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.
Technologies Used in This Code
Core Libraries & Frameworks
PyTorch: Deep learning framework

CUDA: NVIDIA's parallel computing platform for GPU acceleration

C++: For high-performance kernel implementation

PyTorch Specific Components
torch.nn.Module: Base class for neural network modules

torch.nn.functional.F.softmax: Softmax activation function

torch.utils.cpp_extension.load_inline: For inline compilation of CUDA/C++ extensions

PyTorch Tensors: Multi-dimensional arrays with automatic differentiation

CUDA/C++ Implementation Details
CUDA Kernels: Custom GPU kernel (f_divergence_kernel)

Parallel Reduction: Tree-based reduction for sum computation

Shared Memory: Using __shared__ memory for inter-thread communication

Atomic Operations: atomicAdd for thread-safe global memory updates

Strided Access Pattern: Grid-stride loop for efficient memory access

Optimization Techniques
Shared Memory Reduction: Parallel reduction within thread blocks

Grid-Stride Loops: Efficient handling of arbitrary array sizes

Numerical Stability: Epsilon (eps) to prevent division by zero

Memory Coalescing: Optimized memory access patterns

Statistical/Machine Learning Components
F-Divergence: Statistical distance between two probability distributions

Chi-square-like metric: Implementation resembling chi-square divergence

Softmax Normalization: Converting logits to probability distributions

Batch Processing: Averaging over batch dimension

Performance Features
GPU Parallelization: Massively parallel computation across data elements

Fused Operations: Single kernel for complete divergence computation

Optimized Reduction: Efficient sum reduction using shared memory




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, eps=1e-8):
        super(Model, self).__init__()
        self.eps = eps

    def forward(self, p, q):
        p = F.softmax(p, dim=1)
        q = F.softmax(q, dim=1)

        divergence = (p - q) ** 2 / (q + self.eps)
        return torch.sum(divergence, dim=1).mean()


batch_size = 32
num_classes = 1000


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


def get_init_inputs():
    return []