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torch.compile (inductor)

PyTorch · python · MIT

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Vendorable · source mirrored · MITView source →

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

95_CrossEntropyLoss.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-95-crossentropyloss-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
Observed
CrossEntropyLossfp32 · [32768, 4096]
NVIDIA H100
292.0µs±1.35
#1 of 2
2026-03-05
CrossEntropyLossfp32 · [32768, 4096]
NVIDIA H100
329.0µs±0.50
#1 of 2
2026-03-05

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:0e010b71acb63dc19d875418f9264ee3bdeeb8ffd87b1cf2dd603c33d613d008
license declaredMIT
license concludedMIT
imported2026-08-26

Kernel source

95_CrossEntropyLoss.py27 lines
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that computes Cross Entropy Loss for multi-class classification tasks.

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

    def forward(self, predictions, targets):
        return torch.nn.functional.cross_entropy(predictions, targets)

batch_size = 32768
num_classes = 4096
input_shape = (num_classes,)
dim = 1

def get_inputs():
    return [torch.rand(batch_size, *input_shape), torch.randint(0, num_classes, (batch_size,))]

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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