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4a8b8b45
编写于
2月 05, 2021
作者:
L
liuyuhui
提交者:
GitHub
2月 05, 2021
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差异文件
[Kunlun] add gen_bkcl_id_op, support multi XPU cards training using multiprocess (#30858)
上级
39f41cb4
变更
16
隐藏空白更改
内联
并排
Showing
16 changed file
with
661 addition
and
94 deletion
+661
-94
paddle/fluid/operators/collective/CMakeLists.txt
paddle/fluid/operators/collective/CMakeLists.txt
+7
-5
paddle/fluid/operators/collective/c_comm_init_op.cc
paddle/fluid/operators/collective/c_comm_init_op.cc
+46
-16
paddle/fluid/operators/collective/c_gen_bkcl_id_op.cc
paddle/fluid/operators/collective/c_gen_bkcl_id_op.cc
+119
-0
paddle/fluid/operators/collective/gen_bkcl_id_op.cc
paddle/fluid/operators/collective/gen_bkcl_id_op.cc
+194
-0
python/paddle/distributed/fleet/meta_optimizers/common.py
python/paddle/distributed/fleet/meta_optimizers/common.py
+54
-24
python/paddle/distributed/fleet/meta_optimizers/graph_execution_optimizer.py
...ibuted/fleet/meta_optimizers/graph_execution_optimizer.py
+56
-25
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+3
-3
python/paddle/fluid/tests/unittests/test_dist_base.py
python/paddle/fluid/tests/unittests/test_dist_base.py
+22
-14
python/paddle/fluid/tests/unittests/test_dist_mnist_fleet_save.py
...addle/fluid/tests/unittests/test_dist_mnist_fleet_save.py
+1
-1
python/paddle/fluid/tests/unittests/test_dist_mnist_fleetapi.py
.../paddle/fluid/tests/unittests/test_dist_mnist_fleetapi.py
+1
-1
python/paddle/fluid/tests/unittests/test_dist_mnist_with_program.py
...dle/fluid/tests/unittests/test_dist_mnist_with_program.py
+2
-2
python/paddle/fluid/tests/unittests/test_dist_sharding_save.py
...n/paddle/fluid/tests/unittests/test_dist_sharding_save.py
+1
-1
python/paddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py
...ddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py
+18
-1
python/paddle/fluid/tests/unittests/test_parallel_dygraph_unused_variables.py
...tests/unittests/test_parallel_dygraph_unused_variables.py
+1
-1
python/paddle/fluid/tests/unittests/xpu/CMakeLists.txt
python/paddle/fluid/tests/unittests/xpu/CMakeLists.txt
+13
-0
python/paddle/fluid/tests/unittests/xpu/test_gen_bkcl_id_op.py
...n/paddle/fluid/tests/unittests/xpu/test_gen_bkcl_id_op.py
+123
-0
未找到文件。
paddle/fluid/operators/collective/CMakeLists.txt
浏览文件 @
4a8b8b45
...
...
@@ -11,7 +11,7 @@ foreach(src ${OPS})
set_source_files_properties
(
${
src
}
PROPERTIES COMPILE_FLAGS
${
COLLECTIVE_COMPILE_FLAGS
}
)
endforeach
()
register_operators
(
EXCLUDES c_gen_nccl_id_op gen_nccl_id_op DEPS
${
COLLECTIVE_DEPS
}
)
register_operators
(
EXCLUDES c_gen_
bkcl_id_op gen_bkcl_id_op c_gen_
nccl_id_op gen_nccl_id_op DEPS
${
COLLECTIVE_DEPS
}
)
if
(
WITH_NCCL
)
set
(
COLLECTIVE_DEPS
${
COLLECTIVE_DEPS
}
nccl_common collective_helper
)
...
...
@@ -19,13 +19,15 @@ if(WITH_NCCL)
op_library
(
gen_nccl_id_op DEPS
${
COLLECTIVE_DEPS
}
)
endif
()
if
(
WITH_XPU_BKCL
)
set
(
COLLECTIVE_DEPS
${
COLLECTIVE_DEPS
}
collective_helper
)
endif
()
if
(
WITH_GLOO
)
set
(
COLLECTIVE_DEPS
${
COLLECTIVE_DEPS
}
gloo_wrapper
)
endif
()
if
(
WITH_XPU_BKCL
)
set
(
COLLECTIVE_DEPS
${
COLLECTIVE_DEPS
}
collective_helper
)
op_library
(
c_gen_bkcl_id_op DEPS
${
COLLECTIVE_DEPS
}
)
op_library
(
gen_bkcl_id_op DEPS
${
COLLECTIVE_DEPS
}
)
endif
()
set
(
OPERATOR_DEPS
${
OPERATOR_DEPS
}
${
COLLECTIVE_DEPS
}
PARENT_SCOPE
)
set
(
GLOB_COLLECTIVE_DEPS
${
COLLECTIVE_DEPS
}
CACHE INTERNAL
"collective dependency"
)
paddle/fluid/operators/collective/c_comm_init_op.cc
浏览文件 @
4a8b8b45
...
...
@@ -14,6 +14,9 @@ limitations under the License. */
#if defined(PADDLE_WITH_NCCL)
#include <nccl.h>
#endif
#if defined(PADDLE_WITH_XPU_BKCL)
#include "xpu/bkcl.h"
#endif
#include <string>
#include "paddle/fluid/framework/op_registry.h"
...
...
