SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training (2410.15526v2)

Abstract: Recent years have witnessed a clear trend towards LLMs with an ever-increasing number of parameters, as well as the growing training overhead and memory usage. Distributed training, particularly through Sharded Data Parallelism (ShardedDP) which partitions optimizer states among workers, has emerged as a crucial technique to mitigate training time and memory usage. Yet, a major challenge in the scalability of ShardedDP is the intensive communication of weights and gradients. While compression techniques can alleviate this issue, they often result in worse accuracy. Driven by this limitation, we propose SDP4Bit (Toward 4Bit Communication Quantization in Sharded Data Parallelism for LLM Training), which effectively reduces the communication of weights and gradients to nearly 4 bits via two novel techniques: quantization on weight differences, and two-level gradient smooth quantization. Furthermore, SDP4Bit presents an algorithm-system co-design with runtime optimization to minimize the computation overhead of compression. In addition to the theoretical guarantees of convergence, we empirically evaluate the accuracy of SDP4Bit on the pre-training of GPT models with up to 6.7 billion parameters, and the results demonstrate a negligible impact on training loss. Furthermore, speed experiments show that SDP4Bit achieves up to 4.08$\times$ speedup in end-to-end throughput on a scale of 128 GPUs.

• The paper introduces a novel 4-bit quantization technique on weight differences and gradients to reduce communication overhead without compromising convergence.
• It leverages algorithm-system co-design, including buffer reuse and kernel fusion, to optimize training throughput in large-scale LLM setups.
• Empirical tests on GPT models with up to 6.7B parameters confirm maintained accuracy and up to 4.08× speedup using 128 GPUs.

SDP4Bit: Toward Efficient Communication Quantization for LLM Training

The paper presents a novel approach, SDP4Bit, designed to enhance the efficiency of LLM training through Sharded Data Parallelism (ShardedDP). With the rapidly expanding parameters of LLMs, reducing training overhead and managing memory usage has become essential. ShardedDP addresses memory limitations by distributing optimizer states across multiple GPUs, but it also significantly increases communication demands. While traditional methods often compromise accuracy as they reduce communication bandwidth, SDP4Bit offers a promising solution by minimizing communication overhead while maintaining accuracy.

Key Contributions

1. 4-Bit Communication Quantization: SDP4Bit introduces a low-bit communication strategy that effectively reduces the communication of weights and gradients to nearly 4 bits. This approach maintains end-to-end (E2E) training accuracy, which has been a challenge with previous methods such as QSDP or ZeRO++. These models either lacked theoretical convergence guarantees or were constrained by strict assumptions.
2. Quantization Techniques: The authors present two main quantization techniques:
   • Quantization on Weight Differences (qWD): Instead of directly compressing weights, SDP4Bit applies 4-bit quantization on weight differences between iterations. This leads to more effective compression, as weight differences tend to be smaller and more uniformly distributed.
   • Two-Level Gradient Smooth Quantization (TLq-HS): This method applies 8-bit quantization within nodes and 4-bit quantization between nodes, using the Hadamard transform to smooth out outliers, minimizing quantization errors.
3. Algorithm-System Co-Design: SDP4Bit incorporates runtime optimizations, including buffer reuse, operation pruning, and kernel fusion. These enhancements help minimize the computation overhead of the introduced quantization techniques.
4. Theoretical Convergence Guarantees: The paper offers robust theoretical analysis, confirming that SDP4Bit converges at the same rate as ordinary Stochastic Gradient Descent (SGD), without compromising the training accuracy. This is achieved under weaker assumptions compared to existing methodologies.
5. Empirical Evaluation: The strategy was empirically validated on GPT models with up to 6.7 billion parameters. The results demonstrated negligible impact on training loss, maintaining alignment with full-precision results. Additionally, a speedup of up to 4.08× in training throughput was observed with 128 GPUs.

Practical and Theoretical Implications

SDP4Bit significantly enhances the scalability of ShardedDP for LLM training by efficiently managing communication overhead. The theoretical guarantees ensure that the proposed quantization strategies do not lead to convergence issues, thus enabling stable and accurate large-scale model training.

The approach also highlights a potential direction for future research on quantization techniques that focus on differences or transformations to minimize loss of information during compression. The Hadamard smoothing technique, in particular, can be explored further for its potential applications in other areas of distributed computing.

Future Developments

Future work may extend the application of SDP4Bit to other models, such as mixture of experts (MoE) or in areas like computer vision. Additionally, exploring applications in parameter-efficient fine-tuning could unlock new efficiencies in training diverse models beyond LLMs.

In conclusion, SDP4Bit provides an innovative step toward more efficient, scalable, and accurate training methodologies for the ever-growing demands of LLMs. This work facilitates advancements in distributed training, potentially leading to broader applications and optimizations within the field of AI and machine learning.