95 lines
4.9 KiB
Bash
95 lines
4.9 KiB
Bash
#!/usr/bin/env bash
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# Test training benchmark for a model.
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# Usage：bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num}
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function _set_params(){
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model_item=${1:-"model_item"} # (必选) 模型 item
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base_batch_size=${2:-"2"} # (必选) 如果是静态图单进程，则表示每张卡上的BS，需在训练时*卡数
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fp_item=${3:-"fp32"} # (必选) fp32|fp16
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run_mode=${4:-"DP"} # (必选) MP模型并行|DP数据并行|PP流水线并行|混合并行DP1-MP1-PP1|DP1-MP4-PP1
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device_num=${5:-"N1C1"} # (必选) 使用的卡数量，N1C1|N1C8|N4C32 （4机32卡）
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profiling=${PROFILING:-"false"} # (必选) Profiling 开关，默认关闭，通过全局变量传递
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model_repo="PaddleClas" # (必选) 模型套件的名字
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speed_unit="samples/sec" # (必选)速度指标单位
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skip_steps=10 # (必选)解析日志，跳过模型前几个性能不稳定的step
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keyword="ips:" # (必选)解析日志，筛选出性能数据所在行的关键字
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convergence_key="loss:" # (可选)解析日志，筛选出收敛数据所在行的关键字 如：convergence_key="loss:"
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max_epochs=${6:-"1"} # （可选）需保证模型执行时间在5分钟内，需要修改代码提前中断的直接提PR 合入套件；或使用max_epoch参数
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num_workers=${7:-"4"} # (可选)
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# 以下为通用执行命令，无特殊可不用修改
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model_name=${model_item}_bs${base_batch_size}_${fp_item}_${run_mode} # (必填) 且格式不要改动,与竞品名称对齐
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device=${CUDA_VISIBLE_DEVICES//,/ }
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arr=(${device})
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num_gpu_devices=${#arr[*]}
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run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # （必填） TRAIN_LOG_DIR benchmark框架设置该参数为全局变量
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profiling_log_path=${PROFILING_LOG_DIR:-$(pwd)} # （必填） PROFILING_LOG_DIR benchmark框架设置该参数为全局变量
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speed_log_path=${LOG_PATH_INDEX_DIR:-$(pwd)}
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train_log_file=${run_log_path}/${model_repo}_${model_name}_${device_num}_log
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profiling_log_file=${profiling_log_path}/${model_repo}_${model_name}_${device_num}_profiling
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speed_log_file=${speed_log_path}/${model_repo}_${model_name}_${device_num}_speed
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}
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function _train(){
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batch_size=${base_batch_size} # 如果模型跑多卡单进程时,请在_train函数中计算出多卡需要的bs
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echo "current CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES}, model_name=${model_name}, device_num=${device_num}, is profiling=${profiling}"
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if [ ${fp_item} = "fp32" ]; then
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config_file="-c ppcls/configs/ImageNet/ResNet/ResNet50.yaml"
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elif [ ${fp_item} = "amp_fp16" ]; then
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config_file="-c ppcls/configs/ImageNet/ResNet/ResNet50_amp_O1.yaml"
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elif [ ${fp_item} = "pure_fp16" ]; then
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config_file="-c ppcls/configs/ImageNet/ResNet/ResNet50_amp_O2.yaml"
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fi
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if [ ${profiling} = "false" ]; then
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profiling_config=""
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log_file=${train_log_file}
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else
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profiling_config="--profiler_options=\"batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile\""
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log_file=${profiling_log_file}
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fi
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train_cmd="${config_file} -o DataLoader.Train.sampler.batch_size=${base_batch_size} -o Global.epochs=${max_epochs} -o DataLoader.Train.loader.num_workers=${num_workers} ${profiling_config} -o Global.eval_during_train=False"
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# 以下为通用执行命令，无特殊可不用修改
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case ${run_mode} in
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DP) if [[ ${device_num} = "N1C1" ]];then
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echo "run ${run_mode} ${device_num}"
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train_cmd="python ppcls/static/train.py ${train_cmd}"
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else
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rm -rf ./mylog
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train_cmd="python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 ppcls/static/train.py ${train_cmd}"
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fi
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;;
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DP1-MP1-PP1) echo "run run_mode: DP1-MP1-PP1" ;;
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*) echo "choose run_mode "; exit 1;
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esac
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echo "train_cmd: ${train_cmd} log_file: ${log_file}"
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timeout 5m ${train_cmd} > ${log_file} 2>&1
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if [ $? -ne 0 ];then
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echo -e "${model_name}, FAIL"
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else
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echo -e "${model_name}, SUCCESS"
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fi
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# kill -9 `ps -ef|grep 'python'|awk '{print $2}'`
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if [ ${device_num} != "N1C1" -a -d mylog ]; then
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rm ${log_file}
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cp mylog/workerlog.0 ${log_file}
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fi
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cd ../
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}
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function _set_env(){
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#开启gc
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export FLAGS_eager_delete_tensor_gb=0.0
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export FLAGS_fraction_of_gpu_memory_to_use=0.98
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####
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export FLAGS_cudnn_exhaustive_search=1
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export FLAGS_conv_workspace_size_limit=4000 #MB
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}
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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;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
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_set_params $@
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# _train # 如果只产出训练log,不解析,可取消注释
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_set_env
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_run # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只产出训练log可以注掉本行,提交时需打开
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