You write custom CUDA kernels to replace the pytorch operators in the given GeGLU architecture to get speedups.

You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining chunk+gelu+elementwise_mul), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
Technologies Used in This Code
Core Libraries
PyTorch: Deep learning framework

CUDA: NVIDIA GPU parallel computing

C++: Kernel implementation

CUDA Components
CUDA kernel: log_exp_softplus_kernel

CUDA math functions: logf(), expf()

Element-wise parallelism: One thread per element

Mathematical Operations
Logarithm: log(x)

Exponential: exp(y) where y = log(x)

Softplus: log(1 + exp(z)) where z = exp(log(x))

Identity property: log(exp(log(x))) = log(x) (mathematically)

Numerically sensitive: Multiple exp/log operations

Architecture
Simple 1D grid: Standard CUDA block configuration

Element-wise processing: Independent computation per element

Memory efficiency: Direct input-output mapping

Numerical Considerations
Input requirements: x > 0 for log(x) to be defined

Potential overflow: exp(exp(log(x))) could be large

Numerical stability: Multiple floating-point operations




Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, x):
        return F.softplus(torch.exp(torch.log(x)))

batch_size = 4096
dim = 1024

def get_inputs():
    x = torch.rand(batch_size, dim) * 5.0 + 0.01
    return [x]

def get_init_inputs():
    return []