ModuleNotFoundError: No module named 'mmengine'

[52THANOS]

here is my config and warning.

2024-04-08 13:01:43,196] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
test.c
LINK : fatal error LNK1181: 无法打开输入文件“aio.lib”
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.1
 [WARNING]  please install triton==1.0.0 if you want to use sparse attention
04/08 13:01:46 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: win32
    Python: 3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]
    CUDA available: True
    MUSA available: False
    numpy_random_seed: 1493094375
    GPU 0: NVIDIA RTX A4000
    CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.6
    NVCC: Cuda compilation tools, release 11.6, V11.6.112
    MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.38.33135 版
    GCC: n/a
    PyTorch: 2.1.2+cu118
    PyTorch compiling details: PyTorch built with:
  - C++ Version: 199711
  - MSVC 192930151
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 2019
  - LAPACK is enabled (usually provided by MKL)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - 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_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.7
  - Magma 2.5.4
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windo
ws/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /bigobj /FS -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER
 -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE /utf-8 /wd4624 /wd4068 /wd4067 /wd4267 /wd4661 /wd4717 /wd4244 /wd4804 /wd4273, LAPACK_INFO=mkl, PE
RF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=OFF, TORCH_VERSION=2.1.2, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

    TorchVision: 0.16.2+cpu
    OpenCV: 4.9.0
    MMEngine: 0.10.3

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 1493094375
    deterministic: False
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

04/08 13:01:46 - mmengine - INFO - Config:
SYSTEM = 'xtuner.utils.SYSTEM_TEMPLATE.arxiv_gentile'
accumulative_counts = 16
batch_size = 1
betas = (
    0.9,
    0.999,
)
custom_hooks = [
    dict(
        tokenizer=dict(
            padding_side='right',
            pretrained_model_name_or_path='internlm/internlm2-7b',
            trust_remote_code=True,
            type='transformers.AutoTokenizer.from_pretrained'),
        type='xtuner.engine.hooks.DatasetInfoHook'),
    dict(
        evaluation_inputs=[
            'We present InternLM, a multilingual foundational language model with 104B parameters. InternLM is pre-trained on a large corpora with 1.6T tokens wi
th a multi-phase progressive process, and then fine-tuned to align with human preferences. We also developed a training system called Uniscale-LLM for efficient 
large language model training. The evaluation on a number of benchmarks shows that InternLM achieves state-of-the-art performance in multiple aspects, including 
knowledge understanding, reading comprehension, mathematics, and coding. With such well-rounded capabilities, InternLM achieves outstanding performances on compr
ehensive exams, including MMLU, AGIEval, C-Eval and GAOKAO-Bench, without resorting to external tools. On these benchmarks, InternLM not only significantly outpe
rforms open-source models, but also obtains superior performance compared to ChatGPT. Also, InternLM demonstrates excellent capability of understanding Chinese l
anguage and Chinese culture, which makes it a suitable foundation model to support Chinese-oriented language applications. This manuscript gives a detailed study of our results, with benchmarks and examples across a diverse set of knowledge domains and tasks.',
            'In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion
 to 70 billion parameters.\nOur fine-tuned LLMs, called LLAMA 2-CHAT, are optimized for dialogue use cases. Our models outperform open-source chat models on most
 benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed
 description of our approach to fine-tuning and safety improvements of LLAMA 2-CHAT in order to enable the community to build on our work and contribute to the responsible development of LLMs.',
        ],
        every_n_iters=500,
        prompt_template='xtuner.utils.PROMPT_TEMPLATE.default',
        system='xtuner.utils.SYSTEM_TEMPLATE.arxiv_gentile',
        tokenizer=dict(
            padding_side='right',
            pretrained_model_name_or_path='internlm/internlm2-7b',
            trust_remote_code=True,
            type='transformers.AutoTokenizer.from_pretrained'),
        type='xtuner.engine.hooks.EvaluateChatHook'),
]
data_path = './data/data_format/arxiv_data.json'
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 = [
    'We present InternLM, a multilingual foundational language model with 104B parameters. InternLM is pre-trained on a large corpora with 1.6T tokens with a mul
ti-phase progressive process, and then fine-tuned to align with human preferences. We also developed a training system called Uniscale-LLM for efficient large la
nguage model training. The evaluation on a number of benchmarks shows that InternLM achieves state-of-the-art performance in multiple aspects, including knowledg
e understanding, reading comprehension, mathematics, and coding. With such well-rounded capabilities, InternLM achieves outstanding performances on comprehensive
 exams, including MMLU, AGIEval, C-Eval and GAOKAO-Bench, without resorting to external tools. On these benchmarks, InternLM not only significantly outperforms o
pen-source models, but also obtains superior performance compared to ChatGPT. Also, InternLM demonstrates excellent capability of understanding Chinese language 
and Chinese culture, which makes it a suitable foundation model to support Chinese-oriented language applications. This manuscript gives a detailed study of our results, with benchmarks and examples across a diverse set of knowledge domains and tasks.',
    'In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 b
illion parameters.\nOur fine-tuned LLMs, called LLAMA 2-CHAT, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchma
rks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed descrip
tion of our approach to fine-tuning and safety improvements of LLAMA 2-CHAT in order to enable the community to build on our work and contribute to the responsible development of LLMs.',
]
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='internlm/internlm2-7b',
        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',
    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 = 'internlm/internlm2-7b'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.default'
randomness = dict(deterministic=False, seed=None)
resume = False
sampler = 'mmengine.dataset.DefaultSampler'
save_steps = 500
save_total_limit = 2
sequence_parallel_size = 1
tokenizer = dict(
    padding_side='right',
    pretrained_model_name_or_path='internlm/internlm2-7b',
    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(
            data_files=dict(train='./data/data_format/arxiv_data.json'),
            path='json',
            type='datasets.load_dataset'),
        dataset_map_fn='xtuner.dataset.map_fns.arxiv_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.default',
            type='xtuner.dataset.map_fns.template_map_fn_factory'),
        tokenizer=dict(
            padding_side='right',
            pretrained_model_name_or_path='internlm/internlm2-7b',
            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'))
train_dataset = dict(
    dataset=dict(
        data_files=dict(train='./data/data_format/arxiv_data.json'),
        path='json',
        type='datasets.load_dataset'),
    dataset_map_fn='xtuner.dataset.map_fns.arxiv_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.default',
        type='xtuner.dataset.map_fns.template_map_fn_factory'),
    tokenizer=dict(
        padding_side='right',
        pretrained_model_name_or_path='internlm/internlm2-7b',
        trust_remote_code=True,
        type='transformers.AutoTokenizer.from_pretrained'),
    type='xtuner.dataset.process_hf_dataset',
    use_varlen_attn=False)
use_varlen_attn = False
visualizer = None
warmup_ratio = 0.03
weight_decay = 0
work_dir = './work_dirs\\internlm2_7b_qlora_arxiv_gentitle_e3'

