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# FRoD: Full-Rank Efficient Fine-Tuning with Rotational Degrees

FRoD is a parameter-efficient fine-tuning method that combines a shared full-rank basis with sparse learnable
rotational degrees. The adapter update is expressed through fixed projection tensors and trainable coefficients, which
allows FRoD to apply full-rank updates while keeping the number of trained parameters small.

Paper: [Full-Rank Efficient Fine-Tuning with Rotational Degrees](https://doi.org/10.1609/aaai.v40i31.39813).

When saving the adapter parameters, it is possible to avoid storing the projection tensors by setting
`save_projection=False` on the `FrodConfig`. In that case, the projections are restored from the base model weights and
the fixed random seed from `projection_prng_key`. This reduces checkpoint size, but the default is
`save_projection=True` to make checkpoint loading independent of regeneration details.

Compared to LoRA, FRoD can express a full-rank update in each adapted linear layer while training only the diagonal
coefficients and a sparse set of off-diagonal rotation coefficients. This can be useful when a low-rank update is too
restrictive. The trade-off is that FRoD computes fixed projection tensors from the base weights during adapter
injection, which makes setup more expensive and the implementation less broadly supported than LoRA.

Projection initialization can be slow on large models because FRoD runs matrix decompositions over the target module
categories before injecting the adapters. A progress bar is shown by default and can be disabled with
`FrodConfig(progressbar=False)`.

For memory-constrained training, `runtime_offload_base_weight=True` keeps target base weights on CPU when the active
FRoD path does not need them. This is opt-in because PEFT methods usually keep all base parameters on the accelerator
after moving the model and after forward passes.

FRoD currently has the following constraint:

- Only `nn.Linear` and `transformers.pytorch_utils.Conv1D` layers are supported.

## Quickstart

```python
from transformers import AutoModelForSequenceClassification

from peft import FrodConfig, TaskType, get_peft_model

model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-uncased", num_labels=2)

peft_config = FrodConfig(
    task_type=TaskType.SEQ_CLS,
    target_modules=["query", "value"],
    modules_to_save=["classifier"],
    sparse_rate=0.02,
    frod_dropout=0.0,
    runtime_offload_base_weight=True,
)

model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
```

## FrodConfig

[[autodoc]] tuners.frod.config.FrodConfig

## FrodModel

[[autodoc]] tuners.frod.model.FrodModel
