diff --git a/mindformers/parallel_core/training_graph/transformer/utils.py b/mindformers/parallel_core/training_graph/transformer/utils.py
index 46243be006db13f10f1481465371bb62b4b3a303..61bc60b411962678c4080498f9ec31c5793aae1f 100644
--- a/mindformers/parallel_core/training_graph/transformer/utils.py
+++ b/mindformers/parallel_core/training_graph/transformer/utils.py
@@ -159,7 +159,6 @@ class LayerSetting:
         self.pp_interleave_num = pp_interleave_num if use_pp_interleave else 1
         self.offset = np.array(offset, np.int32)
         self._check_inputs()
-        self.offset = np.broadcast_to(self.offset, (self.pp_interleave_num, self.pp))
 
         self.is_zbv = ms.get_auto_parallel_context("pipeline_scheduler") == "zero_bubble_v"
         avg_layer = self.num_layers // (self.pp * self.pp_interleave_num)
@@ -571,17 +570,39 @@ class LayerSetting:
 
     def _check_inputs(self):
         """Check the inputs of offset."""
+        total_stages = self.pp * self.pp_interleave_num
+        if total_stages <= 1:
+            self.offset = np.zeros((1, 1), dtype=np.int32)
+            return
+        base_layers = self.num_layers // total_stages
+        remainder_layers = self.num_layers % total_stages
+
         if self.offset.ndim >= 1 and self.offset.shape[-1] != self.pp:
             raise ValueError(f"offset.shape[-1] should equal to `pp` ({self.pp}), "
                              f"but got ({self.offset.shape[-1]}). `offset`: {self.offset}")
         if self.offset.ndim >= 2 and self.offset.shape[-2] != self.pp_interleave_num:
             raise ValueError(f"offset.shape[-2] should equal to `pp_interleave_num` ({self.pp_interleave_num}), "
                              f"but got ({self.offset.shape[-2]}). `offset`: {self.offset}")
-        if self.offset.sum() != self.num_layers % (self.pp * self.pp_interleave_num):
-            r = self.num_layers % (self.pp * self.pp_interleave_num)
+        if self.offset.sum() != remainder_layers:
             raise ValueError(f"The sum of `offset` ({self.offset.sum()}) should equal to remainder of `num_layers` "
                              f"({self.num_layers}) % (pp ({self.pp}) * pp_interleave_num ({self.pp_interleave_num})) "
-                             f"= {r}")
+                             f"= {remainder_layers}")
+
+        # Broadcast offset
+        self.offset = np.broadcast_to(self.offset, (self.pp_interleave_num, self.pp))
+        actual_layers = self.offset.flatten() + base_layers
+
+        # Ensure head and tail stages have non-negative layer counts (0 is allowed)
+        if actual_layers[0] < 0:
+            raise ValueError(f"Head stage has negative layers. Offset must be ≥ {-base_layers}.")
+        if actual_layers[-1] < 0:
+            raise ValueError(f"Tail stage has negative layers. Offset must be ≥ {-base_layers}.")
+
+        # Ensure all middle stages have at least 1 layer
+        if total_stages > 2:
+            middle_layers = actual_layers[1:-1]
+            if np.any(middle_layers < 1):
+                raise ValueError(f"Some middle stage has fewer than 1 layer. Offset must be ≥ {1 - base_layers}.")
 
     @staticmethod
     def set_pattern_recompute(layer, p_list, add_prim_attr=False, set_on=True, info=''):