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

SimCLR (Simple Contrastive Learning) loss computation

Multi-kernel design: normalization + similarity + loss computation

Row-wise L2 normalization with shared memory reduction

Similarity matrix computation via torch::matmul (all-vs-all)

Positive pair identification via index arithmetic (i↔i+batch_size)

Warp-level reduction utilities for max/sum operations

Numerically stable softmax with max subtraction and temperature scaling

Contiguous tensor handling for memory coalescing

Dynamic kernel configuration for variable batch sizes

Mean reduction across all augmented samples




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

    def forward(self, z_i: torch.Tensor, z_j: torch.Tensor) -> torch.Tensor:
        batch_size = z_i.shape[0]
        z = torch.cat([z_i, z_j], dim=0)

        sim_matrix = torch.matmul(z, z.T) / self.temperature

        mask = torch.eye(2 * batch_size, dtype=torch.bool, device=z.device)
        sim_matrix = sim_matrix.masked_fill(mask, -9e15)

        pos_sim = torch.cat([
            torch.diag(sim_matrix, batch_size),
            torch.diag(sim_matrix, -batch_size)
        ], dim=0)

        loss = -pos_sim + torch.logsumexp(sim_matrix, dim=1)
        loss = loss.mean()

        return loss


batch_size = 16
dim = 128


def get_inputs():
    z_i = torch.randn(batch_size, dim)
    z_i = torch.nn.functional.normalize(z_i, dim=1)
    z_j = torch.randn(batch_size, dim)
    z_j = torch.nn.functional.normalize(z_j, dim=1)
    return [z_i, z_j]


def get_init_inputs():
    temperature = 0.5
    return [temperatur