  We introduce Mini-Sequence Transformer (MsT), a simple and effective
methodology for highly efficient and accurate LLM training with extremely long
sequences. MsT partitions input sequences and iteratively processes
mini-sequences to reduce intermediate memory usage. Integrated with activation
recomputation, it enables significant memory savings in both forward and
backward passes. In experiments with the Llama3-8B model, with MsT, we measure
no degradation in throughput or convergence even with 12x longer sequences than
standard implementations due to our careful memory optimizations. MsT is fully
general, implementation-agnostic, and requires minimal code changes to
integrate with existing LLM training frameworks.
