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#!/bin/bash
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set -xe
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# 运行示例：CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode}
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# 参数说明
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function _set_params(){
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run_mode=${1:-"sp"} # 单卡sp|多卡mp
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batch_size=${2:-"64"}
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fp_item=${3:-"fp32"} # fp32|fp16
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max_iter=${4:-"500"} # 可选，如果需要修改代码提前中断
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model_name=${5:-"model_name"}
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run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
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# 以下不用修改
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device=${CUDA_VISIBLE_DEVICES//,/ }
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arr=(${device})
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num_gpu_devices=${#arr[*]}
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log_file=${run_log_path}/${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
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}
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function _train(){
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echo "Train on ${num_gpu_devices} GPUs"
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echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
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train_cmd="--model_name=${model_name}
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--batch_size=${batch_size}
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--fp=${fp_item} \
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--max_iter=${max_iter} "
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case ${run_mode} in
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sp) train_cmd="python -u tools/train.py "${train_cmd}" ;;
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mp)
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train_cmd="python -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py "${train_cmd}"
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log_parse_file="mylog/workerlog.0" ;;
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*) echo "choose run_mode(sp or mp)"; exit 1;
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esac
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# 以下不用修改
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timeout 15m ${train_cmd} > ${log_file} 2>&1
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if [ $? -ne 0 ];then
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echo -e "${model_name}, FAIL"
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export job_fail_flag=1
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else
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echo -e "${model_name}, SUCCESS"
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export job_fail_flag=0
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fi
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kill -9 `ps -ef|grep 'python'|awk '{print $2}'`
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if [ $run_mode = "mp" -a -d mylog ]; then
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rm ${log_file}
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cp mylog/workerlog.0 ${log_file}
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fi
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}
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_set_params $@
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_train
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