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dde19a0f
编写于
1月 24, 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 quantization freeze pass.
上级
f4dec5cd
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
450 addition
and
19 deletion
+450
-19
paddle/fluid/pybind/ir.cc
paddle/fluid/pybind/ir.cc
+11
-0
python/CMakeLists.txt
python/CMakeLists.txt
+1
-0
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
...ddle/fluid/contrib/slim/quantization/quantization_pass.py
+180
-7
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
+6
-0
python/paddle/fluid/contrib/slim/tests/__init__.py
python/paddle/fluid/contrib/slim/tests/__init__.py
+0
-0
python/paddle/fluid/contrib/slim/tests/configs/config.yaml
python/paddle/fluid/contrib/slim/tests/configs/config.yaml
+1
-1
python/paddle/fluid/contrib/slim/tests/configs/pruners.yaml
python/paddle/fluid/contrib/slim/tests/configs/pruners.yaml
+0
-0
python/paddle/fluid/contrib/slim/tests/configs/pruners_0.yaml
...on/paddle/fluid/contrib/slim/tests/configs/pruners_0.yaml
+0
-0
python/paddle/fluid/contrib/slim/tests/test_factory.py
python/paddle/fluid/contrib/slim/tests/test_factory.py
+1
-1
python/paddle/fluid/contrib/slim/tests/test_graph.py
python/paddle/fluid/contrib/slim/tests/test_graph.py
+80
-0
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
...paddle/fluid/contrib/slim/tests/test_quantization_pass.py
+120
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+50
-10
未找到文件。
paddle/fluid/pybind/ir.cc
浏览文件 @
dde19a0f
...
...
@@ -17,6 +17,7 @@
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
...
...
@@ -27,6 +28,10 @@ namespace py = pybind11;
using
paddle
::
framework
::
ir
::
Graph
;
using
paddle
::
framework
::
ir
::
Node
;
using
paddle
::
framework
::
ir
::
GraphSafeRemoveNodes
;
using
paddle
::
framework
::
ir
::
HasCircle
;
using
paddle
::
framework
::
ir
::
GraphNum
;
using
paddle
::
framework
::
ir
::
TopologySortOperations
;
using
paddle
::
framework
::
ir
::
BuildOperationAdjList
;
using
paddle
::
framework
::
OpDesc
;
using
paddle
::
framework
::
ProgramDesc
;
using
paddle
::
framework
::
VarDesc
;
...
...
@@ -36,6 +41,12 @@ namespace paddle {
namespace
pybind
{
void
BindGraph
(
py
::
module
*
m
)
{
m
->
def
(
"graph_safe_remove_nodes"
,
GraphSafeRemoveNodes
);
m
->
def
(
"has_circle"
,
HasCircle
);
m
->
def
(
"graph_num"
,
GraphNum
);
m
->
def
(
"topology_sort"
,
TopologySortOperations
,
return_value_policy
::
reference
);
m
->
def
(
"build_adjacency_list"
,
BuildOperationAdjList
,
return_value_policy
::
reference
);
py
::
class_
<
Graph
,
std
::
shared_ptr
<
Graph
>>
(
*
m
,
"Graph"
,
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
...
...
python/CMakeLists.txt
浏览文件 @
dde19a0f
...
...
@@ -64,6 +64,7 @@ if (WITH_TESTING)
add_subdirectory
(
paddle/dataset/tests
)
add_subdirectory
(
paddle/fluid/tests
)
add_subdirectory
(
paddle/fluid/contrib/tests
)
add_subdirectory
(
paddle/fluid/contrib/slim/tests
)
endif
()
install
(
DIRECTORY
${
PADDLE_PYTHON_PACKAGE_DIR
}
DESTINATION opt/paddle/share/wheels
...
...
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @
dde19a0f
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
import
collections
import
numpy
as
np
from
....
import
core
from
....framework
import
IrGraph
from
....framework
import
Program
...
...
@@ -88,10 +89,6 @@ class QuantizationTransformPass(object):
self
.
