From f39b0cb6f657dcef6da9bb8953c1c7c0fa6848b7 Mon Sep 17 00:00:00 2001
From: bailang <bailang12@h-partners.com>
Date: Thu, 26 May 2022 10:25:49 +0800
Subject: [PATCH 1/2] =?UTF-8?q?[=E8=87=AA=E7=A0=94][PyTorch]Bert-Squad=5FI?=
 =?UTF-8?q?D0470=5Ffor=5FPyTorch=E6=A8=A1=E5=9E=8B=E4=B8=80=E5=AF=B9?=
 =?UTF-8?q?=E5=A4=9A=E6=A8=A1=E5=9E=8B=E8=84=9A=E6=9C=AC=E8=BF=98=E5=8E=9F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: bailang <bailang12@h-partners.com>
---
 .../test/train_ID0470_large_performance_1p.sh | 196 +++++++++++++++++
 .../test/train_ID0470_large_performance_8p.sh | 201 +++++++++++++++++
 .../train_ID3078_Bert_Squad_performance_1p.sh | 197 +++++++++++++++++
 .../train_ID3078_Bert_Squad_performance_8p.sh | 202 ++++++++++++++++++
 4 files changed, 796 insertions(+)
 create mode 100644 PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_1p.sh
 create mode 100644 PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_8p.sh
 create mode 100644 PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_1p.sh
 create mode 100644 PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_8p.sh

diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_1p.sh b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_1p.sh
new file mode 100644
index 0000000000..60663bcfe0
--- /dev/null
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_1p.sh
@@ -0,0 +1,196 @@
+#!/bin/bash
+
+###############指定训练脚本执行路径###############
+# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${cur_path##*/}
+if [ x"${cur_path_last_dirname}" == x"test" ];then
+    test_path_dir=${cur_path}
+    cd ..
+    cur_path=`pwd`
+else
+    test_path_dir=${cur_path}/test
+fi
+
+#集合通信参数,不需要修改
+export RANK_SIZE=1
+
+RANK_ID_START=0
+
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+ckpt_path=""
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Bert-Squad_ID0470_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=32
+#训练step
+train_steps=
+#学习率
+learning_rate=6e-5
+
+
+#维测参数，precision_mode需要模型审视修改
+precision_mode="allow_fp32_to_fp16"
+
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump		           if or not over detection, default is False
+    --data_dump_flag		     data dump flag, default is False
+    --data_dump_step		     data dump step, default is 10
+    --profiling		           if or not profiling for performance debug, default is False
+    --data_path		           source data of training
+    -h/--help		             show help message
+    "
+    exit 1
+fi
+
+#参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#非平台场景时source 环境变量
+check_etp_flag=`env | grep etp_running_flag`
+etp_flag=`echo ${check_etp_flag#*=}`
+if [ x"${etp_flag}" != x"true" ];then
+    source  ${test_path_dir}/env_npu.sh
+fi
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+ASCEND_DEVICE_ID=0
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path/
+mkdir -p results/SQUAD
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+
+    #创建DeviceID输出目录，不需要修改
+    if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    else
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    fi
+
+    
+    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
+    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
+    nohup python3.7 run_squad.py \
+          --init_checkpoint ${ckpt_path}/bert_large_pretrained_amp.pt \
+          --bert_model bert-large-uncased \
+		  --do_train \
+		  --train_file ${data_path}/train-v1.1.json \
+		  --train_batch_size ${batch_size} \
+		  --do_predict \
+		  --predict_batch_size ${batch_size} \
+		  --predict_file ${data_path}/dev-v1.1.json \
+		  --learning_rate ${learning_rate} \
+		  --num_train_epochs ${train_epochs} \
+		  --seed 1 \
+		  --fp16 \
+		  --max_seq_length 384 \
+		  --doc_stride 128 \
+		  --max_steps 100 \
+		  --use_npu \
+		  --loss_scale 4096 \
+		  --addr=127.0.0.1 \
+		  --vocab_file ${data_path}/data/uncased_L-24_H-1024_A-16/vocab.txt \
+		  --do_eval \
+      --eval_script ${data_path}/evaluate-v1.1.py \
+		  --npu_id ${ASCEND_DEVICE_ID} \
+		  --do_lower_case \
+		  --output_dir ${cur_path}/results \
+		  --config_file bert_config.json \
+		  --json-summary ${test_path_dir}/output/${ASCEND_DEVICE_ID}/dllogger.json> ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+done 
+wait
+
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+#结果打印，不需要修改
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+step_time=`grep 'step_time : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk '{print$13}'| tail -n+3 |awk '{sum+=$1} END {print"",sum/NR}' | sed s/[[:space:]]//g`
+
+FPS=`awk 'BEGIN{printf "%d\n", '$batch_size'/'$step_time'}'`
+
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+#输出训练精度,需要模型审视修改
+train_accuracy=`grep 'F1 : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $10}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#性能看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+#单迭代训练时长
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${FPS}'}'`
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep -r "step_loss :" $test_path_dir/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $19}' > $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+
+#最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}' $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_8p.sh b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_8p.sh
new file mode 100644
index 0000000000..6bb206bfa5
--- /dev/null
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID0470_large_performance_8p.sh
@@ -0,0 +1,201 @@
+#!/bin/bash
+
+###############指定训练脚本执行路径###############
+# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${cur_path##*/}
+if [ x"${cur_path_last_dirname}" == x"test" ];then
+    test_path_dir=${cur_path}
+    cd ..