quantization_config convert to <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>
04/08 13:01:47 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" regi
stry 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.
(base) (venv) PS H:\xtuner> python xtuner/tools/train.py xtuner/configs/internlm/internlm2_7b/internlm2_7b_qlora_arxiv_gentitle_e3.py
[2024-04-08 13:35:48,283] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
test.c
LINK : fatal error LNK1181: 无法打开输入文件“aio.lib”
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.1
 [WARNING]  please install triton==1.0.0 if you want to use sparse attention
04/08 13:35:52 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: win32
    Python: 3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]
    CUDA available: True
    MUSA available: False
    numpy_random_seed: 868136044
    GPU 0: NVIDIA RTX A4000
    CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.6
    NVCC: Cuda compilation tools, release 11.6, V11.6.112
    MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.38.33135 版
    GCC: n/a
    PyTorch: 2.1.2+cu118
    PyTorch compiling details: PyTorch built with:
  - C++ Version: 199711
  - MSVC 192930151
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 2019
  - LAPACK is enabled (usually provided by MKL)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - 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_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.7
  - Magma 2.5.4
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windo
ws/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /bigobj /FS -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER
 -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE /utf-8 /wd4624 /wd4068 /wd4067 /wd4267 /wd4661 /wd4717 /wd4244 /wd4804 /wd4273, LAPACK_INFO=mkl, PE
RF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=OFF, TORCH_VERSION=2.1.2, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

    TorchVision: 0.16.2+cpu
    OpenCV: 4.9.0
    MMEngine: 0.10.3

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 868136044
    deterministic: False
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

