ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers
Quantization techniques are pivotal in reducing the memory and computational demands of deep neural network inference. Existing solutions, such as ZeroQuant, offer dynamic quantization for models like BERT and GPT but overlook crucial memory-bounded operators and the complexities of per-token quantization. Addressing these gaps, we present a novel, fully hardware-enhanced robust optimized post-training W8A8 quantization framework, ZeroQuant-HERO. This framework uniquely integrates both memory bandwidth and compute-intensive operators, aiming for optimal hardware performance. Additionally, it offers flexibility by allowing specific INT8 modules to switch to FP16/BF16 mode, enhancing accuracy.
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Methods
Adam •
Attention Dropout •
BERT •
BPE •
Cosine Annealing •
Dense Connections •
Discriminative Fine-Tuning •
Dropout •
GELU •
GPT •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Weight Decay •
WordPiece
