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#!/usr/bin/env bash
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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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config_path=${2:-"conf/conformer.yaml"}
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decode_config_path=${3:-"conf/tuning/decode.yaml"}
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output=${4:-"exp/conformer"}
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seed=${5:-"0"}
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ngpu=${6:-"1"}
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profiler_options=${7:-"None"}
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batch_size=${8:-"32"}
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fp_item=${9:-"fp32"}
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model_item=${10:-"conformer"}
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benchmark_max_step=0
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run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
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# 添加日志解析需要的参数
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base_batch_size=${batch_size}
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mission_name="语音识别"
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direction_id="1"
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ips_unit="sent./sec"
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skip_steps=10 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
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keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
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index="1"
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model_name=${model_item}_bs${batch_size}_${fp_item}
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# 以下不用修改
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device=${CUDA_VISIBLE_DEVICES//,/ }
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arr=(${device})
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num_gpu_devices=${#arr[*]}
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log_file=${run_log_path}/recoder_${model_item}_${run_mode}_bs${batch_size}_${fp_item}_ngpu${ngpu}
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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="--config=${config_path} \
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--decode_cfg=${decode_config_path} \
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--output=${output} \
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--seed=${seed} \
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--ngpu=${ngpu} \
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--benchmark-batch-size ${batch_size} \
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--benchmark-max-step ${benchmark_max_step} "
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if [ ${profiler_options} != "None" ]; then
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train_cmd=${train_cmd}" --profiler-options=${profiler_options}"
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fi
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case ${run_mode} in
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sp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
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mp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
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*) echo "choose run_mode(sp or mp)"; exit 1;
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esac
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echo ${train_cmd}
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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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trap 'for pid in $(jobs -pr); do kill -KILL $pid; done' INT QUIT TERM
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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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source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
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_set_params $@
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#_train # 如果只想产出训练log,不解析,可取消注释
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_run # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
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