cuda-samples/python/3_FrameworkInterop/customPyTorchKernel/README.md

Sample: PyTorch Custom GPU Operator

Description

This sample demonstrates how to add a custom GPU operation to PyTorch using the cuda.core API. It implements a simple square operation (y = x²) to show the complete workflow from CUDA kernel to PyTorch integration with autograd support.

Requirements

• NVIDIA GPU with Compute Capability 7.0+
• CUDA Toolkit 13.0+
• Python 3.10+
• PyTorch 2.0+
• cuda-python >= 13.0.0
• cuda-core >=1.0.0

Installation

cd python/3_FrameworkInterop/customPyTorchKernel
pip install -r requirements.txt

Windows users: The default torch wheel on PyPI for Windows is CPU-only and will cause torch.cuda.is_available() to return False. Install a CUDA-enabled build from PyTorch's wheel index before (or after) the command above:

pip install torch --index-url https://download.pytorch.org/whl/cu128

Replace cu128 with the wheel suffix matching your installed CUDA driver (e.g. cu121, cu124, cu126, cu128). The driver's CUDA version must be >= the wheel's bundled runtime.

How to Run

# Basic usage
python customPyTorchKernel.py

# Test with more elements
python customPyTorchKernel.py --size 1000000

# Use specific GPU
CUDA_VISIBLE_DEVICES=1 python customPyTorchKernel.py

Expected Output

The sample runs three tests:

1. Forward pass correctness (y = x²)
2. Backward pass correctness (gradient computation)
3. Multi-dimensional tensor support

All tests should pass, confirming the custom operator works correctly with PyTorch's autograd system.

Key Concepts

The sample demonstrates:

• Writing CUDA kernels with grid-stride loops
• Runtime kernel compilation with cuda.core
• PyTorch autograd integration via torch.autograd.Function
• Stream management using PyTorch's current stream
• Kernel caching for performance

The code is self-documenting with inline comments explaining each step.