The authors highlight that PyTorch allows for simplicity, ease of use, flexibility, and with torch.compile, performance as well. They encourage users to copy-paste, fork, and modify the code in the repo, rather than providing another library or framework for people to import. The post concludes by acknowledging the open-source community for their support in scaling LLMs.
Key takeaways:
- The PyTorch team has developed a way to accelerate generative AI models using native PyTorch, achieving speeds almost 10x faster than baseline with no loss of accuracy.
- They have introduced a range of optimizations including Torch.compile, GPU quantization, Speculative Decoding, and Tensor Parallelism, all of which can be implemented in less than 1000 lines of native PyTorch code.
- They have also explored techniques such as int8 and int4 quantization to reduce the size of the weights, and speculative decoding to break the strict serial dependency in autoregressive generation.
- By combining all these techniques, they have achieved state-of-the-art performance numbers, serving Llama-7B at 241 tokens/s and Llama-70B at 80 tokens/s.