create_var
(
...
...
@@ -175,7 +177,8 @@ class QuantizationTransformPass(object):
lod_level
=
var_desc
.
lod_level
(),
persistable
=
var_desc
.
persistable
())
initializer
(
var
,
init_program
.
global_block
())
self
.
_program_exe
.
run
(
program
=
init_program
,
scope
=
self
.
_scope
)
exe
=
Executor
(
self
.
_place
)
exe
.
run
(
program
=
init_program
,
scope
=
self
.
_scope
)
return
graph
...
...
@@ -183,11 +186,11 @@ class QuantizationTransformPass(object):
if
self
.
_weight_quantize_type
==
'range_abs_max'
or
\
self
.
_activation_quantize_type
==
'range_abs_max'
:
counter_name
=
cpt
.
to_text
(
'@STEP_COUNTER@'
)
for
node
in
graph
.
all_vars
():
for
node
in
graph
.
all_var
_node
s
():
if
node
.
name
()
==
counter_name
:
self
.
_global_step
=
node
if
self
.
_global_step
is
None
:
global_step_in
=
graph
.
create_p
aram
_node
(
global_step_in
=
graph
.
create_p
ersistable
_node
(
name
=
counter_name
,
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
shape
=
[
1
],
...
...
@@ -228,14 +231,14 @@ class QuantizationTransformPass(object):
quant_var_node
=
graph
.
create_var_node
(
name
=
self
.
_quantized_var_name
(
var_node
.
name
()),
var_type
=
var_node
.
var
().
type
(),
shape
=
var_node
.
var
().
shape
(),
var_dtype
=
var_node
.
var
().
dtype
())
var_type
=
var_node
.
type
(),
shape
=
var_node
.
shape
(),
var_dtype
=
var_node
.
dtype
())
scale_var_node
=
graph
.
create_var_node
(
name
=
self
.
_quantized_scale_name
(
var_node
.
name
()),
var_type
=
var_node
.
var
().
type
(),
shape
=
var_node
.
var
().
shape
(),
var_dtype
=
var_node
.
var
().
dtype
())
var_type
=
var_node
.
type
(),
shape
=
var_node
.
shape
(),
var_dtype
=
var_node
.
dtype
())
quant_op_node
=
graph
.
create_op_node
(
op_type
=
'fake_quantize_abs_max'
,
attrs
=
{
...
...
@@ -258,15 +261,15 @@ class QuantizationTransformPass(object):
quant_var_node
=
graph
.
create_var_node
(
name
=
self
.
_quantized_var_name
(
var_node
.
name
()),
var_type
=
var_node
.
var
().
type
(),
shape
=
var_node
.
var
().
shape
(),
var_dtype
=
var_node
.
var
().
dtype
())
var_type
=
var_node
.
type
(),
shape
=
var_node
.
shape
(),
var_dtype
=
var_node
.
dtype
())
scale_in_node
=
graph
.
create_p
aram
_node
(
scale_in_node
=
graph
.
create_p
ersistable
_node
(
name
=
self
.
_quantized_scale_name
(
var_node
.
name
()),
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
shape
=
[
1
],
var_dtype
=
var_node
.
var
().
dtype
())
var_dtype
=
var_node
.
dtype
())
self
.
_need_initialized
[
scale_in_node
.
var
()]
=
Constant
(
value
=
0.001
)
scale_out_node
=
graph
.
create_var_node_from_desc
(
scale_in_node
.
var
())
...
...
@@ -275,11 +278,11 @@ class QuantizationTransformPass(object):
if
not
self
.
_is_test
:
# The name of scales_var_node maybe 'scales_0', 'scales_1', etc.
scales_node
=
graph
.
create_p
aram
_node
(
scales_node
=
graph
.
create_p
ersistable
_node
(
name
=
unique_name
.
generate
(
'scales'
),
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
shape
=
[
self
.
_window_size
],
var_dtype
=
var_node
.
var
().
dtype
())
var_dtype
=
var_node
.
dtype
())
self
.
_need_initialized
[
scales_node
.
var
()]
=
Constant
(
value
=
0
)
inputs
[
'Iter'
]
=
self
.
_global_step
outputs
[
'OutScales'
]
=
scales_node
...
...
@@ -314,9 +317,9 @@ class QuantizationTransformPass(object):
dequant_var_node
=
graph
.
create_var_node
(
name
=
self
.
