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

Mesh edge length loss computation (squared Euclidean distance between connected vertices)

Edge list representation with pair-wise vertex indices

3D coordinate difference calculation (x, y, z)

Element-wise parallelization across batch×edges

Fixed block size (256 threads) with dynamic grid sizing

Contiguous tensor handling for memory coalescing

Batch-aware indexing for vertex access

Mean reduction across all edges and batches

Geometry regularization for mesh smoothness






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, vertices, edges):
        v1 = vertices[:, edges[:, 0], :]
        v2 = vertices[:, edges[:, 1], :]
        return torch.mean(torch.sum((v1 - v2) ** 2, dim=2))

batch_size = 16
num_vertices = 1024
num_edges = 3000

def get_inputs():
    vertices = torch.randn(batch_size, num_vertices, 3, requires_grad=True)
    edges = torch.randint(0, num_vertices, (num_edges, 2)).long()
    return [vertices, edges]

def get_init_inputs():
    return []