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体验新版 GitCode,发现更多精彩内容 >>
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832bd720
编写于
2月 15, 2019
作者:
Z
Zhen Wang
提交者:
GitHub
2月 15, 2019
浏览文件
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差异文件
Merge pull request #15610 from wzzju/quantization_inference_passes
Quantization inference passes
上级
4da291c6
bc95a4cc
变更
14
展开全部
隐藏空白更改
内联
并排
Showing
14 changed file
with
1101 addition
and
79 deletion
+1101
-79
paddle/fluid/pybind/ir.cc
paddle/fluid/pybind/ir.cc
+32
-26
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+4
-7
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
+377
-17
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
+372
-0
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
...on/paddle/fluid/contrib/tests/test_quantize_transpiler.py
+5
-3
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+222
-24
未找到文件。
paddle/fluid/pybind/ir.cc
浏览文件 @
832bd720
...
...
@@ -13,10 +13,12 @@
// limitations under the License.
#include "paddle/fluid/pybind/ir.h"
#include <algorithm>
#include <string>
#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 +29,10 @@ 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 +42,12 @@ 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 "
...
...
@@ -46,7 +58,6 @@ void BindGraph(py::module *m) {
.
def
(
"get_float"
,
&
Graph
::
Get
<
float
>
)
.
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
));
})
...
...
@@ -63,11 +74,6 @@ void BindGraph(py::module *m) {
[](
Graph
&
self
,
const
std
::
string
&
attr_name
,
double
attr
)
{
return
self
.
Set
(
attr_name
,
new
double
(
attr
));
})
.
def
(
"set"
,
[](
Graph
&
self
,
const
std
::
string
&
attr_name
,
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
)
{
...
...
@@ -108,42 +114,42 @@ void BindNode(py::module *m) {
.
def
(
"is_op"
,
&
Node
::
IsOp
)
.
def
(
"is_var"
,
&
Node
::
IsVar
)
.
def
(
"is_ctrl_var"
,
&
Node
::
IsCtrlVar
)
.
def
(
"clear_inputs"
,
[](
Node
&
self
)
{
self
.
inputs
.
clear
();
})
.
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
);
}
auto
pos
=
std
::
find_if
(
self
.
inputs
.
begin
(),
self
.
inputs
.
end
(),
[
&
node_id
](
const
Node
*
n
)
{
return
n
->
id
()
==
node_id
;
});
if
(
pos
!=
self
.
inputs
.
end
())
{
self
.
inputs
.
erase
(
pos
);
}
})
.
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
);
}
auto
pos
=
std
::
find
(
self
.
inputs
.
begin
(),
self
.
inputs
.
end
(),
&
node
);
if
(
pos
!=
self
.
inputs
.
end
())
{
self
.
inputs
.
erase
(
pos
);
}
})
.
def
(
"inputs_append"
,
[](
Node
&
self
,
Node
&
node
)
{
self
.
inputs
.
push_back
(
&
node
);
})
.
def
(
"clear_outputs"
,
[](
Node
&
self
)
{
self
.
outputs
.
clear
();
})
.
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
);
}
auto
pos
=
std
::
find_if
(
self
.
outputs
.
begin
(),
self
.
outputs
.
end
(),
[
&
node_id
](
const
Node
*
n
)
{
return
n
->
id
()
==
node_id
;
});
if
(
pos
!=
self
.
outputs
.
end
())
{
self
.
outputs
.
erase
(
pos
);
}
})
.
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
);
}
auto
pos
=
std
::
find
(
self
.
outputs
.
begin
(),
self
.
outputs
.
end
(),
&
node
);
if
(
pos
!=
self
.
outputs
.
end
())
{
self
.
outputs
.
erase
(
pos
);
}
})
.
def
(
"outputs_append"
,
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
832bd720
...
...
@@ -829,8 +829,7 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"disable_profiler"
,
platform
::
DisableProfiler
);
m
.
def
(
"is_profiler_enabled"
,
platform
::
IsProfileEnabled
);
m
.
def
(
"reset_profiler"
,
platform
::
ResetProfiler
);
m
.
def
(
"get_pass"
,
[](
const
py
::
bytes
&
binary_str
)
{
std
::
string
pass_type
(
binary_str
);
m
.
def
(
"get_pass"
,
[](
const
std
::
string
&
pass_type
)
{
auto
pass
=
framework
::
ir
::
PassRegistry
::
Instance
().
