xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism (2411.01738v1)

Abstract: Diffusion models are pivotal for generating high-quality images and videos. Inspired by the success of OpenAI's Sora, the backbone of diffusion models is evolving from U-Net to Transformer, known as Diffusion Transformers (DiTs). However, generating high-quality content necessitates longer sequence lengths, exponentially increasing the computation required for the attention mechanism, and escalating DiTs inference latency. Parallel inference is essential for real-time DiTs deployments, but relying on a single parallel method is impractical due to poor scalability at large scales. This paper introduces xDiT, a comprehensive parallel inference engine for DiTs. After thoroughly investigating existing DiTs parallel approaches, xDiT chooses Sequence Parallel (SP) and PipeFusion, a novel Patch-level Pipeline Parallel method, as intra-image parallel strategies, alongside CFG parallel for inter-image parallelism. xDiT can flexibly combine these parallel approaches in a hybrid manner, offering a robust and scalable solution. Experimental results on two 8xL40 GPUs (PCIe) nodes interconnected by Ethernet and an 8xA100 (NVLink) node showcase xDiT's exceptional scalability across five state-of-the-art DiTs. Notably, we are the first to demonstrate DiTs scalability on Ethernet-connected GPU clusters. xDiT is available at https://github.com/xdit-project/xDiT.

• The paper introduces xDiT, a hybrid parallel inference engine that addresses the quadratic computational challenges in Diffusion Transformers.
• xDiT integrates Sequence Parallelism, PipeFusion, CFG parallelism, and patch-level VAE strategies to enhance scalability and reduce communication overhead.
• Evaluations on diverse GPU clusters demonstrate xDiT’s ability to lower latency and efficiently handle high-resolution tasks, enabling real-time diffusion model applications.

An In-Depth Analysis of xDiT: A Scalable Inference Engine for Diffusion Transformers

The rapid evolution of diffusion models, particularly with the shift toward employing Diffusion Transformers (DiTs), has introduced complex challenges in computational scalability. The paper "xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism" provides a comprehensive solution designed to address these challenges. This paper delineates the development and evaluation of xDiT, a novel parallel inference engine explicitly tailored for DiTs.

Summary and Methodological Innovations

The authors identify the essential requirement for parallel inference when working with DiTs, primarily due to the quadratic scaling of computational demands associated with attention mechanisms in such models. The xDiT system aims to address this requirement by combining various parallelism strategies to enhance scalability and computation efficiency.

1. Diffusion Model Transition and Challenges: Diffusion models have traditionally employed U-Net architectures, but the transition to DiTs has been driven by DiTs' superior capacity and scalability. However, this transition has necessitated handling longer sequence lengths, resulting in exponential growth in computational demands and inference latency. Single-method parallel approaches have demonstrated limited efficiency in meeting these demands.
2. Parallel Strategies in xDiT: The system leverages a hybrid approach by integrating different parallel strategies:
   • Sequence Parallel (SP) and PipeFusion: For intra-image parallelism, the paper adapts SP to DiT blocks and introduces PipeFusion, a patch-level pipeline parallelism. PipeFusion primarily outperforms other methods in communication and memory efficiency by exploiting temporal input redundancy.
   • CFG Parallelism: This addresses inter-image parallelism by separating computation paths for different conditional latents, minimizing communication overhead using AllGather operations.
   • Patch-Level VAE Parallelism: Deployed to mitigate GPU memory limitations when generating high-resolution images, it allows the VAE module to effectively manage higher memory footprints.
3. Hybrid Parallel Approach: The innovation in xDiT lies in its flexible hybridization of these parallel approaches, which proves critical in heterogeneous network environments across different hardware configurations. This allows for the efficient distribution of computational workloads, adapting dynamically to network topologies and hardware capabilities.

Performance and Scalability

The xDiT system has been meticulously evaluated on diverse GPU cluster configurations, demonstrating exceptional scalability and efficiency across different image and video generation DiTs. The authors highlight key findings:

• The combination of PipeFusion and SP yields lower latency and enhanced scalability, particularly notable in environments constrained by communication overhead, such as multi-node GPU clusters connected via Ethernet.
• Hybrid parallelism, incorporating CFG parallel and PipeFusion, achieves significant performance gains, especially in contexts with diverse model architectures and varying input lengths.
• The implementation of patch-level parallelism in the VAE module effectively circumvents potential OOM issues, allowing for the successful handling of extensive image resolutions and contributing to the robustness of the system.

Implications and Future Prospects

The development of xDiT holds substantial implications for the deployment of DiTs in real-time applications, ensuring scalability and efficiency in extensive framework settings. Practically, it provides an adaptable solution for researchers and practitioners working on high-resolution image and video generation or similar computationally demanding tasks.

From a theoretical standpoint, xDiT exemplifies the potential of marrying various parallel methodologies to harness the benefits and flexibility required in large-scale AI deployments. This hybrid approach paves the way for future research exploring dynamic parallelism, potentially integrating more advanced scheduling techniques to further minimize latency and optimize resource allocation across heterogeneous systems.

Conclusion

In summary, the "xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism" paper offers a profound methodological contribution to the field of computational models. The insights and results presented affirm the viability of hybrid parallel paradigms in catering to the demands of next-generation diffusion transformers and lay a foundation for future exploration in massive parallelism and system optimization strategies within the AI domain.