We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[tensor] fix some unittests

[tensor] fix some unittests

[tensor] fix some unittests


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `colossalai/context/process_group_initializer/initializer_sequence.py`
Content:
```
1 #!/usr/bin/env python
2 # -*- encoding: utf-8 -*-
3 import torch.distributed as dist
4 
5 from colossalai.registry import DIST_GROUP_INITIALIZER
6 from .initializer_tensor import Initializer_Tensor
7 from .process_group_initializer import ProcessGroupInitializer
8 from ..parallel_mode import ParallelMode
9 
10 
11 @DIST_GROUP_INITIALIZER.register_module
12 class Initializer_Sequence_DP(ProcessGroupInitializer):
13     """A ProcessGroupInitializer for sequence parallelism all-reduce.
14 
15     In Sequence Parallelism, each GPU holds the full copy of model weights,
16     thus, gradient all-reduce occurs across all processes in the same pipeline stage
17 
18     Args:
19         rank (int): The rank of current process
20         world_size (int): Size of whole communication world
21         config (Config): Running configuration
22         data_parallel_size (int): Size of data parallel
23         pipeline_parallel_size (int): Size of pipeline parallel
24         tensor_parallel_size (int): Size of tensor parallel
25     """
26 
27     def __init__(self, *args, **kwargs):
28         super().__init__(*args, **kwargs)
29         self.dp_size = self.world_size // self.pipeline_parallel_size
30         self.num_group = self.pipeline_parallel_size
31 
32     def init_dist_group(self):
33         """Initialize Sequence Parallel process groups used for gradient all-reduce.
34 
35         Returns:
36             Tuple: A tuple (local_rank, group_world_size, process_group, ranks_in_group, mode).
37         """
38         local_rank = None
39         ranks_in_group = None
40         process_group = None
41         cpu_group = None
42         group_world_size = None
43         mode = ParallelMode.SEQUENCE_DP
44 
45         for i in range(self.num_group):
46             ranks = [i * self.dp_size + j for j in range(self.dp_size)]
47             group = dist.new_group(ranks)
48             group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group
49 
50             if self.rank in ranks:
51                 local_rank = ranks.index(self.rank)
52                 group_world_size = len(ranks)
53                 process_group = group
54                 cpu_group = group_cpu
55                 ranks_in_group = ranks
56 
57         return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
58 
59 
60 @DIST_GROUP_INITIALIZER.register_module
61 class Initializer_Sequence(ProcessGroupInitializer):
62     """A ProcessGroupInitializer for sequence parallelism.
63 
64     Args:
65         rank (int): The rank of current process.
66         world_size (int): Size of whole communication world.
67         config (Config): Running configuration.
68         data_parallel_size (int): Size of data parallel.
69         pipeline_parallel_size (int): Size of pipeline parallel.
70         tensor_parallel_size (int): Size of tensor parallel.
71     """
72 
73     def __init__(self, *args, **kwargs):
74         super().__init__(*args, **kwargs)
75         # reuse tensor parallel initializer code
76         self._sequence_initializer = Initializer_Tensor(*args, **kwargs)
77         self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)
78 
79     def init_dist_group(self):
80         """Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.
81 
82         Sequence parallelism requires 2 process groups. The first is for model forward where several processes
83         exchange partial query, key and value embedding to compute self attention values. The second is for
84         all-reduce to synchronize the model parameters.