_dequantized_var_name
(
var_node
.
name
()),
var_type
=
var_node
.
var
().
type
(),
shape
=
var_node
.
var
().
shape
(),
var_dtype
=
var_node
.
var
().
dtype
())
var_type
=
var_node
.
type
(),
shape
=
var_node
.
shape
(),
var_dtype
=
var_node
.
dtype
())
max_range
=
(
1
<<
(
quant_bits
-
1
))
-
1
dequant_op_node
=
graph
.
create_op_node
(
op_type
=
'fake_dequantize_max_abs'
,
...
...
@@ -400,22 +403,22 @@ class QuantizationFreezePass(object):
Args:
graph(IrGraph): the applied graph.
"""
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
var
s
()]
ops
=
graph
.
all_ops
()
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
node
s
()]
ops
=
graph
.
all_op
_node
s
()
for
op_node
in
ops
:
op_name
=
op_node
.
name
()
if
op_name
in
self
.
_fake_quant_op_names
:
input_arg_name
=
op_node
.
op
().
input
(
'X'
)[
0
]
input_arg_name
=
op_node
.
input
(
'X'
)[
0
]
if
input_arg_name
in
persistable_vars
:
if
self
.
_weight_quantize_type
==
'abs_max'
:
param
=
self
.
_load_var
(
input_arg_name
)
scale_v
=
np
.
max
(
np
.
abs
(
param
))
else
:
scale_v
=
self
.
_load_var
(
op_node
.
op
().
output
(
'OutScale'
)
[
0
])[
0
]
scale_v
=
self
.
_load_var
(
op_node
.
output
(
'OutScale'
)
[
0
])[
0
]
self
.
_var_scale_map
[
input_arg_name
]
=
scale_v
else
:
scale_v
=
graph
.
var_node
(
op_node
.
o
p
().
o
utput
(
'OutScale'
)[
0
])
scale_v
=
graph
.
var_node
(
op_node
.
output
(
'OutScale'
)[
0
])
self
.
_var_scale_map
[
input_arg_name
]
=
scale_v
if
input_arg_name
in
persistable_vars
:
self
.
_remove_fake_quant_and_dequant_op
(
graph
,
op_node
)
...
...
@@ -425,13 +428,13 @@ class QuantizationFreezePass(object):
self
.
_weight_bits
)
self
.
_restore_var
(
input_arg_name
,
quantized_param_v
)
ops
=
graph
.
all_ops
()
ops
=
graph
.
all_op
_node
s
()
for
op_node
in
ops
:
op_name
=
op_node
.
name
()
if
op_name
in
self
.
_fake_dequant_op_names
:
self
.
_remove_fake_quant_and_dequant_op
(
graph
,
op_node
)
ops
=
graph
.
all_ops
()
ops
=
graph
.
all_op
_node
s
()
for
op_node
in
ops
:
op_name
=
op_node
.
name
()
if
op_name
in
self
.
_quantizable_ops
:
...
...
@@ -451,8 +454,8 @@ class QuantizationFreezePass(object):
return
graph
def
_remove_fake_quant_and_dequant_op
(
self
,
graph
,
op_node
):
k
=
op_node
.
o
p
().
o
utput
(
'Out'
)[
0
]
v
=
op_node
.
op
().
input
(
'X'
)[
0
]
k
=
op_node
.
output
(
'Out'
)[
0
]
v
=
op_node
.
input
(
'X'
)[
0
]
if
v
not
in
self
.
_op_input_rename_map
:
self
.
_op_input_rename_map
[
k
]
=
v
else
:
...
...
@@ -462,7 +465,7 @@ class QuantizationFreezePass(object):
def
_insert_post_dequant_op
(
self
,
graph
,
op_node
):
max_range
=
None
scale_var_node
=
None
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
var
s
()]
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
node
s
()]
for
var_node
in
op_node
.
inputs
:
name
=
var_node
.
name
()
if
name
in
self
.
_op_input_rename_map
:
...
...
@@ -480,7 +483,7 @@ class QuantizationFreezePass(object):
original_var_name
)
max_range
=
param_range
*
act_range
/
scale_v
else
:
assert
isinstance
(
scale_v
,
core
.
Node
)
assert
isinstance
(
scale_v
,
Ir
Node
)
scale_var_node
=
self
.
_var_scale_map
[
original_var_name
]
if
len
(
op_node
.
outputs
)
!=
1
:
...
...
@@ -490,9 +493,9 @@ class QuantizationFreezePass(object):
output_var_node
=
op_node
.
outputs
[
0
]
dequant_var_node
=
graph
.
create_var_node
(
name
=
self
.
