# 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 .context import Context ctx = None def launch(): """ Paddle distribution training entry ``python -m paddle.distributed.launch``. Usage: .. code-block:: bash :name: code-block-bash1 python -m paddle.distributed.launch [-h] [--master MASTER] [--rank RANK] [--log_level LOG_LEVEL] [--nnodes NNODES] [--nproc_per_node NPROC_PER_NODE] [--log_dir LOG_DIR] [--run_mode RUN_MODE] [--job_id JOB_ID] [--devices DEVICES] [--host HOST] [--servers SERVERS] [--trainers TRAINERS] [--trainer_num TRAINER_NUM] [--server_num SERVER_NUM] [--gloo_port GLOO_PORT] [--with_gloo WITH_GLOO] [--max_restart MAX_RESTART] [--elastic_level ELASTIC_LEVEL] [--elastic_timeout ELASTIC_TIMEOUT] training_script ... Base Parameters: - ``--master``: The master/rendezvous server, support ``http://`` and ``etcd://``, default with ``http://``. e.g., ``--master=127.0.0.1:8080``. Default ``--master=None``. - ``--rank``: The rank of the node, can be auto assigned by master. Default ``--rank=-1``. - ``--log_level``: The log level to set for logging.setLevel which can be CRITICAL/ERROR/WARNING/INFO/DEBUG/NOTSET, case insensitive. Default ``--log_level=INFO``. - ``--nnodes``: The number of nodes for a distributed job, it can be a range in elastic mode, e.g., ``--nnodes=2:3``. Default ``--nnodes=1``. - ``--nproc_per_node``: The number of processes to launch on a node. In gpu training, it should be less or equal to the gpus number of you system. e.g., ``--nproc_per_node=8`` - ``--log_dir``: The path for each process's log. e.g., ``--log_dir=output_dir``. Default ``--log_dir=log``. - ``--run_mode``: The run mode of job, can be:collective/ps/ps-heter/rpc. e.g., ``--run_mode=ps``. Default ``--run_mode=collective``. - ``--job_id``: The job unique id, it affects the log files' name. e.g., ``--job_id=job1``. Default ``--job_id=default``. - ``--devices``: The selected accelerate devices on nodes, can be gpu/xpu etc.. e.g., ``--devices=0,1,2,3`` will launch four training processes each bound to one device. - ``training_script``: The full path to the single GPU training program/script to be launched in parallel, followed by all the arguments for the training script. e.g., ``training.py`` - ``training_script_args``: The args of training_script. e.g., ``--lr=0.1`` Collective Parameters: - ``--ips``: [DEPRECATED] Paddle cluster nodes ips, e.g., ``--ips=192.168.0.16,192.168.0.17``. Default ``--ips=127.0.0.1``. Parameter-Server Parameters: - ``--servers``: User defined servers ip:port, e.g., ``--servers="192.168.0.16:6170,192.168.0.17:6170"`` - ``--trainers``: User defined trainers ip:port, e.g., ``--trainers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172"`` - ``--workers``: [DEPRECATED] The same as trainers. - ``--trainer_num``: Number of trainers on each node, can be 0. - ``--worker_num``: [DEPRECATED] The same as trainer_num. - ``--server_num``: Number of servers on each node, can be 0. - ``--heter_workers``: User defined heter workers ip1:port1;ip2:port2, e.g., ``--heter_workers="192.168.0.16:6172;192.168.0.17:6172"`` - ``--heter_worker_num``: Number of heter_workers in each stage (It recommend to set when in the emulated distributed environment using single node) - ``--heter_devices``: Type of heter_device in each stage - ``--gloo_port``: Gloo http Port. Default ``--gloo_port=6767``. - ``--with_gloo``: Using gloo or not. Default ``--with_gloo=0``. Elastic Parameters: - ``--max_restart``: The maximum restart times for an elastic job. Default ``--max_restart=3``. - ``--elastic_level``: The elastic level: -1: disable, 0: failed exit, peers hold, 1: internal restart. Default ``--elastic_level=-1``. - ``--elastic_timeout``: Seconds to wait before elastic job begin to train. Default ``--elastic_timeout=30``. IPU Parameters: IPU distributed launch only requires and allowes three arguments ``--devices``, ``training_script`` and ``training_script_args``. The ``--devices`` is the number of IPU devices. e.g., ``--devices=4`` will launch the training program with four IPU devices. The ``training_script`` is only allowed to set as ``ipu``. The ``training_script_args`` includes arguments required by IPU distributed launch and illustrated as below. ``Examples 10`` has provided a example of paddle.distributed.launch with IPUs. - ``--hosts``: The hosts for IPU distributd training. Each host is able to include multiple processes. - ``--nproc_per_host``: The number of processes launched per host. Each process is able to include multiple replicas. - ``--ipus_per_replica``: The number of IPUs requested per replica. Each replica is able to include multiple IPUs. - ``--ipu_partition``: The partition name of IPU devices. - ``--vipu_server``: The ip of the IPU device manager. - ``training_script``: The full path to the IPU distributed training program/script to be launched in parallel. e.g., ``training.py``. - ``training_script_args``: The args of the IPU distributed training program/script. e.g., ``--lr=0.1``. Returns: - ``None`` Examples 0 (master, ip/port auto detection): .. code-block:: bash :name: code-block-example-bash0 # For training on multi node, run the following command in one of the nodes python -m paddle.distributed.launch --nnodes 2 train.py # Then the following info will be print # Copy the following command to other nodes to run. # -------------------------------------------------------------------------------- # python -m paddle.distributed.launch --master 10.0.0.1:38714 --nnodes 2 train.py # -------------------------------------------------------------------------------- # Follow the instruction above and paste the command in other nodes can launch a multi nodes training job. # There are two ways to launch a job with the same command for multi nodes training # 1) using the following command in every nodes, make sure the ip is one of the training node and the port is available on that node # python -m paddle.distributed.launch --master 10.0.0.1:38714 --nnodes 2 train.py # 2) using the following command in every nodes with a independent etcd service # python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2 train.py # This functionality works will for both collective and ps mode and even with other arguments. Examples 1 (collective, single node): .. code-block:: bash :name: code-block-example-bash1 # For training on single node using 4 gpus. python -m paddle.distributed.launch --devices=0,1,2,3 train.py --lr=0.01 Examples 2 (collective, multi node): .. code-block:: bash :name: code-block-example-bash2 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 # On 192.168.0.16: python -m paddle.distributed.launch --devices=0,1,2,3 --master=192.168.0.16:8090 train.py --lr=0.01 # On 192.168.0.17: python -m paddle.distributed.launch --devices=0,1,2,3 --master=192.168.0.16:8090 train.py --lr=0.01 Examples 3 (ps, cpu, single node): .. code-block:: bash :name: code-block-example-bash3 # To simulate distributed environment using single node, e.g., 2 servers and 4 workers. python -m paddle.distributed.launch --server_num=2 --worker_num=4 train.py --lr=0.01 Examples 4 (ps, cpu, multi node): .. code-block:: bash :name: code-block-example-bash4 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server and 2 workers. # On 192.168.0.16: python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 # On 192.168.0.17: python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 # Or with master, the following command run 2 server and 2 trainer on each node. python -m paddle.distributed.launch --master 192.168.0.16:9090 --server_num=2 --trainer_num=2 --nnodes 2 train.py Examples 5 (ps, gpu, single node): .. code-block:: bash :name: code-block-example-bash5 # To simulate distributed environment using single node, e.g., 2 servers and 4 workers, each worker use single gpu. export CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --server_num=2 --worker_num=4 train.py --lr=0.01 Examples 6 (ps, gpu, multi node): .. code-block:: bash :name: code-block-example-bash6 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server and 2 workers. # On 192.168.0.16: export CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 # On 192.168.0.17: export CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 Examples 7 (ps-heter, cpu + gpu, single node): .. code-block:: bash :name: code-block-example-bash7 # To simulate distributed environment using single node, e.g., 2 servers and 4 workers, two workers use gpu, two workers use cpu. export CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.launch --server_num=2 --worker_num=2 --heter_worker_num=2 train.py --lr=0.01 Examples 8 (ps-heter, cpu + gpu, multi node): .. code-block:: bash :name: code-block-example-bash8 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server, 1 gpu worker, 1 cpu worker. # On 192.168.0.16: export CUDA_VISIBLE_DEVICES=0 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.17:6171" --heter_workers="192.168.0.16:6172,192.168.0.17:6172" train.py --lr=0.01 # On 192.168.0.17: export CUDA_VISIBLE_DEVICES=0 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.17:6171" --heter_workers="192.168.0.16:6172,192.168.0.17:6172" train.py --lr=0.01 Examples 9 (elastic): .. code-block:: bash :name: code-block-example-bash9 # With the following command, the job will begin to run immediately if 4 nodes are ready, # or it will run after elastic_timeout if only 2 or 3 nodes ready python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2:4 train.py # once the number of nodes changes between 2:4 during training, the strategy holds Examples 10 (ipu): .. code-block:: bash :name: code-block-example-bash10 # With the following command, the job will begin to run the distributhed program with IPUs # Require `devices` as the number of IPUs # Require `training_script` to be set as `ipu` # Require `training_script_args` as the arguments of IPU distributed training instead of the arguments of the training program/script # Please Check the `IPU Parameters` for details python -m paddle.distributed.launch --devices 4 ipu --hosts=localhost --nproc_per_host=2 --ipus_per_replica=1 --ipu_partition=pod16 --vipu_server=127.0.0.1 train.py Examples 11 (rpc, cpu, single node): .. code-block:: bash :name: code-block-example-bash11 # Training on single node with two local servers python -m paddle.distributed.launch --master 127.0.0.1:8765 --nnodes 1 --nproc_per_node 2 --rank 0 --run_mode rpc train.py Examples 12 (rpc, cpu, multi node): .. code-block:: bash :name: code-block-example-bash12 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 2 servers. # On 192.168.0.16 python -m paddle.distributed.launch --master 192.168.0.16:8765 --nnodes 2 --nproc_per_node 2 --rank 0 --run_mode rpc train.py # On 192.168.0.17 python -m paddle.distributed.launch --master 192.168.0.16:8765 --nnodes 2 --nproc_per_node 2 --rank 1 --run_mode rpc train.py """ # initialize the context to run global ctx ctx = Context() if ctx.is_legacy_mode(): # legacy mode from paddle.distributed.fleet import launch launch.launch() elif ctx.is_auto_tuner_mode(): import copy import json import logging import os import sys import time from ..auto_tuner.recorder import HistoryRecorder from ..auto_tuner.tuner import AutoTuner from ..auto_tuner.utils import ( add_overlap_performance, gen_new_args, gen_new_ctx, read_log, read_step_time_log, ) from . import controllers start_time = time.time() # read user defined tuner config json if not ctx.args.auto_tuner_json.endswith(".json"): raise ValueError("Please use '.json' as the file name suffix.") try: with open(ctx.args.auto_tuner_json, "r") as f: tuner_cfg = json.load(f) except: raise ValueError("Please check your auto tuner json whether valid.") logger = logging.getLogger('auto_tuner') logger.setLevel(logging.INFO) auto_tuner_log_path = os.path.join( os.path.dirname(ctx.args.auto_tuner_json), f'{os.path.basename(ctx.args.auto_tuner_json).split(".")