update

python/graphstorm/config/argument.py

@@ -1726,7 +1726,7 @@ def wd_l2norm(self):
             try:
                 wd_l2norm = float(self._wd_l2norm)
             except:
-                raise ValueError("wd-l2norm must be a floating point " \
+                raise ValueError("wd_l2norm must be a floating point " \
                                  f"but get {self._wd_l2norm}")
             return wd_l2norm
         return 0
@@ -1740,7 +1740,12 @@ def alpha_l2norm(self):
         """
         # pylint: disable=no-member
         if hasattr(self, "_alpha_l2norm"):
-            return self._alpha_l2norm
+            try:
+                alpha_l2norm = float(self._alpha_l2norm)
+            except:
+                raise ValueError("alpha_l2norm must be a floating point " \
+                                 f"but get {self._alpha_l2norm}")
+            return alpha_l2norm
         return .0
 
     @property

tests/unit-tests/test_config.py

@@ -253,6 +253,7 @@ def create_train_config(tmp_path, file_name):
         "topk_model_to_save": 4,
         "save_model_path": os.path.join(tmp_path, "save"),
         "wd_l2norm": 5e-5,
+        "alpha_l2norm": 5e-5,
     }
     with open(os.path.join(tmp_path, file_name+"1.yaml"), "w") as f:
         yaml.dump(yaml_object, f)
@@ -263,6 +264,7 @@ def create_train_config(tmp_path, file_name):
         "topk_model_to_save": 5,
         "save_model_path": os.path.join(tmp_path, "save"),
         "wd_l2norm": "1e-3",
+        "alpha_l2norm": "1e-3",
     }
     with open(os.path.join(tmp_path, file_name+"2.yaml"), "w") as f:
         yaml.dump(yaml_object, f)
@@ -294,6 +296,7 @@ def create_train_config(tmp_path, file_name):
         "early_stop_burnin_rounds": -1,
         "early_stop_rounds": 0,
         "wd_l2norm": "NA",
+        "alpha_l2norm": "NA",
     }
 
     with open(os.path.join(tmp_path, file_name+"_fail.yaml"), "w") as f:
@@ -305,6 +308,7 @@ def create_train_config(tmp_path, file_name):
         "topk_model_to_save": 3,
         "save_model_path": os.path.join(tmp_path, "save"),
         "wd_l2norm": "",
+        "alpha_l2norm": "",
     }
     with open(os.path.join(tmp_path, file_name+"_fail1.yaml"), "w") as f:
         yaml.dump(yaml_object, f)
@@ -355,13 +359,15 @@ def test_train_info():
         config = GSConfig(args)
         assert config.topk_model_to_save == 4
         assert config.wd_l2norm == 5e-5
+        assert config.alpha_l2norm == 5e-5
 
         args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'train_test2.yaml'), local_rank=0)
         config = GSConfig(args)
         assert config.eval_frequency == 1000
         assert config.save_model_frequency == 2000
         assert config.topk_model_to_save == 5
         assert config.wd_l2norm == 1e-3
+        assert config.alpha_l2norm == 1e-3
 
         args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'train_test3.yaml'), local_rank=0)
         config = GSConfig(args)
@@ -387,13 +393,15 @@ def test_train_info():
         check_failure(config, "early_stop_burnin_rounds")
         check_failure(config, "early_stop_rounds")
         check_failure(config, "wd_l2norm")
+        check_failure(config, "alpha_l2norm")
 
         args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'train_test_fail1.yaml'), local_rank=0)
         config = GSConfig(args)
         # in PR # 893 we loose the constraints of model saving frequency and eval frequency
         # so here we do not check failure, but check the topk model argument
         assert config.topk_model_to_save == 3
         check_failure(config, "wd_l2norm")
+        check_failure(config, "alpha_l2norm")
 
 def create_rgcn_config(tmp_path, file_name):
     yaml_object = create_dummpy_config_obj()