triton-cuda-examples-torch

To write custom Triton CUDA kernels for PyTorch, you can use Triton to implement high-performance operations such as matmul, softmax, and layer_norm, then wrap them inside PyTorch models using patterns similar to torch.autograd.Function for seamless integration.

The best way to integrate Triton kernels inside a PyTorch model is by wrapping the Triton CUDA operations using patterns akin to torch.autograd.Function, allowing custom GPU kernels to function as standard PyTorch layers during model execution.

Yes, you need CUDA-capable hardware, the Triton compiler, and a PyTorch installation to reproduce the provided kernel examples, run operations like vector_add, and extend them to real workloads.

Yes, you can use Triton to optimize layer_norm and vector_add operations by implementing vectorized CUDA kernels that execute high-performance GPU computations directly within your PyTorch neural network workflows.

Triton PyTorch kernel examples include common neural network operations such as vector_add, matmul, layer_norm, and softmax, alongside multi-kernel workflows that demonstrate how to package custom GPU operations together.