diff --git a/colossalai/shardformer/policies/deepseek.py b/colossalai/shardformer/policies/deepseek.py
index 07b86cd638c8..8ebda357b380 100644
--- a/colossalai/shardformer/policies/deepseek.py
+++ b/colossalai/shardformer/policies/deepseek.py
@@ -1,3 +1,4 @@
+import warnings
 from functools import partial
 from typing import Callable, Dict, List, Union
 
@@ -39,21 +40,20 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
 
         if self.shard_config.enable_tensor_parallelism:
             raise NotImplementedError("Tensor parallelism is not supported for Deepseek model now.")
-        if getattr(self.shard_config, "ep_group", None) is None:
-            raise ValueError("You must pass in ep_group via shard_config for expert parallel!")
-
-        # expert parallel
-        self.append_or_create_submodule_replacement(
-            description=[
-                SubModuleReplacementDescription(
-                    suffix="mlp",
-                    target_module=EPDeepseekMoE,
-                    kwargs={"ep_group": self.shard_config.ep_group},
-                )
-            ],
-            policy=policy,
-            target_key="DeepseekDecoderLayer",
-        )
+
+        if getattr(self.shard_config, "ep_group", None) is not None:
+            # expert parallel
+            self.append_or_create_submodule_replacement(
+                description=[
+                    SubModuleReplacementDescription(
+                        suffix="mlp",
+                        target_module=EPDeepseekMoE,
+                        kwargs={"ep_group": self.shard_config.ep_group},
+                    )
+                ],
+                policy=policy,
+                target_key="DeepseekDecoderLayer",
+            )
 
         # optimization configuration
         if self.shard_config.enable_fused_normalization:
@@ -82,7 +82,10 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
             )
 
         if self.shard_config.enable_flash_attention:
-            raise NotImplementedError("Flash attention has already been replaced in deepseek.")
+            warnings.warn(
+                "Flash attention has already been replaced in deepseek, and now set enable_flash_attention = False."
+            )
+            self.shard_config.enable_flash_attention = False
 
         return policy
 
diff --git a/tests/test_moe/test_deepseek_layer.py b/tests/test_moe/test_deepseek_layer.py
index 328ffb1de5f8..85cc986959fd 100644
--- a/tests/test_moe/test_deepseek_layer.py
+++ b/tests/test_moe/test_deepseek_layer.py
@@ -25,14 +25,17 @@ def check_deepseek_moe_layer():
         ep_size=dist.get_world_size(),
     )
 
-    config = AutoConfig.from_pretrained("deepseek-ai/deepseek-moe-16b-base", trust_remote_code=True)
-    config.num_hidden_layers = 1
-    config.n_routed_experts = n_experts
-    config.num_experts_per_tok = top_k
-    config.hidden_size = hidden_size
-    config.intermediate_size = hidden_size * 2
-    config.first_k_dense_replace = 0
-    config.num_attention_heads = 2
+    config = AutoConfig.from_pretrained(
+        "deepseek-ai/deepseek-moe-16b-base",
+        num_hidden_layers=1,
+        n_routed_experts=n_experts,
+        num_experts_per_tok=top_k,
+        hidden_size=hidden_size,
+        intermediate_size=hidden_size * 2,
+        first_k_dense_replace=0,
+        num_attention_heads=2,
+        trust_remote_code=True,
+    )
     torch.manual_seed(0)
     # get the moe layer in auto model
     orig_model = AutoModel.from_config(config, trust_remote_code=True).layers[0].mlp.cuda()