MAPL learns task-specific orthogonal compression subspaces per pipeline stage via manifold-constrained optimization and recovers signals with low-overhead anchors, yielding better compression-performance tradeoffs than fixed projections on LLaMA models up to 1B parameters.
Acco: Accumulate while you communicate for communication-overlapped sharded llm training.arXiv preprint arXiv:2406.02613, 2024
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Learned Subspace Compression for Communication-Efficient Pipeline Parallelism
MAPL learns task-specific orthogonal compression subspaces per pipeline stage via manifold-constrained optimization and recovers signals with low-overhead anchors, yielding better compression-performance tradeoffs than fixed projections on LLaMA models up to 1B parameters.