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

Histogram-based loss computation for positive/negative sample distributions

Soft histogram binning with linear interpolation weights

Atomic operations (atomicAdd) for histogram accumulation

Per-bin kernel parallelization with center-based distance calculation

Cumulative distribution function (CDF) computation via torch::cumsum

Probability density function (PDF) normalization

Contiguous tensor handling for input data

Numerical stability with epsilon addition (1e-8)

Dynamic kernel configuration based on element counts

Histogram intersection loss via PDF-CDF product sum




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, num_bins=10, min_val=0.0, max_val=1.0):
        super().__init__()
        self.num_bins = num_bins
        self.min_val = min_val
        self.max_val = max_val
        self.step = (max_val - min_val) / num_bins
        self.centers = torch.linspace(min_val + self.step / 2, max_val - self.step / 2, num_bins)

    def forward(self, pos: torch.Tensor, neg: torch.Tensor) -> torch.Tensor:
        delta = self.step
        centers = self.centers.to(pos.device)

        pos_rep = pos.unsqueeze(1).repeat(1, self.num_bins)
        neg_rep = neg.unsqueeze(1).repeat(1, self.num_bins)

        centers_rep_pos = centers.unsqueeze(0).repeat(pos.size(0), 1)
        centers_rep_neg = centers.unsqueeze(0).repeat(neg.size(0), 1)

        pos_hist = torch.clamp(1 - torch.abs(pos_rep - centers_rep_pos) / delta, min=0)
        neg_hist = torch.clamp(1 - torch.abs(neg_rep - centers_rep_neg) / delta, min=0)

        pos_hist_sum = pos_hist.sum(dim=0)
        neg_hist_sum = neg_hist.sum(dim=0)

        pos_cdf = torch.cumsum(pos_hist_sum, dim=0)
        pos_cdf = pos_cdf / (pos_cdf[-1] + 1e-8)

        neg_pdf = neg_hist_sum / (neg_hist_sum.sum() + 1e-8)

        loss = (neg_pdf * pos_cdf).sum()

        return loss


batch_size = 128
num_features = 512


def get_inputs():
    pos = torch.rand(batch_size, dtype=torch.float32)
    neg = torch.rand(batch_size, dtype=torch.float32)
    return [pos, neg]


def get_init_inputs():
    return [10, 0.0, 1.0]