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#!/usr/bin/env bash
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source test_tipc/common_func.sh
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FILENAME=$1
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# $MODE be one of {'lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer'}
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MODE=$2
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dataline=$(awk 'NR>=1{print}' $FILENAME)
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# Parse params
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IFS=$'\n'
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lines=(${dataline})
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# Training params
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task_name=$(parse_first_value "${lines[1]}")
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model_name=$(parse_second_value "${lines[1]}")
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python=$(func_parser_value "${lines[2]}")
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gpu_list=$(func_parser_value "${lines[3]}")
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train_use_gpu_key=$(func_parser_key "${lines[4]}")
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train_use_gpu_value=$(func_parser_value "${lines[4]}")
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autocast_list=$(func_parser_value "${lines[5]}")
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autocast_key=$(func_parser_key "${lines[5]}")
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epoch_key=$(func_parser_key "${lines[6]}")
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epoch_value=$(func_parser_params "${lines[6]}")
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save_model_key=$(func_parser_key "${lines[7]}")
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train_batch_key=$(func_parser_key "${lines[8]}")
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train_batch_value=$(func_parser_params "${lines[8]}")
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pretrain_model_key=$(func_parser_key "${lines[9]}")
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pretrain_model_value=$(func_parser_value "${lines[9]}")
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train_config_key=$(func_parser_key "${lines[10]}")
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train_config_value=$(func_parser_params "${lines[10]}")
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train_model_name=$(func_parser_value "${lines[11]}")
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train_param_key1=$(func_parser_key "${lines[12]}")
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train_param_value1=$(func_parser_value "${lines[12]}")
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trainer_list=$(func_parser_value "${lines[14]}")
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trainer_norm=$(func_parser_key "${lines[15]}")
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norm_trainer=$(func_parser_value "${lines[15]}")
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pact_key=$(func_parser_key "${lines[16]}")
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pact_trainer=$(func_parser_value "${lines[16]}")
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fpgm_key=$(func_parser_key "${lines[17]}")
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fpgm_trainer=$(func_parser_value "${lines[17]}")
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distill_key=$(func_parser_key "${lines[18]}")
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distill_trainer=$(func_parser_value "${lines[18]}")
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trainer_key1=$(func_parser_key "${lines[19]}")
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trainer_value1=$(func_parser_value "${lines[19]}")
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trainer_key2=$(func_parser_key "${lines[20]}")
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trainer_value2=$(func_parser_value "${lines[20]}")
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eval_py=$(func_parser_value "${lines[23]}")
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eval_key1=$(func_parser_key "${lines[24]}")
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eval_value1=$(func_parser_value "${lines[24]}")
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save_infer_key=$(func_parser_key "${lines[27]}")
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export_weight=$(func_parser_key "${lines[28]}")
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export_shape_key=$(func_parser_key "${lines[29]}")
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export_shape_value=$(func_parser_value "${lines[29]}")
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norm_export=$(func_parser_value "${lines[30]}")
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pact_export=$(func_parser_value "${lines[31]}")
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fpgm_export=$(func_parser_value "${lines[32]}")
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distill_export=$(func_parser_value "${lines[33]}")
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export_key1=$(func_parser_key "${lines[34]}")
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export_value1=$(func_parser_value "${lines[34]}")
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export_key2=$(func_parser_key "${lines[35]}")
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export_value2=$(func_parser_value "${lines[35]}")
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inference_dir=$(func_parser_value "${lines[36]}")
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# Params of inference model
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infer_model_dir_list=$(func_parser_value "${lines[37]}")
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infer_export_list=$(func_parser_value "${lines[38]}")
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infer_is_quant=$(func_parser_value "${lines[39]}")
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# Inference params
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inference_py=$(func_parser_value "${lines[40]}")
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use_gpu_key=$(func_parser_key "${lines[41]}")
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use_gpu_list=$(func_parser_value "${lines[41]}")
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use_mkldnn_key=$(func_parser_key "${lines[42]}")
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use_mkldnn_list=$(func_parser_value "${lines[42]}")
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cpu_threads_key=$(func_parser_key "${lines[43]}")
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cpu_threads_list=$(func_parser_value "${lines[43]}")
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batch_size_key=$(func_parser_key "${lines[44]}")
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batch_size_list=$(func_parser_value "${lines[44]}")
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use_trt_key=$(func_parser_key "${lines[45]}")
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use_trt_list=$(func_parser_value "${lines[45]}")
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precision_key=$(func_parser_key "${lines[46]}")
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precision_list=$(func_parser_value "${lines[46]}")
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infer_model_key=$(func_parser_key "${lines[47]}")
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infer_config_key=$(func_parser_key "${lines[48]}")
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infer_config_value=$(func_parser_value "${lines[48]}")
