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体验新版 GitCode,发现更多精彩内容 >>
提交
4bbfa9ec
编写于
4月 03, 2018
作者:
X
Xin Pan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add feed to ParallelExecutor
上级
a98a3fdc
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
99 addition
and
39 deletion
+99
-39
doc/fluid/api/gen_doc.sh
doc/fluid/api/gen_doc.sh
+0
-0
paddle/fluid/framework/lod_tensor.h
paddle/fluid/framework/lod_tensor.h
+1
-0
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+19
-2
paddle/fluid/framework/parallel_executor.h
paddle/fluid/framework/parallel_executor.h
+5
-1
python/paddle/fluid/parallel_executor.py
python/paddle/fluid/parallel_executor.py
+28
-10
python/paddle/fluid/tests/unittests/test_parallel_executor.py
...on/paddle/fluid/tests/unittests/test_parallel_executor.py
+46
-26
未找到文件。
doc/fluid/api/gen_doc.sh
100755 → 100644
浏览文件 @
4bbfa9ec
文件模式从 100755 更改为 100644
paddle/fluid/framework/lod_tensor.h
浏览文件 @
4bbfa9ec
...
...
@@ -142,6 +142,7 @@ class LoDTensor : public Tensor {
return
(
lod_
)[
level
].
size
()
-
1
;
}
// Split LoDTensor and copy to each place specified in places.
std
::
vector
<
LoDTensor
>
SplitLoDTensor
(
const
std
::
vector
<
platform
::
Place
>
places
)
const
;
...
...
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
4bbfa9ec
...
...
@@ -150,13 +150,30 @@ void ParallelExecutor::BCastParamsToGPUs(
#endif
}
void
ParallelExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
const
std
::
string
&
fetched_var_name
)
{
void
ParallelExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
const
std
::
string
&
fetched_var_name
,
const
std
::
unordered_map
<
std
::
string
,
LoDTensor
>
&
feed_tensors
)
{
platform
::
RecordBlock
b
(
0
);
SplitTensorToPlaces
(
feed_tensors
);
auto
fetch_data
=
member_
->
executor_
->
Run
(
fetch_tensors
);
*
member_
->
global_scope_
->
Var
(
fetched_var_name
)
->
GetMutable
<
FeedFetchList
>
()
=
fetch_data
;
}
void
ParallelExecutor
::
SplitTensorToPlaces
(
const
std
::
unordered_map
<
std
::
string
,
LoDTensor
>
&
feed_tensors
)
{
for
(
auto
it
:
feed_tensors
)
{
auto
lod_tensors
=
it
.
second
.
SplitLoDTensor
(
member_
->
places_
);
for
(
size_t
j
=
0
;
j
<
member_
->
places_
.
size
();
++
j
)
{
// TODO(panxy0718): Do I need to delete this var?
member_
->
local_scopes_
[
j
]
->
Var
(
it
.
first
)
->
GetMutable
<
LoDTensor
>
()
->
ShareDataWith
(
lod_tensors
[
j
]);
}
}
}
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/parallel_executor.h
浏览文件 @
4bbfa9ec
...
...
@@ -42,9 +42,13 @@ class ParallelExecutor {
bool
allow_op_delay
);
void
Run
(
const
std
::
vector
<
std
::
string
>&
fetch_tensors
,
const
std
::
string
&
fetched_var_name
=
"fetched_var"
);
const
std
::
string
&
fetched_var_name
,
const
std
::
unordered_map
<
std
::
string
,
LoDTensor
>&
feed_tensors
);
private:
void
SplitTensorToPlaces
(
const
std
::
unordered_map
<
std
::
string
,
LoDTensor
>&
feed_tensors
);
ParallelExecutorPrivate
*
member_
;
void
BCastParamsToGPUs
(
const
ProgramDesc
&
startup_program
)
const
;
...
...
python/paddle/fluid/parallel_executor.py
浏览文件 @
4bbfa9ec
...
...
@@ -26,25 +26,29 @@ class ParallelExecutor(object):
use_cuda
,
num_threads
=
None
,
allow_op_delay
=
False
):
places
=
[]
self
.
_places
=
[]
self
.
_act_places
=
[]
if
use_cuda
:
for
i
in
xrange
(
core
.
get_cuda_device_count
()):
p
=
core
.
