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.


This CUDA kernel implements optimized Sigmoid Gated Linear Unit (GLU) with:

Memory Optimization:

Vectorized memory access using float4 for 4x bandwidth

Contiguous tensor inputs for coalesced memory access

Direct element-wise computation without temporary storage

Parallelization Strategy:

Grid-stride loop for efficient workload distribution

256 threads per block optimal configuration

Automatic grid size calculation with 65535 block limit

Computational Optimization:

Sigmoid GLU: sigmoid(gate) * activation

Optimized sigmoid: 1.0f / (1.0f + expf(-x))

Efficient indexing for gate and activation components

Work Distribution:

Each thread processes 4 elements via float4

Automatic indexing calculation for gate and activation vectors

Direct multiplication of sigmoid-activated gate with activation

The implementation provides maximum memory throughput for the sigmoid GLU operation through vectorization and efficient parallelization, requiring input feature dimension to be divisible by 8 for optimal performance.

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:
        gate, act = x.chunk(2, dim=-1)
        return torch.sigmoid(gate) * act

batch_size = 128
feature_dim = 4096

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

def get_init_inputs():
    return []