torch.compile (inductor)
PyTorch · python · MIT
Kernel source · 31 lines ↓holds 1 record
Use it
Vendorable · source mirrored · MITView source →
No package. Vendor the mirrored source: 31 lines, MIT.
21_Sigmoid.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-21-sigmoid-torch-compile-inductor?include=source"interfacepython · torch_compile_inductor
symbolModel.forward
Compatibility
measured onNVIDIA H100
declared hardwaredeclared only
architectures—
dtypes
Benchmark evidence
2 measurements across 1 GPU, fastest first.
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:ea9dc0c5ca2c7061b2a3676f7f0679a89e2747e89587969b8b1a51874c084f06
license declaredMIT
license concludedMIT
imported2026-08-26
Kernel source
21_Sigmoid.py31 lines
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Sigmoid activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Sigmoid activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with Sigmoid applied, same shape as input.
"""
return torch.sigmoid(x)
batch_size = 4096
dim = 393216
def get_inputs():
x = torch.rand(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs neededscrolls · 31 lines total
Source code from KernelBench, © 2023 Anne Ouyang, Simon Guo, Azalia Mirhoseini (Scaling Intelligence Lab, Stanford University), MIT License · MIT
Best evidence level for this revision: reported
JSON