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 | // Extended XPU relu kernel (fp32/fp16/bf16/int8) run on the Intel iGPU via SYCL. | |
| // Same logic as relu_xpu/relu.cpp: max for fp32/int8, sign-bit zero for 16-bit. | |
| using namespace sycl; | |
| inline float relu_dev(float x) { return x > 0.f ? x : 0.f; } | |
| inline uint16_t relu_dev(uint16_t x) { return (x & 0x8000u) ? uint16_t(0) : x; } // fp16 & bf16 | |
| inline int8_t relu_dev(int8_t x) { return x > 0 ? x : int8_t(0); } | |
| template<class T> T fillval(size_t i); | |
| template<> float fillval<float>(size_t i) { return ((i&1)?-1.f:1.f)*float(i%97); } | |
| template<> uint16_t fillval<uint16_t>(size_t i){ return (uint16_t)((i&1)?(0x8000u|(i%200)):(i%200)); } | |
| template<> int8_t fillval<int8_t>(size_t i) { return (int8_t)((i&1)?-(int)(i%100):(int)(i%100)); } | |
| template<typename T> | |
| void run(sycl::queue& q, const char* name) { | |
| const size_t n = 64ull*1024*1024; | |
| T* in = malloc_device<T>(n, q); | |
| T* out = malloc_device<T>(n, q); | |
| std::vector<T> h(n), o(n); | |
| for (size_t i=0;i<n;++i) h[i]=fillval<T>(i); | |
| q.memcpy(in, h.data(), n*sizeof(T)).wait(); | |
| auto rn=[&](){ q.parallel_for(range<1>(n), [=](id<1> idx){ size_t i=idx[0]; out[i]=relu_dev(in[i]); }).wait(); }; | |
| for(int w=0;w<5;++w) rn(); | |
| q.memcpy(o.data(), out, n*sizeof(T)).wait(); | |
| bool ok=true; for(size_t i=0;i<n&&ok;++i) if(o[i]!=relu_dev(h[i])) ok=false; | |
| double best=1e30; | |
| for(int r=0;r<12;++r){ auto a=std::chrono::high_resolution_clock::now(); rn(); | |
| auto b=std::chrono::high_resolution_clock::now(); | |
| best=std::min(best,std::chrono::duration<double>(b-a).count()); } | |
| double gb=2.0*n*sizeof(T)/1e9; | |
| printf(" %-12s %dB %5.1f GB/s %6.1f Gel/s %s\n", name, (int)sizeof(T), gb/best, n/best/1e9, ok?"OK":"FAIL"); | |
| free(in,q); free(out,q); | |
| } | |
| int main(){ | |
| queue q{gpu_selector_v}; | |
| printf("iGPU: %s (%u EUs)\n", q.get_device().get_info<info::device::name>().c_str(), | |
| q.get_device().get_info<info::device::max_compute_units>()); | |
| printf("extended XPU relu, all dtypes (64M):\n"); | |
| run<float>(q, "fp32"); | |
| run<uint16_t>(q, "fp16/bf16"); // identical 16-bit sign-bit op | |
| run<int8_t>(q, "int8"); | |
| return 0; | |
| } | |