diff --git a/paddle/fluid/pir/dialect/op_generator/ops_api_gen.py b/paddle/fluid/pir/dialect/op_generator/ops_api_gen.py
index ed5c193aa2eca..fae7a3c9fc283 100644
--- a/paddle/fluid/pir/dialect/op_generator/ops_api_gen.py
+++ b/paddle/fluid/pir/dialect/op_generator/ops_api_gen.py
@@ -82,6 +82,7 @@
     'generate_sequence_xpu',
     'layer_norm_act_xpu',
     'memcpy',
+    'batch_norm_',
     'multi_encoder_xpu',
     'multihead_matmul',
     'squeeze_excitation_block',
@@ -104,7 +105,6 @@
     'add_n_',
     'add_n_with_kernel',
     'assign_value',
-    'batch_norm_',
     'c_allgather',
     'c_allreduce_max',
     'c_allreduce_sum',
diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py
index 4cd13ec19846a..f7d32bb61908b 100644
--- a/python/paddle/nn/layer/norm.py
+++ b/python/paddle/nn/layer/norm.py
@@ -42,6 +42,7 @@
     _global_flags,
     get_default_dtype,
     in_dynamic_or_pir_mode,
+    in_pir_mode,
     no_grad,
 )
 from .. import functional as F
@@ -1056,7 +1057,7 @@ def __init__(
         self._trainable_statistics = trainable_statistics
 
     def forward(self, input):
-        if in_dynamic_or_pir_mode():
+        if in_dynamic_mode():
             batch_norm_out, t1, t2, t3, t4, _ = _C_ops.batch_norm(
                 input,
                 self._mean,
@@ -1072,13 +1073,29 @@ def forward(self, input):
             )
             if self._act is None:
                 return batch_norm_out
-            if in_dynamic_mode():
-                return dygraph_utils._append_activation_in_dygraph(
-                    batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn
-                )
-            else:
-                act_op = getattr(_C_ops, self._act)
-                return act_op(batch_norm_out)
+
+            return dygraph_utils._append_activation_in_dygraph(
+                batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn
+            )
+        elif in_pir_mode():
+            batch_norm_out, t1, t2, t3, t4, _ = _C_ops.batch_norm_(
+                input,
+                self._mean,
+                self._variance,
+                self.weight,
+                self.bias,
+                not self.training,
+                self._momentum,
+                self._epsilon,
+                self._data_layout,
+                self._use_global_stats,
+                self._trainable_statistics,
+            )
+            if self._act is None:
+                return batch_norm_out
+
+            act_op = getattr(_C_ops, self._act)
+            return act_op(batch_norm_out)
         else:
             # create output
             # mean and mean_out share the same memory