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928418a9
编写于
5月 14, 2018
作者:
T
typhoonzero
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into gen_nccl_id_op
上级
5ae0c664
9923be5d
变更
40
显示空白变更内容
内联
并排
Showing
40 changed file
with
588 addition
and
227 deletion
+588
-227
doc/fluid/design/concepts/lod_tensor.md
doc/fluid/design/concepts/lod_tensor.md
+1
-1
paddle/fluid/framework/details/multi_devices_graph_builder.cc
...le/fluid/framework/details/multi_devices_graph_builder.cc
+64
-9
paddle/fluid/framework/details/multi_devices_graph_builder.h
paddle/fluid/framework/details/multi_devices_graph_builder.h
+9
-2
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+7
-5
paddle/fluid/framework/parallel_executor.h
paddle/fluid/framework/parallel_executor.h
+1
-0
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+5
-0
paddle/fluid/operators/detection/CMakeLists.txt
paddle/fluid/operators/detection/CMakeLists.txt
+29
-0
paddle/fluid/operators/detection/bipartite_match_op.cc
paddle/fluid/operators/detection/bipartite_match_op.cc
+0
-0
paddle/fluid/operators/detection/box_coder_op.cc
paddle/fluid/operators/detection/box_coder_op.cc
+1
-1
paddle/fluid/operators/detection/box_coder_op.cu
paddle/fluid/operators/detection/box_coder_op.cu
+1
-1
paddle/fluid/operators/detection/box_coder_op.h
paddle/fluid/operators/detection/box_coder_op.h
+0
-0
paddle/fluid/operators/detection/iou_similarity_op.cc
paddle/fluid/operators/detection/iou_similarity_op.cc
+1
-1
paddle/fluid/operators/detection/iou_similarity_op.cu
paddle/fluid/operators/detection/iou_similarity_op.cu
+1
-1
paddle/fluid/operators/detection/iou_similarity_op.h
paddle/fluid/operators/detection/iou_similarity_op.h
+0
-0
paddle/fluid/operators/detection/mine_hard_examples_op.cc
paddle/fluid/operators/detection/mine_hard_examples_op.cc
+0
-0
paddle/fluid/operators/detection/multiclass_nms_op.cc
paddle/fluid/operators/detection/multiclass_nms_op.cc
+0
-0
paddle/fluid/operators/detection/prior_box_op.cc
paddle/fluid/operators/detection/prior_box_op.cc
+1
-1
paddle/fluid/operators/detection/prior_box_op.cu
paddle/fluid/operators/detection/prior_box_op.cu
+1
-1
paddle/fluid/operators/detection/prior_box_op.h
paddle/fluid/operators/detection/prior_box_op.h
+0
-0
paddle/fluid/operators/detection/target_assign_op.cc
paddle/fluid/operators/detection/target_assign_op.cc
+1
-1
paddle/fluid/operators/detection/target_assign_op.cu
paddle/fluid/operators/detection/target_assign_op.cu
+1
-1
paddle/fluid/operators/detection/target_assign_op.h
paddle/fluid/operators/detection/target_assign_op.h
+0
-0
paddle/fluid/operators/gen_nccl_id_op.cc
paddle/fluid/operators/gen_nccl_id_op.cc
+3
-4
paddle/fluid/operators/math/blas_impl.cu.h
paddle/fluid/operators/math/blas_impl.cu.h
+15
-3
paddle/fluid/operators/math/blas_impl.h
paddle/fluid/operators/math/blas_impl.h
+3
-3
paddle/fluid/operators/math/math_function.cu
paddle/fluid/operators/math/math_function.cu
+4
-3
paddle/fluid/operators/matmul_op.cc
paddle/fluid/operators/matmul_op.cc
+44
-32
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+4
-3
python/paddle/fluid/data_feeder.py
python/paddle/fluid/data_feeder.py
+58
-0
python/paddle/fluid/inferencer.py
python/paddle/fluid/inferencer.py
+32
-21
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+7
-3
python/paddle/fluid/parallel_executor.py
python/paddle/fluid/parallel_executor.py
+5
-0
python/paddle/fluid/tests/book/CMakeLists.txt
python/paddle/fluid/tests/book/CMakeLists.txt
+2
-0
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
+9
-0
python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt
...tests/book/high-level-api/recognize_digits/CMakeLists.txt
+7
-0
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
...-level-api/recognize_digits/test_recognize_digits_conv.py
+47
-30
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
...h-level-api/recognize_digits/test_recognize_digits_mlp.py
+46
-30
python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py
.../book/high-level-api/word2vec/no_test_word2vec_new_api.py
+14
-14
python/paddle/fluid/tests/unittests/test_parallel_executor.py
...on/paddle/fluid/tests/unittests/test_parallel_executor.py
+113
-24
python/paddle/fluid/trainer.py
python/paddle/fluid/trainer.py
+51
-32
未找到文件。
doc/fluid/design/concepts/lod_tensor.md
浏览文件 @
928418a9
...
...
@@ -155,7 +155,7 @@ into offsets
3 2+3 4+5 1+9 2+10 3+12
```
so we know that the first sentence is from word 0 to word 3, and the second sentence from wor
k
3 to word 5.
so we know that the first sentence is from word 0 to word 3, and the second sentence from wor
d
3 to word 5.
Similarly, the lengths in the top level LoD
...
...
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @
928418a9
...
...
@@ -37,20 +37,26 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const
std
::
string
&
loss_var_name
,
const
std
::
unordered_set
<
std
::
string
>
&
params
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
platform
::
NCCLContextMap
*
nccl_ctxs
,
bool
use_default_grad_scale
)
platform
::
NCCLContextMap
*
nccl_ctxs
,
bool
use_default_grad_scale
,
bool
balance_parameter_opt_between_cards
)
:
loss_var_name_
(
loss_var_name
),
places_
(
places
),
local_scopes_
(
local_scopes
),
nccl_ctxs_
(
nccl_ctxs
)
{
nccl_ctxs_
(
nccl_ctxs
),
balance_parameter_opt_between_cards_
(
balance_parameter_opt_between_cards
)
{
#else
MultiDevSSAGraphBuilder
::
MultiDevSSAGraphBuilder
(
const
std
::
vector
<
platform
::
Place
>
&
places
,
const
std
::
string
&
loss_var_name
,
const
std
::
unordered_set
<
std
::
string
>
&
params
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
bool
use_default_grad_scale
)
const
std
::
vector
<
Scope
*>
&
local_scopes
,
bool
use_default_grad_scale
,
bool
balance_parameter_opt_between_cards
)
:
loss_var_name_
(
loss_var_name
),
places_
(
places
),
local_scopes_
(
local_scopes
)
{
local_scopes_
(
local_scopes
),
balance_parameter_opt_between_cards_
(
balance_parameter_opt_between_cards
)
{
#endif
for
(
auto
&
p
:
params
)
{
grad_names_
.
insert
(
GradVarName
(
p
));
...
...
@@ -124,6 +130,12 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
// Find "send" op first for split is in front of send.
OpDesc
*
send_op
=
GetSendOpDesc
(
program
);
size_t
cur_device_id
=
0
;
std
::
vector
<
std
::
unordered_set
<
std
::
string
>>
var_name_on_devices
;
std
::
vector
<
std
::
unordered_set
<
std
::
string
>>
bcast_var_name_set
;
var_name_on_devices
.
resize
(
places_
.
size
());
bcast_var_name_set
.
resize
(
places_
.
size
());
bool
is_forwarding
=
true
;
for
(
auto
*
op
:
program
.
Block
(
0
).
AllOps
())
{
if
(
op
->
Type
()
==
"send"
)
{
...
...
