llamafactory使用8张昇腾910b算力卡lora微调训练qwen2-72b大模型

说明

我需要在昇腾服务器上对Qwen2-72B大模型进行lora微调，改变其自我认知。
我的环境下是8张910B1卡。显存约512GB。

准备：安装llamafactory

请参考官方方法安装llamafactory：https://github.com/hiyouga/LLaMA-Factory

特别强调下，deepspeed一定要按照文档中要求的版本安装，太新或者太旧，多卡训练都有问题。

准备数据集和训练配置

我准备的工作目录如下：

./
├── data
│   ├── dataset_info.json
│   └── self_cognition.json
├── deepspeed
│   └── ds_z3_config.json
├── models
├── start_train.sh
└── train_config.yaml

其中data目录下self_cognition.json是我准备的数据集，他是alpaca格式的，dataset_info.json是数据集的入口配置文件，一会训练要指定它。

dataset_info.json内容如下：

{
"train_data_name": {
    "file_name": "self_cognition.json"
  }
 }

我在这里只写了一个数据集，其实可以配置很多的。

deepspeed目录下的ds_z3_config.json是deepspeed的配置文件，使用多卡训练必须要有这个文件。
这个文件在LLaMA-Factory代码工程下examples/deepspeed下有参考文件，我直接复制了一个过来。
其内容如下：

{
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "zero_allow_untested_optimizer": true,
  "fp16": {
    "enabled": "auto",
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "initial_scale_power": 16,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "bf16": {
    "enabled": "auto"
  },
  "zero_optimization": {
    "stage": 3,
    "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": 1e9,
    "stage3_max_reuse_distance": 1e9,
    "stage3_gather_16bit_weights_on_model_save": true
  }
}

models目录是空的，用来存放训练生成的模型。

train_config.yaml是训练配置文件，起内容如下：

### model
model_name_or_path: /data/xxx/mindformer_share/Qwen2-72B-Instruct/

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all

### ddp
ddp_timeout: 180000000
deepspeed: ./deepspeed/ds_z3_config.json

### dataset
dataset: train_data_name
template: qwen
cutoff_len: 1024
max_samples: 200
overwrite_cache: true
preprocessing_num_workers: 16

### output
output_dir: ./models/
logging_steps: 10
save_steps: 50
plot_loss: true
overwrite_output_dir: true
  #report_to: tensorboard
  #logging_dir: /data/xxx/mindformer_share/llamaFactory/tensorboard/

### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 1.0e-4
num_train_epochs: 40.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true

### eval
val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

其中model_name_or_path用爱指定基础模型的路径，output_dir用来指定训练生成模型的输出路径。num_train_epochs是训练的轮数，max_samples是最大样本的数量，可根据实际情况修改这个值。save_steps是多少步保存一次中间checkpoint。

重点要说下deepspeed配置，如果不指定deepspeed配置文件，则默认使用数据并行，一旦模型无法在单张卡上加载就会出错。而配置了deepspeed之后，则模型会被切分到各张卡上，大模型可以平均分布在多张卡上。

我的训练启动脚本是start_train.sh，其内容如下：

#!/bin/sh

source /usr/local/Ascend/ascend-toolkit/set_env.sh

set -x
#export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

llamafactory-cli train ./train_config.yaml

执行训练

在命令行执行start_train.sh脚本

sh start_train.sh

训练完成后./models目录下文件如下：

./models/
├── adapter_config.json
├── adapter_model.safetensors
├── added_tokens.json
├── all_results.json
├── checkpoint-100
├── checkpoint-150
├── checkpoint-200
├── checkpoint-50
├── eval_results.json
├── merges.txt
├── README.md
├── runs
├── special_tokens_map.json
├── tokenizer_config.json
├── tokenizer.json
├── trainer_log.jsonl
├── trainer_state.json
├── training_args.bin
├── training_loss.png
├── train_results.json
└── vocab.json

我的模型训练了40轮，每50步保存一次模型。通过查看trainer_log.jsonl，发现checkpoint-150之后loss就已经很稳定了，我决定就使用checkpoint-150这个中间结果做后面的操作。

