diff --git a/megatron/mpu/mappings.py b/megatron/mpu/mappings.py
index 1c5e6fb10..d95c5a3df 100644
--- a/megatron/mpu/mappings.py
+++ b/megatron/mpu/mappings.py
@@ -33,17 +33,17 @@ def _reduce(input_):
     if get_model_parallel_world_size() == 1:
         return input_
 
-    # Bf16 convert
+    # upcast to fp32 if using fp32 allreduce
     dt = input_.dtype
-    if dt == torch.bfloat16 and get_fp32_allreduce():
+    if get_fp32_allreduce():
         input_ = input_.float()
 
     # All-reduce.
     torch.distributed.all_reduce(input_, group=get_model_parallel_group())
 
-    # Bf16 convert
-    if dt == torch.bfloat16 and get_fp32_allreduce():
-        input_ = input_.bfloat16()
+    # reconvert to original Bf16/Fp16 dtype
+    if get_fp32_allreduce():
+        input_ = input_.to(dt)
 
     return input_
 
@@ -75,11 +75,6 @@ def _gather(input_):
     if world_size == 1:
         return input_
 
-    # Bf16 convert
-    dt = input_.dtype
-    if dt == torch.bfloat16 and get_fp32_allreduce():
-        input_ = input_.float()
-
     # Size and dimension.
     last_dim = input_.dim() - 1
     rank = get_model_parallel_rank()
@@ -91,10 +86,6 @@ def _gather(input_):
     # Note: torch.cat already creates a contiguous tensor.
     output = torch.cat(tensor_list, dim=last_dim).contiguous()
 
-    # Bf16 convert
-    if dt == torch.bfloat16 and get_fp32_allreduce():
-        output = output.bfloat16()
-
     return output
 
 
@@ -105,9 +96,9 @@ def _reduce_scatter_along_seq_dim(input_, seq_dim):
     if world_size == 1:
         return input_
 
-    # Bf16 convert
+    # upcast to fp32 if using fp32 allreduce
     dt = input_.dtype
-    if dt == torch.bfloat16 and get_fp32_allreduce():
+    if get_fp32_allreduce():
         input_ = input_.float()
 
     assert input_.shape[seq_dim] % world_size == 0
@@ -117,11 +108,9 @@ def _reduce_scatter_along_seq_dim(input_, seq_dim):
     output = torch.empty_like(tensor_list[0])
     torch.distributed.reduce_scatter(output, tensor_list)
 
-    # Bf16 convert
-    if dt == torch.bfloat16 and get_fp32_allreduce():
-        output = (
-            output.bfloat16()
-        )
+    # reconvert to original Bf16/Fp16 dtype
+    if get_fp32_allreduce():
+        input_ = input_.to(dt)
 
     return output
 
@@ -134,11 +123,6 @@ def _gather_along_seq_dim(input_, seq_dim):
     if world_size == 1:
         return input_
 
-    # Bf16 convert
-    dt = input_.dtype
-    if dt == torch.bfloat16 and get_fp32_allreduce():
-        input_ = input_.float()
-
     input_ = input_.contiguous()
     rank = get_model_parallel_rank()
     tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
@@ -146,12 +130,6 @@ def _gather_along_seq_dim(input_, seq_dim):
     torch.distributed.all_gather(tensor_list, input_, group=get_model_parallel_group())
     output = torch.cat(tensor_list, dim=seq_dim)
 
-    # Bf16 convert
-    if dt == torch.bfloat16 and get_fp32_allreduce():
-        output = (
-            output.bfloat16()
-        )
-
     return output
 
 
@@ -259,8 +237,7 @@ def backward(ctx, grad_output):
 
 
 class _GatherFromSequenceParallelRegion(torch.autograd.Function):
-    """All-Gather across sequence parallel region (same region as model parallel region.)
-    """
+    """All-Gather across sequence parallel region (same region as model parallel region.)"""
 
     @staticmethod
     def symbolic(graph, input_, seq_dim):
@@ -297,6 +274,7 @@ def backward(ctx, grad_output):
             None,
         )
 
+
 # -----------------
 # Helper functions.
 # -----------------