Decentralized DORA Tuning

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Abstract

$D^2ORA$ (Decentralized Weight-Decomposed Low-Rank Adaptation) extends the DoRA algorithm to a decentralized environment, enabling distributed training across multiple servers.

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$D^2ORA$ Algorithm

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Step 1: Initialization

We first initialize the parameter matrices of Theia and the low-rank matrices.

• Magnitude Vector Initialization: Initialize the magnitude vector $m$ using the column-wise norm of the pre-trained weight matrix $W_0$:

  $$m = ||W_0||_c$$

• Directional Matrix Initialization: Set the directional matrix $V$ to the pre-trained weight matrix $W_0$:

  $$V = W_0$$

• Low-Rank Matrices Initialization: Initialize the low-rank matrices $B$ and $A$ for the LoRA method.

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Step 2: Decomposition

Decompose the pre-trained weight matrix $W_0$ into its magnitude and direction components. The pre-trained weight is directly derived from the NLP model.

• Magnitude Decomposition:

  $$m = ||W_0||_c$$

• Directional Decomposition:

  $$V = W_0 / ||W_0||_c$$

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Step 3: Distributed Training

For each server in the decentralized network, perform the following:

• Distribution: Distribute the initialized $m$, $V$, $B$, and $A$ to all servers.

• Iterations: For each iteration $t$ from 1 to $T$:

  • Weight Update Calculation: Compute the weight update $\Delta W$ using the low-rank adaptation method:

    $$\Delta W = BA$$

  • Magnitude Vector Update: Update the magnitude vector $m$:

    $$m = m - \eta \nabla_m L(m, V)$$

  • Directional Matrix Update: Update the low-rank matrices $B$ and $A$ with gradient descent:

    $$B = B - \eta \nabla_B L(B, A)$$
    $$A = A - \eta \nabla_A L(B, A)$$

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Step 4: Aggregation

After all iterations are completed, collect updates from all servers and aggregate them:

• Magnitude Vector Aggregation:

  $$m' = \text{aggregate}(m)$$

• Directional Matrix Aggregation:

  $$V' = \text{aggregate}(V)$$

• Low-Rank Matrices Aggregation:

  $$B' = \text{aggregate}(B)$$
  $$A' = \text{aggregate}(A)$$

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Step 5: Merging

Merge the updated components to form the final weight matrix:

• Final Weights Calculation:
  $$W' = m' \frac{V'}{||V'||_c} + B'A'$$

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Step 6: Return Updated Weights

Return the updated weights $W'$ as the final output of the algorithm. We use it as the weight parameters of Theia.

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Summary

D^2ORA enhances the capabilities of DoRA by enabling decentralized training across multiple servers. This approach leverages blockchain technology to ensure trustworthiness, making it suitable for training AI models in a distributed and secure environment.