@@ -23,7 +26,7 @@ namespace framework {
class
Scope
;
}
// namespace framework
}
// namespace paddle
#if
defined(PADDLE_WITH_NC
CL)
#if
(defined PADDLE_WITH_NCCL) || (defined PADDLE_WITH_XPU_BK
CL)
#include "paddle/fluid/platform/collective_helper.h"
#endif
...
...
@@ -39,29 +42,56 @@ class CCommInitOp : public framework::OperatorBase {
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
PADDLE_ENFORCE_EQ
(
is_gpu_place
(
place
),
true
,
PADDLE_ENFORCE_EQ
(
is_gpu_place
(
place
)
||
is_xpu_place
(
place
)
,
true
,
platform
::
errors
::
PreconditionNotMet
(
"CCommInitOp can run on gpu place only."
));
"CCommInitOp can run on gpu
or xpu
place only."
));
auto
var
=
scope
.
FindVar
(
Input
(
"X"
));
PADDLE_ENFORCE_NOT_NULL
(
var
,
platform
::
errors
::
InvalidArgument
(
"Input con not be empty."
));
if
(
is_gpu_place
(
place
))
{
#if defined(PADDLE_WITH_NCCL)
ncclUniqueId
*
nccl_id
=
var
->
GetMutable
<
ncclUniqueId
>
();
int
nranks
=
Attr
<
int
>
(
"nranks"
);
int
rank_id
=
Attr
<
int
>
(
"rank"
);
int
rid
=
Attr
<
int
>
(
"ring_id"
);
int
device_id
=
BOOST_GET_CONST
(
platform
::
CUDAPlace
,
place
).
device
;
if
(
Attr
<
int
>
(
"device_id"
)
>=
0
)
{
device_id
=
Attr
<
int
>
(
"device_id"
);
}
platform
::
NCCLCommContext
::
Instance
().
CreateNCCLComm
(
nccl_id
,
nranks
,
rank_id
,
device_id
,
rid
);
ncclUniqueId
*
nccl_id
=
var
->
GetMutable
<
ncclUniqueId
>
();
int
nranks
=
Attr
<
int
>
(
"nranks"
);
int
rank_id
=
Attr
<
int
>
(
"rank"
);
int
rid
=
Attr
<
int
>
(
"ring_id"
);
int
device_id
=
BOOST_GET_CONST
(
platform
::
CUDAPlace
,
place
).
device
;
if
(
Attr
<
int
>
(
"device_id"
)
>=
0
)
{
device_id
=
Attr
<
int
>
(
"device_id"
);
}
platform
::
NCCLCommContext
::
Instance
().
CreateNCCLComm
(
nccl_id
,
nranks
,
rank_id
,
device_id
,
rid
);
#else
PADDLE_THROW
(
platform
::
errors
::
PreconditionNotMet
(
"PaddlePaddle should compile with GPU."
));
PADDLE_THROW
(
platform
::
errors
::
PreconditionNotMet
(
"PaddlePaddle should compile with GPU."
));
#endif
}
else
if
(
is_xpu_place
(
place
))
{
#if defined(PADDLE_WITH_BKCL)
BKCLUniqueId
*
bkcl_id
=
var
->
GetMutable
<
BKCLUniqueId
>
();
int
nranks
=
Attr
<
int
>
(
"nranks"
);
int
rank_id
=
Attr
<
int
>
(
"rank"
);
int
rid
=
Attr
<
int
>
(
"ring_id"
);
PADDLE_ENFORCE_EQ
(
rid
,
0
,
platform
::
errors
::
OutOfRange
(
"Ring id must equal 0 in multi Kunlun cards training, but got %d"
,
ring_id
));
int
device_id
=
BOOST_GET_CONST
(
platform
::
XPUPlace
,
place
).
device
;
if
(
Attr
<
int
>
(
"device_id"
)
>=
0
)
{
device_id
=
Attr
<
int
>
(
"device_id"
);
}
platform
::
BKCLCommContext
::
Instance
().
CreateBKCLComm
(
bkcl_id
,
nranks
,
rank_id
,
device_id
,
rid
);
#else
PADDLE_THROW
(
platform
::
errors
::
PreconditionNotMet
(
"PaddlePaddle should compile with XPU."
));
#endif
}
else
{
PADDLE_THROW
(
platform
::
errors
::
PreconditionNotMet
(
"CCommInitOp can run on gpu or xpu place only."
));
}
}
};
...
...
paddle/fluid/operators/collective/c_gen_bkcl_id_op.cc
0 → 100644
浏览文件 @
4a8b8b45
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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 <string>
#include "glog/logging.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/var_type_traits.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/gen_comm_id_helper.h"
namespace
paddle
{
namespace
operators
{
static
void
GenBKCLID
(
std
::
vector
<
BKCLUniqueId
>*
bkcl_ids
)
{
for
(
size_t
i
=
0
;
i
<
bkcl_ids
->
size
();
++
i
)
{
BKCLResult_t
ret
=
bkcl_get_unique_id
(
&
(
*
bkcl_ids
)[
i
]);
PADDLE_ENFORCE_EQ
(
BKCL_SUCCESS
,
ret
,
platform
::
errors
::
PreconditionNotMet
(
"bkcl get unique id failed [%d]"
,
ret
));
}
}
static
void
CopyBKCLIDToVar
(
const
std
::
vector
<
BKCLUniqueId
>&
bkcl_ids
,
std
::
function
<
std
::
string
(
size_t
)
>
func
,
const
framework
::
Scope
&
scope
)
{
for
(
size_t
i
=
0
;
i
<
bkcl_ids
.
size
();
++
i
)
{
std
::
string
var_name
=
func
(
i
);
auto
var
=
scope
.