+    cur_path=`pwd`
+else
+    test_path_dir=${cur_path}/test
+fi
+
+
+#集合通信参数,不需要修改
+export RANK_SIZE=8
+RANK_ID_START=0
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+ckpt_path=""
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Bert-Squad_ID0470_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=32
+#训练step
+train_steps=
+#学习率
+learning_rate=2e-4
+
+
+#维测参数，precision_mode需要模型审视修改
+precision_mode="allow_fp32_to_fp16"
+
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump		           if or not over detection, default is False
+    --data_dump_flag		     data dump flag, default is False
+    --data_dump_step		     data dump step, default is 10
+    --profiling		           if or not profiling for performance debug, default is False
+    --data_path		           source data of training
+    -h/--help		             show help message
+    "
+    exit 1
+fi
+
+#参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#非平台场景时source 环境变量
+check_etp_flag=`env | grep etp_running_flag`
+etp_flag=`echo ${check_etp_flag#*=}`
+if [ x"${etp_flag}" != x"true" ];then
+    source  ${test_path_dir}/env_npu.sh
+fi
+
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path
+mkdir -p results/SQUAD
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    export ASCEND_DEVICE_ID=$RANK_ID
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+
+
+
+    #创建DeviceID输出目录，不需要修改
+    if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    else
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    fi
+
+    
+    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
+    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
+    nohup python3.7 run_squad.py \
+          --init_checkpoint ${ckpt_path}/bert_large_pretrained_amp.pt \
+          --bert_model bert-large-uncased \
+		  --do_train \
+		  --train_file ${data_path}/train-v1.1.json \
+		  --train_batch_size ${batch_size} \
+		  --do_predict \
+		  --predict_batch_size ${batch_size} \
+		  --predict_file ${data_path}/dev-v1.1.json \
+		  --learning_rate ${learning_rate} \
+		  --num_train_epochs ${train_epochs} \
+		  --seed 1 \
+		  --fp16 \
+		  --max_seq_length 384 \
+		  --doc_stride 128 \
+		  --max_steps 100 \
+		  --use_npu \
+		  --loss_scale 4096 \
+		  --vocab_file ${data_path}/data/uncased_L-24_H-1024_A-16/vocab.txt \
+		  --do_eval \
+      --eval_script ${data_path}/evaluate-v1.1.py \
+		  --npu_id ${ASCEND_DEVICE_ID} \
+		  --do_lower_case \
+		  --output_dir results/SQUAD \
+		  --config_file bert_config.json \
+		  --num_npu 8 \
+		  --local_rank=$RANK_ID \
+		  --addr=127.0.0.1 \
+		  --json-summary ${test_path_dir}/output/${ASCEND_DEVICE_ID}/dllogger.json> ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+done 
+wait
+
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+ASCEND_DEVICE_ID=0
+#结果打印，不需要修改
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+step_time=`grep 'step_time : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk '{print$13}'| tail -n+3 |awk '{sum+=$1} END {print"",sum/NR}' | sed s/[[:space:]]//g`
+
+FPS=`awk 'BEGIN{printf "%d\n", '$batch_size'/'$step_time'*'$RANK_SIZE'}'`
+
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+#输出训练精度,需要模型审视修改
+train_accuracy=`grep 'F1 : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $10}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#性能看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+#单迭代训练时长
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${FPS}'}'`
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep -r "step_loss :" $test_path_dir/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $19}' > $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+
+#最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}' $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_1p.sh b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_1p.sh
new file mode 100644
index 0000000000..46876ef754
--- /dev/null
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_1p.sh
@@ -0,0 +1,197 @@
+#!/bin/bash
+
+###############指定训练脚本执行路径###############
+# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${cur_path##*/}
+if [ x"${cur_path_last_dirname}" == x"test" ];then
+    test_path_dir=${cur_path}
+    cd ..