@@ -139,12 +151,27 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
}
is_forwarding
=
false
;
}
else
{
int
op_dev_id
=
GetOpDeviceID
(
var_name_on_devices
,
*
op
);
if
(
op_dev_id
==
-
1
)
{
// var on all device
CreateComputationalOps
(
&
result
,
*
op
,
places_
.
size
());
}
else
{
CreateComputationalOp
(
&
result
,
*
op
,
op_dev_id
);
for
(
auto
&
var_name
:
op
->
OutputArgumentNames
())
{
var_name_on_devices
[
op_dev_id
].
emplace
(
var_name
);
}
}
if
(
!
is_forwarding
&&
places_
.
size
()
>
1
)
{
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
for
(
auto
&
og
:
op
->
OutputArgumentNames
())
{
if
(
IsParameterGradientOnce
(
og
,
&
og_has_been_broadcast
))
{
if
(
balance_parameter_opt_between_cards_
)
{
CreateReduceOp
(
&
result
,
og
,
cur_device_id
);
var_name_on_devices
[
cur_device_id
].
emplace
(
og
);
bcast_var_name_set
[
cur_device_id
].
emplace
(
og
.
substr
(
0
,
og
.
size
()
-
strlen
(
kGradVarSuffix
)));
cur_device_id
=
(
cur_device_id
+
1
)
%
places_
.
size
();
}
else
{
if
(
IsSparseGradient
(
var_types
,
og
))
{
CreateReduceOp
(
&
result
,
og
,
0
);
CreateBroadcastOp
(
&
result
,
og
,
0
);
...
...
@@ -156,7 +183,15 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
}
}
}
}
// Insert BCast Ops
for
(
size_t
dev_id
=
0
;
dev_id
<
bcast_var_name_set
.
size
();
++
dev_id
)
{
auto
&
to_bcast_set
=
bcast_var_name_set
[
dev_id
];
for
(
auto
&
bcast_name
:
to_bcast_set
)
{
CreateBroadcastOp
(
&
result
,
bcast_name
,
dev_id
);
}
}
/*
Dependency graph has been constructed. However, there are still data
harzaeds need to be handled.
...
...
@@ -265,6 +300,26 @@ bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
return
is_pg_once
;
}
int
MultiDevSSAGraphBuilder
::
GetOpDeviceID
(
const
std
::
vector
<
std
::
unordered_set
<
std
::
string
>>
&
var_name_on_devices
,
const
OpDesc
&
op
)
const
{
if
(
!
balance_parameter_opt_between_cards_
)
{
return
-
1
;
}
int
var_dev_id
=
-
1
;
for
(
auto
&
var_name
:
op
.
InputArgumentNames
())
{
if
(
var_dev_id
!=
-
1
)
break
;
for
(
size_t
i
=
0
;
i
<
var_name_on_devices
.
size
();
++
i
)
{
if
(
var_name_on_devices
[
i
].
count
(
var_name
))
{
var_dev_id
=
static_cast
<
int
>
(
i
);
break
;
}
}
}
return
var_dev_id
;
}
void
MultiDevSSAGraphBuilder
::
CreateScaleLossGradOp
(
SSAGraph
*
result
)
const
{
for
(
size_t
i
=
0
;
i
<
places_
.
size
();
++
i
)
{
// Insert ScaleCost OpHandle
...
...
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @
928418a9
...
...
@@ -36,13 +36,15 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const
std
::
unordered_set
<
std
::
string
>
&
params
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
platform
::
NCCLContextMap
*
nccl_ctxs
,
bool
use_default_grad_scale
);
bool
use_default_grad_scale
,
bool
balance_parameter_opt_between_cards
);
#else
MultiDevSSAGraphBuilder
(
const
std
::
vector
<
platform
::
Place
>
&
places
,
const
std
::
string
&
loss_var_name
,
const
std
::
unordered_set
<
std
::
string
>
&
params
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
bool
use_default_grad_scale
);
bool
use_default_grad_scale
,
bool
balance_parameter_opt_between_cards
);
#endif
std
::
unique_ptr
<
SSAGraph
>
Build
(
const
ProgramDesc
&
program
)
const
override
;
...
...
@@ -60,6 +62,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
#ifdef PADDLE_WITH_CUDA
platform
::
NCCLContextMap
*
nccl_ctxs_
;
#endif
bool
balance_parameter_opt_between_cards_
;
bool
use_default_grad_scale_
;
bool
IsScaleLossOp
(
const
OpDesc
&
op
)
const
;
...
...
@@ -84,6 +87,10 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const
std
::
string
&
og
,
std
::
unordered_set
<
std
::
string
>
*
og_has_been_broadcast
)
const
;
int
GetOpDeviceID
(
const
std
::
vector
<
std
::
unordered_set
<
std
::
string
>>
&
var_name_on_devices
,
const
OpDesc
&
op
)
const
;
void
InsertNCCLAllReduceOp
(
SSAGraph
*
result
,
const
std
::
string
&
og
)
const
;
void
CreateBroadcastOp
(
SSAGraph
*
result
,
const
std
::
string
&
p_name
,
...
...
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
928418a9
...
...
@@ -58,7 +58,8 @@ ParallelExecutor::ParallelExecutor(
const
std
::
unordered_set
<
std
::
string
>
&
bcast_vars
,
const
ProgramDesc
&
main_program
,
const
std
::
string
&
loss_var_name
,
Scope
*
scope
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
bool
allow_op_delay
,
bool
use_default_grad_scale
,
size_t
num_trainers
,
size_t
trainer_id
)
bool
use_default_grad_scale
,
bool
balance_parameter_opt_between_cards
,
size_t
num_trainers
,
size_t
trainer_id
)
:
member_
(
new
ParallelExecutorPrivate
(
places
))
{
member_
->
global_scope_
=
scope
;
...
...
@@ -99,11 +100,12 @@ ParallelExecutor::ParallelExecutor(
#ifdef PADDLE_WITH_CUDA
details
::
MultiDevSSAGraphBuilder
builder
(
member_
->
places_
,
loss_var_name
,
params
,
member_
->
local_scopes_
,
member_
->
nccl_ctxs_
.
get
(),
use_default_grad_scale
);
member_
->
nccl_ctxs_
.
get
(),
use_default_grad_scale
,
balance_parameter_opt_between_cards
);
#else
details
::
MultiDevSSAGraphBuilder
builder
(
member_
->
places_
,
loss_var_name
,
params
,
member_
->
local_scopes_
,
use_default_grad_scale
);
details
::
MultiDevSSAGraphBuilder
builder
(
member_
->
places_
,
loss_var_name
,
params
,
member_
->
local_scopes_
,
use_default_grad_scale
,
balance_parameter_opt_between_cards
);
#endif
auto
graph
=
builder
.
Build
(
main_program
);
...
...
paddle/fluid/framework/parallel_executor.h
浏览文件 @
928418a9
...
...
@@ -41,6 +41,7 @@ class ParallelExecutor {
const
std
::
string
&
loss_var_name
,
Scope
*
scope
,
const
std
::
vector
<
Scope
*>&
local_scopes
,
bool
allow_op_delay
,
bool
use_default_grad_scale
,
bool
balance_parameter_opt_between_cards
,
size_t
num_trainers
=
0
,
size_t
trainer_id
=
0
);
~
ParallelExecutor
();
...
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
928418a9
...
...
@@ -276,6 +276,11 @@ foreach(src ${READER_LIBRARY})
set
(
OP_LIBRARY
${
src
}
${
OP_LIBRARY
}
)
endforeach
()
add_subdirectory
(
detection
)
foreach
(
src
${
DETECTION_LIBRARY
}
)
set
(
OP_LIBRARY
${
src
}
${
OP_LIBRARY
}
)
endforeach
()
set
(
GLOB_OP_LIB
${
OP_LIBRARY
}
CACHE INTERNAL
"Global OP library"
)
cc_test
(
gather_test SRCS gather_test.cc DEPS tensor
)
...
