diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md
index 5fe833642..413138597 100644
--- a/configs/neox_arguments.md
+++ b/configs/neox_arguments.md
@@ -111,7 +111,7 @@ Logging Arguments
 
 - **git_hash**: str
 
-    Default = 28a5a62
+    Default = 53d0ae8
 
     current git hash of repository
 
@@ -1060,9 +1060,9 @@ Parallelism Arguments
 
     Default = False
 
-    flag to determine whether Megatron-style (https://arxiv.org/abs/2205.05198) Sequence-Parallelism 
-    (sharding acts along seq. dim among TP group for LNs) will be used. Has no effect when model_parallel_size is 1.
-    **Set by user.**
+    flag to determine whether Megatron-style Sequence Parallelism (https://arxiv.org/abs/2205.05198)
+    (Layernorm inputs and activations are sharded across model parallel group) will be used. Has no effect when model_parallel_size is 1.
+    **Set by user, in contrast to neox_args.is_pipe_parallel.**
 
 
 
diff --git a/megatron/mpu/mappings.py b/megatron/mpu/mappings.py
index d95c5a3df..f11d9e6ab 100644
--- a/megatron/mpu/mappings.py
+++ b/megatron/mpu/mappings.py
@@ -101,16 +101,31 @@ def _reduce_scatter_along_seq_dim(input_, seq_dim):
     if get_fp32_allreduce():
         input_ = input_.float()
 
-    assert input_.shape[seq_dim] % world_size == 0
-    tensor_list = list(
-        torch.split(input_, input_.shape[seq_dim] // world_size, seq_dim)
-    )
-    output = torch.empty_like(tensor_list[0])
-    torch.distributed.reduce_scatter(output, tensor_list)
+    dim_size = list(input_.size())
+    assert (
+        isinstance(seq_dim, int) and seq_dim < len(dim_size) and seq_dim >= 0
+    ), "seq_dim must be a valid tensor dim"
+    assert dim_size[seq_dim] % world_size == 0
+
+    if seq_dim == 0:
+        # reduce_scatter_tensor is faster but only works correctly on dimension 0
+        dim_size[seq_dim] = dim_size[seq_dim] // world_size
+        output = torch.empty(
+            dim_size, dtype=input_.dtype, device=torch.cuda.current_device()
+        )
+        torch.distributed.reduce_scatter_tensor(
+            output, input_.contiguous(), group=get_model_parallel_group()
+        )
+    else:
+        tensor_list = list(
+            torch.split(input_, input_.shape[seq_dim] // world_size, seq_dim)
+        )
+        output = torch.empty_like(tensor_list[0])
+        torch.distributed.reduce_scatter(output, tensor_list)
 
     # reconvert to original Bf16/Fp16 dtype
     if get_fp32_allreduce():
-        input_ = input_.to(dt)
+        output = output.to(dt)
 
     return output
 
@@ -123,12 +138,29 @@ def _gather_along_seq_dim(input_, seq_dim):
     if world_size == 1:
         return input_
 
-    input_ = input_.contiguous()
-    rank = get_model_parallel_rank()
-    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
-    tensor_list[rank] = input_
-    torch.distributed.all_gather(tensor_list, input_, group=get_model_parallel_group())
-    output = torch.cat(tensor_list, dim=seq_dim)
+    dim_size = list(input_.size())
+    assert (
+        isinstance(seq_dim, int) and seq_dim < len(dim_size) and seq_dim >= 0
+    ), "seq_dim must be a valid tensor dim"
+    dim_size[seq_dim] = dim_size[seq_dim] * world_size
+
+    if seq_dim == 0:
+        # reduce_gather_tensor is faster but only works correctly on dimension 0
+        output = torch.empty(
+            dim_size, dtype=input_.dtype, device=torch.cuda.current_device()
+        )
+        torch.distributed.all_gather_into_tensor(
+            output, input_.contiguous(), group=get_model_parallel_group()
+        )
+    else:
+        input_ = input_.contiguous()
+        rank = get_model_parallel_rank()
+        tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
+        tensor_list[rank] = input_
+        torch.distributed.all_gather(
+            tensor_list, input_, group=get_model_parallel_group()
+        )
+        output = torch.cat(tensor_list, dim=seq_dim)
 
     return output