FindVar
(
var_name
);
PADDLE_ENFORCE_NOT_NULL
(
var
,
platform
::
errors
::
NotFound
(
"Variable with name %s is not found"
,
var_name
.
c_str
()));
auto
bkcl_id
=
var
->
GetMutable
<
BKCLUniqueId
>
();
memcpy
(
bkcl_id
,
&
bkcl_ids
[
i
],
sizeof
(
BKCLUniqueId
));
}
}
class
CGenBKCLIdOp
:
public
framework
::
OperatorBase
{
public:
CGenBKCLIdOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
dev_place
)
const
override
{
int
rank
=
Attr
<
int
>
(
"rank"
);
framework
::
Scope
&
local_scope
=
scope
.
NewScope
();
std
::
function
<
std
::
string
(
size_t
)
>
func
=
[
&
](
size_t
i
)
->
std
::
string
{
return
Output
(
"Out"
);
};
std
::
vector
<
BKCLUniqueId
>
bkcl_ids
;
bkcl_ids
.
resize
(
1
);
if
(
rank
==
0
)
{
GenBKCLID
(
&
bkcl_ids
);
std
::
vector
<
std
::
string
>
endpoint_list
=
Attr
<
std
::
vector
<
std
::
string
>>
(
"other_endpoints"
);
platform
::
SendBroadCastCommID
(
endpoint_list
,
&
bkcl_ids
);
}
else
{
std
::
string
endpoint
=
Attr
<
std
::
string
>
(
"endpoint"
);
platform
::
RecvBroadCastCommID
(
endpoint
,
&
bkcl_ids
);
}
CopyBKCLIDToVar
(
bkcl_ids
,
func
,
scope
);
scope
.
DeleteScope
(
&
local_scope
);
}
};
class
CGenBKCLIdOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddOutput
(
"Out"
,
"Raw variable contains a BKCL UniqueId instaces."
);
AddComment
(
R"DOC(
CGenBKCLId operator
For trainer 0: generate a new UniqueId and send it to all the other trainers.
For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the server.
)DOC"
);
AddAttr
<
std
::
string
>
(
"endpoint"
,
"(string), e.g. 127.0.0.1:6175 "
"current listen endpoint"
);
AddAttr
<
std
::
vector
<
std
::
string
>>
(
"other_endpoints"
,
"['trainer1_ip:port', 'trainer2_ip:port', ...] "
"list of other trainer endpoints"
)
.
SetDefault
({});
AddAttr
<
int
>
(
"rank"
,
"(int default 0) "
"The rank of the trainer in distributed training."
)
.
SetDefault
(
0
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
c_gen_bkcl_id
,
ops
::
CGenBKCLIdOp
,
ops
::
CGenBKCLIdOpMaker
);
paddle/fluid/operators/collective/gen_bkcl_id_op.cc
0 → 100644
浏览文件 @
4a8b8b45
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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 <ostream>
#include <string>
#include "glog/logging.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/var_type_traits.h"
#include "paddle/fluid/platform/bkcl_helper.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/split.h"
#include "paddle/fluid/platform/gen_comm_id_helper.h"
namespace
paddle
{
namespace
operators
{
static
void
GenBKCLID
(
std
::
vector
<
BKCLUniqueId
>*
bkcl_ids
)
{
for
(
size_t
i
=
0
;
i
<
bkcl_ids
->
size
();
++
i
)
{
BKCLResult_t
ret
=
bkcl_get_unique_id
(
&
(
*
bkcl_ids
)[
i
]);
PADDLE_ENFORCE_EQ
(
BKCL_SUCCESS
,
ret
,
platform
::
errors
::
PreconditionNotMet
(
"bkcl get unique id failed [%d]"
,
ret
));
}
}
static
void
CopyBKCLIDToVar
(
const
std
::
vector
<
BKCLUniqueId
>&
bkcl_ids
,
std
::
function
<
std
::
string
(
size_t
)
>
func
,
const
framework
::
Scope
&
scope
)
{
for
(
size_t
i
=
0
;
i
<
bkcl_ids
.
size
();
++
i
)
{
std
::
string
var_name
=
func
(
i
);
auto
var
=
scope
.
FindVar
(
var_name
);
PADDLE_ENFORCE_NOT_NULL
(
var
,
platform
::
errors
::
NotFound
(
"Variable with name %s is not found"
,
var_name
.
c_str
()));
auto
bkcl_id
=
var
->
GetMutable
<
BKCLUniqueId
>
();
memcpy
(
bkcl_id
,
&
bkcl_ids
[
i
],
sizeof
(
BKCLUniqueId
));
}
}
class
GenBKCLIdOp
:
public
framework
::
OperatorBase
{
public:
GenBKCLIdOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
dev_place
)
const
override
{
std
::
vector
<
std
::
string
>
trainers
=
Attr
<
std
::
vector
<
std
::
string
>>
(
"trainers"
);
int
trainer_id
=
Attr
<
int
>
(
"trainer_id"
);
std
::
string
endpoint
=
trainers
[
trainer_id
];
PADDLE_ENFORCE_GE
(
trainer_id
,
0
,
platform
::
errors
::
InvalidArgument
(
"trainer_id %d is less than 0. Its "
"valid range is [0, trainer_size)"
));
PADDLE_ENFORCE_LT
(
trainer_id
,
static_cast
<
int
>
(
trainers
.