...
paddle/fluid/operators/detection/CMakeLists.txt
0 → 100644
浏览文件 @
928418a9
set
(
LOCAL_DETECTION_LIBS
)
function
(
detection_library TARGET_NAME
)
set
(
oneValueArgs
""
)
set
(
multiValueArgs SRCS DEPS
)
set
(
options
""
)
set
(
common_deps op_registry
)
set
(
pybind_flag 0
)
cmake_parse_arguments
(
detection_library
"
${
options
}
"
"
${
oneValueArgs
}
"
"
${
multiValueArgs
}
"
${
ARGN
}
)
op_library
(
${
TARGET_NAME
}
SRCS
${
detection_library_SRCS
}
DEPS
${
common_deps
}
${
detection_library_DEPS
}
)
set
(
LOCAL_DETECTION_LIBS
${
TARGET_NAME
}
${
LOCAL_DETECTION_LIBS
}
PARENT_SCOPE
)
endfunction
()
detection_library
(
bipartite_match_op SRCS bipartite_match_op.cc
)
detection_library
(
box_coder_op SRCS box_coder_op.cc box_coder_op.cu
)
detection_library
(
iou_similarity_op SRCS iou_similarity_op.cc
iou_similarity_op.cu
)
detection_library
(
mine_hard_examples_op SRCS mine_hard_examples_op.cc
)
detection_library
(
multiclass_nms_op SRCS multiclass_nms_op.cc
)
detection_library
(
prior_box_op SRCS prior_box_op.cc prior_box_op.cu
)
detection_library
(
target_assign_op SRCS target_assign_op.cc
target_assign_op.cu
)
# Export local libraries to parent
set
(
DETECTION_LIBRARY
${
LOCAL_DETECTION_LIBS
}
PARENT_SCOPE
)
paddle/fluid/operators/bipartite_match_op.cc
→
paddle/fluid/operators/
detection/
bipartite_match_op.cc
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/box_coder_op.cc
→
paddle/fluid/operators/
detection/
box_coder_op.cc
浏览文件 @
928418a9
...
...
@@ -9,7 +9,7 @@ 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. */
#include "paddle/fluid/operators/box_coder_op.h"
#include "paddle/fluid/operators/
detection/
box_coder_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/box_coder_op.cu
→
paddle/fluid/operators/
detection/
box_coder_op.cu
浏览文件 @
928418a9
...
...
@@ -9,7 +9,7 @@ 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. */
#include "paddle/fluid/operators/box_coder_op.h"
#include "paddle/fluid/operators/
detection/
box_coder_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace
paddle
{
...
...
paddle/fluid/operators/box_coder_op.h
→
paddle/fluid/operators/
detection/
box_coder_op.h
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/iou_similarity_op.cc
→
paddle/fluid/operators/
detection/
iou_similarity_op.cc
浏览文件 @
928418a9
...
...
@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/operators/iou_similarity_op.h"
#include "paddle/fluid/operators/
detection/
iou_similarity_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/iou_similarity_op.cu
→
paddle/fluid/operators/
detection/
iou_similarity_op.cu
浏览文件 @
928418a9
...
...
@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/operators/iou_similarity_op.h"
#include "paddle/fluid/operators/
detection/
iou_similarity_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
...
...
paddle/fluid/operators/iou_similarity_op.h
→
paddle/fluid/operators/
detection/
iou_similarity_op.h
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/mine_hard_examples_op.cc
→
paddle/fluid/operators/
detection/
mine_hard_examples_op.cc
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/multiclass_nms_op.cc
→
paddle/fluid/operators/
detection/
multiclass_nms_op.cc
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/prior_box_op.cc
→
paddle/fluid/operators/
detection/
prior_box_op.cc
浏览文件 @
928418a9
...
...
@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/operators/prior_box_op.h"
#include "paddle/fluid/operators/
detection/
prior_box_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/prior_box_op.cu
→
paddle/fluid/operators/
detection/
prior_box_op.cu
浏览文件 @
928418a9
...
...
@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/operators/prior_box_op.h"
#include "paddle/fluid/operators/
detection/
prior_box_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/prior_box_op.h
→
paddle/fluid/operators/
detection/
prior_box_op.h
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/target_assign_op.cc
→
paddle/fluid/operators/
detection/
target_assign_op.cc
浏览文件 @
928418a9
...
...
@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/operators/target_assign_op.h"
#include "paddle/fluid/operators/
detection/
target_assign_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/target_assign_op.cu
→
paddle/fluid/operators/
detection/
target_assign_op.cu
浏览文件 @
928418a9
...
...
@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/operators/target_assign_op.h"
#include "paddle/fluid/operators/
detection/
target_assign_op.h"
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/target_assign_op.h
→
paddle/fluid/operators/
detection/
target_assign_op.h
浏览文件 @
928418a9
文件已移动
paddle/fluid/operators/gen_nccl_id_op.cc
浏览文件 @
928418a9
...
...
@@ -83,8 +83,8 @@ class GenNCCLIdOp : public framework::OperatorBase {
rpc_service_
->
SetProgram
(
&
empty_program
);
rpc_service_
->
SetExecutor
(
&
executor
);
s
erver_thread_
.
reset
(
new
std
::
thread
(
std
::
bind
(
&
detail
::
AsyncGRPCServer
::
RunSyncUpdate
,
rpc_service_
))
)
;
s
td
::
thread
server_
thread
(
std
::
bind
(
&
detail
::
AsyncGRPCServer
::
RunSyncUpdate
,
rpc_service_
));
rpc_service_
->
SetCond
(
0
);
VLOG
(
3
)
<<
"start getting nccl id from trainer 0..."
;
auto
recv
=
rpc_service_
->
Get
();
...
...
@@ -92,13 +92,12 @@ class GenNCCLIdOp : public framework::OperatorBase {
rpc_service_
->
ShutDown
();
VLOG
(
3
)
<<
"rpc server stopped"
;
// TODO(wuyi): reinit nccl communicators
server_thread
_
->
join
();
server_thread
.
join
();
delete
rpc_service_
;
}
protected:
mutable
detail
::
AsyncGRPCServer
*
rpc_service_
=
nullptr
;
mutable
std
::
shared_ptr
<
std
::
thread
>
server_thread_
;
};
class
GenNCCLIdOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
...
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @
928418a9
...
...
@@ -96,10 +96,22 @@ struct CUBlas<platform::float16> {
reinterpret_cast
<
__half
*>
(
C
),
ldc
));
}
template
<
typename
...
ARGS
>
static
void
GEMM_BATCH
(
ARGS
...
args
)
{
static
void
GEMM_BATCH
(
cublasHandle_t
handle
,
cublasOperation_t
transa
,
cublasOperation_t
transb
,
int
m
,
int
n
,
int
k
,
const
float16
*
alpha
,
const
float16
*
A
,
int
lda
,
long
long
int
strideA
,
const
float16
*
B
,
// NOLINT
int
ldb
,
long
long
int
strideB
,
// NOLINT
const
float16
*
beta
,
float16
*
C
,
int
ldc
,
long
long
int
strideC
,
// NOLINT
int
batchCount
)
{
#if CUDA_VERSION >= 8000
PADDLE_ENFORCE
(
platform
::
dynload
::
cublasHgemmStridedBatched
(
args
...));
PADDLE_ENFORCE
(
platform
::
dynload
::
cublasHgemmStridedBatched
(
handle
,
transa
,
transb
,
m
,
n
,
k
,
reinterpret_cast
<
const
__half
*>
(
alpha
),
reinterpret_cast
<
const
__half
*>
(
A
),
lda
,
strideA
,
reinterpret_cast
<
const
__half
*>
(
B
),
ldb
,
strideB
,
reinterpret_cast
<
const
__half
*>
(
beta
),
reinterpret_cast
<
__half
*>
(
C
),
ldc
,
strideC
,
batchCount
));
#else
PADDLE_THROW
(
"HgemmStridedBatched is not supported on cuda <= 7.5"
);
#endif
...
...
paddle/fluid/operators/math/blas_impl.h
浏览文件 @
928418a9
...
...
@@ -172,9 +172,9 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
c_array
.
data
(),
&
ldc
,
1
/* group_count */
,
&
batchCount
);
#else
for
(
int
k
=
0
;
k
<
batchCount
;
++
k
)
{
const
float
*
Ak
=
&
A
[
k
*
strideA
];
const
float
*
Bk
=
&
B
[
k
*
strideB
];
float
*
Ck
=
&
C
[
k
*
M
*
N
];
auto
*
Ak
=
&
A
[
k
*
strideA
];
auto
*
Bk
=
&
B
[
k
*
strideB
];
auto
*
Ck
=
&
C
[
k
*
M
*
N
];
this
->
template
GEMM
<
T
>(
transA
,
transB
,
M
,
N
,
K
,
alpha
,
Ak
,
Bk
,
beta
,
Ck
);
}
#endif
...
