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.

CUDA C++ kernel for Russell‑Rao dissimilarity with exponential transformation

Block‑parallel per‑sample processing: each block handles one batch element

Thread‑wise accumulation of element‑wise minimums (local_inter)

Parallel reduction in shared memory using binary tree approach

Russell‑Rao formula: (dim – intersection) / dim where intersection = ∑ min(xᵢ, yᵢ)

Exponential activation: exp(value) applied to the dissimilarity score

Grid‑stride memory access for coalesced reads

PyTorch inline C++/CUDA extension via load_inline



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):
        super(Model, self).__init__()

    def forward(self, x, y):
        n = x.size(1)
        intersection = torch.min(x, y).sum(dim=1)
        resistance = (n - intersection) / n
        return torch.exp(resistance).mean()


batch_size = 16
input_dim = 1024


def get_inputs():
    x = torch.randn(batch_size, input_dim).abs()  # Ensure positive for meaningful fuzzy operations
    y = torch.randn(batch_size, input_dim).abs()
    return [x, y]


def get_init_inputs():
    return []