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/auto_parallel/tensor_shard/deprecated/graph_analysis.py`
Content:
```
1 from dataclasses import dataclass
2 from torch.fx.node import Node
3 from torch.fx.graph import Graph
4 from torch.fx.graph_module import GraphModule
5 from collections import OrderedDict as ODict
6 from typing import List, OrderedDict, Union, Any
7 from colossalai.fx.passes.utils import get_node_module
8 
9 __all__ = ['LiveVariable', 'LiveVariableVector', 'LiveStage', 'GraphAnalyser']
10 
11 
12 @dataclass
13 class LiveVariable:
14     """
15     LiveVariable is a data structure to store the meta information of a variable for liveness analysis.
16     """
17     name: str
18     node: Node
19     is_inplace: bool
20 
21 
22 class LiveVariableVector(list):
23     """
24     LiveVariableVector is a data structure to store the list of LiveVariable objects.
25     """
26 
27     def exists(self, name) -> bool:
28         """
29         Check if a variable has already existed in the current list by name.
30         """
31         for var in self:
32             if name == var.name:
33                 return True
34         return False
35 
36     def get(self, name) -> LiveVariable:
37         for var in self:
38             if name == var.name:
39                 return var
40         raise KeyError(f"Variable {name} is not found")
41 
42     def copy(self) -> "LiveVariableVector":
43         """
44         Create a copy of this vector
45         """
46         vector = LiveVariableVector()
47         for var in self:
48             vector.append(var)
49         return vector
50 
51 
52 @dataclass
53 class LiveStage:
54     """
55     LiveStage is a data structure to record the living variables at this current node.
56     """
57     name: str
58     node: Node
59     all_live_vars: LiveVariableVector
60     unique_live_vars: LiveVariableVector
61 
62 
63 class GraphAnalyser:
64 
65     def __init__(self, gm: GraphModule):
66         self._gm = gm
67         self._graph = gm.graph
68 
69     @property
70     def gm(self) -> GraphModule:
71         """
72         Return the GraphModule object associated with this analyser.
73         """
74         return self._gm
75 
76     @property
77     def graph(self) -> Graph:
78         """
79         Return the Graph object associated with this analyser.
80         """
81         return self._graph
82 
83     def liveness_analysis(self) -> List[LiveStage]:
84         """
85         Analyse the graph to obtain the variable liveness information. This function returns
86         an ordered dictionary where the key is the compute stage ID and the value is a LivenessStage object.
87         """
88         compute_nodes = self.graph.nodes
89         liveness_list = []
90 
91         # checked: record all variables created since the first stage
92         # all: record the live variables only exist until the current stage.
93         #       this can be different from the `checked list`` as some varialbes may be destroyed prior to this stage.
94         # unique: record the unique live variables only exist until the current stage.
95         #       this is different from `all list` as some variables are duplicated.
96         checked_variables = LiveVariableVector()
97         all_live_variables = LiveVariableVector()
98         unique_live_vars = LiveVariableVector()
99 
100         for idx, node in enumerate(compute_nodes):
101             #############################
102             # find new living variables #
103             #############################
104             # detect whether the current op is an in-place op
105             # if it is an in-place op, we would deem it as a duplciate var
106             is_inplace = False
107             if node.op == 'call_function':
108                 # check if this is an inplace op such as torch.nn.functional.relu(x, inplace=True)
109                 if node.kwargs.get('inplace', False):
110                     is_inplace = True
111             elif node.op == 'call_module':
112                 # to check if this is an inplace op such as torch.nn.Relu(inplace=True)
113                 module = get_node_module(node)
114                 if getattr(module, 'inplace', False):
115                     is_inplace = True
116 
117             # add the output var
118             meta = getattr(node, '_meta_data', None)
119             live_var = LiveVariable(name=node.name, node=node, is_inplace=is_inplace)
120             if not is_inplace:
121                 unique_live_vars.append(live_var)
122             checked_variables.append(live_var)
123             all_live_variables.append(live_var)
124 
125             # check if any input is not checked yet
126             for arg in node.args:
127                 if not isinstance(arg, Node):
128                     continue
129                 arg_name = arg.name
130                 if not checked_variables.exists(arg_name):
131                     live_var_from_arg = LiveVariable(name=arg_name, node=node, is_inplace=False)
132                     all_live_variables.append(live_var_from_arg)
133                     checked_variables.append(live_var_from_arg)
134                     unique_live_vars.append(live_var_from_arg)
135 
136             # TODO: add the logic to remove live variables
137             # this should be completed if we are able to trace the backward compute graph
138 
139             # add this stage to liveness dict
140             stage = LiveStage(name=node.name,
141                               node=node,
142                               all_live_vars=all_live_variables.copy(),
143                               unique_live_vars=unique_live_vars.copy())
144             # if a LiveStage is covered by another LiveStage, we just keep the larger one.
