Fix Bad Outputs in Fast Path for GraniteMoeHybrid

[alex-jw-brooks]

This PR fixes garbage output being produced when running granite moe hybrid models on the fast path; this is currently caused by inconsistent handling in has_previous_state on the fast path because the cache param was updated directly in the forward call of the state space layer in the slow path of bamba + updated through modular instead of handling the cache params at the end of forward like other state space models.

Quick repro case:

from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import torch

model_path="ibm-granite/granite-4.0-tiny-preview"
device="cuda"
model = AutoModelForCausalLM.from_pretrained(
        model_path,
        device_map=device,
        torch_dtype=torch.bfloat16,
    )
tokenizer = AutoTokenizer.from_pretrained(
        model_path
)

conv = [{"role": "user", "content":"What is a rattlesnake? "}]

input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)

set_seed(42)
output = model.generate(
    **input_ids,
    max_new_tokens=32,
)

prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
print(prediction)

Current output with mamba_ssm and causal_conv1d installed:

< the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the

vs without it

<think>A rattlesnake is a venomous snake species known for its distinctive rattle at the end of its tail, which it

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@ArthurZucker can you please take a look?

[ArthurZucker]

Makes sense but we should call this in https://github.com/huggingface/transformers/pull/39033/files#diff-75325057d583641e2cd9953d8803fa3ee0759f945df1e965152f28f39015c65cL468 no?
THis would be cleaner as contained in where the cache is updated

[alex-jw-brooks]

Hey @ArthurZucker thanks for the quick review! IMO it's better to keep it out here, since this is independent of the fast/slow path implementations for forward and better aligns with the way other architectures (e.g., falcon, jamba, etc) are updating has_previous_state .

Torch/cuda forwards are also called per ssm layer, but this is a global property, so it'll also set it to True for the 2nd+ ssm layers on the first decode if we move it in, which may caused indexing errors if we aren't careful in checking when to use_precomputed_states in the forward implementations

[ArthurZucker]

Okay! Merging then thanks 🤗

[tdoublep]

Thanks for the fix, we just hit this comparing against transformers in vLLM CI