...
paddle/fluid/operators/math/math_function.cu
浏览文件 @
928418a9
...
...
@@ -35,7 +35,8 @@ template struct SetConstant<platform::CUDADeviceContext, bool>;
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
template struct Transpose<platform::CUDADeviceContext, double, RANK>;
template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
template struct Transpose<platform::CUDADeviceContext, float16, RANK>;
DEFINE_GPU_TRANS
(
1
);
DEFINE_GPU_TRANS
(
2
);
...
...
paddle/fluid/operators/matmul_op.cc
浏览文件 @
928418a9
...
...
@@ -25,7 +25,7 @@ namespace operators {
* Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
* original x_dim is returned.
*/
static
framework
::
DDim
RowMatrixFromVector
(
const
framework
::
DDim
&
x_dim
)
{
static
framework
::
DDim
RowMatrixFromVector
(
const
framework
::
DDim
&
x_dim
)
{
if
(
x_dim
.
size
()
>
1
)
{
return
x_dim
;
}
...
...
@@ -36,7 +36,7 @@ static framework::DDim RowMatrixFromVector(const framework::DDim& x_dim) {
* Get column matrix shape from a vector shape. If the ran of y_dim > 1, the
* original y_dim is returned.
*/
static
framework
::
DDim
ColumnMatrixFromVector
(
const
framework
::
DDim
&
y_dim
)
{
static
framework
::
DDim
ColumnMatrixFromVector
(
const
framework
::
DDim
&
y_dim
)
{
if
(
y_dim
.
size
()
>
1
)
{
return
y_dim
;
}
...
...
@@ -46,12 +46,12 @@ static framework::DDim ColumnMatrixFromVector(const framework::DDim& y_dim) {
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
&
x
=
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
&
x
=
detail
::
Ref
(
context
.
Input
<
framework
::
Tensor
>
(
"X"
),
"Cannot find X"
);
auto
&
y
=
auto
&
y
=
detail
::
Ref
(
context
.
Input
<
framework
::
Tensor
>
(
"Y"
),
"Cannot find Y"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
context
);
...
...
@@ -65,7 +65,7 @@ class MatMulKernel : public framework::OpKernel<T> {
// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
// Identity op if the tensor is not of rank 3.
static
framework
::
Tensor
FoldInitDims
(
const
framework
::
Tensor
&
input
)
{
static
framework
::
Tensor
FoldInitDims
(
const
framework
::
Tensor
&
input
)
{
auto
output
=
input
;
auto
in_dims
=
input
.
dims
();
if
(
in_dims
.
size
()
==
3
)
{
...
...
@@ -78,8 +78,8 @@ static framework::Tensor FoldInitDims(const framework::Tensor& input) {
// (Warning: This requires transposing data and writes into new memory.)
// Identity op if the tensor is not of rank 3.
template
<
typename
DeviceContext
,
typename
T
>
static
framework
::
Tensor
FoldHeadAndLastDims
(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
)
{
static
framework
::
Tensor
FoldHeadAndLastDims
(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
)
{
auto
in_dims
=
input
.
dims
();
if
(
in_dims
.
size
()
!=
3
)
{
return
input
;
...
...
@@ -102,7 +102,7 @@ static framework::Tensor FoldHeadAndLastDims(const DeviceContext& context,
* If transposed, `H,W` will be swapped.
*/
static
void
ReshapeTensorIntoMatrixSequence
(
framework
::
Tensor
*
x
,
const
math
::
MatDescriptor
&
descriptor
)
{
framework
::
Tensor
*
x
,
const
math
::
MatDescriptor
&
descriptor
)
{
int64_t
h
,
w
;
h
=
descriptor
.
height_
;
w
=
descriptor
.
width_
;
...
...
@@ -130,9 +130,9 @@ static void ReshapeTensorIntoMatrixSequence(
* If any of `X` and `Y` has batch size BatchSize, the out will have the
* BatchSize.
*/
static
void
ReshapeXYOutIntoMatrixSequence
(
framework
::
Tensor
*
x
,
framework
::
Tensor
*
y
,
framework
::
Tensor
*
out
,
bool
trans_x
,
static
void
ReshapeXYOutIntoMatrixSequence
(
framework
::
Tensor
*
x
,
framework
::
Tensor
*
y
,
framework
::
Tensor
*
out
,
bool
trans_x
,
bool
trans_y
)
{
auto
x_dim
=
RowMatrixFromVector
(
x
->
dims
());
auto
y_dim
=
ColumnMatrixFromVector
(
y
->
dims
());
...
...
@@ -177,10 +177,10 @@ static void ReshapeXYOutIntoMatrixSequence(framework::Tensor* x,
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
MatMul
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
framework
::
Tensor
*
out
)
const
{
void
MatMul
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
framework
::
Tensor
*
out
)
const
{
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
context
);
auto
mat_dim_a
=
math
::
CreateMatrixDescriptor
(
a
.
dims
(),
0
,
trans_a
);
...
...
@@ -188,18 +188,18 @@ class MatMulGradKernel : public framework::OpKernel<T> {
blas
.
MatMul
(
a
,
mat_dim_a
,
b
,
mat_dim_b
,
T
(
1
),
out
,
T
(
0
));
}
void
CalcInputGrad
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
bool
is_fold_init_dims_a
,
const
framework
::
Tensor
&
b
,
void
CalcInputGrad
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
bool
is_fold_init_dims_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
bool
is_fold_init_dims_b
,
framework
::
Tensor
*
out
)
const
{
framework
::
Tensor
*
out
)
const
{
if
(
out
==
nullptr
)
return
;
bool
need_combine
=
(
a
.
dims
().
size
()
==
3
||
b
.
dims
().
size
()
==
3
)
&&
out
->
dims
().
size
()
==
2
;
if
(
!
need_combine
)
{
MatMul
(
context
,
a
,
trans_a
,
b
,
trans_b
,
out
);
}
else
{
auto
&
ctx
=
context
.
template
device_context
<
DeviceContext
>();
auto
&
ctx
=
context
.
template
device_context
<
DeviceContext
>();
MatMul
(
context
,
is_fold_init_dims_a
?
FoldInitDims
(
a
)
:
FoldHeadAndLastDims
<
DeviceContext
,
T
>
(
ctx
,
a
),
...
...
@@ -210,13 +210,13 @@ class MatMulGradKernel : public framework::OpKernel<T> {
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
x
=
*
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
y
=
*
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
auto
dout
=
*
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dx
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
dx
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
bool
transpose_x
=
context
.
Attr
<
bool
>
(
"transpose_X"
);
bool
transpose_y
=
context
.
Attr
<
bool
>
(
"transpose_Y"
);
...
...
@@ -269,7 +269,7 @@ class MatMulOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
),
"Input(X) of MatMulOp should not be null."
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"Y"
),
...
...
@@ -375,7 +375,7 @@ class MatMulOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"Y"
),
"Input(Y) should not be null"
);
PADDLE_ENFORCE
(
context
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
...
...
@@ -401,7 +401,7 @@ class MatMulOpGradMaker : public framework::SingleGradOpDescMaker {
protected:
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
retv
=
new
framework
::
OpDesc
();
auto
*
retv
=
new
framework
::
OpDesc
();
retv
->
SetType
(
"matmul_grad"
);
retv
->
SetInput
(
"X"
,
Input
(
"X"
));
retv
->
SetInput
(
"Y"
,
Input
(
"Y"
));
...
...
@@ -420,15 +420,27 @@ REGISTER_OPERATOR(matmul, ops::MatMulOp, ops::MatMulOpMaker,
ops
::
MatMulOpGradMaker
);
REGISTER_OPERATOR
(
matmul_grad
,
ops
::
MatMulOpGrad
);
REGISTER_OP_CPU_KERNEL
(
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CPUDeviceContext
,
paddle
::
platform
::
float16
>
);
REGISTER_OP_CPU_KERNEL
(
matmul_grad
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
paddle
::
platform
::
float16
>
);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL
(
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
);
REGISTER_OP_CUDA_KERNEL
(
matmul_grad
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
);
#endif
paddle/fluid/pybind/pybind.cc
浏览文件 @
928418a9
...
