diff --git a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh
index c64f63e125329b1d51d0779527eb6f5d62210114..a5abb3573c576e882c67560ca3fa3dc1f025e795 100644
--- a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh
@@ -28,7 +28,7 @@ batch_size=4
 train_steps=800
 #学习率
 learning_rate=
-
+ffts='None'
 #维测参数，precision_mode需要模型审视修改
 precision_mode="allow_mix_precision"
 #维持参数，以下不需要修改
@@ -70,6 +70,8 @@ do
         mkdir -p ${data_dump_path}
     elif [[ $para == --data_dump_step* ]];then
         data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --profiling* ]];then
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
@@ -99,6 +101,10 @@ sed -i "s|data/|${data_path}/|g" $cur_path/../ascendvsr/config/defaults.py
 sed -i "s|cfg.solver.print_interval = 20|cfg.solver.print_interval = 100|g" $cur_path/../ascendvsr/config/defaults.py
 sed -i "s|2000|${train_steps}|g" $cur_path/../configs/edvr.yaml
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 cd $cur_path/../
@@ -150,7 +156,11 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据
 #吞吐量，不需要修改
@@ -173,4 +183,4 @@ echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.
 echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/cv/detection/SSD-Resnet34_ID0048_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/detection/SSD-Resnet34_ID0048_for_TensorFlow/test/train_performance_1p.sh
index 2e601e6edf3d9fdc8fcf369f0de2c846af2919b4..5a9d324db6e368150784973d889389fa72bb177b 100644
--- a/TensorFlow/built-in/cv/detection/SSD-Resnet34_ID0048_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/detection/SSD-Resnet34_ID0048_for_TensorFlow/test/train_performance_1p.sh
@@ -14,7 +14,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 export ASCEND_GLOBAL_LOG_LEVEL_ETP=3
 
@@ -75,6 +75,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
         mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
@@ -94,7 +96,9 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
-
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 cd $cur_path/../
@@ -151,7 +155,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据
 #吞吐量，不需要修改
 ActualFPS=${FPS}
diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh
index 52e0ef983e7766af01dd4200bcd8c2d449419cce..604af32e6d1e3969b18d06db8ca72890739cd1ec 100644
--- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh
@@ -12,7 +12,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 export ASCEND_GLOBAL_LOG_LEVEL=3
 
@@ -72,6 +72,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
 		export autotune=$autotune
@@ -96,6 +98,10 @@ fi
 cp -r $data_path/data/* ${cur_path}/../data/
 #sed -i "s/total_epoches = 200/total_epoches = 1/g" ${cur_path}/../args_single.py
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改 
 start_time=$(date +%s)
 
@@ -156,7 +162,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据
 grep "fps" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log > FPS.log
 sed -i '1d' FPS.log
diff --git a/TensorFlow/built-in/cv/image_classification/DenseNet121_ID0067_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/DenseNet121_ID0067_for_TensorFlow/test/train_performance_1p.sh
index 61b484ee11868dd17cdef3f3e5cfa825ba6fa7ea..0c272f09e6c15aea742dc9dbacbde198beb5f14b 100644
--- a/TensorFlow/built-in/cv/image_classification/DenseNet121_ID0067_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/DenseNet121_ID0067_for_TensorFlow/test/train_performance_1p.sh
@@ -12,7 +12,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 export ASCEND_GLOBAL_LOG_LEVEL_ETP=3
 
@@ -74,6 +74,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
     fi
@@ -84,6 +86,10 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 
 #训练开始时间，不需要修改
 start_time=$(date +%s)
@@ -146,7 +152,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据，不需要修改
 #吞吐量
 ActualFPS=${FPS}
diff --git a/TensorFlow/built-in/cv/image_classification/InceptionV4_ID0002_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/InceptionV4_ID0002_for_TensorFlow/test/train_performance_1p.sh
index 0e0488de2a3f7f9849ab7280c9b9d1dc2a0f6c5a..a62cd5c25ea7cec48c1358ce82901552c30e90fc 100644
--- a/TensorFlow/built-in/cv/image_classification/InceptionV4_ID0002_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/InceptionV4_ID0002_for_TensorFlow/test/train_performance_1p.sh
@@ -12,7 +12,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 export ASCEND_GLOBAL_LOG_LEVEL_ETP=3
 
