diff --git a/internlm/model/metrics.py b/internlm/model/metrics.py
index 1f54d06..1f2f1c7 100644
--- a/internlm/model/metrics.py
+++ b/internlm/model/metrics.py
@@ -20,13 +20,18 @@ class AccPerplex:
         dataset_types (List[str]): Various data types that will be used in the current training process,
             such as ['en', 'cn', 'code']. The order of the List should be consistent with the type_id specified
             in the dataset. Changed parameters need to be used in conjunction with set_current_type_ids().
+        logits_float32: Use float32 to calculate logits.
     """
 
-    def __init__(self, device, tp_pg, dp_pg, tokenizer=None, dataset_types: List[str] = None):
+    def __init__(
+        self, device, tp_pg, dp_pg, tokenizer=None, dataset_types: List[str] = None, logits_float32: bool = False
+    ):
         self.device = device
         self.right = torch.Tensor([0]).to(device=device)
         self.total = torch.Tensor([0]).to(device=device)
-        self.total_log_probs = torch.Tensor([0]).to(device=device, dtype=torch.float)
+        self.total_log_probs = torch.Tensor([0]).to(
+            device=device, dtype=torch.float if logits_float32 else torch.bfloat16
+        )
         self.tp_pg = tp_pg
         self.dp_pg = dp_pg
         self.tp_local_rank = torch.distributed.get_rank(self.tp_pg)
@@ -34,6 +39,7 @@ class AccPerplex:
         self.total_bytes = torch.Tensor([0]).to(device=device).view(1)
         self.batch_shift = 0
         self.type_ids = None
+        self.logits_float32 = logits_float32
         if dataset_types is not None:
             self.dataset_types = dataset_types
             self.total_type_count = len(dataset_types)
@@ -128,8 +134,9 @@ class AccPerplex:
             # All reduce is needed to get the chunks from other GPUs.
             torch.distributed.all_reduce(predicted_logits, op=torch.distributed.ReduceOp.SUM, group=self.tp_pg)
 
-            predicted_logits = predicted_logits.to(dtype=torch.float)
-            shift_logits = shift_logits.to(dtype=torch.float)
+            if self.logits_float32:
+                predicted_logits = predicted_logits.to(dtype=torch.float)
+                shift_logits = shift_logits.to(dtype=torch.float)
 
             pred_exp_logits = torch.exp(predicted_logits)
             # Sum of exponential of logits along vocab dimension across all GPUs.