_quantizable_grad_ops
=
[
'%s_grad'
%
(
op
)
for
op
in
self
.
_quantizable_ops
]
self
.
_fake_quant_op_types
=
[
'fake_quantize_abs_max'
,
'fake_quantize_range_abs_max'
]
self
.
_fake_dequant_op_types
=
[
'fake_dequantize_max_abs'
]
self
.
_is_test
=
None
self
.
_global_step
=
None
...
...
@@ -102,17 +99,17 @@ class QuantizationTransformPass(object):
self
.
_is_test
=
graph
.
is_test
()
# marked the variable which has been dequantized.
dequantized_vars
=
collections
.
OrderedDict
()
p
arams
=
[
p
.
name
()
for
p
in
graph
.
all_paramete
rs
()]
p
ersistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_va
rs
()]
def
_transform_forward
(
graph
,
op
):
for
var_node
in
op
.
inputs
:
if
var_node
.
name
()
in
dequantized_vars
:
dequant_var_node
=
dequantized_vars
[
var_node
.
name
()]
else
:
quant_bits
=
self
.
_weight_bits
if
var_node
.
name
()
in
p
aram
s
\
quant_bits
=
self
.
_weight_bits
if
var_node
.
name
()
in
p
ersistable_var
s
\
else
self
.
_activation_bits
quant_type
=
self
.
_weight_quantize_type
if
var_node
.
name
()
\
in
p
aram
s
else
self
.
_activation_quantize_type
in
p
ersistable_var
s
else
self
.
_activation_quantize_type
quant_var_node
,
scale_var_node
=
self
.
_insert_quant_op
(
graph
,
var_node
,
quant_bits
,
quant_type
)
dequant_var_node
=
self
.
_insert_dequant_op
(
...
...
@@ -316,3 +313,179 @@ class QuantizationTransformPass(object):
Return the scale name of quantized variable for the input `var_name`.
"""
return
"%s.scale"
%
(
var_name
)
class
QuantizationFreezePass
(
object
):
def
__init__
(
self
,
scope
,
place
,
weight_bits
=
8
,
activation_bits
=
8
,
weight_quantize_type
=
'abs_max'
):
assert
scope
is
not
None
,
\
'The scope cannot be set None.'
assert
place
is
not
None
,
\
'The place cannot be set None.'
self
.
_scope
=
scope
self
.
_place
=
place
self
.
_weight_bits
=
weight_bits
self
.
_activation_bits
=
activation_bits
self
.
_weight_quantize_type
=
weight_quantize_type
self
.
_quantizable_ops
=
[
'conv2d'
,
'depthwise_conv2d'
,
'mul'
]
self
.
_fake_quant_op_names
=
[
'fake_quantize_abs_max'
,
'fake_quantize_range_abs_max'
]
self
.
_fake_dequant_op_names
=
[
'fake_dequantize_max_abs'
]
self
.
_op_input_rename_map
=
collections
.
OrderedDict
()
self
.
_op_output_rename_map
=
collections
.
OrderedDict
()
self
.
_var_scale_map
=
collections
.
OrderedDict
()
def
apply
(
self
,
graph
):
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_vars
()]
ops
=
graph
.
all_ops
()
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
]
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
]
self
.
_var_scale_map
[
input_arg_name
]
=
scale_v
else
:
scale_v
=
graph
.
var_node
(
op_node
.
op
().
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
)
# quantize weight and restore
param_v
=
self
.
_load_var
(
input_arg_name
)
quantized_param_v
=
self
.
_quant
(
param_v
,
scale_v
,
self
.
weight_bits
)
self
.
_restore_var
(
input_arg_name
,
quantized_param_v
)
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
)
for
op_node
in
ops
:
op_name
=
op_node
.
name
()
if
op_name
in
self
.
_quantizable_ops
:
self
.
_insert_post_dequant_op
(
graph
,
op_node
)
for
op_node
in
ops
:
# insert dequant_op after fc/conv, need to rename inputs of the followed ops
for
var_node
in
op_node
.
inputs
:
name
=
var_node
.
name
()
if
name
in
self
.
