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.
CUDA kernel for CrossEntropy-Dice Loss with shared memory parallel reduction.

Optimizations:

Numerically stable sigmoid/log-sigmoid using exp(-|x|).

Parallel tree reduction in shared memory (4 concurrent reductions).

Batch-level parallelism (one block per sample).

Double precision for accuracy.

Kernel computes per batch:

intersection = Σ(p*y) for Dice

sum_probs = Σ(p)

sum_targets = Σ(y)

ce_sum = Σ(BCE loss)

Final loss:
L = α·(1-mean(Dice)) + (1-α)·mean(CE)




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


class Model(nn.Module):
    def __init__(self, alpha=0.5, smooth=1.0):
        super().__init__()
        self.alpha = alpha
        self.smooth = smooth

    def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
        targets_f = targets.float()

        probs = logits.sigmoid()
        probs = probs.flatten(1)
        targets_f_flat = targets_f.flatten(1)

        intersection = (probs * targets_f_flat).sum(dim=1)
        dice = (2.0 * intersection + self.smooth) / (probs.sum(dim=1) + targets_f_flat.sum(dim=1) + self.smooth)
        dice_loss = 1.0 - dice.mean()

        ce_loss = F.binary_cross_entropy_with_logits(logits, targets_f, reduction='mean')

        return self.alpha * dice_loss + (1.0 - self.alpha) * ce_loss


batch_size = 128
feature_dim = 100


def get_inputs():
    logits = torch.randn(batch_size, feature_dim, dtype=torch.float64)
    targets = torch.randint(0, 2, (batch_size, feature_dim), dtype=torch.float64)
    return [logits, targets]


def get_init_inputs():
    return [0.5, 1.0]