Signed-off-by: GuoMengHao <guomenghao@huawei.com>pull/3512/head
GuoMengHao
5 years ago
parent
c22792aab1
commit
2309e7369a
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# Run distribute pretrain
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## description
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The number of D chips can be automatically allocated based on the device_num set in hccl config file, You don not need to specify that.
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## how to use
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For example, if we want to run the distributed training of Bert model on D chip, we can in `/bert/` dir:
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```
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python ./scripts/ascend_distributed_launcher/run_distribute_pretrain.py --run_script_dir ./run_pretrain.py --hyper_parameter_config_dir ./scripts/ascend_distributed_launcher/hyper_parameter_config.ini --data_dir /path/dataset/ --hccl_config_dir model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
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```
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output:
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```
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hccl_config_dir: model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
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the number of logical core: 192
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avg_core_per_rank: 96
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rank_size: 2
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start training for rank 0, device 5:
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rank_id: 0
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device_id: 5
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core nums: 0-95
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epoch_size: 8
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data_dir: /data/small_512/
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schema_dir:
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log file dir: ./LOG5/log.txt
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start training for rank 1, device 6:
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rank_id: 1
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device_id: 6
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core nums: 96-191
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epoch_size: 8
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data_dir: /data/small_512/
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schema_dir:
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log file dir: ./LOG6/log.txt
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```
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## Note
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1. Note that `hccl_2p_56_x.x.x.x.json` can use [hccl_tools.py](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools) to generate.
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2. For hyper parameter, please note that you should customize the scripts `hyper_parameter_config.ini`. Please note that these two hyper parameters are not allowed to be configured here:
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device_id
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device_num
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3. For Other Model, please note that you should customize the option `run_script` and Corresponding `hyper_parameter_config.ini`.
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[config]
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distribute=true
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epoch_size=40
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enable_save_ckpt=true
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enable_lossscale=true
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do_shuffle=true
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enable_data_sink=true
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data_sink_steps=100
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save_checkpoint_path=./checkpoint/
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save_checkpoint_steps=10000
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save_checkpoint_num=1
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""distribute pretrain script"""
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import os
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import json
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import configparser
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import multiprocessing
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from argparse import ArgumentParser
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def parse_args():
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"""
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parse args .
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Args:
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Returns:
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args.
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Examples:
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>>> parse_args()
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"""
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parser = ArgumentParser(description="mindspore distributed training")
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parser.add_argument("--run_script_dir", type=str, default="",
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help="Run script path, it is better to use absolute path")
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parser.add_argument("--hyper_parameter_config_dir", type=str, default="",
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help="Hyper Parameter config path, it is better to use absolute path")
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parser.add_argument("--data_dir", type=str, default="",
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help="Data path, it is better to use absolute path")
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parser.add_argument("--hccl_config_dir", type=str, default="",
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help="Hccl config path, it is better to use absolute path")
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args = parser.parse_args()
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return args
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def distribute_pretrain():
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"""
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distribute pretrain scripts. The number of D chips can be automatically allocated
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based on the device_num set in hccl config file, You don not need to specify that.
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"""
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print("start", __file__)
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args = parse_args()
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run_script = args.run_script_dir
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data_dir = args.data_dir
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cf = configparser.ConfigParser()
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cf.read(args.hyper_parameter_config_dir)
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cfg = dict(cf.items("config"))
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print("hccl_config_dir:", args.hccl_config_dir)
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os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = args.hccl_config_dir
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os.environ['RANK_TABLE_FILE'] = args.hccl_config_dir
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cores = multiprocessing.cpu_count()
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print("the number of logical core:", cores)
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# get device_ips
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device_ips = {}
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with open('/etc/hccn.conf', 'r') as fin:
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for hccn_item in fin.readlines():
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if hccn_item.strip().startswith('address_'):
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device_id, device_ip = hccn_item.split('=')
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device_id = device_id.split('_')[1]
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device_ips[device_id] = device_ip.strip()
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with open(args.hccl_config_dir, "r", encoding="utf-8") as fin:
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hccl_config = json.loads(fin.read())
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rank_size = 0
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for server in hccl_config["server_list"]:
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rank_size += len(server["device"])
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if server["device"][0]["device_ip"] in device_ips.values():
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this_server = server
