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
PyTorch: Deep learning framework

CUDA: GPU parallel computing

C++: Kernel implementation

CUDA Components
CUDA kernel: sign_mul_add_kernel

Element-wise parallelism: One thread per element

Simple branching: Sign extraction logic

Mathematical Operations
Sign function: Extract sign of tensor a (1, 0, or -1)

Element-wise multiplication: sign(a) × b

Element-wise addition: (sign(a) × b) + c

Three-input operation: Combines three tensors

Architecture
Standard CUDA pattern: 1D grid/block configuration

Three tensor inputs: a, b, c of same shape

Conditional logic: Branching for sign extraction





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

    def forward(self, a, b, c):
        return torch.sign(a) * b + c

batch_size = 4096
dim = 1024

def get_inputs():
    a = torch.randn(batch_size, dim)
    b = torch.randn(batch_size, dim)
    c = torch.randn(batch_size, dim)
    return [a, b, c]

def get_init_inputs():
    return []