FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness
- URL: http://arxiv.org/abs/2412.03317v2
- Date: Sun, 19 Jan 2025 08:31:39 GMT
- Title: FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness
- Authors: Vincent Abbott, Gioele Zardini,
- Abstract summary: Methods like FlashAttention have achieved a x6 performance improvement over native PyTorch by avoiding unnecessary data transfers.
This paper extends Neural Circuit Diagrams for deep learning models to consider resource usage and the distribution of tasks across a GPU hierarchy.
We develop a methodology for representing intermediate-level pseudocode with diagrams, allowing hardware-aware algorithms to be derived step-by-step.
- Score: 0.0
- License:
- Abstract: Optimizing deep learning algorithms currently requires slow, manual derivation, potentially leaving much performance untapped. Methods like FlashAttention have achieved a x6 performance improvement over native PyTorch by avoiding unnecessary data transfers, but required three iterations over three years to be developed. Automated compiled methods have consistently lagged behind. This paper extends Neural Circuit Diagrams for deep learning models to consider resource usage and the distribution of tasks across a GPU hierarchy. We show how diagrams can use simple relabellings to derive high-level streaming and tiling optimization strategies along with performance models. We show how this high-level performance model allows the effects of quantization and multi-level GPU hierarchies to be readily considered. We develop a methodology for representing intermediate-level pseudocode with diagrams, allowing hardware-aware algorithms to be derived step-by-step. Finally, we show how our methodology can be used to better understand existing techniques like FlashAttention. This work uses a theoretical framework to link assumptions about GPU behaviour to claims about performance. We aim to lay the groundwork for a scientific approach to GPU optimization where experiments can address clear hypotheses rather than post-hoc rationalizations.
Related papers
- Numerical Pruning for Efficient Autoregressive Models [87.56342118369123]
This paper focuses on compressing decoder-only transformer-based autoregressive models through structural weight pruning.
Specifically, we propose a training-free pruning method that calculates a numerical score with Newton's method for the Attention and modules, respectively.
To verify the effectiveness of our method, we provide both theoretical support and extensive experiments.
arXiv Detail & Related papers (2024-12-17T01:09:23Z) - Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics [10.22111332588471]
We propose a novel deep symbolic optimization learning framework that combines their advantages.
Dso4NS guides the search for mathematical expressions within the high-dimensional discrete symbolic space and then incorporates the highest-performing mathematical expressions into a solver.
Experiments demonstrate the effectiveness of Dso4NS in learning high-quality expressions, outperforming existing approaches on a CPU machine.
arXiv Detail & Related papers (2024-06-14T06:02:14Z) - Using Graph Neural Networks to model the performance of Deep Neural
Networks [2.1151356984322307]
We develop a novel performance model that adopts a graph representation.
Experimental evaluation shows a 7:75x and 12x reduction in prediction error compared to the Halide and TVM models, respectively.
arXiv Detail & Related papers (2021-08-27T20:20:17Z) - Top-KAST: Top-K Always Sparse Training [50.05611544535801]
We propose Top-KAST, a method that preserves constant sparsity throughout training.
We show that it performs comparably to or better than previous works when training models on the established ImageNet benchmark.
In addition to our ImageNet results, we also demonstrate our approach in the domain of language modeling.
arXiv Detail & Related papers (2021-06-07T11:13:05Z) - Faster Meta Update Strategy for Noise-Robust Deep Learning [62.08964100618873]
We introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient with a faster layer-wise approximation.
We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance.
arXiv Detail & Related papers (2021-04-30T16:19:07Z) - Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
We introduce a fast optimization-based meta-learning method for few-shot classification.
Our strategy enables important aspects of the base learner objective to be learned during meta-training.
We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
arXiv Detail & Related papers (2020-10-01T15:59:31Z) - ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators
using Reinforcement Learning [5.251940442946459]
We propose an autonomous strategy called ConfuciuX to find optimized HW resource assignments for a given model and dataflow style.
It converges to the optimized hardware configuration 4.7 to 24 times faster than alternate techniques.
arXiv Detail & Related papers (2020-09-04T04:59:26Z) - Kernel methods through the roof: handling billions of points efficiently [94.31450736250918]
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems.
Recent advances have shown the benefits of a number of algorithmic ideas, for example combining optimization, numerical linear algebra and random projections.
Here, we push these efforts further to develop and test a solver that takes full advantage of GPU hardware.
arXiv Detail & Related papers (2020-06-18T08:16:25Z) - Heterogeneous CPU+GPU Stochastic Gradient Descent Algorithms [1.3249453757295084]
We study training algorithms for deep learning on heterogeneous CPU+GPU architectures.
Our two-fold objective -- maximize convergence rate and resource utilization simultaneously -- makes the problem challenging.
We show that the implementation of these algorithms achieves both faster convergence and higher resource utilization than on several real datasets.
arXiv Detail & Related papers (2020-04-19T05:21:20Z) - Optimizing Streaming Parallelism on Heterogeneous Many-Core
Architectures: A Machine Learning Based Approach [16.702537371391053]
This article presents an automatic approach to derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures.
Our approach employs a performance model to estimate the resulting performance of the target application under a given resource partition and task granularity configuration.
Compared to the single-stream version, our approach achieves a 1.6x and 1.1x speedup on the XeonPhi and the GPU platform, respectively.
arXiv Detail & Related papers (2020-03-05T21:18:21Z) - Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
We introduce a comprehensive benchmark for local features and robust estimation algorithms.
Our pipeline's modular structure allows easy integration, configuration, and combination of different methods.
We show that with proper settings, classical solutions may still outperform the perceived state of the art.
arXiv Detail & Related papers (2020-03-03T15:20:57Z)
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.