百川13B模型发布及魔搭最佳实践-阿里云开发者社区

环境配置和安装

1、本文在4*V100的阿里云ECS的环境配置下运行

2、python>=3.8

服务器连接与环境准备

# 服务器连接 (CentOS)
ssh root@xxx.xxx.xxx.xxx  # 可通过vscode连接
passwd  # 修改root密码
lsb_release -a  # 查看操作系统版本
# 安装git并配置
yum install git
git config --global user.name "baichuan" 
git config --global user.email "baichuan@abc.com"
git config --global init.defaultBranch main
git config --list
# 创建用户, 并设置密码(当然你也可以在root下操作)
useradd -d /home/baichuan -m baichuan
passwd baichuan
su baichuan
# 安装miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# 一直[ENTER], yes即可
sh Miniconda3-latest-Linux-x86_64.sh
# conda虚拟环境搭建
conda create --name baichuan python=3.10
conda activate baichuan
# pip设置全局镜像与相关python包安装
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install modelscope
pip install numpy pandas matplotlib scikit-learn
pip install transformers datasets
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install tqdm
pip install tensorboard
pip install torchmetrics
pip install sentencepiece
pip install accelerate
pip install numpy -U  # Resolve torchmetrics dependencies and update numpy

git clone ModelScope，运行示例代码

#获取示例代码
git clone https://github.com/modelscope/modelscope.git
cd modelscope/examples/pytorch/llm
python baichuan_sft.py

以下介绍代码中的具体细节

进入python环境，获取环境基本信息，代码目录：https://github.com/modelscope/modelscope/blob/master/examples/pytorch/llm/_common.py

from _common import *
device_ids = [0, 1, 2, 3]
logger.info(device_ids)
select_device(device_ids)
seed_everything(42)

模型链接和下载

百川系列模型现已在ModelScope社区开源：

百川-7B

https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary

百川-13B-Base

https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary

百川-13B-Chat

https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Chat/summary

社区支持直接下载模型的repo

模型weights下载

from modelscope.hub.snapshot_download import snapshot_download
model_id = 'baichuan-inc/Baichuan-13B-Base'
model_dir = get_model_dir(model_id, model_revision='v1.0.1')

或者通过如下代码，实现模型下载，以及load model, tokenizer：

模型下载，load model，tokenizer

# ### Loading Model and Tokenizer
WORK_DIR = "runs/baichuan_13B"
LORA_TARGET_MODULES = ["W_pack"]
#
def get_baichuan13B_model_tokenizer(model_dir: str, load_model: bool = True):
    sys.path.insert(0, model_dir)
    from configuration_baichuan import BaichuanConfig
    from tokenization_baichuan import BaichuanTokenizer
    from modeling_baichuan import BaichuanForCausalLM
    model_config = BaichuanConfig.from_pretrained(model_dir)
    model_config.torch_dtype = torch.float16
    logger.info(f'model_config: {model_config}')
    tokenizer = BaichuanTokenizer.from_pretrained(model_dir)
    model = None
    if load_model:
        model = BaichuanForCausalLM.from_pretrained(
            model_dir,
            config=model_config,
            device_map='auto',
            torch_dtype=torch.float16)
    #
    return model, tokenizer
model, tokenizer = get_baichuan13B_model_tokenizer(model_dir)

设置GRADIENT_CHECKPOINTING的训练方式，可以有效降低训练显存

GRADIENT_CHECKPOINTING = True
if GRADIENT_CHECKPOINTING:
    # baichuan13B does not implement the `get_input_embeddings` function
    def get_input_embeddings(self):
        return self.model.embed_tokens
    model.gradient_checkpointing_enable()
    model.__class__.get_input_embeddings = get_input_embeddings.__get__(model)
    model.enable_input_require_grads()
if tokenizer.pad_token_id is None:
    tokenizer.pad_token_id = tokenizer.eos_token_id
#
logger.info(f'bos_token_id: {tokenizer.bos_token_id}, eos_token_id: {tokenizer.eos_token_id}, '
            f'pad_token_id: {tokenizer.pad_token_id}')

