diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
index ba66116478ff30931f7a065ae841b8b38b486054..3d230d237cea024a269d01c0f4992ebe50861fc8 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
@@ -6,6 +6,7 @@ from typing import List
 
 import numpy as np
 import torch
+import torch.distributed as dist
 from msprobe.core.common.file_utils import path_len_exceeds_limit, change_mode
 from msprobe.core.common.log import logger
 from msprobe.core.common.const import Const, OverflowConst, FileCheckConst
@@ -22,7 +23,7 @@ except ImportError:
 
 
 class PytorchDataProcessor(BaseDataProcessor):
-    pytorch_special_type = (torch.device, torch.dtype, torch.Size, torch.Tensor)
+    pytorch_special_type = (torch.device, torch.dtype, torch.Size, torch.Tensor, dist.ProcessGroup)
 
     def __init__(self, config, data_writer):
         super().__init__(config, data_writer)
@@ -105,6 +106,16 @@ class PytorchDataProcessor(BaseDataProcessor):
     def _analyze_torch_size(arg):
         return {"type": "torch.Size", "value": list(arg)}
 
+    @staticmethod
+    def _analyze_process_group(arg):
+        group_info = {"type": "torch.distributed.ProcessGroup", "group_id": id(arg)}
+        try:
+            group_ranks = dist.get_process_group_ranks(arg)
+            group_info.update({"group_ranks": group_ranks})
+        except Exception as e:
+            logger.warning(f"Failed to get process group(id: {id(arg)}) ranks info, the error info: {e}.")
+        return group_info
+
     @classmethod
     def get_special_types(cls):
         return super().get_special_types() + cls.pytorch_special_type
@@ -114,6 +125,8 @@ class PytorchDataProcessor(BaseDataProcessor):
             return self.torch_object_key[suffix_stack[-1]](element)
         if isinstance(element, torch.Size):
             return self._analyze_torch_size(element)
+        if isinstance(element, dist.ProcessGroup):
+            return self._analyze_process_group(element)
         converted_numpy, numpy_type = self._convert_numpy_to_builtin(element)
         if converted_numpy is not element:
             return self._analyze_numpy(converted_numpy, numpy_type)
diff --git a/debug/accuracy_tools/msprobe/test/core_ut/data_dump/data_processor/test_pytorch_processor.py b/debug/accuracy_tools/msprobe/test/core_ut/data_dump/data_processor/test_pytorch_processor.py
index 7d44c49e2ccfe714b6dfc67e6991e4b6ad2109e6..6f28c27dc2fc74d4336ff9d97423755d8d0afef8 100644
--- a/debug/accuracy_tools/msprobe/test/core_ut/data_dump/data_processor/test_pytorch_processor.py
+++ b/debug/accuracy_tools/msprobe/test/core_ut/data_dump/data_processor/test_pytorch_processor.py
@@ -131,6 +131,26 @@ class TestPytorchDataProcessor(unittest.TestCase):
         expected = {'type': 'torch.Size', 'value': [3, 4, 5]}
         self.assertEqual(result, expected)
 
+    @patch('msprobe.core.data_dump.data_processor.pytorch_processor.dist.get_process_group_ranks')
+    def test_analyze_process_group_success(self, mock_get_process_group_ranks):
+        mock_get_process_group_ranks.return_value = [0, 1, 2]
+        arg = MagicMock()
+        group_info = self.processor._analyze_process_group(arg)
+
+        self.assertEqual(group_info["type"], "torch.distributed.ProcessGroup")
+        self.assertEqual(group_info["group_id"], id(arg))
+        self.assertEqual(group_info["group_ranks"], [0, 1, 2])
+
+    @patch('msprobe.core.data_dump.data_processor.pytorch_processor.dist.get_process_group_ranks',
+            side_effect=Exception('Error message'))
+    def test_analyze_process_group_exception(self, _):
+        arg = MagicMock()
+        group_info = self.processor._analyze_process_group(arg)
+
+        self.assertEqual(group_info["type"], "torch.distributed.ProcessGroup")
+        self.assertEqual(group_info["group_id"], id(arg))
+        self.assertNotIn("group_ranks", group_info)
+
     def test_get_special_types(self):
         special_types = self.processor.get_special_types()
         self.assertIn(torch.Tensor, special_types)