ModuleNotFoundError: No module named 'mmengine'

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
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