FEAT Add VB-LoRA (huggingface#2039)

Implements "VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector
Banks"

https://arxiv.org/abs/2405.15179

docs/source/_toctree.yml

@@ -114,6 +114,8 @@
       title: VeRA
     - local: package_reference/fourierft
       title: FourierFT
+    - local: package_reference/vblora
+      title: VB-LoRA
 
     title: Adapters
   - sections:

docs/source/package_reference/vblora.md

@@ -0,0 +1,40 @@
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+
+# VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
+
+## Overview
+
+[VB-LoRA](https://arxiv.org/abs/2405.15179) is a parameter-efficient fine-tuning technique that extends LoRA by learning a fine-grained parameter-sharing scheme at the sub-vector level, achieving significantly higher parameter efficiency. This makes VB-LoRA especially useful in scenarios where storage and transmission costs are critical. It works by decomposing low-rank matrices—from different layers and modules such as K, Q, V, and FFN—into sub-vectors, which are then globally shared through a vector bank.
+
+The abstract from the paper is:
+
+*As the adoption of large language models increases and the need for per-user or per-task model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and its variants, incur substantial storage and transmission costs. To further reduce stored parameters, we introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules and layers by sharing parameters globally via a vector bank. As an instantiation of the paradigm to LoRA, our proposed VB-LoRA composites all the low-rank matrices of LoRA from a shared vector bank with a differentiable top-k admixture module. VB-LoRA achieves extreme parameter efficiency while maintaining comparable or better performance compared to state-of-the-art PEFT methods. Extensive experiments demonstrate the effectiveness of VB-LoRA on natural language understanding, natural language generation, and instruction tuning tasks. When fine-tuning the Llama2-13B model, VB-LoRA only uses 0.4% of LoRA's stored parameters, yet achieves superior results.*
+
+## Usage Tips
+
+- VB-LoRA utilizes a sparse top-k module to learn the sharing machanism. When saving adapter parameters, you can either save only the top-k weights and their indices by setting `save_only_topk_weights = True` in `VBLoRAConfig`, or save all the trainable logits by setting it to `False`. Enabling `save_only_topk_weights = True` significantly reduces storage space; for instance, in Llama2-7B, the storage file size decreases from 308MB to 2.5MB. Note that models saved with `save_only_topk_weights = True` are intended for merging or inference only and cannot be used to resume training.
+
+- VB-LoRA has two sets of training parameters: vector bank parameters and logit parameters. In practice, we found that logit parameters require a higher learning rate, while vector bank parameters require a lower learning rate. When using the AdamW optimizer, typical learning rates are 0.01 for logits and 0.001 for vector bank parameters.
+
+## VBLoRAConfig
+
+[[autodoc]] tuners.vblora.config.VBLoRAConfig
+
+## VBLoRAModel
+
+[[autodoc]] tuners.vblora.model.VBLoRAModel
+