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

Knowledge distillation loss computation (KL divergence with temperature scaling)

Warp-level reduction templates for max and sum operations

Atomic max operation on floats using compare-and-swap (CAS) loop

Numerically stable softmax with max subtraction

Shared memory caching for intermediate statistics (max, sum, KL)

Per-batch parallel processing (one CUDA block per sample)

Temperature scaling applied to logits before softmax

KL divergence calculation: Σ p_teacher·(log(p_teacher) - log(p_student))

Contiguous tensor handling for memory coalescing

Temperature-squared scaling in final loss reduction



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

    def forward(self, student_logits: torch.Tensor, teacher_logits: torch.Tensor) -> torch.Tensor:
        soft_student = F.log_softmax(student_logits / self.temperature, dim=1)
        soft_teacher = F.softmax(teacher_logits / self.temperature, dim=1)
        kl_div = F.kl_div(soft_student, soft_teacher, reduction='batchmean')
        return kl_div * (self.temperature ** 2)


batch_size = 128
num_classes = 1000
temperature = 4.0


def get_inputs():
    student = torch.randn(batch_size, num_classes, requires_grad=True)
    teacher = torch.randn(batch_size, num_classes)
    return [student, teacher]


def get_init_inputs():
    return [temperature]