Add for bert models (#3976)

* add_for_bert_models

* update add_for_bert_models

* update for add_for_bert_models

PaddleNLP/PaddleLARK/BERT/run_classifier.py

@@ -32,6 +32,7 @@ import multiprocessing
 
 import paddle
 import paddle.fluid as fluid
+from paddle.fluid import profiler
 
 import reader.cls as reader
 from model.bert import BertConfig
@@ -93,6 +94,12 @@ data_g.add_arg("do_lower_case", bool, True,
 data_g.add_arg("random_seed",   int,  0,     "Random seed.")
 
 run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+
+# NOTE:profiler args, used for benchmark
+run_type_g.add_arg("profiler_path",                str,    './', "the profiler output file path. (used for benchmark)")
+run_type_g.add_arg("is_profiler",                  int,    0,     "the profiler switch. (used for benchmark)")
+run_type_g.add_arg("max_iter",                     int,    0,     "the max batch nums to train. (used for benchmark)")
+
 run_type_g.add_arg("use_cuda",                     bool,   True,  "If set, use GPU for training.")
 run_type_g.add_arg("use_fast_executor",            bool,   False, "If set, use fast parallel executor (in experiment).")
 run_type_g.add_arg("shuffle",                      bool,   True,  "")
@@ -317,9 +324,17 @@ def main(args):
         time_begin = time.time()
         throughput = []
         ce_info = []
+
+        total_batch_num=0 # used for benchmark
+
         while True:
             try:
                 steps += 1
+
+                total_batch_num += 1 # used for benchmark
+                if args.max_iter and total_batch_num == args.max_iter: # used for benchmark
+                    return
+
                 if steps % args.skip_steps == 0:
                     if args.use_fp16:
                         fetch_list = [loss.name, accuracy.name, scheduled_lr.name, num_seqs.name, loss_scaling.name]
@@ -353,6 +368,13 @@ def main(args):
                     time_end = time.time()
                     used_time = time_end - time_begin
 
+                    # profiler tools
+                    if args.is_profiler and current_epoch == 0 and steps == args.skip_steps:
+                        profiler.start_profiler("All")
+                    elif args.is_profiler and current_epoch == 0 and steps == args.skip_steps * 2:
+                        profiler.stop_profiler("total", args.profiler_path)
+                        return
+
                     log_record = "epoch: {}, progress: {}/{}, step: {}, ave loss: {}, ave acc: {}".format(
                            current_epoch, current_example, num_train_examples,
                            steps, np.sum(total_cost) / np.sum(total_num_seqs),