Summary of Grass: Compute Efficient Low-memory Llm Training with Structured Sparse Gradients, by Aashiq Muhamed et al.
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
by Aashiq Muhamed, Oscar Li, David Woodruff, Mona Diab, Virginia Smith
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach, Grass (GRAdient Structured Sparsification), is a novel optimization method that leverages sparse projections to transform gradients into structured sparse updates. This design reduces memory usage for optimizer states and minimizes gradient memory footprint, computation, and communication costs, leading to substantial throughput improvements. Grass achieves competitive performance to full-rank training and existing projection-based methods, enabling half-precision pretraining of a 13B parameter LLaMA model on a single 40GB A100 GPU and yielding up to a 2throughput improvement on an 8-GPU system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Grass is a new way to train large language models. It helps by using less memory, which makes it faster. This means we can do more work with the same amount of computer power. Grass works as well as other methods, and it even lets us use smaller computers for some jobs. This is important because it will make it easier to do many things that need a lot of computing power. |
Keywords
» Artificial intelligence » Llama » Optimization » Precision » Pretraining
