prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Fix import error for Legacy TorchScript CI on `TorchTensorRTModule`
  - Older versions of TorchScript do not have the `torch._dynamo` import capability
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py`
  Content:
  ```
  1 import copy
  2 import sys
  3 from contextlib import contextmanager
  4 from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
  5 
  6 import torch
  7 
  8 if not torch.__version__.startswith("1"):
  9     import torch._dynamo as torchdynamo
  10 
  11 from torch.fx.passes.infra.pass_base import PassResult
  12 from torch_tensorrt.fx.utils import req_torch_version
  13 from torch_tensorrt.fx.passes.lower_basic_pass_aten import (
  14     compose_bmm,
  15     compose_chunk,
  16     compose_getitem_slice,
  17     remove_ops,
  18     replace_aten_op_with_indices,
  19     replace_aten_reshape_alias_with_replace,
  20     replace_builtin_ops,
  21     replace_inplace_ops,
  22     replace_native_layernorm_with_layernorm,
  23     replace_transpose_mm_op_with_linear,
  24     run_const_fold,
  25 )
  26 from typing_extensions import TypeAlias
  27 
  28 Value: TypeAlias = Union[
  29     Tuple["Value", ...],
  30     List["Value"],
  31     Dict[str, "Value"],
  32 ]
  33 
  34 
  35 class DynamoConfig:
  36     """
  37     Manage Exir-specific configurations of Dynamo.
  38     """
  39 
  40     def __init__(
  41         self,
  42         capture_scalar_outputs: bool = True,
  43         guard_nn_modules: bool = True,
  44         dynamic_shapes: bool = True,
  45         specialize_int: bool = True,
  46         verbose: bool = True,
  47     ) -> None:
  48 
  49         self.capture_scalar_outputs = capture_scalar_outputs
  50         self.guard_nn_modules = guard_nn_modules
  51         self.dynamic_shapes = dynamic_shapes
  52         self.specialize_int = specialize_int
  53         self.verbose = verbose
  54 
  55     def activate(self) -> None:
  56         torchdynamo.config.capture_scalar_outputs = self.capture_scalar_outputs
  57         torchdynamo.config.guard_nn_modules = self.guard_nn_modules
  58         torchdynamo.config.dynamic_shapes = self.dynamic_shapes
  59         torchdynamo.config.specialize_int = self.specialize_int
  60         torchdynamo.config.verbose = self.verbose
  61 
  62     def deactivate(self) -> None:
  63         torchdynamo.config.capture_scalar_outputs = True
  64         torchdynamo.config.guard_nn_modules = True
  65         torchdynamo.config.dynamic_shapes = True
  66         torchdynamo.config.specialize_int = True
  67         torchdynamo.config.verbose = True
  68 
  69 
  70 @contextmanager
  71 def using_config(config: DynamoConfig) -> Generator[DynamoConfig, None, None]:
  72     config.activate()
  73     try:
  74         yield config
  75     finally:
  76         config.deactivate()
  77 
  78 
  79 @contextmanager
  80 def setting_python_recursive_limit(limit: int = 10000) -> Generator[None, None, None]:
  81     """
