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50_conv_standard_2D__square_input__square_kernel.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-50-conv-standard-2d-square-input-square-kernel-torch?include=source"interfacepython · torch_eager
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
Observed
conv standard 2D square input square kernelfp32 · [256, 3, 224, 224]
NVIDIA H100
2.20ms±0.01
#2 of 2
2026-03-05
conv standard 2D square input square kernelfp32 · [256, 3, 224, 224]
NVIDIA H100
2.97ms±0.00
#2 of 2
2026-03-05
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:00276ed20e2092960411065fc43876f76b6744b4d875ab61c9d415ed90e50260
license declaredMIT
license concludedMIT
imported2026-08-26
Kernel source
50_conv_standard_2D__square_input__square_kernel.py22 lines
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes=1000):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2)
def forward(self, x):
x = self.conv1(x)
return x
# Test code
batch_size = 256
num_classes = 1000
def get_inputs():
return [torch.rand(batch_size, 3, 224, 224)]
def get_init_inputs():
return [num_classes]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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