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.
Custom CUDA kernel extension via torch.utils.cpp_extension.load_inline

Projective affine transformation: y = (w_numx + b_num) / (w_denx + b_den)

Element-wise parallelization using CUDA grid-stride loops

Per-channel learnable parameters for numerator and denominator (w_num, b_num, w_den, b_den)

Contiguous tensor handling for all input tensors

Memory-efficient in-place-like computation with torch.empty_like

Auto-tuning block/grid size based on tensor size (up to 65535 blocks)

Division operation for rational transformation




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, num_features=512):
        super().__init__()
        self.w_num = nn.Parameter(torch.ones(1, num_features))
        self.b_num = nn.Parameter(torch.zeros(1, num_features))
        self.w_den = nn.Parameter(torch.zeros(1, num_features))
        self.b_den = nn.Parameter(torch.ones(1, num_features))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        numerator = x * self.w_num + self.b_num
        denominator = x * self.w_den + self.b_den
        return numerator / denominator


batch_size = 128
feature_dim = 512


def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return []