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体验新版 GitCode,发现更多精彩内容 >>
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94b7c1ea
编写于
3月 12, 2019
作者:
Z
Zhen Wang
提交者:
GitHub
3月 12, 2019
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差异文件
Merge pull request #16107 from wzzju/add_graph_clone
Add clone function for IrGraph.
上级
85709f43
b8d1f503
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
131 addition
and
34 deletion
+131
-34
paddle/fluid/framework/ir/graph.cc
paddle/fluid/framework/ir/graph.cc
+34
-1
paddle/fluid/framework/ir/graph.h
paddle/fluid/framework/ir/graph.h
+5
-0
paddle/fluid/framework/ir/node.h
paddle/fluid/framework/ir/node.h
+1
-0
paddle/fluid/pybind/ir.cc
paddle/fluid/pybind/ir.cc
+6
-2
python/paddle/fluid/contrib/slim/tests/test_graph.py
python/paddle/fluid/contrib/slim/tests/test_graph.py
+72
-31
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+13
-0
未找到文件。
paddle/fluid/framework/ir/graph.cc
浏览文件 @
94b7c1ea
...
...
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <unordered_
set
>
#include <unordered_
map
>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/op_proto_maker.h"
...
...
@@ -152,6 +152,39 @@ void Graph::ResolveHazard(
}
}
std
::
shared_ptr
<
Graph
>
Graph
::
Clone
()
{
auto
cloned_graph
=
std
::
make_shared
<
Graph
>
(
this
->
program_
);
cloned_graph
->
ReleaseNodes
();
cloned_graph
->
num_node_created_
=
0
;
std
::
unordered_map
<
ir
::
Node
*
,
ir
::
Node
*>
origin_to_cloned
;
for
(
auto
*
n
:
this
->
node_set_
)
{
ir
::
Node
*
cloned_node
=
nullptr
;
if
(
n
->
IsCtrlVar
())
{
cloned_node
=
cloned_graph
->
CreateControlDepVar
();
}
else
if
(
!
n
->
var_desc_
&&
!
n
->
op_desc_
)
{
// empty node
cloned_node
=
cloned_graph
->
CreateEmptyNode
(
n
->
Name
(),
n
->
NodeType
());
}
else
if
(
n
->
IsVar
())
{
cloned_node
=
cloned_graph
->
CreateVarNode
(
n
->
Var
());
}
else
if
(
n
->
IsOp
())
{
cloned_node
=
cloned_graph
->
CreateOpNode
(
n
->
Op
());
}
if
(
cloned_node
)
{
origin_to_cloned
[
n
]
=
cloned_node
;
}
else
{
PADDLE_THROW
(
"The cloned node's type is not supported!"
);
}
}
for
(
auto
*
n
:
this
->
node_set_
)
{
for
(
auto
it
=
n
->
inputs
.
begin
();
it
!=
n
->
inputs
.
end
();
it
++
)
{
origin_to_cloned
[
n
]
->
inputs
.
push_back
(
origin_to_cloned
[
*
it
]);
}
for
(
auto
it
=
n
->
outputs
.
begin
();
it
!=
n
->
outputs
.
end
();
it
++
)
{
origin_to_cloned
[
n
]
->
outputs
.
push_back
(
origin_to_cloned
[
*
it
]);
}
}
return
cloned_graph
;
}
bool
IsControlDepVar
(
const
ir
::
Node
&
var
)
{
return
var
.
Name
().
find
(
ir
::
Node
::
kControlDepVarName
)
!=
std
::
string
::
npos
;
}
...
...
paddle/fluid/framework/ir/graph.h
浏览文件 @
94b7c1ea
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include <map>
#include <memory>
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/node.h"
...
...
@@ -212,6 +213,10 @@ class Graph {
void
ResolveHazard
(
const
std
::
map
<
std
::
string
,
std
::
vector
<
ir
::
Node
*>>
&
var_nodes
);
// Create a new and duplicated graph.
// WARN: The method only clones the graph structure, not its attributes.
std
::
shared_ptr
<
Graph
>
Clone
();
private:
std
::
map
<
std
::
string
,
std
::
vector
<
ir
::
Node
*>>
InitFromProgram
(
const
ProgramDesc
&
program
);
...
...
paddle/fluid/framework/ir/node.h
浏览文件 @
94b7c1ea
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <typeindex>
#include <typeinfo>
...
...
paddle/fluid/pybind/ir.cc
浏览文件 @
94b7c1ea
...
...
@@ -18,6 +18,7 @@
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
...
...
@@ -54,12 +55,14 @@ void BindGraph(py::module *m) {
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
"`paddle::ir::Graph` for details."
