QLoRA: Quantized Low-Rank Adaptation Forward Pass
MoE, Compression & Scaling DS practice problem on Onlearn.
Difficulty: medium.
Topics: QLoRA: Quantized Low-Rank Adaptation Forward Pass, 4-bit NormalFloat, Double Quantization, Low-Rank Decomposition, Paged Optimizers, Adapter Injection, Linear Algebra, Information Theory, Numerical Analysis, Deep Learning Architectures, Distributed Systems, Matrix Factorization, Quantization Theory, Parameter-Efficient Fine-Tuning, Weight Distribution Analysis, Computational Complexity.
Implement the forward pass of QLoRA (Quantized Low Rank Adaptation), an extension of LoRA that enables fine tuning of large language models on consumer GPUs by quantizing the frozen pretrained weights to 4 bit precision. The frozen weights are stored in 4 bit format to save memory, but are dequantized to full precision during the forward pass. The trainable LoRA matrices (A and B) remain in full precision. Given quantized weights with their scale and zero point, along with LoRA matrices, compute the output.