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 SoftClip activation with:

Memory Optimization:

Dual kernel approach: vectorized float4 with ILP and scalar fallback

Vectorized memory access for sizes divisible by 4

Contiguous tensor inputs for coalesced memory access

Parallelization Strategy:

Grid-stride loop for efficient workload distribution

256 threads per block optimal configuration

Automatic kernel selection based on input size alignment

Instruction-Level Parallelism (ILP): processes 2×float4 per iteration

Computational Optimization:

SoftClip: x + softplus(min - x) - softplus(x - max)

Configurable min, max, and beta parameters

Optimized softplus with early exit for large values (bx > 20)

Precomputed reciprocal: inv_beta = 1.0f / beta

Numerically stable softplus using log1pf(expf(bx))

Work Distribution:

Vectorized kernel processes 8 elements per iteration (2×float4) via ILP

Scalar kernel handles unaligned input sizes

Each thread computes independent SoftClip operations

The implementation provides maximum throughput through vectorization and ILP while maintaining flexibility for various input sizes through automatic kernel selection.


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, min_val=-1.0, max_val=1.0, beta=1.0):
        super().__init__()
        self.min_val = min_val
        self.max_val = max_val
        self.beta = beta

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x + F.softplus(self.min_val - x, beta=self.beta) - F.softplus(x - self.max_val, beta=self.beta)

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, 1.0, 1.0]