tilelang-cuda-examples-torch

To write custom CUDA kernels for PyTorch using TileLang, you define operations like GEMM or elementwise functions with the TileLang DSL, then compile and launch them using proper out_idx conventions and shared memory directly within your PyTorch workflow.

TileLang in PyTorch supports implementing high-performance CUDA operations including GEMM (matmul), elementwise, layernorm, and GEMV kernels, which are ideal for research, benchmarking, and prototyping custom CUDA-enabled workflows.

Yes, you can use TileLang kernels for matrix multiplication and layernorm in PyTorch. The DSL provides complete examples for building and integrating these high-performance CUDA kernels into your existing tensor workflows.

TileLang handles shared memory and kernel launch conventions for PyTorch integration by demonstrating proper out_idx usage and shared-memory allocation, ensuring your custom CUDA kernels execute efficiently within the PyTorch runtime.

Using TileLang instead of raw CUDA C++ for PyTorch simplifies kernel development by providing a higher-level DSL for GEMM and normalization operations, while still enabling shared-memory optimization and direct compilation into high-performance CUDA code.

The limitations of using TileLang CUDA kernels in PyTorch workflows center on its intermediate implementation depth, making it best suited for education, benchmarking, and prototyping rather than immediate production deployment without further optimization.