size
()),
platform
::
errors
::
OutOfRange
(
"trainer_id %d is out of range. Its valid "
"range is [0, trainer_size)"
,
trainer_id
));
int
bkcl_comm_num
=
Attr
<
int
>
(
"bkcl_comm_num"
);
int
use_hierarchical_allreduce
=
Attr
<
bool
>
(
"use_hierarchical_allreduce"
);
int
inter_nranks
=
Attr
<
int
>
(
"hierarchical_allreduce_inter_nranks"
);
int
inter_trainer_id
=
-
1
;
int
exter_trainer_id
=
-
1
;
if
(
use_hierarchical_allreduce
)
{
PADDLE_ENFORCE_GT
(
trainers
.
size
(),
1
,
platform
::
errors
::
PreconditionNotMet
(
"The number of collective trainers %llu <= 1"
,
trainers
.
size
()));
PADDLE_ENFORCE_GT
(
inter_nranks
,
1
,
platform
::
errors
::
PreconditionNotMet
(
"inter_nranks %d <= 1 while in hierarchical allreduce mode"
,
inter_nranks
));
PADDLE_ENFORCE_EQ
(
trainers
.
size
()
%
inter_nranks
,
0
,
platform
::
errors
::
PreconditionNotMet
(
"The number of trainers %llu mod inter_nranks %d is not equal 0"
,
trainers
.
size
(),
inter_nranks
));
inter_trainer_id
=
trainer_id
%
inter_nranks
;
if
(
trainer_id
%
inter_nranks
==
0
)
{
exter_trainer_id
=
trainer_id
/
inter_nranks
;
}
}
std
::
ostringstream
ss
;
for
(
size_t
i
=
0
;
i
<
trainers
.
size
();
i
++
)
{
ss
<<
trainers
[
i
]
<<
","
;
}
VLOG
(
1
)
<<
"trainer_id:"
<<
trainer_id
<<
", use_hierarchical_allreduce:"
<<
use_hierarchical_allreduce
<<
", bkcl_comm_num:"
<<
bkcl_comm_num
<<
", inter_nranks:"
<<
inter_nranks
<<
", inter_trainer_id:"
<<
inter_trainer_id
<<
", exter_trainer_id:"
<<
exter_trainer_id
<<
", trainers:"
<<
ss
.
str
();
int
server_fd
=
-
1
;
std
::
vector
<
BKCLUniqueId
>
bkcl_ids
;
bkcl_ids
.
resize
(
bkcl_comm_num
);
/// 1. init flat
std
::
function
<
std
::
string
(
size_t
)
>
func
=
platform
::
GetFlatBKCLVarName
;
// broadcast unique id
if
(
trainer_id
==
0
)
{
GenBKCLID
(
&
bkcl_ids
);
// server endpoints
std
::
vector
<
std
::
string
>
flat_endpoints
;
flat_endpoints
.
insert
(
flat_endpoints
.
begin
(),
trainers
.
begin
()
+
1
,
trainers
.
end
());
platform
::
SendBroadCastCommID
(
flat_endpoints
,
&
bkcl_ids
);
}
else
{
server_fd
=
platform
::
CreateListenSocket
(
endpoint
);
platform
::
RecvBroadCastCommID
(
server_fd
,
endpoint
,
&
bkcl_ids
);
}
CopyBKCLIDToVar
(
bkcl_ids
,
func
,
scope
);
/*TODO(liuyuhui) Baidu Kunlun Communication Library(BKCL) don't support
hierarchical communication
as NVIDIA Collective Communications Library(NCCL) in multi Nvidia GPU cards,
and will support it later.
*/
// close socket server
if
(
trainer_id
!=
0
)
{
platform
::
CloseSocket
(
server_fd
);
}
}
};
class
GenBKCLIdOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddOutput
(
"BKCLID"
,
"Raw variable contains a BKCL UniqueId instaces."
);
AddComment
(
R"DOC(
GenBKCLId operator
For trainer 0: generate a new UniqueId and send it to all the other trainers.
For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the server.
)DOC"
);
AddAttr
<
std
::
vector
<
std
::
string
>>
(
"trainers"
,
"['trainer0_ip:port', 'trainer1_ip:port', ...] "
"list of all trainer endpoints"
)
.
SetDefault
({});
AddAttr
<
int
>
(
"trainer_id"
,
"(int) "
"The index of the trainer in distributed training."
);
AddAttr
<
int
>
(
"bkcl_comm_num"
,
"(int default 1) "
"The number of bkcl communicator num."
)
.
SetDefault
(
1
);
AddAttr
<
bool
>
(
"use_hierarchical_allreduce"
,
"(bool default false) "
"Wheter to use hierarchical allreduce."
)
.
SetDefault
(
false
);
AddAttr
<
int
>
(
"hierarchical_allreduce_inter_nranks"
,
"(int default 1) "
"Wheter to use hierarchical allreduce."
)
.
SetDefault
(
-
1
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
gen_bkcl_id
,
ops
::
GenBKCLIdOp
,
ops
::
GenBKCLIdOpMaker
);
python/paddle/distributed/fleet/meta_optimizers/common.py
浏览文件 @
4a8b8b45
...
...
@@ -74,30 +74,60 @@ class CollectiveHelper(object):
wait_server_ready
(
other_endpoints
)
block
=
program
.
global_block
()
nccl_id_var
=
block
.
create_var
(
name
=
unique_name
.
generate
(
'nccl_id'
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
block
.
append_op
(
type
=
'c_gen_nccl_id'
,
inputs
=
{},
outputs
=
{
'Out'
:
nccl_id_var
},
attrs
=
{
'rank'
:
rank
,
'endpoint'
:
current_endpoint
,
'other_endpoints'
:
other_endpoints
,
OP_ROLE_KEY
:
OpRole
.
