Dynamo无法跟踪GraphModule中部分函数，导致同一系列模型生成的hash值相同

[fangfangssj]

同一系列模型生成的hash相同，例如qwen系列前面的头结构是一样的，第一次提取可以提取到完整的计算图，第二次提取的时候，里面有一个torch无法跟踪的torch._C._functorch.PyCapsule._vmap_increment_nesting函数，第二次提取计算图到这里就结束了，只有一小部分，大概只有百分之一的计算图，第二次已经跟踪了的部分生成一个hash值，所以所有qwen系列hash算出来都是一样的，同理，llama，gpt等系列模型也是如此。

/root/miniconda3/lib/python3.12/site-packages/torch/_dynamo/variables/functions.py:1262: UserWarning: Dynamo does not know how to trace the builtin `torch._C._functorch.PyCapsule._vmap_increment_nesting.` This function is either a Python builtin (e.g. _warnings.warn) or a third-party C/C++ Python extension (perhaps created with pybind).
If it is a Python builtin, please file an issue on GitHub so the PyTorch team can add support for it and see the next case for a workaround.
If it is a third-party C/C++ Python extension, please either wrap it into a PyTorch-understood custom operator (see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html for more details) or, if it is traceable, use `torch.compiler.allow_in_graph`.
  torch._dynamo.utils.warn_once(explanation + "\n" + "\n".join(hints))
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/root/GraphNet/graph_net/torch/check_redundant_incrementally.py", line 85, in <module>
    main(args=args)
  File "/root/GraphNet/graph_net/torch/check_redundant_incrementally.py", line 65, in main
    graph_hash not in graph_hash2graph_net_model_path
AssertionError: Redundant models detected. old-model-path:/root/GraphNet/samples/transformers-auto-model/distilgpt2/graph_hash.txt, new-model-path:/root/graphnet_workspace/Corianas/1.3b/graph_hash.txt.

[fangfangssj]

#222 中提供了对应的脚本，去掉transformers库里面的vmap操作