Breaking change about AWQ Fused modules due to Attention Refactor
Author: fanqiNO1
Pull request: no
Labels: name: bug

### System Info

transformers==5.0.0dev
autoawq==0.2.9
autoawq_kernels==0.0.9
torch==2.6.0+cu124

### Who can help?

Due to PR #35235, the `past_key_values` is no longer a returned value of attention modules.

However, when using AWQ models with Fused modules [AWQ Fused modules docs](https://huggingface.co/docs/transformers/main/en/quantization/awq#fused-modules), there will be an error like issue #38554

```bash
    hidden_states, _ = self.self_attn(
ValueError: too many values to unpack (expected 2)
```

So we can hack the `awq.modules.fused.attn.QuantAttentionFused` to avoid returning `past_key_values`. Therefore, I create a primary PR #41909 to fix it.

However, for special `rope_type` such as LLaMA3, the RoPE implementation in AutoAWQ will cause error, since `awq.modules.fused.attn.RoPE` supports default RoPE only.

Maybe we can implement and maintain `AwqRoPE` and `AwqQuantAttentionFused` in `transformers.integrations.awq`? Or we can maintain `huggingface/AutoAWQ` as `casper-hansen/AutoAWQ` is archived.

I'd like to refine my PR to help transformers fix this bug!

@SunMarc @MekkCyber

### Information

- [ ] The official example scripts
- [x] 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

```python

from transformers import AwqConfig, AutoModelForCausalLM, AutoTokenizer


# model_path = "./llama-3.1-8b-instruct-awq"
model_path = "./qwen2.5-7b-instruct-awq"
# model_path = "./qwen3-8b-awq"

awq_config = AwqConfig(
    bits=4,
    do_fuse=True,
    fuse_max_seq_len=8192
)

model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=awq_config).to("cuda:0")
print(model)
tokenizer = AutoTokenizer.from_pretrained(model_path)

max_new_tokens = 1024 if "qwen3" in model_path else 32


messages = []

prompt1 = "What is the result of 3+5?"
messages.append({"role": "user", "content": prompt1})
text1 = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs1 = tokenizer(text1, return_tensors="pt").to("cuda:0")

generated_ids1 = model.generate(**inputs1, max_new_tokens=max_new_tokens)
output_ids1 = generated_ids1[0, len(inputs1.input_ids[0]) :].tolist()
output1 = tokenizer.decode(output_ids1, skip_special_tokens=True)
messages.append({"role": "assistant", "content": output1})
print("Output 1:", output1)

prompt2 = "What about adding 10 to that result?"
messages.append({"role": "user", "content": prompt2})
text2 = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs2 = tokenizer(text2, return_tensors="pt").to("cuda:0")

generated_ids2 = model.generate(**inputs2, max_new_tokens=max_new_tokens)
output_ids2 = generated_ids2[0, len(inputs2.input_ids[0]) :].tolist()
output2 = tokenizer.decode(output_ids2, skip_special_tokens=True)
messages.append({"role": "assistant", "content": output2})
print("Output 2:", output2)

```

### Expected behavior

There is no error.

Comments:
I think this issue and the related PR need some help.

@SunMarc @MekkCyber 

Hey @fanqiNO1, your help is appreciated ! For now I think it will be easier to just monkey patch `awq.modules.fused.attn.QuantAttentionFused` as you did but for rope, maybe it will be worth to maintain an AWQRoPE

Thanks for your response! I will try to implement an AWQRoPE later this week and update my PR. 😸 

However, we should hack `QuantAttentionFused` to replace `awq.modules.fused.attn.RoPE` with our `AWQRoPE`. I will try to do it~