SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators (2410.10714v2)

Abstract: LLMs have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of pseudo-random generators to encode and compress model weights. Specifically, for each block of weights, we find a seed that is fed into a Linear Feedback Shift Register (LFSR) during inference to efficiently generate a random matrix. This matrix is then linearly combined with compressed coefficients to reconstruct the weight block. SeedLM reduces memory access and leverages idle compute cycles during inference, effectively speeding up memory-bound tasks by trading compute for fewer memory accesses. Unlike state-of-the-art compression methods that rely on calibration data, our approach is data-free and generalizes well across diverse tasks. Our experiments with Llama 3 70B, which is particularly challenging to compress, show that SeedLM achieves significantly better zero-shot accuracy retention at 4- and 3-bit than state-of-the-art techniques, while maintaining performance comparable to FP16 baselines. Additionally, FPGA-based tests demonstrate that 4-bit SeedLM, as model size increases to 70B, approaches a 4x speed-up over an FP16 Llama 2/3 baseline.

• The paper introduces a novel method that compresses LLM weights by storing PRNG seeds and projection coefficients instead of raw weight values.
• It employs LFSRs to project weight matrices, achieving 3-4 bit compression while maintaining minimal accuracy loss.
• The technique reduces memory transfer and accelerates inference by 4x, highlighting its potential for energy-efficient deployments.

An Expert Analysis of SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

The paper introduces SeedLM, a novel technique aimed at mitigating the memory access bottleneck in the deployment of LLMs, like Llama 3 70B, by employing a method that leverages seeds of pseudo-random generators for weight compression. SeedLM presents a significant departure from traditional post-training compression methodologies by offering a data-free compression, which stands distinct in its avoidance of calibration data—an element often integral to existing techniques.

Methodology and Key Results

The core innovation of SeedLM lies in its unique use of Linear Feedback Shift Registers (LFSRs) in projecting weight matrices to a pseudo-random basis. Instead of storing traditional weight values, SeedLM compresses weights by storing only the generator seed and a set of projection coefficients. Through this strategy, the LFSR is utilized to efficiently recreate weight matrices during inference, offering a novel approach that trades increased computational effort for reduced memory access.

A key strength of SeedLM is its ability to compress LLM weights to 3-4 bits while maintaining minimal accuracy loss. Notably, the implementation shows strong numerical outcomes when applied to models like Llama 3 70B. Specifically, SeedLM achieves a significant reduction in memory transfer requirements and a 4x speed-up in inference tasks compared to FP16 baselines, evidencing its efficacy in enhancing throughput for memory-bound tasks via FPGA-based implementations.

Implications and Future Directions

The implications of SeedLM's approach are profound for both theoretical and practical domains of AI. Theoretically, it opens new avenues in compression techniques by introducing deterministic offline algorithms that challenge the convention of data-dependent calibrations. Practically, its promise of energy efficiency for large-scale model deployment aligns closely with the growing demand for high-performance, low-power AI systems in industry applications.

Future research may explore fine-tuning this compression method for various architectures and exploring its integration across diverse AI tasks. Moreover, expanding the SeedLM framework to include additional hardware configurations and testing under varied data distributions could further broaden its applicability.

In conclusion, while SeedLM demonstrates an impressive capability in weight compression without heavy reliance on calibration datasets, the continuous evolution in AI model architectures and hardware advancements necessitates ongoing adaptations and optimizations. Such developments could solidify SeedLM's position as a mainstay in efficient model deployment strategies, particularly in scenarios constrained by bandwidth and energy limitations.