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

Earth Mover's Distance (EMD) loss computation (1D Wasserstein distance)

Shared memory utilities for reduction, scan, and softmax operations

Inclusive prefix scan for cumulative distribution function (CDF) computation

Softmax normalization with max and sum reductions

CDF-based distance via squared difference between cumulative distributions

Dynamic thread allocation to match number of classes

Per-batch parallel processing (one CUDA block per sample)

Contiguous tensor handling for memory coalescing

Numerical stability with max subtraction in softmax




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

    def forward(self, pred, target):
        pred_prob = F.softmax(pred, dim=1)
        pred_cdf = torch.cumsum(pred_prob, dim=1)
        target_cdf = torch.cumsum(target, dim=1)
        return torch.mean(torch.square(pred_cdf - target_cdf))

batch_size = 32
num_classes = 128

def get_inputs():
    pred = torch.randn(batch_size, num_classes, requires_grad=True)
    target = torch.softmax(torch.randn(batch_size, num_classes), dim=1)
    return [pred, target]

def get_init_inputs():
    return []