From ff0fa7659f148bb45e3086e4e3b1abecdfb3048a Mon Sep 17 00:00:00 2001 From: huangting4201 <1538303371@qq.com> Date: Tue, 8 Aug 2023 11:18:15 +0800 Subject: [PATCH] feat(monitor): support monitor and alert (#175) * feat(monitor): support monitor and alert * feat(monitor.py): fix demo error * feat(monitor.py): move cmd monitor args to config file * feat(hybrid_zero_optim.py): if overflow occurs send alert msg * feat(monitor.py): remove alert msg filter * feat(monitor.py): optimize class MonitorTracker * feat(monitor.py): optimize code * feat(monitor.py): optimize code * feat(monitor.py): optimize code * feat(monitor.py): optimize code * feat(train.py): update print to log * style(ci): fix lint error * fix(utils/evaluation.py): remove useless code * fix(model/modeling_internlm.py): fix lint error --------- Co-authored-by: huangting4201 <huangting3@sensetime.com> --- internlm/initialize/launch.py | 8 +- internlm/model/embedding.py | 4 +- internlm/model/linear.py | 20 +- internlm/model/modeling_internlm.py | 3 +- internlm/model/utils.py | 17 +- internlm/monitor/__init__.py | 4 + internlm/monitor/alert.py | 53 ++++ internlm/monitor/monitor.py | 226 ++++++++++++++++++ internlm/monitor/utils.py | 32 +++ .../solver/optimizer/hybrid_zero_optim.py | 2 + internlm/utils/common.py | 12 - internlm/utils/evaluation.py | 17 +- train.py | 50 +++- 13 files changed, 399 insertions(+), 49 deletions(-) create mode 100644 internlm/monitor/__init__.py create mode 100644 internlm/monitor/alert.py create mode 100644 internlm/monitor/monitor.py create mode 100644 internlm/monitor/utils.py diff --git a/internlm/initialize/launch.py b/internlm/initialize/launch.py index 1f60adc..dee6ffd 100644 --- a/internlm/initialize/launch.py +++ b/internlm/initialize/launch.py @@ -202,7 +202,13 @@ def args_sanity_check(): if "sequence_parallel" not in gpc.config.model: gpc.config.model._add_item("sequence_parallel", False) else: - assert not (gpc.config.model.sequence_parallel is True and gpc.config.model.use_flash_attn is False), "sequence parallel does not support use_flash_attn=False" + assert not ( + gpc.config.model.sequence_parallel is True and gpc.config.model.use_flash_attn is False + ), "sequence parallel does not support use_flash_attn=False" + + # feishu webhook address for alerting + if "alert_address" not in gpc.config: + gpc.config._add_item("alert_address", None) def launch( diff --git a/internlm/model/embedding.py b/internlm/model/embedding.py index 0951ccd..d35b9c1 100644 --- a/internlm/model/embedding.py +++ b/internlm/model/embedding.py @@ -55,10 +55,10 @@ class Embedding1D(nn.Module): output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) output = gather_forward_split_backward(output_parallel, ParallelMode.TENSOR, dim=-1) - + if gpc.config.model.sequence_parallel: output = split_forward_gather_backward(output, ParallelMode.TENSOR, dim=1) - + return output diff --git a/internlm/model/linear.py b/internlm/model/linear.py index 2fa249c..50b4bf0 100644 --- a/internlm/model/linear.py +++ b/internlm/model/linear.py @@ -58,7 +58,11 @@ class ScaleColumnParallelLinear(nn.Linear): else: weight = self.weight return fused_dense_func_torch( - input, weight, self.bias, process_group=self.process_group, sequence_parallel=gpc.config.model.sequence_parallel + input, + weight, + self.bias, + process_group=self.process_group, + sequence_parallel=gpc.config.model.sequence_parallel, ) @@ -103,7 +107,11 @@ class RewardModelLinear(ScaleColumnParallelLinear): else: weight = self.weight return fused_dense_func_torch( - input, weight, self.bias, process_group=self.process_group, sequence_parallel=gpc.config.model.sequence_parallel + input, + weight, + self.bias, + process_group=self.process_group, + sequence_parallel=gpc.config.model.sequence_parallel, ) @@ -170,7 +178,13 @@ class FeedForward(nn.Module): dtype=dtype, ) self.w2 = ColumnParallelLinearTorch( - in_features, hidden_features, process_group, bias, sequence_parallel=gpc.config.model.sequence_parallel, device=device, dtype=dtype + in_features, + hidden_features, + process_group, + bias, + sequence_parallel=gpc.config.model.sequence_parallel, + device=device, + dtype=dtype, ) self.w3 = RowParallelLinearTorch( hidden_features, diff --git a/internlm/model/modeling_internlm.py b/internlm/model/modeling_internlm.py index 31138fa..4a7a4ee 100644 --- a/internlm/model/modeling_internlm.py +++ b/internlm/model/modeling_internlm.py @@ -31,6 +31,7 @@ MODEL_TYPE = "INTERNLM" logger = get_logger(__file__) RMSNorm = try_import_RMSNorm() + class PackedFlashBaseLayer1D(nn.Module): """ 1D Packed Flash Base Layer. @@ -461,7 +462,7 @@ def build_model_with_cfg( use_scaled_init: bool = True, use_swiglu: bool = True, use_flash_attn: bool = True, - sequence_parallel: bool = False, + sequence_parallel: bool = False, # pylint: disable=W0613 ): """ Builde model with config diff --git a/internlm/model/utils.py b/internlm/model/utils.py index a84f058..8b80af2 100644 --- a/internlm/model/utils.py +++ b/internlm/model/utils.py @@ -16,6 +16,9 @@ from torch.cuda.amp import custom_bwd from torch.distributed import ProcessGroup from internlm.core.context import global_context as gpc +from internlm.utils.logger import get_logger + +logger = get_logger(__file__) def _split(input_, parallel_mode, dim=-1): @@ -84,6 +87,7 @@ class _GatherForwardSplitBackward(torch.autograd.Function): def gather_forward_split_backward(input_, parallel_mode, dim): return _GatherForwardSplitBackward.apply(input_, parallel_mode, dim) + def linear_bias_wgrad_torch(input, grad_output, has_d_bias): assert input.dtype == grad_output.dtype grad_weight = torch.matmul(grad_output.t(), input) @@ -157,10 +161,11 @@ def fused_dense_func_torch( else: return FusedDenseFuncTorch.apply(x, weight, bias, return_residual, process_group, sequence_parallel) + class _SplitForwardGatherBackward(torch.autograd.Function): """ Split the input and keep only the corresponding chuck to the rank. - + Args: input_: input matrix. parallel_mode: parallel mode. @@ -180,7 +185,7 @@ class _SplitForwardGatherBackward(torch.autograd.Function): @staticmethod def backward(ctx, grad_output): return _gather(grad_output, ctx.mode, ctx.dim), None, None - + def split_forward_gather_backward(input_, parallel_mode, dim): return _SplitForwardGatherBackward.apply(input_, parallel_mode, dim) @@ -189,14 +194,14 @@ def split_forward_gather_backward(input_, parallel_mode, dim): def try_import_RMSNorm(): """ Try import MixFusedRMSNorm from apex, if failed, return our RMSNorm - + """ try: from apex.normalization.fused_layer_norm import MixedFusedRMSNorm as RMSNorm + return RMSNorm - except ModuleNotFoundError as e: - from internlm.utils.logger import get_logger - logger = get_logger(__file__) + except ModuleNotFoundError: logger.warn("The torch implementation for MixFusedRMSNorm is slower than apex. Please note this!") from internlm.model.norm import RMSNormTorch as RMSNorm + return RMSNorm diff --git a/internlm/monitor/__init__.py b/internlm/monitor/__init__.py new file mode 100644 index 0000000..b100cde --- /dev/null +++ b/internlm/monitor/__init__.py @@ -0,0 +1,4 @@ +from .monitor import initialize_monitor_manager, send_alert_message +from .utils import set_env_var + +__all__ = ["send_alert_message", "initialize_monitor_manager", "set_env_var"] diff --git a/internlm/monitor/alert.py b/internlm/monitor/alert.py new file mode 100644 index 0000000..78b6040 --- /dev/null +++ b/internlm/monitor/alert.py @@ -0,0 +1,53 @@ +import json +import time + +import requests + + +def send_feishu_msg_with_webhook(webhook: str, title: str, message: str): + """ + Use Feishu robot to send messages with the given webhook. + + Args: + webhook (str): The webhook to be used to send message. + title (str): The message title. + message (str): The message body. + + Returns: + The response from the request. Or catch the exception and return None. + + Raises: + Exception: An exception rasied by the HTTP post request. + + """ + + headers = {"Content-Type": "application/json;charset=utf-8"} + msg_body = { + "timestamp": int(time.time()), + "msg_type": "post", + "content": { + "post": { + "zh_cn": { + "title": title, + "content": [ + [ + { + "tag": "text", + "text": message, + }, + ], + ], + }, + }, + }, + } + + try: + res = requests.post(webhook, data=json.dumps(msg_body), headers=headers, timeout=30) + res = res.json() + print(f"Feishu webhook response: {res}") + except Exception as err: # pylint: disable=W0703 + print(f"HTTP Post error: {err}") + res = None + + return res diff --git a/internlm/monitor/monitor.py b/internlm/monitor/monitor.py new file mode 100644 index 0000000..ca5cf55 --- /dev/null +++ b/internlm/monitor/monitor.py @@ -0,0 +1,226 @@ +import os +import signal +import socket +import time +from contextlib import contextmanager +from threading import Thread + +from internlm.core.context import global_context as gpc +from internlm.monitor.alert import send_feishu_msg_with_webhook +from internlm.utils.common import SingletonMeta + +from .utils import get_job_key, set_env_var + + +def send_alert_message(address: str = None, title: str = None, message: str = None): + """ + Send alert messages to the given Feishu webhook address in log rank. + + Args: + address (str): The alert address to be used to send message, defaults to None. + title (str): The message title, defaults to None. + message (str): The message body, defaults to None. + """ + + if address is not None and gpc.is_rank_for_log(): + send_feishu_msg_with_webhook( + webhook=address, + title=title if title else get_job_key(), + message=message, + ) + + +class MonitorTracker(Thread): + """ + Track job status and alert to Feishu during job training. + + Args: + alert_address (str): The Feishu webhook address for sending alerting messages. + check_interval (float): The interval in seconds for monitoring checks. Defaults to 300. + loss_spike_limit (float): The threshold for detecting loss value spikes. Defaults to 1.5. + """ + + def __init__( + self, + alert_address: str, + check_interval: float = 300, + loss_spike_limit: float = 1.5, + ): + super().__init__() + self.alert_address = alert_address + self.check_interval = check_interval + self.loss_spike_limit = loss_spike_limit + self.last_active_time = -1 + self.last_loss_value = -1 + self.stopped = False + self.start() + + def run(self): + """ + start the monitor tracker. + """ + + while not self.stopped: + try: + self._check_stuck() + self._check_loss_spike() + except Exception: + continue + time.sleep(self.check_interval) + + def _check_stuck(self): + """ + Check training status for potential stuck condition. + """ + + new_active_time = -1 + if os.getenv("LAST_ACTIVE_TIMESTAMP") is not None: + new_active_time = os.getenv("LAST_ACTIVE_TIMESTAMP") + if int(new_active_time) <= int(self.last_active_time) and new_active_time != -1: + self._send_alert("Training may be in stuck status, please check it.") + self.last_active_time = new_active_time + + def _check_loss_spike(self): + """ + Check for loss value spikes. + """ + + if gpc.is_rank_for_log(): + new_loss_value = -1 + new_step_id = -1 + if os.getenv("LOSS") is not None: + new_loss_value = os.getenv("LOSS") + if os.getenv("STEP_ID") is not None: + new_step_id = os.getenv("STEP_ID") + + if (float(new_loss_value) / float(self.last_loss_value)) > self.loss_spike_limit and new_loss_value != -1: + assert int(new_step_id) >= 0 + self._send_alert( + f"Checking periodically: Loss spike may be happened in step {new_step_id}, " + f"loss value from {self.last_loss_value} to {new_loss_value}, please check it." + ) + + self.last_loss_value = new_loss_value + + def _send_alert(self, message): + """ + Send alerting message to the Feishu webhook address. + + Args: + message (str): The alerting message to be sent. + """ + + send_alert_message( + address=self.alert_address, + message=message, + ) + + def stop(self): + """ + Stop the monitor tracker. + """ + + self.stopped = True + + +class MonitorManager(metaclass=SingletonMeta): + """ + Monitor Manager for managing monitor thread and monitoring training status. + """ + + def __init__(self, loss_spike_limit: float = 1.5) -> None: + self.monitor_thread = None + self.loss_spike_limit = loss_spike_limit + self.last_step_loss = -1 + + def monitor_loss_spike(self, alert_address: str = None, step_count: int = 0, cur_step_loss: float = 0.0): + """Check loss value, if loss spike occurs, send alert message to Feishu.""" + set_env_var(key="LOSS", value=cur_step_loss) + set_env_var(key="STEP_ID", value=step_count) + + if self.last_step_loss != -1 and cur_step_loss > self.loss_spike_limit * self.last_step_loss: + send_alert_message( + address=alert_address, + message=( + f"Checking step by step: Loss spike may be happened in step {step_count}, " + f"loss value from {self.last_step_loss} to {cur_step_loss}, please check it." + ), + ) + self.last_step_loss = cur_step_loss + + def monitor_exception(self, alert_address: str = None, excp_info: str = None): + """Catch and format exception information, send alert message to Feishu.""" + filtered_trace = excp_info.split("\n")[-10:] + format_trace = "" + for line in filtered_trace: + format_trace += "\n" + line + send_alert_message( + address=alert_address, + message=f"Catch Exception from {socket.gethostname()} with rank id {gpc.get_global_rank()}:{format_trace}", + ) + + def handle_sigterm(self, alert_address: str = None): + """Catch SIGTERM signal, and send alert message to Feishu.""" + + def sigterm_handler(sys_signal, frame): + print("receive frame: ", frame) + print("receive signal: ", sys_signal) + send_alert_message( + address=alert_address, + message=f"Process received signal {signal} and exited.", + ) + + signal.signal(signal.SIGTERM, sigterm_handler) + + def start_monitor( + self, + job_name: str, + alert_address: str, + monitor_interval_seconds: int = 300, + loss_spike_limit: float = 1.5, + ): + """ + Initialize and start monitor thread for checking training job status, loss spike and so on. + + Args: + job_name (str): The training job name. + alert_address (str): The Feishu webhook address for sending alert messages. + monitor_interval_seconds (int): The time of monitor interval in seconds, defaults to 300. + loss_spike_limit (float): The limit multiple of current loss to previous loss value, which means loss spike + may be occurs, defaults to 1.5. + """ + + # initialize some variables for monitoring + set_env_var(key="JOB_NAME", value=job_name) + + # start a monitor thread, periodically check the training status + self.monitor_thread = MonitorTracker( + alert_address=alert_address, + check_interval=monitor_interval_seconds, + loss_spike_limit=loss_spike_limit, + ) + + def stop_monitor(self): + """Stop the monitor and alert thread.""" + if self.monitor_thread is not None: + self.monitor_thread.stop() + + +monitor_manager = MonitorManager() + + +@contextmanager +def initialize_monitor_manager(job_name: str = None, alert_address: str = None): + if alert_address is not None: + try: + monitor_manager.start_monitor(job_name=job_name, alert_address=alert_address) + monitor_manager.handle_sigterm(alert_address=alert_address) + send_alert_message(address=alert_address, message=f"Training in {socket.gethostname()} is starting.") + yield + finally: + send_alert_message( + address=gpc.config.alert_address, message=f"Training in {socket.gethostname()} completed." + ) + monitor_manager.stop_monitor() + else: + yield diff --git a/internlm/monitor/utils.py b/internlm/monitor/utils.py new file mode 100644 index 0000000..f64c7dc --- /dev/null +++ b/internlm/monitor/utils.py @@ -0,0 +1,32 @@ +import os +from datetime import datetime + + +def now_time(): + return datetime.now().strftime("%b%d_%H-%M-%S") + + +def set_env_var(key, value): + os.environ[str(key)] = str(value) + + +def get_job_id(): + job_id = "none" + if os.getenv("SLURM_JOB_ID") is not None: + job_id = os.getenv("SLURM_JOB_ID") + elif os.getenv("K8S_WORKSPACE_ID") is not None: + job_id = os.getenv("K8S_WORKSPACE_ID") + + return