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update tipc log #841

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3aa6591
update tipc log
zhengya01 Sep 21, 2022
b704ebb
update tipc log
zhengya01 Sep 21, 2022
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6 changes: 4 additions & 2 deletions test_tipc/common_func.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -65,9 +65,11 @@ function status_check(){
last_status=$1 # the exit code
run_command=$2
run_log=$3
model_name=$4
log_path=$5
if [ $last_status -eq 0 ]; then
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
echo -e "\033[33m Run successfully with command - ${model_name} - ${run_command} - ${log_path} \033[0m" | tee -a ${run_log}
else
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
echo -e "\033[33m Run failed with command - ${model_name} - ${run_command} - ${log_path} \033[0m" | tee -a ${run_log}
fi
}
47 changes: 29 additions & 18 deletions test_tipc/test_train_inference_python.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,8 @@ if [ ${MODE} = "klquant_whole_infer" ]; then
infer_value1=$(func_parser_value "${lines[17]}")
fi

LOG_PATH="./test_tipc/output"
WORK_PATH=$(pwd)
LOG_PATH="$(pwd)/test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log"

Expand All @@ -149,6 +150,7 @@ function func_inference(){
_log_path=$4
_img_dir=$5
_flag_quant=$6
_gpu=$7
# inference
for use_gpu in ${use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
Expand All @@ -167,7 +169,7 @@ function func_inference(){
fi # skip when quant model inference but precision is not int8
set_precision=$(func_set_params "${precision_key}" "${precision}")

_save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
_save_log_path="${_log_path}/python_infer_cpu_gpus_${_gpu}_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
Expand All @@ -178,7 +180,7 @@ function func_inference(){
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
status_check $last_status "${command}" "${status_log}" "${model_name}" "${_save_log_path}"
done
done
done
Expand All @@ -196,7 +198,7 @@ function func_inference(){
continue
fi
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
_save_log_path="${_log_path}/python_infer_gpu_gpus_${_gpu}_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
Expand All @@ -208,7 +210,7 @@ function func_inference(){
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
status_check $last_status "${command}" "${status_log}" "${model_name}" "${_save_log_path}"

done
done
Expand Down Expand Up @@ -391,8 +393,8 @@ else
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
if [ ${#ips} -le 26 ];then
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
nodes=1
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
else
IFS=","
ips_array=(${ips})
Expand All @@ -408,10 +410,11 @@ else

set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
_train_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}.log"
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} >${_train_log} 2>&1"
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
status_check $? "${cmd}" "${status_log}" "${model_name}" "${_train_log}"

elif [ ${#ips} -le 26 ];then # train with multi-gpu
# run pserver
Expand All @@ -421,10 +424,11 @@ else
cur_port=${PADDLE_PSERVER_PORT_ARRAY[$i]}
echo "PADDLE WILL START PSERVER "$cur_port
export PADDLE_PORT=${cur_port}
cmd="${python} ${SC}"
_train_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_pserver_${cur_port}.log"
cmd="${python} ${SC} >${_train_log} 2>&1"
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
status_check $? "${cmd}" "${status_log}" "${model_name}" "${_train_log}"
done

# run trainer
Expand All @@ -433,16 +437,20 @@ else
do
echo "PADDLE WILL START Trainer "$i
export PADDLE_TRAINER_ID=$i
cmd="${python} ${SC}"
_train_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_trainer_${i}.log"
cmd="${python} ${SC} ${_train_log} 2>&1"
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
status_check $? "${cmd}" "${status_log}" "${model_name}" "${_train_log}"
done
else # train with multi-machine
_train_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}.log"
cmd="${python} -m paddle.distributed.launch --ips=${ips} --devices=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
last_status=${PIPESTATUS[0]}
cat ${WORK_PATH}/log/workerlog.0
status_check ${last_status} "${cmd}" "${status_log}" "${model_name}" "${_train_log}"

fi
# run train
Expand All @@ -454,20 +462,23 @@ else
fi
# run eval
if [ ${eval_py} != "null" ]; then
_eval_log="${LOG_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} ${set_eval_params1}"
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1} >${_eval_log} 2>&1"
eval $eval_cmd
status_check $? "${eval_cmd}" "${status_log}"
status_check $? "${eval_cmd}" "${status_log}" "${model_name}" "${_eval_log}"
fi
# run export model
if [ ${run_export} != "null" ]; then
# run export model
_export_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_export.log"
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
save_infer_path="${save_log}"
set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key} >${_export_log} 2>&1"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
status_check $? "${export_cmd}" "${status_log}" "${model_name}" "${_export_log}"

#run inference
eval $env
Expand All @@ -477,7 +488,7 @@ else
else
infer_model_dir=${save_infer_path}
fi
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" "${gpu}"

eval "unset CUDA_VISIBLE_DEVICES"
fi
Expand Down