diff --git a/PaddleNLP/benchmark/bert/README.md b/PaddleNLP/benchmark/bert/README.md
index 0948c3e677fa4c2c64572b2d6e22a922a508556c..684b63af41452b222b41d391025f564a64d76a48 100644
--- a/PaddleNLP/benchmark/bert/README.md
+++ b/PaddleNLP/benchmark/bert/README.md
@@ -20,7 +20,7 @@ export DATA_DIR=${HOME}/bert_data/wikicorpus_en
 ### 运行模型训练脚本
 ```shell
 
-1. 如果是需要多单机多卡/多机多卡训练，则使用下面的命令进行训练
+1. 如果是需要单机多卡/多机多卡训练，则使用下面的命令进行训练
 unset CUDA_VISIBLE_DEVICES
 fleetrun --gpus 0,1,2,3 ./run_pretrain.py \
     --model_type bert \
diff --git a/PaddleNLP/benchmark/bert/run_pretrain_single.py b/PaddleNLP/benchmark/bert/run_pretrain_single.py
index 1bd2104907657e963503b72e609469e10c37c014..9641ffbd05a46f5fbfba4a9b55b27466ee120aaa 100644
--- a/PaddleNLP/benchmark/bert/run_pretrain_single.py
+++ b/PaddleNLP/benchmark/bert/run_pretrain_single.py
@@ -266,21 +266,34 @@ def do_train(args):
         for f_id in range(0, len(files)):
             train_data_loader, _ = create_pretraining_dataset(
                 files[f_id], args.max_predictions_per_seq, args, data_holders)
+            train_reader_cost = 0.0
+            train_run_cost = 0.0
+            total_samples = 0
+            reader_start = time.time()
             for step, batch in enumerate(train_data_loader):
+                train_reader_cost += time.time() - reader_start
                 global_step += 1
+                train_start = time.time()
                 loss_return = exe.run(main_program,\
                     feed=batch,
                     fetch_list=[loss])
+                train_run_cost += time.time() - train_start
+                total_samples += args.batch_size
                 # In the new 2.0 api, must call this function to change the learning_rate
                 lr_scheduler.step()
                 if global_step % args.logging_steps == 0:
-                    time_cost = time.time() - tic_train
                     print(
-                        "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s, ips: %.2f sequences/s"
-                        % (global_step, epoch, step, loss_return[0],
-                           args.logging_steps / time_cost,
-                           args.logging_steps * args.batch_size / time_cost))
-                    tic_train = time.time()
+                        "global step: %d, epoch: %d, batch: %d, loss: %f, "
+                        "avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f sequences/sec"
+                        %
+                        (global_step, epoch, step, loss_return[0],
+                         train_reader_cost / args.logging_steps,
+                         (train_reader_cost + train_run_cost) /
+                         args.logging_steps, total_samples / args.logging_steps,
+                         total_samples / (train_reader_cost + train_run_cost)))
+                    train_reader_cost = 0.0
+                    train_run_cost = 0.0
+                    total_samples = 0
                 if global_step % args.save_steps == 0:
                     output_dir = os.path.join(args.output_dir,
                                               "model_%d" % global_step)
@@ -290,8 +303,10 @@ def do_train(args):
                     paddle.fluid.io.save_params(exe, output_dir)
                     tokenizer.save_pretrained(output_dir)
                 if global_step >= args.max_steps:
+                    reader_start = time.time()
                     del train_data_loader
                     return
+                reader_start = time.time()
             del train_data_loader
         epoch += 1