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# Fusion mapping (experimental feature)

Fusion mapping provides an opt-in way to replace model submodules at load time while preserving the original checkpoint format.

It builds on:

- [Monkey patching](./monkey_patching) to swap module classes before model instantiation.
- [Dynamic weight loading](./weightconverter) to map weights between the original and fused runtime layouts.

> [!WARNING]
> Fusion mapping is an experimental loading feature. It changes the runtime module structure and may affect model behavior. Use it only when you explicitly want a fused runtime layout.

## Quick start

Fusion is enabled through [`~PreTrainedModel.from_pretrained`] with `fusion_config`:

```python
from transformers import AutoModelForImageTextToText


model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen2-VL-2B-Instruct",
    fusion_config={"patch_embeddings": True},
)
```

By default, no fusion is applied.
If `fusion_config` is stored in the model config, `from_pretrained()` will reuse it automatically.

## How it works

Fusion registration happens before the model is instantiated:

1. [`~PreTrainedModel.from_pretrained`] uses the explicit `fusion_config` argument or falls back to `config.fusion_config`.
2. The fusion registry validates the requested fusion names.
3. Each enabled fusion meta-initializes the target model class, optionally filters candidate modules by name, and uses `is_fusable(...)` to discover compatible module classes.
4. Fused replacement classes are registered through [`~transformers.monkey_patching.register_patch_mapping`].
5. Matching [`~WeightTransform`] rules are generated from the config so checkpoint loading can map weights into the fused runtime layout.
6. By default, [`~PreTrainedModel.save_pretrained`] uses the reverse conversion path to restore the original checkpoint layout. Pass `save_original_format=False` to keep the converted runtime layout instead.

This lets a fusion use a different runtime module structure while still loading from the original checkpoint format, and by default saving back to it as well.

Note: With the current monkey-patching mechanism, fusion registration is class-level: one compatible module class maps to one fused replacement class.

## Current fusion families

Currently, `fusion_config` supports one fusion family:

- `patch_embeddings`
  Enable with:

  ```python
  fusion_config = {"patch_embeddings": True}
  ```

  Effect:
  Replaces compatible `nn.Conv3d` patch embedding projections with equivalent flattened `nn.Linear` projections at runtime.

## Extending fusion mapping

To add a new fusion family:

1. Add an `is_fusable` predicate.
   This decides whether a discovered module is compatible with the fusion.
2. Optionally add `target_modules_patterns`.
   This makes the discovery step more explicit by pre-filtering candidate module names before `is_fusable(...)`.
3. Add a `make_fused_class` factory.
   This returns the runtime replacement class for a compatible module class.
4. Add a `make_transforms` factory if the fused layout needs checkpoint conversion.
   This returns the [`~WeightTransform`] rules that map weights between the original and fused layouts for a given config.
5. Register the new `ModuleFusionSpec` in [`fusion_mapping.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/fusion_mapping.py).

Once registered, the new fusion becomes available through `fusion_config`.

## Internal API

[[autodoc]] fusion_mapping.ModuleFusionSpec

[[autodoc]] fusion_mapping.PatchEmbeddingsFusionSpec

[[autodoc]] fusion_mapping._register_module_fusion

[[autodoc]] fusion_mapping.register_fusion_patches
