kernel-craft 0.1.0

CUDA kernels for machine learning systems optimization

kernel-craft Python API

CUDA convolution kernels exposed to Python with numpy and PyTorch support.

Installation

Build from source with CMake:

cd /path/to/kernel-craft
mkdir build && cd build
cmake ..
make kernel_craft_python

The module will be at src/python/build/kernel_craft_python.cpython-*.so.

Usage

import sys
sys.path.insert(0, 'src/python/build')

import kernel_craft_python as kc
import numpy as np

# Input: 2D float32 numpy array
input = np.random.randn(256, 256).astype(np.float32)
kernel = np.random.randn(3, 3).astype(np.float32)

# Naive convolution
out = kc.conv_naive(input, kernel)  # -> np.ndarray

# Tiled convolution with configurable tile size
out = kc.conv_tiled(input, kernel, tile_w=8, tile_h=8)  # -> np.ndarray

Version

import kernel_craft_python as kc
print(kc.__version__)  # "0.1.0"

PyTorch Tensors

import torch
import kernel_craft_python as kc

# Input: 2D float32 PyTorch tensor on CUDA
input = torch.rand(256, 256, dtype=torch.float32, device='cuda')
kernel = torch.rand(3, 3, dtype=torch.float32, device='cuda')

# Naive convolution
out = kc.conv_naive(input, kernel)  # -> torch.Tensor on GPU

# Tiled convolution
out = kc.conv_tiled(input, kernel, tile_w=16, tile_h=16)  # -> torch.Tensor on GPU

API Reference

Function	Input Type	Output Type
conv_naive(input, kernel)	np.ndarray or Tensor	np.ndarray or Tensor
conv_tiled(input, kernel, tile_w, tile_h)	np.ndarray or Tensor	np.ndarray or Tensor

Parameters

• input: Input image (2D, float32)
• kernel: Convolution kernel (2D, float32, odd dimension)
• tile_w: Tile width for tiled convolution (default: 8)
• tile_h: Tile height for tiled convolution (default: 8)

Supported Tile Sizes

• 8x8 (default, best overall performance)
• 16x16
• 32x32

Error Handling

All functions raise RuntimeError with descriptive messages for:

• Invalid input dimensions (must be 2D)
• Invalid kernel dimensions (must be 2D, square, odd-sized)
• Invalid dtype (must be float32)