Get
(
pass_type
);
return
std
::
shared_ptr
<
framework
::
ir
::
Pass
>
(
std
::
move
(
pass
));
});
...
...
@@ -838,10 +837,9 @@ All parameter, weight, gradient are variables in Paddle.
py
::
class_
<
ir
::
Pass
,
std
::
shared_ptr
<
ir
::
Pass
>>
pass
(
m
,
"Pass"
);
pass
.
def
(
py
::
init
())
.
def
(
"has"
,
&
ir
::
Pass
::
Has
)
.
def
(
"set"
,
[](
ir
::
Pass
&
self
,
const
std
::
string
&
attr_name
,
const
ProgramDesc
&
attr
)
{
return
self
.
Set
(
attr_name
,
new
ProgramDesc
(
attr
));
.
def
(
"set_not_owned"
,
[](
ir
::
Pass
&
self
,
const
std
::
string
&
attr_name
,
ProgramDesc
&
attr
)
{
self
.
SetNotOwned
<
ProgramDesc
>
(
attr_name
,
&
attr
);
})
.
def
(
"set"
,
...
...
@@ -850,7 +848,6 @@ All parameter, weight, gradient are variables in Paddle.
})
.
def
(
"set"
,
[](
ir
::
Pass
&
self
,
const
std
::
string
&
name
,
int
val
)
{
self
.
Set
<
const
int
>
(
name
,
new
int
(
val
));
})
.
def
(
"get_program"
,
&
ir
::
Pass
::
Get
<
ProgramDesc
>
)
.
def
(
"type"
,
&
ir
::
Pass
::
Type
)
.
def
(
"apply"
,
[](
ir
::
Pass
&
self
,
std
::
shared_ptr
<
ir
::
Graph
>
graph
)
{
std
::
unique_ptr
<
ir
::
Graph
>
origin_graph
(
graph
.
get
());
...
...
python/CMakeLists.txt
浏览文件 @
832bd720
...
...
@@ -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 @
832bd720
此差异已折叠。
点击以展开。
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
0 → 100644
浏览文件 @
832bd720
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 @
832bd720
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 @
832bd720
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 @
832bd720
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 @
832bd720
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 @
832bd720
...
...
@@ -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 @
832bd720
# 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 @
832bd720
...
...
@@ -17,9 +17,12 @@ import random
import
numpy
as
np
import
paddle.fluid
as
fluid
import
six
from
paddle.fluid.framework
import
Program
import
paddle
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid.contrib.slim.quantization
import
QuantizationTransformPass
from
paddle.fluid.contrib.slim.quantization
import
QuantizationFreezePass
from
paddle.fluid.contrib.slim.quantization
import
ConvertToInt8Pass
from
paddle.fluid.contrib.slim.quantization
import
TransformForMobilePass
from
paddle.fluid
import
core
...
...
@@ -65,6 +68,28 @@ 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 +196,177 @@ class TestQuantizationTransformPass(unittest.TestCase):
self
.
residual_block_quant
(
'range_abs_max'
)
class
TestQuantizationFreezePass
(
unittest
.
TestCase
):
def
freeze_graph
(
self
,
use_cuda
,
seed
,
quant_type
):
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_program
.
desc
),
for_test
=
True
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
scope
=
fluid
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
exe
.
run
(
startup
)
transform_pass
=
QuantizationTransformPass
(
scope
=
scope
,
program_exe
=
exe
,
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
():
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
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test'
+
dev_name
+
quant_type
,
marked_nodes
)
quantized_main_program
=
main_graph
.
to_program
()
quantized_test_program
=
test_graph
.
to_program
()
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
),
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
.
scope_guard
(
scope
):
for
_
in
range
(
iters
):
data
=
next
(
train_reader
())
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))
test_data
=
next
(
test_reader
())
with
fluid
.
program_guard
(
quantized_test_program
):
w_var
=
fluid
.
framework
.
