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.

PyTorch C++/CUDA Extension: Inline compilation using torch.utils.cpp_extension.load_inline.

Vectorized CUDA Kernel: Uses float4 memory loads/stores for 4‑element SIMD-like processing to increase memory throughput.

Fused Multiply-Add (FMA) Control: Compilation flag -fmad=false disables automatic FMA to preserve numerical precision in subtraction‑squared sequence.

Two‑Stage Processing:

Vectorized main loop processes aligned float4 chunks.

Scalar tail loop handles remaining elements not divisible by 4.

CUDA Intrinsics: Uses __fsub_rn and __fmul_rn for rounded single‑precision arithmetic.

Batch‑Style Kernel Launch: Configures threads and blocks based on tensor size, capped at 65535 blocks.

Per‑Feature‑Map MSE: Computes squared differences for each pair of feature maps in input_features and target_features.

Flexible Reduction: Supports 'mean' (default) and 'sum' reduction across feature maps, with automatic fallback to mean.

Automatic GPU Transfer: Moves tensors to CUDA if not already on GPU before kernel launch.





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, reduction='mean'):
        super().__init__()
        self.reduction = reduction
        self.mse_loss = nn.MSELoss(reduction=reduction)

    def forward(self, input_features: list[torch.Tensor], target_features: list[torch.Tensor]) -> torch.Tensor:
        loss = 0.0
        for i in range(len(input_features)):
            loss += self.mse_loss(input_features[i], target_features[i])
        return loss


batch_size = 256
feature_shapes = [(64, 64, 64), (128, 32, 32), (256, 16, 16)]


def get_inputs():
    input_features = []
    target_features = []
    for c, h, w in feature_shapes:
        input_features.append(torch.randn(batch_size, c, h, w, dtype=torch.float32))
        target_features.append(torch.randn(batch_size, c, h, w, dtype=torch.float32))
    return [input_features, target_features]


def get_init_inputs():
    return ['mean']