85 
86         Returns:
87             List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:
88                 A Sequence parallelism's information in list of tuples.
89         """
90 
91         parallel_setting = []
92 
93         local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \
94             self._sequence_initializer.init_dist_group()
95         # change mode to sequence
96         mode = ParallelMode.SEQUENCE
97 
98         parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode))
99         parallel_setting.append(self._sequence_dp_initializer.init_dist_group())
100         return parallel_setting
101
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/colossalai/context/process_group_initializer/initializer_sequence.py b/colossalai/context/process_group_initializer/initializer_sequence.py
--- a/colossalai/context/process_group_initializer/initializer_sequence.py
+++ b/colossalai/context/process_group_initializer/initializer_sequence.py
@@ -3,9 +3,10 @@
 import torch.distributed as dist
 
 from colossalai.registry import DIST_GROUP_INITIALIZER
+
+from ..parallel_mode import ParallelMode
 from .initializer_tensor import Initializer_Tensor
 from .process_group_initializer import ProcessGroupInitializer
-from ..parallel_mode import ParallelMode
 
 
 @DIST_GROUP_INITIALIZER.register_module

{"golden_diff": "diff --git a/colossalai/context/process_group_initializer/initializer_sequence.py b/colossalai/context/process_group_initializer/initializer_sequence.py\n--- a/colossalai/context/process_group_initializer/initializer_sequence.py\n+++ b/colossalai/context/process_group_initializer/initializer_sequence.py\n@@ -3,9 +3,10 @@\n import torch.distributed as dist\n \n from colossalai.registry import DIST_GROUP_INITIALIZER\n+\n+from ..parallel_mode import ParallelMode\n from .initializer_tensor import Initializer_Tensor\n from .process_group_initializer import ProcessGroupInitializer\n-from ..parallel_mode import ParallelMode\n \n \n @DIST_GROUP_INITIALIZER.register_module\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\nimport torch.distributed as dist\n\nfrom colossalai.registry import DIST_GROUP_INITIALIZER\nfrom .initializer_tensor import Initializer_Tensor\nfrom .process_group_initializer import ProcessGroupInitializer\nfrom ..parallel_mode import ParallelMode\n\n\n@DIST_GROUP_INITIALIZER.register_module\nclass Initializer_Sequence_DP(ProcessGroupInitializer):\n    \"\"\"A ProcessGroupInitializer for sequence parallelism all-reduce.\n\n    In Sequence Parallelism, each GPU holds the full copy of model weights,\n    thus, gradient all-reduce occurs across all processes in the same pipeline stage\n\n    Args:\n        rank (int): The rank of current process\n        world_size (int): Size of whole communication world\n        config (Config): Running configuration\n        data_parallel_size (int): Size of data parallel\n        pipeline_parallel_size (int): Size of pipeline parallel\n        tensor_parallel_size (int): Size of tensor parallel\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.dp_size = self.world_size // self.pipeline_parallel_size\n        self.num_group = self.pipeline_parallel_size\n\n    def init_dist_group(self):\n        \"\"\"Initialize Sequence Parallel process groups used for gradient all-reduce.\n\n        Returns:\n            Tuple: A tuple (local_rank, group_world_size, process_group, ranks_in_group, mode).\n        \"\"\"\n        local_rank = None\n        ranks_in_group = None\n        process_group = None\n        cpu_group = None\n        group_world_size = None\n        mode = ParallelMode.SEQUENCE_DP\n\n        for i in range(self.num_group):\n            ranks = [i * self.dp_size + j for j in range(self.dp_size)]\n            group = dist.new_group(ranks)\n            group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group\n\n            if self.rank in ranks:\n                local_rank = ranks.index(self.rank)\n                group_world_size = len(ranks)\n                process_group = group\n                cpu_group = group_cpu\n                ranks_in_group = ranks\n\n        return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode\n\n\n@DIST_GROUP_INITIALIZER.register_module\nclass Initializer_Sequence(ProcessGroupInitializer):\n    \"\"\"A ProcessGroupInitializer for sequence parallelism.\n\n    Args:\n        rank (int): The rank of current process.\n        world_size (int): Size of whole communication world.\n        config (Config): Running configuration.\n        data_parallel_size (int): Size of data parallel.\n        pipeline_parallel_size (int): Size of pipeline parallel.\n        tensor_parallel_size (int): Size of tensor parallel.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        # reuse tensor parallel initializer code\n        self._sequence_initializer = Initializer_Tensor(*args, **kwargs)\n        self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)\n\n    def init_dist_group(self):\n        \"\"\"Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.