importlib.metadata.PackageNotFoundError: No package metadata was found for xtuner

反复按照教程安装出现这个问题：
(xtuner0.1.9) root@intern-studio:~/xtuner019/xtuner# xtuner
Traceback (most recent call last):
File "/root/.local/bin/xtuner", line 33, in
sys.exit(load_entry_point('xtuner', 'console_scripts', 'xtuner')())
File "/root/.local/bin/xtuner", line 22, in importlib_load_entry_point
for entry_point in distribution(dist_name).entry_points
File "/share/conda_envs/internlm-base/lib/python3.10/importlib/metadata/init.py", line 969, in
distribution return Distribution.from_name(distribution_name)
File "/share/conda_envs/internlm-base/lib/python3.10/importlib/metadata/init.py", line 548, in
from_name raise PackageNotFoundError(name)
importlib.metadata.PackageNotFoundError: No package metadata was found for xtuner

之前可以训练，后来更换参数和数据报以下错误，就直接尝试在base环境里安装就出现上面的错误。
nohup: ignoring input
[2024-01-11 19:09:22,787] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-01-11 19:10:00,577] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
01/11 19:10:22 - mmengine - INFO -

System environment:
sys.platform: linux
Python: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 716082487
GPU 0: NVIDIA A100-SXM4-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.7, V11.7.99
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.0.1
PyTorch compiling details: PyTorch built with:

• GCC 9.3

• C++ Version: 201703

• Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications

• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)

• OpenMP 201511 (a.k.a. OpenMP 4.5)

• LAPACK is enabled (usually provided by MKL)

• NNPACK is enabled

• CPU capability usage: AVX2

• CUDA Runtime 11.7

• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37

• CuDNN 8.5

• Magma 2.6.1

• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -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=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -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=2.0.1, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

  TorchVision: 0.15.2
  OpenCV: 4.9.0
  MMEngine: 0.10.2

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

01/11 19:10:22 - mmengine - INFO - Config:
SYSTEM = ''
accumulative_counts = 16
batch_size = 1
betas = (
0.9,
0.999,
)
custom_hooks = [
dict(
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/model/Shanghai_AI_Laboratory/internlm-chat-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.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.internlm_chat',
system='',
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/model/Shanghai_AI_Laboratory/internlm-chat-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.EvaluateChatHook'),
]
data_path = '/root/code/xturn/grade-school-math/grade_school_math/data/new'
dataloader_num_workers = 0
default_hooks = dict(
checkpoint=dict(interval=1, type='mmengine.hooks.CheckpointHook'),
logger=dict(interval=10, 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'
lr = 0.0002
max_epochs = 3
max_length = 2048
max_norm = 1
model = dict(
llm=dict(
pretrained_model_name_or_path=
'/root/model/Shanghai_AI_Laboratory/internlm-chat-7b',
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')
optim_type = 'bitsandbytes.optim.PagedAdamW32bit'
optim_wrapper = dict(
optimizer=dict(
betas=(
0.9,
0.999,
),
lr=0.0002,
type='bitsandbytes.optim.PagedAdamW32bit',
weight_decay=0),
type='DeepSpeedOptimWrapper')
pack_to_max_length = True
param_scheduler = dict(
T_max=3,
by_epoch=True,
convert_to_iter_based=True,
eta_min=0.0,
type='mmengine.optim.CosineAnnealingLR')
pretrained_model_name_or_path = '/root/model/Shanghai_AI_Laboratory/internlm-chat-7b'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.internlm_chat'
randomness = dict(deterministic=False, seed=None)
resume = False
runner_type = 'FlexibleRunner'
strategy = dict(
config=dict(
bf16=dict(enabled=True),
fp16=dict(enabled=False, initial_scale_power=16),
gradient_accumulation_steps='auto',
gradient_clipping='auto',
train_micro_batch_size_per_gpu='auto',
zero_allow_untested_optimizer=True,
zero_force_ds_cpu_optimizer=False,
zero_optimization=dict(overlap_comm=True, stage=2)),
exclude_frozen_parameters=True,
gradient_accumulation_steps=16,
gradient_clipping=1,
train_micro_batch_size_per_gpu=1,
type='DeepSpeedStrategy')
tokenizer = dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/model/Shanghai_AI_Laboratory/internlm-chat-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained')
train_cfg = dict(by_epoch=True, max_epochs=3, val_interval=1)
train_dataloader = dict(
batch_size=1,
collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'),
dataset=dict(
dataset=dict(
path=
'/root/code/xturn/grade-school-math/grade_school_math/data/new',
type='datasets.load_dataset'),
dataset_map_fn='xtuner.dataset.map_fns.oasst1_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.internlm_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/model/Shanghai_AI_Laboratory/internlm-chat-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset'),
num_workers=0,
sampler=dict(shuffle=True, type='mmengine.dataset.DefaultSampler'))
train_dataset = dict(
dataset=dict(
path='/root/code/xturn/grade-school-math/grade_school_math/data/new',
type='datasets.load_dataset'),
dataset_map_fn='xtuner.dataset.map_fns.oasst1_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.internlm_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/model/Shanghai_AI_Laboratory/internlm-chat-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset')
visualizer = None
weight_decay = 0
work_dir = './work_dirs/internlm_chat_7b_qlora_oasst1_e3_copy'

