Resolves the issue with evaluation on step2 for single GPU

applications/DeepSpeed-Chat/training/utils/model/model_utils.py

@@ -14,6 +14,7 @@
 
 from .reward_model import RewardModel
 from ..utils import load_state_dict_into_model
+from ..utils import print_rank_0
 
 
 def configure_dropout(model_config, dropout):
@@ -130,8 +131,8 @@ def create_critic_model(model_name_or_path,
     critic_model = create_hf_model(AutoModel, model_name_or_path, tokenizer,
                                    ds_config, rlhf_training, dropout)
     end = time.time()
-    if torch.distributed.get_rank() == 0:
-        print(f"> Creating model from_config took {end - start} seconds")
+    print_rank_0(f">Creating model from_config took {end - start} seconds",
+                 None)
 
     critic_model = RewardModel(
         critic_model,
@@ -152,8 +153,8 @@ def create_critic_model(model_name_or_path,
         start = time.time()
         model_ckpt_state_dict = torch.load(model_ckpt_path, map_location='cpu')
         end = time.time()
-        if torch.distributed.get_rank() == 0:
-            print(f"> torch.load took {end - start} seconds")
+        print_rank_0(f">Creating model from_config took {end - start} seconds",
+                     None)
 
         # load critic model from checkpoint with zero-stage 3 compatibility
         # this functionality may be moved to DS checkpoint load API in future
@@ -163,7 +164,8 @@ def create_critic_model(model_name_or_path,
                                    "",
                                    zero_stage=zero_stage)
         end = time.time()
-        if torch.distributed.get_rank() == 0:
-            print(f"> Loading model state dict took {end - start} seconds")
+
+        print_rank_0(f">Creating model from_config took {end - start} seconds",
+                     None)
 
     return critic_model

applications/DeepSpeed-Chat/training/utils/utils.py

@@ -14,9 +14,22 @@
 import torch.nn as nn
 
 
-def print_rank_0(msg, rank=0):
-    if rank <= 0:
+def print_rank_0(msg, rank=None):
+    if rank is not None and rank <= 0:
         print(msg)
+    elif is_rank_0():
+        print(msg)
+
+
+def is_rank_0():
+    """Check whether it is rank 0."""
+    if torch.distributed.is_initialized():
+        if torch.distributed.get_rank() == 0:
+            return True
+        else:
+            return False
+    else:
+        return True
 
 
 def to_device(batch, device):