_dequantized_var_name
(
output_var_node
.
name
()),
var_type
=
output_var_node
.
var
().
type
(),
shape
=
output_var_node
.
var
().
shape
(),
var_dtype
=
output_var_node
.
var
().
dtype
())
var_type
=
output_var_node
.
type
(),
shape
=
output_var_node
.
shape
(),
var_dtype
=
output_var_node
.
dtype
())
dequant_op_node
=
graph
.
create_op_node
(
op_type
=
'fake_dequantize_max_abs'
,
attrs
=
{
...
...
@@ -517,14 +520,19 @@ class QuantizationFreezePass(object):
def
_remove_unused_var_nodes
(
self
,
graph
):
all_used_vars
=
set
()
ops
=
graph
.
all_ops
()
ops
=
graph
.
all_op
_node
s
()
for
op_node
in
ops
:
for
input_node
in
op_node
.
inputs
:
all_used_vars
.
add
(
input_node
)
for
output_node
in
op_node
.
outputs
:
all_used_vars
.
add
(
output_node
)
all_unused_vars
=
graph
.
all_vars
()
-
all_used_vars
all_used_vars
=
{
n
.
node
for
n
in
all_used_vars
}
all_unused_vars
=
{
n
for
n
in
filter
(
lambda
node
:
node
.
node
not
in
all_used_vars
,
graph
.
all_var_nodes
())
}
graph
.
safe_remove_nodes
(
all_unused_vars
)
def
_original_var_name
(
self
,
var_name
):
...
...
@@ -583,8 +591,8 @@ class ConvertToInt8Pass(object):
Args:
graph(IrGraph): the applied graph.
"""
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
var
s
()]
ops
=
graph
.
all_ops
()
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_
node
s
()]
ops
=
graph
.
all_op
_node
s
()
input_map
=
{}
for
op_node
in
ops
:
op_name
=
op_node
.
name
()
...
...
@@ -605,10 +613,10 @@ class ConvertToInt8Pass(object):
def
_convert_to_int8
(
self
,
graph
,
var_node
):
int8_var_node_name
=
var_node
.
name
()
+
".int8"
int8_var_node
=
graph
.
create_p
aram
_node
(
int8_var_node
=
graph
.
create_p
ersistable
_node
(
name
=
cpt
.
to_text
(
int8_var_node_name
),
var_type
=
var_node
.
var
().
type
(),
shape
=
var_node
.
var
().
shape
(),
var_type
=
var_node
.
type
(),
shape
=
var_node
.
shape
(),
var_dtype
=
core
.
VarDesc
.
VarType
.
INT8
)
array
=
self
.
_load_var
(
var_node
.
name
())
self
.
_scope
.
var
(
int8_var_node_name
)
...
...
@@ -624,14 +632,19 @@ class ConvertToInt8Pass(object):
def
_remove_unused_var_nodes
(
self
,
graph
):
all_used_vars
=
set
()
ops
=
graph
.
all_ops
()
ops
=
graph
.
all_op
_node
s
()
for
op_node
in
ops
:
for
input_node
in
op_node
.
inputs
:
all_used_vars
.
add
(
input_node
)
for
output_node
in
op_node
.
outputs
:
all_used_vars
.
add
(
output_node
)
all_unused_vars
=
graph
.
all_vars
()
-
all_used_vars
all_used_vars
=
{
n
.
node
for
n
in
all_used_vars
}
all_unused_vars
=
{
n
for
n
in
filter
(
lambda
node
:
node
.
node
not
in
all_used_vars
,
graph
.
all_var_nodes
())
}
graph
.
safe_remove_nodes
(
all_unused_vars
)
...
...
@@ -655,11 +668,11 @@ class TransformForMobilePass(object):
Args:
graph(IrGraph): the graph will be transformed.
"""
ops
=
graph
.
all_ops
()
ops
=
graph
.
all_op
_node
s
()
for
op_node
in
ops
:
name
=
op_node
.
name
()
if
name
in
self
.
_fake_quant_op_names
:
op_node
.
op
().
set_type
(
'quantize'
)
op_node
.
set_type
(
'quantize'
)
quant_node
=
graph
.
create_op_node_from_desc
(
op_node
.
op
())
for
input_node
in
op_node
.
inputs
:
graph
.
link_to
(
input_node
,
quant_node
)
...
...
@@ -667,7 +680,7 @@ class TransformForMobilePass(object):
graph
.
link_to
(
quant_node
,
output_node
)
graph
.
safe_remove_nodes
(
op_node
)
if
name
in
self
.
