Instructions to use SuperexponentialAI/relu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use SuperexponentialAI/relu with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("SuperexponentialAI/relu") - Notebooks
- Google Colab
- Kaggle
Optimized relu: cpu/cuda/xpu, 1.2-1.85x faster on RTX 4090, benchmarked vs upstream and torch.relu
e873e70 verified | import platform | |
| import torch | |
| import torch.nn.functional as F | |
| import relu | |
| def test_relu(): | |
| if platform.system() == "Darwin": | |
| device = torch.device("mps") | |
| elif hasattr(torch, "xpu") and torch.xpu.is_available(): | |
| device = torch.device("xpu") | |
| elif torch.version.cuda is not None and torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| x = torch.randn(1024, 1024, dtype=torch.float32, device=device) | |
| torch.testing.assert_allclose(F.relu(x), relu.relu(x)) | |