[0]}_auto_tuner.log', ) handler = logging.FileHandler(auto_tuner_log_path, mode="w") handler.setLevel(logging.INFO) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) handler.setFormatter(formatter) logger.addHandler(handler) # copy training script args if ctx.args.training_script.endswith('.py'): if os.environ.get("WITH_COVERAGE") == "ON": entrypoint = [ sys.executable, "-u", "-m", "coverage", "run", "--branch", "-p", ctx.args.training_script, ] else: entrypoint = [sys.executable, "-u", ctx.args.training_script] else: entrypoint = [ctx.args.training_script] entrypoint.extend(ctx.args.training_script_args) raw_args = copy.deepcopy(ctx.args.training_script_args) # get nodes and gpus from args if not ctx.args.devices: gpus_per_node = 8 else: gpus_per_node = len(ctx.args.devices.split(",")) nnodes = ctx.args.nnodes if isinstance(nnodes, str): nnodes = int(nnodes.split(":")[0]) else: nnodes = int(nnodes) tuner_cfg["nodes"] = nnodes tuner_cfg["gpus_per_node"] = gpus_per_node tuner_cfg["num_gpus"] = gpus_per_node * tuner_cfg["nodes"] if not tuner_cfg.get("search_algo", None): tuner_cfg["search_algo"] = {"name": "grid"} mode = tuner_cfg.get("mode", None) history_file_path = os.path.join( os.path.dirname(ctx.args.auto_tuner_json), f'{os.path.basename(ctx.args.auto_tuner_json).split(".")[0]}_history.csv', ) sorted_ips = [] ip = None if nnodes > 1: from .utils.etcd_client import ETCDClient assert "etcd://" in ctx.args.master master_ip, port = ctx.args.master.strip("etcd://").split(':') client = ETCDClient(host=master_ip, port=port) client.delete("best_cfg") client.delete_prefix("auto_tuner") import socket try: hostname = socket.gethostname() ip = socket.gethostbyname(socket.getfqdn(hostname)) except: ip = '127.0.0.1' assert ip != '127.0.0.1' if tuner_cfg["search_algo"].get("estimated_num_gpus", None): # get all machine ips and sort them # to avoid etcd deleting key and adding key at the same time time.sleep(5) path = f"auto_tuner/ip/{ip}" while not client.put(path, f"{ip}".encode('latin-1')): time.sleep(1) ips = list(client.get_prefix("auto_tuner/ip/")) size = len(ips) while size != nnodes: time.sleep(1) client.put(path, f"{ip}".encode('latin-1')) ips = list(client.get_prefix("auto_tuner/ip/")) size = len(ips) sorted_ips = sorted([i[0].decode() for i in ips]) logger.info( f"The total count of nodes is {len(sorted_ips)} and sorted ips are {sorted_ips}." ) # get max time per task run max_time_per_task = tuner_cfg.get("max_time_per_task", 1800) tuner_cfg["max_time_per_task"] = max_time_per_task ctx.max_time_per_task = max_time_per_task # warmup warmup_time = ( max_time_per_task if "warmup_time" not in tuner_cfg else tuner_cfg.get("warmup_time") ) # max_search_time max_search_time = tuner_cfg.get("max_search_time", None) is_first_task = True # build history recorder recorder = HistoryRecorder() job_id = 0 error_task_nums = 0 ctx.args.max_restart = -1 raw_ctx = copy.deepcopy(ctx) # gbs search if ( tuner_cfg.get('model_cfg', {}).get('global_batch_size', 'auto') == "auto" ): # adjust micron batch size until out of memory to get best global batch size gbs_tuner_cfg = copy.deepcopy(tuner_cfg) gbs_tuner_cfg["search_algo"] = "gbs" gbs_tuner = AutoTuner(gbs_tuner_cfg) gbs_cur_cfg = gbs_tuner.search_once() best_gbs = None while gbs_cur_cfg: ctx = copy.deepcopy(raw_ctx) log_dir = "GBSSearch/GBS{}_DP{}_MP{}_PP{}_Sharding_degree_{}_stage_{}_MBS{}_Recompute_{}_granularity_{}".format( gbs_cur_cfg["global_batch_size"], gbs_cur_cfg["dp_degree"], gbs_cur_cfg["mp_degree"], gbs_cur_cfg["pp_degree"], gbs_cur_cfg["sharding_degree"], gbs_cur_cfg["sharding_stage"], gbs_cur_cfg["micro_batch_size"], gbs_cur_cfg["use_recompute"], gbs_cur_cfg["recompute_granularity"], ) ctx.args.log_dir = log_dir # every task has own job id job_id += 1 task_job_id = "gbs_tuner_" + str(job_id) ctx.args.job_id = task_job_id # generate script args of task gbs_new_args = gen_new_args( raw_args, gbs_cur_cfg, gbs_tuner_cfg ) ctx.args.training_script_args = gbs_new_args # launch task ctx.logger.info( "Launch task from auto tuner: job_id {}, log_dir {}, config {}".format( task_job_id, log_dir, gbs_cur_cfg ) ) logger.info( "Launch task from auto tuner: job_id {}, log_dir {}, config {}".format( task_job_id, log_dir, gbs_cur_cfg ) ) c = controllers.init(ctx) c.run() # process generated result # TODO diffentiate out of memory and no loss(maybe over time) # TODO integragte memory and metric read metric, mem, err = read_log( path=ctx.args.log_dir, metric_file="workerlog.0", target_metric=tuner_cfg["metric_cfg"]["name"], memory_file=f"{ctx.args.job_id}.gpu.log", ) if err & (1 << 0): ctx.logger.warning( f"Read metric failed for parameters: {log_dir}" ) logger.warning( f"Read metric failed for parameters: {log_dir}" ) # for pruner use gbs_cur_cfg['time'] = -1 gbs_cur_cfg[tuner_cfg['metric_cfg']['name']] = None gbs_cur_cfg["max_mem_usage"] = mem if err & (1 << 1): ctx.logger.warning( f"Out of memory for parameters: {log_dir}" ) logger.warning(f"Out of memory for parameters: {log_dir}") # for pruner use gbs_cur_cfg['time'] = -1 gbs_cur_cfg[tuner_cfg['metric_cfg']['name']] = None gbs_cur_cfg["max_mem_usage"] = "OOM" # not err & (1 << 1): do not record memory usage when out of memory if err & (1 << 2) and not err & (1 << 1): ctx.logger.warning( f"Read memory usage failed for parameters: {log_dir}" ) logger.warning( f"Read memory usage failed for parameters: {log_dir}" ) gbs_cur_cfg["max_mem_usage"] = None if not err: # for pruner use gbs_cur_cfg['time'] = metric gbs_cur_cfg[tuner_cfg['metric_cfg']['name']] = metric gbs_cur_cfg["max_mem_usage"] = mem if err & (1 << 0) or err & (1 << 1): # no metric or out of memory, end gbs search break # store and update args for next round gbs_cur_cfg["job_id"] = job_id best_gbs = gbs_cur_cfg["global_batch_size"] recorder.add_cfg(**gbs_cur_cfg) c.finalize(exit=False) recorder.store_history("./tuner_gbs_history.csv") # new cfgs for next round gbs_new_cfg = gbs_tuner.search_once() gbs_cur_cfg = copy.deepcopy(gbs_new_cfg) gbs_tuner.add_cfg(gbs_cur_cfg) # per task launch interval time.sleep(3) # prevent no valid global batch size found if best_gbs is None: raise ValueError( "No valid global batch size found, check memory or valid search time. cur_tuner_cfg{}".format( gbs_tuner_cfg ) ) # set best global batch size to tuner cfg tuner_cfg["model_cfg"]["global_batch_size"] = best_gbs recorder.store_history("./tuner_gbs_history.csv") recorder.clean_history() end_time = time.time() ctx.logger.info( f"AtuoTuner for GBS search ends in {end_time-start_time}s." ) logger.info( f"AtuoTuner for GBS search ends in {end_time-start_time}s." ) # build AutoTuner to get new config auto_tuner = AutoTuner(tuner_cfg) logger.info( f"Launch {len(auto_tuner.algo.all_tasks)} tasks by auto tuner: " ) cur_cfg = auto_tuner.search_once() auto_tuner.add_cfg(cur_cfg) assert cur_cfg is not None, "No config can run." while cur_cfg: task_start_time = time.time() ctx = copy.deepcopy(raw_ctx) if is_first_task: ctx.max_time_per_task = warmup_time is_first_task = False # auto tuner supports dp, mp, pp, micro batch size, sharding, recompute by default and every task has own log dir global_batch_size = ( cur_cfg["global_batch_size"] if "global_batch_size" in cur_cfg else tuner_cfg["model_cfg"]["global_batch_size"] ) acc_steps = ( global_batch_size // cur_cfg["dp_degree"] // cur_cfg["sharding_degree"] // cur_cfg["micro_batch_size"] ) cur_cfg["acc_steps"] = acc_steps cur_cfg["global_batch_size"] = global_batch_size if "sharding_overlap" in cur_cfg: log_dir = "GBS{}_DP{}_MP{}_PP{}_VPP{}_Sharding{}_Stage{}_MBS{}_Recompute_{}_Granularity_{}_AccStep{}_Overlap_{}".format( global_batch_size, cur_cfg["dp_degree"], cur_cfg["mp_degree"], cur_cfg["pp_degree"], cur_cfg["vpp_degree"], cur_cfg["sharding_degree"], cur_cfg["sharding_stage"], cur_cfg["micro_batch_size"], cur_cfg["use_recompute"], cur_cfg["recompute_granularity"], cur_cfg["acc_steps"], cur_cfg["sharding_overlap"], ) else: log_dir = "GBS{}_DP{}_MP{}_PP{}_VPP{}_Sharding{}_Stage{}_MBS{}_Recompute_{}_Granularity_{}_AccStep{}".format( global_batch_size, cur_cfg["dp_degree"], cur_cfg["mp_degree"], cur_cfg["pp_degree"], cur_cfg["vpp_degree"], cur_cfg["sharding_degree"], cur_cfg["sharding_stage"], cur_cfg["micro_batch_size"], cur_cfg["use_recompute"], cur_cfg["recompute_granularity"], cur_cfg["acc_steps"], ) ctx.args.log_dir = os.path.join( os.path.dirname(ctx.args.auto_tuner_json), log_dir ) # every task has own job id job_id += 1 task_job_id = "auto_tuner_" + str(job_id) ctx.args.job_id = task_job_id # generate script args of task new_args = gen_new_args(raw_args, cur_cfg, tuner_cfg) ctx.args.training_script_args = new_args # launch task ctx.logger.info( f"Launch task: job_id {task_job_id}, log_dir {log_dir}" ) logger.info(f"Launch task: job_id {task_job_id}, log_dir {log_dir}") # in single dp estimation scene, just some nodes not all nodes run ctx = gen_new_ctx(ctx, cur_cfg, tuner_cfg) actual_nnodes = int(ctx.args.nnodes.split(":")[0]) if sorted_ips: actual_exec_ips = sorted_ips[:actual_nnodes] if ip not in actual_exec_ips: cur_cfg = client.get(f"auto_tuner/{log_dir}")[0] wait_start_time = time.time() while not cur_cfg: wait_end_time = time.time() if ( wait_end_time - wait_start_time > tuner_cfg["max_time_per_task"] + 30 ): raise ValueError(f"Wait {log_dir} failed") time.sleep(3) cur_cfg = client.get(f"auto_tuner/{log_dir}")[0] logger.info( f"Receive that task {log_dir} has ended by etcd." ) ctx.logger.info( f"Receive that task {log_dir} has ended by etcd." ) cur_cfg = json.loads(cur_cfg.decode()) auto_tuner.history_cfgs.pop(-1) auto_tuner.add_cfg(cur_cfg) recorder.add_cfg(**cur_cfg) cur_best_cfgs, err = recorder.get_best( metric=tuner_cfg['metric_cfg']['name'], direction=tuner_cfg['metric_cfg'][ 'OptimizationDirection' ], ) if not err: ctx.logger.info(f"Current best config: {cur_best_cfgs}") logger.info(f"Current best config: {cur_best_cfgs}") else: ctx.logger.info( "Get best config failed. Currently no config can be run." ) logger.info( "Get best config failed. Currently no config can be run." ) if ( "sharding_overlap" in cur_cfg and cur_cfg["sharding_overlap"] ): add_overlap_performance( cur_cfg, tuner_cfg, recorder.history ) has_error = cur_cfg["has_error"] if has_error: error_task_nums += 1 error_info = cur_cfg["error_info"] task_nums = len(auto_tuner.algo.all_tasks) cur_task_id = auto_tuner.algo.idx ctx.logger.info( "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format( cur_task_id, task_nums, cur_task_id - job_id, error_task_nums, error_info, round( (task_nums - cur_task_id) * max_time_per_task / 60, 2, ), ) ) logger.info( "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format( cur_task_id, task_nums, cur_task_id - job_id, error_task_nums, error_info, round( (task_nums - cur_task_id) * max_time_per_task / 60, 2, ), ) ) recorder.store_history(history_file_path) # generate a new config new_cfg = auto_tuner.search_once() cur_cfg = copy.deepcopy(new_cfg) auto_tuner.add_cfg(cur_cfg) continue c = controllers.init(ctx) # for single dp estimation and not run sharding overlap if tuner_cfg["search_algo"]["name"] != "grid": # estimated_num_gpus means need single dp estimation if "estimated_num_gpus" in tuner_cfg["search_algo"]: if cur_cfg["sharding_degree"] == 1: os.environ["FLAGS_shard_bypass_dygraph_optimizer"] = "1" else: os.environ["FLAGS_shard_bypass_dygraph_optimizer"] = "0" c.run() task_end_time = time.time() cur_cfg["exec_time"] = round(task_end_time - task_start_time, 2) ctx.logger.info( "Task: job_id {}, log_dir {} ended in {}s".format( task_job_id, log_dir, cur_cfg["exec_time"] ) ) logger.info( "Task: job_id {}, log_dir {} ended in {}s".format( task_job_id, log_dir, cur_cfg["exec_time"] ) ) # process generated result metric, mem, err = read_log( path=ctx.args.log_dir, metric_file="workerlog.0", target_metric=tuner_cfg["metric_cfg"]["name"], memory_file=f"{ctx.args.job_id}.gpu.log", ) # sync sigint timeout_flag = True OOM_flag = err & (1 << 1) if actual_nnodes > 1: path = f"auto_tuner/{job_id}/{ip}" if OOM_flag: while not client.put(path, "OOM".encode('latin-1')): time.sleep(1) ctx.logger.info(f"Put OOM to {path}") logger.info(f"Put OOM to {path}") elif hasattr(c, 'sigint') and c.sigint == 14: while not client.put(path, "OK".encode('latin-1')): time.sleep(1) ctx.logger.info(f"Put OK to {path}") logger.info(f"Put OK to {path}") elif not hasattr(c, 'sigint') and c.pod.exit_code == 0: while not client.put(path, "OK".encode('latin-1')): time.sleep(1) ctx.logger.info(f"Put OK to {path}") logger.info(f"Put OK to {path}") else: while not client.put(path, "Error".encode('latin-1')): time.sleep(1) ctx.logger.info(f"Put Error to {path}") logger.info(f"Put Error to {path}") result = list(client.get_prefix(f"auto_tuner/{job_id}/")) size = len(result) while size != actual_nnodes: time.sleep(1) result = list(client.get_prefix(f"auto_tuner/{job_id}/")) size = len(result) status = [i[0].decode() for i in result] ctx.logger.info(f"Status of auto_tuner/{job_id}/: {status}") logger.info(f"Status of auto_tuner/{job_id}/: {status}") if "OOM" in status: timeout_flag = False OOM_flag = True elif "OK" not in status: timeout_flag = False has_error = False if err & (1 << 0): ctx.logger.warning(f"Read metric of {log_dir} failed.") logger.warning(f"Read metric of {log_dir} failed.") # for pruner use cur_cfg['time'] = -1 cur_cfg[tuner_cfg['metric_cfg']['name']] = None cur_cfg["max_mem_usage"] = mem if not OOM_flag else "OOM" has_error = True if err & (1 << 1): ctx.logger.warning(f"{log_dir} OOM.") logger.warning(f"{log_dir} OOM.") # for pruner use cur_cfg['time'] = -1 cur_cfg[tuner_cfg['metric_cfg']['name']] = None cur_cfg["max_mem_usage"] = "OOM" has_error = True # not err & (1 << 1): do not record memory usage when out of memory if err & (1 << 2) and not err & (1 << 1): ctx.logger.warning(f"Read memory usage of {log_dir} failed.") logger.warning(f"Read memory usage of {log_dir} failed.") cur_cfg["max_mem_usage"] = None if not OOM_flag else "OOM" if not has_error and timeout_flag: # for pruner use cur_cfg['time'] = metric cur_cfg[tuner_cfg['metric_cfg']['name']] = metric cur_cfg["max_mem_usage"] = mem if not OOM_flag else "OOM" if not has_error and not timeout_flag: cur_cfg['time'] = -1 cur_cfg[tuner_cfg['metric_cfg']['name']] = None cur_cfg["max_mem_usage"] = None if not OOM_flag else "OOM" if tuner_cfg['metric_cfg']['name'] not in cur_cfg: cur_cfg[tuner_cfg['metric_cfg']['name']] = None cur_cfg['job_id'] = job_id # multi dp conversion if ( "conversion" in tuner_cfg["search_algo"] and "step_time" in tuner_cfg["search_algo"]["conversion"] and "sharding_overlap" not in cur_cfg ): single_dp_performance = cur_cfg[tuner_cfg['metric_cfg']['name']] step_time_metric = tuner_cfg["search_algo"]["conversion"][ "step_time" ] step_time = read_step_time_log( path=ctx.args.log_dir, file="workerlog.0", target_metric=step_time_metric, ) # set default comm_bw = tuner_cfg["search_algo"]["conversion"].get( "comm_bw", [100] ) model_size_b = int( tuner_cfg["search_algo"]["conversion"].get( "model_size_b", 7 ) ) amp = tuner_cfg["search_algo"]["conversion"].get("amp", False) for bw in comm_bw: if amp: comm_time = model_size_b * (4 + 2) / bw else: comm_time = model_size_b * 4 / bw multi_dp_performace = ( round( step_time / (step_time + comm_time) * single_dp_performance, 5, ) if single_dp_performance and step_time else None ) cur_cfg[ f"bw_{bw}_{tuner_cfg['metric_cfg']['name']}" ] = multi_dp_performace cur_cfg["has_error"] = has_error if has_error: error_task_nums += 1 error_info = None cur_cfg["error_info"] = error_info task_nums = len(auto_tuner.algo.all_tasks) cur_task_id = auto_tuner.algo.idx ctx.logger.info( "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format( cur_task_id, task_nums, cur_task_id - job_id, error_task_nums, error_info, round( (task_nums - cur_task_id) * max_time_per_task / 60, 2, ), ) ) logger.info( "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format( cur_task_id, task_nums, cur_task_id - job_id, error_task_nums, error_info, round( (task_nums - cur_task_id) * max_time_per_task / 60, 2, ), ) ) # sync for single dp if sorted_ips: master_ip = sorted_ips[0] if ip == master_ip: while not client.put( f"auto_tuner/{log_dir}", json.dumps(cur_cfg).encode('latin-1'), ): time.sleep(1) logger.info(f"{ip} put auto_tuner/{log_dir} successfully.") recorder.add_cfg(**cur_cfg) cur_best_cfgs, err = recorder.get_best( metric=tuner_cfg['metric_cfg']['name'], direction=tuner_cfg['metric_cfg']['OptimizationDirection'], ) if not err: ctx.logger.info(f"Current best config: {cur_best_cfgs}") logger.info(f"Current best config: {cur_best_cfgs}") else: ctx.logger.info("Get best config failed, no config can be run.") logger.info("Get best config failed, no config can be run.") # record history if "sharding_overlap" in cur_cfg and cur_cfg["sharding_overlap"]: add_overlap_performance(cur_cfg, tuner_cfg, recorder.history) recorder.store_history(history_file_path) c.finalize(exit=False) # generate a new config new_cfg = auto_tuner.search_once() cur_cfg = copy.deepcopy(new_cfg) auto_tuner.add_cfg(cur_cfg) # per task launch interval self_pid = str(os.getpid()) processes = os.popen( "fuser -v /dev/nvidia* |awk '{for(i=1;i<=NF;i++) print $i;}'" ).readlines() for process in processes: pid = str(process.strip()) if pid != self_pid: os.system("kill -9 " + pid) time.sleep(3) end_time = time.time() if max_search_time and (end_time - start_time) > int( max_search_time ): break recorder.store_history(history_file_path) # get best config to run best_cfg = None ctx = copy.deepcopy(raw_ctx) if nnodes > 1: collective_master_ip = os.environ.get("COLLECTIVE_MASTER_IP", None) assert collective_master_ip is not None if ip == collective_master_ip: best_cfg, err = recorder.get_best( metric=tuner_cfg['metric_cfg']['name'], direction=tuner_cfg['metric_cfg']['OptimizationDirection'], mode=mode, ) if err: raise ValueError( "Get best config failed. Currently there are no appropriate configs." ) data = json.dumps(best_cfg) while not client.put("best_cfg", data): time.sleep(1) continue else: for i in range(10): try: data = client.get("best_cfg")[0].decode() best_cfg = json.loads(data) except Exception as e: ctx.logger.warning(e) logger.warning(e) time.sleep(2) if best_cfg: break assert best_cfg else: best_cfg, err = recorder.get_best( metric=tuner_cfg['metric_cfg']['name'], direction=tuner_cfg['metric_cfg']['OptimizationDirection'], mode=mode, ) if err: raise ValueError( "Get best config failed. Currently there are no appropriate configs." ) assert best_cfg and best_cfg["time"] != -1 end_time = time.time() ctx.logger.info(f"AutoTuner ended in {end_time-start_time}s.") logger.info(f"AutoTuner ended in {end_time-start_time}s.") # launch best cfg # estimation search need not run best cfg if not tuner_cfg.get("run_best", True) or tuner_cfg["search_algo"].get( "estimated_num_gpus", None ): sys.exit() new_args = gen_new_args(raw_args, best_cfg, tuner_cfg, run_best=True) ctx.run_best = True ctx.args.training_script_args = new_args ctx.args.job_id = "best_cfg" ctx.logger.info(f"Launch best cfg: {best_cfg}") logger.info(f"Launch best cfg: {best_cfg}") ctx.args.log_dir = ctx.args.log_dir = os.path.join( os.path.dirname(ctx.args.auto_tuner_json), "best_cfg" ) # run best cfg c = controllers.init(ctx) c.run() c.finalize(exit=True) else: from . import controllers # initialize the selected controller c = controllers.init(ctx) # run the pods c.run() # manager or just wait pod c.finalize() if __name__ == "__main__": launch()