Handle HF dynamic cache

[t-vi]

Dynamic cache currently is updated during tracing, this is not good.

    from transformers.models.llama import LlamaForCausalLM, LlamaConfig
    from transformers import DynamicCache

    model_id = "meta-llama/Llama-3.2-1B"

    config_args = LlamaConfig.get_config_dict(model_id)[0]
    config_args['num_hidden_layers'] = 1
    with torch.device('cuda'):
        model = LlamaForCausalLM(LlamaConfig(**config_args)).to(torch.bfloat16).requires_grad_(False).eval()

    jm = thunder.jit(model)

    j_past_key_values = DynamicCache()
    args1 = dict(
        cache_position=torch.tensor([0, 1, 2, 3, 4, 5], device='cuda:0'),
        input_ids=torch.tensor([[128000,    791,   1401,    311,   2324,    374]], device='cuda:0'),
        inputs_embeds=None,
        attention_mask=torch.tensor([[1, 1, 1, 1, 1, 1]], device='cuda:0'),
        use_cache=True, return_dict= True
    )
    res = jm(past_key_values=j_past_key_values, **args1)

gives

>       res = jm(past_key_values=j_past_key_values, **args1)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py:1736: in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py:1747: in _call_impl
    return forward_call(*args, **kwargs)
thunder/core/module.py:80: in forward
    res = self._forward_fn(*args, **kwargs)
thunder/__init__.py:736: in fn_
    cache_entry, inps, pro_to_epi = get_computation_and_inputs(*args, **kwargs)
thunder/core/langctxs.py:136: in _fn
    result = fn(*args, **kwargs)
thunder/__init__.py:232: in cache_info_wrapper
    res = fn(*args, **kwargs)
thunder/__init__.py:633: in get_computation_and_inputs
    inps, pro_to_epi = pro(*args, **kwargs)
thunder.prologue_0:190: in prologue
    check_number_type_and_value(i192, 0)
thunder/executors/pythonex.py:103: in _check_number_type_and_value_impl
    utils.check(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

cond = False, s = <function _check_number_type_and_value_impl.<locals>.<lambda> at 0x7fc162c6bf60>, exception_type = <class 'RuntimeError'>

    def check(cond: bool, s: Callable[[], str], exception_type: type[Exception] = RuntimeError) -> None:
        """Helper function for raising an error_type (default: RuntimeError) if a boolean condition fails.
    
        s is a callable producing a string to avoid string construction if the error check is passed.
        """
        if not cond:
>           raise exception_type(s())
E           RuntimeError: Expected 6 to be equal to and have the type of 0

thunder/core/baseutils.py:107: RuntimeError

This is useful for llama inference.