You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/tests/benchmark/conformer/run_benchmark.sh

74 lines
3.0 KiB

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

#!/usr/bin/env bash
set -xe
# 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=${2:-"conf/conformer.yaml"}
output=${3:-"exp/conformer"}
seed=${4:-"0"}
ngpu=${5:-"1"}
profiler_options=${6:-"None"}
batch_size=${7:-"32"}
fp_item=${8:-"fp32"}
model_item=${9:-"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=${config_path}
--output=${output}
--seed=${seed}
--ngpu=${ngpu}
--profiler-options "${profiler_options}"
--benchmark-batch-size ${batch_size}
--benchmark-max-step ${benchmark_max_step} "
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
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