  82     Temporarily increase the python interpreter stack recursion limit.
  83     This is mostly used for pickling large scale modules.
  84     """
  85     default = sys.getrecursionlimit()
  86     if limit > default:
  87         sys.setrecursionlimit(limit)
  88     try:
  89         yield
  90     finally:
  91         sys.setrecursionlimit(default)
  92 
  93 
  94 @req_torch_version("2.dev")
  95 def dynamo_trace(
  96     f: Callable[..., Value],
  97     # pyre-ignore
  98     args: Tuple[Any, ...],
  99     aten_graph: bool,
  100     tracing_mode: str = "real",
  101     dynamo_config: Optional[DynamoConfig] = None,
  102 ) -> Tuple[torch.fx.GraphModule, Set]:
  103     """
  104     TODO: Once we fully migrate to torchdynamo frontend, we will remove
  105     this config option alltogether.  For now, it helps with quick
  106     experiments with playing around with TorchDynamo
  107     """
  108     if dynamo_config is None:
  109         dynamo_config = DynamoConfig()
  110     with using_config(dynamo_config), setting_python_recursive_limit(2000):
  111         torchdynamo.reset()
  112         try:
  113             return torchdynamo.export(
  114                 f,
  115                 *copy.deepcopy(args),
  116                 aten_graph=aten_graph,
  117                 tracing_mode=tracing_mode,
  118             )
  119         except torchdynamo.exc.Unsupported as exc:
  120             raise RuntimeError(
  121                 "The user code is using a feature we don't support. "
  122                 "Please try torchdynamo.explain() to get possible the reasons",
  123             ) from exc
  124         except Exception as exc:
  125             raise RuntimeError(
  126                 "torchdynamo internal error occured. Please see above stacktrace"
  127             ) from exc
  128 
  129 
  130 @req_torch_version("2.dev")
  131 def trace(f, args, *rest):
  132     graph_module, guards = dynamo_trace(f, args, True, "symbolic")
  133     return graph_module, guards
  134 
  135 
  136 @req_torch_version("2.dev")
  137 def opt_trace(f, args, *rest):
  138     """
  139     Optimized trace with necessary passes which re-compose some ops or replace some ops
  140     These passes should be general and functional purpose
  141     """
  142     passes_list = [
  143         compose_bmm,
  144         compose_chunk,
  145         compose_getitem_slice,
  146         replace_aten_reshape_alias_with_replace,
  147         replace_aten_op_with_indices,
  148         replace_transpose_mm_op_with_linear,  # after compose_bmm
  149         replace_native_layernorm_with_layernorm,
  150         remove_ops,
  151         replace_builtin_ops,  # after replace_native_layernorm_with_layernorm
  152         replace_inplace_ops,  # remove it once functionalization is enabled
  153     ]
  154 
  155     fx_module, _ = trace(f, args)
  156     print(fx_module.graph)
  157     for passes in passes_list:
  158         pr: PassResult = passes(fx_module)
  159         fx_module = pr.graph_module
  160 
  161     fx_module(*args)
  162 
  163     fx_module = run_const_fold(fx_module)
  164     print(fx_module.graph)
  165     return fx_module
  166
  ```
  
  Path: `py/torch_tensorrt/dynamo/__init__.py`
  Content:
  ```
  1 from torch_tensorrt.dynamo import fx_ts_compat
  2 from .backend import compile
  3
  ```
  --- 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/py/torch_tensorrt/dynamo/__init__.py b/py/torch_tensorrt/dynamo/__init__.py
  --- a/py/torch_tensorrt/dynamo/__init__.py
  +++ b/py/torch_tensorrt/dynamo/__init__.py
  @@ -1,2 +1,6 @@
  -from torch_tensorrt.dynamo import fx_ts_compat
  -from .backend import compile
  +import torch
  +from packaging import version
  +
  +if version.parse(torch.__version__) >= version.parse("2.1.dev"):
  +    from torch_tensorrt.dynamo import fx_ts_compat
  +    from .backend import compile
  diff --git a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py
  --- a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py
  +++ b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py
  @@ -2,10 +2,11 @@
   import sys
   from contextlib import contextmanager
   from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
