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 & Frameworks
PyTorch: Deep learning framework

CUDA: NVIDIA's parallel computing platform for GPU acceleration

CUDA/C++ Components
CUDA kernel: logit_sigmoid_shift_kernel

CUDA math functions: logf(), expf()

Element-wise parallelism: One thread per tensor element

Mathematical Operations
Logit transform: log(x/(1-x))

Sigmoid activation: 1/(1+exp(-y))

Additive shift: Output + shift value

Numerically sensitive: Division and log operations

Architecture
Simple 1D grid: Standard CUDA block/grid configuration

Memory efficiency: Direct element-wise computation

PyTorch integration: Custom CUDA extension module





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

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

    def forward(self, x):
        return torch.sigmoid(torch.logit(x)) + self.shift

batch_size = 4096
dim = 1024

def get_inputs():
    x = torch.rand(batch_size, dim) * 0.999 + 0.0005
    return [x]

def get_init_inputs():
    return [0.5]