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os.environ['RANK_SIZE'] = str(rank_size)
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print("total rank size:", rank_size)
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print("this server rank size:", len(this_server["device"]))
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avg_core_per_rank = int(int(cores) / len(this_server["device"]))
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core_gap = avg_core_per_rank - 1
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print("avg_core_per_rank:", avg_core_per_rank)
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count = 0
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for instance in this_server["device"]:
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device_id = instance["device_id"]
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rank_id = instance["rank_id"]
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print("\nstart training for rank " + str(rank_id) + ", device " + str(device_id) + ":")
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print("rank_id:", rank_id)
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print("device_id:", device_id)
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start = count * int(avg_core_per_rank)
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count += 1
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end = start + core_gap
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cmdopt = str(start) + "-" + str(end)
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os.environ["DEVICE_ID"] = device_id
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os.environ["RANK_ID"] = rank_id
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os.environ["DEPLOY_MODE"] = "0"
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os.environ["GE_USE_STATIC_MEMORY"] = "1"
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os.system("rm -rf LOG" + str(device_id))
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os.system("mkdir ./LOG" + str(device_id))
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os.system("cp *.py ./LOG" + str(device_id))
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os.system("mkdir -p ./LOG" + str(device_id) + "/ms_log")
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os.system("env > ./LOG" + str(device_id) + "/env.log")
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cur_dir = os.getcwd()
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os.environ["GLOG_log_dir"] = cur_dir + "/LOG" + str(device_id) + "/ms_log"
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os.environ["GLOG_logtostderr"] = "0"
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print("core_nums:", cmdopt)
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print("epoch_size:", str(cfg['epoch_size']))
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print("data_dir:", data_dir)
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print("log_file_dir: ./LOG" + str(device_id) + "/log.txt")
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cmd = 'taskset -c ' + cmdopt + ' python ' + run_script + " "
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opt = " ".join(["--" + key + "=" + str(cfg[key]) for key in cfg.keys()])
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if ('device_id' in opt) or ('device_num' in opt) or ('data_dir' in opt):
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raise ValueError("hyper_parameter_config.ini can not setting 'device_id',"
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" 'device_num' or 'data_dir'! ")
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cmd += opt
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cmd += " --data_dir=" + data_dir
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cmd += ' --device_id=' + str(device_id) + ' --device_num=' \
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+ str(rank_size) + ' >./LOG' + str(device_id) + '/log.txt 2>&1 &'
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os.system(cmd)
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if __name__ == "__main__":
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distribute_pretrain()
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@ -0,0 +1,48 @@
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# Run distribute pretrain
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## description
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The number of D chips can be automatically allocated based on the device_num set in hccl config file, You don not need to specify that.
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## how to use
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For example, if we want to run the distributed training of Bert model on D chip, we can in `/bert/` dir:
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```
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python model_zoo/utils/ascend_distributed_launcher/run_distribute_pretrain.py --run_script_dir ./run_pretrain.py --hyper_parameter_config_dir model_zoo/utils/ascend_distributed_launcher/hyper_parameter_config.ini --data_dir /path/dataset/ --hccl_config_dir model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
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```
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output:
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```
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hccl_config_dir: model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
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the number of logical core: 192
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avg_core_per_rank: 96
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rank_size: 2
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start training for rank 0, device 5:
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rank_id: 0
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device_id: 5
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core nums: 0-95
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epoch_size: 8
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data_dir: /data/small_512/
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schema_dir:
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log file dir: ./LOG5/log.txt
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start training for rank 1, device 6:
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rank_id: 1
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device_id: 6
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core nums: 96-191
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epoch_size: 8
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data_dir: /data/small_512/
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schema_dir:
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log file dir: ./LOG6/log.txt
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```
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## Note
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1. Note that `hccl_2p_56_x.x.x.x.json` can use [hccl_tools.py](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools) to generate.
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2. For hyper parameter, please note that you should customize the scripts `hyper_parameter_config.ini`. Please note that these two hyper parameters are not allowed to be configured here:
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device_id
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device_num
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3. For Other Model, please note that you should customize the option `run_script` and Corresponding `hyper_parameter_config.ini`.
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@ -0,0 +1,11 @@
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[config]
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distribute=true
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epoch_size=40
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enable_save_ckpt=true
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enable_lossscale=true
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do_shuffle=true
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enable_data_sink=true
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data_sink_steps=100
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save_checkpoint_path=./checkpoint/
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save_checkpoint_steps=10000
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save_checkpoint_num=1
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@ -0,0 +1,142 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""distribute pretrain script"""
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import os
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import json
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import configparser
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import multiprocessing
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from argparse import ArgumentParser
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def parse_args():
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"""
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parse args .
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Args:
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Returns:
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args.