  +from packaging import version
   
   import torch
   
  -if not torch.__version__.startswith("1"):
  +if version.parse(torch.__version__) >= version.parse("2.dev"):
       import torch._dynamo as torchdynamo
   
   from torch.fx.passes.infra.pass_base import PassResult
  
verification_info: {"golden_diff": "diff --git a/py/torch_tensorrt/dynamo/__init__.py b/py/torch_tensorrt/dynamo/__init__.py\n--- a/py/torch_tensorrt/dynamo/__init__.py\n+++ b/py/torch_tensorrt/dynamo/__init__.py\n@@ -1,2 +1,6 @@\n-from torch_tensorrt.dynamo import fx_ts_compat\n-from .backend import compile\n+import torch\n+from packaging import version\n+\n+if version.parse(torch.__version__) >= version.parse(\"2.1.dev\"):\n+    from torch_tensorrt.dynamo import fx_ts_compat\n+    from .backend import compile\ndiff --git a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py\n--- a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py\n+++ b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py\n@@ -2,10 +2,11 @@\n import sys\n from contextlib import contextmanager\n from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union\n+from packaging import version\n \n import torch\n \n-if not torch.__version__.startswith(\"1\"):\n+if version.parse(torch.__version__) >= version.parse(\"2.dev\"):\n     import torch._dynamo as torchdynamo\n \n from torch.fx.passes.infra.pass_base import PassResult\n", "issue": "Fix import error for Legacy TorchScript CI on `TorchTensorRTModule`\n- Older versions of TorchScript do not have the `torch._dynamo` import capability\n", "before_files": [{"content": "import copy\nimport sys\nfrom contextlib import contextmanager\nfrom typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union\n\nimport torch\n\nif not torch.__version__.startswith(\"1\"):\n    import torch._dynamo as torchdynamo\n\nfrom torch.fx.passes.infra.pass_base import PassResult\nfrom torch_tensorrt.fx.utils import req_torch_version\nfrom torch_tensorrt.fx.passes.lower_basic_pass_aten import (\n    compose_bmm,\n    compose_chunk,\n    compose_getitem_slice,\n    remove_ops,\n    replace_aten_op_with_indices,\n    replace_aten_reshape_alias_with_replace,\n    replace_builtin_ops,\n    replace_inplace_ops,\n    replace_native_layernorm_with_layernorm,\n    replace_transpose_mm_op_with_linear,\n    run_const_fold,\n)\nfrom typing_extensions import TypeAlias\n\nValue: TypeAlias = Union[\n    Tuple[\"Value\", ...],\n    List[\"Value\"],\n    Dict[str, \"Value\"],\n]\n\n\nclass DynamoConfig:\n    \"\"\"\n    Manage Exir-specific configurations of Dynamo.\n    \"\"\"\n\n    def __init__(\n        self,\n        capture_scalar_outputs: bool = True,\n        guard_nn_modules: bool = True,\n        dynamic_shapes: bool = True,\n        specialize_int: bool = True,\n        verbose: bool = True,\n    ) -> None:\n\n        self.capture_scalar_outputs = capture_scalar_outputs\n        self.guard_nn_modules = guard_nn_modules\n        self.dynamic_shapes = dynamic_shapes\n        self.specialize_int = specialize_int\n        self.verbose = verbose\n\n    def activate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = self.capture_scalar_outputs\n        torchdynamo.config.guard_nn_modules = self.guard_nn_modules\n        torchdynamo.config.dynamic_shapes = self.dynamic_shapes\n        torchdynamo.config.specialize_int = self.specialize_int\n        torchdynamo.config.verbose = self.verbose\n\n    def deactivate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = True\n        torchdynamo.config.guard_nn_modules = True\n        torchdynamo.config.dynamic_shapes = True\n        torchdynamo.config.specialize_int = True\n        torchdynamo.config.verbose = True\n\n\n@contextmanager\ndef using_config(config: DynamoConfig) -> Generator[DynamoConfig, None, None]:\n    config.activate()\n    try:\n        yield config\n    finally:\n        config.deactivate()\n\n\n@contextmanager\ndef setting_python_recursive_limit(limit: int = 10000) -> Generator[None, None, None]:\n    \"\"\"\n    Temporarily increase the python interpreter stack recursion limit.\n    This is mostly used for pickling large scale modules.