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 Winsorized normalization with fixed dimension (1024)

Bitonic sort in shared memory for quantile computation

Linear interpolation to estimate 5th and 95th percentiles (Q_LOW=0.05, Q_HIGH=0.95)

Winsorizing (clipping): values below lower bound or above upper bound are clipped

Two‑pass statistics: mean and variance computed after clipping

Warp‑level reduction using __shfl_down_sync for sum and sum of squares

Shared‑memory broadcast for mean and standard deviation

Vectorized load/store via float4 for coalesced memory access

Block‑parallel processing: one block per batch element, 256 threads per block

Standard normalization with epsilon for numerical stability

PyTorch inline C++/CUDA extension via load_inline




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__()
        self.limits = (0.05, 0.95)

    def forward(self, x):
        lower = torch.quantile(x, self.limits[0], dim=-1, keepdim=True)
        upper = torch.quantile(x, self.limits[1], dim=-1, keepdim=True)
        x_clamped = torch.clamp(x, min=lower, max=upper)

        mean = x_clamped.mean(dim=-1, keepdim=True)
        std = x_clamped.std(dim=-1, keepdim=True)

        return (x_clamped - mean) / (std + 1e-8)


batch_size = 16
dim = 1024


def get_inputs():
    x = torch.randn(batch_size, dim, device='cuda', dtype=torch.float32)
    return [x]


def get_init_inputs():
    return []