模型推理

以Baichuan-13B-Base和Baichuan-13B-Chat为例

Baichuan-13B-base推理代码

from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
import torch
from modelscope import snapshot_download, Model
model_dir = snapshot_download("baichuan-inc/Baichuan-13B-Base")
model = Model.from_pretrained(model_dir, device_map="balanced", trust_remote_code=True, torch_dtype=torch.float16)
text_generation_zh  = pipeline(task=Tasks.text_generation, model=model)
text_generation_zh._model_prepare = True
result_zh = text_generation_zh('今天天气是真的', min_length=10, max_length=512, num_beams=3,temperature=0.8,do_sample=False, early_stopping=True,top_k=50,top_p=0.8, repetition_penalty=1.2, length_penalty=1.2, no_repeat_ngram_size=6)
print(result_zh)

Baichuan-13B-chat推理代码

import torch
from modelscope import snapshot_download, Model
model_dir = snapshot_download("baichuan-inc/Baichuan-13B-Chat", revision='v1.0.3')
model = Model.from_pretrained(model_dir, device_map="balanced", trust_remote_code=True, torch_dtype=torch.float16)
messages = []
messages.append({"role": "user", "content": "世界上第二高的山峰是哪一座？"})
response = model(messages)
print(response)

数据集链接及下载

社区推荐数据集为：alpaca-gpt4-data-zh，alpaca-gpt4-data-en。该数据集目前也在魔搭社区上开源了，可以使用ModelScope SDK直接下载数据集：

from modelscope import MsDataset
dataset_zh = MsDataset.load("AI-ModelScope/alpaca-gpt4-data-zh", split="train")
dataset_en = MsDataset.load("AI-ModelScope/alpaca-gpt4-data-en", split="train")
print(len(dataset_zh["instruction"]))
print(len(dataset_en["instruction"]))
print(dataset_zh[0])
"""Out
48818
52002
{'instruction': '保持健康的三个提示。', 'input': None, 'output': '以下是保持健康的三个提示：\n\n1. 保持身体活动。每天做适当的身体运动，如散步、跑步或游泳，能促进心血管健康，增强肌肉力量，并有助于减少体重。\n\n2. 均衡饮食。每天食用新鲜的蔬菜、水果、全谷物和脂肪含量低的蛋白质食物，避免高糖、高脂肪和加工食品，以保持健康的饮食习惯。\n\n3. 睡眠充足。睡眠对人体健康至关重要，成年人每天应保证 7-8 小时的睡眠。良好的睡眠有助于减轻压力，促进身体恢复，并提高注意力和记忆力。'}
"""

模型训练最佳实践

微调过程分为如下几步：

prompt的设计
使用ModelScope提供的微调方法构建最终模型
使用ModelScope提供的Trainer对模型进行微调

准备Lora：

# ### Preparing lora
LORA_RANK = 8
LORA_ALPHA = 32
LORA_DROPOUT_P = 0.1
lora_config = LoRAConfig(
    replace_modules=LORA_TARGET_MODULES,
    rank=LORA_RANK,
    lora_alpha=LORA_ALPHA,
    lora_dropout=LORA_DROPOUT_P)
logger.info(f'lora_config: {lora_config}')
Swift.prepare_model(model, lora_config)
#
show_freeze_layers(model)
print_model_info(model)
_p = list(model.parameters())[100]
logger.info(f'device: {_p.device}, dtype: {_p.dtype}')
model.bfloat16()

导入datasets

# ### Loading Dataset
tokenize_function = partial(tokenize_function, tokenizer=tokenizer)
train_dataset, val_dataset = get_alpaca_en_zh_dataset(tokenize_function)
# Data analysis
stat_dataset(train_dataset)
stat_dataset(val_dataset)
data_collate_fn = partial(data_collate_fn, tokenizer=tokenizer)
print_examples(train_dataset[0], tokenizer)