\n    \"\"\"\n    default = sys.getrecursionlimit()\n    if limit > default:\n        sys.setrecursionlimit(limit)\n    try:\n        yield\n    finally:\n        sys.setrecursionlimit(default)\n\n\n@req_torch_version(\"2.dev\")\ndef dynamo_trace(\n    f: Callable[..., Value],\n    # pyre-ignore\n    args: Tuple[Any, ...],\n    aten_graph: bool,\n    tracing_mode: str = \"real\",\n    dynamo_config: Optional[DynamoConfig] = None,\n) -> Tuple[torch.fx.GraphModule, Set]:\n    \"\"\"\n    TODO: Once we fully migrate to torchdynamo frontend, we will remove\n    this config option alltogether.  For now, it helps with quick\n    experiments with playing around with TorchDynamo\n    \"\"\"\n    if dynamo_config is None:\n        dynamo_config = DynamoConfig()\n    with using_config(dynamo_config), setting_python_recursive_limit(2000):\n        torchdynamo.reset()\n        try:\n            return torchdynamo.export(\n                f,\n                *copy.deepcopy(args),\n                aten_graph=aten_graph,\n                tracing_mode=tracing_mode,\n            )\n        except torchdynamo.exc.Unsupported as exc:\n            raise RuntimeError(\n                \"The user code is using a feature we don't support. \"\n                \"Please try torchdynamo.explain() to get possible the reasons\",\n            ) from exc\n        except Exception as exc:\n            raise RuntimeError(\n                \"torchdynamo internal error occured. Please see above stacktrace\"\n            ) from exc\n\n\n@req_torch_version(\"2.dev\")\ndef trace(f, args, *rest):\n    graph_module, guards = dynamo_trace(f, args, True, \"symbolic\")\n    return graph_module, guards\n\n\n@req_torch_version(\"2.dev\")\ndef opt_trace(f, args, *rest):\n    \"\"\"\n    Optimized trace with necessary passes which re-compose some ops or replace some ops\n    These passes should be general and functional purpose\n    \"\"\"\n    passes_list = [\n        compose_bmm,\n        compose_chunk,\n        compose_getitem_slice,\n        replace_aten_reshape_alias_with_replace,\n        replace_aten_op_with_indices,\n        replace_transpose_mm_op_with_linear,  # after compose_bmm\n        replace_native_layernorm_with_layernorm,\n        remove_ops,\n        replace_builtin_ops,  # after replace_native_layernorm_with_layernorm\n        replace_inplace_ops,  # remove it once functionalization is enabled\n    ]\n\n    fx_module, _ = trace(f, args)\n    print(fx_module.graph)\n    for passes in passes_list:\n        pr: PassResult = passes(fx_module)\n        fx_module = pr.graph_module\n\n    fx_module(*args)\n\n    fx_module = run_const_fold(fx_module)\n    print(fx_module.graph)\n    return fx_module\n", "path": "py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py"}, {"content": "from torch_tensorrt.dynamo import fx_ts_compat\nfrom .backend import compile\n", "path": "py/torch_tensorrt/dynamo/__init__.py"}], "after_files": [{"content": "import copy\nimport sys\nfrom contextlib import contextmanager\nfrom typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union\nfrom packaging import version\n\nimport torch\n\nif version.parse(torch.__version__) >= version.parse(\"2.dev\"):\n    import torch._dynamo as torchdynamo\n\nfrom torch.fx.passes.infra.pass_base import PassResult\nfrom torch_tensorrt.fx.utils import req_torch_version\nfrom torch_tensorrt.fx.passes.lower_basic_pass_aten import (\n    compose_bmm,\n    compose_chunk,\n    compose_getitem_slice,\n    remove_ops,\n    replace_aten_op_with_indices,\n    replace_aten_reshape_alias_with_replace,\n    replace_builtin_ops,\n    replace_inplace_ops,\n    replace_native_layernorm_with_layernorm,\n    replace_transpose_mm_op_with_linear,\n    run_const_fold,\n)\nfrom typing_extensions import TypeAlias\n\nValue: TypeAlias = Union[\n    Tuple[\"Value\", ...],\n    List[\"Value\"],\n    Dict[str, \"Value\"],\n]\n\n\nclass DynamoConfig:\n    \"\"\"\n    Manage Exir-specific configurations of Dynamo.