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PRETRAINED_MODEL_PATH="/root/notebook/common_data/Meta-Llama-3-8B" # huggingface or local model path
cd ColossalAI/examples/inference/
colossalai run --nproc_per_node 1 llama_generation.py -m $PRETRAINED_MODEL_PATH --max_length 80

colossalai run --nproc_per_node 2 llama_generation.py -m $PRETRAINED_MODEL_PATH --max_length 80 --tp_size 2

PRETRAINED_MODEL_PATH="/root/notebook/common_data/Meta-Llama-3-8B"
git pull # update example benchmark from branch feature/colossal-infer
cd ColossalAI/examples/inference/
python benchmark_llama3.py -m llama3-8b -b 32 -s 128 -o 256 -p $PRETRAINED_MODEL_PATH

cd /root/ColossalAI
BUILD_EXT=1 pip install .

mkdir /root/training_outputs
mkdir /root/training_outputs/checkpoints
mkdir /root/training_outputs/configs
mkdir /root/training_outputs/tensorboards

cd /root/ColossalAI/applications/Colossal-LLaMA/
cp hostfile.example hostfile

{"source": "", "target": "Lionel Andrés Messi(Spanish pronunciation: [ljoˈnel anˈdɾes ˈmesi] (i); born 24 June 1987), also known as Leo Messi, is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer club Inter Miami and the Argentina national team.", "category": "sports"}
{"source": "猜谜语：一身卷卷细毛，吃的青青野草，过了数九寒冬，无私献出白毛。（打一动物）", "target": "白羊", "category": "riddle"}

python prepare_pretrain_dataset.py \
    --data_input_dirs "Directory that contains one or more JSONL file(s)>" \
    --tokenizer_dir "Tokenizer directory" \
    --data_output_dirs "Spliced tokenized output" \
    --max_length 8192 \
    --num_spliced_dataset_bins 10

{"messages": [{"from": "human", "content": "What are the three primary colors?"}, {"from": "assistant", "content": "The three primary colors are red, blue, and yellow."}]}
{"messages": [{"from": "human", "content": "解释个人电脑和服务器之间的区别。"}, {"from": "assistant", "content": "个人电脑和服务器是两种不同类型的计算机系统，它们的主要区别在于用途、硬件配置和性能。个人电脑，顾名思义，是为个人使用而设计的计算机。它们通常用于日常的工作、娱乐和学习，可以运行各种各样的应用程序和游戏。个人电脑的硬件配置一般是按照标准配置来设计的，不过也可以根据个人需求进行定制。而服务器是为了满足大量用户的需求而设计的计算机系统，它们通常用于为用户提供各种网络服务，如网站、电子邮件和文件传输等。服务器通常需要高性能的硬件配置，并且可以承受高负载和长时间的运行。由于服务器需要支持大量用户的访问，它们通常配备多核处理器、大容量内存和大容量硬盘驱动器，以提高系统的运行速度和稳定性。总之，个人电脑和服务器之间的主要区别在于它们的用途、硬件配置和性能。个人电脑用于个人使用，而服务器用于支持大量用户的访问。服务器的硬件配置通常比个人电脑更高，以保证系统的性能和稳定性。"}]}

python prepare_sft_dataset.py \
    --data_input_dirs "Directory that contains one or more JSONL file(s)>" \
    --tokenizer_dir "Tokenizer directory" \
    --data_output_dirs "Spliced tokenized output" \
    --max_length 8192 \
    --num_spliced_dataset_bins 10 \
    --llama_version 3

cp train.example.sh train.sh
#更新训练脚本

PROJECT_NAME="LLaMA-3-8B-cpt"
PARENT_SAVE_DIR="/root/training_outputs/checkpoints/" # Path to a folder to save checkpoints
PARENT_TENSORBOARD_DIR="/root/training_outputs/tensorboards/" # Path to a folder to save logs
PARENT_CONFIG_FILE="/root/training_outputs/configs/" # Path to a folder to save training config logs
PRETRAINED_MODEL_PATH="/root/notebook/common_data/Meta-Llama-3-8B" # huggingface or local model path


# 以预置已处理数据集为例
declare -a dataset=(
    /root/notebook/common_data/tokenized-cpt-data/arrow/part-00000
    /root/notebook/common_data/tokenized-cpt-data/arrow/part-00001
    /root/notebook/common_data/tokenized-cpt-data/arrow/part-00002
)

TIMESTAMP=$(date +%Y-%m-%d-%H-%M-%S)
FULL_PROJECT_NAME="${PROJECT_NAME}-${TIMESTAMP}"
SAVE_DIR="${PARENT_SAVE_DIR}${FULL_PROJECT_NAME}"
CONFIG_FILE="${PARENT_CONFIG_FILE}${FULL_PROJECT_NAME}.json"

colossalai run --nproc_per_node 8 --hostfile hostfile --master_port 31312 train.py \
    --pretrained $PRETRAINED_MODEL_PATH \
    --dataset ${dataset[@]} \
    --plugin "zero2" \
    --save_interval 400 \
    --save_dir $SAVE_DIR \
    --tensorboard_dir $TENSORBOARD_DIR \
    --config_file $CONFIG_FILE \
    --num_epochs 1 \
    --micro_batch_size 2 \
    --lr 1e-4 \
    --mixed_precision "bf16" \
    --grad_clip 1.0 \
    --weight_decay 0.01 \
    --warmup_steps 100 \
    --use_grad_checkpoint \
    --use_flash_attn \

git clone https://github.com/hpcaitech/ColossalAI
cd ColossalAI/examples/language/llama
BUILD_EXT=1 pip install -U git+https://github.com/hpcaitech/ColossalAI
pip install -r requirements.txt
export PYTHONPATH=$(realpath ..)