{"current_steps": 10, "total_steps": 200, "loss": 4.1278, "lr": 4e-05, "epoch": 2.0, "percentage": 5.0, "elapsed_time": "0:03:03", "remaining_time": "0:58:11"}
{"current_steps": 20, "total_steps": 200, "loss": 2.4438, "lr": 9e-05, "epoch": 4.0, "percentage": 10.0, "elapsed_time": "0:05:48", "remaining_time": "0:52:20"}
{"current_steps": 30, "total_steps": 200, "loss": 1.0016, "lr": 9.951340343707852e-05, "epoch": 6.0, "percentage": 15.0, "elapsed_time": "0:08:35", "remaining_time": "0:48:39"}
{"current_steps": 40, "total_steps": 200, "loss": 0.4434, "lr": 9.755282581475769e-05, "epoch": 8.0, "percentage": 20.0, "elapsed_time": "0:11:18", "remaining_time": "0:45:13"}
{"current_steps": 50, "total_steps": 200, "loss": 0.0837, "lr": 9.414737964294636e-05, "epoch": 10.0, "percentage": 25.0, "elapsed_time": "0:13:59", "remaining_time": "0:41:58"}
{"current_steps": 60, "total_steps": 200, "loss": 0.0096, "lr": 8.940053768033609e-05, "epoch": 12.0, "percentage": 30.0, "elapsed_time": "0:17:36", "remaining_time": "0:41:06"}
{"current_steps": 70, "total_steps": 200, "loss": 0.0059, "lr": 8.345653031794292e-05, "epoch": 14.0, "percentage": 35.0, "elapsed_time": "0:20:22", "remaining_time": "0:37:50"}
{"current_steps": 80, "total_steps": 200, "loss": 0.0019, "lr": 7.649596321166024e-05, "epoch": 16.0, "percentage": 40.0, "elapsed_time": "0:23:07", "remaining_time": "0:34:41"}
{"current_steps": 90, "total_steps": 200, "loss": 0.0026, "lr": 6.873032967079561e-05, "epoch": 18.0, "percentage": 45.0, "elapsed_time": "0:25:51", "remaining_time": "0:31:36"}
{"current_steps": 100, "total_steps": 200, "loss": 0.0011, "lr": 6.0395584540887963e-05, "epoch": 20.0, "percentage": 50.0, "elapsed_time": "0:28:36", "remaining_time": "0:28:36"}
{"current_steps": 110, "total_steps": 200, "loss": 0.0007, "lr": 5.174497483512506e-05, "epoch": 22.0, "percentage": 55.0, "elapsed_time": "0:32:03", "remaining_time": "0:26:13"}
{"current_steps": 120, "total_steps": 200, "loss": 0.001, "lr": 4.3041344951996746e-05, "epoch": 24.0, "percentage": 60.0, "elapsed_time": "0:34:44", "remaining_time": "0:23:09"}
{"current_steps": 130, "total_steps": 200, "loss": 0.0009, "lr": 3.4549150281252636e-05, "epoch": 26.0, "percentage": 65.0, "elapsed_time": "0:37:25", "remaining_time": "0:20:08"}
{"current_steps": 140, "total_steps": 200, "loss": 0.0008, "lr": 2.6526421860705473e-05, "epoch": 28.0, "percentage": 70.0, "elapsed_time": "0:40:08", "remaining_time": "0:17:12"}
{"current_steps": 150, "total_steps": 200, "loss": 0.0009, "lr": 1.9216926233717085e-05, "epoch": 30.0, "percentage": 75.0, "elapsed_time": "0:42:48", "remaining_time": "0:14:16"}
{"current_steps": 160, "total_steps": 200, "loss": 0.0009, "lr": 1.2842758726130283e-05, "epoch": 32.0, "percentage": 80.0, "elapsed_time": "0:46:37", "remaining_time": "0:11:39"}
{"current_steps": 170, "total_steps": 200, "loss": 0.0007, "lr": 7.597595192178702e-06, "epoch": 34.0, "percentage": 85.0, "elapsed_time": "0:49:21", "remaining_time": "0:08:42"}
{"current_steps": 180, "total_steps": 200, "loss": 0.0007, "lr": 3.6408072716606346e-06, "epoch": 36.0, "percentage": 90.0, "elapsed_time": "0:52:05", "remaining_time": "0:05:47"}
{"current_steps": 190, "total_steps": 200, "loss": 0.0007, "lr": 1.0926199633097157e-06, "epoch": 38.0, "percentage": 95.0, "elapsed_time": "0:54:53", "remaining_time": "0:02:53"}
{"current_steps": 200, "total_steps": 200, "loss": 0.0007, "lr": 3.04586490452119e-08, "epoch": 40.0, "percentage": 100.0, "elapsed_time": "0:57:36", "remaining_time": "0:00:00"}
{"current_steps": 200, "total_steps": 200, "epoch": 40.0, "percentage": 100.0, "elapsed_time": "0:58:26", "remaining_time": "0:00:00"}

