add ce for lac

dygraph/lac/train.py

@@ -44,10 +44,11 @@ def do_train(args):
     with fluid.dygraph.guard(place):
         if args.use_data_parallel:
             strategy = fluid.dygraph.parallel.prepare_context()
-        #fluid.default_startup_program().random_seed = 102
-        #fluid.default_main_program().random_seed = 102
-        #np.random.seed(102)
-        #random.seed(102)
+        if args.enable_ce:
+            fluid.default_startup_program().random_seed = 102
+            fluid.default_main_program().random_seed = 102
+            np.random.seed(102)
+            random.seed(102)
         train_loader = reader.create_dataloader(
             args,
             file_name=args.train_data,
@@ -102,6 +103,8 @@ def do_train(args):
                 (precision, recall, f1, end_time - start_time))
             model.train()
 
+        ce_time = []
+        ce_infor = []
         for epoch_id in range(args.epoch):
             for batch in train_loader():
                 words, targets, length = batch
@@ -129,6 +132,8 @@ def do_train(args):
 
                     print("[train] step = %d, loss = %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time %.5f" % (
                         step, avg_cost, precision, recall, f1_score, end_time - start_time))
+                    ce_time.append(end_time - start_time)
+                    ce_infor.append([precision, recall, f1_score])
 
                 if step % args.validation_steps == 0:
                     test_process(test_loader, chunk_evaluator)
@@ -139,7 +144,24 @@ def do_train(args):
                     paddle.fluid.save_dygraph(model.state_dict(), save_path)
                 step += 1
 
-        
+        if args.enable_ce and fluid.dygraph.parallel.Env().local_rank == 0:
+            card_num = fluid.core.get_cuda_device_count()
+            _p = 0
+            _r = 0
+            _f1 = 0
+            _time = 0
+            try:
+                _time = ce_time[-1]
+                _p = ce_infor[-1][0]
+                _r = ce_infor[-1][1]
+                _f1 = ce_infor[-1][2]
+            except:
+                print("ce info error")
+            print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
+            print("kpis\ttrain_p_card%s\t%f" % (card_num, _p))
+            print("kpis\ttrain_r_card%s\t%f" % (card_num, _r))
+            print("kpis\ttrain_f1_card%s\t%f" % (card_num, _f1))
+
 
 if __name__ == "__main__":
     # 参数控制可以根据需求使用argparse，yaml或者json