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

Mexican hat wavelet transformation: y = (1 - ((x-t)/s)²)·exp(-0.5·((x-t)/s)²)

Element-wise parallelization using CUDA grid-stride loops

Per-channel wavelet parameters (scale, translation as learnable parameters)

Mathematical operations: scaling, shifting, exponentiation (expf)

Contiguous tensor handling for all input tensors

Fused arithmetic operations with __fsub_rn, __fmul_rn, __fdiv_rn for precision

Memory-efficient in-place-like computation with torch.empty_like

Auto-tuning block/grid size based on tensor size (up to 65535 blocks)

Compiler flags for precision control (-fmad=false)




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, num_features=512):
        super().__init__()
        self.num_features = num_features
        self.scale = nn.Parameter(torch.ones(1, num_features))
        self.translation = nn.Parameter(torch.zeros(1, num_features))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        z = (x - self.translation) / self.scale
        return (1 - z.pow(2)) * torch.exp(-0.5 * z.pow(2))


batch_size = 128
feature_dim = 512


def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return []