SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop (SPD), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs. In detail, we first propose a block design that…
Large language model (LLM) training has surged in popularity over the last year with the release of several popular models such as Llama 2, Falcon, and Mistral. Customers are now pre-training and fine-tuning LLMs ranging from 1 billion to over 175 billion parameters to optimize model performance for applications across…
This article is divided into five parts; they are: • An Example of Tensor Parallelism • Setting Up Tensor Parallelism • Preparing Model for Tensor Parallelism • Train a Model with Tensor Parallelism • Combining Tensor Parallelism with FSDP Tensor parallelism originated from the Megatron-LM paper.