cuda-c-examples-torch

You compile and run custom CUDA C kernels in PyTorch by using the load_inline function to build C++ sources into a lightweight extension, exposing GPU kernel functions directly to Python.

You can accelerate common GPU workloads like vector_add, relu, matmul, softmax, and layernorm by implementing custom CUDA kernels and calling them directly from Python via the inline extension.

No, you do not need to leave Python to compile CUDA code for PyTorch operations. The load_inline mechanism compiles C++ sources directly within your workflow, enabling seamless creation of high-performance ops.

The best way to integrate high-performance custom ops without a full C++ extension is using a minimal inline pattern with load_inline, allowing you to compile and execute CUDA C kernels directly from Python.

This inline CUDA C compilation approach requires no external package dependencies, though you must install the required system build tools to compile the C++ sources and expose the Python-callable kernels.

You should use load_inline for CUDA C kernels when standard PyTorch operations lack the performance needed for critical tasks, enabling direct GPU kernel prototyping for vector operations and matrix multiplication.