)
.
def
(
py
::
init
<
const
ProgramDesc
&>
())
.
def
(
"clone"
,
&
Graph
::
Clone
)
.
def
(
"has"
,
&
Graph
::
Has
)
.
def
(
"get_int"
,
&
Graph
::
Get
<
int
>
)
.
def
(
"get_float"
,
&
Graph
::
Get
<
float
>
)
.
def
(
"get_double"
,
&
Graph
::
Get
<
double
>
)
.
def
(
"get_string"
,
&
Graph
::
Get
<
std
::
string
>
)
.
def
(
"get_marked_nodes"
,
&
Graph
::
Get
<
std
::
unordered_set
<
const
Node
*>>
)
.
def
(
"get_marked_nodes"
,
&
Graph
::
Get
<
std
::
unordered_set
<
const
Node
*>>
,
return_value_policy
::
reference
)
.
def
(
"set"
,
[](
Graph
&
self
,
const
std
::
string
&
attr_name
,
int
attr
)
{
return
self
.
Set
(
attr_name
,
new
int
(
attr
));
})
.
def
(
"set"
,
...
...
@@ -103,7 +106,8 @@ void BindGraph(py::module *m) {
.
def
(
"retrieve_node"
,
&
Graph
::
RetrieveNode
,
return_value_policy
::
reference
)
.
def
(
"resolve_hazard"
,
&
Graph
::
ResolveHazard
)
.
def
(
"origin_program_desc"
,
&
Graph
::
OriginProgram
);
.
def
(
"origin_program_desc"
,
&
Graph
::
OriginProgram
,
return_value_policy
::
reference
);
}
void
BindNode
(
py
::
module
*
m
)
{
...
...
python/paddle/fluid/contrib/slim/tests/test_graph.py
浏览文件 @
94b7c1ea
...
...
@@ -13,58 +13,92 @@
# limitations under the license.
from
__future__
import
print_function
import
os
import
six
import
unittest
import
paddle
import
paddle.fluid
as
fluid
import
six
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid
import
core
os
.
environ
[
"CUDA_VISIBLE_DEVICES"
]
=
"0"
os
.
environ
[
"CPU_NUM"
]
=
"1"
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'
)
def
conv_block
():
img
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
1
,
28
,
28
],
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
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
[
img
,
label
],
avg_loss
class
TestGraph
(
unittest
.
TestCase
):
def
test_graph_functions
(
self
):
def
graph_apis
(
self
,
use_cuda
=
False
,
for_ci
=
True
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
residual_block
(
2
)
feeds
,
loss
=
conv_block
(
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
backup_graph
=
graph
.
clone
()
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
len
(
backup_graph
.
all_nodes
()))
build_strategy
=
fluid
.
BuildStrategy
()
build_strategy
.
memory_optimize
=
False
build_strategy
.
enable_inplace
=
False
origin_binary
=
fluid
.
CompiledProgram
(
graph
.
graph
).
with_data_parallel
(
loss_name
=
loss
.
name
,
build_strategy
=
build_strategy
)
backup_binary
=
fluid
.
CompiledProgram
(
backup_graph
.
graph
).
with_data_parallel
(
loss_name
=
loss
.
name
,
build_strategy
=
build_strategy
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
iters
=
5
batch_size
=
8
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
feeds
,
place
=
place
)
def
train
(
binary
):
for
_
in
range
(
iters
):
data
=
next
(
train_reader
())
loss_v
=
exe
.
run
(
binary
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
loss
.
name
])
print
(
'{}: {}'
.
format
(
'loss'
,
loss_v
))
train
(
origin_binary
)
train
(
backup_binary
)
marked_nodes
=
set
()
for
op
in
graph
.
all_op_nodes
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
marked_nodes
.
add
(
op
)
graph
.
draw
(
'.'
,
'residual'
,
marked_nodes
)
if
not
for_ci
:
graph
.
draw
(
'.'
,
'residual'
,
marked_nodes
)
backup_marked_nodes
=
set
()
for
op
in
backup_graph
.
all_op_nodes
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
backup_marked_nodes
.
add
(
op
)
backup_graph
.
draw
(
'.'
,
'backup'
,
backup_marked_nodes
)
self
.
assertFalse
(
graph
.
has_circle
())
self
.
assertEqual
(
graph
.
graph_num
(),
1
)
nodes
=
graph
.
topology_sort
()
...
...
@@ -75,6 +109,13 @@ class TestGraph(unittest.TestCase):
graph
.
safe_remove_nodes
(
marked_nodes
)
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
nodes_num
-
len
(
marked_nodes
))
def
test_graph_apis_cpu
(
self
):
self
.
graph_apis
(
use_cuda
=
False
,
for_ci
=
True
)
def
test_graph_apis_cuda
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
self
.
graph_apis
(
use_cuda
=
True
,
for_ci
=
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/framework.py
浏览文件 @
94b7c1ea
...
...
@@ -2002,6 +2002,19 @@ class IrGraph(object):
self
.
graph
=
graph
self
.
_for_test
=
for_test
def
clone
(
self
):
"""
Create a new and duplicated IrGraph.
Warns:
The method only clones the graph structure, not its attributes.
Returns:
IrGraph: A new and duplicated graph.
"""
g
=
self
.
graph
.
clone
()
return
IrGraph
(
g
,
self
.
_for_test
)
def
is_test
(
self
):
"""
If the graph is used for testing, the function returns true. Otherwise, returns false.
...
...
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