torch.compile (inductor)
PyTorch · python · MIT
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88_MinGPTNewGelu.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-88-mingptnewgelu-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 digestsha256:4d94ec988aaf847585df636399267acf5aa84c33b944f888fd232b1939257415
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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