diff --git a/debug/accuracy_tools/grad_tool/grad_comparator.py b/debug/accuracy_tools/grad_tool/grad_comparator.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cd3768e0c8ec24e48c28b2f06aeb2a09b0fba47
--- /dev/null
+++ b/debug/accuracy_tools/grad_tool/grad_comparator.py
@@ -0,0 +1,104 @@
+import os
+import torch
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+from grad_tool.utils import write_csv, path_check
+
+
+class GradComparator:
+    @staticmethod
+    def compare(path1: str, path2: str, output_dir):
+        steps = GradComparator._get_steps(path1, path2)
+        if len(steps) == 0:
+            raise Exception("no step for comparison")
+        similarities = {}
+        print(f"the following steps will be compared:\n{steps}")
+        for step in tqdm(steps, desc="culculate similarities"):
+            pt_files = GradComparator._get_pt_files(path1, path2, step)
+            same_count_summary = 0
+            total_count_summary = 0
+            for pt_file in pt_files:
+                pt1 = f'{path1}/step_{step}/{pt_file}'
+                pt2 = f'{path2}/step_{step}/{pt_file}'
+                same_count, total_count = GradComparator._calc_similarity(pt1, pt2)
+                same_count_summary += same_count
+                total_count_summary += total_count
+                if pt_file not in similarities:
+                    similarities[pt_file] = []
+                if total_count == 0:
+                    similarities[pt_file].append(0)
+                else:
+                    similarities[pt_file].append(same_count / total_count)
+            if "summary" not in similarities:
+                similarities["summary"] = []
+            if total_count_summary == 0:
+                similarities["summary"].append(0)
+            else:
+                similarities["summary"].append(same_count_summary / total_count_summary)
+        if not os.path.isdir(output_dir):
+            os.makedirs(output_dir)
+        GradComparator._save_similar(similarities, steps, output_dir)
+
+    @staticmethod
+    def _get_steps(path1: str, path2: str):
+        path_check(path1, isdir=True)
+        path_check(path2, isdir=True)
+        steps = []
+        for folder1 in os.listdir(path1):
+            splits = folder1.split('_')
+            if len(splits) == 0 or splits[0] != 'step' or not splits[1].isdigit():
+                continue
+
+            folder2 = f'{path2}/{folder1}'
+            if not os.path.exists(folder2):
+                continue
+            steps.append(int(splits[1]))
+        steps = sorted(steps)
+        return steps
+
+    @staticmethod
+    def _get_pt_files(path1: str, path2: str, step: int):
+        path1 = f'{path1}/step_{step}'
+        path2 = f'{path2}/step_{step}'
+        path_check(path1, isdir=True)
+        path_check(path2, isdir=True)
+        pt_files = []
+        for folder1 in os.listdir(path1):
+            splits = folder1.split('.')
+            if len(splits) < 1 or splits[-1] != 'pt':
+                continue
+            folder2 = f'{path2}/{folder1}'
+            if not os.path.exists(folder2):
+                continue
+            pt_files.append(folder1)
+        return sorted(pt_files)
+
+    @staticmethod
+    def _save_similar(similarities: [float], steps: [int], output_dir: str):
+        if len(similarities) == 0:
+            raise Exception(f"length of similarities is 0")
+        for key, value in tqdm(similarities.items(), desc="save similarities"):
+            if len(value) != len(steps):
+                raise Exception(f"similarities length of {key}:{len(value)} not equal steps:{len(steps)}")
+            plt.plot(steps, value)
+            plt.xlabel('steps')
+            plt.ylabel('similarities')
+            plt.title(f'{key}_similarities')
+            plt.savefig(f'{output_dir}/{key}_similarities.png')
+            plt.close()
+            head_tuple = tuple(['step'] + [str(step) for step in steps])
+            write_csv(f"{output_dir}/{key}_similarities.csv", [['similarity'] + value], head_tuple)
+
+    @staticmethod
+    def _calc_similarity(pt_file1: str, pt_file2: str):
+        tensor1 = torch.load(pt_file1)
+        tensor2 = torch.load(pt_file2)
+        if tensor1.shape != tensor2.shape:
+            raise Exception(f"tensor shape is not equal: {pt_file1}, {pt_file2}")
+        if tensor1.dtype != torch.bool:
+            raise Exception(f"tensor type is not bool: {pt_file1}")
+        if tensor2.dtype != torch.bool:
+            raise Exception(f"tensor type is not bool: {pt_file2}")
+        same_count = (tensor1 == tensor2).sum().item()
+        total_count = tensor1.numel()
+        return same_count, total_count
\ No newline at end of file
diff --git a/debug/accuracy_tools/grad_tool/utils.py b/debug/accuracy_tools/grad_tool/utils.py
index 4e563d9928112bb95017ba7b8ec7bd281cb0832d..7417668a6febb30898284db8e59a4a44a0a284d9 100644
--- a/debug/accuracy_tools/grad_tool/utils.py
+++ b/debug/accuracy_tools/grad_tool/utils.py
@@ -5,6 +5,7 @@ import torch
 import pandas as pd
 from ptdbg_ascend.src.python.ptdbg_ascend.common.file_check_util import FileOpen, create_directory, \
     check_link, FileChecker, FileCheckConst
+from ptdbg_ascend.src.python.ptdbg_ascend.common.utils import check_file_or_directory_path
 
 
 class ListCache(list):
@@ -115,3 +116,7 @@ def get_rank_id(tensor):
     if rank is not None:
         return rank
     return os.getpid()
+
+
+def path_check(path, isdir=False):
+    check_file_or_directory_path(path, isdir)
\ No newline at end of file