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.
Custom CUDA kernel extension via torch.utils.cpp_extension.load_inline

Channel-wise mean computation with parallel reduction

Warp/block-level reduction optimizations using __shfl_down_sync

Dual kernel strategy: fused vs. vectorized based on feature dimension size

Vectorized memory access using float4 for coalesced loads

Conditional kernel selection: fused kernel for small features, separate kernels for large

Memory-efficient intermediate storage for channel means

Fast math optimizations with --use_fast_math flag

Batch-parallel processing with one CUDA block per batch element




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):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        channel_mean = x.mean(dim=1, keepdim=True)
        gate = torch.sigmoid(x)
        return channel_mean * gate


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 []