01/11 19:10:23 - 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.
01/11 19:10:24 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook

before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DatasetInfoHook
(NORMAL ) EvaluateChatHook
(VERY_LOW ) CheckpointHook

before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook

before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook

after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) EvaluateChatHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

before_val:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) DatasetInfoHook

before_val_epoch:
(NORMAL ) IterTimerHook

before_val_iter:
(NORMAL ) IterTimerHook

after_val_iter:
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

after_val:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) EvaluateChatHook

after_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) EvaluateChatHook
(VERY_LOW ) CheckpointHook

before_test:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) DatasetInfoHook

before_test_epoch:
(NORMAL ) IterTimerHook

before_test_iter:
(NORMAL ) IterTimerHook

after_test_iter:
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_test:
(VERY_HIGH ) RuntimeInfoHook

after_run:
(BELOW_NORMAL) LoggerHook

01/11 19:10:26 - mmengine - WARNING - Dataset Dataset has no metainfo. dataset_meta in visualizer will be None.
quantization_config convert to <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>

Loading checkpoint shards: 0%| | 0/8 [00:00<?, ?it/s]
Loading checkpoint shards: 12%|█▎ | 1/8 [00:03<00:23, 3.34s/it]
Loading checkpoint shards: 25%|██▌ | 2/8 [00:06<00:19, 3.30s/it]
Loading checkpoint shards: 38%|███▊ | 3/8 [00:09<00:16, 3.25s/it]
Loading checkpoint shards: 50%|█████ | 4/8 [00:12<00:12, 3.17s/it]
Loading checkpoint shards: 62%|██████▎ | 5/8 [00:16<00:09, 3.20s/it]
Loading checkpoint shards: 75%|███████▌ | 6/8 [00:20<00:07, 3.65s/it]
Loading checkpoint shards: 88%|████████▊ | 7/8 [00:23<00:03, 3.43s/it]
Loading checkpoint shards: 100%|██████████| 8/8 [00:24<00:00, 2.55s/it]
Loading checkpoint shards: 100%|██████████| 8/8 [00:24<00:00, 3.04s/it]
01/11 19:10:53 - mmengine - INFO - dispatch internlm attn forward
01/11 19:10:53 - mmengine - WARNING - Due to the implementation of the PyTorch version of flash attention, even when the output_attentions flag is set to True, it is not possible to return the attn_weights.
[2024-01-11 19:11:48,486] [INFO] [logging.py:96:log_dist] [Rank -1] DeepSpeed info: version=0.12.6, git-hash=unknown, git-branch=unknown
[2024-01-11 19:11:48,486] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-01-11 19:11:48,486] [INFO] [comm.py:652:init_distributed] Not using the DeepSpeed or dist launchers, attempting to detect MPI environment...
[2024-01-11 19:11:48,653] [INFO] [comm.py:702:mpi_discovery] Discovered MPI settings of world_rank=0, local_rank=0, world_size=1, master_addr=192.168.237.56, master_port=29500
[2024-01-11 19:11:48,653] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
[2024-01-11 19:11:49,014] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
[2024-01-11 19:11:49,019] [INFO] [logging.py:96:log_dist] [Rank 0] Using client Optimizer as basic optimizer
[2024-01-11 19:11:49,019] [INFO] [logging.py:96:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
[2024-01-11 19:11:49,089] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = PagedAdamW32bit
[2024-01-11 19:11:49,089] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=PagedAdamW32bit type=<class 'bitsandbytes.optim.adamw.PagedAdamW32bit'>
[2024-01-11 19:11:49,089] [WARNING] [engine.py:1166:do_optimizer_sanity_check] **** You are using ZeRO with an untested optimizer, proceed with caution *****
[2024-01-11 19:11:49,089] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
[2024-01-11 19:11:49,090] [INFO] [stage_1_and_2.py:148:init] Reduce bucket size 500,000,000
[2024-01-11 19:11:49,090] [INFO] [stage_1_and_2.py:149:init] Allgather bucket size 500,000,000
[2024-01-11 19:11:49,090] [INFO] [stage_1_and_2.py:150:init] CPU Offload: False
[2024-01-11 19:11:49,090] [INFO] [stage_1_and_2.py:151:init] Round robin gradient partitioning: False
[2024-01-11 19:11:51,497] [INFO] [utils.py:791:see_memory_usage] Before initializing optimizer states
[2024-01-11 19:11:51,498] [INFO] [utils.py:792:see_memory_usage] MA 5.63 GB Max_MA 5.93 GB CA 6.31 GB Max_CA 6 GB
[2024-01-11 19:11:51,498] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory: used = 105.09 GB, percent = 5.2%
[2024-01-11 19:11:51,748] [INFO] [utils.py:791:see_memory_usage] After initializing optimizer states
[2024-01-11 19:11:51,748] [INFO] [utils.py:792:see_memory_usage] MA 5.63 GB Max_MA 6.23 GB CA 6.91 GB Max_CA 7 GB
[2024-01-11 19:11:51,749] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory: used = 105.32 GB, percent = 5.2%
[2024-01-11 19:11:51,749] [INFO] [stage_1_and_2.py:516:init] optimizer state initialized
[2024-01-11 19:11:51,870] [INFO] [utils.py:791:see_memory_usage] After initializing ZeRO optimizer
[2024-01-11 19:11:51,871] [INFO] [utils.py:792:see_memory_usage] MA 5.63 GB Max_MA 5.63 GB CA 6.91 GB Max_CA 7 GB