@@ -73,6 +73,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
         mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
@@ -93,6 +95,10 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 cd $cur_path/../
@@ -151,6 +157,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据
 #吞吐量，不需要修改
@@ -173,4 +184,4 @@ echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.
 echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/cv/image_classification/MobileNetV2_ID0074_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/MobileNetV2_ID0074_for_TensorFlow/test/train_performance_1p.sh
index 3c9d27f2da5623f27d5b02b5d6d653aaf76552cb..59b8dfdc1704dc2fb7cdd4969f375258148dfaea 100644
--- a/TensorFlow/built-in/cv/image_classification/MobileNetV2_ID0074_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/MobileNetV2_ID0074_for_TensorFlow/test/train_performance_1p.sh
@@ -15,7 +15,7 @@ export HCCL_CONNECT_TIMEOUT=600
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 export ASCEND_GLOBAL_LOG_LEVEL_ETP=3
 
@@ -77,6 +77,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
         export autotune=$autotune
@@ -98,7 +100,9 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
-
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -176,6 +180,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据
 #吞吐量，不需要修改
diff --git a/TensorFlow/built-in/cv/image_classification/ResNet101_ID0063_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/ResNet101_ID0063_for_TensorFlow/test/train_performance_1p.sh
index 9e816f6d149330ef6d75f91f555cba7648899f29..259868fe1d8cacc8be7d38d4ed7f31cdaad22821 100644
--- a/TensorFlow/built-in/cv/image_classification/ResNet101_ID0063_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/ResNet101_ID0063_for_TensorFlow/test/train_performance_1p.sh
@@ -3,6 +3,7 @@
 cur_path=`pwd`
 # 数据集路径,保持为空,不需要修改
 data_path=""
+ffts='None'
 #/autotest/CI_daily/ModelZoo_Resnet101_TF_Atlas/data/resnet50/imagenet_TF
 #集合通信参数,不需要修改
 
@@ -73,6 +74,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
         mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
@@ -93,6 +96,10 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 cd $cur_path/../
@@ -153,6 +160,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据
 #吞吐量，不需要修改
diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh
index 48d4a07342cb7cff45a86f0ee9a33530ba56d196..48c689f74015938507571b04776ed2a376652ade 100644
--- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh
@@ -12,7 +12,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 export ASCEND_GLOBAL_LOG_LEVEL_ETP=3
 
@@ -77,6 +77,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
         mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
@@ -106,6 +108,10 @@ sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g"  $cur_path/../src/configs/res5
 sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g"  $cur_path/../src/configs/res50_256bs_1p.py
 
 cp data_loader.py $cur_path/../src/data_loader/resnet50/
+
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 cd $cur_path/../
@@ -166,9 +172,14 @@ if [[ ${fp32} == "--fp32" ]];then
   CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'perf'
 elif [[ ${hf32} == "--hf32" ]];then
   CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'hf32'_'perf'
+elif [[ ${ffts} == "--ffts" ]];then
+  CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
 else
   CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
 fi
+
+
+
 ##获取性能数据
 #吞吐量，不需要修改
 ActualFPS=${FPS}
diff --git a/TensorFlow/built-in/cv/image_classification/Resnet50v1.5_ID1721_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/Resnet50v1.5_ID1721_for_TensorFlow/test/train_performance_1p.sh
index f42f0844adc13c1ea3d6b643f5199630cad3f53f..d7eaecf97f6a5e9c53737247cd598c913cb10a5a 100644
--- a/TensorFlow/built-in/cv/image_classification/Resnet50v1.5_ID1721_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/Resnet50v1.5_ID1721_for_TensorFlow/test/train_performance_1p.sh
@@ -12,7 +12,7 @@ export PYTHONPATH=${cur_path}/../:$PYTHONPATH
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #设置默认日志级别,不需要修改
 #export ASCEND_GLOBAL_LOG_LEVEL_ETP=1
 
@@ -65,6 +65,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --autotune* ]];then
         autotune=`echo ${para#*=}`
         mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
@@ -87,7 +89,9 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
-
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -143,7 +147,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 #获取性能数据，不需要修改
 #吞吐量
 ActualFPS=${FPS}
diff --git a/TensorFlow/built-in/cv/image_synthesis/SRGAN_ID1881_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_synthesis/SRGAN_ID1881_for_TensorFlow/test/train_performance_1p.sh
index c1f19bd34764ad52651e31b107abc96286299a90..fa33bd3c13097207d79cbb4ea552a38f8f81a75f 100644
--- a/TensorFlow/built-in/cv/image_synthesis/SRGAN_ID1881_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_synthesis/SRGAN_ID1881_for_TensorFlow/test/train_performance_1p.sh
@@ -21,7 +21,7 @@ learning_rate=1e-5
 
