Transformer based architectures have become de-facto models used for a range of Natural Language Processing tasks. In particular, the BERT based models achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However, BERT based models have a prohibitive memory footprint and latency. As a result, deploying BERT based models in resource constrained environments has become a challenging task. In this work, we perform an extensive analysis of fine-tuned BERT models using second order Hessian information, and we use our results to propose a novel method for quantizing BERT models to ultra low precision. In particular, we propose a new group-wise quantization scheme, and we use Hessian-based mix-precision method to compress the model further. We extensively test our proposed method on BERT downstream tasks of SST-2, MNLI, CoNLL-03, and SQuAD. We can achieve comparable performance to baseline with at most 2.3% performance degradation, even with ultra-low precision quantization down to 2 bits, corresponding up to 13× compression of the model parameters, and up to 4× compression of the embedding table as well as activations. Among all tasks, we observed the highest performance loss for BERT fine-tuned on SQuAD. By probing into the Hessian based analysis as well as visualization, we show that this is related to the fact that current training/fine-tuning strategy of BERT does not converge for SQuAD.

/pdf/q-bert-hessian-based-ultra-low-precision-quantization-of-3ar40x24be.pdf

Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT

This chapter provides approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of current methods. Over the past decade, people have observed significant improvements in the accuracy of Neural Networks (NNs) for a wide range of problems, often achieved by highly over-parameterized models. Achieving efficient, real-time NNs with optimal accuracy requires rethinking the design, training, and deployment of NN models. Model distillation involves training a large model and then using it as a teacher to train a more compact model. Loosely related to NN quantization is work in neuroscience that suggests that the human brain stores information in a discrete/quantized form, rather than in a continuous form. Gray and Neuhoff have written a very nice survey of the history of quantization up to 1998. 

A Survey of Quantization Methods for Efficient Neural Network Inference

Recently, pre-trained Transformer [1] based language models such as BERT [2] and GPT [3], have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large amount of parameters. The emergence of even larger and more accurate models such as GPT2 [4] and Megatron11https://github.com/NVIDIA/Megatron-LM, suggest a trend of large pre-trained Transformer models. However, using these large models in production environments is a complex task requiring a large amount of compute, memory and power resources. In this work we show how to perform quantization-aware training during the fine-tuning phase of BERT in order to compress BERT by 4x with minimal accuracy loss. Furthermore, the produced quantized model can accelerate inference speed if it is optimized for 8bit Integer supporting hardware.

Q8BERT: Quantized 8Bit BERT

Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of quantization parameters and evaluate their choices on a wide range of neural network models for different application domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are more difficult to quantize, such as MobileNets and BERT-large.

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.

Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive for many edge processors, and it has been a challenge to deploy these models for edge applications and devices that have resource constraints. While quantization can be a viable solution to this, previous work on quantizing Transformer based models uses floating-point arithmetic during inference, thus limiting model deployment on many edge processors. In this work, we propose a novel integer-only quantization scheme for Transformer based models that quantizes the entire inference process. In particular, we demonstrate how to approximate nonlinear operations in Transformer architectures, e.g., GELU, Softmax, and Layer Normalization, with lightweight integer computations. We use those approximations in our method, I-BERT, with an end-to-end integer-only inference, and without any floating point calculation. We test our approach on GLUE downstream tasks using RoBERTaBase and RoBERTa-Large. For both cases, with an 8bit integer-only quantization scheme, I-BERT achieves similar accuracy as compared to the full-precision baseline.

