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 HardSwish 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:

Optimized HardSwish: x * min(max(x + 3, 0), 6) * 1/6

Precomputed reciprocal: 0.166666667f instead of division

Branchless clamping using fminf(fmaxf())

Work Distribution:

Vectorized main loop processes 4 elements per thread via float4

Scalar tail handles remaining elements (n % 4)

Each thread computes independent HardSwish operations

The implementation maximizes throughput through vectorization while maintaining complete coverage for any input size through tail processing.






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):
        super().__init__()
        self.act = nn.Hardswish()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.act(x)

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 []