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.

Dual‑Kernel Strategy:

Element‑wise Kernel: Computes per‑element squared difference (student - teacher)^2 for reduction='none'.

Vectorized Reduction Kernel: Uses float4 loads for high‑throughput, shared‑memory tree reduction for 'mean'/'sum'.

Vectorized Processing: Main loop uses float4 (4‑element SIMD‑style) memory loads/stores for aligned data.

Shared‑Memory Parallel Reduction: Tree‑based sum across threads with extern __shared__ memory.

Atomic Finalization: atomicAdd accumulates block sums into a single‑element tensor.

Reduction Mode Control: Python passes integer mode (0=none, 1=mean, 2=sum) to select kernel and post‑processing.

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

Block/Thread Configuration: 256 threads per block, grid size capped at 1024 for reduction kernel.

Numerical Safety: Checks tensor sizes and CUDA device 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, logits_student: torch.Tensor, logits_teacher: torch.Tensor) -> torch.Tensor:
        return self.mse_loss(logits_student, logits_teacher)


batch_size = 256
num_classes = 1000


def get_inputs():
    logits_student = torch.randn(batch_size, num_classes, dtype=torch.float32)
    logits_teacher = torch.randn(batch_size, num_classes, dtype=torch.float32)
    return [logits_student, logits_teacher]


def get_init_inputs():
    return ['mean']