+    cur_path=`pwd`
+else
+    test_path_dir=${cur_path}/test
+fi
+
+#集合通信参数,不需要修改
+export RANK_SIZE=1
+
+RANK_ID_START=0
+
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+ckpt_path=""
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Bert-Squad_ID3078_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=32
+#训练step
+train_steps=
+#学习率
+learning_rate=6e-5
+
+
+#维测参数，precision_mode需要模型审视修改
+precision_mode="allow_fp32_to_fp16"
+
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump		           if or not over detection, default is False
+    --data_dump_flag		     data dump flag, default is False
+    --data_dump_step		     data dump step, default is 10
+    --profiling		           if or not profiling for performance debug, default is False
+    --data_path		           source data of training
+    -h/--help		             show help message
+    "
+    exit 1
+fi
+
+#参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#非平台场景时source 环境变量
+check_etp_flag=`env | grep etp_running_flag`
+etp_flag=`echo ${check_etp_flag#*=}`
+if [ x"${etp_flag}" != x"true" ];then
+    source  ${test_path_dir}/env_npu.sh
+fi
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+ASCEND_DEVICE_ID=0
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path/
+mkdir -p results/SQUAD
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+
+    #创建DeviceID输出目录，不需要修改
+    if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    else
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    fi
+
+    
+    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
+    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
+    nohup python3.7 run_squad.py \
+          --init_checkpoint ${ckpt_path}/bert_large_pretrained_amp.pt \
+          --bert_model bert-large-uncased \
+		  --do_train \
+		  --train_file ${data_path}/train-v1.1.json \
+		  --train_batch_size ${batch_size} \
+		  --do_predict \
+		  --predict_batch_size ${batch_size} \
+		  --predict_file ${data_path}/dev-v1.1.json \
+		  --learning_rate ${learning_rate} \
+		  --num_train_epochs ${train_epochs} \
+		  --seed 1 \
+		  --fp16 \
+		  --max_seq_length 384 \
+		  --doc_stride 128 \
+		  --max_steps 100 \
+		  --use_npu \
+		  --loss_scale 4096 \
+		  --addr=127.0.0.1 \
+		  --vocab_file ${data_path}/data/uncased_L-24_H-1024_A-16/vocab.txt \
+		  --do_eval \
+      --eval_script ${data_path}/evaluate-v1.1.py \
+		  --npu_id ${ASCEND_DEVICE_ID} \
+		  --do_lower_case \
+		  --output_dir ${cur_path}/results \
+		  --config_file bert_config.json \
+                   --graph_mode \
+		  --json-summary ${test_path_dir}/output/${ASCEND_DEVICE_ID}/dllogger.json> ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+done 
+wait
+
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+#结果打印，不需要修改
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+step_time=`grep 'step_time : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk '{print$13}'| tail -n+3 |awk '{sum+=$1} END {print"",sum/NR}' | sed s/[[:space:]]//g`
+
+FPS=`awk 'BEGIN{printf "%d\n", '$batch_size'/'$step_time'}'`
+
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+#输出训练精度,需要模型审视修改
+train_accuracy=`grep 'F1 : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $10}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#性能看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+#单迭代训练时长
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${FPS}'}'`
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep -r "step_loss :" $test_path_dir/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $19}' > $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+
+#最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}' $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_8p.sh b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_8p.sh
new file mode 100644
index 0000000000..2669e818c9
--- /dev/null
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_ID3078_Bert_Squad_performance_8p.sh
@@ -0,0 +1,202 @@
+#!/bin/bash
+
+###############指定训练脚本执行路径###############
+# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${cur_path##*/}
+if [ x"${cur_path_last_dirname}" == x"test" ];then
+    test_path_dir=${cur_path}
+    cd ..
