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体验新版 GitCode,发现更多精彩内容 >>
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efb4d5c2
编写于
7月 27, 2022
作者:
R
ronnywang
提交者:
GitHub
7月 27, 2022
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差异文件
[CustomDevice] add process_group_xccl ut (#44632)
* [CustomDevice] add process_group_xccl ut * update
上级
2a5437a2
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
466 addition
and
0 deletion
+466
-0
python/paddle/fluid/tests/custom_runtime/CMakeLists.txt
python/paddle/fluid/tests/custom_runtime/CMakeLists.txt
+1
-0
python/paddle/fluid/tests/custom_runtime/custom_device_multi_process_collective.py
.../custom_runtime/custom_device_multi_process_collective.py
+42
-0
python/paddle/fluid/tests/custom_runtime/process_group_xccl.py
...n/paddle/fluid/tests/custom_runtime/process_group_xccl.py
+241
-0
python/paddle/fluid/tests/custom_runtime/test_collective_process_group_xccl.py
...ests/custom_runtime/test_collective_process_group_xccl.py
+154
-0
python/paddle/fluid/tests/custom_runtime/test_fleet_launch_custom_device.sh
...d/tests/custom_runtime/test_fleet_launch_custom_device.sh
+28
-0
未找到文件。
python/paddle/fluid/tests/custom_runtime/CMakeLists.txt
浏览文件 @
efb4d5c2
...
...
@@ -5,6 +5,7 @@ if(WITH_CUSTOM_DEVICE)
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
list
(
REMOVE_ITEM TEST_OPS
"test_collective_process_group_xccl"
)
foreach
(
TEST_OP
${
TEST_OPS
}
)
py_test
(
${
TEST_OP
}
SRCS
${
TEST_OP
}
.py
)
endforeach
()
...
...
python/paddle/fluid/tests/custom_runtime/custom_device_multi_process_collective.py
0 → 100644
浏览文件 @
efb4d5c2
# Copyright (c) 2019 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
os
import
sys
import
time
def
train
(
prefix
):
selected_accelerators
=
os
.
getenv
(
"FLAGS_selected_accelerators"
)
selected_custom_devices
=
os
.
getenv
(
"FLAGS_selected_custom_cpus"
)
trainer_id
=
int
(
os
.
getenv
(
"PADDLE_TRAINER_ID"
))
worker_endpoints_env
=
os
.
getenv
(
"PADDLE_TRAINER_ENDPOINTS"
)
current_endpoint
=
os
.
getenv
(
"PADDLE_CURRENT_ENDPOINT"
)
worker_endpoints
=
worker_endpoints_env
trainers_num
=
len
(
worker_endpoints
.
split
(
','
))
device_ids
=
os
.
getenv
(
"PADDLE_WORLD_DEVICE_IDS"
)
current_device_id
=
os
.
getenv
(
"PADDLE_LOCAL_DEVICE_IDS"
)
details
=
"selected_accelerators:{} selected_custom_devices:{} worker_endpoints:{} trainers_num:{} current_endpoint:{} trainer_id:{} device_ids:{} device_id:{}"
\
.
format
(
selected_accelerators
,
selected_custom_devices
,
worker_endpoints
,
trainers_num
,
current_endpoint
,
trainer_id
,
device_ids
,
current_device_id
)
print
(
details
)
with
open
(
"multi_process_{}.check_{}.log"
.
format
(
prefix
,
trainer_id
),
"w"
)
as
f
:
f
.
write
(
details
)
if
__name__
==
'__main__'
:
prefix
=
sys
.
argv
[
1
]
train
(
prefix
)
python/paddle/fluid/tests/custom_runtime/process_group_xccl.py
0 → 100644
浏览文件 @
efb4d5c2
# Copyright (c) 2022 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.
from
__future__
import
print_function
import
unittest
import
random
import
numpy
as
np
import
os
import
shutil
import
paddle
from
paddle.fluid
import
core
from
datetime
import
timedelta
import
paddle.fluid.core
as
core
from
paddle.fluid.framework
import
_test_eager_guard
from
paddle.fluid.dygraph.parallel
import
ParallelEnv
def
init_process_group
(
strategy
=
None
):
nranks
=
ParallelEnv
().
nranks
rank
=
ParallelEnv
().
local_rank
is_master
=
True
if
rank
==
0
else
False
store
=
paddle
.
fluid
.
core
.
TCPStore
(
"127.0.0.1"
,
6173
,
is_master
,
nranks
)
pg_group
=
core
.