Forward
})
block
.
append_op
(
type
=
'c_comm_init'
,
inputs
=
{
'X'
:
nccl_id_var
},
outputs
=
{},
attrs
=
{
'nranks'
:
nranks
,
'rank'
:
rank
,
'ring_id'
:
ring_id
,
OP_ROLE_KEY
:
OpRole
.
Forward
})
if
core
.
is_compiled_with_cuda
():
comm_id_var
=
block
.
create_var
(
name
=
unique_name
.
generate
(
'nccl_id'
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
block
.
append_op
(
type
=
'c_gen_nccl_id'
,
inputs
=
{},
outputs
=
{
'Out'
:
comm_id_var
},
attrs
=
{
'rank'
:
rank
,
'endpoint'
:
current_endpoint
,
'other_endpoints'
:
other_endpoints
,
OP_ROLE_KEY
:
OpRole
.
Forward
})
block
.
append_op
(
type
=
'c_comm_init'
,
inputs
=
{
'X'
:
comm_id_var
},
outputs
=
{},
attrs
=
{
'nranks'
:
nranks
,
'rank'
:
rank
,
'ring_id'
:
ring_id
,
OP_ROLE_KEY
:
OpRole
.
Forward
})
elif
core
.
is_compiled_with_xpu
():
comm_id_var
=
block
.
create_var
(
name
=
unique_name
.
generate
(
'bkcl_id'
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
block
.
append_op
(
type
=
'c_gen_bkcl_id'
,
inputs
=
{},
outputs
=
{
'Out'
:
comm_id_var
},
attrs
=
{
'rank'
:
rank
,
'endpoint'
:
current_endpoint
,
'other_endpoints'
:
other_endpoints
,
OP_ROLE_KEY
:
OpRole
.
Forward
})
block
.
append_op
(
type
=
'c_comm_init'
,
inputs
=
{
'X'
:
comm_id_var
},
outputs
=
{},
attrs
=
{
'nranks'
:
nranks
,
'rank'
:
rank
,
'ring_id'
:
ring_id
,
OP_ROLE_KEY
:
OpRole
.
Forward
})
else
:
raise
ValueError
(
"comm_id must be generated in paddlepaddle-xpu or paddlepaddle-xpu."
)
def
_wait
(
self
,
current_endpoint
,
endpoints
):
assert
(
self
.
wait_port
)
...
...
python/paddle/distributed/fleet/meta_optimizers/graph_execution_optimizer.py
浏览文件 @
4a8b8b45
...
...
@@ -64,39 +64,70 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
if
trainer_id
==
0
:
wait_server_ready
(
other_trainers
)
nccl_id_var
=
startup_program
.
global_block
().
create_var
(
name
=
"NCCLID"
,
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
if
core
.
is_compiled_with_cuda
():
comm_id_var
=
startup_program
.
global_block
().
create_var
(
name
=
"NCCLID"
,
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
for
i
in
range
(
1
,
build_strategy
.
nccl_comm_num
):
startup_program
.
global_block
().
create_var
(
name
=
"NCCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
if
build_strategy
.
use_hierarchical_allreduce
:
for
i
in
range
(
0
,
build_strategy
.
nccl_comm_num
):
for
i
in
range
(
1
,
build_strategy
.
nccl_comm_num
):
startup_program
.
global_block
().
create_var
(
name
=
"
Hierarchical_inter_
NCCLID_{}"
.
format
(
i
),
name
=
"NCCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
if
build_strategy
.
use_hierarchical_allreduce
:
for
i
in
range
(
0
,
build_strategy
.
nccl_comm_num
):
startup_program
.
global_block
().
create_var
(
name
=
"Hierarchical_inter_NCCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
startup_program
.
global_block
().
create_var
(
name
=
"Hierarchical_exter_NCCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
startup_program
.
global_block
().
append_op
(
type
=
"gen_nccl_id"
,
inputs
=
{},
outputs
=
{
"NCCLID"
:
comm_id_var
},
attrs
=
{
"trainers"
:
trainer_endpoints
,
"trainer_id"
:
trainer_id
,
"nccl_comm_num"
:
build_strategy
.
nccl_comm_num
,
"use_hierarchical_allreduce"
:
build_strategy
.
use_hierarchical_allreduce
,
"hierarchical_allreduce_inter_ranks"
:
build_strategy
.
hierarchical_allreduce_inter_nranks
})
elif
core
.
is_compiled_with_xpu
():
comm_id_var
=
startup_program
.
global_block
().
create_var
(
name
=
"BKCLID"
,
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
#NOTE(liuyuhui) Baidu Kunlun Communication Library(BKCL) currently do not support multi machines.
assert
build_strategy
.
bkcl_comm_num
==
1
,
\
"Baidu Kunlun Communication Library(BKCL) currently do not support multi machines."
for
i
in
range
(
1
,
build_strategy
.
bkcl_comm_num
):
startup_program
.
global_block
().
create_var
(
name
=
"
Hierarchical_exter_NC
CLID_{}"
.
format
(
i
),
name
=
"
BK
CLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
startup_program
.
global_block
().
append_op
(
type
=
"gen_nccl_id"
,
inputs
=
{},
outputs
=
{
"NCCLID"
:
nccl_id_var
},
attrs
=
{
"trainers"
:
trainer_endpoints
,
"trainer_id"
:
trainer_id
,
"nccl_comm_num"
:
build_strategy
.
