oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes

In this paper, we introduce the range of oBERTa language models, an easy-to-use set of language models which allows Natural Language Processing (NLP) practitioners to obtain between 3.8 and 24.3 times faster models without expertise in model compression. Specifically, oBERTa extends existing work on pruning, knowledge distillation, and quantization and leverages frozen embeddings improves distillation and model initialization to deliver higher accuracy on a broad range of transfer tasks. In generating oBERTa, we explore how the highly optimized RoBERTa differs from the BERT for pruning during pre-training and finetuning. We find it less amenable to compression during fine-tuning. We explore the use of oBERTa on seven representative NLP tasks and find that the improved compression techniques allow a pruned oBERTa model to match the performance of BERTbase and exceed the performance of Prune OFA Large on the SQUAD V1.1 Question Answering dataset, despite being 8x and 2x, respectively faster in inference. We release our code, training regimes, and associated model for broad usage to encourage usage and experimentation

Sparse*BERT: Sparse Models are Robust

Large Language Models have become the core architecture upon which most modern natural language processing (NLP) systems build. These models can consistently deliver impressive accuracy and robustness across tasks and domains, but their high computational overhead can make inference difficult and expensive. To make the usage of these models less costly recent work has explored leveraging structured and unstructured pruning, quantization, and distillation as ways to improve inference speed and decrease size. This paper studies how models pruned using Gradual Unstructured Magnitude Pruning can transfer between domains and tasks. Our experimentation shows that models that are pruned during pretraining using general domain masked language models can transfer to novel domains and tasks without extensive hyperparameter exploration or specialized approaches. We demonstrate that our general sparse model Sparse*BERT can become SparseBioBERT simply by pretraining the compressed architecture on unstructured biomedical text. Moreover, we show that SparseBioBERT can match the quality of BioBERT with only 10\% of the parameters.

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

Pre-trained Transformer-based language models have become a key building block for natural language processing (NLP) tasks. While these models are extremely accurate, they can be too large and computationally intensive to run on standard deployments. A variety of compression methods, including distillation, quantization, structured and unstructured pruning are known to be applicable to decrease model size and increase inference speed. In this context, this paper's contributions are two-fold. We begin with an in-depth study of the accuracy-compression trade-off for unstructured weight pruning in the context of BERT models, and introduce Optimal BERT Surgeon (O-BERT-S), an efficient and accurate weight pruning method based on approximate second-order information, which we show to yield state-of-the-art results in terms of the compression/accuracy trade-off. Specifically, Optimal BERT Surgeon extends existing work on second-order pruning by allowing for pruning blocks of weights, and by being applicable at BERT scale. Second, we investigate the impact of this pruning method when compounding compression approaches for Transformer-based models, which allows us to combine state-of-the-art structured and unstructured pruning together with quantization, in order to obtain highly compressed, but accurate models. The resulting compression framework is powerful, yet general and efficient: we apply it to both the fine-tuning and pre-training stages of language tasks, to obtain state-of-the-art results on the accuracy-compression trade-off with relatively simple compression recipes. For example, we obtain 10x model size compression with < 1% relative drop in accuracy to the dense BERT-base, 10x end-to-end CPU-inference speedup with < 2% relative drop in accuracy, and 29x inference speedups with < 7.5% relative accuracy drop.

How Well Do Sparse Imagenet Models Transfer?

Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream," specialized datasets. Generally, it is understood that more accurate models on the "upstream" dataset will provide better transfer accuracy "downstream". In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned - that is, compressed by sparsifiying their connections. Specifically, we consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, re-growth and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods.