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体验新版 GitCode,发现更多精彩内容 >>
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提交
e4cfe477
编写于
4月 13, 2018
作者:
Y
Yu Yang
提交者:
GitHub
4月 13, 2018
浏览文件
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差异文件
Merge pull request #9898 from reyoung/feature/mix_cpu_gpu_op
Feature/mix cpu gpu op
上级
9bc0c23b
ed2d7d7d
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
247 addition
and
29 deletion
+247
-29
paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc
paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc
+78
-23
paddle/fluid/framework/details/op_handle_base.cc
paddle/fluid/framework/details/op_handle_base.cc
+16
-0
paddle/fluid/framework/details/op_handle_base.h
paddle/fluid/framework/details/op_handle_base.h
+3
-0
python/paddle/fluid/tests/book/test_label_semantic_roles.py
python/paddle/fluid/tests/book/test_label_semantic_roles.py
+5
-6
python/paddle/fluid/tests/unittests/test_parallel_executor.py
...on/paddle/fluid/tests/unittests/test_parallel_executor.py
+145
-0
未找到文件。
paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc
浏览文件 @
e4cfe477
...
...
@@ -14,6 +14,8 @@
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#include <algorithm>
namespace
paddle
{
namespace
framework
{
namespace
details
{
...
...
@@ -27,6 +29,32 @@ NCCLAllReduceOpHandle::NCCLAllReduceOpHandle(
}
}
struct
ReduceLoDTensor
{
const
std
::
vector
<
LoDTensor
>
&
src_tensors_
;
LoDTensor
&
dst_tensor_
;
ReduceLoDTensor
(
const
std
::
vector
<
LoDTensor
>
&
src
,
LoDTensor
*
dst
)
:
src_tensors_
(
src
),
dst_tensor_
(
*
dst
)
{}
template
<
typename
T
>
void
operator
()()
const
{
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
());
std
::
copy
(
t0
.
data
<
T
>
(),
t0
.
data
<
T
>
()
+
t0
.
numel
(),
dst
);
for
(
size_t
i
=
1
;
i
<
src_tensors_
.
size
();
++
i
)
{
auto
&
t
=
src_tensors_
[
i
];
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
,
[](
T
a
,
T
b
)
->
T
{
return
a
+
b
;
});
}
}
};
void
NCCLAllReduceOpHandle
::
RunImpl
()
{
if
(
inputs_
.
size
()
==
1
)
{
return
;
// No need to all reduce when GPU count = 1;
...
...
@@ -41,40 +69,67 @@ void NCCLAllReduceOpHandle::RunImpl() {
int
dtype
=
-
1
;
size_t
numel
=
0
;
std
::
vector
<
std
::
function
<
void
()
>>
all_reduce_call
s
;
std
::
vector
<
LoDTensor
>
lod_tensor
s
;
for
(
size_t
i
=
0
;
i
<
local_scopes_
.
size
();
++
i
)
{
auto
&
p
=
places_
[
i
];
auto
*
s
=
local_scopes_
[
i
];
int
dev_id
=
boost
::
get
<
platform
::
CUDAPlace
>
(
p
).
device
;
auto
&
lod_tensor
=
s
->
FindVar
(
var_name
)
->
Get
<
LoDTensor
>
();
void
*
buffer
=
const_cast
<
void
*>
(
lod_tensor
.
data
<
void
>
());
lod_tensors
.
emplace_back
(
lod_tensor
);
}
if
(
dtype
==
-
1
)
{
dtype
=
platform
::
ToNCCLDataType
(
lod_tensor
.
type
());
}
if
(
platform
::
is_gpu_place
(
lod_tensors
[
0
].
place
()))
{
std
::
vector
<
std
::
function
<
void
()
>>
all_reduce_calls
;
for
(
size_t
i
=
0
;
i
<
local_scopes_
.
size
();
++
i
)
{
auto
&
p
=
places_
[
i
];
auto
&
lod_tensor
=
lod_tensors
[
i
];
void
*
buffer
=
const_cast
<
void
*>
(
lod_tensor
.
data
<
void
>
());
if
(
numel
==
0
)
{
numel
=
static_cast
<
size_t
>
(
lod_tensor
.
numel
());
}
if
(
dtype
==
-
1
)
{
dtype
=
platform
::
ToNCCLDataType
(
lod_tensor
.
type
());
}
auto
&
nccl_ctx
=
nccl_ctxs_
.
at
(
dev_id
);
auto
stream
=
nccl_ctx
.
stream
();
auto
comm
=
nccl_ctx
.
comm_
;
all_reduce_calls
.
emplace_back
([
=
]
{
PADDLE_ENFORCE
(
platform
::
dynload
::
ncclAllReduce
(
buffer
,
buffer
,
numel
,
static_cast
<
ncclDataType_t
>
(
dtype
),
ncclSum
,
comm
,
stream
));
if
(
numel
==
0
)
{
numel
=
static_cast
<
size_t
>
(
lod_tensor
.
