Скачать или смотреть Lec 14 | Efficient LLMs: Part 04

In thie lecture, we cover two more game-changing techniques! First, we explore Pipeline Parallelism, a strategy where different layers of a model are placed on a pipeline of GPUs. We'll discuss scheduling methods like 1F1B (One-Forward-One-Backward) that minimize the idle "bubble" time and make this approach highly effective.

Then, we shift from system-level parallelism to a brilliant algorithmic optimization: Flash Attention. To understand its magic, we'll first cover the basics of the GPU memory hierarchy. You'll then see how Flash Attention reorders the computation to be IO-aware, avoiding slow memory transfers and leading to dramatic speedups and memory savings for the core component of the Transformer. ✨

Resources 📚
For slides and other course materials, please visit the Course Website (lcs2.in/llm2501)

Suggested Readings 📖
PipeDream: Fast and Efficient Pipeline Parallel DNN Training (https://arxiv.org/pdf/1806.03377)
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (https://arxiv.org/abs/2205.14135v2)
FlashAttention Blog Post (  / eli5-flash-attention  )

#PipelineParallelism #FlashAttention #LLM #GPU #DeepLearning #DistributedTraining #AI