#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
DIR=`pwd`

# Guide:
# This script supports distributed training on multi-gpu workers (as well as single-worker training).
# Please set the options below according to the comments.
# For multi-gpu workers training, these options should be manually set for each worker.
# After setting the options, please run the script on each worker.

# Number of GPUs per GPU worker
GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')

# Number of GPU workers, for single-worker training, please set to 1
NNODES=${WORLD_SIZE:-1} # modified to adapt cluster

# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
NODE_RANK=${RANK:-0} # modified to adapt cluster

# The ip address of the rank-0 worker, for single-worker training, please set to localhost
MASTER_ADDR=${MASTER_ADDR:-localhost}

# The port for communication
MASTER_PORT=${MASTER_PORT:-6001}

MODEL="Qwen2.5-7B"
DATA="train.jsonl"

function usage() {
    echo '
Usage: bash finetune/finetune_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
'
}

while [[ "$1" != "" ]]; do
    case $1 in
        -m | --model )
            shift
            MODEL=$1
            ;;
        -d | --data )
            shift
            DATA=$1
            ;;
        -h | --help )
            usage
            exit 0
            ;;
        * )
            echo "Unknown argument ${1}"
            exit 1
            ;;
    esac
    shift
done

DISTRIBUTED_ARGS="
    --nproc_per_node $GPUS_PER_NODE \
    --nnodes $NNODES \
    --node_rank $NODE_RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT
"

echo $DISTRIBUTED_ARGS

filename=$(basename "$0")

# 去掉扩展名（假设是 .sh）
base="${filename%.sh}"

# per_device_train_batch_size 4 for 7B 8Gpu Zero 3
torchrun $DISTRIBUTED_ARGS training_scripts/finetune_qwen_base.py \
    --model_name_or_path $MODEL \
    --data_path $DATA \
    --bf16 True \
    --output_dir ckpts/$base \
    --num_train_epochs 3 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy "no" \
    --save_strategy "epoch" \
    --save_steps 1000 \
    --save_total_limit 10 \
    --learning_rate 1e-5 \
    --weight_decay 0.1 \
    --adam_beta2 0.95 \
    --warmup_ratio 0.1 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --report_to "none" \
    --model_max_length 4096 \
    --gradient_checkpointing True \
    --lazy_preprocess True \
    --deepspeed training_scripts/ds_config_zero3.json
