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LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

Summary

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Authors propose Low-Rank Adaptation (LoRA) that freezes the pretrained model weights and injects trainable rank decomposition matrices.
W0+ΔW=W0+BAW_0 + \Delta W = W_0 + BA
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LoRA reduced the number of trainable parameters and improved efficiency without no critical drop in accuracy and no additional inference latency.
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GPT-3 175B: 1/10,000 times # of parameters, 1/3 GPU memory
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LoRA is task- and model-agnostic. (Can be applied to many fine-tuning regime)

Related works

(Typical) Adapter layers

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Most of the adapter layers introduce inference latency.
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Houlsby et al. (2019) - two adapter layers
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Lin et al. (2020)
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Since the batch size of most online inference is 1, this induces noticeable increase in inference time.

Low-rank structures in Deep Learning

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Low-rank structure:
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The property that the tensors of the neural network (usually weight matrices) can be approximated or decomposed into a combination of low-rank matrices or tensors.
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Have been observed in various deep learning tasks (especially in over-parametrized neural networks)

Fine-tuning

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Full fine-tuning:
max⁡Φ∑(x,y)∈Z∑t=1∣y∣log⁡(PΦ(yt∣x,y<t))\max_{\Phi} \sum_{(x, y) \in \mathcal{Z}}\sum_{t=1}^{|y|} \log (P_{\Phi}(y_t | x, y_{<t}))
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The model is initialized to pre-trained weights Φ\Phi.
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And updated to Φ0+ΔΦ\Phi_{0} + \Delta \Phi
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Extremely compute-intensive for large models (such as GPT-3 with 175 Billion parameters)

LoRA

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Low-rank Adaptation
W0+ΔW=W0+BAW_0 + \Delta W = W_0 + BA
max⁡Θ∑(x,y)∈Z∑t=1∣y∣log⁡(pΦ0+ΔΦ(Θ)(yt∣x,y<t))\max_{\Theta} \sum_{(x, y) \in \mathcal{Z}}\sum_{t=1}^{|y|} \log (p_{\Phi_0 + \Delta \Phi (\Theta)}(y_t|x, y_{<t}))
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Task-specific parameter increment ΔΦ=ΔΦ(Θ)\Delta\Phi = \Delta\Phi(\Theta) is encoded by much smaller sized set of parameters Θ\Theta. → Compute- and memory-efficient!
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Applying to Transformer
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LoRA is applied to only self-attention module weights - Wq,Wk,Wv,WoW_q, W_k, W_v, W_o
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Not applied to MLP module

Results

Further analysis

Optimal rank rr for LoRA?

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LoRA performs well even with a very small rr
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Increasing rr does not cover a more meaningful subspace

Subspace similarity between different rr

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Directions of top singular vector overlaps significantly, while others are not.
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