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.

CUDA Optimization Strategies:

Parallel Reduction

Warp shuffle operations (__shfl_down_sync)

Shared memory for block-level reduction

Double precision accumulation

Memory Access

contiguous() tensors for coalesced access

__restrict__ pointers

Sequential memory access per thread

Computation Optimization

Avoids redundant sqrt call by reusing squared distance

Compiler flag: -O3

__forceinline__ for reduction functions

Kernel Design

One block per sample, 256 threads per block

Threads process feature dimension with stride

Final mean reduction on PyTorch side

Numerical Stability

Double precision for distance calculation

Explicit bounds checking (max(0, margin-dist))




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, margin=2.0):
        super().__init__()
        self.margin = margin

    def forward(self, x1: torch.Tensor, x2: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        dist = F.pairwise_distance(x1, x2, keepdim=True)

        loss_con = (1 - y) * torch.pow(dist, 2)
        loss_dis = y * torch.pow(torch.clamp(self.margin - dist, min=0.0), 2)

        loss = 0.5 * (loss_con + loss_dis)
        return loss.mean()


batch_size = 128
feature_dim = 512


def get_inputs():
    x1 = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    x2 = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    y = torch.randint(0, 2, (batch_size, 1), dtype=torch.float32)
    return [x1, x2, y]


def get_init_inputs():
    return [2.0]