numel
());
}
int
dev_id
=
boost
::
get
<
platform
::
CUDAPlace
>
(
p
).
device
;
auto
&
nccl_ctx
=
nccl_ctxs_
.
at
(
dev_id
);
auto
stream
=
nccl_ctx
.
stream
();
auto
comm
=
nccl_ctx
.
comm_
;
all_reduce_calls
.
emplace_back
([
=
]
{
PADDLE_ENFORCE
(
platform
::
dynload
::
ncclAllReduce
(
buffer
,
buffer
,
numel
,
static_cast
<
ncclDataType_t
>
(
dtype
),
ncclSum
,
comm
,
stream
));
});
}
this
->
RunAndRecordEvent
([
&
]
{
platform
::
NCCLGroupGuard
guard
;
for
(
auto
&
call
:
all_reduce_calls
)
{
call
();
}
});
}
}
else
{
// Special handle CPU only Operator's gradient. Like CRF
auto
&
trg
=
*
this
->
local_scopes_
[
0
]
->
Var
()
->
GetMutable
<
framework
::
LoDTensor
>
();
// Reduce All Tensor to trg in CPU
ReduceLoDTensor
func
(
lod_tensors
,
&
trg
);
VisitDataType
(
ToDataType
(
lod_tensors
[
0
].
type
()),
func
);
this
->
RunAndRecordEvent
([
&
]
{
platform
::
NCCLGroupGuard
guard
;
for
(
auto
&
call
:
all_reduce_calls
)
{
call
();
for
(
size_t
i
=
0
;
i
<
local_scopes_
.
size
();
++
i
)
{
auto
&
scope
=
local_scopes_
[
i
];
auto
&
p
=
places_
[
i
];
auto
*
var
=
scope
->
FindVar
(
var_name
);
auto
*
dev_ctx
=
dev_ctxes_
[
p
];
RunAndRecordEvent
(
p
,
[
&
trg
,
var
,
dev_ctx
,
p
]
{
auto
&
tensor_gpu
=
*
var
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
&
tensor_cpu
=
trg
;
TensorCopy
(
tensor_cpu
,
p
,
*
dev_ctx
,
&
tensor_gpu
);
});
}
}
);
}
}
}
...
...
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @
e4cfe477
...
...
@@ -107,6 +107,22 @@ void OpHandleBase::RunAndRecordEvent(const std::function<void()> &callback) {
#endif
}
void
OpHandleBase
::
RunAndRecordEvent
(
platform
::
Place
p
,
const
std
::
function
<
void
()
>
&
callback
)
{
#ifdef PADDLE_WITH_CUDA
if
(
platform
::
is_cpu_place
(
p
)
||
events_
.
empty
())
{
callback
();
}
else
{
auto
*
ctx
=
dev_ctxes_
.
at
(
p
);
auto
*
cuda_ctx
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
ctx
);
cuda_ctx
->
RecordEvent
(
events_
.
at
(
boost
::
get
<
platform
::
CUDAPlace
>
(
p
).
device
),
callback
);
}
#else
callback
();
#endif
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @
e4cfe477
...
...
@@ -64,6 +64,9 @@ class OpHandleBase {
protected:
void
RunAndRecordEvent
(
const
std
::
function
<
void
()
>
&
callback
);
void
RunAndRecordEvent
(
platform
::
Place
p
,
const
std
::
function
<
void
()
>
&
callback
);
virtual
void
RunImpl
()
=
0
;
};
...
...
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
e4cfe477
...
...
@@ -12,17 +12,16 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
contextlib
import
math
import
numpy
as
np
import
os
import
time
import
unittest
import
paddle
import
paddle.dataset.conll05
as
conll05
import
paddle.fluid
as
fluid
from
paddle.fluid.initializer
import
init_on_cpu
import
contextlib
import
time
import
unittest
import
os
word_dict
,
verb_dict
,
label_dict
=
conll05
.
get_dict
()
word_dict_len
=
len
(
word_dict
)
...
...
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @
e4cfe477
...
...
