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 code implements IoU (Intersection over Union) + tanh activation with CUDA optimizations:

Element-wise parallelism - Each thread processes one box pair independently (no inter-thread communication).

Direct IoU computation - Computes intersection area with min/max operations and union area.

Numerical stability - Adds 1e-6 to denominator to avoid division by zero.

Fused activation - Applies tanh to IoU value in same kernel.

Memory coalescing - Accesses 4 consecutive float values per box (x1,y1,x2,y2).

Simple grid-stride mapping - Standard 1D grid/block for independent box pairs.

No shared memory needed - Pure element-wise computation without reduction.

CUDA math functions - Uses fmaxf, fminf, and tanh for hardware acceleration.

Boundary safety - Ensures non-negative widths/heights with fmaxf(0.0f, ...).

Batch processing - Handles N box pairs in parallel.


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):
        super(Model, self).__init__()

    def forward(self, box1, box2):
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]

        inter_x1 = torch.max(b1_x1, b2_x1)
        inter_y1 = torch.max(b1_y1, b2_y1)
        inter_x2 = torch.min(b1_x2, b2_x2)
        inter_y2 = torch.min(b1_y2, b2_y2)

        inter_area = (inter_x2 - inter_x1).clamp(min=0) * (inter_y2 - inter_y1).clamp(min=0)

        area1 = (b1_x2 - b1_x1).clamp(min=0) * (b1_y2 - b1_y1).clamp(min=0)
        area2 = (b2_x2 - b2_x1).clamp(min=0) * (b2_y2 - b2_y1).clamp(min=0)

        union_area = area1 + area2 - inter_area
        iou = inter_area / (union_area + 1e-6)

        return torch.tanh(iou).mean()

batch_size = 16
input_dim = 4

def get_inputs():
    box1 = torch.rand(batch_size, 4)
    box1[:, 2:] += box1[:, :2]
    box2 = torch.rand(batch_size, 4)
    box2[:, 2:] += box2[:, :2]
    return [box1, box2]

def get_init_inputs():
    return []