04/08 13:35:52 - mmengine - INFO - Config:
SYSTEM = 'xtuner.utils.SYSTEM_TEMPLATE.arxiv_gentile'
accumulative_counts = 16
batch_size = 1
betas = (
    0.9,
    0.999,
)
custom_hooks = [
    dict(
        tokenizer=dict(
            padding_side='right',
            pretrained_model_name_or_path='internlm/internlm2-7b',
            trust_remote_code=True,
            type='transformers.AutoTokenizer.from_pretrained'),
        type='xtuner.engine.hooks.DatasetInfoHook'),
    dict(
        evaluation_inputs=[
            'We present InternLM, a multilingual foundational language model with 104B parameters. InternLM is pre-trained on a large corpora with 1.6T tokens wi
th a multi-phase progressive process, and then fine-tuned to align with human preferences. We also developed a training system called Uniscale-LLM for efficient 
large language model training. The evaluation on a number of benchmarks shows that InternLM achieves state-of-the-art performance in multiple aspects, including 
knowledge understanding, reading comprehension, mathematics, and coding. With such well-rounded capabilities, InternLM achieves outstanding performances on compr
ehensive exams, including MMLU, AGIEval, C-Eval and GAOKAO-Bench, without resorting to external tools. On these benchmarks, InternLM not only significantly outpe
rforms open-source models, but also obtains superior performance compared to ChatGPT. Also, InternLM demonstrates excellent capability of understanding Chinese l
anguage and Chinese culture, which makes it a suitable foundation model to support Chinese-oriented language applications. This manuscript gives a detailed study of our results, with benchmarks and examples across a diverse set of knowledge domains and tasks.',
            'In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion
 to 70 billion parameters.\nOur fine-tuned LLMs, called LLAMA 2-CHAT, are optimized for dialogue use cases. Our models outperform open-source chat models on most
 benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed
 description of our approach to fine-tuning and safety improvements of LLAMA 2-CHAT in order to enable the community to build on our work and contribute to the responsible development of LLMs.',
        ],
        every_n_iters=500,
        prompt_template='xtuner.utils.PROMPT_TEMPLATE.default',
        system='xtuner.utils.SYSTEM_TEMPLATE.arxiv_gentile',
        tokenizer=dict(
            padding_side='right',
            pretrained_model_name_or_path='internlm/internlm2-7b',
            trust_remote_code=True,
            type='transformers.AutoTokenizer.from_pretrained'),
        type='xtuner.engine.hooks.EvaluateChatHook'),
]
data_path = './data/data_format/arxiv_data.json'
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 = [
    'We present InternLM, a multilingual foundational language model with 104B parameters. InternLM is pre-trained on a large corpora with 1.6T tokens with a mul
ti-phase progressive process, and then fine-tuned to align with human preferences. We also developed a training system called Uniscale-LLM for efficient large la
nguage model training. The evaluation on a number of benchmarks shows that InternLM achieves state-of-the-art performance in multiple aspects, including knowledg
e understanding, reading comprehension, mathematics, and coding. With such well-rounded capabilities, InternLM achieves outstanding performances on comprehensive
 exams, including MMLU, AGIEval, C-Eval and GAOKAO-Bench, without resorting to external tools. On these benchmarks, InternLM not only significantly outperforms o
pen-source models, but also obtains superior performance compared to ChatGPT. Also, InternLM demonstrates excellent capability of understanding Chinese language 
and Chinese culture, which makes it a suitable foundation model to support Chinese-oriented language applications. This manuscript gives a detailed study of our results, with benchmarks and examples across a diverse set of knowledge domains and tasks.',
    'In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 b
illion parameters.\nOur fine-tuned LLMs, called LLAMA 2-CHAT, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchma
rks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed descrip
tion of our approach to fine-tuning and safety improvements of LLAMA 2-CHAT in order to enable the community to build on our work and contribute to the responsible development of LLMs.',
]
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='internlm/internlm2-7b',
        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',
    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 = 'internlm/internlm2-7b'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.default'
randomness = dict(deterministic=False, seed=None)
resume = False
sampler = 'mmengine.dataset.DefaultSampler'
save_steps = 500
save_total_limit = 2
sequence_parallel_size = 1
tokenizer = dict(
    padding_side='right',
    pretrained_model_name_or_path='internlm/internlm2-7b',
    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(
            data_files=dict(train='./data/data_format/arxiv_data.json'),
            path='json',
            type='datasets.load_dataset'),
        dataset_map_fn='xtuner.dataset.map_fns.arxiv_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.default',
            type='xtuner.dataset.map_fns.template_map_fn_factory'),
        tokenizer=dict(
            padding_side='right',
            pretrained_model_name_or_path='internlm/internlm2-7b',
            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'))
train_dataset = dict(
    dataset=dict(
        data_files=dict(train='./data/data_format/arxiv_data.json'),
        path='json',
        type='datasets.load_dataset'),
    dataset_map_fn='xtuner.dataset.map_fns.arxiv_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.default',
        type='xtuner.dataset.map_fns.template_map_fn_factory'),
    tokenizer=dict(
        padding_side='right',
        pretrained_model_name_or_path='internlm/internlm2-7b',
        trust_remote_code=True,
        type='transformers.AutoTokenizer.from_pretrained'),
    type='xtuner.dataset.process_hf_dataset',
    use_varlen_attn=False)
use_varlen_attn = False
visualizer = None
warmup_ratio = 0.03
weight_decay = 0
work_dir = './work_dirs\\internlm2_7b_qlora_arxi mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" regi
stry 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.v_gentitle_e3'

the program stopped at here. Only giving some warnings

[52THANOS]