job_id + + +def get_job_name(): + job_name = f"unknown-{now_time()}" + if os.getenv("JOB_NAME") is not None: + job_name = os.getenv("JOB_NAME") + + return job_name + + +def get_job_key(): + return f"{get_job_id()}_{get_job_name()}" diff --git a/internlm/solver/optimizer/hybrid_zero_optim.py b/internlm/solver/optimizer/hybrid_zero_optim.py index 116ffc2..618b772 100644 --- a/internlm/solver/optimizer/hybrid_zero_optim.py +++ b/internlm/solver/optimizer/hybrid_zero_optim.py @@ -28,6 +28,7 @@ from internlm.solver.optimizer.utils import ( from internlm.utils.common import get_current_device from internlm.utils.logger import get_logger from internlm.utils.megatron_timers import megatron_timer as timer +from internlm.monitor import send_alert_message from .utils import compute_norm @@ -542,6 +543,7 @@ class HybridZeroOptimizer(BaseOptimizer): if found_inf: if gpc.is_rank_for_log(): logger.warning("Overflow occurs, please check it.") + send_alert_message(address=gpc.config.alert_address, message="Overflow occurs, please check it.") self._grad_store._averaged_gradients = dict() self.zero_grad() return False, None diff --git a/internlm/utils/common.py b/internlm/utils/common.py index 584078f..d479284 100644 --- a/internlm/utils/common.py +++ b/internlm/utils/common.py @@ -34,18 +34,6 @@ def get_master_node(): return result -def get_process_rank(): - proc_rank = -1 - if os.getenv("SLURM_PROCID") is not None: - proc_rank = int(os.getenv("SLURM_PROCID")) - elif os.getenv("RANK") is not None: - # In k8s env, we use $RANK. - proc_rank = int(os.getenv("RANK")) - - # assert proc_rank != -1, "get_process_rank cant't get right process rank!" - return proc_rank - - def move_norm_to_cuda(norm: Union[float, torch.Tensor]) -> Union[float, torch.Tensor]: if torch.is_tensor(norm) and norm.device.type != "cuda": norm = norm.to(torch.cuda.current_device()) diff --git a/internlm/utils/evaluation.py b/internlm/utils/evaluation.py index 8424e16..d10f0c1 100644 --- a/internlm/utils/evaluation.py +++ b/internlm/utils/evaluation.py @@ -6,8 +6,8 @@ from tqdm import tqdm from internlm.core.context import ParallelMode from internlm.core.context import global_context as gpc -from internlm.model.metrics import AccPerplex from internlm.core.scheduler import SchedulerMetricHook +from internlm.model.metrics import AccPerplex @contextmanager @@ -90,15 +90,9 @@ def evaluate_on_val_dls( total_val_bsz = len(batch[1]) assert total_val_bsz % data_cfg.micro_bsz == 0 num_microbatches = total_val_bsz // data_cfg.micro_bsz - if gpc.config.model.sequence_parallel: - sequence_world_size = gpc.get_world_size(ParallelMode.TENSOR) - tensor_shape = torch.Size( - [data_cfg.micro_bsz, batch[0]["input_ids"].shape[1] // sequence_world_size, gpc.config.HIDDEN_SIZE] - ) - else: - tensor_shape = torch.Size( - [data_cfg.micro_bsz, batch[0]["input_ids"].shape[1], gpc.config.HIDDEN_SIZE] - ) + tensor_shape = torch.Size( + [data_cfg.micro_bsz, batch[0]["input_ids"].shape[1], gpc.config.HIDDEN_SIZE] + ) with switch_evaluation_pipeline_scheduler( trainer=trainer, @@ -114,7 +108,6 @@ def evaluate_on_val_dls( assert total_val_bsz % data_cfg.micro_bsz == 0 grad_accum_size = total_val_bsz // data_cfg.micro_bsz grad_accum_batch_size = data_cfg.micro_bsz - # import pdb; pdb.set_trace() with switch_evaluation_no_pipeline_scheduler( trainer=trainer, grad_accum_size=grad_accum_size, @@ -170,4 +163,4 @@ def switch_sequence_parallel_mode(): gpc.config.model.sequence_parallel = False yield finally: - gpc.config.model.sequence_parallel = prev_mode \ No newline at end of file + gpc.config.model.sequence_parallel = prev_mode diff --git a/train.py b/train.py index 59729e7..fa8d130 100644 --- a/train.py +++ b/train.py @@ -30,6 +30,8 @@ from internlm.data.packed_dataset import ( from internlm.data.utils import DATASET_TYPE_IDS_MAP, unpack_data from internlm.model.loss import FlashGPTLMLoss from internlm.model.metrics import AccPerplex +from internlm.monitor import initialize_monitor_manager, send_alert_message, set_env_var +from internlm.monitor.monitor import monitor_manager as mm from internlm.solver.beta2_scheduler import Beta2Scheduler from internlm.solver.lr_scheduler import FineTuneCosineAnnealingWarmupLR from internlm.solver.optimizer import HybridZeroOptimizer @@ -37,7 +39,6 @@ from internlm.utils.common import ( BatchSkipper, get_master_node, get_megatron_flops, - get_process_rank, launch_time, parse_args, ) @@ -92,6 +93,15 @@ def initialize_distributed_env(config: str, launcher: str = "slurm", master_port def initialize_llm_logger(start_time: str): + """ + Initialize customed uniscale logger. + + Args: + start_time (str): The launch time of current training job. + + Returns: The instance of uniscale logger. + """ + uniscale_logger = initialize_uniscale_logger( job_name=gpc.config.JOB_NAME, launch_time=start_time, file_name=get_parallel_log_file_name() ) @@ -213,6 +223,8 @@ def get_train_data_loader(num_worker: int = 0): def get_validation_data_loader(num_worker: int = 0): + """Generate and return the validation data loader.""" + data_cfg = gpc.config.data if not data_cfg.valid_folder: @@ -327,6 +339,8 @@ def record_current_batch_training_metrics( Print some training metrics of current batch. """ + set_env_var(key="LAST_ACTIVE_TIMESTAMP", value=int(time.time())) + if success_update in (0, True): train_state.num_consumed_tokens += batch[1].nelement() * gpc.get_world_size(ParallelMode.DATA) if is_no_pp_or_last_stage(): @@ -405,12 +419,11 @@ def record_current_batch_training_metrics( else: logger.info(line) + # if loss spike occurs, send alert info to feishu + mm.monitor_loss_spike(alert_address=gpc.config.alert_address, step_count=batch_count, cur_step_loss=loss.item()) + def main(args): - # initialize distributed environment - initialize_distributed_env(config=args.config, launcher=args.launcher, master_port=args.port, seed=args.seed) - assert hasattr(gpc, "config") and gpc.config is not None - # init setting skip_batches = gpc.config.data.skip_batches total_steps = gpc.config.data.total_steps @@ -477,8 +490,8 @@ def main(args): model_load_path = load_model_only_folder else: logger.info( - f"===========New Run {current_time} on host:{socket.gethostname()}," - f"tp:{gpc.get_local_rank(ParallelMode.TENSOR)},pp={gpc.get_local_rank(ParallelMode.PIPELINE)}," + f"===========New Run {current_time} on host:{socket.gethostname()},rank={gpc.get_global_rank()}," + f"tp={gpc.get_local_rank(ParallelMode.TENSOR)},pp={gpc.get_local_rank(ParallelMode.PIPELINE)}," f"dp={gpc.get_local_rank(ParallelMode.DATA)}===========" ) @@ -594,6 +607,9 @@ def main(args): train_state.inf_nan_skip_batches += 1 # record the amount of updating parameters unsuccessfully. if grad_norm == -99.0 and gpc.is_rank_for_log(): # -99.0 encodes a specific failure case logger.warning(f"Warning: skip parameter update at step {batch_count}.") + send_alert_message( + address=gpc.config.alert_address, message=f"Warning: skip parameter update at step {batch_count}." + ) # calculate and record the training metrics, eg. loss, accuracy and so on. record_current_batch_training_metrics( @@ -646,9 +662,19 @@ def main(args): if __name__ == "__main__": args = parse_args() + hostname = socket.gethostname() - try: - main(args) - except Exception: - print(f"Raise exception from {socket.gethostname()} with proc id: {get_process_rank()}") - traceback.print_exc() + # initialize distributed environment + initialize_distributed_env(config=args.config, launcher=args.launcher, master_port=args.port, seed=args.seed) + assert hasattr(gpc, "config") and gpc.config is not None + + # initialize monitor manager context + with initialize_monitor_manager(job_name=gpc.config.JOB_NAME, alert_address=gpc.config.alert_address): + try: + main(args) + except Exception: + logger.error( + f"Raise exception from {hostname} with rank id: {gpc.get_global_rank()}", + exc_info=traceback.format_exc(), + ) + mm.monitor_exception(alert_address=gpc.config.alert_address, excp_info=traceback.format_exc())