[2024-01-11 19:11:51,871] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory: used = 105.39 GB, percent = 5.2%
[2024-01-11 19:11:51,882] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = PagedAdamW32bit
[2024-01-11 19:11:51,882] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using client LR scheduler
[2024-01-11 19:11:51,882] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = None
[2024-01-11 19:11:51,882] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[0.0002], mom=[(0.9, 0.999)]
[2024-01-11 19:11:51,886] [INFO] [config.py:984:print] DeepSpeedEngine configuration:
[2024-01-11 19:11:51,886] [INFO] [config.py:988:print] activation_checkpointing_config {
"partition_activations": false,
"contiguous_memory_optimization": false,
"cpu_checkpointing": false,
"number_checkpoints": null,
"synchronize_checkpoint_boundary": false,
"profile": false
}
[2024-01-11 19:11:51,886] [INFO] [config.py:988:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
[2024-01-11 19:11:51,886] [INFO] [config.py:988:print] amp_enabled .................. False
[2024-01-11 19:11:51,886] [INFO] [config.py:988:print] amp_params ................... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] autotuning_config ............ {
"enabled": false,
"start_step": null,
"end_step": null,
"metric_path": null,
"arg_mappings": null,
"metric": "throughput",
"model_info": null,
"results_dir": "autotuning_results",
"exps_dir": "autotuning_exps",
"overwrite": true,
"fast": true,
"start_profile_step": 3,
"end_profile_step": 5,
"tuner_type": "gridsearch",
"tuner_early_stopping": 5,
"tuner_num_trials": 50,
"model_info_path": null,
"mp_size": 1,
"max_train_batch_size": null,
"min_train_batch_size": 1,
"max_train_micro_batch_size_per_gpu": 1.024000e+03,
"min_train_micro_batch_size_per_gpu": 1,
"num_tuning_micro_batch_sizes": 3
}
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] bfloat16_enabled ............. True
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] checkpoint_parallel_write_pipeline False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] checkpoint_tag_validation_enabled True
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] checkpoint_tag_validation_fail False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7f0b4ba0bee0>
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] communication_data_type ...... None
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] curriculum_enabled_legacy .... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] curriculum_params_legacy ..... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] data_efficiency_enabled ...... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] dataloader_drop_last ......... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] disable_allgather ............ False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] dump_state ................... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] dynamic_loss_scale_args ...... None
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_enabled ........... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_gas_boundary_resolution 1
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_layer_name ........ bert.encoder.layer
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_layer_num ......... 0
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_max_iter .......... 100
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_stability ......... 1e-06
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_tol ............... 0.01
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] eigenvalue_verbose ........... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] elasticity_enabled ........... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] flops_profiler_config ........ {
"enabled": false,
"recompute_fwd_factor": 0.0,
"profile_step": 1,
"module_depth": -1,
"top_modules": 1,
"detailed": true,
"output_file": null
}
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] fp16_auto_cast ............... None
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] fp16_enabled ................. False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] fp16_master_weights_and_gradients False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] global_rank .................. 0
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] grad_accum_dtype ............. None
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] gradient_accumulation_steps .. 16
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] gradient_clipping ............ 1
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] gradient_predivide_factor .... 1.0
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] graph_harvesting ............. False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] initial_dynamic_scale ........ 1
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] load_universal_checkpoint .... False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] loss_scale ................... 1.0