_op_output_rename_map
:
old_in
=
graph
.
var_node
(
name
)
new_in
=
graph
.
var_node
(
self
.
_op_output_rename_map
[
name
])
graph
.
update_input_link
(
old_in
,
new_in
,
op_node
)
# remove the unused var node in the graph
self
.
_remove_unused_var_nodes
(
graph
)
def
_remove_fake_quant_and_dequant_op
(
self
,
graph
,
op_node
):
k
=
op_node
.
op
().
output
(
'Out'
)[
0
]
v
=
op_node
.
op
().
input
(
'X'
)[
0
]
if
v
not
in
self
.
_op_input_rename_map
:
self
.
_op_input_rename_map
[
k
]
=
v
else
:
self
.
_op_input_rename_map
[
k
]
=
self
.
_op_input_rename_map
[
v
]
graph
.
save_remove_nodes
(
op_node
)
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_vars
()]
for
var_node
in
op_node
.
op
().
inputs
:
name
=
var_node
.
name
()
if
name
in
self
.
_op_input_rename_map
:
old_in
=
graph
.
var_node
(
name
)
new_in
=
graph
.
var_node
(
self
.
_op_input_rename_map
[
name
])
graph
.
update_input_link
(
old_in
,
new_in
,
op_node
)
original_var_name
=
self
.
_original_var_name
(
name
)
if
original_var_name
in
persistable_vars
:
param_range
=
(
1
<<
(
self
.
_weight_bits
-
1
))
-
1
act_range
=
(
1
<<
(
self
.
_activation_bits
-
1
))
-
1
scale_v
=
self
.
_var_scale_map
[
original_var_name
]
assert
self
.
_is_float
(
scale_v
),
'The scale of parameter %s is not a float.'
%
(
original_var_name
)
max_range
=
param_range
*
act_range
/
scale_v
else
:
assert
isinstance
(
scale_v
,
core
.
Node
)
scale_var_node
=
self
.
_var_scale_map
[
original_var_name
]
if
len
(
op_node
.
op
().
outputs
)
!=
1
:
raise
ValueError
(
"Only support one output, but op %s has"
" more than one output."
%
(
op_node
.
name
()))
output_var_node
=
op_node
.
op
().
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
())
dequant_op_node
=
graph
.
create_op_node
(
op_type
=
'fake_dequantize_max_abs'
,
attrs
=
{
'max_range'
:
float
(
max_range
)},
inputs
=
{
'X'
:
output_var_node
,
'Scale'
:
scale_var_node
},
outputs
=
{
'Out'
:
dequant_var_node
})
graph
.
link_to
(
output_var_node
,
dequant_op_node
)
graph
.
link_to
(
scale_var_node
,
dequant_op_node
)
graph
.
link_to
(
dequant_op_node
,
dequant_var_node
)
self
.
_op_output_rename_map
[
output_var_node
.
name
(
)]
=
dequant_var_node
.
name
()
return
dequant_var_node
def
_load_var
(
self
,
name
):
return
np
.
array
(
self
.
_scope
.
find_var
(
name
).
get_tensor
())
def
_restore_var
(
self
,
name
,
arr
):
t
=
self
.
_scope
.
find_var
(
name
).
get_tensor
()
t
.
set
(
arr
,
self
.
_place
)
def
_remove_unused_var_nodes
(
self
,
graph
):
all_used_vars
=
set
()
ops
=
graph
.
all_ops
()
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
graph
.
safe_remove_nodes
(
all_unused_vars
)
def
_original_var_name
(
self
,
var_name
):
"""
Return the original variable name.
"""
if
var_name
.
endswith
(
'.quantized.dequantized'
):
return
var_name
[:
-
len
(
'.quantized.dequantized'
)]
if
var_name
.
endswith
(
'.quantized'
):
return
var_name
[:
-
len
(
'.quantized'
)]
if
var_name
.
endswith
(
'.dequantized'
):
return
var_name
[:
-
len
(
'.dequantized'
)]
if
var_name
.
endswith
(
'.scale'
):
return
var_name
[:
-
len
(
'.scale'
)]
else
:
return
var_name
def
_dequantized_var_name
(
self
,
var_name
):
"""
Return dequantized variable name for the input `var_name`.