_get_var
(
'conv2d_1.w_0.quantized'
,
quantized_test_program
)
# Testing
with
fluid
.
scope_guard
(
scope
):
test_loss1
,
w_quant
=
exe
.
run
(
program
=
quantized_test_program
,
feed
=
feeder
.
feed
(
test_data
),
fetch_list
=
[
loss
,
w_var
])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass
=
QuantizationFreezePass
(
scope
=
scope
,
place
=
place
)
freeze_pass
.
apply
(
test_graph
)
marked_nodes
=
set
()
for
op
in
test_graph
.
all_ops
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test_freeze'
+
dev_name
+
quant_type
,
marked_nodes
)
server_program
=
test_graph
.
to_program
()
with
fluid
.
scope_guard
(
scope
):
test_loss2
,
=
exe
.
run
(
program
=
server_program
,
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))
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)))
# 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
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test_int8'
+
dev_name
+
quant_type
,
marked_nodes
)
server_program_int8
=
test_graph
.
to_program
()
# Save the 8-bit parameter and model file.
with
fluid
.
scope_guard
(
scope
):
fluid
.
io
.
save_inference_model
(
'server_int8'
+
dev_name
+
quant_type
,
[
'image'
,
'label'
],
[
loss
],
exe
,
server_program_int8
)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[
infer
,
feed
,
fetch
]
=
fluid
.
io
.
load_inference_model
(
'server_int8'
+
dev_name
+
quant_type
,
exe
)
# Check the loaded 8-bit weight.
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)))
mobile_pass
=
TransformForMobilePass
()
mobile_pass
.
apply
(
test_graph
)
marked_nodes
=
set
()
for
op
in
test_graph
.
all_ops
():
if
op
.
name
().
find
(
'quantize'
)
>
-
1
:
marked_nodes
.
add
(
op
)
test_graph
.
draw
(
'.'
,
'test_mobile'
+
dev_name
+
quant_type
,
marked_nodes
)
mobile_program
=
test_graph
.
to_program
()
with
fluid
.
scope_guard
(
scope
):
fluid
.
io
.
save_inference_model
(
'mobile_int8'
+
dev_name
+
quant_type
,
[
'image'
,
'label'
],
[
loss
],
exe
,
mobile_program
)
def
test_freeze_graph_cuda_dynamic
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
with
fluid
.
unique_name
.
guard
():
self
.
freeze_graph
(
True
,
seed
=
1
,
quant_type
=
'abs_max'
)
def
test_freeze_graph_cpu_dynamic
(
self
):
with
fluid
.
unique_name
.
guard
():
self
.
freeze_graph
(
False
,
seed
=
2
,
quant_type
=
'abs_max'
)
def
test_freeze_graph_cuda_static
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
with
fluid
.
unique_name
.
guard
():
self
.
freeze_graph
(
True
,
seed
=
1
,
quant_type
=
'range_abs_max'
)
def
test_freeze_graph_cpu_static
(
self
):
with
fluid
.
unique_name
.
guard
():
self
.
freeze_graph
(
False
,
seed
=
2
,
quant_type
=
'range_abs_max'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
浏览文件 @
832bd720
...
...
@@ -204,9 +204,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
build_program
(
test_program
,
startup
,
True
)
test_program
=
test_program
.
clone
(
for_test
=
True
)
quant_transpiler
=
QuantizeTranspiler
()
quant_transpiler
.
training_transpile
(
main
)
quant_transpiler
.
training_transpile
(
test_program
)
quant_type
=
'range_abs_max'
# 'range_abs_max' or 'abs_max'
quant_transpiler
=
QuantizeTranspiler
(
activation_quantize_type
=
quant_type
)
quant_transpiler
.
training_transpile
(
main
,
startup
)
quant_transpiler
.
training_transpile
(
test_program
,
startup
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
...
...
python/paddle/fluid/framework.py
浏览文件 @
832bd720
...
...
@@ -16,6 +16,8 @@ from __future__ import print_function
import
collections
from
collections
import
defaultdict
from
collections
import
Iterable
import
contextlib
from
.wrapped_decorator
import
signature_safe_contextmanager
import
os
import
re
...
...
@@ -1529,12 +1531,16 @@ class Block(object):
class
IrGraph
(
object
):
"""
IrGraph uses core.Graph as the delegation to accomplish the manipulation.
Python IrGraph. Beneath it is a core.Graph, which is used for
create a c++ Ir Pass Graph. An IrGraph is just a graph view of
a Program. In an IrGraph, both Variables and Operators are graph
nodes.
