This AI Paper Demonstrates An End-to-End Training Flow on An Large Language Model LLM-13 Billion GPT-Using Sparsity And Dataflow

Abstraction: End-to-end sparse LLM training on SambaNova RDU hardware

Key points:

• SambaNova Systems paper demonstrates sparsity integrated into the full training cycle of a 13B-parameter GPT model
• Sparse training on SambaNova RDU achieves speedups over NVIDIA A100 while maintaining accuracy comparable to dense baseline
• Accuracy depends on many factors: structured vs. unstructured vs. semi-structured sparsity, percentage of weight/activation sparsity, training schedule
• Conventional hardware (A100, TPU, CS-2, Graphcore IPU) struggles to exploit sparsity efficiently due to fixed dense compute paradigms
• Sparsity mimics neuron connection patterns in the human brain to reduce compute intensity
• Result: equivalent zero-shot and few-shot accuracy metrics to the dense 13B GPT baseline with significant hardware efficiency gains

Connections: Sambanova · Nvidia · Model Sparsity · Large Language Models · ML Hardware · LLM Training

Source: https://www.marktechpost.com/2023/04/16/this-ai-paper-demonstrates-an-end-to-end-training-flow-on-an-large-language-model-llm-13-billion-gpt-using-sparsity-and-dataflow/