Place
()
p
.
set_place
(
core
.
CUDAPlace
(
i
))
places
.
append
(
p
)
self
.
_act_places
.
append
(
core
.
CUDAPlace
(
i
))
p
.
set_place
(
self
.
_act_places
[
-
1
])
self
.
_places
.
append
(
p
)
else
:
for
i
in
xrange
(
multiprocessing
.
cpu_count
()):
p
=
core
.
Place
()
p
.
set_place
(
core
.
CPUPlace
())
places
.
append
(
p
)
self
.
_act_places
.
append
(
core
.
CPUPlace
(
i
))
p
.
set_place
(
self
.
_act_places
[
-
1
])
self
.
_places
.
append
(
p
)
assert
self
.
_places
,
"no place for execution"
if
num_threads
is
None
:
if
use_cuda
:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
num_threads
=
len
(
places
)
num_threads
=
len
(
self
.
_
places
)
else
:
min
(
len
(
places
)
*
2
,
multiprocessing
.
cpu_count
())
min
(
len
(
self
.
_
places
)
*
2
,
multiprocessing
.
cpu_count
())
startup
=
framework
.
default_startup_program
()
main
=
framework
.
default_main_program
()
...
...
@@ -53,7 +57,7 @@ class ParallelExecutor(object):
self
.
executor
=
core
.
ParallelExecutor
(
num_threads
,
True
if
use_cuda
else
False
,
# use_event
places
,
self
.
_
places
,
set
([
p
.
name
for
p
in
main
.
global_block
().
iter_parameters
()
if
not
p
.
stop_gradient
...
...
@@ -65,8 +69,22 @@ class ParallelExecutor(object):
allow_op_delay
)
self
.
scope
=
scope
def
run
(
self
,
fetch_list
):
def
run
(
self
,
fetch_list
,
feed_dict
=
{}):
"""
:param fetch_list: A list of variable names that will be fetched.
:param feed_dict: A dict mapping for feed variable name to LoDTensor
or numpy array.
:return: fetched value list.
"""
feed_tensor_dict
=
{}
for
i
,
feed_name
in
enumerate
(
feed_dict
):
feed_tensor
=
feed_dict
[
feed_name
]
if
not
isinstance
(
feed_tensor
,
core
.
LoDTensor
):
feed_tensor
=
core
.
LoDTensor
()
feed_tensor
.
set
(
feed_dict
[
feed_name
],
self
.
_act_places
[
0
])
feed_tensor_dict
[
feed_name
]
=
feed_tensor
fetch_var_name
=
'@FETCHED_VAR_NAME@'
self
.
executor
.
run
(
fetch_list
,
fetch_var_name
)
self
.
executor
.
run
(
fetch_list
,
fetch_var_name
,
feed_tensor_dict
)
arr
=
self
.
scope
.
find_var
(
fetch_var_name
).
get_lod_tensor_array
()
return
[
arr
[
i
]
for
i
in
range
(
len
(
arr
))]
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @
4bbfa9ec
...
...
@@ -21,13 +21,17 @@ import paddle.dataset.mnist as mnist
import
paddle.dataset.wmt16
as
wmt16
def
simple_fc_net
():
reader
=
fluid
.
layers
.
open_recordio_file
(
filename
=
'./mnist.recordio'
,
shapes
=
[[
-
1
,
784
],
[
-
1
,
1
]],
lod_levels
=
[
0
,
0
],
dtypes
=
[
'float32'
,
'int64'
])
img
,
label
=
fluid
.
layers
.
read_file
(
reader
)
def
simple_fc_net
(
use_feed
):
if
use_feed
:
img
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
784
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
else
:
reader
=
fluid
.
layers
.
open_recordio_file
(
filename
=
'./mnist.recordio'
,
shapes
=
[[
-
1
,
784
],
[
-
1
,
1
]],
lod_levels
=
[
0
,
0
],
dtypes
=
[
'float32'
,
'int64'
])
img
,
label
=
fluid
.
layers
.
read_file
(
reader
)
hidden
=
img
for
_
in
xrange
(
4
):
hidden
=
fluid
.
layers
.
fc
(
...
...
