diff --git a/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py b/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py
index 70c4b826973a01805ac8db9b1105dc8d9dcd7923..61095de62cf693d53c4088593f644617476186bb 100644
--- a/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py
+++ b/debug/accuracy_tools/msprobe/core/single_save/single_comparator.py
@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import os
+import re
 import multiprocessing
 from dataclasses import dataclass
 
@@ -70,6 +71,9 @@ class SingleComparator:
         比较两个NumPy数组，计算最大绝对误差、最大相对误差和相同元素的百分比
         """
         # 计算每个维度上的最小尺寸
+        if array1.ndim != array2.ndim:
+            array1 = array1.flatten()
+            array2 = array2.flatten()
         min_shape = [min(s1, s2) for s1, s2 in zip(array1.shape, array2.shape)]
         # 截取数组到相同的形状
         sliced_array1 = array1[tuple(slice(0, s) for s in min_shape)]
@@ -176,9 +180,18 @@ class SingleComparator:
                 continue
             for step, step_path in cls.get_steps(tag_path):
                 for rank, rank_path in cls.get_ranks(step_path):
-                    for micro_step, micro_step_path in cls.get_micro_steps(rank_path):
-                        for array_id, array_path in cls.get_arrays(micro_step_path):
-                            array_paths.setdefault(tag, []).append((step, rank, micro_step, array_id, array_path))
+                    for item in os.listdir(rank_path):
+                        next_path = os.path.join(rank_path, item)
+                        if re.match(r"micro_step(\d+)", item):
+                            micro_step = re.match(r"micro_step(\d+)", item).group(1)
+                            for array_id, array_path in cls.get_arrays(next_path):
+                                array_paths.setdefault(tag, []).append(
+                                    (step, rank, int(micro_step), array_id, array_path))
+                        elif re.match(r"\w{1,100}_(\d{1,100})\.npy", item):
+                            array_id = re.match(r"\w{1,100}_(\d{1,100})\.npy", item).group(1)
+                            array_paths.setdefault(tag, []).append((step, rank, 0, int(array_id), next_path))
+                        else:
+                            array_paths.setdefault(tag, []).append((step, rank, 0, 0, next_path))
         return array_paths
 
     @classmethod