This Ai Paper From Bytedance Introduces Megascale-infer: A Disaggregated Expert Parallelism System For Efficient And Scalable Moe-based Llm Serving

Large connection models are built connected transformer architectures and powerfulness applications for illustration chat, codification generation, and search, but their increasing standard pinch billions of parameters makes businesslike computation progressively challenging. Scaling specified systems while maintaining debased latency and precocious throughput puts unit connected algorithm creation and system-level optimization. Effectively serving these models now requires observant orchestration of memory, communication, and compute resources.

A captious situation successful this abstraction is really sparsity, introduced done Mixture-of-Experts (MoE) models, affects conclusion performance. These models selectively activate a subset of feed-forward networks per input, reducing computational load. However, this selective activation leads to underutilization of hardware. During inference, attraction modules go bottlenecks owed to predominant representation entree to key-value caches, while nan FFN modules go idle because each receives a mini fraction of tokens. As a result, GPU utilization drops significantly, particularly during decoding, creating inefficiencies and inflating operational costs.

While immoderate methods for illustration vLLM and TensorRT-LLM person attempted to reside conclusion scaling done parallelism and optimized kernels, these solutions stay constrained. They process nan exemplary holistically, meaning they cannot independently set scaling for different components. As MoE models turn successful size and sparsity, this attack leads to smaller progressive batches per expert, weakening nan benefits of batching for FFNs. Moreover, tensor and pipeline parallelism approaches adhd connection overhead, particularly crossed nodes, which becomes a limiting facet successful multi-GPU environments.

ByteDance and Peking University researchers person introduced MegaScale-Infer, a strategy that rethinks nan architecture of MoE serving. Instead of serving nan exemplary arsenic a monolithic block, nan researchers disaggregate nan attraction and FFN modules, deploying them connected abstracted GPUs. This separation enables customized scaling and parallelism strategies tailored to nan circumstantial needs of each module. Attention modules, which are memory-intensive, are replicated to aggregate requests, while FFN modules are scaled utilizing master parallelism. The strategy besides supports heterogeneous GPU deployment, assigning cost-effective memory-heavy GPUs to attraction tasks and compute-optimized GPUs to FFNs. This disaggregation dramatically improves assets usage and elasticity successful deployment.

To further optimize performance, MegaScale-Infer employs a ping-pong pipeline parallelism strategy. The thought is to break down batches of requests into smaller micro-batches that alternate betwixt attraction and FFN modules, ensuring that neither constituent sits idle. The strategy determines nan optimal number of micro-batches required to support precocious utilization, considering compute time, connection latency, and hardware setup. For example, if nan connection clip is little than half nan compute time, astatine slightest 3 micro-batches are used. Further, nan strategy integrates a high-performance M2N connection room that avoids unnecessary GPU-to-CPU information copies, reducing latency and instability. This room replaces nan accepted All-to-All routing pinch a much businesslike sender-receiver exemplary designed specifically for MoE’s token dispatch pattern.

MegaScale-Infer was tested connected aggregate large-scale MoE models, including Mixtral 8×22B, DBRX, and a scaled civilization exemplary pinch 317 cardinal parameters. In experiments connected homogeneous setups utilizing NVIDIA Ampere GPUs, MegaScale-Infer improved per-GPU decoding throughput by up to 2.56× compared to vLLM and 1.28× complete TensorRT-LLM. The scaled exemplary achieved a 7.11× summation complete vLLM and a 1.90× summation complete TensorRT-LLM. On heterogeneous clusters pinch H20 GPUs for attraction and L40S for FFNs, nan strategy achieved up to 3.24× and 1.86× higher throughput per dollar than nan baselines. Its M2N connection room delivered up to 4.2× higher throughput and 68.2% little latency than NCCL.

This insubstantial presents a clear problem of underutilized GPUs during MoE conclusion and offers a applicable solution by modularizing nan architecture. The projected disaggregation strategy, mixed pinch micro-batch pipelining and a civilization connection protocol, substantially impacts serving ratio and cost.

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