ProcessGroupCustom
(
store
,
rank
,
nranks
,
paddle
.
CustomPlace
(
ParallelEnv
().
device_type
,
ParallelEnv
().
device_id
))
return
pg_group
class
TestProcessGroupFp32
(
unittest
.
TestCase
):
def
setUp
(
self
):
paddle
.
seed
(
2022
)
random
.
seed
(
2022
)
np
.
random
.
seed
(
2022
)
self
.
config
()
def
config
(
self
):
self
.
dtype
=
"float32"
self
.
shape
=
(
2
,
10
,
5
)
def
test_create_process_group_xccl
(
self
):
with
_test_eager_guard
():
paddle
.
set_device
(
'custom_cpu:%d'
%
paddle
.
distributed
.
ParallelEnv
().
dev_id
)
pg
=
init_process_group
()
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_y
=
paddle
.
to_tensor
(
y
)
sum_result
=
tensor_x
+
tensor_y
if
pg
.
rank
()
==
0
:
task
=
pg
.
allreduce
(
tensor_x
)
task
.
wait
()
# assert np.array_equal(tensor_x, sum_result)
else
:
task
=
pg
.
allreduce
(
tensor_y
)
task
.
wait
()
# assert np.array_equal(tensor_y, sum_result)
print
(
"test allreduce sum api ok"
)
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_y
=
paddle
.
to_tensor
(
y
)
max_result
=
paddle
.
maximum
(
tensor_x
,
tensor_y
)
if
pg
.
rank
()
==
0
:
task
=
pg
.
allreduce
(
tensor_x
,
core
.
ReduceOp
.
MAX
)
task
.
wait
()
# assert np.array_equal(tensor_x, max_result)
else
:
task
=
pg
.
allreduce
(
tensor_y
,
core
.
ReduceOp
.
MAX
)
task
.
wait
()
# assert np.array_equal(tensor_y, max_result)
print
(
"test allreduce max api ok"
)
# test broadcast
# rank 0
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
# rank 1
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_y
=
paddle
.
to_tensor
(
y
)
broadcast_result
=
paddle
.
assign
(
tensor_x
)
if
pg
.
rank
()
==
0
:
task
=
pg
.
broadcast
(
tensor_x
,
0
)
task
.
synchronize
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
assert
task
.
is_completed
()
# assert np.array_equal(broadcast_result, tensor_x)
else
:
task
=
pg
.
broadcast
(
tensor_y
,
0
)
task
.
synchronize
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
assert
task
.
is_completed
()
# assert np.array_equal(broadcast_result, tensor_y)
print
(
"test broadcast api ok"
)
# test barrier
# rank 0
if
pg
.
rank
()
==
0
:
task
=
pg
.
barrier
()
task
.
wait
()
# rank 1
else
:
task
=
pg
.
barrier
()
task
.
wait
()
print
(
"test barrier api ok
\n
"
)
return
# test allgather
# rank 0
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
tensor_y
=
paddle
.
to_tensor
(
y
)
out_shape
=
list
(
self
.
shape
)
out_shape
[
0
]
*=
2
out
=
np
.
random
.
random
(
out_shape
).
astype
(
self
.
dtype
)
tensor_out
=
paddle
.
to_tensor
(
out
)
if
pg
.
rank
()
==
0
:
task
=
pg
.
all_gather
(
tensor_x
,
tensor_out
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
# rank 1
else
:
task
=
pg
.
all_gather
(
tensor_y
,
tensor_out
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
out_1
=
paddle
.
slice
(
tensor_out
,
[
0
],
[
0
],
[
out_shape
[
0
]
//
2
])
out_2
=
paddle
.
slice
(
tensor_out
,
[
0
],
[
out_shape
[
0
]
//
2
],
[
out_shape
[
0
]])
# assert np.array_equal(tensor_x, out_1)
# assert np.array_equal(tensor_y, out_2)
print
(
"test allgather api ok
\n
"
)
# test alltoall
# rank 0
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
out1
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
out2
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
tensor_y
=
paddle
.
to_tensor
(
y
)
tensor_out1
=
paddle
.
to_tensor
(
out1
)
tensor_out2
=
paddle
.
to_tensor
(
out2
)
raw_tensor_x_2
=
paddle
.
slice
(
tensor_x
,
[
0
],
[
self
.
shape
[
0
]
//
2
],
[
self
.
shape
[
0
]])
raw_tensor_y_1
=
paddle
.
slice
(
tensor_y
,
[
0
],
[
0
],
[
self
.
