add tokens per sec; test=develop (#4875)

PaddleNLP/seq2seq/seq2seq/train.py

@@ -97,8 +97,7 @@ def main():
                 dropout=dropout)
         loss = model.build_graph()
         inference_program = train_program.clone(for_test=True)
-        clip=fluid.clip.GradientClipByGlobalNorm(
-            clip_norm=max_grad_norm)
+        clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=max_grad_norm)
         lr = args.learning_rate
         opt_type = args.optimizer
         if opt_type == "sgd":
@@ -190,8 +189,10 @@ def main():
             total_loss = 0
             word_count = 0.0
             batch_times = []
+            time_interval = 0.0
+            batch_start_time = time.time()
+            epoch_word_count = 0.0
             for batch_id, batch in enumerate(train_data_iter):
-                batch_start_time = time.time()
                 input_data_feed, word_num = prepare_input(
                     batch, epoch_id=epoch_id)
                 word_count += word_num
@@ -206,27 +207,34 @@ def main():
                 batch_end_time = time.time()
                 batch_time = batch_end_time - batch_start_time
                 batch_times.append(batch_time)
+                time_interval += batch_time
+                epoch_word_count += word_num
 
                 if batch_id > 0 and batch_id % 100 == 0:
-                    print("-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f" %
-                          (epoch_id, batch_id, batch_time,
-                           np.exp(total_loss / word_count)))
+                    print(
+                        "-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f; speed: %0.5f tokens/sec"
+                        % (epoch_id, batch_id, batch_time,
+                           np.exp(total_loss / word_count),
+                           word_count / time_interval))
                     ce_ppl.append(np.exp(total_loss / word_count))
                     total_loss = 0.0
                     word_count = 0.0
+                    time_interval = 0.0
 
                 # profiler tools
                 if args.profile and epoch_id == 0 and batch_id == 100:
                     profiler.reset_profiler()
                 elif args.profile and epoch_id == 0 and batch_id == 105:
                     return
+                batch_start_time = time.time()
 
             end_time = time.time()
             epoch_time = end_time - start_time
             ce_time.append(epoch_time)
             print(
-                "\nTrain epoch:[%d]; Epoch Time: %.5f; avg_time: %.5f s/step\n"
-                % (epoch_id, epoch_time, sum(batch_times) / len(batch_times)))
+                "\nTrain epoch:[%d]; Epoch Time: %.5f; avg_time: %.5f s/step; speed: %0.5f tokens/sec\n"
+                % (epoch_id, epoch_time, sum(batch_times) / len(batch_times),
+                   epoch_word_count / sum(batch_times)))
 
             if not args.profile:
                 save_path = os.path.join(args.model_path,