"""
return
"%s.dequantized"
%
(
var_name
)
def
_is_float
(
v
):
return
isinstance
(
v
,
float
)
or
isinstance
(
v
,
np
.
float32
)
\
or
isinstance
(
v
,
np
.
float64
)
def
_quant
(
x
,
scale
,
num_bits
):
return
np
.
round
(
x
/
scale
*
((
1
<<
(
num_bits
-
1
))
-
1
))
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
0 → 100644
浏览文件 @
dde19a0f
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
foreach
(
src
${
TEST_OPS
}
)
py_test
(
${
src
}
SRCS
${
src
}
.py
)
endforeach
()
python/paddle/fluid/contrib/slim/
unitest
/__init__.py
→
python/paddle/fluid/contrib/slim/
tests
/__init__.py
浏览文件 @
dde19a0f
文件已移动
python/paddle/fluid/contrib/slim/
unitest
/configs/config.yaml
→
python/paddle/fluid/contrib/slim/
tests
/configs/config.yaml
浏览文件 @
dde19a0f
version
:
1.0
include
:
[
"
./
unitest/configs/pruners.yaml"
,
"
./unitest
/configs/pruners_0.yaml"
]
include
:
[
"
./
configs/pruners.yaml"
,
"
.
/configs/pruners_0.yaml"
]
pruners
:
pruner_1
:
class
:
'
RatioPruner'
...
...
python/paddle/fluid/contrib/slim/
unitest
/configs/pruners.yaml
→
python/paddle/fluid/contrib/slim/
tests
/configs/pruners.yaml
浏览文件 @
dde19a0f
文件已移动
python/paddle/fluid/contrib/slim/
unitest
/configs/pruners_0.yaml
→
python/paddle/fluid/contrib/slim/
tests
/configs/pruners_0.yaml
浏览文件 @
dde19a0f
文件已移动
python/paddle/fluid/contrib/slim/
unitest
/test_factory.py
→
python/paddle/fluid/contrib/slim/
tests
/test_factory.py
浏览文件 @
dde19a0f
...
...
@@ -18,7 +18,7 @@ import unittest
class
TestFactory
(
unittest
.
TestCase
):
def
test_parse
(
self
):
factory
=
ConfigFactory
(
'./
unitest/
configs/config.yaml'
)
factory
=
ConfigFactory
(
'./configs/config.yaml'
)
pruner
=
factory
.
instance
(
'pruner_1'
)
self
.
assertEquals
(
pruner
.
ratios
[
'conv1_1.w'
],
0.3
)
...
...
python/paddle/fluid/contrib/slim/tests/test_graph.py
0 → 100644
浏览文件 @
dde19a0f
# 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.
from
__future__
import
print_function
import
unittest
import
paddle.fluid
as
fluid
import
six
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid
import
core
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
TestGraph
(
unittest
.
TestCase
):
def
test_graph_functions
(
self
):
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
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
marked_nodes
=
set
()
for
op
in
graph
.
all_ops
():
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
()))
nodes_map
=
graph
.
build_adjacency_list
()
self
.
assertEqual
(
len
(
nodes_map
),
len
(
graph
.
all_ops
()))
nodes_num
=
len
(
graph
.
all_nodes
())
graph
.
safe_remove_nodes
(
marked_nodes
)
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
nodes_num
-
len
(
marked_nodes
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/contrib/slim/
unitest
/test_quantization_pass.py
→
python/paddle/fluid/contrib/slim/
tests
/test_quantization_pass.py
浏览文件 @
dde19a0f
...
...
