A transformer layer is built from familiar operations: linear projections, attention, normalization, activation functions, and residual additions. A direct implementation may launch a separate accelerator kernel for every operation and write every intermediate tensor to device memory. The mathematics is correct, but the execution can spend more time moving values and launching kernels than performing useful arithmetic.
Fast attention kernels and operator fusion reorganize this work. They keep intermediate values close to the arithmetic units, combine operations, and reuse compiled execution plans. These optimizations normally leave the model weights and mathematical function unchanged, although small numerical differences can arise from operation order and precision.
Fast Attention Kernels and Operator Fusion.Photo by Chris Boland. Some rights reserved.
Note: Starting with this post, you will run the code that depends on vLLM. You can install vLLM with python -m pip install vllm. This environment should have a CUDA-enabled PyTorch build that matches your driver so that torch.compile() can use CUDA graphs. Also note that running the code may download model checkpoints and other data. Be sure you have enough disk space.
Source: Machine Learning Mastery — Read the full article on the original site ↗