Adafactor

Author: Aolei Cao (ac3237), Ziyang Li (zl986), Junjia Liang (jl4439) (ChemE 6800 Fall 2024)

Stewards: Nathan Preuss, Wei-Han Chen, Tianqi Xiao, Guoqing Hu

Problem formulation

1. Objective

Minimize the loss function $ f(x) $, where $ x \in R^n $ and $ x $ is the weight vector to be optimized.

2. Parameters

• Gradient:

$ G_t = \nabla f(x_{t-1}) $

• Second moment estimate:

$ \hat{V}_t = \hat{\beta}_{2t} \hat{V}_{t-1} + (1 - \hat{\beta}_{2t})(G_t^2 + \epsilon_1 1_n) $

• Where:

$ \hat{V}_t $ is the running average of the squared gradient. $ \hat{\beta}_{2t} $ is the corrected decay parameter. $ \epsilon_1 $ is a regularization constant.

• Step size:

 $ \alpha_t = \max(\epsilon_2, \text{RMS}(x_{t-1})) \rho_t $
 ** Where:
   * $ \rho_t $ is the relative step size.
   * $ \epsilon_2 $ is a regularization constant.
   * $ \text{RMS} $ is the root mean square, defined as:
     $ u_{xt} = \frac{-g_{xt}}{\sqrt{\hat{v}_{xt}}} $
     $ \text{RMS}(U_t) = \text{RMS}_{x \in X}(u_{xt}) = \sqrt{\text{Mean}_{x \in X}\left(\frac{(g_{xt})^2}{\hat{v}_{xt}}\right)} $

3. Problem Formulation

Adafactor for Weighted Vectors

Inputs:

• Initial point: $ X_0 \in \mathbb{R}^n $
• Relative step sizes: $ \rho_t $ for $ t = 1 $ to $ T $
• Second moment decay: $ \hat{\beta}_{2t} $ for $ t = 1 $ to $ T $, with $ \hat{\beta}_{21} = 0 $
• Regularization constants: $ \epsilon_1, \epsilon_2 $
• Clipping threshold: $ d $

Algorithm:

1. For $ t = 1 $ to $ T $:
   1. Compute adaptive step size:

  $ \alpha_t = \max(\epsilon_2, \text{RMS}(X_{t-1})) \rho_t $

1. 1. Compute gradient:

  $ G_t = \nabla f_t(X_{t-1}) $

1. 1. Update second moment estimate:

  $ \hat{V}_t = \hat{\beta}_{2t} \hat{V}_{t-1} + (1 - \hat{\beta}_{2t})(G_t^2 + \epsilon_1 1_n) $

1. 1. Compute normalized gradient:

  $ U_t = \frac{G_t}{\sqrt{\hat{V}_t}} $

1. 1. Apply clipping:

  $ \hat{U}_t = \frac{U_t}{\max(1, \text{RMS}(U_t) / d)} $

1. 1. Update parameter:

  $ X_t = X_{t-1} - \alpha_t \hat{U}_t $

1. End for

Adafactor for Weighted Matrices

Inputs:

• Initial point: $ X_0 \in \mathbb{R}^{n \times m} $
• Relative step sizes: $ \rho_t $ for $ t = 1 $ to $ T $
• Second moment decay: $ \hat{\beta}_{2t} $ for $ t = 1 $ to $ T $, with $ \hat{\beta}_{21} = 0 $
• Regularization constants: $ \epsilon_1, \epsilon_2 $
• Clipping threshold: $ d $

Algorithm:

1. For $ t = 1 $ to $ T $:
   1. Compute adaptive step size:

  $ \alpha_t = \max(\epsilon_2, \text{RMS}(X_{t-1})) \rho_t $

1. 1. Compute gradient:

  $ G_t = \nabla f_t(X_{t-1}) $

1. 1. Update row-wise second moment:

  $ R_t = \hat{\beta}_{2t} R_{t-1} + (1 - \hat{\beta}_{2t})(G_t^2 + \epsilon_1 1_n 1_m^T) 1_m $

1. 1. Update column-wise second moment:

  $ C_t = \hat{\beta}_{2t} C_{t-1} + (1 - \hat{\beta}_{2t}) 1_n^T (G_t^2 + \epsilon_1 1_n 1_m^T) $

1. 1. Update overall second moment estimate:

  $ \hat{V}_t = \frac{R_t C_t}{1_n^T R_t} $

1. 1. Compute normalized gradient:

  $ U_t = \frac{G_t}{\sqrt{\hat{V}_t}} $

1. 1. Apply clipping:

  $ \hat{U}_t = \frac{U_t}{\max(1, \text{RMS}(U_t) / d)} $

1. 1. Update parameter:

  $ X_t = X_{t-1} - \alpha_t \hat{U}_t $

1. End for

4. Proposed Hyperparameters for Adafactor

• Regularization constant 1: $ \epsilon_1 = 10^{-30} $
• Regularization constant 2: $ \epsilon_2 = 10^{-3} $
• Clipping threshold: $ d = 1 $
• Relative step size: $ \rho_t = \min(10^{-2}, 1/\sqrt{t}) $
• Second moment decay: $ \hat{\beta}_{2t} = 1 - t^{-0.8} $