【AIGC】Baichuan2-13B-Chat模型微调_baichuan2-13b-chat-4bits微调

环境

微调框架：LLaMA-Efficient-Tuning
训练机器：4*RTX3090TI (24G显存)
python环境：python3.8, 安装requirements.txt依赖包

一、Lora微调

1、准备数据集

2、训练及测试

1）创建模型输出目录

mkdir -p models/baichuan2_13b_chat/train_models/baichuan2_13b_chat_multi_gpus_03_epoch100/train_model
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2）创建deepspeed配置文件目录

mkdir -p models/baichuan2_13b_chat/deepspeed_config
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3）创建deepspeed配置文件

vi models/baichuan2_13b_chat/deepspeed_config/ds_config_baichuan2_13b_chat_multi_gpus_03_epoch100.json
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{
  "bf16": {
    "enabled": true
  },
  "fp16": {
    "enabled": "auto",
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "initial_scale_power": 16,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "optimizer": {
    "type": "AdamW",
    "params": {
      "lr": "auto",
      "betas": "auto",
      "eps": "auto",
      "weight_decay": "auto"
    }
  },
  "scheduler": {
    "type": "WarmupDecayLR",
    "params": {
      "last_batch_iteration": -1,
      "total_num_steps": "auto",
      "warmup_min_lr": "auto",
      "warmup_max_lr": "auto",
      "warmup_num_steps": "auto"
    }
  },
  "zero_optimization": {
    "stage": 3,
    "offload_optimizer": {
      "device": "cpu",
      "pin_memory": true
    },
    "offload_param": {
      "device": "cpu",
      "pin_memory": true
    },
    "overlap_comm": true,
    "contiguous_gradients": true,
    "sub_group_size": 1e9,
    "reduce_bucket_size": "auto",
    "stage3_prefetch_bucket_size": "auto",
    "stage3_param_persistence_threshold": "auto",
    "stage3_max_live_parameters": 2e9,
    "stage3_max_reuse_distance": 2e9,
    "stage3_gather_16bit_weights_on_model_save": true
  },
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "steps_per_print": 2000,
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "wall_clock_breakdown": false
}
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4）训练模型

deepspeed --num_nodes 1 --num_gpus 4 --master_port=9901 src/train_bash.py \
    --stage sft \
    --model_name_or_path baichuan-inc/Baichuan2-13B-Chat \
    --do_train \
    --dataset example1 \
    --template baichuan2 \
    --finetuning_type lora \
    --lora_rank 16 \
    --lora_target W_pack,o_proj,gate_proj,down_proj,up_proj \
    --output_dir models/baichuan2_13b_chat/train_models/baichuan2_13b_chat_multi_gpus_03_epoch100/train_model \
    --overwrite_cache \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --preprocessing_num_workers 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 100 \
    --learning_rate 5e-3 \
    --max_grad_norm 0.5 \
    --num_train_epochs 300.0 \
    --evaluation_strategy steps \
    --plot_loss \
    --bf16 \
    --deepspeed models/baichuan2_13b_chat/deepspeed_config/ds_config_baichuan2_13b_chat_multi_gpus_03_epoch100.json
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[INFO|trainer.py:1686] 2023-09-19 04:07:47,607 >> ***** Running training *****
[INFO|trainer.py:1687] 2023-09-19 04:07:47,607 >>   Num examples = 94
[INFO|trainer.py:1688] 2023-09-19 04:07:47,608 >>   Num Epochs = 300
[INFO|trainer.py:1689] 2023-09-19 04:07:47,608 >>   Instantaneous batch size per device = 4
[INFO|trainer.py:1692] 2023-09-19 04:07:47,608 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:1693] 2023-09-19 04:07:47,608 >>   Gradient Accumulation steps = 4
[INFO|trainer.py:1694] 2023-09-19 04:07:47,608 >>   Total optimization steps = 300
[INFO|trainer.py:1695] 2023-09-19 04:07:47,612 >>   Number of trainable parameters = 55,787,520
{'loss': 7.7023, 'learning_rate': 0.00488255033557047, 'epoch': 6.67}                                                                                        
{'loss': 7.0675, 'learning_rate': 0.004714765100671141, 'epoch': 13.33}                                                                                      
  8%|█████████▊                                                                                                           | 25/300 [17:10<3:07:01, 40.81s/it]

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5. 测试模型

python src/cli_demo.py \
    --model_name_or_path baichuan-inc/Baichuan2-13B-Chat \
    --template baichuan2 \
    --finetuning_type lora \
    --checkpoint_dir models/baichuan2_13b_chat/train_models/baichuan2_13b_chat_multi_gpus_03_epoch100/train_model
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3、注意事项：

1）我用的是3090TI显卡，使用fp16精度时，训练结果始终没有效果，而且训练到后面有（loss为0）的问题。这个不清楚时什么原因。所以需要采用bf16，deepspeed配置文件中要将bf16配置为true。训练时添加参数–bf16 。
所以如果显卡不是 3090TI ，可以尝试用 --fp16。

Refer:

• 使用baichuan2-13B进行微调时出现loss全为0的情况
• Deepspeed zero3对Baichuan系的13b-chat进行微调，微调效果失效（Baichuan-13b-chat和Baichuan2-13b-chat都尝试过）

2）deepspeed中 stage 需要选择 3 。尝试过 2 ，内存会溢出。
3) 报错：AttributeError: 'Parameter' object has no attribute 'ds_status' ; 解决办法：关闭验证集，比如 --val_size 0.01, --load_best_model_at_end

Refer:

• Baichuan2-13B-Base微调eval报错

4. 此次训练，loss还没降下去，测试效果不太理想。但是知识库微调成功，只是表达凌乱。所以建议如果知识库不大的话，尽量用单卡训练，效果更好。