配置Config

# Setting Config
cfg_file = os.path.join(model_dir, 'configuration.json')
#
BATCH_SIZE = 1
MAX_EPOCHS = 1
T_max = get_T_max(len(train_dataset), BATCH_SIZE, MAX_EPOCHS, True)
WORK_DIR = get_work_dir(WORK_DIR)
EVAL_INTERVAL = 200
CONFIG = Config({
    'train': {
        'dataloader': {
            'batch_size_per_gpu': BATCH_SIZE,
            'workers_per_gpu': 1,
            'shuffle': True,
            'drop_last': True,
            'pin_memory': True
        },
        'max_epochs': MAX_EPOCHS,
        'work_dir': WORK_DIR,
        'optimizer': {
            'type': 'AdamW',
            'lr': 1e-4,
            'weight_decay': 0.01,
            'options': {
                'cumulative_iters': 16, 'grad_clip': {
                    'norm_type': 2,
                    'max_norm': 2.0
                }
            }
        },
        'lr_scheduler': {
            'type': 'CosineAnnealingLR',
            'T_max': T_max,
            'eta_min': 1e-5,
            'options': {
                'by_epoch': False,
                'warmup': {
                    'type': 'LinearWarmup',
                    'warmup_ratio': 0.1,
                    'warmup_iters': 200
                }
            }
        },
        'hooks': [
            {'type': 'CheckpointHook', 'by_epoch': False, 'interval': EVAL_INTERVAL, 'max_checkpoint_num': 1},
            {'type': 'EvaluationHook', 'by_epoch': False, 'interval': EVAL_INTERVAL},
            {'type': 'BestCkptSaverHook',
                'metric_key': 'acc',
                'save_best': True, 'rule': 'max', 'max_checkpoint_num': 1},
            {'type': 'TextLoggerHook',
                'by_epoch': True,  # Whether EpochBasedTrainer is used
                'interval': 5},
            {'type': 'TensorboardHook', 'by_epoch': False, 'interval': 5}
        ]
    },
    'evaluation': {
        'dataloader': {
            'batch_size_per_gpu': BATCH_SIZE,
            'workers_per_gpu': 1,
            'shuffle': False,
            'drop_last': False,
            'pin_memory': True
        },
        'metrics': [
            {'type': 'my_metric', 'vocab_size': tokenizer.vocab_size}
        ]
    }
})

开启微调

# ### Finetuning
def cfg_modify_fn(cfg: Config) -> Config:
    cfg.update(CONFIG)
    return cfg
trainer = EpochBasedTrainer(
    model=model,
    cfg_file=cfg_file,
    data_collator=data_collate_fn,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    remove_unused_data=True,
    seed=42,
    device='cpu',  # No placement for model, leave the model to `device_map`
    cfg_modify_fn=cfg_modify_fn,
)
trainer.train()

可视化

Tensorboard 命令: (e.g.)

tensorboard --logdir /home/baichuan/my_git/modelscope/runs/baichuan_13B/v0-20230711-172449 --port 6006

# ### Visualization
tb_dir = os.path.join(WORK_DIR, 'tensorboard_output')
plot_image(tb_dir, ['loss'], 0.9)

资源消耗

Baichuan-13B-base用lora的方式训练的显存占用如下，大约为40G

推理结果

import sys
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from modelscope import snapshot_download
from modelscope.swift import LoRAConfig, Swift
LORA_CKPT_FPATH = '/path/to/your/xxx.pth'
#
model_dir = snapshot_download('baichuan-inc/Baichuan-13B-Base', 'v1.0.1')
sys.path.insert(0, model_dir)
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_dir,
    device_map='auto',
    torch_dtype=torch.float16,
    trust_remote_code=True)
model.bfloat16()  # Consistent with training
#
lora_config = LoRAConfig(
    replace_modules=['W_pack'],
    rank=32,
    lora_alpha=8,
    lora_dropout=0,
    pretrained_weights=LORA_CKPT_FPATH)
Swift.prepare_model(model, lora_config)
#
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
PROMPT = """### 用户
{instruction}
### AI助手
<s>"""
instruction = '今天睡眠不好怎么办？'
text = PROMPT.format(instruction=instruction)
input_ids = tokenizer(text, return_tensors='pt')['input_ids'].to('cuda')
generate_ids = model.generate(
    input_ids=input_ids,
    max_new_tokens=512,
    streamer=streamer,
    pad_token_id=tokenizer.pad_token_id,
    temperature=0.7,
    top_k=50,
    do_sample=True)

开源代码链接：https://github.com/modelscope/modelscope/blob/master/examples/pytorch/llm/baichuan_sft.py

百川13B模型发布及魔搭最佳实践

准备Lora：

导入datasets

配置Config

ModelScope模型即服务

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