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save_log_key=$(func_parser_key "${lines[49]}")
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benchmark_key=$(func_parser_key "${lines[50]}")
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benchmark_value=$(func_parser_value "${lines[50]}")
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infer_key1=$(func_parser_key "${lines[51]}")
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infer_value1=$(func_parser_value "${lines[51]}")
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infer_key2=$(func_parser_key "${lines[52]}")
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infer_value2=$(func_parser_value "${lines[52]}")
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OUT_PATH="./test_tipc/output/${task_name}/${model_name}/${MODE}"
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mkdir -p ${OUT_PATH}
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status_log="${OUT_PATH}/results_python.log"
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echo "------------------------ ${MODE} ------------------------" >> "${status_log}"
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# Parse extra args
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parse_extra_args "${lines[@]}"
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for params in ${extra_args[*]}; do
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IFS=':'
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arr=(${params})
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key=${arr[0]}
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value=${arr[1]}
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:
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done
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function func_inference() {
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local IFS='|'
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local _python=$1
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local _script="$2"
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local _model_dir="$3"
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local _log_path="$4"
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local _config="$5"
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local set_infer_config=$(func_set_params "${infer_config_key}" "${_config}")
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local set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
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local set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
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local set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
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local set_infer_params2=$(func_set_params "${infer_key2}" "${infer_value2}")
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# Do inference
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for use_gpu in ${use_gpu_list[*]}; do
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local set_device=$(func_set_params "${use_gpu_key}" "${use_gpu}")
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if [ ${use_gpu} = 'False' ] || [ ${use_gpu} = 'cpu' ]; then
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for use_mkldnn in ${use_mkldnn_list[*]}; do
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if [ ${use_mkldnn} = 'False' ]; then
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continue
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fi
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for precision in ${precision_list[*]}; do
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if [ ${use_mkldnn} = 'False' ] && [ ${precision} = 'fp16' ]; then
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continue
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fi # Skip when enable fp16 but disable mkldnn
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for threads in ${cpu_threads_list[*]}; do
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for batch_size in ${batch_size_list[*]}; do
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local _save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
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local infer_value1="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}_results"
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local set_mkldnn=$(func_set_params "${use_mkldnn_key}" "${use_mkldnn}")
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local set_precision=$(func_set_params "${precision_key}" "${precision}")
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local set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
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local set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
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local cmd="${_python} ${_script} ${set_config} ${set_device} ${set_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_benchmark} ${set_precision} ${set_infer_params1} ${set_infer_params2}"
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echo ${cmd}
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run_command "${cmd}" "${_save_log_path}"
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last_status=${PIPESTATUS[0]}
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status_check ${last_status} "${cmd}" "${status_log}" "${model_name}"
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done
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done
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done
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done
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elif [ ${use_gpu} = 'True' ] || [ ${use_gpu} = 'gpu' ]; then
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for use_trt in ${use_trt_list[*]}; do
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for precision in ${precision_list[*]}; do
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if [ ${precision} = 'fp16' ] && [ ${use_trt} = 'False' ]; then
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continue
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fi # Skip when enable fp16 but disable trt
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for batch_size in ${batch_size_list[*]}; do
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local _save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
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local infer_value1="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}_results"
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local set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
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local set_precision=$(func_set_params "${precision_key}" "${precision}")
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local set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
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local cmd="${_python} ${_script} ${set_config} ${set_device} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_benchmark} ${set_infer_params2}"
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echo ${cmd}
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run_command "${cmd}" "${_save_log_path}"
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${cmd}" "${status_log}" "${model_name}"
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done
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done
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done
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else
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echo "Currently, hardwares other than CPU and GPU are not supported!"
