torch.compile (inductor)
PyTorch · python · MIT
Kernel source · 28 lines ↓holds 1 record
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68_Matmul_Min_Subtract.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l2-68-matmul-min-subtract-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.
Operation / workload
Hardware
Latency
Rank
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Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:a53a08d0bfd1f0d2dad54b59b5d42954e3b828dc52a1b06e2618f62ee24a7d6e
license declaredMIT
license concludedMIT
imported2026-08-26
Kernel source
68_Matmul_Min_Subtract.py28 lines
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a matrix multiplication, applies minimum, and subtracts a constant.
"""
def __init__(self, in_features, out_features, constant):
super(Model, self).__init__()
self.linear = nn.Linear(in_features, out_features)
self.constant = nn.Parameter(torch.tensor(constant))
def forward(self, x):
x = self.linear(x)
x = torch.min(x, self.constant)
x = x - self.constant
return x
batch_size = 128
in_features = 16384
out_features = 16384
constant = 2.0
def get_inputs():
return [torch.rand(batch_size, in_features)]
def get_init_inputs():
return [in_features, out_features, constant]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
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