add ce for dygraph/bert (#4323)

dygraph/bert/run_classifier.py

@@ -84,6 +84,7 @@ run_type_g.add_arg("task_name",                    str,    None,
 run_type_g.add_arg("do_train",                     bool,   True,  "Whether to perform training.")
 run_type_g.add_arg("do_test",                      bool,   False,  "Whether to perform evaluation on test data set.")
 run_type_g.add_arg("use_data_parallel", bool, False,  "The flag indicating whether to shuffle instances in each pass.")
+run_type_g.add_arg("enable_ce", bool, False,	 help="The flag indicating whether to run the task for continuous evaluation.")
 
 args = parser.parse_args()
 
@@ -184,6 +185,8 @@ def train(args):
         steps = 0
         time_begin = time.time()
 
+        ce_time = []
+        ce_acc = []
         for batch in train_data_generator():
             data_ids = create_data(batch)
             loss, accuracy, num_seqs = cls_model(data_ids)
@@ -197,6 +200,8 @@ def train(args):
                 current_example, current_epoch = processor.get_train_progress()
                 localtime = time.asctime(time.localtime(time.time()))
                 print("%s, epoch: %s, steps: %s, dy_graph loss: %f, acc: %f, speed: %f steps/s" % (localtime, current_epoch, steps, loss.numpy(), accuracy.numpy(), args.skip_steps / used_time))
+                ce_time.append(used_time)
+                ce_acc.append(accuracy.numpy())
                 time_begin = time.time()
 
             if steps != 0 and steps % args.save_steps == 0 and fluid.dygraph.parallel.Env().local_rank == 0:
@@ -220,6 +225,18 @@ def train(args):
                 optimizer.optimizer.state_dict(),
                 save_path)
             print("Save model parameters and optimizer status at %s" % save_path)
+
+        if args.enable_ce:
+            _acc = 0
+            _time = 0
+            try:
+                _time = ce_time[-1]
+                _acc = ce_acc[-1]
+            except:
+                print("ce info error")
+            print("kpis\ttrain_duration_card%s\t%s" % (dev_count, _time))
+            print("kpis\ttrain_acc_card%s\t%f" % (dev_count, _acc))
+
         return cls_model
 
 def predict(args, cls_model = None):