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benchmark_train.sh
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#!/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
# or
# bash run_benchmark_train.sh config.txt
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=(${cur_dir})
lastIndex=$((${#arr[@]}-1))
echo ${arr[lastIndex]}
}
FILENAME=$1
# copy FILENAME as new
new_filename="./test_tipc/benchmark_train.txt"
cmd=`yes|cp $FILENAME $new_filename`
FILENAME=$new_filename
# MODE must be one of ['benchmark_train']
MODE=$2
PARAMS=$3
# bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt benchmark_train dynamic_bs8_null_DP_N1C1
IFS=$'\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 $line_num + 1`
epoch=$(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 $line_num + 1`
run_mode=$(func_parser_value "${lines[line_num]}")
line_num=`expr $line_num + 1`
fp_items="null"
line_num=`expr $line_num + 1`
device_num=$(func_parser_value "${lines[line_num]}")
line_num=`expr $line_num + 1`
gpu_config=$(func_parser_value "${lines[line_num]}")
line_num=`expr $line_num + 1`
cpu_config=$(func_parser_value "${lines[line_num]}")
flags_value=$(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"
# The number of lines in which train params can be replaced.
line_python=3
line_gpuid=4
line_precision=6
line_epoch=7
line_batchsize=9
line_profile=13
line_eval_py=24
line_export_py=30
if [ ${MODE} = "benchmark_train" ]; then
line_runmode=56
fi
func_sed_params "$FILENAME" "${line_eval_py}" "null"
func_sed_params "$FILENAME" "${line_export_py}" "null"
func_sed_params "$FILENAME" "${line_python}" "$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_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[3]}
func_sed_params "$FILENAME" "${line_runmode}" "$run_mode"
device_num=${params_list[4]}
IFS=";"
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" "${line_precision}" "$precision"
func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$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_mode}_${device_num}_profiling"
func_sed_params "$FILENAME" "${line_gpuid}" "0" # sed used gpu_id
# set profile_option params
tmp=`sed -i "${line_profile}s/.*/${profile_option}/" "${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_mode}_${device_num}_log"
speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
func_sed_params "$FILENAME" "${line_profile}" "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_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} \
--fp_item ${precision} \
--keyword ips: \
--skip_steps 2 \
--device_num ${device_num} \
--speed_unit example/s \
--convergence_key auc: "
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_mode}_${device_num}_log"
speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id" # sed used gpu_id
func_sed_params "$FILENAME" "${line_profile}" "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_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} \
--fp_item ${precision} \
--keyword ips: \
--skip_steps 2 \
--device_num ${device_num} \
--speed_unit example/s \
--convergence_key auc: "
echo $cmd
eval $cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${cmd}" "${status_log}"
fi
done
done
done