"""
def
__init__
(
self
,
graph
,
for_test
=
False
):
"""
Construct the IrGraph using core.Graph.
Construct an IrGraph using core.Graph.
Args:
graph(core.Graph): C++ Graph.
for_test(bool): True for the test graph and false for the train graph.
...
...
@@ -1545,23 +1551,81 @@ class IrGraph(object):
self
.
_for_test
=
for_test
def
is_test
(
self
):
"""
If the graph is used for testing, the function returns true. Otherwise, returns false.
"""
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 all nodes included in the graph as a set.
"""
return
{
node
for
node
in
self
.
graph
.
nodes
()}
def
all_vars
(
self
):
"""
Return all variable nodes included in the graph as a set.
"""
return
{
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_var
()}
def
all_persistable_vars
(
self
):
"""
Return all persistable variable nodes included in the graph as a set.
"""
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 all operator nodes included in the graph as a set.
"""
return
{
node
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_op
()}
def
var_node
(
self
,
name
):
"""
Get a variable node by name from the 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:
core.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
):
"""
Create a persistable variable node in the graph. In IrGraph,
it can not distinguish between persistable variables and parameters.
Args:
name(str): the name of the persistable variable node.
vart_type(core.VarDesc.VarType): the type of the persistable variable node.
shape(list): the shape of the persistable variable node.
var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.
Returns:
core.Node: the created persistable variable node.
"""
var_desc
=
core
.
VarDesc
(
name
)
var_desc
.
set_type
(
var_type
)
var_desc
.
set_shape
(
shape
)
...
...
@@ -1570,6 +1634,20 @@ class IrGraph(object):
return
self
.
graph
.
create_var_node
(
var_desc
)
def
create_var_node
(
self
,
name
,
var_type
,
shape
,
var_dtype
):
"""
Create a variable node in the graph. The created variable node is
not persistable.
Args:
name(str): the name of the variable node.
vart_type(core.VarDesc.VarType): the type of the variable node.
shape(list): the shape of the variable node.
var_dtype(core.VarDesc.VarType): the data type of the variable node.
Returns:
core.Node: the created variable node.
"""
var_desc
=
core
.
VarDesc
(
name
)
var_desc
.
set_type
(
var_type
)
var_desc
.
set_shape
(
shape
)
...
...
@@ -1577,19 +1655,41 @@ class IrGraph(object):
return
self
.
graph
.
create_var_node
(
var_desc
)
def
create_var_node_from_desc
(
self
,
var_desc
):
"""
Create a variable node by using an existing VarDesc in the graph.
Depend on the giving VarDesc, the created variable node may be persistable.
Args:
var_desc(core.VarDesc): the giving variable description.
Returns:
core.Node: the created variable node.
"""
return
self
.
graph
.
create_var_node
(
var_desc
)
def
create_op_node
(
self
,
op_type
,
attrs
,
inputs
,
outputs
):
"""
Create a operator node in the graph.
Args:
op_type(str): the type of the operator node.
attrs(dict): the attributes of the operator node.
inputs(dict): the inputs of the operator node.
outputs(dict): the outpus of the operator node.
Returns:
core.Node: the created operator node.
"""
op_desc
=
core
.
OpDesc
()
op_desc
.
set_type
(
op_type
)
for
attr
,
value
in
attrs
.
iteritems
(
):
for
attr
,
value
in
six
.
iteritems
(
attrs
):
self
.
_update_desc_attr
(
op_desc
,
attr
,
value
)
for
input_name
,
var_nodes
in
inputs
.
iteritems
(
):
for
input_name
,
var_nodes
in
six
.
iteritems
(
inputs
):
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
(
):
for
output_name
,
var_nodes
in
six
.
iteritems
(
outputs
):
if
not
isinstance
(
var_nodes
,
list
):
var_nodes
=
[
var_nodes
]
op_desc
.
set_output
(
output_name
,
...
...
@@ -1597,11 +1697,29 @@ class IrGraph(object):
return
self
.
graph
.
create_op_node
(
op_desc
)
def
create_op_node_from_desc
(
self
,
op_desc
):
"""
Create a operator node by using an existing OpDesc in the graph.
Args:
op_desc(core.VarDesc): the giving operator description.
Returns:
core.Node: the created operator node.
"""
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.'
"""
Update the input's link of a operator node.
