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. Algorithms

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:

• For $t=1$ to $T$:
  • Compute adaptive step size: $\alpha _{t}=\max(\epsilon _{2},{\text{RMS}}(X_{t-1}))\rho _{t}$
  • Compute gradient: $G_{t}=\nabla f_{t}(X_{t-1})$
  • Update second moment estimate: ${\hat {V}}_{t}={\hat {\beta }}_{2t}{\hat {V}}_{t-1}+(1-{\hat {\beta }}_{2t})(G_{t}^{2}+\epsilon _{1}1_{n})$
  • Compute normalized gradient: $U_{t}={\frac {G_{t}}{\sqrt {{\hat {V}}_{t}}}}$
  • Apply clipping: ${\hat {U}}_{t}={\frac {U_{t}}{\max(1,{\text{RMS}}(U_{t})/d)}}$
  • Update parameter: $X_{t}=X_{t-1}-\alpha _{t}{\hat {U}}_{t}$
• 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:

• For $t=1$ to $T$:
  • Compute adaptive step size: $\alpha _{t}=\max(\epsilon _{2},{\text{RMS}}(X_{t-1}))\rho _{t}$
  • Compute gradient: $G_{t}=\nabla f_{t}(X_{t-1})$
  • Update row-wise second moment: $R_{t}={\hat {\beta }}_{2t}R_{t-1}+(1-{\hat {\beta }}_{2t})(G_{t}^{2}+\epsilon _{1}1_{n}1_{m}^{T})1_{m}$
  • Update column-wise second moment: $C_{t}={\hat {\beta }}_{2t}C_{t-1}+(1-{\hat {\beta }}_{2t})1_{n}^{T}(G_{t}^{2}+\epsilon _{1}1_{n}1_{m}^{T})$
  • Update overall second moment estimate: ${\hat {V}}_{t}={\frac {R_{t}C_{t}}{1_{n}^{T}R_{t}}}$
  • Compute normalized gradient: $U_{t}={\frac {G_{t}}{\sqrt {{\hat {V}}_{t}}}}$
  • Apply clipping: ${\hat {U}}_{t}={\frac {U_{t}}{\max(1,{\text{RMS}}(U_{t})/d)}}$
  • Update parameter: $X_{t}=X_{t-1}-\alpha _{t}{\hat {U}}_{t}$
• 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: ${\hat {\beta }}_{2t}=1-t^{-0.8}$

Numerical Examples

Step-by-step instructions for determining the result of the first iteration.

Problem setup

Initial weights ($X_{0}$​):

$X_{0}={\begin{bmatrix}0.7&-0.5&0.9\\-1.1&0.8&-1.6\\1.2&-0.7&0.4\end{bmatrix}}$

Initial gradient (​$G_{t}$):

Gradient of the loss function with respect to X

$G_{t}={\begin{bmatrix}0.3&-0.2&0.4\\-0.5&0.6&-0.1\\0.2&-0.4&0.3\end{bmatrix}}$

Hyperparameters setup

$\epsilon _{1}=10^{-30}$ (Minimum learning rate scaling factor))

$\epsilon _{2}=10^{-3}$ (Regularization constant)

$d=1$ (Clipping threshold)

$\rho _{t}=\min(10^{-2},1/{\sqrt {t}})$ (Relative step size)

${\hat {\beta }}_{2t}=1-t^{-0.8}$ (Second moment decay)

Step 1: Learning Rate Scaling

Define the relative step size

$\rho _{1}=\min(10^{-2},1/{\sqrt {1}})=10^{-2}$

Step 1.1: Root Mean Square(RMS) calculation for $X_{0}$

Root Mean Square(RMS) calculation for $X_{0}$

RMS formula

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle RMS(X_{0})={\sqrt {{\tfrac {1}{n}}\textstyle \sum _{i=1}^{n}\displaystyle X_{0}[i]^{2}}}}

Substitute the initial weights

$RMS(X_{0})={\sqrt {{\tfrac {1}{9}}(0.72^{2}+(-0.5)^{2}+0.9^{2}+(-1.1)^{2}+0.8^{2}+(-0.6)^{2}+1.2^{2}+(-0.7)^{2}+0.4^{2})}}$

$RMS(X_{0})={\sqrt {\frac {6.85}{9}}}\approx 0.806$

Step 1.2: Find the Learning Rate Scaling ($\alpha _{t}$​):

Learning rate formula

$\alpha _{1}=max(\epsilon _{2},RMS(X_{0}))\cdot p_{1}$

Substitute the RMS

$\alpha _{1}=max(0.001,0.806)\cdot 0.01=0.00806$

Step 2: Compute $G_{t}^{2}$​ (Element-wise Square of Gradient)