\n    \"\"\"\n\n    def __init__(\n        self,\n        capture_scalar_outputs: bool = True,\n        guard_nn_modules: bool = True,\n        dynamic_shapes: bool = True,\n        specialize_int: bool = True,\n        verbose: bool = True,\n    ) -> None:\n\n        self.capture_scalar_outputs = capture_scalar_outputs\n        self.guard_nn_modules = guard_nn_modules\n        self.dynamic_shapes = dynamic_shapes\n        self.specialize_int = specialize_int\n        self.verbose = verbose\n\n    def activate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = self.capture_scalar_outputs\n        torchdynamo.config.guard_nn_modules = self.guard_nn_modules\n        torchdynamo.config.dynamic_shapes = self.dynamic_shapes\n        torchdynamo.config.specialize_int = self.specialize_int\n        torchdynamo.config.verbose = self.verbose\n\n    def deactivate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = True\n        torchdynamo.config.guard_nn_modules = True\n        torchdynamo.config.dynamic_shapes = True\n        torchdynamo.config.specialize_int = True\n        torchdynamo.config.verbose = True\n\n\n@contextmanager\ndef using_config(config: DynamoConfig) -> Generator[DynamoConfig, None, None]:\n    config.activate()\n    try:\n        yield config\n    finally:\n        config.deactivate()\n\n\n@contextmanager\ndef setting_python_recursive_limit(limit: int = 10000) -> Generator[None, None, None]:\n    \"\"\"\n    Temporarily increase the python interpreter stack recursion limit.\n    This is mostly used for pickling large scale modules.\n    \"\"\"\n    default = sys.getrecursionlimit()\n    if limit > default:\n        sys.setrecursionlimit(limit)\n    try:\n        yield\n    finally:\n        sys.setrecursionlimit(default)\n\n\n@req_torch_version(\"2.dev\")\ndef dynamo_trace(\n    f: Callable[..., Value],\n    # pyre-ignore\n    args: Tuple[Any, ...],\n    aten_graph: bool,\n    tracing_mode: str = \"real\",\n    dynamo_config: Optional[DynamoConfig] = None,\n) -> Tuple[torch.fx.GraphModule, Set]:\n    \"\"\"\n    TODO: Once we fully migrate to torchdynamo frontend, we will remove\n    this config option alltogether.  For now, it helps with quick\n    experiments with playing around with TorchDynamo\n    \"\"\"\n    if dynamo_config is None:\n        dynamo_config = DynamoConfig()\n    with using_config(dynamo_config), setting_python_recursive_limit(2000):\n        torchdynamo.reset()\n        try:\n            return torchdynamo.export(\n                f,\n                *copy.deepcopy(args),\n                aten_graph=aten_graph,\n                tracing_mode=tracing_mode,\n            )\n        except torchdynamo.exc.Unsupported as exc:\n            raise RuntimeError(\n                \"The user code is using a feature we don't support. \"\n                \"Please try torchdynamo.explain() to get possible the reasons\",\n            ) from exc\n        except Exception as exc:\n            raise RuntimeError(\n                \"torchdynamo internal error occured. Please see above stacktrace\"\n            ) from exc\n\n\n@req_torch_version(\"2.dev\")\ndef trace(f, args, *rest):\n    graph_module, guards = dynamo_trace(f, args, True, \"symbolic\")\n    return graph_module, guards\n\n\n@req_torch_version(\"2.dev\")\ndef opt_trace(f, args, *rest):\n    \"\"\"\n    Optimized trace with necessary passes which re-compose some ops or replace some ops\n    These passes should be general and functional purpose\n    \"\"\"\n    passes_list = [\n        compose_bmm,\n        compose_chunk,\n        compose_getitem_slice,\n        replace_aten_reshape_alias_with_replace,\n        replace_aten_op_with_indices,\n        replace_transpose_mm_op_with_linear,  # after compose_bmm\n        replace_native_layernorm_with_layernorm,\n        remove_ops,\n        replace_builtin_ops,  # after replace_native_layernorm_with_layernorm\n        replace_inplace_ops,  # remove it once functionalization is enabled\n    ]\n\n    fx_module, _ = trace(f, args)\n    print(fx_module.graph)\n    for passes in passes_list:\n        pr: PassResult = passes(fx_module)\n        fx_module = pr.graph_module\n\n    fx_module(*args)\n\n    fx_module = run_const_fold(fx_module)\n    print(fx_module.graph)\n    return fx_module\n", "path": "py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py"}, {"content": "import torch\nfrom packaging import version\n\nif version.parse(torch.__version__) >= version.parse(\"2.1.dev\"):\n    from torch_tensorrt.dynamo import fx_ts_compat\n    from .backend import compile\n", "path": "py/torch_tensorrt/dynamo/__init__.py"}]}