145             replace = False
146             for index, prev_stage in enumerate(liveness_list):
147                 all_covered = True
148                 for ele in prev_stage.unique_live_vars:
149                     if ele not in stage.unique_live_vars:
150                         all_covered = False
151                         break
152                 if all_covered:
153                     replace = True
154                     break
155             if replace:
156                 liveness_list[index] = stage
157             else:
158                 liveness_list.append(stage)
159 
160         return liveness_list
161 
162     def get_alias_set(self):
163         pass
164
```
--- 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/auto_parallel/tensor_shard/deprecated/graph_analysis.py b/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py
--- a/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py
+++ b/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py
@@ -1,9 +1,11 @@
+from collections import OrderedDict as ODict
 from dataclasses import dataclass
-from torch.fx.node import Node
+from typing import Any, List, OrderedDict, Union
+
 from torch.fx.graph import Graph
 from torch.fx.graph_module import GraphModule
-from collections import OrderedDict as ODict
-from typing import List, OrderedDict, Union, Any
+from torch.fx.node import Node
+
 from colossalai.fx.passes.utils import get_node_module
 
 __all__ = ['LiveVariable', 'LiveVariableVector', 'LiveStage', 'GraphAnalyser']

{"golden_diff": "diff --git a/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py b/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py\n--- a/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py\n+++ b/colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py\n@@ -1,9 +1,11 @@\n+from collections import OrderedDict as ODict\n from dataclasses import dataclass\n-from torch.fx.node import Node\n+from typing import Any, List, OrderedDict, Union\n+\n from torch.fx.graph import Graph\n from torch.fx.graph_module import GraphModule\n-from collections import OrderedDict as ODict\n-from typing import List, OrderedDict, Union, Any\n+from torch.fx.node import Node\n+\n from colossalai.fx.passes.utils import get_node_module\n \n __all__ = ['LiveVariable', 'LiveVariableVector', 'LiveStage', 'GraphAnalyser']\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from dataclasses import dataclass\nfrom torch.fx.node import Node\nfrom torch.fx.graph import Graph\nfrom torch.fx.graph_module import GraphModule\nfrom collections import OrderedDict as ODict\nfrom typing import List, OrderedDict, Union, Any\nfrom colossalai.fx.passes.utils import get_node_module\n\n__all__ = ['LiveVariable', 'LiveVariableVector', 'LiveStage', 'GraphAnalyser']\n\n\n@dataclass\nclass LiveVariable:\n    \"\"\"\n    LiveVariable is a data structure to store the meta information of a variable for liveness analysis.\n    \"\"\"\n    name: str\n    node: Node\n    is_inplace: bool\n\n\nclass LiveVariableVector(list):\n    \"\"\"\n    LiveVariableVector is a data structure to store the list of LiveVariable objects.\n    \"\"\"\n\n    def exists(self, name) -> bool:\n        \"\"\"\n        Check if a variable has already existed in the current list by name.\n        \"\"\"\n        for var in self:\n            if name == var.name:\n                return True\n        return False\n\n    def get(self, name) -> LiveVariable:\n        for var in self:\n            if name == var.name:\n                return var\n        raise KeyError(f\"Variable {name} is not found\")\n\n    def copy(self) -> \"LiveVariableVector\":\n        \"\"\"\n        Create a copy of this vector\n        \"\"\"\n        vector = LiveVariableVector()\n        for var in self:\n            vector.append(var)\n        return vector\n\n\n@dataclass\nclass LiveStage:\n    \"\"\"\n    LiveStage is a data structure to record the living variables at this current node.\n    \"\"\"\n    name: str\n    node: Node\n    all_live_vars: LiveVariableVector\n    unique_live_vars: LiveVariableVector\n\n\nclass GraphAnalyser:\n\n    def __init__(self, gm: GraphModule):\n        self._gm = gm\n        self._graph = gm.graph\n\n    @property\n    def gm(self) -> GraphModule:\n        \"\"\"\n        Return the GraphModule object associated with this analyser.\n        \"\"\"\n        return self._gm\n\n    @property\n    def graph(self) -> Graph:\n        \"\"\"\n        Return the Graph object associated with this analyser.\n        \"\"\"\n        return self._graph\n\n    def liveness_analysis(self) -> List[LiveStage]:\n        \"\"\"\n        Analyse the graph to obtain the variable liveness information. This function returns\n        an ordered dictionary where the key is the compute stage ID and the value is a LivenessStage object.