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Examples:
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>>> parse_args()
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"""
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parser = ArgumentParser(description="mindspore distributed training")
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parser.add_argument("--run_script_dir", type=str, default="",
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help="Run script path, it is better to use absolute path")
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parser.add_argument("--hyper_parameter_config_dir", type=str, default="",
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help="Hyper Parameter config path, it is better to use absolute path")
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parser.add_argument("--data_dir", type=str, default="",
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help="Data path, it is better to use absolute path")
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parser.add_argument("--hccl_config_dir", type=str, default="",
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help="Hccl config path, it is better to use absolute path")
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args = parser.parse_args()
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return args
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def distribute_pretrain():
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"""
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distribute pretrain scripts. The number of D chips can be automatically allocated
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based on the device_num set in hccl config file, You don not need to specify that.
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"""
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print("start", __file__)
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args = parse_args()
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run_script = args.run_script_dir
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data_dir = args.data_dir
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cf = configparser.ConfigParser()
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cf.read(args.hyper_parameter_config_dir)
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cfg = dict(cf.items("config"))
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print("hccl_config_dir:", args.hccl_config_dir)
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os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = args.hccl_config_dir
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os.environ['RANK_TABLE_FILE'] = args.hccl_config_dir
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cores = multiprocessing.cpu_count()
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print("the number of logical core:", cores)
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# get device_ips
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device_ips = {}
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with open('/etc/hccn.conf', 'r') as fin:
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for hccn_item in fin.readlines():
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if hccn_item.strip().startswith('address_'):
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device_id, device_ip = hccn_item.split('=')
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device_id = device_id.split('_')[1]
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device_ips[device_id] = device_ip.strip()
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with open(args.hccl_config_dir, "r", encoding="utf-8") as fin:
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hccl_config = json.loads(fin.read())
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rank_size = 0
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for server in hccl_config["server_list"]:
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rank_size += len(server["device"])
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if server["device"][0]["device_ip"] in device_ips.values():
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this_server = server
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os.environ['RANK_SIZE'] = str(rank_size)
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print("total rank size:", rank_size)
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print("this server rank size:", len(this_server["device"]))
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avg_core_per_rank = int(int(cores) / len(this_server["device"]))
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core_gap = avg_core_per_rank - 1
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print("avg_core_per_rank:", avg_core_per_rank)
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count = 0
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for instance in this_server["device"]:
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device_id = instance["device_id"]
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rank_id = instance["rank_id"]
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print("\nstart training for rank " + str(rank_id) + ", device " + str(device_id) + ":")
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print("rank_id:", rank_id)
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print("device_id:", device_id)
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start = count * int(avg_core_per_rank)
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count += 1
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end = start + core_gap
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cmdopt = str(start) + "-" + str(end)
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os.environ["DEVICE_ID"] = device_id
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os.environ["RANK_ID"] = rank_id
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os.environ["DEPLOY_MODE"] = "0"
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os.environ["GE_USE_STATIC_MEMORY"] = "1"
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os.system("rm -rf LOG" + str(device_id))
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os.system("mkdir ./LOG" + str(device_id))
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os.system("cp *.py ./LOG" + str(device_id))
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os.system("mkdir -p ./LOG" + str(device_id) + "/ms_log")
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os.system("env > ./LOG" + str(device_id) + "/env.log")
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cur_dir = os.getcwd()
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os.environ["GLOG_log_dir"] = cur_dir + "/LOG" + str(device_id) + "/ms_log"
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os.environ["GLOG_logtostderr"] = "0"
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print("core_nums:", cmdopt)
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print("epoch_size:", str(cfg['epoch_size']))
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print("data_dir:", data_dir)
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print("log_file_dir: ./LOG" + str(device_id) + "/log.txt")
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cmd = 'taskset -c ' + cmdopt + ' python ' + run_script + " "
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opt = " ".join(["--" + key + "=" + str(cfg[key]) for key in cfg.keys()])
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if ('device_id' in opt) or ('device_num' in opt) or ('data_dir' in opt):
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raise ValueError("hyper_parameter_config.ini can not setting 'device_id',"
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" 'device_num' or 'data_dir'! ")
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cmd += opt
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cmd += " --data_dir=" + data_dir
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cmd += ' --device_id=' + str(device_id) + ' --device_num=' \
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+ str(rank_size) + ' >./LOG' + str(device_id) + '/log.txt 2>&1 &'
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os.system(cmd)
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if __name__ == "__main__":
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distribute_pretrain()
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