You write custom CUDA kernels to replace the PyTorch operators in the given EvoNorm 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 normalization+affine_transform+nonlinear_gating), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.

Techniques used:

Fused kernel design: Single kernel computes entire IoU loss pipeline

Element-wise parallelism: One thread per bounding box pair

Memory optimization: Coalesced memory access with contiguous tensors

CUDA math intrinsics: fmaxf, fminf for efficient min/max operations

Numerical stability: EPSILON (1e-6f) to prevent division by zero

Grid-stride loops: Efficient thread scheduling with block size 256

Fast math compilation: --use_fast_math flag for optimized math operations

Inline CUDA extension: Runtime kernel compilation in PyTorch

Key optimization features:

No intermediate tensors: Eliminates Python-CUDA data transfer overhead

Direct coordinate access: Bypasses PyTorch's split() operation

Branch-free computation: Minimal conditional logic in kernel

Batch-level parallelism: Perfect for object detection workloads

Memory locality: Sequential access pattern for pred/target coordinates

Performance advantages:

~5-10x reduction in kernel launches vs Python version

Eliminates temporary tensor allocations

Better cache utilization through fused operations

Optimal for real-time object detection training


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


B, COORD = 32, 4
EPS = 1e-6


class IoULossBatch(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        px1, py1, px2, py2 = pred.split(1, dim=1)
        tx1, ty1, tx2, ty2 = target.split(1, dim=1)

        # 1. 计算交集区域坐标
        ix1 = torch.max(px1, tx1)
        iy1 = torch.max(py1, ty1)
        ix2 = torch.min(px2, tx2)
        iy2 = torch.min(py2, ty2)

        # 2. 计算交集区域面积 (确保宽度/高度非负)
        iw = torch.max(ix2 - ix1, torch.tensor(0.0).to(pred.device))
        ih = torch.max(iy2 - iy1, torch.tensor(0.0).to(pred.device))
        intersection = iw * ih

        # 3. 计算预测框和目标框的面积
        area_p = (px2 - px1) * (py2 - py1)
        area_t = (tx2 - tx1) * (ty2 - ty1)

        # 4. 计算并集区域面积: Union = Area_p + Area_t - Intersection
        union = area_p + area_t - intersection

        # 5. 计算 IoU
        iou = intersection / (union + EPS)

        # 6. 计算 IoU Loss (1 - IoU)
        loss = 1.0 - iou

        return loss.mean()


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.op = IoULossBatch()

    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        return self.op(pred, target)


def get_inputs():
    torch.manual_seed(42)
    # 随机生成坐标 (0, 100) 范围的基数
    base = torch.rand(B, COORD) * 100

    # 构建预测框 (确保 x1 < x2 且 y1 < y2)
    pred = torch.empty_like(base)
    pred[:, 0] = torch.min(base[:, 0], base[:, 2])
    pred[:, 1] = torch.min(base[:, 1], base[:, 3])
    pred[:, 2] = torch.max(base[:, 0], base[:, 2])
    pred[:, 3] = torch.max(base[:, 1], base[:, 3])

    # 构建目标框
    target = torch.empty_like(base)
    target[:, 0] = torch.min(base[:, 0] + 5, base[:, 2] + 5)
    target[:, 1] = torch.min(base[:, 1] + 5, base[:, 3] + 5)
    target[:, 2] = torch.max(base[:, 0] + 5, base[:, 2] + 5)
    target[:, 3] = torch.max(base[:, 1] + 5, base[:, 3] + 5)

    return [pred, target]


def get_init_inputs():
    return []