 #参数配置
 data_path=""
-
+ffts='None'
 if [[ $1 == --help || $1 == --h ]];then
    echo "usage:./train_performance_1p.sh"
    exit 1
@@ -31,6 +31,8 @@ for para in $*
 do
    if [[ $para == --data_path* ]];then
       data_path=`echo ${para#*=}`
+   elif [[ $para == --ffts* ]];then
+      ffts=`echo ${para#*=}
    fi
    
    if [[ $para == --conda_name* ]];then
@@ -72,6 +74,9 @@ else
 fi
 wait
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 start=$(date +%s)
 nohup python3 main.py > $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
 wait
@@ -120,7 +125,11 @@ echo "Final Performance images/sec : $FPS"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据，不需要修改
 #吞吐量
 ActualFPS=${FPS}
diff --git a/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/test/train_performance_1p.sh
index 6208416c36ac4b18a2ebb6ecb71d94a00fd022f6..89b55f87e6b83cb805335474bfd649f7d388f8fd 100644
--- a/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/test/train_performance_1p.sh
@@ -22,7 +22,7 @@ learning_rate=0.00001375
 
 #参数配置
 data_path=""
-
+ffts='None'
 if [[ $1 == --help || $1 == --h ]];then
    echo "usage:./train_performance_1p.sh"
    exit 1
@@ -32,6 +32,8 @@ for para in $*
 do
    if [[ $para == --data_path* ]];then
       data_path=`echo ${para#*=}`
+   elif [[ $para == --ffts* ]];then
+      ffts=`echo ${para#*=}`
    fi
 done
 
@@ -53,6 +55,10 @@ else
 fi
 wait
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 start=$(date +%s)
 nohup python3 -m albert.run_pretraining \
               --input_file=${data_path}/data/tf_news_2016_zh_raw_news2016zh_1.tfrecord \
@@ -99,8 +105,11 @@ echo "Final Train Accuracy : ${train_accuracy}"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据，不需要修改
 #吞吐量
 ActualFPS=${FPS}
@@ -123,4 +132,4 @@ echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${Cas
 echo "TrainingTime = ${TrainingTime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/nlp/Bert-NER_ID0797_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/nlp/Bert-NER_ID0797_for_TensorFlow/test/train_performance_1p.sh
index f27d80c0b91d499b79ef5cd975cbb430294689ef..f5d09b6216e8599ea68daaac35d1397f82e7b600 100644
--- a/TensorFlow/built-in/nlp/Bert-NER_ID0797_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/nlp/Bert-NER_ID0797_for_TensorFlow/test/train_performance_1p.sh
@@ -39,7 +39,7 @@ data_dump_flag=False
 data_dump_step="10"
 profiling=False
 autotune=False
-
+ffts='None'
 # 帮助信息，不需要修改
 if [[ $1 == --help || $1 == -h ]];then
     echo"usage:./train_performance_1P.sh <args>"
@@ -71,6 +71,8 @@ do
         mkdir -p ${data_dump_path}
     elif [[ $para == --data_dump_step* ]];then
         data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --profiling* ]];then
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
@@ -92,6 +94,10 @@ sed -i "s|#num_train_steps = 100|num_train_steps = 25|g" $cur_path/../BERT_NER.p
 mkdir -p $cur_path/../output
 cp -r $data_path/result_dir $cur_path/../output/
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -161,7 +167,11 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据，不需要修改
 #吞吐量
diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh
index 0d67fba344620ab05afedff404e4acbe7f2869e6..d8bcb692c7eaf953a930e29c2fa58e1f7f3103f1 100644
--- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh
+++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh
@@ -11,7 +11,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #基础参数 需要模型审视修改
 #网络名称，同目录名称
 Network="Bertsquad_ID0495_for_TensorFlow"
@@ -60,6 +60,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
     fi
@@ -78,6 +80,9 @@ init_checkpoint=${data_path}/model/bert_model.ckpt
 train_file=${data_path}/dataset/train-v1.1_small.json
 predict_file=${data_path}/dataset/dev-v1.1.json
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -126,7 +131,11 @@ echo "E2E training Duration sec: $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 #获取性能数据
 step_per_sec=`grep "global_step/sec:" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk -F 'global_step/sec:' '{print $2}'|awk 'END {print $1}'`
 
diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh
index 7aad180ab0d2b3b4d3f6f62bbf87e9739a6c2766..b1fdedf89c2c815a7592f4c4dc12e9e601d5fe1b 100644
--- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh
+++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh
@@ -10,7 +10,7 @@ RANK_ID_START=0
 
