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PaddleSpeech/tests/benchmark/conformer/run_benchmark.sh

77 lines
3.1 KiB

#!/usr/bin/env bash
# 运行示例CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode}
# 参数说明
function _set_params(){
run_mode=${1:-"sp"} # 单卡sp|多卡mp
config_path=${2:-"conf/conformer.yaml"}
decode_config_path=${3:-"conf/tuning/decode.yaml"}
output=${4:-"exp/conformer"}
seed=${5:-"0"}
ngpu=${6:-"1"}
profiler_options=${7:-"None"}
batch_size=${8:-"32"}
fp_item=${9:-"fp32"}
model_item=${10:-"conformer"}
benchmark_max_step=0
run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
# 添加日志解析需要的参数
base_batch_size=${batch_size}
mission_name="语音识别"
direction_id="1"
ips_unit="sent./sec"
skip_steps=10 # 解析日志有些模型前几个step耗时长需要跳过 (必填)
keyword="ips:" # 解析日志,筛选出数据所在行的关键字 (必填)
index="1"
model_name=${model_item}_bs${batch_size}_${fp_item}
# 以下不用修改
device=${CUDA_VISIBLE_DEVICES//,/ }
arr=(${device})
num_gpu_devices=${#arr[*]}
log_file=${run_log_path}/recoder_${model_item}_${run_mode}_bs${batch_size}_${fp_item}_ngpu${ngpu}
}
function _train(){
echo "Train on ${num_gpu_devices} GPUs"
echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
train_cmd="--config=${config_path} \
--decode_cfg=${decode_config_path} \
--output=${output} \
--seed=${seed} \
--ngpu=${ngpu} \
--benchmark-batch-size ${batch_size} \
--benchmark-max-step ${benchmark_max_step} "
if [ ${profiler_options} != "None" ]; then
train_cmd=${train_cmd}" --profiler-options=${profiler_options}"
fi
case ${run_mode} in
sp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
mp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
*) echo "choose run_mode(sp or mp)"; exit 1;
esac
echo ${train_cmd}
# 以下不用修改
3 years ago
timeout 15m ${train_cmd} > ${log_file} 2>&1
if [ $? -ne 0 ];then
echo -e "${model_name}, FAIL"
export job_fail_flag=1
else
echo -e "${model_name}, SUCCESS"
export job_fail_flag=0
fi
trap 'for pid in $(jobs -pr); do kill -KILL $pid; done' INT QUIT TERM
if [ $run_mode = "mp" -a -d mylog ]; then
rm ${log_file}
cp mylog/workerlog.0 ${log_file}
fi
}
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
_set_params $@
#_train # 如果只想产出训练log,不解析,可取消注释
_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