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体验新版 GitCode,发现更多精彩内容 >>
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提交
a3ac54b6
编写于
7月 20, 2018
作者:
C
chengduo
提交者:
GitHub
7月 20, 2018
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Fix Reduce functor (#12262)
* Fix Reduce and Gather * Fix unit test
上级
6c981e7d
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
89 addition
and
25 deletion
+89
-25
paddle/fluid/framework/details/reduce_and_gather.h
paddle/fluid/framework/details/reduce_and_gather.h
+6
-4
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
...dle/fluid/tests/unittests/test_parallel_executor_mnist.py
+83
-21
未找到文件。
paddle/fluid/framework/details/reduce_and_gather.h
浏览文件 @
a3ac54b6
...
...
@@ -35,14 +35,16 @@ struct ReduceLoDTensor {
PADDLE_ENFORCE
(
!
src_tensors_
.
empty
());
auto
&
t0
=
*
src_tensors_
[
0
];
PADDLE_ENFORCE_NE
(
t0
.
numel
(),
0
);
dst_tensor_
.
Resize
(
t0
.
dims
());
T
*
dst
=
dst_tensor_
.
mutable_data
<
T
>
(
platform
::
CPUPlace
());
if
(
dst
!=
t0
.
data
<
T
>
())
{
std
::
copy
(
t0
.
data
<
T
>
(),
t0
.
data
<
T
>
()
+
t0
.
numel
(),
dst
);
}
for
(
size_t
i
=
1
;
i
<
src_tensors_
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
src_tensors_
.
size
();
++
i
)
{
auto
&
t
=
*
src_tensors_
[
i
];
if
(
dst
==
t
.
data
<
T
>
())
{
continue
;
}
PADDLE_ENFORCE_EQ
(
t
.
dims
(),
t0
.
dims
());
PADDLE_ENFORCE_EQ
(
t
.
type
(),
t0
.
type
());
std
::
transform
(
t
.
data
<
T
>
(),
t
.
data
<
T
>
()
+
t
.
numel
(),
dst
,
dst
,
...
...
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @
a3ac54b6
...
...
@@ -102,6 +102,16 @@ class TestMNIST(TestParallelExecutorBase):
fluid
.
recordio_writer
.
convert_reader_to_recordio_file
(
MNIST_RECORDIO_FILE
,
reader
,
feeder
)
def
_init_data
(
self
,
random
=
True
):
np
.
random
.
seed
(
5
)
if
random
:
img
=
np
.
random
.
random
(
size
=
[
32
,
784
]).
astype
(
np
.
float32
)
else
:
img
=
np
.
ones
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
return
img
,
label
# simple_fc
def
check_simple_fc_convergence
(
self
,
use_cuda
,
use_reduce
=
False
):
if
use_cuda
and
not
core
.
is_compiled_with_cuda
():
return
...
...
@@ -109,8 +119,8 @@ class TestMNIST(TestParallelExecutorBase):
self
.
check_network_convergence
(
simple_fc_net
,
use_cuda
=
use_cuda
,
allow_op_delay
=
True
)
img
=
np
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
img
,
label
=
self
.
_init_data
(
)
self
.
check_network_convergence
(
simple_fc_net
,
feed_dict
=
{
"image"
:
img
,
...
...
@@ -118,6 +128,37 @@ class TestMNIST(TestParallelExecutorBase):
use_cuda
=
use_cuda
,
use_reduce
=
use_reduce
)
def
check_simple_fc_convergence_with_Reduce
(
self
,
use_cuda
):
if
use_cuda
and
not
core
.
is_compiled_with_cuda
():
return
self
.
check_network_convergence
(
simple_fc_net
,
use_cuda
=
use_cuda
,
use_reduce
=
True
)
self
.
check_network_convergence
(
simple_fc_net
,
use_cuda
=
use_cuda
,
allow_op_delay
=
True
,
use_reduce
=
True
)
img
,
label
=
self
.
_init_data
()
all_reduce_first_loss
,
all_reduce_last_loss
=
self
.
check_network_convergence
(
simple_fc_net
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
,
use_reduce
=
False
)
reduce_first_loss
,
reduce_last_loss
=
self
.
check_network_convergence
(
simple_fc_net
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
,
use_reduce
=
True
)
for
loss
in
zip
(
all_reduce_first_loss
,
reduce_first_loss
):
self
.
assertAlmostEquals
(
loss
[
0
],
loss
[
1
],
delta
=
1e-6
)
for
loss
in
zip
(
all_reduce_last_loss
,
reduce_last_loss
):
self
.
assertAlmostEquals
(
loss
[
0
],
loss
[
1
],
delta
=
1e-6
)
def
test_simple_fc
(
self
):
# use_cuda
self
.
check_simple_fc_convergence
(
True
)
...
