In this blog post, we talk about all the upgrades in-depth, and how they become part of the transformers toolkit so other models (current and future) can benefit from them. Providing clean implementations of new methods in transformers also allows the community to quickly understand and adopt them. Frameworks such as MLX, llama.cpp or vLLM can use the transformers code as a reference to build their own implementations.

Best part: Most of these features should work across all major models within transformers!

A kernel is a specialized, compact program that runs on accelerators to execute tasks like matrix multiplications, activations, or normalizations. In eager PyTorch, operations trigger individual kernels sequentially, which is straightforward but can incur extra memory transfers and launch overheads. PyTorch 2.0's torch.compile with backends like TorchInductor addresses this by automatically fusing and optimizing kernels, delivering 2–10× performance gains.

In addition, the community has created custom kernels for frequent combinations of operations, not just individual PyTorch ops like matmul. For example, Flash Attention was created to optimize the critical attention block that defines the transformers architecture, and is present in many models including most LLMs. By carefully combining all the attention operations inside a single kernel, memory transfers are minimized, memory use is reduced, and speedups can be achieved.

The problem is that all these various kernels are available in separate libraries, which creates a dependency bloat if they were to be added to the transformers library. Furthermore, these kernels are not just Python code, they consist of low-level cuda code, glued together with C++ and exposed through a Python layer.

Source link