\n\n        Sequence parallelism requires 2 process groups. The first is for model forward where several processes\n        exchange partial query, key and value embedding to compute self attention values. The second is for\n        all-reduce to synchronize the model parameters.\n\n        Returns:\n            List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:\n                A Sequence parallelism's information in list of tuples.\n        \"\"\"\n\n        parallel_setting = []\n\n        local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \\\n            self._sequence_initializer.init_dist_group()\n        # change mode to sequence\n        mode = ParallelMode.SEQUENCE\n\n        parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode))\n        parallel_setting.append(self._sequence_dp_initializer.init_dist_group())\n        return parallel_setting\n", "path": "colossalai/context/process_group_initializer/initializer_sequence.py"}], "after_files": [{"content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\nimport torch.distributed as dist\n\nfrom colossalai.registry import DIST_GROUP_INITIALIZER\n\nfrom ..parallel_mode import ParallelMode\nfrom .initializer_tensor import Initializer_Tensor\nfrom .process_group_initializer import ProcessGroupInitializer\n\n\n@DIST_GROUP_INITIALIZER.register_module\nclass Initializer_Sequence_DP(ProcessGroupInitializer):\n    \"\"\"A ProcessGroupInitializer for sequence parallelism all-reduce.\n\n    In Sequence Parallelism, each GPU holds the full copy of model weights,\n    thus, gradient all-reduce occurs across all processes in the same pipeline stage\n\n    Args:\n        rank (int): The rank of current process\n        world_size (int): Size of whole communication world\n        config (Config): Running configuration\n        data_parallel_size (int): Size of data parallel\n        pipeline_parallel_size (int): Size of pipeline parallel\n        tensor_parallel_size (int): Size of tensor parallel\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.dp_size = self.world_size // self.pipeline_parallel_size\n        self.num_group = self.pipeline_parallel_size\n\n    def init_dist_group(self):\n        \"\"\"Initialize Sequence Parallel process groups used for gradient all-reduce.\n\n        Returns:\n            Tuple: A tuple (local_rank, group_world_size, process_group, ranks_in_group, mode).\n        \"\"\"\n        local_rank = None\n        ranks_in_group = None\n        process_group = None\n        cpu_group = None\n        group_world_size = None\n        mode = ParallelMode.SEQUENCE_DP\n\n        for i in range(self.num_group):\n            ranks = [i * self.dp_size + j for j in range(self.dp_size)]\n            group = dist.new_group(ranks)\n            group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group\n\n            if self.rank in ranks:\n                local_rank = ranks.index(self.rank)\n                group_world_size = len(ranks)\n                process_group = group\n                cpu_group = group_cpu\n                ranks_in_group = ranks\n\n        return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode\n\n\n@DIST_GROUP_INITIALIZER.register_module\nclass Initializer_Sequence(ProcessGroupInitializer):\n    \"\"\"A ProcessGroupInitializer for sequence parallelism.\n\n    Args:\n        rank (int): The rank of current process.\n        world_size (int): Size of whole communication world.\n        config (Config): Running configuration.\n        data_parallel_size (int): Size of data parallel.\n        pipeline_parallel_size (int): Size of pipeline parallel.\n        tensor_parallel_size (int): Size of tensor parallel.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        # reuse tensor parallel initializer code\n        self._sequence_initializer = Initializer_Tensor(*args, **kwargs)\n        self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)\n\n    def init_dist_group(self):\n        \"\"\"Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.\n\n        Sequence parallelism requires 2 process groups. The first is for model forward where several processes\n        exchange partial query, key and value embedding to compute self attention values. The second is for\n        all-reduce to synchronize the model parameters.\n\n        Returns:\n            List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:\n                A Sequence parallelism's information in list of tuples.\n        \"\"\"\n\n        parallel_setting = []\n\n        local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \\\n            self._sequence_initializer.init_dist_group()\n        # change mode to sequence\n        mode = ParallelMode.SEQUENCE\n\n        parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode))\n        parallel_setting.append(self._sequence_dp_initializer.init_dist_group())\n        return parallel_setting\n", "path": "colossalai/context/process_group_initializer/initializer_sequence.py"}]}