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 ELU 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:

ELU GLU: elu(gate, alpha) * activation

Configurable alpha parameter for ELU

Fast math compilation flags for optimized expf()

Branching ELU: x > 0 ? x : alpha * (exp(x) - 1)

Work Distribution:

Each thread processes 4 elements via float4

Automatic indexing for gate and activation components

Direct multiplication of ELU-activated gate with activation

The implementation provides maximum throughput through vectorization and fast math optimizations, requiring input feature dimension to be divisible by 8 for optimal performance with configurable ELU alpha parameter.






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, alpha=1.0):
        super().__init__()
        self.alpha = alpha

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate, act = x.chunk(2, dim=-1)
        return F.elu(gate, alpha=self.alpha) * act

batch_size = 128
feature_dim = 1024

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

def get_init_inputs():
    return [1.0]