LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
arXiv preprint arXiv:2102.02888 , year=
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DeepSpeed-Ulysses keeps communication volume constant for sequence-parallel attention when sequence length and device count scale together, delivering 2.5x faster training on 4x longer sequences than prior SOTA.
SignSGD with pre-sign dithering and a calibrated hybrid switch to SGD achieves 92.18% accuracy on CIFAR-10 with ResNet-18, outperforming pure SGD and SignSGD, plus better results than Adam on CIFAR-100.
citing papers explorer
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LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
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DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
DeepSpeed-Ulysses keeps communication volume constant for sequence-parallel attention when sequence length and device count scale together, delivering 2.5x faster training on 4x longer sequences than prior SOTA.
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Enhancing SignSGD: Small-Batch Convergence Analysis and a Hybrid Switching Strategy
SignSGD with pre-sign dithering and a calibrated hybrid switch to SGD achieves 92.18% accuracy on CIFAR-10 with ResNet-18, outperforming pure SGD and SignSGD, plus better results than Adam on CIFAR-100.