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+ffts='None'
 #基础参数，需要模型审视修改
 #网络名称，同目录名称
 Network="Bert-base_ID0060_for_TensorFlow"
@@ -68,6 +68,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
     fi
@@ -79,6 +81,10 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 #进入训练脚本目录，需要模型审视修改
@@ -149,7 +155,11 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
 grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh
index 0b4b153fdf2437db87debcf9b7f4b50d76df5b6f..3474ab1076c3deca262d2c0cc781829326f78cf5 100644
--- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh
+++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh
@@ -22,7 +22,7 @@ batch_size=24
 train_steps=200
 #学习率
 learning_rate=
-
+ffts='None'
 #维测参数，precision_mode需要模型审视修改
 #precision_mode="allow_mix_precision"
 #维持参数，以下不需要修改
@@ -64,6 +64,8 @@ do
         mkdir -p ${data_dump_path}
     elif [[ $para == --data_dump_step* ]];then
         data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --profiling* ]];then
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
@@ -81,6 +83,10 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 #进入训练脚本目录，需要模型审视修改
@@ -152,8 +158,11 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
 grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
@@ -170,4 +179,4 @@ echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.
 echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/nlp/Roberta_ID2366_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/nlp/Roberta_ID2366_for_TensorFlow/test/train_performance_1p.sh
index 76a87a1d89bfb44a4f78c3f0642b259183d7f93c..5b1241b02c5346a52fadcca09117faa959f8912f 100644
--- a/TensorFlow/built-in/nlp/Roberta_ID2366_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/nlp/Roberta_ID2366_for_TensorFlow/test/train_performance_1p.sh
@@ -15,7 +15,7 @@ Network="Roberta_ID2366_for_TensorFlow"
 batch_size=32
 num_train_epochs=1
 
-
+ffts='None'
 # 帮助信息，不需要修改
 if [[ $1 == --help || $1 == -h ]];then
     echo"usage:./train_performance_1p.sh <args>"
@@ -50,6 +50,8 @@ do
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
         mkdir -p ${profiling_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
         echo "${data_path}"
@@ -70,6 +72,10 @@ fi
 cd $cur_path/../
 #参数修改
 sed -i "s|max_steps=num_train_steps|max_steps=100|g" run_classifier.py
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
@@ -121,6 +127,11 @@ echo "E2E Training Duration sec : ${e2e_time}"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 CaseName="${Network}_bs${BatchSize}_${RANK_SIZE}p_perf"
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据，不需要修改
 #吞吐量
 ActualFPS=${FPS}
diff --git a/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_finetune.sh b/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_finetune.sh
index 5552d4802689ea63e0ca9ee0bd02429be75597a0..3dc7c0d69a2eb03328cf29a756fb3587b3404279 100644
--- a/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_finetune.sh
+++ b/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_finetune.sh
@@ -9,7 +9,7 @@ data_path=""
 Network="albert_xlarge_zh_ID2355_for_TensorFlow"
 export JOB_ID=10087
 RANK_SIZE=1
-
+ffts='None'
 #npu param
 task_name=lcqmc_pair   
 do_train=true   
@@ -53,6 +53,8 @@ do
 		do_train=`echo ${para#*=}`
 	elif [[ $para == --do_eval* ]];then
 		do_eval=`echo ${para#*=}`
+	elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
 	elif [[ $para == --max_seq_length* ]];then
 		max_seq_length=`echo ${para#*=}`
 	elif [[ $para == --train_batch_size* ]];then
@@ -82,6 +84,10 @@ fi
 
