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# 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37
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# 执行目录：需说明
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CUR_DIR=${PWD} # PaddleSpeech/tests/benchmark/conformer
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cd ../../../
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log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # benchmark系统指定该参数,不需要跑profile时,log_path指向存speed的目录
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cd ${CUR_DIR}
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sed -i '/set\ -xe/d' run_benchmark.sh
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#cd **
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pushd ../../../examples/aishell/asr1
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# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
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# 2 拷贝该模型需要数据、预训练模型
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source path.sh
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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mkdir -p conf/benchmark
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#yq e ".training.accum_grad=1" conf/conformer.yaml > conf/benchmark/conformer.yaml
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cp conf/conformer.yaml conf/benchmark/conformer.yaml
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sed -i "s/ accum_grad: 2/ accum_grad: 1/g" conf/benchmark/conformer.yaml
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fp_item_list=(fp32)
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bs_item=(16)
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config_path=conf/benchmark/conformer.yaml
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decode_config_path=conf/tuning/decode.yaml
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seed=0
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output=exp/conformer
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profiler_options=None
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model_item=conformer
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for fp_item in ${fp_item_list[@]}; do
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for bs_item in ${bs_item[@]}
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do
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rm exp -rf
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log_name=speech_${model_item}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
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echo "index is speed, 8gpus, run_mode is multi_process, begin, conformer"
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run_mode=mp
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ngpu=8
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${decode_config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
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sleep 60
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log_name=speech_${model_item}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
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echo "index is speed, 1gpus, begin, ${log_name}"
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run_mode=sp
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ngpu=1
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CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${decode_config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_1gpus 2>&1 # (5min)
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sleep 60
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done
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done
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popd
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