nccl_comm_num
,
"use_hierarchical_allreduce"
:
build_strategy
.
use_hierarchical_allreduce
,
"hierarchical_allreduce_inter_ranks"
:
build_strategy
.
hierarchical_allreduce_inter_nranks
})
startup_program
.
global_block
().
append_op
(
type
=
"gen_bkcl_id"
,
inputs
=
{},
outputs
=
{
"BKCLID"
:
comm_id_var
},
attrs
=
{
"trainers"
:
trainer_endpoints
,
"trainer_id"
:
trainer_id
,
"nccl_comm_num"
:
build_strategy
.
nccl_comm_num
,
"use_hierarchical_allreduce"
:
build_strategy
.
use_hierarchical_allreduce
,
"hierarchical_allreduce_inter_ranks"
:
build_strategy
.
hierarchical_allreduce_inter_nranks
})
else
:
raise
ValueError
(
"comm_id must be generated in paddlepaddle-xpu or paddlepaddle-gpu."
)
def
_try_to_compile
(
self
,
startup_program
,
main_program
,
loss
):
dist_strategy
=
self
.
user_defined_strategy
...
...
python/paddle/fluid/framework.py
浏览文件 @
4a8b8b45
...
...
@@ -2057,9 +2057,9 @@ class Operator(object):
'feed'
,
'fetch'
,
'recurrent'
,
'go'
,
'rnn_memory_helper_grad'
,
'conditional_block'
,
'while'
,
'send'
,
'recv'
,
'listen_and_serv'
,
'fl_listen_and_serv'
,
'ncclInit'
,
'select'
,
'checkpoint_notify'
,
'gen_
nccl_id'
,
'c_gen_nccl_id'
,
'c_comm_init'
,
'c_sync_calc_stream
'
,
'c_
sync_comm_stream'
,
'queue_generator'
,
'dequeue'
,
'enqueue
'
,
'heter_listen_and_serv'
'gen_
bkcl_id'
,
'c_gen_bkcl_id'
,
'gen_nccl_id'
,
'c_gen_nccl_id
'
,
'c_
comm_init'
,
'c_sync_calc_stream'
,
'c_sync_comm_stream
'
,
'
queue_generator'
,
'dequeue'
,
'enqueue'
,
'
heter_listen_and_serv'
}
def
__init__
(
self
,
...
...
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @
4a8b8b45
...
...
@@ -186,8 +186,8 @@ class TestDistRunnerBase(object):
fleet
.
save_inference_model
(
exe
,
infer_save_dir_fleet
,
feeded_var_names
,
[
avg_cost
])
def
run_
gpu
_fleet_api_trainer
(
self
,
args
):
assert
args
.
update_method
==
"nccl2"
def
run_
use
_fleet_api_trainer
(
self
,
args
):
assert
args
.
update_method
==
"nccl2"
or
"bkcl"
self
.
lr
=
args
.
lr
...
...
@@ -207,7 +207,7 @@ class TestDistRunnerBase(object):
role
=
role_maker
.
PaddleCloudRoleMaker
(
is_collective
=
True
)
fleet
.
init
(
role
)
print_to_err
(
"
gpu
_fleet"
,
"fleet.node_num:"
)
print_to_err
(
"
use
_fleet"
,
"fleet.node_num:"
)
# "fleet.node_id:", fleet.node_id(),
# "fleet.trainer_num:", fleet.worker_num())
...
...
@@ -217,8 +217,16 @@ class TestDistRunnerBase(object):
trainer_prog
=
fleet
.
_origin_program
dist_prog
=
fleet
.
main_program
device_id
=
int
(
os
.
getenv
(
"FLAGS_selected_gpus"
,
"0"
))
place
=
fluid
.
CUDAPlace
(
device_id
)
if
fluid
.
core
.
is_compiled_with_cuda
():
device_id
=
int
(
os
.
getenv
(
"FLAGS_selected_gpus"
,
"0"
))
place
=
fluid
.
CUDAPlace
(
device_id
)
elif
fluid
.
core
.
is_compiled_with_xpu
():
device_id
=
int
(
os
.
getenv
(
"FLAGS_selected_xpus"
,
"0"
))
place
=
fluid
.
XPUPlace
(
device_id
)
else
:
raise
ValueError
(
"fleet dygraph api must in paddlepaddle-xpu or paddlepaddle-gpu."
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
...
...
@@ -550,7 +558,7 @@ class TestParallelDyGraphRunnerBase(object):
model
.
clear_gradients
()
return
out_losses
def
run_
gpu
_fleet_api_trainer
(
self
,
args
):
def
run_
use
_fleet_api_trainer
(
self
,
args
):
import
paddle.distributed.fleet
as
fleet
import
paddle.distributed.fleet.base.role_maker
as
role_maker
# 1. enable dygraph
...
...
@@ -566,12 +574,12 @@ class TestParallelDyGraphRunnerBase(object):
args
.
trainer_id
=
paddle
.
distributed
.
get_rank
()
# 3. init parallel env
if
args
.
update_method
==
"nccl2"
:
if
args
.
update_method
==
"nccl2"
or
"bkcl"
:
fleet
.
init
(
is_collective
=
True
)
# 4. train model
model
,
train_reader
,
opt
=
self
.
get_model
()
if
args
.
update_method
==
"nccl2"
:
if
args
.
update_method
==
"nccl2"
or
"bkcl"
:
opt
=
fleet
.
distributed_optimizer
(
opt
)
model
=
fleet
.
distributed_model
(
model
)
...
...
