diff --git a/internlm/train/training_internlm.py b/internlm/train/training_internlm.py
index 1e36a21..1f68af9 100644
--- a/internlm/train/training_internlm.py
+++ b/internlm/train/training_internlm.py
@@ -53,6 +53,7 @@ from internlm.utils.common import DummyProfile
 from internlm.utils.logger import get_logger
 from internlm.utils.megatron_timers import megatron_timer as timer
 from internlm.utils.parallel import (
+    check_sequence_parallel,
     set_model_params_layer_name,
     sync_model_param,
     sync_model_param_within_tp,
@@ -111,6 +112,10 @@ def initialize_model():
     # if fsdp enabled, wrap the model
     model = wrap_FSDP_model(model)
 
+    # check whether the norm module has IS_SEQUENCE_PARALLEL attribute
+    if gpc.config.parallel.sequence_parallel is True:
+        check_sequence_parallel(model)
+
     return model
 
 
diff --git a/internlm/utils/parallel.py b/internlm/utils/parallel.py
index 9b70fc8..e6bb18f 100644
--- a/internlm/utils/parallel.py
+++ b/internlm/utils/parallel.py
@@ -4,11 +4,14 @@
 import torch.distributed as dist
 from torch import nn
 
-from internlm.core.context import IS_TENSOR_PARALLEL, ParallelMode
+from internlm.core.context import IS_SEQUENCE_PARALLEL, IS_TENSOR_PARALLEL, ParallelMode
 from internlm.core.context import global_context as gpc
 from internlm.core.naive_amp import NaiveAMPModel
+from internlm.model.utils import try_import_RMSNorm
 from internlm.solver.pipeline_utils import partition_uniform
 
+RMSNorm = try_import_RMSNorm()
+
 
 def is_model_parallel_parameter(p):
     return hasattr(p, IS_TENSOR_PARALLEL) and getattr(p, IS_TENSOR_PARALLEL)
@@ -98,3 +101,26 @@ def set_model_params_layer_name(model):
                     layer_param_name = f"{layer_name}-{param_name}"
                     param.__setattr__("layer_name", layer_name)
                     param.__setattr__("param_name", f"{layer_name}-{param_name}")
+
+
+def check_sequence_parallel(model):
+    """
+    check whether the norm module has IS_SEQUENCE_PARALLEL attribute.
+    when the sequence_parallel is True, the norm module should have the IS_SEQUENCE_PARALLEL attribute
+    to illustrate the norm should conduct the all-reduce for its grad.
+    """
+
+    if not isinstance(model, nn.ModuleList):
+        model = [model]
+
+    for _chunk in model:
+        if isinstance(_chunk, NaiveAMPModel):
+            _chunk = _chunk.model
+
+        for _, module in _chunk.named_modules():
+            if isinstance(module, (RMSNorm, nn.LayerNorm)):
+                for param in module.parameters():
+                    assert hasattr(param, IS_SEQUENCE_PARALLEL), (
+                        "when the gpc.config.parallel.sequence parallel is True,"
+                        "the params of norm module should have IS_SEQUENCE_PARALLEL attribute"
+                    )