合并lora模型到基础模型

merge方法1：llamafactory-cli export

llamafactory的命令行工具自带了lora合并功能，参考源码工程目录examples/merge_lora/下的配置文件编写一个合并的配置文件即可。

首先编写一个合并用的配置文件qwen_merge_lora_config.yaml：

### Note: DO NOT use quantized model or quantization_bit when merging lora adapters

### model
model_name_or_path: /data/xxx/mindformer_share/Qwen2-72B-Instruct/
adapter_name_or_path: /data/xxx/mindformer_share/llamaFactory/models/checkpoint-150/
template: qwen
finetuning_type: lora

### export
export_dir: /data/xxx/mindformer_share/llamaFactory/export_merge_lora/
export_size: 2
export_device: cpu  # 也可以写npu
export_legacy_format: false

上面文件中，model_name_or_path是基础模型路径，adapter_name_or_path是lora训练输出路径，export_dir是合并后输出的模型路径。template时模型的架构，跟lora训练配置里一致即可。

然后在命令行窗口执行：

llamafactory-cli export qwen_merge_lora_config.yaml

执行完毕，在export_dir定义路径下就是合并后的完整模型。

merge方法2：

也可以使用python，通过调用peft来合并lora模型。

import torch
import torch_npu
from torch_npu.npu import amp
from torch_npu.contrib import transfer_to_npu
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
from peft import PeftModel


model_path="/data/xxx/mindformer_share/Qwen2-72B-Instruct/"
lora_path="/data/xxx/mindformer_share/llamaFactory/models/checkpoint-150/"
merge_path="./export_python_merge"


print(f"Loading the Base model from {model_path}")
tokenizer = AutoTokenizer.from_pretrained(model_path,
    revision="v2.0",
    use_fast=False,
    trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(model_path,
    revision="v2.0",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True)
    #trust_remote_code=True).eval().half().npu()

print(f"Loading the LoRA from {lora_path}")
lora_model = PeftModel.from_pretrained(
        base_model,
        lora_path,
        torch_dtype=torch.float16,
    )


print("Applying the LoRA")
model = lora_model.merge_and_unload()

print(f"Saving the target model to {merge_path}")
model.save_pretrained(merge_path)
print(f"Saving the tokenizer to {merge_path}")
tokenizer.save_pretrained(merge_path)

上述代码中model_path是基础模型路径，lora_path是lora模型目录，merge_path是合并模型输出路径。

2种合并方法的比较

其中方法1中，export_device设置为cpu时，内存至少占140G（大约时模型尺寸大小），cpu会占用到100个核，NPU资源几乎不占。

方法1中export_device设置为npu时整体效果和方法2差不多。

2种方法合并模型的时间都约15分钟。

测试合并后的模型

合并后的模型就和huggingface上下载的模型使用同样的方法测试。这是我测试qwen2-72b合并后的模型的代码。

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "npu" # the device to load the model onto

#model_path="/data/yuanll/mindformer_share/Qwen2-72B-Instruct/"
#model_path="./export_merge_lora"
model_path="./export_python_merge"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

#{"role": "system", "content": "You are a helpful assistant."},
prompt = "告诉我你的身份和创造者"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"response:{response}")

参考资料

在昇腾开发环境合并baichuan2-13B模型的lora文件

Qwen/Qwen2-72B-Instruct

LLaMA-Factory 实战（一）：采用 LoRA 方式对QWen2 做指令微调

LLaMA-Factory 8卡4090 deepspeed zero3 微调Qwen14B-chat

原文地址：https://blog.csdn.net/yuanlulu/article/details/145161415

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