...
@@ -503,12 +503,13 @@ All parameter, weight, gradient are variables in Paddle.
const
ProgramDesc
&
main_program
,
const
std
::
string
&
loss_var_name
,
Scope
*
scope
,
std
::
vector
<
Scope
*>
&
local_scopes
,
bool
allow_op_delay
,
bool
use_default_grad_scale
,
size_t
num_trainers
,
size_t
trainer_id
)
{
bool
balance_parameter_opt_between_cards
,
size_t
num_trainers
,
size_t
trainer_id
)
{
new
(
&
self
)
ParallelExecutor
(
num_threads
,
use_event
,
places
,
params
,
bcast_vars
,
main_program
,
loss_var_name
,
scope
,
local_scopes
,
allow_op_delay
,
use_default_grad_scale
,
num_trainers
,
trainer_id
);
allow_op_delay
,
use_default_grad_scale
,
balance_parameter_opt_between_cards
,
num_trainers
,
trainer_id
);
})
.
def
(
"bcast_params"
,
&
ParallelExecutor
::
BCastParamsToGPUs
)
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
...
...
python/paddle/fluid/data_feeder.py
浏览文件 @
928418a9
...
...
@@ -16,6 +16,7 @@ from __future__ import print_function
import
core
import
numpy
import
six.moves
as
six
import
multiprocessing
from
framework
import
Variable
,
default_main_program
...
...
@@ -116,3 +117,60 @@ class DataFeeder(object):
for
each_name
,
each_converter
in
six
.
zip
(
self
.
feed_names
,
converter
):
ret_dict
[
each_name
]
=
each_converter
.
done
()
return
ret_dict
def
feed_parallel
(
self
,
iterable
,
num_places
=
None
):
if
isinstance
(
self
.
place
,
core
.
CUDAPlace
):
places
=
[
core
.
CUDAPlace
(
i
)
for
i
in
six
.
xrange
(
self
.
_get_number_of_places_
(
num_places
))
]
else
:
places
=
[
core
.
CPUPlace
()
for
_
in
six
.
xrange
(
self
.
_get_number_of_places_
(
num_places
))
]
if
len
(
iterable
)
!=
len
(
places
):
raise
ValueError
(
"feed_parallel takes multiple mini-batches. Each "
"mini-batch will be feed on each device. The "
"number of devices and number of mini-batches "
"must be same."
)
place
=
self
.
place
for
p
,
batch
in
six
.
zip
(
places
,
iterable
):
self
.
place
=
p
yield
self
.
feed
(
batch
)
self
.
place
=
place
def
_get_number_of_places_
(
self
,
num_places
):
if
num_places
is
not
None
:
return
int
(
num_places
)
elif
isinstance
(
self
.
place
,
core
.
CUDAPlace
):
return
core
.
get_cuda_device_count
()
else
:
return
multiprocessing
.
cpu_count
()
def
decorate_reader
(
self
,
reader
,
multi_devices
,
num_places
=
None
,
drop_last
=
True
):
def
__reader_creator__
():
if
not
multi_devices
:
for
item
in
reader
():
yield
self
.
feed
(
item
)
else
:
num
=
self
.
_get_number_of_places_
(
num_places
)
item
=
[]
for
batch
in
reader
():
item
.
append
(
batch
)
if
len
(
item
)
==
num
:
yield
list
(
self
.
feed_parallel
(
item
,
num
))
item
=
[]
if
not
drop_last
and
len
(
item
)
!=
0
:
raise
ValueError
(
"The data batch which cannot fit for devices will be "
"dropped is not implementation. Other strategies are "
"not implemented"
)
return
__reader_creator__
python/paddle/fluid/inferencer.py
浏览文件 @
928418a9
...
...
@@ -16,31 +16,42 @@ import core
import
framework
import
executor
import
io
from
trainer
import
check_and_get_place
__all__
=
[
'Inferencer'
,
]
class
Inferencer
(
object
):
def
__init__
(
self
,
network_func
,
param_path
=
None
,
place
=
None
):
# 1. we need to generate a framework.Program by calling
# network_func. Reference: fluid.program_guard in test_word2vec.py
# 2. move the default_main_program to self.program.
# 3. run the default_startup program.
# 4. load params from param_path into scope
def
__init__
(
self
,
param_path
,
place
=
None
):
"""
:param param_path: the path where the inference model is saved by fluid.io.save_inference_model
:param place: place to do the inference
"""
self
.
param_path
=
param_path
self
.
scope
=
core
.
Scope
()
self
.
place
=
place
self
.
startup_program
=
framework
.
Program
()
# TODO: generate the startup_program with network_func
exe
=
executor
.
Executor
(
place
)
exe
.
run
(
self
.
startup_program
,
scope
=
self
.
scope
)
if
param_path
:
self
.
exe
=
executor
.
Executor
(
check_and_get_place
(
place
))
with
executor
.
scope_guard
(
self
.
scope
):
# load params from param_path into scope
io
.
load_persistables
(
exe
,
dirname
=
param_path
)
def
infer
(
self
,
inputs
):
# run self.program
pass
[
self
.
inference_program
,
_
,
self
.
fetch_targets
]
=
io
.
load_inference_model
(
executor
=
self
.
exe
,
dirname
=
param_path
)
def
infer
(
self
,
inputs
,
return_numpy
=
True
):
"""
:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
to get the predict value
:param return_numpy: if return numpy value for row tensor
:return: the predict value of the inference model
"""
if
not
isinstance
(
inputs
,
dict
):
raise
ValueError
(
"inputs should be a map of {'input_name': input_var}"
)
with
executor
.
scope_guard
(
self
.
scope
):
results
=
self
.
exe
.
run
(
self
.
inference_program
,
feed
=
inputs
,
fetch_list
=
self
.
fetch_targets
,
return_numpy
=
return_numpy
)
return
results
python/paddle/fluid/io.py
浏览文件 @
928418a9
...
...
@@ -263,6 +263,9 @@ def get_inference_program(target_vars, main_program=None):
def
prepend_feed_ops
(
inference_program
,
feed_target_names
,
feed_holder_name
=
'feed'
):
if
len
(
feed_target_names
)
==
0
:
return
global_block
=
inference_program
.
global_block
()
feed_var
=
global_block
.
create_var
(
name
=
feed_holder_name
,
...
...
@@ -323,6 +326,7 @@ def save_inference_model(dirname,
if
isinstance
(
feeded_var_names
,
basestring
):
feeded_var_names
=
[
feeded_var_names
]
else
:
if
len
(
feeded_var_names
)
>
0
:
if
not
(
bool
(
feeded_var_names
)
and
all
(
isinstance
(
name
,
basestring
)
for
name
in
feeded_var_names
)):
raise
ValueError
(
"'feed_var_names' should be a list of str."
)
...
...
python/paddle/fluid/parallel_executor.py
浏览文件 @
928418a9
...
...
@@ -31,6 +31,7 @@ class ParallelExecutor(object):
allow_op_delay
=
False
,
share_vars_from
=
None
,
use_default_grad_scale
=
True
,
balance_parameter_opt_between_cards
=
False
,
num_trainers
=
0
,
trainer_id
=
0
):
"""
...
...
@@ -53,6 +54,9 @@ class ParallelExecutor(object):
gradients of each device and scaled gradients would be
aggregated. Otherwise, a customized scale value should be fed
to the network.
balance_parameter_opt_between_cards(bool, default True): Whether
updating different gradients on different cards. Currently, it
is not recommended.
num_trainers(int, default 0): If greater than 0, NCCL will be
initialized with multpile rank of nodes, each node should have
same number of GPUs. Distributed training will be enabled then.
...
...
@@ -137,6 +141,7 @@ class ParallelExecutor(object):
local_scopes
,
allow_op_delay
,
use_default_grad_scale
,
balance_parameter_opt_between_cards
,
num_trainers
,
trainer_id
)
self
.
scope
=
scope
...