 cp -r $data_path/albert_config $cur_path/
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #执行训练
 cd $cur_path
 sed -i "s|max_steps=num_train_steps|max_steps=100|g" run_classifier.py
@@ -118,7 +124,11 @@ echo "Final Performance images/sec : $FPS"
 #训练用例信息，不需要修改
 BatchSize=${train_batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据，不需要修改
 #吞吐量
diff --git a/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_pretrain.sh b/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_pretrain.sh
index 6b6ac7dad88a9829be8b404e847a7533d6d8286b..0e5db676984e2642f971392bf9f61ab2035c7f2c 100644
--- a/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_pretrain.sh
+++ b/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/test/train_performance_1p_pretrain.sh
@@ -6,7 +6,7 @@ cur_path=`pwd`/../
 Network="albert_xlarge_zh_ID2348_for_TensorFlow"
 RankSize=1
 export RANK_SIZE=1
-
+ffts='None'
 #npu param
 do_train=true
 max_seq_length=512
@@ -51,6 +51,8 @@ do
 		do_train=`echo ${para#*=}`
 	elif [[ $para == --do_eval* ]];then
 		do_eval=`echo ${para#*=}`
+	elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
 	elif [[ $para == --max_seq_length* ]];then
 		max_seq_length=`echo ${para#*=}`
 	elif [[ $para == --train_batch_size* ]];then
@@ -75,6 +77,10 @@ else
    mkdir -p $cur_path/test/output/$ASCEND_DEVICE_ID
 fi
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 cd $cur_path
 start=$(date +%s)
 nohup python3 -u run_pretraining.py \
@@ -112,7 +118,11 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${train_batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'perf'
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 
 ##获取性能数据，不需要修改
 #吞吐量
diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh
index f192262101cc2881d480bfb3f5ac3a28ff13a438..374c1f88e009e186afeacb9d4ef1de08f12ea83b 100644
--- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh
+++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh
@@ -24,7 +24,7 @@ batch_size=128
 train_steps=
 #学习率
 learning_rate=
-
+ffts='None'
 #维测参数，precision_mode需要模型审视修改
 precision_mode="allow_fp32_to_fp16"
 #维持参数，以下不需要修改
@@ -62,6 +62,8 @@ do
         data_dump_flag=`echo ${para#*=}`
         data_dump_path=${cur_path}/output/data_dump
         mkdir -p ${data_dump_path}
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --data_dump_step* ]];then
         data_dump_step=`echo ${para#*=}`
     elif [[ $para == --profiling* ]];then
@@ -87,6 +89,10 @@ cd $cur_path/../examples
 
 sed -i "s|epochs=10|epochs=5|g" run_fwfm.py
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
 do
     #设置环境变量，不需要修改
@@ -137,8 +143,11 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据，不需要修改
 #吞吐量
 
diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh
index e2b5df89968d6a4fcd8e31b106503e1eab1f1baf..03ce6c03b3a32eab6b16c902800b0d7a427a5d05 100644
--- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh
+++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh
@@ -24,7 +24,7 @@ batch_size=128
 train_steps=
 #学习率
 learning_rate=
-
+ffts='None'
 #维测参数，precision_mode需要模型审视修改
 precision_mode="allow_fp32_to_fp16"
 #维持参数，以下不需要修改
@@ -64,6 +64,8 @@ do
         mkdir -p ${data_dump_path}
     elif [[ $para == --data_dump_step* ]];then
         data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --profiling* ]];then
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
@@ -79,6 +81,10 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -135,9 +141,11 @@ BatchSize=${batch_size}
 DeviceType=`uname -m`
 
 if [[ $precision_mode == "must_keep_origin_dtype" ]];then
-        CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'perf'
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'perf'
+elif [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
 else
-		CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
 fi
 
 ##获取性能数据，不需要修改
diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh
index 53fdf2d0970dd0e4d3362181cda056b79aca5c44..ae2acfd649e047ab685b1bde1b2c6829b15973ec 100644
--- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh
+++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh
@@ -24,7 +24,7 @@ batch_size=128
 train_steps=
 #学习率
 learning_rate=
-
+ffts='None'
 #维测参数，precision_mode需要模型审视修改
 precision_mode="allow_fp32_to_fp16"
 #维持参数，以下不需要修改
@@ -65,6 +65,8 @@ do
         mkdir -p ${data_dump_path}
     elif [[ $para == --data_dump_step* ]];then
         data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --ffts* ]];then
+        ffts=`echo ${para#*=}`
     elif [[ $para == --profiling* ]];then
         profiling=`echo ${para#*=}`
         profiling_dump_path=${cur_path}/output/profiling
@@ -80,6 +82,10 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+if [[ ${ffts} == "--ffts" ]];then
+   export ASCEND_ENHANCE_ENABLE=1
+fi
+
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
@@ -134,8 +140,11 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
-
+if [[ ${ffts} == "--ffts" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'_'ffts'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 ##获取性能数据，不需要修改
 #吞吐量