@@ -505,3 +505,148 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
train_loss
,
test_loss
,
atol
=
1e-8
),
"Train loss: "
+
str
(
train_loss
)
+
"
\n
Test loss:"
+
str
(
test_loss
))
import
paddle.dataset.conll05
as
conll05
import
paddle.fluid
as
fluid
word_dict
,
verb_dict
,
label_dict
=
conll05
.
get_dict
()
word_dict_len
=
len
(
word_dict
)
label_dict_len
=
len
(
label_dict
)
pred_dict_len
=
len
(
verb_dict
)
mark_dict_len
=
2
word_dim
=
32
mark_dim
=
5
hidden_dim
=
512
depth
=
8
mix_hidden_lr
=
1e-3
embedding_name
=
'emb'
def
db_lstm
(
word
,
predicate
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
,
mark
,
**
ignored
):
# 8 features
predicate_embedding
=
fluid
.
layers
.
embedding
(
input
=
predicate
,
size
=
[
pred_dict_len
,
word_dim
],
dtype
=
'float32'
,
param_attr
=
'vemb'
)
mark_embedding
=
fluid
.
layers
.
embedding
(
input
=
mark
,
size
=
[
mark_dict_len
,
mark_dim
],
dtype
=
'float32'
)
word_input
=
[
word
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
]
emb_layers
=
[
fluid
.
layers
.
embedding
(
size
=
[
word_dict_len
,
word_dim
],
input
=
x
,
param_attr
=
fluid
.
ParamAttr
(
name
=
embedding_name
,
trainable
=
False
))
for
x
in
word_input
]
emb_layers
.
append
(
predicate_embedding
)
emb_layers
.
append
(
mark_embedding
)
hidden_0_layers
=
[
fluid
.
layers
.
fc
(
input
=
emb
,
size
=
hidden_dim
,
act
=
'tanh'
)
for
emb
in
emb_layers
]
hidden_0
=
fluid
.
layers
.
sums
(
input
=
hidden_0_layers
)
lstm_0
=
fluid
.
layers
.
dynamic_lstm
(
input
=
hidden_0
,
size
=
hidden_dim
,
candidate_activation
=
'relu'
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'sigmoid'
)
# stack L-LSTM and R-LSTM with direct edges
input_tmp
=
[
hidden_0
,
lstm_0
]
for
i
in
range
(
1
,
depth
):
mix_hidden
=
fluid
.
layers
.
sums
(
input
=
[
fluid
.
layers
.
fc
(
input
=
input_tmp
[
0
],
size
=
hidden_dim
,
act
=
'tanh'
),
fluid
.
layers
.
fc
(
input
=
input_tmp
[
1
],
size
=
hidden_dim
,
act
=
'tanh'
)
])
lstm
=
fluid
.
layers
.
dynamic_lstm
(
input
=
mix_hidden
,
size
=
hidden_dim
,
candidate_activation
=
'relu'
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'sigmoid'
,
is_reverse
=
((
i
%
2
)
==
1
))
input_tmp
=
[
mix_hidden
,
lstm
]
feature_out
=
fluid
.
layers
.
sums
(
input
=
[
fluid
.
layers
.
fc
(
input
=
input_tmp
[
0
],
size
=
label_dict_len
,
act
=
'tanh'
),
fluid
.
layers
.
fc
(
input
=
input_tmp
[
1
],
size
=
label_dict_len
,
act
=
'tanh'
)
])
return
feature_out
class
TestCRFModel
(
unittest
.
TestCase
):
def
test_all
(
self
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
word
=
fluid
.
layers
.
data
(
name
=
'word_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
predicate
=
fluid
.
layers
.
data
(
name
=
'verb_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_n2
=
fluid
.
layers
.
data
(
name
=
'ctx_n2_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_n1
=
fluid
.
layers
.
data
(
name
=
'ctx_n1_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_0
=
fluid
.
layers
.
data
(
name
=
'ctx_0_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_p1
=
fluid
.
layers
.
data
(
name
=
'ctx_p1_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_p2
=
fluid
.
layers
.
data
(
name
=
'ctx_p2_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
mark
=
fluid
.
layers
.
data
(
name
=
'mark_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
feature_out
=
db_lstm
(
**
locals
())
target
=
fluid
.
layers
.
data
(
name
=
'target'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
crf_cost
=
fluid
.
layers
.
linear_chain_crf
(
input
=
feature_out
,
label
=
target
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'crfw'
,
learning_rate
=
1e-1
))
avg_cost
=
fluid
.
layers
.
mean
(
crf_cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
fluid
.
layers
.
exponential_decay
(
learning_rate
=
0.01
,
decay_steps
=
100000
,
decay_rate
=
0.5
,
staircase
=
True
))
sgd_optimizer
.
minimize
(
avg_cost
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
conll05
.
test
(),
buf_size
=
8192
),
batch_size
=
16
)
place
=
fluid
.
CUDAPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
pe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
loss_name
=
avg_cost
.
name
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
word
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
,
predicate
,
mark
,
target
],
place
=
fluid
.
CPUPlace
())
data
=
train_data
()
for
i
in
xrange
(
10
):
cur_batch
=
next
(
data
)
print
map
(
numpy
.
array
,
pe
.
run
(
feed_dict
=
feeder
.
feed
(
cur_batch
),
fetch_list
=
[
avg_cost
.
name
]))[
0
]
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