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

PyTorch Tensors: Multi-dimensional arrays

torch::empty_like(): Tensor creation with same properties

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

Element-Wise Parallelism: One thread per tensor element

Simple Grid/Block Configuration: Standard 1D parallelization pattern

Conditional Branching: GPU-friendly threshold comparison

Activation/Processing Components
Threshold Function: Binary thresholding operation

Scaling Operation: Multiplication by scale factor

Negation Operation: Sign inversion (multiplication by -1)

Conditional Activation: Different behavior above/below threshold

Mathematical Operations
Comparison Operation: x > threshold check

Multiplication: x * scale scaling

Negation: -(value) sign inversion

Zero Assignment: Below-threshold values set to 0

Optimization Techniques
Simple Branching: GPU-optimized conditional logic

Fused Operations: Threshold, scale, and negate in single kernel

Memory Coalescing: Straightforward memory access pattern

Element-Wise Independence: No inter-element dependencies

Performance Features
Massive Parallelization: GPU acceleration for thresholding operation

Minimal Memory Traffic: Direct computation to output

Low Computational Cost: Simple arithmetic and comparison

Deterministic Output: Predictable, piecewise function

Unique Implementation Aspects
Composite Operation: Threshold + scale + negate in one step

Two-Parameter Design: Tunable threshold and scale values

Zero/Non-Zero Output: Below-threshold values always zero

Negative Activation: Above-threshold outputs are always negative

Custom Activation: Specialized non-linear function

Potential Applications
Sparse Activation: Creates sparse negative activations

Feature Selection: Threshold-based feature suppression

Custom Regularization: Specialized activation for specific tasks

Signal Processing: Threshold-based signal modification




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, threshold, scale):
        super(Model, self).__init__()
        self.threshold = threshold
        self.scale = scale

    def forward(self, x):
        return torch.where(x > self.threshold, -x * self.scale, torch.tensor(0.0, dtype=x.dtype, device=x.device))

batch_size = 1024
dim = 1024

def get_inputs():
    x = torch.randn(batch_size, dim)
    return [x]

def get_init_inputs():
    return [0.5, 2.0]