/pdf/i-bert-integer-only-bert-quantization-32celt4zsl.pdf

I-BERT: Integer-only BERT Quantization

In this work, we quantize a trained Transformer machine language translation model leveraging INT8/VNNI instructions in the latest Intel$^\circledR$ Xeon$^\circledR$ Cascade Lake processors to improve inference performance while maintaining less than 0.5$\%$ drop in accuracy. To the best of our knowledge, this is the first attempt in the industry to quantize the Transformer model. This has high impact as it clearly demonstrates the various complexities of quantizing the language translation model. We present novel quantization techniques directly in TensorFlow to opportunistically replace 32-bit floating point (FP32) computations with 8-bit integers (INT8) and transform the FP32 computational graph. We also present a bin-packing parallel batching technique to maximize CPU utilization. Overall, our optimizations with INT8/VNNI deliver 1.5X improvement over the best FP32 performance. Furthermore, it reveals the opportunities and challenges to boost performance of quantized deep learning inference and establishes best practices to run inference with high efficiency on Intel CPUs.

Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model.

Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources. In this paper, we propose a method (BeamCLIP) that can effectively transfer the representations of a large pre-trained multimodal model (CLIP-ViT) into a small target model (e.g., ResNet-18). For unsupervised transfer, we introduce cross-modal similarity matching (CSM) that enables a student model to learn the representations of a teacher model by matching the relative similarity distribution across text prompt embeddings. To better encode the text prompts, we design context-based prompt augmentation (CPA) that can alleviate the lexical ambiguity of input text prompts. Our experiments show that unsupervised representation transfer of a pre-trained vision-language model enables a small ResNet-18 to achieve a better ImageNet-1K top-1 linear probe accuracy (66.2%) than vision-only self-supervised learning (SSL) methods (e.g., SimCLR: 51.8%, SwAV: 63.7%), while closing the gap with supervised learning (69.8%).

Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

Existing approaches to train neural networks that use large images require to either crop or down-sample data during pre-processing, use small batch sizes, or split the model across devices mainly due to the prohibitively limited memory capacity available on GPUs and emerging accelerators. These techniques often lead to longer time to convergence or time to train (TTT), and in some cases, lower model accuracy. CPUs, on the other hand, can leverage significant amounts of memory. While much work has been done on parallelizing neural network training on multiple CPUs, little attention has been given to tune neural network training with large images on CPUs. In this work, we train a multi-scale convolutional neural network (M-CNN) to classify large biomedical images for high content screening in one hour. The ability to leverage large memory capacity on CPUs enables us to scale to larger batch sizes without having to crop or down-sample the input images. In conjunction with large batch sizes, we find a generalized methodology of linearly scaling of learning rate and train M-CNN to state-of-the-art (SOTA) accuracy of 99% within one hour. We achieve fast time to convergence using 128 two socket Intel Xeon 6148 processor nodes with 192GB DDR4 memory connected with 100Gbps Intel Omnipath architecture.

Training Multiscale-CNN for Large Microscopy Image Classification in One Hour

Existing approaches to train neural networks that use large images require to either crop or down-sample data during pre-processing, use small batch sizes, or split the model across devices mainly due to the prohibitively limited memory capacity available on GPUs and emerging accelerators. These techniques often lead to longer time to convergence or time to train (TTT), and in some cases, lower model accuracy. CPUs, on the other hand, can leverage significant amounts of memory. While much work has been done on parallelizing neural network training on multiple CPUs, little attention has been given to tune neural network training with large images on CPUs. In this work, we train a multi-scale convolutional neural network (M-CNN) to classify large biomedical images for high content screening in one hour. The ability to leverage large memory capacity on CPUs enables us to scale to larger batch sizes without having to crop or down-sample the input images. In conjunction with large batch sizes, we find a generalized methodology of linearly scaling of learning rate and train M-CNN to state-of-the-art (SOTA) accuracy of 99% within one hour. We achieve fast time to convergence using 128 two socket Intel$\circledR $ Xeon$\circledR $ 6148 processor nodes with 192 GB DDR4 memory connected with 100 Gbps Intel$\circledR $ Omnipath architecture.

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Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model.

Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

Training Multiscale-CNN for Large Microscopy Image Classification in One Hour

Training Multiscale-CNN for Large Microscopy Image Classification in One Hour