+    cur_path=`pwd`
+else
+    test_path_dir=${cur_path}/test
+fi
+
+
+#集合通信参数,不需要修改
+export RANK_SIZE=8
+RANK_ID_START=0
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+ckpt_path=""
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Bert-Squad_ID0470_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=32
+#训练step
+train_steps=
+#学习率
+learning_rate=2e-4
+
+
+#维测参数，precision_mode需要模型审视修改
+precision_mode="allow_fp32_to_fp16"
+
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump		           if or not over detection, default is False
+    --data_dump_flag		     data dump flag, default is False
+    --data_dump_step		     data dump step, default is 10
+    --profiling		           if or not profiling for performance debug, default is False
+    --data_path		           source data of training
+    -h/--help		             show help message
+    "
+    exit 1
+fi
+
+#参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#非平台场景时source 环境变量
+check_etp_flag=`env | grep etp_running_flag`
+etp_flag=`echo ${check_etp_flag#*=}`
+if [ x"${etp_flag}" != x"true" ];then
+    source  ${test_path_dir}/env_npu.sh
+fi
+
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path
+mkdir -p results/SQUAD
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    export ASCEND_DEVICE_ID=$RANK_ID
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+
+
+
+    #创建DeviceID输出目录，不需要修改
+    if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    else
+        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
+    fi
+
+    
+    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
+    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
+    nohup python3.7 run_squad.py \
+          --init_checkpoint ${ckpt_path}/bert_large_pretrained_amp.pt \
+          --bert_model bert-large-uncased \
+		  --do_train \
+		  --train_file ${data_path}/train-v1.1.json \
+		  --train_batch_size ${batch_size} \
+		  --do_predict \
+		  --predict_batch_size ${batch_size} \
+		  --predict_file ${data_path}/dev-v1.1.json \
+		  --learning_rate ${learning_rate} \
+		  --num_train_epochs ${train_epochs} \
+		  --seed 1 \
+		  --fp16 \
+		  --max_seq_length 384 \
+		  --doc_stride 128 \
+		  --max_steps 100 \
+		  --use_npu \
+		  --loss_scale 4096 \
+		  --vocab_file ${data_path}/data/uncased_L-24_H-1024_A-16/vocab.txt \
+		  --do_eval \
+      --eval_script ${data_path}/evaluate-v1.1.py \
+		  --npu_id ${ASCEND_DEVICE_ID} \
+		  --do_lower_case \
+		  --output_dir results/SQUAD \
+		  --config_file bert_config.json \
+		  --num_npu 8 \
+		  --local_rank=$RANK_ID \
+		  --addr=127.0.0.1 \
+		  --json-summary ${test_path_dir}/output/${ASCEND_DEVICE_ID}/dllogger.json> ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+done 
+wait
+
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+ASCEND_DEVICE_ID=0
+
+#结果打印，不需要修改
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+step_time=`grep 'step_time : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk '{print$13}'| tail -n+3 |awk '{sum+=$1} END {print"",sum/NR}' | sed s/[[:space:]]//g`
+
+FPS=`awk 'BEGIN{printf "%d\n", '$batch_size'/'$step_time'*'$RANK_SIZE'}'`
+
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+#输出训练精度,需要模型审视修改
+train_accuracy=`grep 'F1 : ' $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $10}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#性能看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+#单迭代训练时长
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${FPS}'}'`
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep -r "step_loss :" $test_path_dir/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $19}' > $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+
+#最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}' $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
-- 
Gitee


From d790e3d088293bd281ec368251272a08877d58bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=99=BD=E6=B5=AA?= <bailang12@h-partners.com>
Date: Thu, 26 May 2022 02:31:50 +0000
Subject: [PATCH 2/2] update
 PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_large_performance_8p.sh.

---
 .../test/train_large_performance_8p.sh                        | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_large_performance_8p.sh b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_large_performance_8p.sh
index 61f8f4bb64..6bb206bfa5 100644
--- a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_large_performance_8p.sh
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_large_performance_8p.sh
@@ -95,7 +95,7 @@ fi
 
 #训练开始时间，不需要修改
 start_time=$(date +%s)
-ASCEND_DEVICE_ID=0
+
 #进入训练脚本目录，需要模型审视修改
 cd $cur_path
 mkdir -p results/SQUAD
@@ -154,7 +154,7 @@ wait
 #训练结束时间，不需要修改
 end_time=$(date +%s)
 e2e_time=$(( $end_time - $start_time ))
-
+ASCEND_DEVICE_ID=0
 #结果打印，不需要修改
 echo "------------------ Final result ------------------"
 #输出性能FPS，需要模型审视修改
-- 
Gitee