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 element‑wise Tukey’s biweight loss

PyTorch C++/CUDA extension via load_inline

Contiguous memory access with .contiguous()

Grid‑stride loop pattern: 256 threads per block, dynamic block count

Branch‑based robust loss using threshold c

Fused kernel computes loss per element and returns the mean

Numerically stable implementation using pre‑computed constants

Custom autograd‑compatible loss module in PyTorch




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

    def forward(self, input, target):
        diff = input - target
        c_sq = self.c ** 2
        scale = c_sq / 6.0

        inlier_mask = torch.abs(diff) <= self.c

        u_sq = (diff / self.c) ** 2
        term = 1.0 - u_sq
        inlier_loss = scale * (1.0 - term ** 3)

        loss = torch.where(inlier_mask, inlier_loss, torch.tensor(scale, device=input.device, dtype=input.dtype))

        return loss.mean()


batch_size = 16
dim = 1024


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


def get_init_inputs():
    c = 4.685
    return [c]