triton-cuda-examples-torch

Integrate Triton CUDA kernels with PyTorch models by implementing custom operators using the Triton DSL, wrapping them in autograd-compatible modules, and executing ready-to-run examples like vector_add and matmul directly within your PyTorch workflow.

Yes, you can use Triton kernels for PyTorch autograd by wrapping them in compatible modules, allowing you to execute custom operators and benchmark their performance directly on CUDA devices.

You can accelerate custom operators including vector_add, matmul, layer_norm, and softmax using the provided Triton CUDA examples, which demonstrate kernel implementation, wrapping, and performance benchmarking within PyTorch.

No extensive CUDA experience is required to wrap Triton kernels in PyTorch, as the Triton DSL simplifies kernel development and the provided examples guide you through integrating and benchmarking custom operators seamlessly.

Use the Triton DSL instead of standard CUDA kernels to simplify custom operator development for PyTorch, leveraging guided examples and wrappers to accelerate prototyping, integration, and benchmarking without low-level CUDA boilerplate.

The best way to start prototyping custom GPU kernels for PyTorch is by running the included Triton examples, which demonstrate how to implement, wrap, and benchmark operators like layer_norm and softmax directly within your models.