The generative AI boom is compute-bound. It has the unique property that adding more compute directly results in a better product. Usually, R&D investment is more directly tied to how valuable a product was, and that relationship is markedly sublinear. But this is not currently so with artificial intelligence and, as a result, a predominant factor driving the industry today is simply the cost of training and inference. 

While we don’t know the true numbers, we’ve heard from reputable sources that the supply of compute is so constrained, demand outstrips it by a factor of 10(!) So we think it’s fair to say that, right now, access to compute resources — at the lowest total cost — has become a determining factor for the success of AI companies.

In fact, we’ve seen many companies spend more than 80% of their total capital raised on compute resources!

In this post, we try to break down the cost factors for an AI company. The absolute numbers will of course change over time, but we don’t see immediate relief from AI companies being bound by their access to compute resources. So, hopefully, this is a helpful framework for thinking through the landscape. Read More

We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4× more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher. Read More

When it comes to training large AI models, people will think about using thousands of GPUs, expensive training costs, and only a few tech giants can afford them. While AI users, like researchers from startups or universities, could do nothing but get overwhelmed by news about large models~

Now, a PC with only one GPU can train GPT with up to 18 billion parameters, and a laptop can also train a model with more than one billion parameters. Compared with the existing mainstream solutions, the parameter capacity can be increased by more than ten times!

Such a significant improvement comes from Colossal-AI, which is an efficient training system for general large AI models. Best of all, it’s completely open-sourced and requires only minimal modifications to allow existing deep learning projects to be trained with much larger models on a single consumer-grade graphics card, allowing everyone to train large AI models at home! In particular, it makes downstream tasks and application deployments such as large AI model fine-tuning and inference much easier! Read More

Recent developments in large-scale machine learning suggest that by scaling up data, model size and training time properly, one might observe that improvements in pre-training would transfer favorably to most downstream tasks. In this work, we systematically study this phenomena and establish that, as we increase the upstream accuracy, the performance of downstream tasks saturates. In particular, we investigate more than 4800 experiments on Vision Transformers, MLP-Mixers and ResNets with number of parameters ranging from ten million to ten billion, trained on the largest scale of available image data (JFT, ImageNet21K) and evaluated on more than 20 downstream image recognition tasks. We propose a model for downstream performance that reflects the saturation phenomena and captures the nonlinear relationship in performance of upstream and downstream tasks. Delving deeper to understand the reasons that give rise to these phenomena, we show that the saturation behavior we observe is closely related to the way that representations evolve through the layers of the models. We showcase an even more extreme scenario where performance on upstream and downstream are at odds with each other. That is, to have a better downstream performance, we need to hurt upstream accuracy. Read More

#performance

Much of an ML model’s learning results depend on the model’s learning rate. The learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Since it influences the extent to which newly acquired information overrides old information, it metaphorically represents the speed at which a machine learning model “learns”.

The importance of Learning Rate can’t be underestimated. That is why there is a lot of research towards both discovering new learning rate schedules (how LR should change over time) and comparing existing ones. Researchers at Google AI, Tel Aviv University, and Princeton collaborated together to write Disentangling Adaptive Gradient Methods from Learning Rates. The paper looks at “how adaptive gradient methods interact with the learning rate schedule.” In this article, I will share some interesting takeaways from the paper that might help you in your ML journeys.  Read More

Stateful optimizers maintain gradient statistics over time, e.g., the exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can be used to accelerate optimization compared to plain stochastic gradient descent, but uses memory that might otherwise be allocated to model parameters, thereby limiting the maximum size of models trained in practice. In this paper, we develop the first optimizers that use 8-bit statistics while maintaining the performance levels of using 32-bit optimizer states. To overcome the resulting computational, quantization, and stability challenges, we develop block-wise dynamic quantization. Block-wise quantization divides input tensors into smaller blocks that are independently quantized. Each block is processed in parallel across cores, yielding faster optimization and high precision quantization. To maintain stability and performance, we combine block-wise quantization with two additional changes: (1) dynamic quantization, a form of non-linear optimization that is precise for both large and small magnitude values, and (2) a stable imbedding layer to reduce gradient variance that comes from the highly non-uniform distribution of input tokens in language models. As a result, our 8-bit optimizers maintain 32-bit performance with a small fraction of the memory footprint on a range of tasks, including 1.5B parameter language modeling, GLUE finetuning, ImageNet classification, WMT’14 machine translation, MoCo v2 contrastive ImageNet retraining+finetuning, and RoBERTa pretraining, without changes to the original optimizer hyperparameters. We open-source1 our 8-bit optimizers as a drop-in replacement that only requires a two-line code change. Read More

It seems to be a minority view nowadays to believe in Moore’s Law, the routine doubling of transistor density roughly every couple of years, or even the much gentler claim, that There’s Plenty [more] Room at the Bottom. There’s even a quip for it: the number of people predicting the death of Moore’s law doubles every two years. This is not merely a populist view by the uninformed.

…Besides mere physical inevitability, improvements to transistor density are taking an economic toll. Building the fabs that manufacture transistors is becoming very expensive, as high as $20 billion each, and TSMC expects to spend $100 billion just over the three years to expand capacity. This cost increases with each cutting-edge node.

This bleak industry view contrasts with the massively increasing demands of scale from AI, that has become a center of attention, in large part due to OpenAI’s attention on the question, and their successful results with their various GPT-derived models. There, too, the economic factor exacerbates the divide; models around GPT-3’s size are the domain of only a few eager companies, and whereas before there was an opportunity to reap quick advances from scaling single- or few-machine models to datacenter scale, now all compute advances require new hardware of some kind, whether better computer architectures or bigger (pricier) data centers. Read More

Although hierarchical structures are popular in recent vision transformers, they require sophisticated designs and massive datasets to work well. In this work, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical manner. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture with minor code changes upon the original vision transformer and obtains improved performance compared to existing methods. Our empirical results show that the proposed method NesT converges faster and requires much less training data to achieve good generalization. For example, a NesT with 68M parameters trained on ImageNet for 100/300 epochs achieves 82.3%/83.8% accuracy evaluated on 224 × 224 image size, outperforming previous methods with up to 57% parameter reduction. Training a NesT with 6M parameters from scratch on CIFAR10 achieves 96% accuracy using a single GPU, setting a new state of the art for vision transformers. Beyond image classification, we extend the key idea to image generation and show NesT leads to a strong decoder that is 8×faster than previous transformer based generators. Furthermore, we also propose a novel method for visually interpreting the learned model. Read More