@@ -42,13 +46,18 @@ def simple_fc_net():
return
loss
def
fc_with_batchnorm
():
reader
=
fluid
.
layers
.
open_recordio_file
(
filename
=
'./mnist.recordio'
,
shapes
=
[[
-
1
,
784
],
[
-
1
,
1
]],
lod_levels
=
[
0
,
0
],
dtypes
=
[
'float32'
,
'int64'
])
img
,
label
=
fluid
.
layers
.
read_file
(
reader
)
def
fc_with_batchnorm
(
use_feed
):
if
use_feed
:
img
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
784
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
else
:
reader
=
fluid
.
layers
.
open_recordio_file
(
filename
=
'./mnist.recordio'
,
shapes
=
[[
-
1
,
784
],
[
-
1
,
1
]],
lod_levels
=
[
0
,
0
],
dtypes
=
[
'float32'
,
'int64'
])
img
,
label
=
fluid
.
layers
.
read_file
(
reader
)
hidden
=
img
for
_
in
xrange
(
1
):
hidden
=
fluid
.
layers
.
fc
(
...
...
@@ -135,7 +144,9 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
scale
,
act
=
'relu'
)
def
SE_ResNeXt152Small
(
batch_size
=
2
):
def
SE_ResNeXt152Small
(
batch_size
=
2
,
use_feed
=
False
):
assert
not
use_feed
,
"SE_ResNeXt doesn't support feed yet"
img
=
fluid
.
layers
.
fill_constant
(
shape
=
[
batch_size
,
3
,
224
,
224
],
dtype
=
'float32'
,
value
=
0.0
)
label
=
fluid
.
layers
.
fill_constant
(
...
...
@@ -185,30 +196,28 @@ class TestParallelExecutorBase(unittest.TestCase):
memory_opt
=
True
,
iter
=
10
,
batch_size
=
None
,
allow_op_delay
=
False
):
allow_op_delay
=
False
,
feed_dict
=
{}):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
method
()
loss
=
method
(
use_feed
=
len
(
feed_dict
)
>
0
)
adam
=
fluid
.
optimizer
.
Adam
()
adam
.
minimize
(
loss
)
if
memory_opt
:
fluid
.
memory_optimize
(
main
)
exe
=
fluid
.
ParallelExecutor
(
loss_name
=
loss
.
name
,
use_cuda
=
True
,
allow_op_delay
=
allow_op_delay
)
exe
=
fluid
.
ParallelExecutor
(
loss_name
=
loss
.
name
,
use_cuda
=
True
)
if
batch_size
is
not
None
:
batch_size
*=
fluid
.
core
.
get_cuda_device_count
()
begin
=
time
.
time
()
first_loss
,
=
exe
.
run
([
loss
.
name
])
first_loss
,
=
exe
.
run
([
loss
.
name
]
,
feed_dict
=
feed_dict
)
first_loss
=
numpy
.
array
(
first_loss
)
for
i
in
xrange
(
iter
):
exe
.
run
([])
exe
.
run
([]
,
feed_dict
=
feed_dict
)
last_loss
,
=
exe
.
run
([
loss
.
name
])
last_loss
,
=
exe
.
run
([
loss
.
name
]
,
feed_dict
=
feed_dict
)
end
=
time
.
time
()
if
batch_size
is
not
None
:
...
...
@@ -242,9 +251,19 @@ class TestMNIST(TestParallelExecutorBase):
self
.
check_network_convergence
(
simple_fc_net
)
self
.
check_network_convergence
(
simple_fc_net
,
allow_op_delay
=
True
)
img
=
numpy
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
numpy
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
self
.
check_network_convergence
(
simple_fc_net
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
})
def
test_batchnorm_fc
(
self
):
self
.
check_network_convergence
(
fc_with_batchnorm
)
self
.
check_network_convergence
(
fc_with_batchnorm
,
allow_op_delay
=
True
)
img
=
numpy
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
numpy
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
self
.
check_network_convergence
(
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
})
class
TestResnet
(
TestParallelExecutorBase
):
...
...
@@ -400,7 +419,8 @@ def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
import
transformer_model
def
transformer
():
def
transformer
(
use_feed
):
assert
not
use_feed
,
"transfomer doesn't support feed yet"
return
transformer_model
.
transformer
(
ModelHyperParams
.
src_vocab_size
+
1
,
ModelHyperParams
.
trg_vocab_size
+
1
,
ModelHyperParams
.
max_length
+
1
,
...
...
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