未验证 提交 7158061a 编写于 作者: G gongweibao 提交者: GitHub

Add startup bash files of test_ascend_group. (#30645)

Add startup bash files of test_ascend_group
上级 e4287ca6
......@@ -39,6 +39,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_fleet_run_random_port)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_async)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_cloud)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_ascend)
list(APPEND MIXED_DIST_TEST_OPS test_ascend_group)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_nproc)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_api_input)
list(APPEND MIXED_DIST_TEST_OPS test_collective_optimizer)
......@@ -524,6 +525,7 @@ if(WITH_DISTRIBUTE)
bash_test_modules(test_fleet_launch_cloud START_BASH test_fleet_launch_cloud.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch_ascend START_BASH test_fleet_launch_ascend.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch_nproc START_BASH test_fleet_launch_nproc.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_ascend_group START_BASH test_ascend_group.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
# port range (20000, 23000) is reserved for dist-ops
set(dist_ut_port 20001)
......
# 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
import paddle.fluid as fluid
from paddle.fluid import unique_name
import paddle.fluid.core as core
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.distributed import fleet
paddle.enable_static()
OpRole = core.op_proto_and_checker_maker.OpRole
OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
role = fleet.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
def init_communicator(startup_program, main_program, current_endpoint, endpoints, ring_id):
nranks = len(endpoints)
other_endpoints = endpoints[:]
other_endpoints.remove(current_endpoint)
group_rank=endpoints.index(current_endpoint)
assert group_rank >=0
block = startup_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': group_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': group_rank,
'ring_id': ring_id,
OP_ROLE_KEY: OpRole.Forward,
})
block.create_var(
name="data",
persistable=True,
dtype='float32')
with fluid.program_guard(main_program):
op_type="c_allreduce_sum"
data=fluid.layers.fill_constant(shape=[1], dtype='float32', value=2.5)
helper = LayerHelper(op_type, **locals())
helper.append_op(
type=op_type,
inputs={'X': [data]},
outputs={'Out': [data]},
attrs={'ring_id': ring_id,
'use_calc_stream': True})
def train(world_endpoints, world_device_ids, local_device_ids,local_rank):
startup_programs=[]
main_programs=[]
#trainer_endpoints=["127.0.0.1:6071","127.0.0.1:6072","127.0.0.1:6073","127.0.0.1:6074"]
trainer_endpoints=world_endpoints
groups=[[], [], []]
groups[0]=[trainer_endpoints[0], trainer_endpoints[1]]
groups[1]=[trainer_endpoints[2], trainer_endpoints[3]]
groups[2]=[trainer_endpoints[0], trainer_endpoints[2]]
for i in range(len(trainer_endpoints)):
startup_programs.append(fluid.Program())
main_programs.append(fluid.Program())
for idx, group in enumerate(groups):
for te in group:
te_idx = trainer_endpoints.index(te)
startup_program = startup_programs[te_idx]
main_program=main_programs[te_idx]
init_communicator(startup_program, main_program, te, group, idx)
print(len(startup_programs))
print(startup_programs[local_rank])
print(main_programs[local_rank])
worker_endpoints=fleet.worker_endpoints()
world_device_ids=fleet.world_device_ids()
local_device_ids=fleet.local_device_ids()
local_rank=int(fleet.local_rank())
print("worker_endpoints:", worker_endpoints)
print("world_device_ids:", world_device_ids)
print("local_device_ids:", local_device_ids)
print("local_rank:", local_rank)
train(worker_endpoints, world_device_ids,local_device_ids,local_rank)
#!/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
cluster_node_ips="127.0.0.1"
export PADDLE_TRAINERS_NUM=4
export POD_IP=127.0.0.1
export PADDLE_TRAINERS=127.0.0.1
export PADDLE_TRAINER_ID=0
export PADDLE_PORT=35789
export TRAINER_PORTS_NUM=4
distributed_args="--ips=${cluster_node_ips} --ascend_npus=0,1,2,3 --log_dir=testlog"
python -m paddle.distributed.fleet.launch ${distributed_args} \
ascend_group.py fleetascendgroup
\ No newline at end of file
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