diff --git a/colossalai/shardformer/layer/attn.py b/colossalai/shardformer/layer/attn.py
index 1d8a89ce0..1a175f426 100644
--- a/colossalai/shardformer/layer/attn.py
+++ b/colossalai/shardformer/layer/attn.py
@@ -488,7 +488,7 @@ class RingAttention(torch.autograd.Function):
         q,  # (B, H, Sq, D)
         k,
         v,
-        sp_group,
+        sp_axis,
         attention_mask_type,
         cu_seqlens=None,
         max_seqlen=None,
@@ -499,7 +499,6 @@ class RingAttention(torch.autograd.Function):
         return_softmax=False,
         inner_ring_size=None,
         pg_mesh=None,
-        sp_axis=None,
         **kwargs,
     ):
         """
@@ -546,7 +545,7 @@ class RingAttention(torch.autograd.Function):
         assert pg_mesh is not None, f"Error: The pg mesh is None! please check the process group initialization."
 
         clone_pg = lambda pg: dist.new_group(dist.get_process_group_ranks(pg))
-
+        sp_group = pg_mesh.get_group_along_axis(sp_axis)
         if inner_ring_size != None:
             RingAttention.SP_GROUP = sp_group
             inner_ring_group, inter_ring_group = RingAttention.get_double_ring_groups(sp_axis, pg_mesh, inner_ring_size)
diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py
index 1eaed167c..90ffba8cf 100644
--- a/colossalai/shardformer/modeling/gpt2.py
+++ b/colossalai/shardformer/modeling/gpt2.py
@@ -857,19 +857,18 @@ def get_gpt2_flash_attention_forward(shard_config: Optional[ShardConfig] = None)
         dropout_p = self.attn_dropout.p if self.training else 0.0
 
         sp_mode = shard_config.sequence_parallelism_mode
-        sp_group = shard_config.sequence_parallel_process_group
+        shard_config.sequence_parallel_process_group
         if sp_mode == "ring_attn":
             attn_output = RingAttention.attention(
                 query,
                 key,
                 value,
-                sp_group,
+                sp_axis=shard_config.sp_axis,
                 **attention_mask,
                 dropout_p=dropout_p,
                 scale=scale,
                 inner_ring_size=shard_config.inner_ring_size,
                 pg_mesh=shard_config.pg_mesh,
-                sp_axis=shard_config.sp_axis,
             )
         else:
             attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p, scale=scale)
diff --git a/colossalai/shardformer/modeling/llama.py b/colossalai/shardformer/modeling/llama.py
index ca0751f04..2e6363060 100644
--- a/colossalai/shardformer/modeling/llama.py
+++ b/colossalai/shardformer/modeling/llama.py
@@ -568,11 +568,10 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                 query_states,
                 key_states,
                 value_states,
-                sp_group,
+                sp_axis=shard_config.sp_axis,
                 **attention_mask,
                 inner_ring_size=shard_config.inner_ring_size,
                 pg_mesh=shard_config.pg_mesh,
-                sp_axis=shard_config.sp_axis,
             )
 
         elif shard_config.enable_flash_attention:
diff --git a/tests/test_shardformer/test_layer/test_ring_attn.py b/tests/test_shardformer/test_layer/test_ring_attn.py
index 23e8e5b78..6ebd8da73 100644
--- a/tests/test_shardformer/test_layer/test_ring_attn.py
+++ b/tests/test_shardformer/test_layer/test_ring_attn.py
@@ -44,12 +44,11 @@ def check_ring_attn(seq_len, bs, nheads, d, dtype, inner_ring_size):
         q,
         k,
         v,
-        sp_group,
+        sp_axis,
         AttnMaskType.CAUSAL,
         return_softmax=True,
         inner_ring_size=inner_ring_size,
         pg_mesh=pg_mesh,
-        sp_axis=sp_axis,
     )
     ring_out = ring_out.transpose(1, 2)
     out, lse, _ = flash_attn_qkvpacked_func(
@@ -88,6 +87,7 @@ def check_packed_seq(seqlen, bs, nheads, d, dtype):
     device = get_current_device()
     sp_group = dist.group.WORLD
     sp_size = dist.get_world_size()
+    sp_axis = 2
     atol = rtol = 7e-3
     torch.cuda.manual_seed(2)
     # Prepare varlen attention mask
@@ -128,11 +128,11 @@ def check_packed_seq(seqlen, bs, nheads, d, dtype):
         q_ring,
         k_ring,
         v_ring,
-        sp_group,
+        sp_axis,
         **mask_info,
         pad_output=False,
         return_softmax=True,
-        pg_mesh=dist.group.WORLD,
+        pg_mesh=ProcessGroupMesh(1, 1, sp_size, 1),
         # deterministic=True
     )
     ring_out = ring_out.transpose(1, 2).reshape(-1, nheads, d)