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 | // kernels-community/relu XPU backend kernel (SYCL parallel_for), torch-free. | |
| // Runs on whatever SYCL device is present (here: the i9 CPU via OpenCL). | |
| using namespace sycl; | |
| int main(){ | |
| queue q{default_selector_v}; | |
| printf("SYCL device: %s\n", q.get_device().get_info<info::device::name>().c_str()); | |
| const size_t n = 256ull*1024*1024; // 1 GB/array | |
| float* in = malloc_shared<float>(n, q); | |
| float* out = malloc_shared<float>(n, q); | |
| for(size_t i=0;i<n;++i) in[i]=((i&1)?-1.f:1.f)*float(i%97); | |
| double gb = 2.0*n*sizeof(float)/1e9; | |
| // same body as relu_xpu/relu.cpp | |
| auto run=[&](){ q.parallel_for(range<1>(n),[=](id<1> i){ out[i]=in[i]>0.f?in[i]:0.f; }).wait(); }; | |
| for(int w=0;w<3;w++) run(); | |
| bool ok=true; for(size_t i=0;i<n&&ok;i++){ float e=in[i]>0?in[i]:0; if(out[i]!=e) ok=false; } | |
| double best=1e30; | |
| for(int r=0;r<8;r++){ | |
| auto t0=std::chrono::high_resolution_clock::now(); run(); | |
| auto t1=std::chrono::high_resolution_clock::now(); | |
| best=std::min(best,std::chrono::duration<double>(t1-t0).count()); | |
| } | |
| printf("SYCL relu (xpu backend): %.1f GB/s (%.2f ms) %s\n", gb/best, best*1e3, ok?"OK":"FAIL"); | |
| free(in,q); free(out,q); | |
| return 0; | |
| } | |