@@ -606,7 +614,7 @@ def runtime_main(test_class):
parser
.
add_argument
(
'--enable_backward_deps'
,
action
=
'store_true'
)
parser
.
add_argument
(
'--use_hallreduce'
,
action
=
'store_true'
)
parser
.
add_argument
(
'--use_pipeline'
,
action
=
'store_true'
)
parser
.
add_argument
(
'--
gpu
_fleet_api'
,
action
=
'store_true'
)
parser
.
add_argument
(
'--
use
_fleet_api'
,
action
=
'store_true'
)
parser
.
add_argument
(
'--use_local_sgd'
,
action
=
'store_true'
)
parser
.
add_argument
(
'--ut4grad_allreduce'
,
action
=
'store_true'
)
parser
.
add_argument
(
...
...
@@ -644,8 +652,8 @@ def runtime_main(test_class):
model
=
test_class
()
if
args
.
role
==
"pserver"
and
args
.
update_method
==
"pserver"
:
model
.
run_pserver
(
args
)
elif
args
.
gpu
_fleet_api
:
model
.
run_
gpu
_fleet_api_trainer
(
args
)
elif
args
.
use
_fleet_api
:
model
.
run_
use
_fleet_api_trainer
(
args
)
elif
args
.
use_pipeline
:
model
.
run_pipeline_trainer
(
args
)
else
:
...
...
@@ -708,7 +716,7 @@ class TestDistBase(unittest.TestCase):
self
.
_dygraph
=
False
self
.
_nccl_comm_num
=
1
self
.
_enable_backward_deps
=
False
self
.
_
gpu
_fleet_api
=
False
self
.
_
use
_fleet_api
=
False
self
.
_use_local_sgd
=
False
self
.
_ut4grad_allreduce
=
False
self
.
_use_hallreduce
=
False
...
...
@@ -1020,8 +1028,8 @@ class TestDistBase(unittest.TestCase):
if
self
.
_fuse_all_reduce
is
not
None
:
tr_cmd
+=
" --fuse_all_reduce {}"
.
format
(
self
.
_fuse_all_reduce
)
if
self
.
_
gpu
_fleet_api
:
tr_cmd
+=
" --
gpu
_fleet_api"
if
self
.
_
use
_fleet_api
:
tr_cmd
+=
" --
use
_fleet_api"
if
self
.
_use_local_sgd
:
tr_cmd
+=
" --use_local_sgd"
if
self
.
_ut4grad_allreduce
:
...
...
python/paddle/fluid/tests/unittests/test_dist_mnist_fleet_save.py
浏览文件 @
4a8b8b45
...
...
@@ -28,7 +28,7 @@ class TestDistMnistFleetSave(TestDistBase):
self
.
_use_reduce
=
False
self
.
_use_reader_alloc
=
False
self
.
_nccl2_mode
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
self
.
_save_model
=
True
def
_rm_temp_files
(
self
,
dirname
):
...
...
python/paddle/fluid/tests/unittests/test_dist_mnist_fleetapi.py
浏览文件 @
4a8b8b45
...
...
@@ -26,7 +26,7 @@ class TestDistMnistNCCL2FleetApi(TestDistBase):
self
.
_use_reduce
=
False
self
.
_use_reader_alloc
=
False
self
.
_nccl2_mode
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
self
.
_sync_batch_norm
=
True
def
test_dist_train
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_dist_mnist_with_program.py
浏览文件 @
4a8b8b45
...
...
@@ -26,7 +26,7 @@ class TestDistMnistLocalSGDFleetApi(TestDistBase):
self
.
_use_reduce
=
False
self
.
_use_reader_alloc
=
False
self
.
_nccl2_mode
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
self
.
_use_local_sgd
=
True
def
test_dist_train
(
self
):
...
...
@@ -41,7 +41,7 @@ class TestDistMnistGradAllReduceFleetApi(TestDistBase):
self
.
_use_reduce
=
False
self
.
_use_reader_alloc
=
False
self
.
_nccl2_mode
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
self
.
_ut4grad_allreduce
=
True
def
test_dist_train
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_dist_sharding_save.py
浏览文件 @
4a8b8b45
...
...
@@ -28,7 +28,7 @@ class TestDistMnistFleetSave(TestDistBase):
self
.
_use_reduce
=
False
self
.
_use_reader_alloc
=
False
self
.
_nccl2_mode
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
self
.
_sharding_save
=
True
self
.
_enforce_place
=
"GPU"
...
...
python/paddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py
浏览文件 @
4a8b8b45
...
...
@@ -71,7 +71,7 @@ class TestFleetDygraphMnist(TestDistBase):
self
.
_sync_mode
=
False
self
.
_nccl2_mode
=
True
self
.
_dygraph
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
def
test_mnist
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
...
...
@@ -82,5 +82,22 @@ class TestFleetDygraphMnist(TestDistBase):
log_name
=
flag_name
)
class
TestFleetDygraphMnistXPU
(
TestDistBase
):
def
_setup_config
(
self
):
self
.
_sync_mode
=
False
self
.
_bkcl_mode
=
True
self
.
_dygraph
=
True
self
.
_enforce_place
=
"XPU"
self
.
_use_fleet_api
=
True
def
test_mnist
(
self
):
if
fluid
.
core
.
is_compiled_with_xpu
():
self
.
check_with_place
(
"parallel_dygraph_mnist.py"
,
delta
=
1e-1
,
check_error_log
=
True
,
log_name
=
flag_name
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_parallel_dygraph_unused_variables.py
浏览文件 @
4a8b8b45
...
