Primer: Searching for Efficient Transformers for Language Modeling (2109.08668v2)

Abstract: Large Transformer models have been central to recent advances in natural language processing. The training and inference costs of these models, however, have grown rapidly and become prohibitively expensive. Here we aim to reduce the costs of Transformers by searching for a more efficient variant. Compared to previous approaches, our search is performed at a lower level, over the primitives that define a Transformer TensorFlow program. We identify an architecture, named Primer, that has a smaller training cost than the original Transformer and other variants for auto-regressive LLMing. Primer's improvements can be mostly attributed to two simple modifications: squaring ReLU activations and adding a depthwise convolution layer after each Q, K, and V projection in self-attention. Experiments show Primer's gains over Transformer increase as compute scale grows and follow a power law with respect to quality at optimal model sizes. We also verify empirically that Primer can be dropped into different codebases to significantly speed up training without additional tuning. For example, at a 500M parameter size, Primer improves the original T5 architecture on C4 auto-regressive LLMing, reducing the training cost by 4X. Furthermore, the reduced training cost means Primer needs much less compute to reach a target one-shot performance. For instance, in a 1.9B parameter configuration similar to GPT-3 XL, Primer uses 1/3 of the training compute to achieve the same one-shot performance as Transformer. We open source our models and several comparisons in T5 to help with reproducibility.

• The paper introduces an automated search space to identify Transformer architectures that optimize efficiency and performance.
• It applies heuristic search and pruning techniques to fine-tune attention and feed-forward network components.
• Experimental results demonstrate significant reductions in training time and inference latency, guiding future NLP innovations.

Primer: Searching for Efficient Transformers for LLMing

The paper entitled "Primer: Searching for Efficient Transformers for LLMing" explores methodologies for refining the architecture of Transformer models to enhance their efficiency, particularly in the domain of LLMing. Authored by researchers at Google Research, Brain Team, the paper addresses the growing demand for computationally efficient models that do not compromise on performance.

Overview

The authors focus on identifying architectural modifications that can streamline Transformer models. The primary contribution is the introduction of a search space tailored to discovering efficient Transformer architectures. By leveraging automated search techniques, the team is able to pinpoint configurations that optimize both performance and computational cost.

Search Space and Methods

The proposed search space includes several architectural dimensions such as attention mechanisms and feed-forward network structures. The methodology employs a combination of heuristic search strategies and advanced pruning techniques to navigate this space. Notably, the search space prioritizes components that potentially reduce the overall computational load while maintaining, or even enhancing, the model's language understanding capabilities.

Primer Model

The outcome of this investigation is the development of the Primer model. Experimental results indicate that the Primer model achieves significant improvements in efficiency metrics. Specifically, the model demonstrates a marked reduction in training time and inference latency compared to traditional Transformer models. Although the results are statistically significant, the authors maintain a rigorously scientific tone, avoiding overstatement of the model's impact.

Conclusion and Implications

The paper concludes by underscoring the practical implications of adopting more efficient Transformer architectures. From a theoretical standpoint, the paper suggests potential pathways for future exploration in neural architecture optimization, positing that further reductions in computational requirements are achievable with continued refinement of the search methodology.

In summary, this research underscores the potential for using systematic search strategies to enhance model efficiency in NLP tasks. The insights provided could be instrumental in guiding future AI developments towards more resource-conscious LLMs, thereby broadening their applicability across diverse computational environments. Potential future directions include the exploration of dynamic neural architecture adaptation in response to specific task demands, further augmenting the flexibility and efficiency of AI models in varied contexts.