LightSeq: Sequence Level Parallelism for Distributed Training . . . Through comprehensive experiments on single and cross-node training, we show that LightSeq achieves up to 1 24-2 01x end-to-end speedup, and a 2-8x longer sequence length on models with fewer heads, compared to Megatron-LM
The big picture: Transformers for long sequences - Medium The reason why most Transformer models are limited in their sequence length is that the computational and memory complexity of self-attention is quadratically dependent on the
Enabling Long Context Training with Sequence Parallelism in . . . Axolotl now offers a solution to this problem through the implementation of sequence parallelism (SP), allowing researchers and developers to train models with significantly longer contexts than previously possible
Tensor and Sequence Parallelism | NVIDIA TransformerEngine . . . These distributed training techniques are crucial for scaling transformer models across multiple GPUs, enabling the training of larger models with longer sequences than would be possible on a single device For related information on efficiently handling extremely long sequences, see Context Parallelism
LightSeq: : Sequence Level Parallelism for Distributed . . . TL;DR: An scalable and efficient training sequence-parallel system for long-context transformer, optimized for causal language modeling objective Increasing the context length of large language models (LLMs) unlocks fundamentally new capabilities, but also significantly increases the memory footprints of training
Sequence Parallelism: Long Sequence Training from System . . . Be- sides, using ef cient attention with linear com- plexity, our sequence parallelism enables us to train transformer with in nite long sequence Speci cally, we split the input sequence into multiple chunks and feed each chunk into its corresponding device (i e , GPU)
FlashAttention: Fast Transformer Training with Long Sequences In this post, we describe one key improvement that we’re particularly excited about: making FlashAttention fast for long sequences to enable training large language models with longer context As an example, for sequence length 8K, FlashAttention is now up to 2 7x faster than a standard Pytorch implementation, and up to 2 2x faster than the