mirror of https://github.com/hpcaitech/ColossalAI
60 lines
2.7 KiB
Bash
Executable File
60 lines
2.7 KiB
Bash
Executable File
set_n_least_used_CUDA_VISIBLE_DEVICES() {
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local n=${1:-"9999"}
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echo "GPU Memory Usage:"
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local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv |
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tail -n +2 |
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nl -v 0 |
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tee /dev/tty |
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sort -g -k 2 |
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awk '{print $1}' |
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head -n $n)
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export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g')
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echo "Now CUDA_VISIBLE_DEVICES is set to:"
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echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
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}
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set_n_least_used_CUDA_VISIBLE_DEVICES 4
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PROJECT_NAME="sft"
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PARENT_SAVE_DIR="/home/nvme-share/home/yeanbang/data/model/hh_rlhf_sheared_llama" # Path to a folder to save checkpoints
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PARENT_TENSORBOARD_DIR="/home/nvme-share/home/yeanbang/data/experiments/sft/log" # Path to a folder to save logs
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PARENT_CONFIG_FILE="/home/nvme-share/home/yeanbang/data/experiments/kto/log" # Path to a folder to save training config logs
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PRETRAINED_MODEL_PATH="/home/nvme-share/share/models/Sheared-LLaMA-1.3B" # huggingface or local model path
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PRETRAINED_TOKENIZER_PATH="/home/nvme-share/share/models/Sheared-LLaMA-1.3B" # huggingface or local tokenizer path
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declare -a dataset=(
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00000
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00001
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00002
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00003
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00004
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00005
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00006
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00007
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00008
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/home/nvme-share/home/yeanbang/data/experiments/sft/arrow/part-00009
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)
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TIMESTAMP=$(date +%Y-%m-%d-%H-%M-%S)
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FULL_PROJECT_NAME="${PROJECT_NAME}-${TIMESTAMP}"
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SAVE_DIR="${PARENT_SAVE_DIR}${FULL_PROJECT_NAME}"
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CONFIG_FILE="${PARENT_CONFIG_FILE}-${FULL_PROJECT_NAME}.json"
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echo $(which colossalai)
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echo $(which python)
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# the real batch size for gradient descent is number_of_node_in_hostfile * nproc_per_node * train_batch_size
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colossalai run --nproc_per_node 4 --master_port 31312 --hostfile ./hostfile train_sft.py \
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--pretrain $PRETRAINED_MODEL_PATH \
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--tokenizer_dir $PRETRAINED_TOKENIZER_PATH \
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--save_interval 2000 \
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--dataset ${dataset[@]} \
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--save_path $SAVE_DIR \
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--config_file $CONFIG_FILE \
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--lora_rank 0 \
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--plugin zero2 \
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--batch_size 8 \
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--max_epochs 1 \
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--accumulation_steps 2 \
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--lr 5e-5 \
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--max_len 4096 \
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--grad_checkpoint \
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--use_flash_attn
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