...
@@ -53,7 +53,7 @@ class TestFleetDygraphMnist(TestDistBase):
self
.
_sync_mode
=
False
self
.
_nccl2_mode
=
True
self
.
_dygraph
=
True
self
.
_
gpu
_fleet_api
=
True
self
.
_
use
_fleet_api
=
True
def
test_mnist
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
...
...
python/paddle/fluid/tests/unittests/xpu/CMakeLists.txt
浏览文件 @
4a8b8b45
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
if
(
WITH_XPU_BKCL
)
list
(
REMOVE_ITEM TEST_OPS
"test_gen_bkcl_id_op"
)
endif
()
file
(
GLOB DIST_TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_dist_*.py"
)
if
(
WITH_XPU_BKCL
)
list
(
APPEND DIST_TEST_OPS test_gen_bkcl_id_op
)
endif
()
list
(
REMOVE_ITEM TEST_OPS test_concat_op_xpu
)
list
(
REMOVE_ITEM TEST_OPS test_mean_op_xpu
)
...
...
@@ -8,5 +17,9 @@ foreach(TEST_OP ${TEST_OPS})
py_test_modules
(
${
TEST_OP
}
MODULES
${
TEST_OP
}
)
endforeach
(
TEST_OP
)
foreach
(
TEST_OP
${
DIST_TEST_OPS
}
)
py_test_modules
(
${
TEST_OP
}
MODULES
${
TEST_OP
}
)
endforeach
(
TEST_OP
)
set_tests_properties
(
test_mul_op_xpu PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_conv2d_op_xpu PROPERTIES TIMEOUT 120
)
python/paddle/fluid/tests/unittests/xpu/test_gen_bkcl_id_op.py
0 → 100644
浏览文件 @
4a8b8b45
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
import
unittest
import
os
import
copy
import
sys
sys
.
path
.
append
(
".."
)
from
launch_function_helper
import
wait
,
_find_free_port
from
multiprocessing
import
Pool
,
Process
from
threading
import
Thread
os
.
environ
[
'GLOG_vmodule'
]
=
str
(
"gen_bkcl_id_op*=10,gen_comm_id*=10"
)
import
paddle
from
paddle.fluid
import
core
paddle
.
enable_static
()
def
run_gen_bkc_id
(
attr
):
bkcl_comm_num
=
attr
[
'bkcl_comm_num'
]
use_hallreduce
=
attr
[
'use_hierarchical_allreduce'
]
startup_program
=
paddle
.
static
.
default_startup_program
()
main_program
=
paddle
.
static
.
default_main_program
()
with
paddle
.
static
.
program_guard
(
main_program
,
startup_program
):
bkcl_id_var
=
startup_program
.
global_block
().
create_var
(
name
=
"BKCLID"
,
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
for
i
in
range
(
1
,
bkcl_comm_num
):
startup_program
.
global_block
().
create_var
(
name
=
"BKCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
if
use_hallreduce
:
for
i
in
range
(
0
,
bkcl_comm_num
):
startup_program
.
global_block
().
create_var
(
name
=
"Hierarchical_inter_BKCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
startup_program
.
global_block
().
create_var
(
name
=
"Hierarchical_exter_BKCLID_{}"
.
format
(
i
),
persistable
=
True
,
type
=
core
.
VarDesc
.
VarType
.
RAW
)
startup_program
.
global_block
().
append_op
(
type
=
"gen_bkcl_id"
,
inputs
=
{},
outputs
=
{
"BKCLID"
:
bkcl_id_var
},
attrs
=
attr
)
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_program
)
class
TestGenBKCLIdOp
(
unittest
.
TestCase
):
def
setUp
(
self
):
try
:
self
.
_dist_ut_port_0
=
int
(
os
.
environ
[
"PADDLE_DIST_UT_PORT"
])
except
Exception
as
e
:
self
.
_dist_ut_port_0
=
_find_free_port
(
set
())
def
gen_bkcl_id
(
self
,
nranks
=
2
):
bkcl_comm_num
=
1
if
nranks
==
2
:
use_hallreduce
=
False
hallreduce_inter_nranks
=
-
1
elif
nranks
==
4
:
use_hallreduce
=
True
hallreduce_inter_nranks
=
2
port
=
self
.
_dist_ut_port_0
trainers
=
[]
for
i
in
range
(
nranks
):
trainers
.
append
(
'127.0.0.1:{}'
.
format
(
port
+
i
))
attr
=
{
"trainers"
:
trainers
,
"trainer_id"
:
0
,
"bkcl_comm_num"
:
bkcl_comm_num
,
"use_hierarchical_allreduce"
:
use_hallreduce
,
"hierarchical_allreduce_inter_nranks"
:
hallreduce_inter_nranks
,
}
procs
=
[]
for
i
in
range
(
nranks
):
attr
[
'trainer_id'
]
=
i
# NOTE: multiprocessing cannot be covered by coverage
p
=
Process
(
target
=
run_gen_bkc_id
,
args
=
(
attr
,
))
p
.
start
()
procs
.
append
(
p
)
wait
(
procs
,
timeout
=
120
)
def
test_flat
(
self
):
print
(
">>> test gen flat bkcl id"
)
self
.
gen_bkcl_id
(
2
)
print
(
"<<< end test gen flat bkcl id"
)
print
()
def
test_hierarchical
(
self
):
print
(
">>> test gen hierarchical bkcl id"
)
self
.
gen_bkcl_id
(
4
)
print
(
"<<< end test gen hierarchical bkcl id"
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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