shape
[
0
]
//
2
])
if
pg
.
rank
()
==
0
:
task
=
pg
.
alltoall
(
tensor_x
,
tensor_out1
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
# rank 1
else
:
task
=
pg
.
alltoall
(
tensor_y
,
tensor_out2
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
out1_2
=
paddle
.
slice
(
tensor_out1
,
[
0
],
[
self
.
shape
[
0
]
//
2
],
[
self
.
shape
[
0
]])
out2_1
=
paddle
.
slice
(
tensor_out2
,
[
0
],
[
0
],
[
self
.
shape
[
0
]
//
2
])
# if pg.rank() == 0:
# assert np.array_equal(out1_2.numpy(), raw_tensor_y_1.numpy())
# else:
# assert np.array_equal(out2_1, raw_tensor_x_2)
print
(
"test alltoall api ok
\n
"
)
# test Reduce
# rank 0
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
tensor_y
=
paddle
.
to_tensor
(
y
)
sum_result
=
tensor_x
+
tensor_y
if
pg
.
rank
()
==
0
:
task
=
pg
.
reduce
(
tensor_x
,
0
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
# rank 1
else
:
task
=
pg
.
reduce
(
tensor_y
,
0
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
# if pg.rank() == 0:
# assert np.array_equal(tensor_x, sum_result)
print
(
"test reduce sum api ok
\n
"
)
# test Scatter
# rank 0
in_shape
=
list
(
self
.
shape
)
in_shape
[
0
]
*=
2
x
=
np
.
random
.
random
(
in_shape
).
astype
(
self
.
dtype
)
y
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
tensor_x
=
paddle
.
to_tensor
(
x
)
tensor_y
=
paddle
.
to_tensor
(
y
)
if
pg
.
rank
()
==
0
:
task
=
pg
.
scatter
(
tensor_x
,
tensor_y
,
0
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
# rank 1
else
:
task
=
pg
.
scatter
(
tensor_x
,
tensor_y
,
0
)
task
.
wait
()
# paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
out1
=
paddle
.
slice
(
tensor_x
,
[
0
],
[
0
],
[
self
.
shape
[
0
]])
out2
=
paddle
.
slice
(
tensor_x
,
[
0
],
[
self
.
shape
[
0
]],
[
self
.
shape
[
0
]
*
2
])
# if pg.rank() == 0:
# assert np.array_equal(tensor_y, out1)
# else:
# assert np.array_equal(tensor_y, out2)
print
(
"test scatter api ok
\n
"
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/custom_runtime/test_collective_process_group_xccl.py
0 → 100644
浏览文件 @
efb4d5c2
# Copyright (c) 2022 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.
from
__future__
import
print_function
import
unittest
import
os
import
sys
import
copy
import
subprocess
import
time
def
start_local_trainers
(
cluster
,
pod
,
training_script
,
training_script_args
,
eager_mode
=
True
,
log_dir
=
None
):
from
paddle.distributed.utils
import
find_free_ports
,
watch_local_trainers
,
get_cluster
,
TrainerProc
current_env
=
copy
.
copy
(
os
.
environ
.
copy
())
#paddle broadcast ncclUniqueId use socket, and
#proxy maybe make trainers unreachable, so delete them.
#if we set them to "", grpc will log error message "bad uri"
#so just delete them.
current_env
.
pop
(
"http_proxy"
,
None
)
current_env
.
pop
(
"https_proxy"
,
None
)
procs
=
[]
os
.
system
(
"rm -rf log && mkdir -p log"
)
for
idx
,
t
in
enumerate
(
pod
.
trainers
):
proc_env
=
{
"FLAGS_selected_custom_cpus"
:
"%s"
%
","
.
join
([
str
(
g
)
for
g
in
t
.
gpus
]),
"PADDLE_TRAINER_ID"
:
"%d"
%
t
.
rank
,
"PADDLE_CURRENT_ENDPOINT"
:
"%s"
%
t
.
endpoint
,
"PADDLE_TRAINERS_NUM"
:
"%d"
%
cluster
.
trainers_nranks
(),
"PADDLE_TRAINER_ENDPOINTS"
:
","
.
join
(
cluster
.
trainers_endpoints
()),
"PADDLE_DISTRI_CUSTOM_DEVICE_TYPE"
:
"custom_cpu"
,
}
if
not
eager_mode
:
proc_env
[
"FLAGS_enable_eager_mode"
]
=
"%d"
%
0
current_env
.
update
(
proc_env
)
print
(
"trainer proc env:{}"
.
format
(
current_env
))
if
os
.
getenv
(
'WITH_COVERAGE'
,
'OFF'
)
==
'ON'
:
cmd
=
"python -m coverage run --branch -p "
+
training_script
else
:
cmd
=
"python -u "
+
training_script
print
(
"start trainer proc:{} env:{}"
.
format
(
cmd
,
proc_env
))
fn
=
open
(
"workerlog.%d"
%
idx
,
"a"
)
proc
=
subprocess
.
