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 a Parametric Tanh-Leaky Unit (PTLU) activation with similar optimizations as previous kernels, plus a branch divergence consideration:

Vectorized Memory Operations: Uses float4 loads/stores to process 4 elements per instruction, improving memory bandwidth utilization.

Coalesced Memory Access: Threads access contiguous memory locations via vectorized operations, enabling efficient memory coalescing.

Fast Math & Loop Unrolling: Compiler flags enable fast approximate math (tanhf) and implicit loop unrolling improves instruction-level parallelism.

Performance Consideration: The conditional branch (if (x > 0.0f)) within the elementwise operation may cause thread divergence within warps, potentially reducing parallelism efficiency when threads in the same warp process both positive and negative values. However, the tanhf computation is avoided for positive inputs, saving computational cost.

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

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        y_neg = self.beta * torch.tanh(x)

        return torch.where(x > 0, self.alpha * x, y_neg)


batch_size = 128
feature_dim = 512


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


def get_init_inputs():
    return [1.0, 1.0]