Enhancing adaptive rounding (adaround) and low-rank adaptation rounding (lora-rounding) for larger degrees of freedom

A method for adjusting weights of a machine learning (ML) model, comprising:

• generating a first matrix of quantized weight values by rounding values of an input matrix of weight values for the ML model;
• applying an activation function to a second matrix, wherein the second matrix is generated based on a third matrix and a fourth matrix of a first matrix pair, and wherein the activation function constrains values of the second matrix to 0 and 1;
• applying the activation function to a fifth matrix, wherein the fifth matrix is based on a sixth matrix and seventh matrix of a second matrix pair;
• generating a positive second matrix by applying a positive factor to the second matrix;
• generating a negative fifth matrix by applying a negative factor to the fifth matrix;
• summing the first matrix of quantized weight values with the positive second matrix and the negative fifth matrix to generate an output matrix of quantized weight values;
• and
• outputting the output matrix of quantized weight values.

A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to:

• generate a first matrix of quantized weight values by rounding values of an input matrix of weight values for a machine learning (ML) model;
• apply an activation function to a second matrix, wherein the second matrix is generated based on a third matrix and a fourth matrix of a first matrix pair, and wherein the activation function constrains values of the second matrix to 0 and 1;
• apply the activation function to a fifth matrix, wherein the fifth matrix is based on a sixth matrix and seventh matrix of a second matrix pair;
• generate a positive second matrix by applying a positive factor to the second matrix;
• generate a negative fifth matrix by applying a negative factor to the fifth matrix;
• sum the first matrix of quantized weight values with the positive second matrix and the negative fifth matrix to generate an output matrix of quantized weight values;
• and
• output the output matrix of quantized weight values.