can't torch.export.export tinyllama model
Author: heshuju
Pull request: no
Labels: name: bug

### System Info

I need to convert the model into IR for further processing, but I encountered a problem.
I'm encountering several similar issues where torch.dynamo is unable to trace certain model objects and methods. Could this be due to incorrect usage on my part? What would be the recommended approach to resolve this?

version:
pytorch:2.4 cpu
transformers: 4.53.2
error msg:
```
/bin/python /home/x/iree/tinyllama/demo.py
Traceback (most recent call last):
  File "/home/x/iree/tinyllama/demo.py", line 55, in <module>
    prefill_ep = export(prefill_wrapper,
  File "/home/x/.local/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
    return _export(
  File "/home/x/.local/lib/python3.10/site-packages/torch/export/_trace.py", line 945, in wrapper
    raise e
  File "/home/x/.local/lib/python3.10/site-packages/torch/export/_trace.py", line 928, in wrapper
    ep = fn(*args, **kwargs)

......

torch._dynamo.exc.Unsupported: call_method GetAttrVariable(UserDefinedObjectVariable(AttentionMaskInterface), _global_mapping) __contains__ [ConstantVariable()] {}

from user code:
   File "/home/x/iree/tinyllama/demo.py", line 17, in forward
    out = self.model(input_ids=input_ids,
  File "/home/x/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/x/.local/lib/python3.10/site-packages/transformers/utils/generic.py", line 943, in wrapper
    output = func(self, *args, **kwargs)
  File "/home/x/.local/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 553, in forward
    outputs: BaseModelOutputWithPast = self.model(
  File "/home/x/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/x/.local/lib/python3.10/site-packages/transformers/utils/generic.py", line 943, in wrapper
    output = func(self, *args, **kwargs)
  File "/home/x/.local/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 419, in forward
    causal_mask = create_causal_mask(
  File "/home/x/.local/lib/python3.10/site-packages/transformers/masking_utils.py", line 753, in create_causal_mask
    early_exit, attention_mask, packed_sequence_mask, kv_length, kv_offset = _preprocess_mask_arguments(
  File "/home/x/.local/lib/python3.10/site-packages/transformers/masking_utils.py", line 683, in _preprocess_mask_arguments
    if config._attn_implementation not in ALL_MASK_ATTENTION_FUNCTIONS._global_mapping:

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
```


### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```
import torch
from transformers import AutoModelForCausalLM
from torch.export import export, Dim

model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
model.eval()

class PrefillWrapper(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, input_ids, attention_mask):
        out = self.model(input_ids=input_ids,
                         attention_mask=attention_mask,
                         use_cache=True)
        return (out.logits, *out.past_key_values)

class DecodeWrapper(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, input_ids, attention_mask, *flat_kvs):
        n_layers = len(flat_kvs) // 2
        pkv = tuple((flat_kvs[2*i], flat_kvs[2*i+1]) for i in range(n_layers))
        out = self.model(input_ids=input_ids,
                         attention_mask=attention_mask,
                         past_key_values=pkv,
                         use_cache=True)
        return (out.logits, *out.past_key_values)

prefill_wrapper = PrefillWrapper(model)
decode_wrapper  = DecodeWrapper(model)

B, T = 2, 16
vocab = 32000
input_ids = torch.randint(0, vocab, (B, T), dtype=torch.long)
attention_mask = torch.ones((B, T), dtype=torch.long)

batch = Dim("batch", min=1, max=4)
seq   = Dim("seq",   min=1, max=1024)

prefill_dyn = {
    "input_ids":      {0: batch, 1: seq},
    "attention_mask": {0: batch, 1: seq},
}

prefill_ep = export(prefill_wrapper,
                    args=(input_ids, attention_mask),
                    dynamic_shapes=prefill_dyn)

with torch.no_grad():
    prefill_out = prefill_wrapper(input_ids, attention_mask)
    past_key_values = prefill_out[1:]       # (k0,v0,k1,v1,...)

decode_input_ids      = input_ids[:, -1:]   # [B, 1]
decode_attention_mask = attention_mask      # [B, T]

kv_seq = Dim("kv_seq", min=1, max=1024)
decode_dyn = {
    "input_ids":      {0: batch, 1: Dim("one", min=1, max=1)},
    "attention_mask": {0: batch, 1: seq},
}

for idx, kv in enumerate(past_key_values):
    decode_dyn[f"flat_kvs_{idx}"] = {0: batch, 2: kv_seq}

decode_ep = export(decode_wrapper,
                   args=(decode_input_ids, decode_attention_mask, *past_key_values),
                   dynamic_shapes=decode_dyn)
```



### Expected behavior

I think it can export 2 ir. prefill and decode.


Comments:
Looking at the stacktrace and logs, it looks like a Pytorch issue. Perhaps it would be best to open an  issue there.

> Looking at the stacktrace and logs, it looks like a Pytorch issue. Perhaps it would be best to open an issue there.

I think you are correct. The updated version has generated a new issue.