prompt: 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()
  
  ```
  
golden_diff: 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']
  
verification_info: {"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"}]}