@@ -65,6 +65,28 @@ def residual_block(num):
return
loss
def
conv_net
(
img
,
label
):
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
img
,
filter_size
=
5
,
num_filters
=
20
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
conv_pool_1
=
fluid
.
layers
.
batch_norm
(
conv_pool_1
)
conv_pool_2
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
conv_pool_1
,
filter_size
=
5
,
num_filters
=
50
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
prediction
=
fluid
.
layers
.
fc
(
input
=
conv_pool_2
,
size
=
10
,
act
=
'softmax'
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
return
avg_loss
class
TestQuantizationTransformPass
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
quantizable_op_and_inputs
=
{
...
...
@@ -171,5 +193,103 @@ class TestQuantizationTransformPass(unittest.TestCase):
self
.
residual_block_quant
(
'range_abs_max'
)
class
TestQuantizeTranspiler
(
unittest
.
TestCase
):
def
freeze_graph
(
self
,
use_cuda
,
seed
):
def
build_program
(
main
,
startup
,
is_test
):
main
.
random_seed
=
seed
startup
.
random_seed
=
seed
with
fluid
.
unique_name
.
guard
():
with
fluid
.
program_guard
(
main
,
startup
):
img
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
loss
=
conv_net
(
img
,
label
)
if
not
is_test
:
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
return
[
img
,
label
],
loss
random
.
seed
(
0
)
np
.
random
.
seed
(
0
)
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
test_program
=
fluid
.
Program
()
feeds
,
loss
=
build_program
(
main
,
startup
,
False
)
build_program
(
test_program
,
startup
,
True
)
test_program
=
test_program
.
clone
(
for_test
=
True
)
main_graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
test_graph
=
IrGraph
(
core
.
Graph
(
test_graph
.
desc
),
for_test
=
True
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
transform_pass
=
QuantizationTransformPass
(
scope
=
fluid
.
global_scope
(),
program_exe
=
exe
)
iters
=
5
batch_size
=
8
class_num
=
10
exe
.
run
(
startup
)
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
batch_size
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
feeds
,
place
=
place
)
with
fluid
.
program_guard
(
main
):
for
_
in
range
(
iters
):
data
=
next
(
train_reader
())
loss_v
=
exe
.
run
(
program
=
main
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
loss
])
with
fluid
.
program_guard
(
test_program
):
test_data
=
next
(
test_reader
())
w_var
=
fluid
.
framework
.
_get_var
(
'conv2d_1.w_0.quantized'
,
test_program
)
# Testing during training
test_loss1
,
w_quant
=
exe
.
run
(
program
=
test_program
,
feed
=
feeder
.
feed
(
test_data
),
fetch_list
=
[
loss
,
w_var
])
# Freeze program for inference, but the weight of fc/conv is still float type.
quant_transpiler
.
freeze_program
(
test_program
,
place
)
test_loss2
,
=
exe
.
run
(
program
=
test_program
,
feed
=
feeder
.
feed
(
test_data
),
fetch_list
=
[
loss
])
self
.
assertAlmostEqual
(
test_loss1
,
test_loss2
,
delta
=
5e-3
)
w_freeze
=
np
.
array
(
fluid
.
global_scope
().
find_var
(
'conv2d_1.w_0'
)
.
get_tensor
())
# fail: -432.0 != -433.0, this is due to the calculation precision
#self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
# Convert parameter to 8-bit.
quant_transpiler
.
convert_to_int8
(
test_program
,
place
)
# Save the 8-bit parameter and model file.
fluid
.
io
.
save_inference_model
(
'model_8bit'
,
[
'image'
,
'label'
],
[
loss
],
exe
,
test_program
)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[
infer
,
feed
,
fetch
]
=
fluid
.
io
.
load_inference_model
(
'model_8bit'
,
exe
)
# Check the loaded 8-bit weight.
w_8bit
=
np
.
array
(
fluid
.
global_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
))
def
not_test_freeze_program_cuda
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
with
fluid
.
unique_name
.
guard
():
self
.
freeze_program
(
True
,
seed
=
1
)
def
not_test_freeze_program_cpu
(
self
):
with
fluid
.
unique_name
.
guard
():
self
.
freeze_program
(
False
,
seed
=
2
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/framework.py
浏览文件 @
dde19a0f
...
