Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients

• URL: http://arxiv.org/abs/2406.17660v1
• Date: Tue, 25 Jun 2024 15:50:32 GMT
• Title: Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
• Authors: Aashiq Muhamed, Oscar Li, David Woodruff, Mona Diab, Virginia Smith,
• Abstract summary: Large language model (LLM) training and finetuning are often bottlenecked by limited GPU memory. We propose Grass (GRAdient Stuctured Sparsification), a novel approach that leverages sparse projections to transform gradients into structured sparse updates.
• Score: 24.58231358634904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) training and finetuning are often bottlenecked by limited GPU memory. While existing projection-based optimization methods address this by projecting gradients into a lower-dimensional subspace to reduce optimizer state memory, they typically rely on dense projection matrices, which can introduce computational and memory overheads. In this work, we propose Grass (GRAdient Stuctured Sparsification), a novel approach that leverages sparse projections to transform gradients into structured sparse updates. This design not only significantly reduces memory usage for optimizer states but also minimizes gradient memory footprint, computation, and communication costs, leading to substantial throughput improvements. Extensive experiments on pretraining and finetuning tasks demonstrate that Grass achieves competitive performance to full-rank training and existing projection-based methods. Notably, Grass enables half-precision pretraining of a 13B parameter LLaMA model on a single 40GB A100 GPU--a feat infeasible for previous methods--and yields up to a $2\times$ throughput improvement on an 8-GPU system. Code can be found at https://github.com/aashiqmuhamed/GRASS .

Related papers

• APOLLO: SGD-like Memory, AdamW-level Performance [61.53444035835778]
  Large language models (LLMs) are notoriously memory-intensive during training. Various memory-efficient Scals have been proposed to reduce memory usage. They face critical challenges: (i) costly SVD operations; (ii) significant performance trade-offs compared to AdamW; and (iii) still substantial memory overhead to maintain competitive performance.
  arXiv  Detail & Related papers  (2024-12-06T18:55:34Z)
• COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection [11.655821671462427]
  We present COAP, a memory-efficient method that minimizes computational overhead while maintaining training performance. For LLaMA-1B, it reduces memory by 61% with only 2% additional time cost, achieving the same PPL as AdamW. With 8-bit quantization, COAP cuts memory by 81% and 4x speedup over GaLore for LLaVA-v1.5-7B fine-tuning, while delivering higher accuracy.
  arXiv  Detail & Related papers  (2024-11-26T03:50:52Z)
• FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training [51.39495282347475]
  We introduce $textttFRUGAL$ ($textbfF$ull-$textbfR$ank $textbfU$pdates with $textbfG$r$textbfA$dient sp$textbfL$itting, a new memory-efficient optimization framework. Our framework can be integrated with various low-rank update selection techniques, including GaLore and BAdam.
  arXiv  Detail & Related papers  (2024-11-12T14:41:07Z)
• BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks [19.007090250576585]
  BlockLLM is an approach inspired by block coordinate descent. It achieves state-of-the-art performance in both finetuning and pretraining tasks.
  arXiv  Detail & Related papers  (2024-06-25T05:45:12Z)
• VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections [35.133698935322634]
  Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. We identify and characterise the important components needed for effective model convergence using gradient descent. This result leads us to a cheap and memory-efficient algorithm for both fine-tuning and pre-training LLMs.
  arXiv  Detail & Related papers  (2024-05-28T09:23:14Z)
• GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection [133.45193150403537]
  Training Large Language Models (LLMs) presents significant memory challenges due to the growing size of weights and GPU states. In this work, we propose Gradient Low-Rank Projection (GaLore) as a memory-efficient training strategy. Our 8-bit GaLore further reduces memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline.
  arXiv  Detail & Related papers  (2024-03-06T07:29:57Z)
• AdaLomo: Low-memory Optimization with Adaptive Learning Rate [59.64965955386855]
  We introduce low-memory optimization with adaptive learning rate (AdaLomo) for large language models. AdaLomo results on par with AdamW, while significantly reducing memory requirements, thereby lowering the hardware barrier to training large language models.
  arXiv  Detail & Related papers  (2023-10-16T09:04:28Z)
• Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model [89.8764435351222]
  We propose a new family of unbiased estimators called WTA-CRS, for matrix production with reduced variance. Our work provides both theoretical and experimental evidence that, in the context of tuning transformers, our proposed estimators exhibit lower variance compared to existing ones.
  arXiv  Detail & Related papers  (2023-05-24T15:52:08Z)
• On-Device Training Under 256KB Memory [62.95579393237751]
  We propose an algorithm-system co-design framework to make on-device training possible with only 256KB of memory. Our framework is the first solution to enable tiny on-device training of convolutional neural networks under 256KB and 1MB Flash.
  arXiv  Detail & Related papers  (2022-06-30T17:59:08Z)
• Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models [0.0]
  We analyse the shortest possible training time for different configurations of distributed training. We introduce two new methods, textitlayered gradient accumulation and textitmodular pipeline parallelism, which together cut the shortest training time by half.
  arXiv  Detail & Related papers  (2021-06-04T19:21:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.