无论什么模型，给上训练都会戛然而止，似乎都没有进入到导入数据的部分

[LZHgrla]

尝试一下这几行代码能否正常运行？

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_path = "internlm/internlm2-chat-7b"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map='auto',
    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'))
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)

[52THANOS]

尝试一下这几行代码能否正常运行？

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_path = "internlm/internlm2-chat-7b"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map='auto',
    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'))
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)

可以的

[LZHgrla]

@52THANOS
xtuner 代码有修改过吗？ 没有报错确实没有办法检查。可以尝试一下训练 alpaca 数据什么的，不要做任何修改，看能不能正常训练

[52THANOS]

@52THANOS xtuner 代码有修改过吗？ 没有报错确实没有办法检查。可以尝试一下训练 alpaca 数据什么的，不要做任何修改，看能不能正常训练

好的我去试试

[52THANOS]

可是我用gemma的config也这样，直接就结束了，alpaca是会自动下载的吗

[LZHgrla]

@52THANOS alpaca 会自动下载。排除一下数据集的影响，再就是检查一下 train.py 是否有改动？

[52THANOS]

@52THANOS alpaca 会自动下载。排除一下数据集的影响，再就是检查一下 train.py 是否有改

这是为什么我明明已经安装了

[LZHgrla]

你python所对应的环境有安装mmengine吗？ 参考 #388 #324

[52THANOS]

你python所对应的环境有安装mmengine吗？ 参考 #388 #324

我是单卡貌似不是这个问题，我用xtuner train xtuner/configs/internlm/internlm2_7b/internlm2_7b_qlora_alpaca_e3.py
和 python xtuner/tools/train.py xtuner/configs/internlm/internlm2_7b/internlm2_7b_qlora_alpaca_e3.py 有什么不一样呢

[52THANOS]

你python所对应的环境有安装mmengine吗？ 参考 #388 #324

这两个启动方式的错误感觉都和mmengine有关系

[LZHgrla]

@52THANOS
可以重新创建虚拟环境，重新安装

[52THANOS]

@52THANOS 可以重新创建虚拟环境，重新安装

依旧不行，失败了

[LZHgrla]

@52THANOS

python -c "import sys;print(sys.executable)"

查看一下打印出来的 python 路径是哪一个？

[52THANOS]

@52THANOS

python -c "import sys;print(sys.executable)"

查看一下打印出来的 python 路径是哪一个？

就是我当前环境下的exe

[LZHgrla]

那 ModuleNotFoundError 应该就是安装的问题，没有安装上mmengine

[52THANOS]

那 ModuleNotFoundError 应该就是安装的问题，没有安装上mmengine

可是我无论是用openmim安装还是直接安装mmengine，他好像都显示有问题

[LZHgrla]

创建新环境，直接安装 pip install mmengine。如果还是找不到mmengine 那估计就是 conda 或系统问题了

[52THANOS]

创建新环境，直接安装 pip install mmengine。如果还是找不到mmengine 那估计就是 conda 或系统问题了

我都能直接ctrol进去，但是他就是显示没有。我想问一下，xtuner train 和python tools/train.py 启动有啥区别啊

[LZHgrla]

@52THANOS
xtuner 命令的入口函数在这里

xtuner/xtuner/entry_point.py

Line 244 in bd6fe4c

def cli():

xtuner train 使用该入口，调用 python tools/train

[52THANOS]

@52THANOS xtuner 命令的入口函数在这里

xtuner/xtuner/entry_point.py

Line 244 in bd6fe4c

def cli():

xtuner train 使用该入口，调用 python tools/train

那看来还是mmengine的问题，我自己之前在用openmmlab的时候都没啥问题

[LZHgrla]

@52THANOS xtuner 命令的入口函数在这里

xtuner/xtuner/entry_point.py

Line 244 in bd6fe4c

def cli():

xtuner train 使用该入口，调用 python tools/train

那看来还是mmengine的问题，我自己之前在用openmmlab的时候都没啥问题

是的，可以逐行 debug 一下 tools/train.py