Square the gradient value

$G_{t}^{2}={\begin{bmatrix}0.3^{2}&(-0.2)^{2}&0.4^{2}\\(-0.5)^{2}&0.6^{2}&(-0.1)^{2}\\0.2^{2}&(-0.4)^{2}&0.3^{2}\end{bmatrix}}$

$G_{t}^{2}={\begin{bmatrix}0.09&0.04&0.16\\0.25&0.36&0.01\\0.04&0.16&0.09\end{bmatrix}}$

Step 3: Find the moment estimate

Step 3.1: Compute row moments ($R_{t}$)

This equation computes the row-wise second moments ($R_{t}$ ​) as an exponential moving average of past moments ($R_{t-1}$) and the current row-wise mean of squared gradients ( $G_{t}^{2}$​ ), with a balance controlled by (${\hat {\beta }}_{2t}$).

For $G_{t}^{2}=\mathbb {R} ^{m\times n}$

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle R_{t}={\hat {\beta _{2t}}}\cdot R_{t-1}+(1-{\hat {\beta }})\cdot ({\tfrac {1}{m}}\textstyle \sum _{j=1}^{m}\displaystyle G_{t}^{2}[i,j]+\epsilon _{1})}

Since ${\hat {\beta }}_{2t}=1-t^{-0.8}$, for first iteration: ${\hat {\beta }}_{21}=0$. And because $\epsilon _{1}$ is too small, we ignore it. The update of Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle R_{1}} is:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle R_{1}={\tfrac {1}{m}}\textstyle \sum _{j=1}^{m}\displaystyle G_{t}^{2}[i,j]}

Row-wise mean ($R_{t}$):

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle R_1 = \begin{bmatrix} \tfrac{0.09+0.04+0.16}{3} \\ \tfrac{0.25+0.36+0.01}{3}\\\tfrac{0.04+0.16+0.09}{3} \end{bmatrix} = \begin{bmatrix} 0.0967\\ 0.2067\\0.0967\end{bmatrix} }

Step 3.2: Compute column moments (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C_t} )

The process is same as row moments

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C_t = \hat{\beta}\cdot C_{{t-1}} + (1-\hat{\beta})\cdot (\tfrac{1}{n}\textstyle \sum_{j=1}^n \displaystyle G^{2}_t[i,j]+\epsilon_1) }

Column-wise mean:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C_1 = \begin{bmatrix} \tfrac{0.09+025+0.04}{3} \\ \tfrac{0.04+0.36+0.16}{3}\\\tfrac{0.16+0.01+0.09}{3} \end{bmatrix} = \begin{bmatrix} 0.1267\\ 0.1867\\0.0867\end{bmatrix} }

Step 3.3: Second Moment Estimate (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle V_t} ​)

The Second Moment Estimate is calculated as the outer product of the row moments (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle R_t} ​) and column moments (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C_t} ​).

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle V_t = R_t \otimes C_t}

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle V_t = \begin{bmatrix} 0.0967\\0.2067\\0.0967 \end{bmatrix} \otimes \begin{bmatrix} 0.1267&0.1867&0.0867\\ \end{bmatrix} }

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle V_t = \begin{bmatrix} 0.0122&0.0180&0.0084\\ 0.0262&0.0386&0.0179\\ 0.0122&0.0180&0.0084\end{bmatrix} }

Step 4: Update the vector (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t } )

step 4.1: Find the vector value of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t }

Formula of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t }

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t = \frac{G_t}{\sqrt{V_t+\epsilon_1}} }

Substitute Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C_t} and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle V_t}

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_1 = \frac{\begin{bmatrix}0.3&-0.2&0.4 \\ -0.5&0.6&-0.1\\0.2&-0.4&0.3 \end{bmatrix}}{\sqrt{\begin{bmatrix} 0.0122&0.0180&0.0084\\ 0.0262&0.0386&0.0179\\0.0122&0.0180&0.0084 \end{bmatrix}}} }

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_1 = \begin{bmatrix} 2.711&-1.489&4.370\\-3.090&3.055&-0.747\\1.807&-2.978&3.278 \end{bmatrix} }

step 4.2: Clipped Update Vector Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \hat{U_t} }

Formula of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \hat{U_t} }

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \hat{U_t} = \frac{U_t}{max(1,\tfrac{RMS(U_t)}{d}) } }

Calculate RMS of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t }

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle RMS(U_t) = \sqrt{\tfrac{1}{9} \sum_{i=1}^9 U_t[i]^2} \approx 3.303 }

Since RMS(Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t } ​)>d, scale Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U_t } ​ by Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \tfrac{1}{3.303} }

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \hat{U_t} = \begin{bmatrix} 0.965&-0.53&1.556 \\-1.1&1.088&-0.266\\0.664&-1.06&1.167 \end{bmatrix} }

Step 4: Weight Update (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X_1 } )