...
@@ -125,14 +166,15 @@ class TestMNIST(TestParallelExecutorBase):
def
test_simple_fc_with_new_strategy
(
self
):
# use_cuda, use_reduce
self
.
check_simple_fc_convergence
(
True
,
True
)
self
.
check_simple_fc_convergence
(
False
,
Tru
e
)
self
.
check_simple_fc_convergence
_with_Reduce
(
True
)
self
.
check_simple_fc_convergence
_with_Reduce
(
Fals
e
)
def
check_simple_fc_parallel_accuracy
(
self
,
use_cuda
,
use_reduce
=
False
):
def
check_simple_fc_parallel_accuracy
(
self
,
use_cuda
):
if
use_cuda
and
not
core
.
is_compiled_with_cuda
():
return
img
=
np
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
img
,
label
=
self
.
_init_data
(
random
=
False
)
single_first_loss
,
single_last_loss
=
self
.
check_network_convergence
(
method
=
simple_fc_net
,
seed
=
1000
,
...
...
@@ -146,8 +188,7 @@ class TestMNIST(TestParallelExecutorBase):
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
,
use_parallel_executor
=
True
,
use_reduce
=
use_reduce
)
use_parallel_executor
=
True
)
for
p_f
in
parallel_first_loss
:
self
.
assertAlmostEquals
(
p_f
,
single_first_loss
[
0
],
delta
=
1e-6
)
...
...
@@ -158,32 +199,53 @@ class TestMNIST(TestParallelExecutorBase):
self
.
check_simple_fc_parallel_accuracy
(
True
)
self
.
check_simple_fc_parallel_accuracy
(
False
)
def
test_simple_fc_parallel_accuracy_with_new_strategy
(
self
):
# use_cuda, use_reduce
self
.
check_simple_fc_parallel_accuracy
(
True
,
True
)
self
.
check_simple_fc_parallel_accuracy
(
False
,
True
)
def
check_batchnorm_fc_convergence
(
self
,
use_cuda
,
use_reduce
=
False
):
def
check_batchnorm_fc_convergence
(
self
,
use_cuda
):
if
use_cuda
and
not
core
.
is_compiled_with_cuda
():
return
self
.
check_network_convergence
(
fc_with_batchnorm
,
use_cuda
=
use_cuda
)
img
=
np
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
img
,
label
=
self
.
_init_data
()
self
.
check_network_convergence
(
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
)
def
check_batchnorm_fc_convergence_use_reduce
(
self
,
use_cuda
):
if
use_cuda
and
not
core
.
is_compiled_with_cuda
():
return
self
.
check_network_convergence
(
fc_with_batchnorm
,
use_cuda
=
use_cuda
,
use_reduce
=
True
)
img
,
label
=
self
.
_init_data
()
all_reduce_first_loss
,
all_reduce_last_loss
=
self
.
check_network_convergence
(
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
,
use_reduce
=
use_reduce
)
use_reduce
=
False
)
reduce_first_loss
,
reduce_last_loss
=
self
.
check_network_convergence
(
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
,
use_reduce
=
True
)
for
loss
in
zip
(
all_reduce_first_loss
,
reduce_first_loss
):
self
.
assertAlmostEquals
(
loss
[
0
],
loss
[
1
],
delta
=
1e-6
)
for
loss
in
zip
(
all_reduce_last_loss
,
reduce_last_loss
):
self
.
assertAlmostEquals
(
loss
[
0
],
loss
[
1
],
delta
=
1e-4
)
def
test_batchnorm_fc
(
self
):
self
.
check_batchnorm_fc_convergence
(
True
)
self
.
check_batchnorm_fc_convergence
(
False
)
def
test_batchnorm_fc_with_new_strategy
(
self
):
# use_cuda, use_reduce
self
.
check_batchnorm_fc_convergence
(
True
,
True
)
self
.
check_batchnorm_fc_convergence
(
False
,
True
)
self
.
check_batchnorm_fc_convergence_use_reduce
(
True
)
self
.
check_batchnorm_fc_convergence_use_reduce
(
False
)
if
__name__
==
'__main__'
:
...
...
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