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] memory_breakdown ............. False
[2024-01-11 19:11:51,887] [INFO] [config.py:988:print] mics_hierarchial_params_gather False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] mics_shard_size .............. -1
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') enabled=False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] nebula_config ................ {
"enabled": false,
"persistent_storage_path": null,
"persistent_time_interval": 100,
"num_of_version_in_retention": 2,
"enable_nebula_load": true,
"load_path": null
}
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] optimizer_legacy_fusion ...... False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] optimizer_name ............... None
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] optimizer_params ............. None
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] pld_enabled .................. False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] pld_params ................... False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] prescale_gradients ........... False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] scheduler_name ............... None
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] scheduler_params ............. None
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] seq_parallel_communication_data_type torch.float32
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] sparse_attention ............. None
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] sparse_gradients_enabled ..... False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] steps_per_print .............. 10000000000000
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] train_batch_size ............. 16
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] train_micro_batch_size_per_gpu 1
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] use_data_before_expert_parallel False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] use_node_local_storage ....... False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] wall_clock_breakdown ......... False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] weight_quantization_config ... None
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] world_size ................... 1
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] zero_allow_untested_optimizer True
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500,000,000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500,000,000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=None sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50,000,000 param_persistence_threshold=100,000 model_persistence_threshold=sys.maxsize max_live_parameters=1,000,000,000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] zero_enabled ................. True
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] zero_force_ds_cpu_optimizer .. False
[2024-01-11 19:11:51,888] [INFO] [config.py:988:print] zero_optimization_stage ...... 2
[2024-01-11 19:11:51,888] [INFO] [config.py:974:print_user_config] json = {
"gradient_accumulation_steps": 16,
"train_micro_batch_size_per_gpu": 1,
"gradient_clipping": 1,
"zero_allow_untested_optimizer": true,
"zero_force_ds_cpu_optimizer": false,
"zero_optimization": {
"stage": 2,
"overlap_comm": true
},
"fp16": {
"enabled": false,
"initial_scale_power": 16
},
"bf16": {
"enabled": true
},
"steps_per_print": 1.000000e+13
}
Traceback (most recent call last):
File "/root/xtuner019/xtuner/xtuner/tools/train.py", line 260, in
main()
File "/root/xtuner019/xtuner/xtuner/tools/train.py", line 256, in main
runner.train()
File "/root/.local/lib/python3.10/site-packages/mmengine/runner/_flexible_runner.py", line 1182, in train
self.strategy.prepare(
File "/root/.local/lib/python3.10/site-packages/mmengine/_strategy/deepspeed.py", line 389, in prepare
self.param_schedulers = self.build_param_scheduler(
File "/root/.local/lib/python3.10/site-packages/mmengine/_strategy/base.py", line 658, in build_param_scheduler
param_schedulers = self._build_param_scheduler(
File "/root/.local/lib/python3.10/site-packages/mmengine/_strategy/base.py", line 563, in _build_param_scheduler
PARAM_SCHEDULERS.build(
File "/root/.local/lib/python3.10/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "/root/.local/lib/python3.10/site-packages/mmengine/registry/build_functions.py", line 294, in build_scheduler_from_cfg
return scheduler_cls.build_iter_from_epoch( # type: ignore
File "/root/.local/lib/python3.10/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 663, in build_iter_from_epoch
assert epoch_length is not None and epoch_length > 0,
AssertionError: epoch_length must be a positive integer, but got 0.
请各位大佬看看

Bắt đầu với traceback từ /root/.local/bin/xtuner và việc tra cứu entry-point của importlib.metadata, sau đó tái hiện quá trình cài đặt được ghi nhận trong các môi trường Python đã báo cáo. Xác định môi trường nào sở hữu launcher và metadata của package; hoàn tất khi thiết lập được ghi nhận khởi chạy xtuner mà không gặp PackageNotFoundError.