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PyTorch eager

PyTorch · python · MIT

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Vendorable · source mirrored · MITView source →

No package. Vendor the mirrored source: 27 lines, MIT.

94_MSELoss.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-94-mseloss-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
MSELossfp32 · [32768, 32768]
NVIDIA H100
8.40ms±0.02
#2 of 2
2026-03-05
MSELossfp32 · [32768, 32768]
NVIDIA H100
13.6ms±0.00
#2 of 2
2026-03-05

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:256ae074a7d4814c9dba0d56679c406fbe4ba101fa7fd765386c3ff65198e9d0
license declaredMIT
license concludedMIT
imported2026-08-26

Kernel source

94_MSELoss.py27 lines
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that computes the Mean Squared Error loss for regression tasks.

    Parameters:
        None
    """
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, predictions, targets):
        return torch.mean((predictions - targets) ** 2)

batch_size = 32768
input_shape = (32768,)
dim = 1

def get_inputs():
    scale = torch.rand(())
    return [torch.rand(batch_size, *input_shape)*scale, torch.rand(batch_size, *input_shape)]

def get_init_inputs():
    return []

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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