训练时卡死在"Checkpoints will be saved to...”

[No360201]

配置文件如下：

Copyright (c) OpenMMLab. All rights reserved.

"""Data format:

[{ "messages": [ { "role": "system", "content": "xxx." }, { "role": "user", "content": "xxx." }, { "role": "assistant", "content": "xxx.", "loss": false}, { "role": "user", "content": "xxx." }, { "role": "assistant", "content": "xxx.", "loss": true} ] }, ... ] """ # noqa: E501 import torch from datasets import load_dataset from mmengine.dataset import DefaultSampler from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, LoggerHook, ParamSchedulerHook) from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR from peft import LoraConfig from torch.optim import AdamW from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig)

from xtuner.dataset import process_hf_dataset from xtuner.dataset.collate_fns import default_collate_fn from xtuner.dataset.map_fns import openai_map_fn, template_map_fn_factory from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook, VarlenAttnArgsToMessageHubHook) from xtuner.engine.runner import TrainLoop from xtuner.model import SupervisedFinetune from xtuner.utils import PROMPT_TEMPLATE from xtuner.parallel.sequence import SequenceParallelSampler

#######################################################################

PART 1 Settings

#######################################################################

Model

pretrained_model_name_or_path = 'deepseek-ai/DeepSeek-V2-Lite-Chat' use_varlen_attn = False

Data

data_files = [数据地址]

prompt_template = PROMPT_TEMPLATE.deepseek_coder max_length = 2048 pack_to_max_length = False

parallel

sequence_parallel_size = 1

Scheduler & Optimizer

batch_size = 1 # per_device accumulative_counts = 16 # bs = 1 GPU 1 batch_size_per_device 16 acc dataloader_num_workers = 0 max_epochs = 10 optim_type = AdamW lr = 2e-4 betas = (0.9, 0.999) weight_decay = 0 max_norm = 1 # grad clip warmup_ratio = 0.03

Save

save_steps = 500 save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)

Evaluate the generation performance during the training

evaluation_freq = 100 SYSTEM = '' evaluation_inputs = [ ('写一个Python函数，将十六进制颜色代码（如#0066ee）转换为对应的' '红、绿、蓝（RGB）三个颜色分量值，并以元组的形式返回。'), ('Write a Python function that takes a hexadecimal color code ' '(e.g., #0066ee) as input and converts it into the corresponding ' 'red, green, and blue (RGB) color component values.') ]

#######################################################################

PART 2 Model & Tokenizer

####################################################################### tokenizer = dict( type=AutoTokenizer.from_pretrained, pretrained_model_name_or_path=pretrained_model_name_or_path, trust_remote_code=True, padding_side='right')

model = dict( type=SupervisedFinetune, use_varlen_attn=use_varlen_attn, llm=dict( type=AutoModelForCausalLM.from_pretrained, pretrained_model_name_or_path=pretrained_model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16, quantization_config=dict( type=BitsAndBytesConfig, load_in_4bit=True, load_in_8bit=False, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4')), lora=dict( type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.1, bias='none', task_type='CAUSAL_LM'))

#######################################################################

PART 3 Dataset & Dataloader

####################################################################### train_dataset = dict( type=process_hf_dataset, dataset=dict(type=load_dataset, path='json', data_files=data_files), tokenizer=tokenizer, max_length=max_length, dataset_map_fn=openai_map_fn, template_map_fn=dict( type=template_map_fn_factory, template=prompt_template), remove_unused_columns=True, shuffle_before_pack=True, pack_to_max_length=pack_to_max_length, use_varlen_attn=use_varlen_attn)

train_dataloader = dict( batch_size=batch_size, num_workers=dataloader_num_workers, dataset=train_dataset, sampler=dict(type=DefaultSampler, shuffle=True), collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))

#######################################################################

PART 4 Scheduler & Optimizer

#######################################################################

optimizer

optim_wrapper = dict( type=AmpOptimWrapper, optimizer=dict( type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), accumulative_counts=accumulative_counts, loss_scale='dynamic', dtype='float16')

learning policy

More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501

param_scheduler = [ dict( type=LinearLR, start_factor=1e-5, by_epoch=True, begin=0, end=warmup_ratio max_epochs, convert_to_iter_based=True), dict( type=CosineAnnealingLR, eta_min=0.0, by_epoch=True, begin=warmup_ratio max_epochs, end=max_epochs, convert_to_iter_based=True) ]

train, val, test setting

train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

#######################################################################

PART 5 Runtime

#######################################################################

Log the dialogue periodically during the training process, optional

custom_hooks = [ dict(type=DatasetInfoHook, tokenizer=tokenizer), dict( type=EvaluateChatHook, tokenizer=tokenizer, every_n_iters=evaluation_freq, evaluation_inputs=evaluation_inputs, system=SYSTEM, prompt_template=prompt_template) ]

if use_varlen_attn: custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]

configure default hooks

default_hooks = dict(

record the time of every iteration.

timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
checkpoint=dict(
    type=CheckpointHook,
    by_epoch=False,
    interval=save_steps,
    max_keep_ckpts=save_total_limit),
# set sampler seed in distributed evrionment.
sampler_seed=dict(type=DistSamplerSeedHook),

)

configure environment

env_cfg = dict(

whether to enable cudnn benchmark

cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),

)

set visualizer

visualizer = None

set log level

log_level = 'INFO'

load from which checkpoint

load_from = None

whether to resume training from the loaded checkpoint

resume = False

Defaults to use random seed and disable deterministic

randomness = dict(seed=None, deterministic=False)

set log processor

log_processor = dict(by_epoch=False)

启动脚本如下： export NCCL_IB_GID_INDEX=3 export MKL_THREADING_LAYER=GNU NPROC_PER_NODE=8 xtuner train ${CONFIG_PATH} --deepspeed deepspeed_zero3_offload --work-dir ${OUTPUTS} xtuner的版本是0.1.23 pack_to_max_length=True or False，都会卡死，请问是什么问题

[No360201]

@HIT-cwh 麻烦看一下谢谢