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.
PyTorch C++/CUDA Extension: Inline compilation using torch.utils.cpp_extension.load_inline.

Two‑Stage Custom CUDA Kernel:

Parallel L1 Difference Reduction: Computes per‑batch L1 distance between two tensors (img1/img2 and z1/z2) using shared‑memory reduction.

Ratio Computation Kernel: Calculates z_diff / (img_diff + eps) element‑wise.

Shared‑Memory Parallel Reduction: Uses block‑level reduction with __shared__ memory and a tree‑based sum pattern.

Batch‑Level Parallelism: Each batch processed by a separate CUDA block in the reduction step.

Automatic Mean Reduction: Returns the mean of the per‑batch ratio values directly from the CUDA wrapper.




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, eps=1e-5):
        super().__init__()
        self.eps = eps

    def forward(self, img1: torch.Tensor, img2: torch.Tensor, z1: torch.Tensor, z2: torch.Tensor) -> torch.Tensor:
        img_diff = torch.abs(img1 - img2).view(img1.size(0), -1).mean(dim=1)
        z_diff = torch.abs(z1 - z2).view(z1.size(0), -1).mean(dim=1)

        loss = z_diff / (img_diff + self.eps)
        return loss.mean()


batch_size = 32
c, h, w = 3, 64, 64
z_dim = 128


def get_inputs():
    img1 = torch.randn(batch_size, c, h, w, dtype=torch.float32)
    img2 = torch.randn(batch_size, c, h, w, dtype=torch.float32)
    z1 = torch.randn(batch_size, z_dim, dtype=torch.float32)
    z2 = torch.randn(batch_size, z_dim, dtype=torch.float32)
    return [img1, img2, z1, z2]


def get_init_inputs():
    return [1e-5]