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 LeakyReGLU (Leaky ReLU Gated Linear Unit) 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:

LeakyReGLU: leaky_relu(gate, slope) * activation

Configurable negative slope parameter

Branching Leaky ReLU: x > 0 ? x : x * slope

Efficient element-wise multiplication

Work Distribution:

Each thread processes 4 elements via float4

Automatic indexing for gate and activation components

Direct multiplication of Leaky ReLU-activated gate with activation

The implementation provides maximum throughput through vectorization, requiring input feature dimension to be divisible by 8 for optimal performance with configurable negative slope 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, negative_slope=0.01):
        super().__init__()
        self.negative_slope = negative_slope

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate, act = x.chunk(2, dim=-1)
        return F.leaky_relu(gate, negative_slope=self.negative_slope) * 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 [0.01]