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#!/usr/bin/env 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=${2:-"8"}
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fp_item=${3:-"fp32"} # fp32|fp16
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max_iter=${4:-"500"} # 可选，如果需要修改代码提前中断
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model_item=${5:-"model_item"}
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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=${batch_size}
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mission_name="语音合成"
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direction_id="1"
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ips_unit="sequences/sec"
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skip_steps=10 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
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keyword="avg_ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
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index="1"
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model_name=${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}/${model_item}_${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="--batch-size=${batch_size}\
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--max-iter=${max_iter}
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--train-metadata=dump/train/norm/metadata.jsonl \
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--dev-metadata=dump/dev/norm/metadata.jsonl \
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--config=examples/csmsc/voc1/conf/default.yaml \
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--output-dir=exp/default \
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--run-benchmark=true"
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case ${run_mode} in
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sp) train_cmd="python paddlespeech/t2s/exps/gan_vocoder/parallelwave_gan/train.py --ngpu=1 ${train_cmd}" ;;
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mp) rm -rf ./mylog
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train_cmd="python paddlespeech/t2s/exps/gan_vocoder/parallelwave_gan/train.py --ngpu=8 ${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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bash tests/test_tipc/barrier.sh
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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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