...
python/paddle/fluid/tests/book/CMakeLists.txt
浏览文件 @
928418a9
...
...
@@ -5,3 +5,5 @@ string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach
(
src
${
TEST_OPS
}
)
py_test
(
${
src
}
SRCS
${
src
}
.py
)
endforeach
()
add_subdirectory
(
high-level-api
)
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
0 → 100644
浏览文件 @
928418a9
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
# default test
foreach
(
src
${
TEST_OPS
}
)
py_test
(
${
src
}
SRCS
${
src
}
.py
)
endforeach
()
add_subdirectory
(
recognize_digits
)
python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt
0 → 100644
浏览文件 @
928418a9
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
# default test
foreach
(
src
${
TEST_OPS
}
)
py_test
(
${
src
}
SRCS
${
src
}
.py
)
endforeach
()
python/paddle/fluid/tests/book/high-level-api/recognize_digits/
no
test_recognize_digits_conv.py
→
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
浏览文件 @
928418a9
...
...
@@ -21,7 +21,6 @@ import unittest
import
math
import
sys
import
os
import
paddle.v2.dataset
as
dataset
BATCH_SIZE
=
64
...
...
@@ -54,47 +53,65 @@ def train_program():
predict
=
inference_program
()
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
return
avg_cost
,
acc
# acc = fluid.layers.accuracy(input=predict, label=label)
# return avg_cost, acc
return
avg_cost
def
train
(
use_cuda
,
save_dirname
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
trainer
=
fluid
.
Trainer
(
train_program
,
place
=
place
,
optimizer
=
optimizer
)
trainer
=
fluid
.
Trainer
(
train_func
=
train_program
,
infer_func
=
inference_program
,
place
=
place
,
optimizer
=
optimizer
)
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
EndIteration
):
avg_cost
,
acc
=
event
.
values
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
(
event
.
batch_id
+
1
)
%
10
==
0
:
test_metrics
=
trainer
.
test
(
reader
=
dataset
.
mnist
.
test
())
avg_cost_set
=
test_metrics
[
0
]
acc_set
=
test_metrics
[
1
]
# get test acc and loss
acc
=
numpy
.
array
(
acc_set
).
mean
()
avg_cost
=
numpy
.
array
(
avg_cost_set
).
mean
()
if
float
(
acc
)
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
save_dirname
)
else
:
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
batch_id
+
1
,
float
(
avg_cost
),
float
(
acc
)))
if
math
.
isnan
(
float
(
avg_cost
)):
sys
.
exit
(
"got NaN loss, training failed."
)
if
isinstance
(
event
,
fluid
.
EndEpochEvent
):
# if (event.epoch + 1) % 10 == 0:
# trainer.save_params(save_dirname)
trainer
.
save_inference_model
(
save_dirname
)
# TODO: Uncomment this part once we are sure that .train is working
# test_reader = paddle.batch(
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
# test_metrics = trainer.test(reader=test_reader)
# avg_cost_set = test_metrics[0]
# acc_set = test_metrics[1]
#
# # get test acc and loss
# acc = numpy.array(acc_set).mean()
# avg_cost = numpy.array(avg_cost_set).mean()
#
# print("avg_cost: %s" % avg_cost)
# print("acc : %s" % acc)
#
# if float(acc) > 0.2: # Smaller value to increase CI speed
# trainer.save_params(save_dirname)
# else:
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
# event.epoch + 1, float(avg_cost), float(acc)))
# if math.isnan(float(avg_cost)):
# sys.exit("got NaN loss, training failed.")
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
trainer
.
train
(
reader
=
dataset
.
mnist
.
train
(),
num_pass
=
100
,
event_handler
=
event_handler
)
num_epochs
=
1
,
event_handler
=
event_handler
,
reader
=
train_reader
,
feed_order
=
[
'img'
,
'label'
])
def
infer
(
use_cuda
,
save_dirname
=
None
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
inference_program
,
param_path
=
save_dirname
,
place
=
place
)
inferencer
=
fluid
.
Inferencer
(
param_path
=
save_dirname
,
place
=
place
)
batch_size
=
1
tensor_img
=
numpy
.
random
.
uniform
(
-
1.0
,
1.0
,
...
...
@@ -114,5 +131,5 @@ def main(use_cuda):
if
__name__
==
'__main__'
:
for
use_cuda
in
(
False
,
True
):
main
(
use_cuda
=
use_cuda
)
#
for use_cuda in (False, True):
main
(
use_cuda
=
False
)
python/paddle/fluid/tests/book/high-level-api/recognize_digits/
no
test_recognize_digits_mlp.py
→
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
浏览文件 @
928418a9
...
...
@@ -21,7 +21,6 @@ import unittest
import
math
import
sys
import
os
import
paddle.v2.dataset
as
dataset
BATCH_SIZE
=
64
...
...
@@ -41,47 +40,64 @@ def train_program():
predict
=
inference_program
()
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
return
avg_cost
,
acc
# acc = fluid.layers.accuracy(input=predict, label=label)
# return avg_cost, acc
return
avg_cost
def
train
(
use_cuda
,
save_dirname
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
trainer
=
fluid
.
Trainer
(
train_program
,
place
=
place
,
optimizer
=
optimizer
)
trainer
=
fluid
.
Trainer
(
train_func
=
train_program
,
infer_func
=
inference_program
,
place
=
place
,
optimizer
=
optimizer
)
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
EndIteration
):
avg_cost
,
acc
=
event
.
values
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
(
event
.
batch_id
+
1
)
%
10
==
0
:
test_metrics
=
trainer
.
test
(
reader
=
dataset
.
mnist
.
test
())
avg_cost_set
=
test_metrics
[
0
]
acc_set
=
test_metrics
[
1
]
# get test acc and loss
acc
=
numpy
.
array
(
acc_set
).
mean
()
avg_cost
=
numpy
.
array
(
avg_cost_set
).
mean
()
if
float
(
acc
)
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
save_dirname
)
else
:
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
batch_id
+
1
,
float
(
avg_cost
),
float
(
acc
)))
if
math
.
isnan
(
float
(
avg_cost
)):
sys
.
exit
(
"got NaN loss, training failed."
)
if
isinstance
(
event
,
fluid
.
EndEpochEvent
):
# if (event.epoch + 1) % 10 == 0:
trainer
.
save_inference_model
(
save_dirname
)
# TODO: Uncomment this part once we are sure that .train is working
# test_reader = paddle.batch(
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
# test_metrics = trainer.test(reader=test_reader)
# avg_cost_set = test_metrics[0]
# acc_set = test_metrics[1]
#
# # get test acc and loss
# acc = numpy.array(acc_set).mean()
# avg_cost = numpy.array(avg_cost_set).mean()
#
# print("avg_cost: %s" % avg_cost)
# print("acc : %s" % acc)
#
# if float(acc) > 0.2: # Smaller value to increase CI speed
# trainer.save_params(save_dirname)
# else:
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
# event.epoch + 1, float(avg_cost), float(acc)))
# if math.isnan(float(avg_cost)):
# sys.exit("got NaN loss, training failed.")
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
trainer
.
train
(
reader
=
dataset
.
mnist
.
train
(),
num_pass
=
100
,
event_handler
=
event_handler
)
num_epochs
=
1
,
event_handler
=
event_handler
,
reader
=
train_reader
,
feed_order
=
[
'img'
,
'label'
])
def
infer
(
use_cuda
,
save_dirname
=
None
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
inference_program
,
param_path
=
save_dirname
,
place
=
place
)
inferencer
=
fluid
.
Inferencer
(
param_path
=
save_dirname
,
place
=
place
)
batch_size
=
1
tensor_img
=
numpy
.
random
.
uniform
(
-
1.0
,
1.0
,
...
...
@@ -101,5 +117,5 @@ def main(use_cuda):
if
__name__
==
'__main__'
:
for
use_cuda
in
(
False
,
True
):
main
(
use_cuda
=
use_cuda
)
#
for use_cuda in (False, True):
main
(
use_cuda
=
False
)
python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py
浏览文件 @
928418a9
...