_fake_dequant_op_names
:
op_node
.
op
().
set_type
(
'dequantize'
)
op_node
.
set_type
(
'dequantize'
)
dequant_node
=
graph
.
create_op_node_from_desc
(
op_node
.
op
())
for
input_node
in
op_node
.
inputs
:
graph
.
link_to
(
input_node
,
dequant_node
)
...
...
python/paddle/fluid/contrib/slim/tests/test_graph.py
浏览文件 @
e00c7a2e
...
...
@@ -61,16 +61,16 @@ class TestGraph(unittest.TestCase):
opt
.
minimize
(
loss
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
marked_nodes
=
set
()
for
op
in
graph
.
all_ops
():
for
op
in
graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
marked_nodes
.
add
(
op
)
graph
.
draw
(
'.'
,
'residual'
,
marked_nodes
)
self
.
assertFalse
(
graph
.
has_circle
())
self
.
assertEqual
(
graph
.
graph_num
(),
1
)
nodes
=
graph
.
topology_sort
()
self
.
assertEqual
(
len
(
nodes
),
len
(
graph
.
all_ops
()))
self
.
assertEqual
(
len
(
nodes
),
len
(
graph
.
all_op
_node
s
()))
nodes_map
=
graph
.
build_adjacency_list
()
self
.
assertEqual
(
len
(
nodes_map
),
len
(
graph
.
all_ops
()))
self
.
assertEqual
(
len
(
nodes_map
),
len
(
graph
.
all_op
_node
s
()))
nodes_num
=
len
(
graph
.
all_nodes
())
graph
.
safe_remove_nodes
(
marked_nodes
)
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
nodes_num
-
len
(
marked_nodes
))
...
...
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @
e00c7a2e
...
...
@@ -130,15 +130,16 @@ class TestQuantizationTransformPass(unittest.TestCase):
loss
=
linear_fc
(
3
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
transform_pass
=
QuantizationTransformPass
(
scope
=
fluid
.
global_scope
(),
p
rogram_exe
=
ex
e
,
p
lace
=
plac
e
,
activation_quantize_type
=
quant_type
)
transform_pass
.
apply
(
graph
)
marked_nodes
=
set
()
for
op
in
graph
.
all_ops
():
for
op
in
graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
graph
.
draw
(
'.'
,
'quantize_fc_'
+
quant_type
,
marked_nodes
)
...
...
@@ -146,7 +147,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
self
.
check_program
(
transform_pass
,
program
)
val_graph
=
IrGraph
(
core
.
Graph
(
program
.
desc
),
for_test
=
False
)
val_marked_nodes
=
set
()
for
op
in
val_graph
.
all_ops
():
for
op
in
val_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
val_marked_nodes
.
add
(
op
)
val_graph
.
draw
(
'.'
,
'val_fc_'
+
quant_type
,
val_marked_nodes
)
...
...
@@ -166,15 +167,16 @@ class TestQuantizationTransformPass(unittest.TestCase):
loss
=
residual_block
(
2
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
transform_pass
=
QuantizationTransformPass
(
scope
=
fluid
.
global_scope
(),
p
rogram_exe
=
ex
e
,
p
lace
=
plac
e
,
activation_quantize_type
=
quant_type
)
transform_pass
.
apply
(
graph
)
marked_nodes
=
set
()
for
op
in
graph
.
all_ops
():
for
op
in
graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
graph
.
draw
(
'.'
,
'quantize_residual_'
+
quant_type
,
marked_nodes
)
...
...
@@ -182,7 +184,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
self
.
check_program
(
transform_pass
,
program
)
val_graph
=
IrGraph
(
core
.
Graph
(
program
.
desc
),
for_test
=
False
)
val_marked_nodes
=
set
()
for
op
in
val_graph
.
all_ops
():
for
op
in
val_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
val_marked_nodes
.
add
(
op
)
val_graph
.
draw
(
'.'
,
'val_residual_'
+
quant_type
,
val_marked_nodes
)
...
...
@@ -231,17 +233,17 @@ class TestQuantizationFreezePass(unittest.TestCase):
with
fluid
.
scope_guard
(
scope
):
exe
.
run
(
startup
)
transform_pass
=
QuantizationTransformPass
(
scope
=
scope
,
p
rogram_exe
=
ex
e
,
activation_quantize_type
=
quant_type
)
scope
=
scope
,
p
lace
=
plac
e
,
activation_quantize_type
=
quant_type
)
transform_pass
.
apply
(
main_graph
)
transform_pass
.
apply
(
test_graph
)
dev_name
=
'_gpu_'
if
use_cuda
else
'_cpu_'
marked_nodes
=
set
()
for
op
in
main_graph
.
all_ops
():
for
op
in
main_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
main_graph
.
draw
(
'.'