...
@@ -1533,20 +1533,47 @@ class IrGraph(object):
def
is_test
(
self
):
return
self
.
_for_test
def
all_parameters
(
self
):
param_nodes
=
set
()
for
node
in
self
.
graph
.
nodes
():
if
node
.
is_var
()
and
node
.
var
()
is
not
None
and
node
.
var
(
).
persistable
():
param_nodes
.
add
(
node
)
return
param_nodes
def
all_nodes
(
self
):
return
{
node
for
node
in
self
.
graph
.
nodes
()}
def
all_vars
(
self
):
return
{
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_var
()}
def
all_persistable_vars
(
self
):
persistable_nodes
=
set
()
for
node
in
self
.
graph
.
nodes
():
if
node
.
is_var
()
and
node
.
var
()
is
not
None
and
node
.
var
(
).
persistable
():
persistable_nodes
.
add
(
node
)
return
persistable_nodes
def
all_ops
(
self
):
return
{
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_op
()}
def
var_node
(
self
,
name
):
"""
Get a variable node by name from this graph.
Args:
name(str): the name of the variable node.
Raises:
ValueError: The If input's type is not str, or this graph
doesn't have a variable with the giving name.
Returns:
Node: the variable node with the giving name.
"""
if
not
isinstance
(
name
,
six
.
string_types
):
raise
TypeError
(
"var require string as parameter, but get %s instead."
%
(
type
(
name
)))
target_var_node
=
None
var_nodes
=
self
.
all_vars
()
for
var_node
in
var_nodes
:
if
var_node
.
name
()
==
name
:
target_var_node
=
var_node
if
target_var_node
is
None
:
raise
ValueError
(
"var_node %s not in this graph"
%
name
)
return
target_var_node
def
create_param_node
(
self
,
name
,
var_type
,
shape
,
var_dtype
):
var_desc
=
core
.
VarDesc
(
name
)
var_desc
.
set_type
(
var_type
)
...
...
@@ -1586,8 +1613,9 @@ class IrGraph(object):
return
self
.
graph
.
create_op_node
(
op_desc
)
def
update_input_link
(
self
,
old_input_node
,
new_input_node
,
op_node
):
assert
old_input_node
in
self
.
graph
.
nodes
()
and
new_input_node
in
self
.
graph
.
nodes
()
and
\
op_node
in
self
.
graph
.
nodes
(),
'Th three arguments must be in the graph nodes.'
assert
old_input_node
in
self
.
graph
.
nodes
()
and
new_input_node
in
\
self
.
graph
.
nodes
()
and
op_node
in
self
.
graph
.
nodes
(),
\
'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
old_input_node
.
outputs_remove
(
op_node
)
op_node
.
inputs_remove
(
old_input_node
)
new_input_node
.
outputs_append
(
op_node
)
...
...
@@ -1596,7 +1624,7 @@ class IrGraph(object):
def
link_to
(
self
,
node_in
,
node_out
):
assert
node_in
in
self
.
graph
.
nodes
()
and
node_out
in
self
.
graph
.
nodes
(),
\
'Th
two arguments
must be in the graph nodes.'
'Th
e two arguments(node_in&node_out)
must be in the graph nodes.'
node_in
.
outputs_append
(
node_out
)
node_out
.
inputs_append
(
node_in
)
...
...
@@ -1605,6 +1633,18 @@ class IrGraph(object):
remove_nodes
=
set
(
remove_nodes
)
core
.
graph_safe_remove_nodes
(
self
.
graph
,
remove_nodes
)
def
has_circle
(
self
):
return
core
.
has_circle
(
self
.
graph
)
def
graph_num
(
self
):
return
core
.
graph_num
(
self
.
graph
)
def
topology_sort
(
self
):
return
core
.
topology_sort
(
self
.
graph
)
def
build_adjacency_list
(
self
):
return
core
.
build_adjacency_list
(
self
.
graph
)
def
draw
(
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'
...
...
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