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 Soft Exponential activation with:

Memory Optimization:

Dual kernel approach: vectorized float4 and scalar fallback

Vectorized memory access for spatial dimensions divisible by 4

Contiguous tensor inputs for coalesced memory access

Channel-wise alpha parameter access

Parallelization Strategy:

Grid-stride loop for efficient workload distribution

256 threads per block optimal configuration

Automatic kernel selection based on spatial dimension alignment

Dynamic channel indexing: (i / spatial_size) % channels

Computational Optimization:

Soft Exponential with three regimes:

α ≈ 0: Identity function x

α > 0: Exponential regime (exp(αx) - 1)/α + α

α < 0: Logarithmic regime -log(1 - α(x + α))/α

Numerically stable implementation with epsilon check fabsf(a) < 1e-6f

Branch protection for logarithmic regime: val > 0 ? -log(val)/a : 0

Work Distribution:

Vectorized kernel processes 4 elements per thread via float4

Scalar kernel handles unaligned spatial dimensions

Each thread computes independent Soft Exponential operations

The implementation provides maximum throughput through vectorization while maintaining numerical stability across all alpha regimes with per-channel parameterization.




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


class Model(nn.Module):
    def __init__(self, in_features, alpha=0.0):
        super().__init__()
        self.alpha = nn.Parameter(torch.full((in_features,), alpha))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shape = [1] * x.dim()
        shape[1] = -1
        a = self.alpha.view(shape)

        condition_zero = (a == 0)
        condition_pos = (a > 0)
        condition_neg = (a < 0)

        res = torch.zeros_like(x)

        if condition_zero.any():
            res = torch.where(condition_zero, x, res)

        if condition_pos.any():
            res = torch.where(condition_pos, (torch.exp(a * x) - 1.0) / a + a, res)

        if condition_neg.any():
            res = torch.where(condition_neg, -torch.log(1.0 - a * (x + a)) / a, res)

        return res


batch_size = 128
in_features = 64
height = 64
width = 64


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


def get_init_inputs():
    return [in_features, 0.5]