,
'main'
+
dev_name
+
quant_type
,
marked_nodes
)
marked_nodes
=
set
()
for
op
in
test_graph
.
all_ops
():
for
op
in
test_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test'
+
dev_name
+
quant_type
,
marked_nodes
)
...
...
@@ -251,11 +253,6 @@ class TestQuantizationFreezePass(unittest.TestCase):
iters
=
5
batch_size
=
8
#train_exe = fluid.ParallelExecutor(
# main_program=quantized_main_program,
# use_cuda=bool(use_cuda),
# loss_name=loss.name,
# scope=scope)
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
...
...
@@ -269,9 +266,7 @@ class TestQuantizationFreezePass(unittest.TestCase):
loss_v
=
exe
.
run
(
program
=
quantized_main_program
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
loss
])
#loss_v = train_exe.run(feed=feeder.feed(data),
# fetch_list=[loss.name])
#print('{}: {}'.format('loss' + dev_name + quant_type, loss_v))
print
(
'{}: {}'
.
format
(
'loss'
+
dev_name
+
quant_type
,
loss_v
))
test_data
=
next
(
test_reader
())
with
fluid
.
program_guard
(
quantized_test_program
):
...
...
@@ -287,7 +282,7 @@ class TestQuantizationFreezePass(unittest.TestCase):
freeze_pass
=
QuantizationFreezePass
(
scope
=
scope
,
place
=
place
)
freeze_pass
.
apply
(
test_graph
)
marked_nodes
=
set
()
for
op
in
test_graph
.
all_ops
():
for
op
in
test_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test_freeze'
+
dev_name
+
quant_type
,
...
...
@@ -299,21 +294,21 @@ class TestQuantizationFreezePass(unittest.TestCase):
feed
=
feeder
.
feed
(
test_data
),
fetch_list
=
[
loss
])
self
.
assertAlmostEqual
(
test_loss1
,
test_loss2
,
delta
=
5e-3
)
#
print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1))
#
print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2))
print
(
'{}: {}'
.
format
(
'test_loss1'
+
dev_name
+
quant_type
,
test_loss1
))
print
(
'{}: {}'
.
format
(
'test_loss2'
+
dev_name
+
quant_type
,
test_loss2
))
w_freeze
=
np
.
array
(
scope
.
find_var
(
'conv2d_1.w_0'
).
get_tensor
())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
#
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
#
np.sum(w_freeze)))
#
print('{}: {}'.format('w_quant' + dev_name + quant_type,
#
np.sum(w_quant)))
print
(
'{}: {}'
.
format
(
'w_freeze'
+
dev_name
+
quant_type
,
np
.
sum
(
w_freeze
)))
print
(
'{}: {}'
.
format
(
'w_quant'
+
dev_name
+
quant_type
,
np
.
sum
(
w_quant
)))
# Convert parameter to 8-bit.
convert_int8_pass
=
ConvertToInt8Pass
(
scope
=
scope
,
place
=
place
)
convert_int8_pass
.
apply
(
test_graph
)
marked_nodes
=
set
()
for
op
in
test_graph
.
all_ops
():
for
op
in
test_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test_int8'
+
dev_name
+
quant_type
,
marked_nodes
)
...
...
@@ -330,14 +325,14 @@ class TestQuantizationFreezePass(unittest.TestCase):
w_8bit
=
np
.
array
(
scope
.
find_var
(
'conv2d_1.w_0.int8'
).
get_tensor
())
self
.
assertEqual
(
w_8bit
.
dtype
,
np
.
int8
)
self
.
assertEqual
(
np
.
sum
(
w_8bit
),
np
.
sum
(
w_freeze
))
#
print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit)))
#
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
#
np.sum(w_freeze)))
print
(
'{}: {}'
.
format
(
'w_8bit'
+
dev_name
+
quant_type
,
np
.
sum
(
w_8bit
)))
print
(
'{}: {}'
.
format
(
'w_freeze'
+
dev_name
+
quant_type
,
np
.
sum
(
w_freeze
)))
mobile_pass
=
TransformForMobilePass
()
mobile_pass
.
apply
(
test_graph
)
marked_nodes
=
set
()
for
op
in
test_graph
.
all_ops
():
for
op
in
test_graph
.
all_op
_node
s
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test_mobile'
+
dev_name
+
quant_type
,
...
...
python/paddle/fluid/framework.py
浏览文件 @
e00c7a2e
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