Gemma3nForCaulsalLM cannot use StaticCache
Author: ChrisStewie404
Pull request: no
Labels: name: bug

### System Info

- `transformers` version: 4.57.0
- Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35
- Python version: 3.9.23
- Huggingface_hub version: 0.34.1
- Safetensors version: 0.5.3
- Accelerate version: 1.9.0
- Accelerate config:    not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.4.0+cu121 (CUDA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA GeForce RTX 4090 D

### Who can help?

Hi @zucchini-nlp 
Does Gemma3n currently support StaticCache? I am trying to improve vRAM utilization by swichting the KV-cache setting from dynamic to static. Offloading cache had been tried as well, but turns out it was simply too slow and (unexpectedly) memory inefficient.  
Not sure if there's anything that I'm doing obviously wrong. Thanks in advance!

### Information

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

### Tasks

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

### Reproduction

I have downloaded [Gemma3n-E4B-it](https://huggingface.co/google/gemma-3n-E4B-it?clone=true) to local directory `model/gemma-3n-E4B-it`.
```python
from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM
from transformers import StaticCache
from transformers.models.gemma3n import Gemma3nForConditionalGeneration, Gemma3nForCausalLM
from PIL import Image
import argparse
from tqdm import tqdm
import numpy as np
import torch
import time
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

import torch._dynamo

MAX_NEW_TOKENS = 20

if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', '-b', type=int, default=1)
    args = parser.parse_args()

    model_path  = 'model/gemma-3n-E4B-it'
    model = Gemma3nForConditionalGeneration.from_pretrained(model_path, device_map="auto",dtype=torch.bfloat16,).eval()
    # model = Gemma3nForCausalLM.from_pretrained(model_path, dtype=torch.bfloat16).cuda().eval()

    processor = AutoProcessor.from_pretrained(model_path)
    # processor = AutoTokenizer.from_pretrained(model_path)

    messages1 = [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."}]
        },
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Hello there."}
            ]
        }
    ]

    inputs = processor.apply_chat_template(
        [messages1] * args.batch_size, 
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        padding=True, # enable padding
        return_tensors="pt",
    ).to(model.device)

    input_len = inputs["input_ids"].shape[-1]

    with torch.inference_mode():
        generations = model.generate(**inputs, max_new_tokens= MAX_NEW_TOKENS, do_sample=False, cache_implementation="static") 
        generations = generations[:, input_len:] # get batch output
        decoded = processor.batch_decode(generations, skip_special_tokens=True) # batch decode
```





### Expected behavior

Running the above file will produce problems concerning `torch._dynamo`. I have also tried downgrading the `transformers`' version to 4.57.1/2/3, but none of them worked. 
According to my observation, this problem seems to be strongly related to static cache, since using dynamic cache( the default cache) and offloaded dynamic cache worked out just fine. Yet setting `cache_implementaion` to `static` or `offloaded_static` will produce exactly the same error.
```bash
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00,  1.38it/s]
The following generation flags are not valid and may be ignored: ['top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_inductor/compile_fx.py:150: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
  warnings.warn(
Traceback (most recent call last):
  File "/root/autodl-tmp/gemma/./run.py", line 104, in <module>
    generations = model.generate(**inputs, max_new_tokens= MAX_NEW_TOKENS, do_sample=False, past_key_values=past_key_values) # do_sample = False, no sampling.
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/generation/utils.py", line 2564, in generate
    result = decoding_method(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/generation/utils.py", line 2787, in _sample
    outputs = model_forward(**model_inputs, return_dict=True)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 433, in _fn
    return fn(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/utils/generic.py", line 918, in wrapper
    output = func(self, *args, **kwargs)
  File "/root/autodl-tmp/gemma/modeling_gemma3n.py", line 2288, in forward
    outputs = self.model(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/utils/generic.py", line 918, in wrapper
    output = func(self, *args, **kwargs)
  File "/root/autodl-tmp/gemma/modeling_gemma3n.py", line 2127, in forward
    outputs = self.language_model(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/utils/generic.py", line 918, in wrapper
    output = func(self, *args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/models/gemma3n/modeling_gemma3n.py", line 1675, in forward
    layer_outputs = decoder_layer(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/modeling_layers.py", line 94, in __call__
    return super().__call__(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/utils/deprecation.py", line 172, in wrapped_func
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/transformers/models/gemma3n/modeling_gemma3n.py", line 1436, in forward
    attn, self_attn_weights = self.self_attn(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 1116, in __call__
    return self._torchdynamo_orig_callable(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 948, in __call__
    result = self._inner_convert(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 472, in __call__
    return _compile(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_utils_internal.py", line 84, in wrapper_function
    return StrobelightCompileTimeProfiler.profile_compile_time(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_strobelight/compile_time_profiler.py", line 129, in profile_compile_time
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 817, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 231, in time_wrapper
    r = func(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 636, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/bytecode_transformation.py", line 1185, in transform_code_object
    transformations(instructions, code_options)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 178, in _fn
    return fn(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 582, in transform
    tracer.run()
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 2451, in run
    super().run()
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 893, in run
    while self.step():
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 805, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 2642, in RETURN_VALUE
    self._return(inst)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 2627, in _return
    self.output.compile_subgraph(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 1123, in compile_subgraph
    self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root)
  File "/root/miniconda3/envs/llm/lib/python3.9/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 1318, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 231, in time_wrapper
    r = func(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 1409, in call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 1390, in call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/repro/after_dynamo.py", line 129, in __call__
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/__init__.py", line 1951, in __call__
    return compile_fx(model_, inputs_, config_patches=self.config)
  File "/root/miniconda3/envs/llm/lib/python3.9/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_inductor/compile_fx.py", line 1261, in compile_fx
    return compile_fx(
  File "/root/miniconda3/envs/llm/lib/python3.9/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_inductor/compile_fx.py", line 1505, in compile_fx
    return aot_autograd(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/backends/common.py", line 69, in __call__
    cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 954, in aot_module_simplified
    compiled_fn, _ = create_aot_dispatcher_function(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 231, in time_wrapper
    r = func(*args, **kwargs)
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 687, in create_aot_dispatcher_function
    compiled_fn, fw_metadata = compiler_fn(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 125, in aot_dispatch_base
    flat_fn, flat_args, fw_metadata = pre_compile(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 1939, in pre_compile
    flat_fn, flat_args, fw_metadata = wrapper.pre_compile(
  File "/root/miniconda3/envs/llm/lib/python3.9/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 848, in pre_compile
    tracing_context.guards_context.aotautograd_guards.append(  # type: ignore[attr-defined]
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
AttributeError: 'list' object has no attribute 'guards_context'

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


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
```

Comments:
Updating the torch version(2.8.0+cu128) will solve this problem, case closed