Popen
(
cmd
.
split
(
" "
),
env
=
current_env
,
stdout
=
fn
,
stderr
=
fn
)
tp
=
TrainerProc
()
tp
.
proc
=
proc
tp
.
rank
=
t
.
rank
tp
.
log_fn
=
fn
tp
.
cmd
=
cmd
procs
.
append
(
tp
)
return
procs
def
get_cluster_from_args
(
selected_gpus
):
from
paddle.distributed.utils
import
find_free_ports
,
watch_local_trainers
,
get_cluster
,
TrainerProc
cluster_node_ips
=
'127.0.0.1'
node_ip
=
'127.0.0.1'
node_ips
=
[
x
.
strip
()
for
x
in
cluster_node_ips
.
split
(
','
)]
node_ips
.
index
(
node_ip
)
free_ports
=
None
free_ports
=
find_free_ports
(
len
(
selected_gpus
))
if
free_ports
is
not
None
:
free_ports
=
list
(
free_ports
)
trainer_endpoints
=
[]
for
ip
in
node_ips
:
trainer_endpoints
.
append
([
"%s:%d"
%
(
ip
,
port
)
for
port
in
free_ports
])
return
get_cluster
(
node_ips
,
node_ip
,
trainer_endpoints
,
selected_gpus
)
class
TestMultipleCustomCPU
(
unittest
.
TestCase
):
def
run_mnist_2custom_cpu
(
self
,
target_file_name
,
eager_mode
=
True
):
from
paddle.distributed.utils
import
find_free_ports
,
watch_local_trainers
,
get_cluster
,
TrainerProc
selected_devices
=
[
0
,
1
]
cluster
=
None
pod
=
None
cluster
,
pod
=
get_cluster_from_args
(
selected_devices
)
procs
=
start_local_trainers
(
cluster
,
pod
,
eager_mode
=
eager_mode
,
training_script
=
target_file_name
,
training_script_args
=
[])
while
True
:
alive
=
watch_local_trainers
(
procs
,
cluster
.
trainers_endpoints
())
if
not
alive
:
print
(
"Local procs complete, POD info:{}"
.
format
(
pod
))
break
time
.
sleep
(
3
)
class
TestProcessGroup
(
TestMultipleCustomCPU
):
def
setUp
(
self
):
# compile so and set to current path
cur_dir
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
cmd
=
'rm -rf PaddleCustomDevice && git clone https://github.com/PaddlePaddle/PaddleCustomDevice.git && cd PaddleCustomDevice/backends/custom_cpu && mkdir build && cd build && cmake .. && make -j8'
os
.
system
(
cmd
)
# set environment for loading and registering compiled custom kernels
# only valid in current process
os
.
environ
[
'CUSTOM_DEVICE_ROOT'
]
=
os
.
path
.
join
(
cur_dir
,
'PaddleCustomDevice/backends/custom_cpu/build'
)
def
test_process_group_xccl
(
self
):
from
paddle.distributed.utils
import
find_free_ports
,
watch_local_trainers
,
get_cluster
,
TrainerProc
self
.
run_mnist_2custom_cpu
(
'process_group_xccl.py'
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/custom_runtime/test_fleet_launch_custom_device.sh
0 → 100644
浏览文件 @
efb4d5c2
#!/bin/bash
# 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.
set
-e
rm
-rf
PaddleCustomDevice
&&
git clone https://github.com/PaddlePaddle/PaddleCustomDevice.git
&&
pushd
PaddleCustomDevice/backends/custom_cpu
&&
mkdir
build
&&
pushd
build
&&
cmake ..
&&
make
-j8
&&
popd
&&
popd
echo
"begin test use custom_cpu"
export
FLAGS_selected_custom_cpus
=
0,1
distributed_args
=
"--ips=127.0.0.1 --backend=xccl --custom_device_type=custom_cpu --custom_devices=0,1 --run_mode=collective --log_dir=testlog"
python
-m
paddle.distributed.fleet.launch
${
distributed_args
}
custom_device_multi_process_collective.py fleetlaunch_custom_cpu
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