Args:
old_input_node(core.Node): the old input node of the giving op_node.
new_input_node(core.Node): the new input node of the giving op_node.
op_node(core.Node): the operator node that is needed to update input's link.
"""
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
)
...
...
@@ -1609,17 +1727,85 @@ class IrGraph(object):
op_node
.
op
().
_rename_input
(
old_input_node
.
name
(),
new_input_node
.
name
())
def
link_to
(
self
,
node_in
,
node_out
):
"""
Connect two nodes.
Args:
node_in(core.Node): the input node.
node_out(core.Node): the output node.
"""
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
)
def
safe_remove_nodes
(
self
,
remove_nodes
):
"""
Remove nodes safely since links connected to these removed nodes are
also removed.
Args:
remove_nodes(set): the nodes prepared to be removed.
"""
if
not
isinstance
(
remove_nodes
,
set
):
remove_nodes
=
set
(
remove_nodes
)
if
isinstance
(
remove_nodes
,
Iterable
):
remove_nodes
=
set
(
remove_nodes
)
else
:
remove_nodes
=
{
remove_nodes
}
core
.
graph_safe_remove_nodes
(
self
.
graph
,
remove_nodes
)
def
draw
(
self
,
save_path
,
name
,
marked_nodes
=
None
):
def
has_circle
(
self
):
"""
Check if the graph has a circle.
Returns:
bool: True if the graph has a circle else False.
"""
return
core
.
has_circle
(
self
.
graph
)
def
graph_num
(
self
):
"""
Count the number of unconnected graphs in this graph.
Returns:
int: the number of unconnected graphs.
"""
return
core
.
graph_num
(
self
.
graph
)
def
topology_sort
(
self
):
"""
Perform the topology sort operation on the graph.
Notes: the `graph` cannot contain a circle.
Returns:
set(core.Node): nodes in topology order.
"""
return
core
.
topology_sort
(
self
.
graph
)
def
build_adjacency_list
(
self
):
"""
Build an adjacency list of operations for the `graph`.
Returns:
dict{core.Node: set(core.Node)}: the adjacency list.
"""
return
core
.
build_adjacency_list
(
self
.
graph
)
def
draw
(
self
,
save_path
,
name
,
marked_nodes
=
None
,
remove_ctr_var
=
True
):
"""
Draw the graph. If `dot` command is installed, the drawn graph
will be saved as pdf file type, otherwise dot file type is used.
Args:
save_path(str): the save path of drawn graph.
name(str): the name of drawn graph.
marked_nodes(set(core.Node)): nodes that are needed to be marked.
Default value is None.
remove_ctr_var(bool): If it is set True, all control variable nodes
in the graph will be removed. Default value is True.
"""
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
\
...
...
@@ -1629,15 +1815,17 @@ class IrGraph(object):
print
(
'The {} is saved as the dot filetype.'
.
format
(
dot_file_path
))
remove_ctr_vars
=
set
()
if
remove_ctr_var
:
remove_ctr_vars
=
set
()
for
node
in
self
.
graph
.
nodes
():
if
node
.
is_ctrl_var
():
remove_ctr_vars
.
add
(
node
)
self
.
safe_remove_nodes
(
remove_ctr_vars
)
ops_num
=
0
for
node
in
self
.
graph
.
nodes
():
if
node
.
is_ctrl_var
():
remove_ctr_vars
.
add
(
node
)
elif
node
.
is_op
():
if
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
)
...
...
@@ -1652,10 +1840,20 @@ class IrGraph(object):
_convert_to_pdf
(
viz_dot_path
)
def
to_program
(
self
):
"""
Convert the graph into a Program.
Notes: When the graph includes backward operator nodes, the
conversion process may be failed. Usually, this function is
only used to convert a test graph.
Returns:
Program: a program converted from the graph.
"""
convert_pass
=
core
.
get_pass
(
'graph_to_program_pass'
)
convert_pass
.
set
(
'program'
,
Program
().
desc
)
desc
=
core
.
ProgramDesc
()
convert_pass
.
set_not_owned
(
'program'
,
desc
)
convert_pass
.
apply
(
self
.
graph
)
desc
=
convert_pass
.
get_program
(
'program'
)
program
=
Program
.
_construct_from_desc
(
desc
)
return
program
...
...
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