Instructions to use Ashiedu/fused-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use Ashiedu/fused-split with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("Ashiedu/fused-split") - Notebooks
- Google Colab
- Kaggle
File size: 460 Bytes
bc2cb62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import torch
from kernels.benchmark import Benchmark
class FusedSplitBenchmark(Benchmark):
def setup(self):
self.size = 1024
self.input = torch.randn(self.size, self.size, device=self.device)
self.out = torch.empty_like(self.input)
def benchmark_base(self):
self.kernel.fused_split(self.out, self.input)
def verify_base(self) -> torch.Tensor:
# Reference implementation
return self.input.clone() |