\n        \"\"\"\n        compute_nodes = self.graph.nodes\n        liveness_list = []\n\n        # checked: record all variables created since the first stage\n        # all: record the live variables only exist until the current stage.\n        #       this can be different from the `checked list`` as some varialbes may be destroyed prior to this stage.\n        # unique: record the unique live variables only exist until the current stage.\n        #       this is different from `all list` as some variables are duplicated.\n        checked_variables = LiveVariableVector()\n        all_live_variables = LiveVariableVector()\n        unique_live_vars = LiveVariableVector()\n\n        for idx, node in enumerate(compute_nodes):\n            #############################\n            # find new living variables #\n            #############################\n            # detect whether the current op is an in-place op\n            # if it is an in-place op, we would deem it as a duplciate var\n            is_inplace = False\n            if node.op == 'call_function':\n                # check if this is an inplace op such as torch.nn.functional.relu(x, inplace=True)\n                if node.kwargs.get('inplace', False):\n                    is_inplace = True\n            elif node.op == 'call_module':\n                # to check if this is an inplace op such as torch.nn.Relu(inplace=True)\n                module = get_node_module(node)\n                if getattr(module, 'inplace', False):\n                    is_inplace = True\n\n            # add the output var\n            meta = getattr(node, '_meta_data', None)\n            live_var = LiveVariable(name=node.name, node=node, is_inplace=is_inplace)\n            if not is_inplace:\n                unique_live_vars.append(live_var)\n            checked_variables.append(live_var)\n            all_live_variables.append(live_var)\n\n            # check if any input is not checked yet\n            for arg in node.args:\n                if not isinstance(arg, Node):\n                    continue\n                arg_name = arg.name\n                if not checked_variables.exists(arg_name):\n                    live_var_from_arg = LiveVariable(name=arg_name, node=node, is_inplace=False)\n                    all_live_variables.append(live_var_from_arg)\n                    checked_variables.append(live_var_from_arg)\n                    unique_live_vars.append(live_var_from_arg)\n\n            # TODO: add the logic to remove live variables\n            # this should be completed if we are able to trace the backward compute graph\n\n            # add this stage to liveness dict\n            stage = LiveStage(name=node.name,\n                              node=node,\n                              all_live_vars=all_live_variables.copy(),\n                              unique_live_vars=unique_live_vars.copy())\n            # if a LiveStage is covered by another LiveStage, we just keep the larger one.\n            replace = False\n            for index, prev_stage in enumerate(liveness_list):\n                all_covered = True\n                for ele in prev_stage.unique_live_vars:\n                    if ele not in stage.unique_live_vars:\n                        all_covered = False\n                        break\n                if all_covered:\n                    replace = True\n                    break\n            if replace:\n                liveness_list[index] = stage\n            else:\n                liveness_list.append(stage)\n\n        return liveness_list\n\n    def get_alias_set(self):\n        pass\n", "path": "colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py"}], "after_files": [{"content": "from collections import OrderedDict as ODict\nfrom dataclasses import dataclass\nfrom typing import Any, List, OrderedDict, Union\n\nfrom torch.fx.graph import Graph\nfrom torch.fx.graph_module import GraphModule\nfrom torch.fx.node import Node\n\nfrom colossalai.fx.passes.utils import get_node_module\n\n__all__ = ['LiveVariable', 'LiveVariableVector', 'LiveStage', 'GraphAnalyser']\n\n\n@dataclass\nclass LiveVariable:\n    \"\"\"\n    LiveVariable is a data structure to store the meta information of a variable for liveness analysis.\n    \"\"\"\n    name: str\n    node: Node\n    is_inplace: bool\n\n\nclass LiveVariableVector(list):\n    \"\"\"\n    LiveVariableVector is a data structure to store the list of LiveVariable objects.\n    \"\"\"\n\n    def exists(self, name) -> bool:\n        \"\"\"\n        Check if a variable has already existed in the current list by name.\n        \"\"\"\n        for var in self:\n            if name == var.name:\n                return True\n        return False\n\n    def get(self, name) -> LiveVariable:\n        for var in self:\n            if name == var.name:\n                return var\n        raise KeyError(f\"Variable {name} is not found\")\n\n    def copy(self) -> \"LiveVariableVector\":\n        \"\"\"\n        Create a copy of this vector\n        \"\"\"\n        vector = LiveVariableVector()\n        for var in self:\n            vector.append(var)\n        return vector\n\n\n@dataclass\nclass LiveStage:\n    \"\"\"\n    LiveStage is a data structure to record the living variables at this current node.