无法启动训练，似乎是mmengine有问题

[Dominic23331]

我在训练时输出以下内容后，程序就停止了，请问这种情况该如何解决？
`2024-05-15 09:29:44.939294: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-05-15 09:29:44.939347: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-05-15 09:29:44.940554: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[2024-05-15 09:29:49,373] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
2024-05-15 09:30:12.273661: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-05-15 09:30:12.273709: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-05-15 09:30:12.274819: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[2024-05-15 09:30:16,168] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
05/15 09:30:19 - mmengine - INFO -

System environment:
sys.platform: linux
Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 1102040617
GPU 0: B1.gpu.medium
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.140
GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.1.0a0+32f93b1
PyTorch compiling details: PyTorch built with:

• GCC 11.2

• C++ Version: 201703

• Intel(R) oneAPI Math Kernel Library Version 2021.1-Product Build 20201104 for Intel(R) 64 architecture applications

• Intel(R) MKL-DNN v2.7.3 (Git Hash N/A)

• OpenMP 201511 (a.k.a. OpenMP 4.5)

• LAPACK is enabled (usually provided by MKL)

• NNPACK is enabled

• CPU capability usage: AVX2

• CUDA Runtime 12.2

• NVCC architecture flags: -gencode;arch=compute_52,code=sm_52;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_72,code=sm_72;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_87,code=sm_87;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_90,code=compute_90

• CuDNN 8.9.5

• Magma 2.6.2

• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.2, CUDNN_VERSION=8.9.5, CXX_COMPILER=/opt/rh/gcc-toolset-11/root/usr/bin/c++, CXX_FLAGS=-fno-gnu-unique -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

  TorchVision: 0.16.0a0
  OpenCV: 4.7.0
  MMEngine: 0.10.4

Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 1102040617
deterministic: False
Distributed launcher: none
Distributed training: False
GPU number: 1

05/15 09:30:19 - mmengine - INFO - Config:
SYSTEM = 'xtuner.utils.SYSTEM_TEMPLATE.alpaca'
accumulative_counts = 16
alpaca_en = dict(
dataset=dict(path='./alpaca', type='datasets.load_dataset'),
dataset_map_fn='xtuner.dataset.map_fns.alpaca_map_fn',
max_length=2048,
pack_to_max_length=True,
remove_unused_columns=True,
shuffle_before_pack=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.chatglm3',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
encode_special_tokens=True,
padding_side='left',
pretrained_model_name_or_path='/gemini/pretrain',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset',
use_varlen_attn=False)
alpaca_en_path = './alpaca'
batch_size = 1
betas = (
0.9,
0.999,
)
custom_hooks = [
dict(
tokenizer=dict(
encode_special_tokens=True,
padding_side='left',
pretrained_model_name_or_path='/gemini/pretrain',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.DatasetInfoHook'),
dict(
evaluation_inputs=[
'请给我介绍五个上海的景点',
'Please tell me five scenic spots in Shanghai',
],
every_n_iters=500,
prompt_template='xtuner.utils.PROMPT_TEMPLATE.chatglm3',
system='xtuner.utils.SYSTEM_TEMPLATE.alpaca',
tokenizer=dict(
encode_special_tokens=True,
padding_side='left',
pretrained_model_name_or_path='/gemini/pretrain',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.EvaluateChatHook'),
]
dataloader_num_workers = 0
default_hooks = dict(
checkpoint=dict(
by_epoch=False,
interval=500,
max_keep_ckpts=2,
type='mmengine.hooks.CheckpointHook'),
logger=dict(
interval=10,
log_metric_by_epoch=False,
type='mmengine.hooks.LoggerHook'),
param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'),
sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'),
timer=dict(type='mmengine.hooks.IterTimerHook'))
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
evaluation_freq = 500
evaluation_inputs = [
'请给我介绍五个上海的景点',
'Please tell me five scenic spots in Shanghai',
]
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
lr = 0.0002
max_epochs = 3
max_length = 2048
max_norm = 1
model = dict(
llm=dict(
pretrained_model_name_or_path='/gemini/pretrain',
quantization_config=dict(
bnb_4bit_compute_dtype='torch.float16',
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
llm_int8_has_fp16_weight=False,
llm_int8_threshold=6.0,
load_in_4bit=True,
load_in_8bit=False,
type='transformers.BitsAndBytesConfig'),
torch_dtype='torch.float16',
trust_remote_code=True,
type='transformers.AutoModelForCausalLM.from_pretrained'),
lora=dict(
bias='none',
lora_alpha=16,
lora_dropout=0.1,
r=64,
task_type='CAUSAL_LM',
type='peft.LoraConfig'),
type='xtuner.model.SupervisedFinetune',
use_varlen_attn=False)
optim_type = 'torch.optim.AdamW'
optim_wrapper = dict(
accumulative_counts=16,
clip_grad=dict(error_if_nonfinite=False, max_norm=1),
dtype='float16',
loss_scale='dynamic',
optimizer=dict(
betas=(
0.9,
0.999,
),
lr=0.0002,
type='torch.optim.AdamW',
weight_decay=0),
type='mmengine.optim.AmpOptimWrapper')
pack_to_max_length = True
param_scheduler = [
dict(
begin=0,
by_epoch=True,
convert_to_iter_based=True,
end=0.09,
start_factor=1e-05,
type='mmengine.optim.LinearLR'),
dict(
begin=0.09,
by_epoch=True,
convert_to_iter_based=True,
end=3,
eta_min=0.0,
type='mmengine.optim.CosineAnnealingLR'),
]
pretrained_model_name_or_path = '/gemini/pretrain'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.chatglm3'
randomness = dict(deterministic=False, seed=None)
resume = False
save_steps = 500
save_total_limit = 2
tokenizer = dict(
encode_special_tokens=True,
padding_side='left',
pretrained_model_name_or_path='/gemini/pretrain',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained')
train_cfg = dict(max_epochs=3, type='xtuner.engine.runner.TrainLoop')
train_dataloader = dict(
batch_size=1,
collate_fn=dict(
type='xtuner.dataset.collate_fns.default_collate_fn',
use_varlen_attn=False),
dataset=dict(
dataset=dict(path='./alpaca', type='datasets.load_dataset'),
dataset_map_fn='xtuner.dataset.map_fns.alpaca_map_fn',
max_length=2048,
pack_to_max_length=True,
remove_unused_columns=True,
shuffle_before_pack=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.chatglm3',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
encode_special_tokens=True,
padding_side='left',
pretrained_model_name_or_path='/gemini/pretrain',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset',
use_varlen_attn=False),
num_workers=0,
sampler=dict(shuffle=True, type='mmengine.dataset.DefaultSampler'))
use_varlen_attn = False
visualizer = None
warmup_ratio = 0.03
weight_decay = 0
work_dir = './work_dirs/chatglm3_6b_base_qlora_alpaca_e3_copy'

quantization_config convert to <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>
05/15 09:30:19 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
low_cpu_mem_usage was None, now set to True since model is quantized.`

[hhaAndroid]

请问解决了吗？你贴的信息有点多，麻烦可以说一下是哪个配置文件？启动命令？以及你是否哪里做了修改吗