...
@@ -99,45 +99,45 @@ def train(use_cuda, is_sparse, save_path):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
def
event_handler
(
event
):
# print type(event)
if
isinstance
(
event
,
fluid
.
EndEpochEvent
):
outs
=
trainer
.
test
(
reader
=
test_reader
)
avg_cost
=
outs
[
0
]
print
(
"loss= "
,
avg_cost
)
if
avg_cost
<
5.0
:
trainer
.
save_
params
(
save_path
)
trainer
.
save_
inference_model
(
save_path
)
return
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
trainer
=
fluid
.
Trainer
(
partial
(
train_program
,
is_sparse
),
partial
(
inference_program
,
is_sparse
),
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
),
place
=
place
)
trainer
.
train
(
reader
=
train_reader
,
num_epochs
=
1
00
,
event_handler
=
event_handler
)
reader
=
train_reader
,
num_epochs
=
1
,
event_handler
=
event_handler
)
def
infer
(
use_cuda
,
is_sparse
,
save_path
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
partial
(
inference_program
,
is_sparse
),
param_path
=
save_path
,
place
=
place
)
inferencer
=
fluid
.
Inferencer
(
param_path
=
save_path
,
place
=
place
)
lod
=
[
0
,
1
]
first_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
second_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
third_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
fourth_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
result
=
inferencer
.
infer
({
result
=
inferencer
.
infer
(
{
'firstw'
:
first_word
,
'secondw'
:
second_word
,
'thirdw'
:
third_word
,
'forthw'
:
fourth_word
})
print
(
result
)
},
return_numpy
=
False
)
print
(
np
.
array
(
result
[
0
]))
def
main
(
use_cuda
,
is_sparse
):
...
...
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @
928418a9
...
...
@@ -205,7 +205,8 @@ class TestParallelExecutorBase(unittest.TestCase):
allow_op_delay
=
False
,
feed_dict
=
None
,
seed
=
None
,
use_parallel_executor
=
True
):
use_parallel_executor
=
True
,
balance_parameter_opt_between_cards
=
False
):
def
run_executor
(
exe
,
feed
,
fetch_list
,
program
=
None
):
if
isinstance
(
exe
,
fluid
.
ParallelExecutor
):
res
=
exe
.
run
(
fetch_list
=
fetch_list
,
feed
=
feed
)
...
...
@@ -234,7 +235,11 @@ class TestParallelExecutorBase(unittest.TestCase):
if
use_parallel_executor
:
exe
=
fluid
.
ParallelExecutor
(
True
,
loss_name
=
loss
.
name
,
allow_op_delay
=
allow_op_delay
)
True
,
loss_name
=
loss
.
name
,
allow_op_delay
=
allow_op_delay
,
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
else
:
exe
=
fluid
.
Executor
(
place
=
place
)
...
...
@@ -280,20 +285,27 @@ class TestMNIST(TestParallelExecutorBase):
fluid
.
recordio_writer
.
convert_reader_to_recordio_file
(
'./mnist.recordio'
,
reader
,
feeder
)
def
check_simple_fc_convergence
(
self
):
def
check_simple_fc_convergence
(
self
,
balance_parameter_opt_between_cards
):
self
.
check_network_convergence
(
simple_fc_net
)
self
.
check_network_convergence
(
simple_fc_net
,
allow_op_delay
=
True
)
img
=
np
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
self
.
check_network_convergence
(
simple_fc_net
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
})
simple_fc_net
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
def
test_simple_fc
(
self
):
self
.
check_simple_fc_convergence
()
self
.
check_simple_fc_convergence
(
False
)
def
test_simple_fc_with_new_strategy
(
self
):
self
.
check_simple_fc_convergence
(
True
)
def
check_simple_fc_parallel_accuracy
(
self
):
def
check_simple_fc_parallel_accuracy
(
self
,
balance_parameter_opt_between_cards
):
img
=
np
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
single_first_loss
,
single_last_loss
=
self
.
check_network_convergence
(
...
...
@@ -307,7 +319,9 @@ class TestMNIST(TestParallelExecutorBase):
seed
=
1000
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_parallel_executor
=
True
)
use_parallel_executor
=
True
,
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
for
p_f
in
parallel_first_loss
:
self
.
assertAlmostEquals
(
p_f
,
single_first_loss
[
0
],
delta
=
1e-6
)
...
...
@@ -315,18 +329,28 @@ class TestMNIST(TestParallelExecutorBase):
self
.
assertAlmostEquals
(
p_l
,
single_last_loss
[
0
],
delta
=
1e-6
)
def
test_simple_fc_parallel_accuracy
(
self
):
self
.
check_simple_fc_parallel_accuracy
()
self
.
check_simple_fc_parallel_accuracy
(
False
)
def
check_batchnorm_fc_convergence
(
self
):
def
test_simple_fc_parallel_accuracy_with_new_strategy
(
self
):
self
.
check_simple_fc_parallel_accuracy
(
True
)
def
check_batchnorm_fc_convergence
(
self
,
balance_parameter_opt_between_cards
):
self
.
check_network_convergence
(
fc_with_batchnorm
)
img
=
np
.
zeros
(
shape
=
[
32
,
784
],
dtype
=
'float32'
)
label
=
np
.
ones
(
shape
=
[
32
,
1
],
dtype
=
'int64'
)
self
.
check_network_convergence
(
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
})
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
def
test_batchnorm_fc
(
self
):
self
.
check_batchnorm_fc_convergence
()
self
.
check_batchnorm_fc_convergence
(
False
)
def
test_batchnorm_fc_with_new_strategy
(
self
):
self
.
check_batchnorm_fc_convergence
(
True
)
class
TestResnet
(
TestParallelExecutorBase
):
...
...
@@ -348,17 +372,22 @@ class TestResnet(TestParallelExecutorBase):
# fluid.recordio_writer.convert_reader_to_recordio_file(
# "./flowers.recordio", reader, feeder, compressor=fluid.core.RecordIOWriter.Compressor.NoCompress)
def
check_resnet_convergence
(
self
):
def
check_resnet_convergence
(
self
,
balance_parameter_opt_between_cards
):
import
functools
batch_size
=
2
self
.
check_network_convergence
(
functools
.
partial
(
SE_ResNeXt50Small
,
batch_size
=
batch_size
),
iter
=
20
,
batch_size
=
batch_size
)
batch_size
=
batch_size
,
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
def
test_resnet
(
self
):
self
.
check_resnet_convergence
()
self
.
check_resnet_convergence
(
False
)
def
test_resnet_with_new_strategy
(
self
):
self
.
check_resnet_convergence
(
True
)
class
ModelHyperParams
(
object
):
...
...
@@ -519,7 +548,7 @@ class TestTransformer(TestParallelExecutorBase):
class
ParallelExecutorTestingDuringTraining
(
unittest
.
TestCase
):
def
check_network_convergence
(
self
):
def
check_network_convergence
(
self
,
balance_parameter_opt_between_cards
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
...
...
@@ -539,12 +568,18 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
feed_dict
=
{
'image'
:
image
,
'label'
:
label
}
train_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
loss_name
=
loss
.
name
,
main_program
=
main
)
use_cuda
=
True
,
loss_name
=
loss
.
name
,
main_program
=
main
,
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
test_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
main_program
=
test_program
,
share_vars_from
=
train_exe
)
share_vars_from
=
train_exe
,
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
for
i
in
xrange
(
5
):
test_loss
,
=
test_exe
.
run
([
loss
.
name
],
feed
=
feed_dict
)
...
...
@@ -558,8 +593,11 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
"Train loss: "
+
str
(
train_loss
)
+
"
\n
Test loss:"
+
str
(
test_loss
))
def
test_parallel
(
self
):
self
.
check_network_convergence
()
def
test_parallel_testing
(
self
):
self
.
check_network_convergence
(
False
)
def
test_parallel_testing_with_new_strategy
(
self
):
self
.
check_network_convergence
(
True
)
import
paddle.dataset.conll05
as
conll05
...
