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# Pretrain Example
> \[!IMPORTANT\]
> Data must be used in conjunction with the corresponding map_fn.
## Data
`./data.json`
```json
[{
"toy_text": "I am an artificial intelligence (AI) assistant named InternLM. I was created by the Shanghai AI Laboratory and my purpose is to assist users with various tasks through natural language processing technology."
},
{
"toy_text": "I am an artificial intelligence programmed to assist with various types of tasks, including answering questions, providing information, and performing automated processes."
}]
```
## Map Function
`./map_fn.py`
```python
def pretrain_map_fn(example):
return {
'conversation': [{
'input': '',
'output': example['toy_text'].strip()
}]
}
```
## Config
Based on [internlm_7b_qlora_json_e3](../../../xtuner/configs/internlm/internlm_7b/internlm_7b_qlora_json_e3.py).
```diff
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from datasets import load_dataset
+ from mmengine.config import read_base
from mmengine.dataset import DefaultSampler
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR
from peft import LoraConfig
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
from xtuner.dataset import process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
-from xtuner.dataset.map_fns import template_map_fn_factory
-from xtuner.engine.hooks import DatasetInfoHook, EvaluateChatHook
+from xtuner.engine.hooks import DatasetInfoHook
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune
-from xtuner.utils import PROMPT_TEMPLATE
+with read_base():
+ from .map_fn import single_turn_map_fn as dataset_map_fn
+
#######################################################################
# PART 1 Settings #
#######################################################################
# Model
pretrained_model_name_or_path = 'internlm/internlm-7b'
# Data
-data_path = 'path/to/your/json_data'
+data_path = './data.json'
-prompt_template = PROMPT_TEMPLATE.default
max_length = 2048
pack_to_max_length = True
# Scheduler & Optimizer
batch_size = 1 # per_device
accumulative_counts = 16
dataloader_num_workers = 0
max_epochs = 3
optim_type = AdamW
lr = 2e-4
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1 # grad clip
# Save
save_steps = 500
save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
# Evaluate the generation performance during the training
evaluation_freq = 500
SYSTEM = ''
evaluation_inputs = [
'请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai'
]
#######################################################################
# PART 2 Model & Tokenizer #
#######################################################################
tokenizer = dict(
type=AutoTokenizer.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
padding_side='right')
model = dict(
type=SupervisedFinetune,
llm=dict(
type=AutoModelForCausalLM.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
torch_dtype=torch.float16,
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')),
lora=dict(
type=LoraConfig,
r=64,
lora_alpha=16,
lora_dropout=0.1,
bias='none',
task_type='CAUSAL_LM'))
#######################################################################
# PART 3 Dataset & Dataloader #
#######################################################################
train_dataset = dict(
type=process_hf_dataset,
dataset=dict(
type=load_dataset, path='json', data_files=dict(train=data_path)),
tokenizer=tokenizer,
max_length=max_length,
+ dataset_map_fn=dataset_map_fn,
- template_map_fn=dict(
- type=template_map_fn_factory, template=prompt_template),
+ template_map_fn=None,
remove_unused_columns=True,
shuffle_before_pack=True,
pack_to_max_length=pack_to_max_length)
train_dataloader = dict(
batch_size=batch_size,
num_workers=dataloader_num_workers,
dataset=train_dataset,
sampler=dict(type=DefaultSampler, shuffle=True),
collate_fn=dict(type=default_collate_fn))
#######################################################################
# PART 4 Scheduler & Optimizer #
#######################################################################
# optimizer
optim_wrapper = dict(
type=AmpOptimWrapper,
optimizer=dict(
type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
accumulative_counts=accumulative_counts,
loss_scale='dynamic',
dtype='float16')
# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
param_scheduler = dict(
type=CosineAnnealingLR,
eta_min=0.0,
by_epoch=True,
end=max_epochs,
convert_to_iter_based=True)
# train, val, test setting
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
#######################################################################
# PART 5 Runtime #
#######################################################################
# Log the dialogue periodically during the training process, optional
-custom_hooks = [
- dict(type=DatasetInfoHook, tokenizer=tokenizer),
- dict(
- type=EvaluateChatHook,
- tokenizer=tokenizer,
- every_n_iters=evaluation_freq,
- evaluation_inputs=evaluation_inputs,
- system=SYSTEM,
- prompt_template=prompt_template)
-]
+custom_hooks = [dict(type=DatasetInfoHook, tokenizer=tokenizer)]
# configure default hooks
default_hooks = dict(
# record the time of every iteration.
timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
checkpoint=dict(
type=CheckpointHook,
by_epoch=False,
interval=save_steps,
max_keep_ckpts=save_total_limit),
# set sampler seed in distributed evrionment.
sampler_seed=dict(type=DistSamplerSeedHook),
)
# configure environment
env_cfg = dict(
# whether to enable cudnn benchmark
cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),
)
# set visualizer
visualizer = None
# set log level
log_level = 'INFO'
# load from which checkpoint
load_from = None
# whether to resume training from the loaded checkpoint
resume = False
# Defaults to use random seed and disable `deterministic`
randomness = dict(seed=None, deterministic=False)
# set log processor
log_processor = dict(by_epoch=False)
```
## Quick Start
```bash
cd ./examples/demo_data/pretrain
xtuner train config.py
```
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