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fi
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done
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}
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if [ ${MODE} = 'whole_infer' ]; then
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GPUID=$3
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if [ ${#GPUID} -le 0 ]; then
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env=""
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else
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env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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fi
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if [ ${infer_model_dir_list} == 'null' ]; then
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echo -e "\033[33m No inference model is specified! \033[0m"
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exit 1
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fi
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# Set CUDA_VISIBLE_DEVICES
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eval ${env}
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export count=0
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IFS='|'
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infer_run_exports=(${infer_export_list})
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for infer_model in ${infer_model_dir_list[*]}; do
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# Run export
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if [ ${infer_run_exports[count]} != 'null' ]; then
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save_infer_dir="${infer_model}/static"
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set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
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set_export_shape=$(func_set_params "${export_shape_key}" "${export_shape_value}")
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set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
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export_cmd="${python} ${infer_run_exports[count]} ${set_export_weight} ${set_save_infer_key} ${set_export_shape}"
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echo ${infer_run_exports[count]}
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eval ${export_cmd}
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status_export=$?
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status_check ${status_export} "${export_cmd}" "${status_log}" "${model_name}"
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else
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save_infer_dir=${infer_model}
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fi
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# Run inference
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func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${OUT_PATH}" "${infer_config_value}"
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count=$((${count} + 1))
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done
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else
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IFS='|'
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export count=0
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USE_GPU_KEY=(${train_use_gpu_value})
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for gpu in ${gpu_list[*]}; do
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train_use_gpu=${USE_GPU_KEY[count]}
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count=$((${count} + 1))
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ips=""
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if [ ${gpu} = '-1' ]; then
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env=""
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elif [ ${#gpu} -le 1 ]; then
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env="export CUDA_VISIBLE_DEVICES=${gpu}"
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eval ${env}
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elif [ ${#gpu} -le 15 ]; then
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IFS=','
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array=(${gpu})
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env="export CUDA_VISIBLE_DEVICES=${array[0]}"
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IFS='|'
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else
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IFS=';'
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array=(${gpu})
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ips=${array[0]}
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gpu=${array[1]}
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IFS='|'
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env=""
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fi
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for autocast in ${autocast_list[*]}; do
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if [ ${autocast} = 'amp' ]; then
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set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
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else
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set_amp_config=""
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fi
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for trainer in ${trainer_list[*]}; do
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if [ ${trainer} = ${pact_key} ]; then
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run_train=${pact_trainer}
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run_export=${pact_export}
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elif [ ${trainer} = "${fpgm_key}" ]; then
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run_train=${fpgm_trainer}
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run_export=${fpgm_export}
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elif [ ${trainer} = "${distill_key}" ]; then
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run_train=${distill_trainer}
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run_export=${distill_export}
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elif [ ${trainer} = ${trainer_key1} ]; then
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run_train=${trainer_value1}
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run_export=${export_value1}
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elif [[ ${trainer} = ${trainer_key2} ]]; then
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run_train=${trainer_value2}
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run_export=${export_value2}
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else
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run_train=${norm_trainer}
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run_export=${norm_export}
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fi
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if [ ${run_train} = 'null' ]; then
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continue
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fi
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set_config=$(func_set_params "${train_config_key}" "${train_config_value}")
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set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
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set_epoch=$(func_set_params "${epoch_key}" "${epoch_value}")
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set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
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set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
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set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
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set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
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# If length of ips >= 15, then it is seen as multi-machine.
