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 RANSAC‑style outlier rejection with two‑stage processing

PyTorch C++/CUDA extension via load_inline

Euclidean distance computation per point pair (L2 norm across feature dimension)

Parallel reduction in shared memory for sum of distances (tree‑based)

Global atomic addition (atomicAdd) to accumulate sum across blocks

Normalized distance‑based thresholding: reject if dist / mean_dist < threshold

Dynamic shared memory allocation for reduction scratchpad

Grid‑stride launch with 256 threads per block

Output binary mask (1.0 = inlier, 0.0 = outlier)






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

    def forward(self, src, tgt):
        diff = src - tgt
        dist = torch.norm(diff, p=2, dim=1)
        mean_dist = dist.mean()
        norm_dist = dist / (mean_dist + 1e-8)
        mask = (norm_dist < self.threshold).float()
        return mask


batch_size = 4096
dim = 64


def get_inputs():
    src = torch.randn(batch_size, dim, device='cuda')
    tgt = torch.randn(batch_size, dim, device='cuda')
    return [src, tgt]


def get_init_inputs():
    threshold = 1.5
    return [threshold]