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 Smooth ReLU (SmeLU) activation with:

Memory Optimization:

Vectorized memory access using float4 for 4x bandwidth

Contiguous tensor inputs for coalesced memory access

Separate handling for vectorized main loop and scalar tail

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:

SmeLU activation with configurable beta parameter

Precomputed reciprocal: inv_4beta = 1.0f / (4.0f * beta)

Fast math compilation flags for optimized arithmetic

Branching implementation:

For |x| < beta: (x + beta)² / (4 * beta)

For x ≥ beta: x

For x ≤ -beta: 0

Work Distribution:

Vectorized main loop processes 4 elements per thread via float4

Scalar tail handles remaining elements (n % 4)

Each thread computes independent SmeLU operations

The implementation provides maximum throughput through vectorization while maintaining the smooth transition characteristic of SmeLU activation with configurable beta 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, beta=2.0):
        super().__init__()
        self.beta = beta

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        abs_x = torch.abs(x)
        return torch.where(
            abs_x < self.beta,
            torch.pow(x + self.beta, 2) / (4.0 * self.beta),
            F.relu(x)
        )

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 [2