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# 15 is the min length of ips info for multi-machine: 0.0.0.0,0.0.0.0
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if [ ${#ips} -le 15 ]; then
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save_dir="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
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nodes=1
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else
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IFS=','
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ips_array=(${ips})
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IFS='|'
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nodes=${#ips_array[@]}
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save_dir="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
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fi
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log_path="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}.log"
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# Load pretrained model from norm training if current trainer is pact or fpgm trainer.
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if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
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set_pretrain="${load_norm_train_model}"
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fi
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set_save_model=$(func_set_params "${save_model_key}" "${save_dir}")
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if [ ${#gpu} -le 2 ]; then # Train with cpu or single gpu
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cmd="${python} ${run_train} ${set_config} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
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elif [ ${#ips} -le 15 ]; then # Train with multi-gpu
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|
|
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_config} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
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else # Train with multi-machine
|
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|
|
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_config} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
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fi
|
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|
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echo ${cmd}
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# Run train
|
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|
run_command "${cmd}" "${log_path}"
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|
status_check $? "${cmd}" "${status_log}" "${model_name}"
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|
if [[ "${cmd}" == *'paddle.distributed.launch'* ]]; then
|
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|
|
cat log/workerlog.0 >> ${log_path}
|
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|
|
fi
|
|
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|
|
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_dir}/${train_model_name}/model.pdparams")
|
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|
|
# Save norm trained models to set pretrain for pact training and fpgm training
|
|
|
|
|
if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1 ]; then
|
|
|
|
|
load_norm_train_model=${set_eval_pretrain}
|
|
|
|
|
fi
|
|
|
|
|
# Run evaluation
|
|
|
|
|
if [ ${eval_py} != 'null' ]; then
|
|
|
|
|
log_path="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_eval.log"
|
|
|
|
|
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
|
|
|
|
|
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
|
|
|
|
|
run_command "${eval_cmd}" "${log_path}"
|
|
|
|
|
status_check $? "${eval_cmd}" "${status_log}" "${model_name}"
|
|
|
|
|
fi
|
|
|
|
|
# Run export model
|
|
|
|
|
if [ ${run_export} != 'null' ]; then
|
|
|
|
|
log_path="${OUT_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_export.log"
|
|
|
|
|
save_infer_path="${save_dir}/static"
|
|
|
|
|
set_export_weight=$(func_set_params "${export_weight}" "${save_dir}/${train_model_name}")
|
|
|
|
|
set_export_shape=$(func_set_params "${export_shape_key}" "${export_shape_value}")
|
|
|
|
|
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
|
|
|
|
|
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key} ${set_export_shape}"
|
|
|
|
|
run_command "${export_cmd}" "${log_path}"
|
|
|
|
|
status_check $? "${export_cmd}" "${status_log}" "${model_name}"
|
|
|
|
|
|
|
|
|
|
# Run inference
|
|
|
|
|
eval ${env}
|
|
|
|
|
if [[ ${inference_dir} != 'null' ]] && [[ ${inference_dir} != '##' ]]; then
|
|
|
|
|
infer_model_dir="${save_infer_path}/${inference_dir}"
|
|
|
|
|
else
|
|
|
|
|
infer_model_dir=${save_infer_path}
|
|
|
|
|
fi
|
|
|
|
|
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${OUT_PATH}" "${train_config_value}"
|
|
|
|
|
|
|
|
|
|
eval "unset CUDA_VISIBLE_DEVICES"
|
|
|
|
|
fi
|
|
|
|
|
done # Done with: for trainer in ${trainer_list[*]}; do
|
|
|
|
|
done # Done with: for autocast in ${autocast_list[*]}; do
|
|
|
|
|
done # Done with: for gpu in ${gpu_list[*]}; do
|
|
|
|
|
fi # End if [ ${MODE} = 'infer' ]; then
|
