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Widely popular transformer-based NLP models such as BERT and Turing-NLG haveenormous capacity trending to billions of parameters. Current execution methodsdemand brute-force resources such as HBM devices and high speedinterconnectivity for data parallelism. In this paper, we introduce a newrelay-style execution technique called L2L (layer-to-layer) where at any givenmoment, the device memory is primarily populated only with the executinglayer(s)'s footprint. The model resides in the DRAM memory attached to either aCPU or an FPGA as an entity we call eager param-server (EPS). To overcome thebandwidth issues of shuttling parameters to and from EPS, the model is executeda layer at a time across many micro-batches instead of the conventional methodof minibatches over whole model. L2L is implemented using 16GB V100 devices forBERT-Large running it with a device batch size of up to 256. Our results show45% reduction in memory and 40% increase in the throughput compared to thestate-of-the-art baseline. L2L is also able to fit models up to 50 Billionparameters on a machine with a single 16GB V100 and 512GB CPU memory andwithout requiring any model partitioning. L2L scales to arbitrary depthallowing researchers to develop on affordable devices which is a big steptoward democratizing AI. By running the optimizer in the host EPS, we show anew form of mixed precision for faster throughput and convergence. In addition,the EPS enables dynamic neural architecture approaches by varying layers acrossiterations. Finally, we also propose and demonstrate a constant memoryvariation of L2L and we propose future enhancements. This work has beenperformed on GPUs first, but also targeted towards all high TFLOPS/Wattaccelerators.

Training Large Neural Networks with Constant Memory using a New Execution Algorithm