Enable MTP support for Qwen3.5
Author: curnane-lab
Pull request: yes

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# What does this PR do?

Extends the generic Multi-Token Prediction (MTP) support from #46229 to Qwen3.5 dense models, so that `model.generate(..., use_mtp=True)` works for Qwen3.5 checkpoints that ship MTP layers.

Qwen3.6 dense checkpoints reuse the same `model_type` (`qwen3_5`) and model classes, so they are covered as well.

## Changes

- `Qwen3_5TextConfig` / `Qwen3_5Config`: add `num_mtp_layers` (default `0`).
- `MtpModel`:
  - Filter `_keys_to_ignore_on_load_unexpected` patterns so non-MTP multimodal weights (e.g. `^model.visual.*`) are not mistaken for MTP weights.
  - Support MRoPE-style nD `position_ids` (shape `[4, B, L]` used by Qwen3.5) and roll them correctly across MTP depths.
- `MTPCandidateGenerator`:
  - Resolve the text tower via `base_model.language_model` for multimodal models.
  - Slice nD `position_ids` with `...` instead of assuming 2D ids.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3.5-4B",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-4B")

inputs = tokenizer("Hello world", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_mtp=True, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Notes / follow-ups

- **Dense models only** — This PR only touches the dense `qwen3_5` model family.  
  Qwen3.5 / Qwen3.6 MoE (`qwen3_5_moe`) can be supported by applying the same
  `num_mtp_layers` config change to `Qwen3_5MoeTextConfig`.

- **Training loss stays outside the core library** — The changes here are
  intentionally limited to inference. They provide enough hooks for external
  frameworks (e.g., LlamaFactory) to attach and train an external `MtpModel`.


Comments:
## Note on relationship with PR #45618 (MTPCandidateGenerator)

I noticed that PR #45618 by @ArthurZucker introduces a `MTPCandidateGenerator` class that integrates MTP into the `generate()` pipeline for speculative decoding. I want to clarify the complementary relationship between these two PRs:

**This PR (#45638)** focuses on the **training side** of MTP:
- Adds MTP architecture modules (`Qwen3_5MTPLayer`, `Qwen3_5MTP`) to the model definition
- Computes MTP auxiliary loss during training (controlled by `output_mtp_loss` config/parameter)
- Follows the MoE auxiliary loss pattern (e.g., Mixtral's `output_router_logits`) for runtime control
- Enables training with MTP loss for improved training efficiency

**PR #45618** focuses on the **inference side** of MTP:
- Introduces `MTPCandidateGenerator` as a universal `CandidateGenerator` subclass
- Enables MTP-based speculative decoding during `generate()` for inference acceleration
- Decouples MTP layers from model files into a reusable generator component
- Adds `GenerationMode.MTP_DECODING` to the generation pipeline

These two approaches are **complementary** rather than conflicting — one addresses training-time MTP loss, the other addresses inference-time MTP speculative decoding.

That said, I appreciate the architectural direction of PR #45618 in decoupling MTP from individual model files. Once #45618 is merged, I'm happy to adapt this PR to align with the `MTPCandidateGenerator` architecture if the maintainers prefer, while preserving the training-time MTP loss computation that this PR enables.

I'd welcome any feedback from the maintainers on the preferred long-term architecture for MTP support in transformers. 🙏

> For training I need to think a bit good sir! We don't want to "pollute" the code with inference specific stuff so we might have a new folder for this

Thanks for the quick response, @ArthurZucker — completely agree with the principle of keeping inference-specific and training-specific code separated.
A few thoughts to help shape the direction, whenever you've had time to think it through:

**Shared MTP module location**. If MTP layers (Qwen3_5MTPLayer, Qwen3_5MTP) live in a dedicated folder (e.g. transformers/mtp/ or under transformers/generation/mtp/), both this PR and #45618 could import from the same place. Training-side code would consume them to compute auxiliary loss; inference-side code would consume them through MTPCandidateGenerator. Happy to refactor in this direction if that matches what you have in mind.
**Training entry point**. The training-side surface is small — essentially an output_mtp_loss flag (mirroring output_router_logits in Mixtral) and the loss computation in the forward pass. I can keep this minimal and model-agnostic so it doesn't bleed into inference paths.
**Order of merging**. I'm fine waiting for #45618 to land first and then rebasing this PR on top of the agreed structure, if that's easier for review. Alternatively, if you'd prefer to land the shared MTP module location as a small prerequisite PR, I can prepare that too.

Could you let me know which layout you'd prefer once you've had a chance to think it over? I'd rather align with your preferred architecture upfront than refactor twice. 🙏

Hi @Cyrilvallez @vasqu @ArthurZucker — Since you all reviewed or authored the inference-side MTP support in #46229, I’d really appreciate your eyes on this training-side MTP implementation for Qwen3.5. It’s intentionally kept separate from the generate path, so the two PRs should be complementary. THX!

@curnane-lab sorry but we will first design around the other PR to design our whole MTP solution. Ideally including training from the get-go as well. It's a bit unclear atm if we really want to add separate implementations as per model so I would see this PR as a draft for now until we clear and merge #46229

Hi @vasqu, the `security-gate / bandit-check` failure is a false positive: `actions/checkout` now refuses to checkout fork PR code under `pull_request_target` unless `allow-unsafe-pr-checkout: true` is set, so Bandit never actually ran. Could you please manually approve the CI workflows? The previous `tests_torch [shard 5/8]` failure has already been fixed in this push.

> Hi @vasqu, the security-gate / bandit-check failure is a false positive: actions/checkout now refuses to checkout fork PR code under pull_request_target unless allow-unsafe-pr-checkout: true is set, so Bandit never actually ran. Could you please manually approve the CI workflows? The previous tests_torch [shard 5/8] failure has already been fixed in this push.

Yes it's a false positive, investigating. Also pinged Cyril to review because he has the best overview for this

Thank you for your contribution 🤗!

## CI Security Gate — automatic approval blocked

This PR was **not** automatically approved for CI because the security gate failed.

**Possible reasons:**
- The PR touches 50 or more files — only PRs with fewer than 50 changed files are automatically approved
- A changed file is outside the allowed directories (`src/`, `tests/`, `docs/`, `utils/`), has a disallowed extension (only `.py`, `.txt`, `.md` permitted outside `tests/` and `docs/`), or is not `.md`/`.yml` inside `docs/`
- A new high-severity security issue was detected in the changed Python files (Bandit check)

See the [workflow run](https://github.com/huggingface/transformers/actions/runs/29549680465) for the exact violations.

A maintainer can review and manually approve CI if a finding is a false positive.

**[For maintainers]** Suggested jobs to run (before merge)

run-slow: qwen3_5

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### CI recap

**Dashboard:** [View test results in Grafana](https://transformers-ci.lor-e.huggingface.cool/d/pytest-observability-by-pr/pytest-observability-branch?var-pr=45638)
**Latest run:** [29680332141:2](https://github.com/huggingface/transformers/actions/runs/29680332141)
**Result:** `success` | **Jobs:** 15 | **Tests:** 172,162 | **Failures:** 0 | **Duration:** 16h 2m

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Hi @Cyrilvallez,  could you review this PR? The current Qwen3.5 CI failure is the `_is_stateful` guard from #46229 — `test_generate_with_mtp` reaches `generate(use_mtp=True)` and gets rejected because Qwen3.5 is stateful (GatedDeltaNet state can't roll back during verification). I've skipped the test for stateful models, so this PR is now scoped to config + MtpModel loading/MRoPE support.