The Distributed Execution of vLLM | HackerNoon

Table of Links

Abstract and 1 Introduction

2 Background and 2.1 Transformer-Based Large Language Models

2.2 LLM Service & Autoregressive Generation

2.3 Batching Techniques for LLMs

3 Memory Challenges in LLM Serving

3.1 Memory Management in Existing Systems

4 Method and 4.1 PagedAttention

4.2 KV Cache Manager

4.3 Decoding with PagedAttention and vLLM

4.4 Application to Other Decoding Scenarios

4.5 Scheduling and Preemption

4.6 Distributed Execution

5 Implementation

6 Evaluation and 6.1 Experimental Setup

6.2 Basic Sampling

6.3 Parallel Sampling and Beam Search

6.4 Shared prefix

6.5 Chatbot

7 Ablation Studies

8 Discussion

9 Related Work

10 Conclusion, Acknowledgement and References

4.6 Distributed Execution

Many LLMs have parameter sizes exceeding the capacity of a single GPU [5, 9]. Therefore, it is necessary to partition them across distributed GPUs and execute them in a model parallel fashion [28, 63]. This calls for a memory manager capable of handling distributed memory. vLLM is effective in distributed settings by supporting the widely used Megatron-LM style tensor model parallelism strategy on Transformers [47]. This strategy adheres to an SPMD (Single Program Multiple Data) execution schedule, wherein the linear layers are partitioned

to perform block-wise matrix multiplication, and the the GPUs constantly synchronize intermediate results via an all-reduce operation. Specifically, the attention operator is split on the attention head dimension, each SPMD process takes care of a subset of attention heads in multi-head attention.

We observe that even with model parallel execution, each model shard still processes the same set of input tokens, thus requiring the KV Cache for the same positions. Therefore, vLLM features a single KV cache manager within the centralized scheduler, as in Fig. 4. Different GPU workers share the manager, as well as the mapping from logical blocks to physical blocks. This common mapping allows GPU workers to execute the model with the physical blocks provided by the scheduler for each input request. Although each GPU worker has the same physical block IDs, a worker only stores a portion of the KV cache for its corresponding attention heads.

In each step, the scheduler first prepares the message with input token IDs for each request in the batch, as well as the block table for each request. Next, the scheduler broadcasts this control message to the GPU workers. Then, the GPU workers start to execute the model with the input token IDs. In the attention layers, the GPU workers read the KV cache according to the block table in the control message.

During execution, the GPU workers synchronize the intermediate results with the all-reduce communication primitive without the coordination of the scheduler, as in [47]. In the end, the GPU workers send the sampled tokens of this iteration back to the scheduler. In summary, GPU workers do not need to synchronize on memory management as they only need to receive all the memory management information at the beginning of each decoding iteration along with the step inputs.