#!/bin/bash source test_tipc/common_func.sh # set env python=python export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3` export model_commit=$(git log|head -n1|awk '{print $2}') export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`) export frame_version=${str_tmp%%.post*} export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`) # run benchmark sh # Usage: # bash run_benchmark_train.sh config.txt params function func_parser_params(){ strs=$1 IFS="=" array=(${strs}) tmp=${array[1]} echo ${tmp} } function func_sed_params(){ filename=$1 line=$2 param_value=$3 params=`sed -n "${line}p" $filename` IFS=":" array=(${params}) key=${array[0]} value=${array[1]} if [[ $value =~ 'benchmark_train' ]];then IFS='=' _val=(${value}) param_value="${_val[0]}=${param_value}" fi new_params="${key}:${param_value}" IFS=";" cmd="sed -i '${line}s/.*/${new_params}/' '${filename}'" eval $cmd } function set_gpu_id(){ string=$1 _str=${string:1:6} IFS="C" arr=(${_str}) M=${arr[0]} P=${arr[1]} gn=`expr $P - 1` gpu_num=`expr $gn / $M` seq=`seq -s "," 0 $gpu_num` echo $seq } function get_repo_name(){ IFS=";" cur_dir=$(pwd) IFS="/" arr=(${cur_dir}) echo ${arr[-1]} } FILENAME=$1 cp FILENAME as new FILENAME new_filename="./test_tipc/benchmark_train.txt" cmd=`yes|cp $FILENAME $new_filename` FILENAME=$new_filename # MODE be one of ['benchmark_train'] MODE=$2 PARAMS=$3 # bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2.0/train_benchmark.txt benchmark_train dynamic_bs8_null_SingleP_DP_N1C1 IFS=$'\n' # parser params from train_benchmark.txt dataline=`cat $FILENAME` # parser params IFS=$'\n' lines=(${dataline}) model_name=$(func_parser_value "${lines[1]}") # 获取benchmark_params所在的行数 line_num=`grep -n "train_benchmark_params" $FILENAME | cut -d ":" -f 1` # for train log parser batch_size=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` fp_items=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` epoch=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` profile_option_key=$(func_parser_key "${lines[line_num]}") profile_option_params=$(func_parser_value "${lines[line_num]}") profile_option="${profile_option_key}:${profile_option_params}" line_num=`expr $line_num + 1` flags_value=$(func_parser_value "${lines[line_num]}") # set flags IFS=";" flags_list=(${flags_value}) for _flag in ${flags_list[*]}; do cmd="export ${_flag}" eval $cmd done # set log_name repo_name=$(get_repo_name ) SAVE_LOG=${BENCHMARK_LOG_DIR:-$(pwd)} # */benchmark_log mkdir -p "${SAVE_LOG}/benchmark_log/" status_log="${SAVE_LOG}/benchmark_log/results.log" # set eval and export as null # line eval_py: 24 # line export_py: 30 func_sed_params "$FILENAME" "24" "null" func_sed_params "$FILENAME" "30" "null" func_sed_params "$FILENAME" "3" "$python" # if params if [ ! -n "$PARAMS" ] ;then # PARAMS input is not a word. IFS="|" batch_size_list=(${batch_size}) fp_items_list=(${fp_items}) device_num_list=(N1C4) run_mode="DP" else # parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_process_type}_${run_mode}_${device_num} IFS="_" params_list=(${PARAMS}) model_type=${params_list[0]} batch_size=${params_list[1]} batch_size=`echo ${batch_size} | tr -cd "[0-9]" ` precision=${params_list[2]} run_process_type=${params_list[3]} run_mode=${params_list[4]} device_num=${params_list[5]} IFS=";" if [ ${precision} = "null" ];then precision="fp32" fi fp_items_list=($precision) batch_size_list=($batch_size) device_num_list=($device_num) fi IFS="|" for batch_size in ${batch_size_list[*]}; do for precision in ${fp_items_list[*]}; do for device_num in ${device_num_list[*]}; do # sed batchsize and precision func_sed_params "$FILENAME" "6" "$precision" func_sed_params "$FILENAME" "9" "$MODE=$batch_size" func_sed_params "$FILENAME" "7" "$MODE=$epoch" gpu_id=$(set_gpu_id $device_num) if [ ${#gpu_id} -le 1 ];then run_process_type="SingleP" log_path="$SAVE_LOG/profiling_log" mkdir -p $log_path log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_profiling" func_sed_params "$FILENAME" "4" "0" # sed used gpu_id # set profile_option params tmp=`sed -i "13s/.*/${profile_option}/" "${FILENAME}"` # run test_train_inference_python.sh cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 " echo $cmd eval $cmd eval "cat ${log_path}/${log_name}" # without profile log_path="$SAVE_LOG/train_log" speed_log_path="$SAVE_LOG/index" mkdir -p $log_path mkdir -p $speed_log_path log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log" speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed" func_sed_params "$FILENAME" "13" "null" # sed profile_id as null cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 " echo $cmd job_bt=`date '+%Y%m%d%H%M%S'` eval $cmd job_et=`date '+%Y%m%d%H%M%S'` export model_run_time=$((${job_et}-${job_bt})) eval "cat ${log_path}/${log_name}" # parser log _model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}" cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \ --speed_log_file '${speed_log_path}/${speed_log_name}' \ --model_name ${_model_name} \ --base_batch_size ${batch_size} \ --run_mode ${run_mode} \ --run_process_type ${run_process_type} \ --fp_item ${precision} \ --keyword ips: \ --skip_steps 2 \ --device_num ${device_num} \ --speed_unit samples/s \ --convergence_key loss: " echo $cmd eval $cmd last_status=${PIPESTATUS[0]} status_check $last_status "${cmd}" "${status_log}" else IFS=";" unset_env=`unset CUDA_VISIBLE_DEVICES` run_process_type="MultiP" log_path="$SAVE_LOG/train_log" speed_log_path="$SAVE_LOG/index" mkdir -p $log_path mkdir -p $speed_log_path log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log" speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed" func_sed_params "$FILENAME" "4" "$gpu_id" # sed used gpu_id func_sed_params "$FILENAME" "13" "null" # sed --profile_option as null cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 " echo $cmd job_bt=`date '+%Y%m%d%H%M%S'` eval $cmd job_et=`date '+%Y%m%d%H%M%S'` export model_run_time=$((${job_et}-${job_bt})) eval "cat ${log_path}/${log_name}" # parser log _model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}" cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \ --speed_log_file '${speed_log_path}/${speed_log_name}' \ --model_name ${_model_name} \ --base_batch_size ${batch_size} \ --run_mode ${run_mode} \ --run_process_type ${run_process_type} \ --fp_item ${precision} \ --keyword ips: \ --skip_steps 2 \ --device_num ${device_num} \ --speed_unit images/s \ --convergence_key loss: " echo $cmd eval $cmd last_status=${PIPESTATUS[0]} status_check $last_status "${cmd}" "${status_log}" fi done done done