process_untokenized_datasets使用自定义map_fn时无法正确导出新config

使用以下方法导入map_fn:
```python
with read_base():
from .my_map_fn import my_map_fn
```

运行process_untokenized_datasets，导出数据后，报错：
```python
Processed dataset has been saved to /mnt/home/xxxxxxx/llm/xtuner/examples/test
Traceback (most recent call last):
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf/pytree/pytree_utils.py", line 113, in ParseCodeToTree
tree = parser_driver.parse_string(code, debug=False)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf_third_party/_ylib2to3/pgen2/driver.py", line 188, in parse_string
return self.parse_tokens(tokens, debug)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf_third_party/_ylib2to3/pgen2/driver.py", line 157, in parse_tokens
if p.addtoken(type, value, (prefix, start)):
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf_third_party/_ylib2to3/pgen2/parse.py", line 230, in addtoken
return self._addtoken(ilabel, type, value, context)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf_third_party/_ylib2to3/pgen2/parse.py", line 313, in _addtoken
raise ParseError('bad input', type, value, context)
yapf_third_party._ylib2to3.pgen2.parse.ParseError: bad input: type=20, value='<', context=('', (91, 14))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf/yapflib/yapf_api.py", line 198, in FormatCode
tree = pytree_utils.ParseCodeToTree(unformatted_source)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf/pytree/pytree_utils.py", line 116, in ParseCodeToTree
ast.parse(code)
File "/mnt/data/conda/envs/llm/lib/python3.10/ast.py", line 50, in parse
return compile(source, filename, mode, flags,
File "", line 91
my_map_fn=
^
SyntaxError: invalid syntax

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/mmengine/config/config.py", line 1483, in pretty_text
text, _ = FormatCode(text, style_config=yapf_style)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/yapf/yapflib/yapf_api.py", line 201, in FormatCode
raise errors.YapfError(errors.FormatErrorMsg(e))
yapf.yapflib.errors.YapfError: :91:15: invalid syntax

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/mnt/home/xxxxxxx/llm/xtuner/xtuner/tools/process_untokenized_datasets.py", line 73, in
modified_cfg.dump(modified_cfg_save_path)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/mmengine/config/config.py", line 1575, in dump
f.write(self.pretty_text)
File "/mnt/data/conda/envs/llm/lib/python3.10/site-packages/mmengine/config/config.py", line 1488, in pretty_text
raise SyntaxError('Failed to format the config file, please '
SyntaxError: Failed to format the config file, please check the syntax of:
accumulative_counts=4
batch_size=16
betas=(
0.9,
0.999,
)
custom_hooks=[
dict(tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='/mnt/data/xxxxxxx/llm/pretrained_model/internlm2_5-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.DatasetInfoHook'),
]
dataloader_num_workers=2
default_hooks=dict(
checkpoint=dict(
by_epoch=False,
interval=1000,
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))
load_from=None
log_level='INFO'
log_processor=dict(
by_epoch=False)
lr=1e-05
max_epochs=4
max_length=512
max_norm=1
model=dict(
llm=dict(
pretrained_model_name_or_path='/mnt/data/xxxxxxx/llm/pretrained_model/internlm2_5-7b',
trust_remote_code=True,
type='transformers.AutoModelForCausalLM.from_pretrained'),
type='xtuner.model.SupervisedFinetune',
use_varlen_attn=False)
optim_type='torch.optim.AdamW'
optim_wrapper=dict(
accumulative_counts=4,
clip_grad=dict(
error_if_nonfinite=False,
max_norm=1),
dtype='bfloat16',
loss_scale='dynamic',
optimizer=dict(
betas=(
0.9,
0.999,
),
lr=1e-05,
type='torch.optim.AdamW',
weight_decay=0),
type='mmengine.optim.AmpOptimWrapper')
pack_to_max_length=False
param_scheduler=[
dict(begin=0,
by_epoch=True,
convert_to_iter_based=True,
end=0.12,
start_factor=0.025,
type='mmengine.optim.LinearLR'),
dict(begin=0.12,
by_epoch=True,
convert_to_iter_based=True,
end=4,
eta_min=1.0000000000000002e-06,
type='mmengine.optim.CosineAnnealingLR'),
]
pretrained_model_name_or_path='/mnt/data/xxxxxxx/llm/pretrained_model/internlm2_5-7b'
prompt_template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat'
randomness=dict(
deterministic=False,
seed=None)
my_map_fn=
resume=False
sampler='mmengine.dataset.DefaultSampler'
save_steps=1000
save_total_limit=2
sequence_parallel_size=1
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='/mnt/data/xxxxxxx/llm/pretrained_model/internlm2_5-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained')
train_cfg=dict(
max_epochs=4,
type='xtuner.engine.runner.TrainLoop')
train_data_files=[
'/mnt/home/xxxxxxx/llm/xtuner/xtuner/configs/llm/internlm2_5_7b/example_data.jsonl',
]
train_dataloader=dict(
batch_size=16,
collate_fn=dict(
type='xtuner.dataset.collate_fns.default_collate_fn',
use_varlen_attn=False),
dataset=dict(
dataset=dict(
dataset_path='/mnt/home/xxxxxxx/llm/xtuner/examples/test',
type='datasets.load_from_disk'),
dataset_map_fn=None,
do_dataset_tokenization=False,
input_ids_with_output=False,
max_dataset_length=None,
max_length=None,
pack_to_max_length=False,
remove_unused_columns=False,
rename_maps=[
],
split=None,
template_map_fn=None,
tokenizer=None,
type='xtuner.dataset.process_hf_dataset'),
num_workers=2,
sampler=dict(
seed=1024,
shuffle=True,
type='xtuner.dataset.samplers.InternRepoSampler'))
train_dataset=dict(
dataset=dict(
data_files=[
'/mnt/home/xxxxxxx/llm/xtuner/xtuner/configs/llm/internlm2_5_7b/example_data.jsonl',
],
path='json',
type='datasets.load_dataset'),
dataset_map_fn=,
max_length=512,
pack_to_max_length=False,
remove_unused_columns=True,
shuffle_before_pack=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='/mnt/data/xxxxxxx/llm/pretrained_model/internlm2_5-7b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset',
use_varlen_attn=False)
use_varlen_attn=False
visualizer=dict(
type='mmengine.visualization.Visualizer',
vis_backends=[
dict(type='mmengine.visualization.TensorboardVisBackend'),
])
warmup_ratio=0.03
weight_decay=0
```