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 Parametric ReLU (PReLU) 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 slope parameter access

Parallelization Strategy:

Grid-stride loop for efficient workload distribution

256 threads per block optimal configuration

Automatic grid size calculation with 65535 block limit

Dynamic kernel selection based on spatial dimension alignment

Computational Optimization:

Branching PReLU activation: val < 0 ? val * slope : val

Fast math compilation flags for optimized arithmetic

Efficient channel indexing: (i / spatial_size) % channels

Work Distribution:

Vectorized kernel processes 4 elements per thread via float4

Scalar kernel handles unaligned spatial dimensions

Each thread computes independent PReLU operations

The implementation maximizes memory throughput through vectorization while maintaining flexibility for various input shapes 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

class Model(nn.Module):
    def __init__(self, num_features=64, init=0.25):
        super().__init__()
        self.act = nn.PReLU(num_parameters=num_features, init=init)

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

batch_size = 128
num_features = 64
height = 64
width = 64

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

def get_init_inputs():
    return [num_features, 0.25]