\n    \"\"\"\n    name: str\n    node: Node\n    all_live_vars: LiveVariableVector\n    unique_live_vars: LiveVariableVector\n\n\nclass GraphAnalyser:\n\n    def __init__(self, gm: GraphModule):\n        self._gm = gm\n        self._graph = gm.graph\n\n    @property\n    def gm(self) -> GraphModule:\n        \"\"\"\n        Return the GraphModule object associated with this analyser.\n        \"\"\"\n        return self._gm\n\n    @property\n    def graph(self) -> Graph:\n        \"\"\"\n        Return the Graph object associated with this analyser.\n        \"\"\"\n        return self._graph\n\n    def liveness_analysis(self) -> List[LiveStage]:\n        \"\"\"\n        Analyse the graph to obtain the variable liveness information. This function returns\n        an ordered dictionary where the key is the compute stage ID and the value is a LivenessStage object.\n        \"\"\"\n        compute_nodes = self.graph.nodes\n        liveness_list = []\n\n        # checked: record all variables created since the first stage\n        # all: record the live variables only exist until the current stage.\n        #       this can be different from the `checked list`` as some varialbes may be destroyed prior to this stage.\n        # unique: record the unique live variables only exist until the current stage.\n        #       this is different from `all list` as some variables are duplicated.\n        checked_variables = LiveVariableVector()\n        all_live_variables = LiveVariableVector()\n        unique_live_vars = LiveVariableVector()\n\n        for idx, node in enumerate(compute_nodes):\n            #############################\n            # find new living variables #\n            #############################\n            # detect whether the current op is an in-place op\n            # if it is an in-place op, we would deem it as a duplciate var\n            is_inplace = False\n            if node.op == 'call_function':\n                # check if this is an inplace op such as torch.nn.functional.relu(x, inplace=True)\n                if node.kwargs.get('inplace', False):\n                    is_inplace = True\n            elif node.op == 'call_module':\n                # to check if this is an inplace op such as torch.nn.Relu(inplace=True)\n                module = get_node_module(node)\n                if getattr(module, 'inplace', False):\n                    is_inplace = True\n\n            # add the output var\n            meta = getattr(node, '_meta_data', None)\n            live_var = LiveVariable(name=node.name, node=node, is_inplace=is_inplace)\n            if not is_inplace:\n                unique_live_vars.append(live_var)\n            checked_variables.append(live_var)\n            all_live_variables.append(live_var)\n\n            # check if any input is not checked yet\n            for arg in node.args:\n                if not isinstance(arg, Node):\n                    continue\n                arg_name = arg.name\n                if not checked_variables.exists(arg_name):\n                    live_var_from_arg = LiveVariable(name=arg_name, node=node, is_inplace=False)\n                    all_live_variables.append(live_var_from_arg)\n                    checked_variables.append(live_var_from_arg)\n                    unique_live_vars.append(live_var_from_arg)\n\n            # TODO: add the logic to remove live variables\n            # this should be completed if we are able to trace the backward compute graph\n\n            # add this stage to liveness dict\n            stage = LiveStage(name=node.name,\n                              node=node,\n                              all_live_vars=all_live_variables.copy(),\n                              unique_live_vars=unique_live_vars.copy())\n            # if a LiveStage is covered by another LiveStage, we just keep the larger one.\n            replace = False\n            for index, prev_stage in enumerate(liveness_list):\n                all_covered = True\n                for ele in prev_stage.unique_live_vars:\n                    if ele not in stage.unique_live_vars:\n                        all_covered = False\n                        break\n                if all_covered:\n                    replace = True\n                    break\n            if replace:\n                liveness_list[index] = stage\n            else:\n                liveness_list.append(stage)\n\n        return liveness_list\n\n    def get_alias_set(self):\n        pass\n", "path": "colossalai/auto_parallel/tensor_shard/deprecated/graph_analysis.py"}]}