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:

Memory Access

contiguous() for memory coalescing

Coalesced global memory reads

Data reuse via L2 cache

Computation

Online max/sum calculation in single pass

Use expf and reciprocal multiplication

Compiler flags: -O3, --use_fast_math

Parallelization

One thread per spatial position (N,H,W)

Fixed 256 threads, auto-calculated blocks

__restrict__ pointers for alias analysis

Numerical Stability

Online max updates with exponential scaling

Prevents overflow

Large Tensor Support

long long indexing prevents overflow





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().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Input: (N, C, H, W)
        Output: (N, C, H, W), Softmax along dim=1 (C)
        """
        return F.softmax(x, dim=1)

batch_size = 32
channels = 64
height = 128
width = 128

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

def get_init_inputs():
    return []