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PyTorch · python · MIT

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Vendorable · source mirrored · MITView source →

No package. Vendor the mirrored source: 26 lines, MIT.

88_MinGPTNewGelu.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-88-mingptnewgelu-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
MinGPTNewGelufp32 · [8192, 8192]
NVIDIA H100
1.65ms±0.00
#2 of 2
2026-03-05
MinGPTNewGelufp32 · [8192, 8192]
NVIDIA H100
2.64ms±0.00
#2 of 2
2026-03-05

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:a72294f18f859e5c1ba9902da34969d20f0b98e9cac750ad75cfd5ba5fa07af4
license declaredMIT
license concludedMIT
imported2026-08-26

Kernel source

88_MinGPTNewGelu.py26 lines
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

# From https://github.com/karpathy/minGPT/blob/master/mingpt/model.py

class Model(nn.Module):
    """
    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
    Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, x):
        return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))

batch_size = 8192
dim = 8192

def get_inputs():
    return [torch.rand(batch_size, dim)]

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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