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 adding weight‑decay term to gradients

Element‑wise fused operation: out = gradient + weight × decay

Grid‑stride processing: each thread handles one element of weight/gradient tensors

Coalesced memory access with contiguous tensors

PyTorch inline C++/CUDA extension via load_inline

Custom CUDA architecture targeting using TORCH_CUDA_ARCH_LIST




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, decay=0.01):
        super(Model, self).__init__()
        self.decay = decay

    def forward(self, w, g):
        return g + w * self.decay

batch_size = 16
input_dim = 1024

def get_inputs():
    w = torch.randn(batch_size, input_dim)
    g = torch.randn(batch_size, input_dim)
    return [w, g]

def get_init_inputs():
    return [0.01]