...
@@ -579,7 +617,7 @@ embedding_name = 'emb'
def
db_lstm
(
word
,
predicate
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
,
mark
,
is_sparse
,
**
ignored
):
is_sparse
,
balance_parameter_opt_between_cards
,
**
ignored
):
# 8 features
predicate_embedding
=
fluid
.
layers
.
embedding
(
input
=
predicate
,
...
...
@@ -648,7 +686,9 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
class
TestCRFModel
(
unittest
.
TestCase
):
def
check_network_convergence
(
self
,
is_sparse
):
def
check_network_convergence
(
self
,
is_sparse
,
balance_parameter_opt_between_cards
=
False
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
...
...
@@ -696,7 +736,11 @@ class TestCRFModel(unittest.TestCase):
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
pe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
loss_name
=
avg_cost
.
name
)
pe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
loss_name
=
avg_cost
.
name
,
balance_parameter_opt_between_cards
=
balance_parameter_opt_between_cards
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
...
...
@@ -718,6 +762,14 @@ class TestCRFModel(unittest.TestCase):
def
test_update_dense_parameter
(
self
):
self
.
check_network_convergence
(
is_sparse
=
False
)
def
test_update_sparse_parameter_with_new_strategy
(
self
):
self
.
check_network_convergence
(
is_sparse
=
False
,
balance_parameter_opt_between_cards
=
True
)
def
test_update_dense_parameter_with_new_strategy
(
self
):
self
.
check_network_convergence
(
is_sparse
=
False
,
balance_parameter_opt_between_cards
=
True
)
# test fetch all the variables of global_block
...
...
@@ -796,5 +848,42 @@ class TestFetchOp(unittest.TestCase):
self
.
parallel_exe
(
train_inputs
,
seed
=
1
)
class
TestFeedParallel
(
unittest
.
TestCase
):
def
test_main
(
self
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
startup
.
random_seed
=
1
with
fluid
.
scope_guard
(
fluid
.
core
.
Scope
()):
with
fluid
.
program_guard
(
main
,
startup
):
data
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
out
=
Lenet
(
data
,
class_dim
=
102
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
loss
=
fluid
.
layers
.
mean
(
loss
)
opt
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
0.1
,
momentum
=
0.9
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
1e-4
))
opt
.
minimize
(
loss
)
place
=
fluid
.
CUDAPlace
(
0
)
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
data
,
label
])
reader
=
feeder
.
decorate_reader
(
paddle
.
batch
(
flowers
.
train
(),
batch_size
=
16
),
multi_devices
=
True
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
pe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
loss_name
=
loss
.
name
,
main_program
=
main
)
for
batch_id
,
data
in
enumerate
(
reader
()):
loss_np
=
np
.
array
(
pe
.
run
(
feed
=
data
,
fetch_list
=
[
loss
.
name
])[
0
])
print
batch_id
,
loss_np
if
batch_id
==
2
:
break
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/trainer.py
浏览文件 @
928418a9
...
...
@@ -19,7 +19,7 @@ import executor
import
data_feeder
import
contextlib
import
io
import
transpiler
import
unique_name
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
import
optimizer
as
opt_module
...
...
@@ -56,26 +56,62 @@ class EndStepEvent(object):
self
.
step
=
step_id
def
check_and_get_place
(
place
):
"""
Check the type of place or get the default place
Args:
place(None|core.CUDAPlace|core.CPUPlace): the place that trainer will be executed on.
Raises:
TypeError if the type mismatched.
Returns:
the original place if it is not None.
if fluid is compiled with CUDA, returns CUDAPlace(0) by default.
Otherwise returns CPUPlace by default.
"""
if
place
is
None
:
if
core
.
is_compiled_with_cuda
():
return
core
.
CUDAPlace
(
0
)
else
:
return
core
.
CPUPlace
()
else
:
if
not
isinstance
(
place
,
core
.
CUDAPlace
)
and
not
isinstance
(
place
,
core
.
CPUPlace
):
raise
TypeError
(
"Place should be either CUDAPlace or CPUPlace"
)
return
place
class
Trainer
(
object
):
"""
Args:
program_func(callable): A function which will return loss. The loss must be a scaler.
train_func(callable): A function which will return loss. The loss must be a scalar.
infer_func(callable): A function which will return predict, used to save inference model
optimizer(optimizer.Optimizer): The optimizer should be an instance of Optimizer
place: The device place of this trainer.
"""
def
__init__
(
self
,
program_func
,
optimizer
,
param_path
=
None
,
place
=
None
):
def
__init__
(
self
,
train_func
,
infer_func
,
optimizer
,
param_path
=
None
,
place
=
None
):
# 1. we need to generate a framework.Program by calling
# program_func. Reference: fluid.program_guard in
# test_word2vec.py
if
not
isinstance
(
optimizer
,
opt_module
.
Optimizer
):
raise
TypeError
(
"The optimizer should be an instance of Optimizer"
)
self
.
infer_func
=
infer_func
self
.
scope
=
core
.
Scope
()
self
.
startup_program
=
framework
.
Program
()
self
.
train_program
=
framework
.
Program
()
with
framework
.
program_guard
(
self
.
train_program
,
self
.
startup_program
):
program_func_outs
=
program
_func
()
program_func_outs
=
train
_func
()
self
.
test_outputs
=
program_func_outs
if
isinstance
(
program_func_outs
,
list
)
else
[
program_func_outs
]
self
.
test_program
=
self
.
train_program
.
clone
()
...
...
@@ -86,9 +122,9 @@ class Trainer(object):
loss
=
self
.
test_outputs
[
0
]
optimize_ops
,
params_grads
=
optimizer
.
minimize
(
loss
)
self
.
place
=
Trainer
.
_
check_and_get_place
(
place
)
self
.
place
=
check_and_get_place
(
place
)
self
.
dist_transpile_if_necessary
(
optimize_ops
,
params_grads
)
self
.
_
dist_transpile_if_necessary
(
optimize_ops
,
params_grads
)
# 2. move the default_main_program to self.program and run the
# default_startup program on an empty core.Scope()
...
...
@@ -101,7 +137,7 @@ class Trainer(object):
# load params from param_path into scope
io
.
load_persistables
(
exe
,
dirname
=
param_path
)
def
dist_transpile_if_necessary
(
self
,
optimize_ops
,
params_grads
):
def
_
dist_transpile_if_necessary
(
self
,
optimize_ops
,
params_grads
):
if
"PADDLE_TRAINING_ROLE"
not
in
os
.
environ
:
return
...
...
@@ -190,31 +226,14 @@ class Trainer(object):
exe
=
executor
.
Executor
(
self
.
place
)
io
.
save_persistables
(
exe
,
dirname
=
param_path
)
@
staticmethod
def
_check_and_get_place
(
place
):
"""
Check the type of place or get the default place
Args:
place(None|core.CUDAPlace|core.CPUPlace): the place that trainer will be executed on.
Raises:
TypeError if the type mismatched.
Returns:
the original place if it is not None.
if fluid is compiled with CUDA, returns CUDAPlace(0) by default.
Otherwise returns CPUPlace by default.
"""
if
place
is
None
:
if
core
.
is_compiled_with_cuda
():
return
core
.
CUDAPlace
(
0
)
else
:
return
core
.
CPUPlace
()
else
:
if
not
isinstance
(
place
,
core
.
CUDAPlace
)
and
not
isinstance
(
place
,
core
.
CPUPlace
):
raise
TypeError
(
"Place should be either CUDAPlace or CPUPlace"
)
return
place
def
save_inference_model
(
self
,
model_path
):
inference_program
=
framework
.
Program
()
with
framework
.
program_guard
(
inference_program
):
with
unique_name
.
guard
():
predict_var
=
self
.
infer_func
()
predict_var
=
self
.
train_program
.
block
(
0
).
var
(
predict_var
.
name
)
exe
=
executor
.
Executor
(
self
.
place
)
io
.
save_inference_model
(
model_path
,
[],
[
predict_var
],
exe
)
@
contextlib
.
contextmanager
def
_prog_and_scope_guard
(
self
):
...
...
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