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 Optimization Strategies:

Vectorized Memory Access

Uses float4 for 4-element vector loads

Reduces memory instructions by 4x

Better memory bandwidth utilization

Grid-Stride Loop

Processes elements with grid-stride pattern

Handles arbitrary tensor sizes

Better GPU occupancy

Two-Level Reduction

Warp shuffle operations for fast reduction

Shared memory for block-level results

Final reduction on PyTorch side

Branch Optimization

Precomputes delta_sq_half outside loop

Efficient conditional for Huber loss

Minimal branching in vectorized path

Performance Tuning

Fixed 256 threads per block

Block count capped at 1024

Compiler flag: -O3

Tail Handling

Separate non-vectorized path for remainder

Maintains correctness for all sizes

Minimal performance impact

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, delta=1.0):
        super().__init__()
        self.loss = nn.HuberLoss(reduction='mean', delta=delta)

    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        return self.loss(x, y)

batch_size = 512
feature_dim = 4096

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

def get_init_inputs():
    return [1.0]