The Case for Co-Designing Model Architectures with Hardware

Abstract: While GPUs are responsible for training the vast majority of state-of-the-art deep learning models, the implications of their architecture are often overlooked when designing new deep learning (DL) models. As a consequence, modifying a DL model to be more amenable to the target hardware can significantly improve the runtime performance of DL training and inference. In this paper, we provide a set of guidelines for users to maximize the runtime performance of their transformer models. These guidelines have been created by carefully considering the impact of various model hyperparameters controlling model shape on the efficiency of the underlying computation kernels executed on the GPU. We find the throughput of models with efficient model shapes is up to 39\% higher while preserving accuracy compared to models with a similar number of parameters but with unoptimized shapes.

• The paper demonstrates that optimizing transformer architecture with GPU-aware hyperparameters can boost throughput by up to 39%.
• The paper shows that aligning matrix dimensions to multiples of 64 FP16 elements significantly enhances Tensor Core performance.
• The paper advocates a co-design approach that customizes model parameters to hardware specs, reducing training time and resource waste.

The Case for Co-Designing Model Architectures with Hardware

This paper addresses an often-overlooked aspect of transformer-based deep learning (DL) models: the co-design of model architectures alongside hardware specifications. As transformer models continue to dominate fields such as natural language processing, optimizing their training and inference efficiency is paramount. This paper presents guidelines that promise to enhance the performance of transformers on GPUs by refining model hyperparameters with an understanding of the underlying GPU architecture.

Key Findings

The authors provide compelling evidence that adhering to certain architectural guidelines can lead to significant improvements in GPU throughput without compromising model accuracy. For instance, models optimized for efficient matrix shapes showcased up to a 39% increase in throughput. Such optimization is especially crucial as even minor inefficiencies in hardware utilization can lead to substantial amounts of wasted resources at scale.

Transformer models rely heavily on Operations such as General Matrix Multiplications (GEMMs). The research highlights that ensuring the dimensions of matrices are favorable to Tensor Core operations can considerably boost performance. Specifically, matrix dimensions should align as multiples of 64 FP16 elements for most efficiency on NVIDIA GPUs. Further, understanding and addressing tile and wave quantization effects are emphasized as strategies to minimize compute wastage.

Implications and Future Directions

The paper highlights the necessity of marrying model and hardware design processes, arguing that model dimensions should be customized with hardware specifications at the forefront. This approach could redefine best practices in DL model development, especially as hardware continues to evolve rapidly. Notably, it underlines the importance of considering the architecture of GPUs, primarily streaming multiprocessors (SMs) and Tensor Cores.

From a theoretical standpoint, the work suggests a shift towards a more hardware-conscious model architecture design paradigm. Practically, these guidelines facilitate more efficient usage of computational resources over the model's lifecycle, minimizing training times and inference costs.

Future research could extend this work by evaluating different hardware types, moving beyond GPUs to more exotic accelerators, or considering the implications of varied parallelism strategies. Furthermore, exploring co-design strategies across different DL architectures, such as encoder-decoder models, could offer additional insights.

Conclusion

This study provides a substantial contribution to the DL community by systematically exploring the dependency between model architecture and hardware efficiency. By designing transformers with GPU specifics in mind, researchers and practitioners can significantly enhance the efficiency of model training and deployment. This work serves as a valuable reference for future endeavors in optimizing deep learning operations for cutting-edge hardware.