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 (affine_leaky_clamp_kernel)

CUDA Math Functions: fmaf() (fused multiply-add), fmaxf(), fminf()

Element-Wise Parallelism: One thread per tensor element

Simple Grid/Block Configuration: Standard 1D parallelization pattern

Activation Function Components
Affine Transformation: Linear scaling and shifting

Leaky ReLU: Modified ReLU with non-zero negative slope

Value Clamping: Hard limits on output range

Fused Operations: Multiple operations in single kernel

Mathematical Operations
Fused Multiply-Add: Efficient scale*x + shift computation

Conditional Activation: Positive pass-through, negative scaling

Range Limiting: Enforce min_val ≤ output ≤ max_val

Element-Wise Processing: Independent processing per element

Optimization Techniques
Fused Kernel Design: Single kernel combines multiple operations

FMA Optimization: Use of fused multiply-add instruction

Branching Efficiency: Simple conditional statements

Memory Coalescing: Straightforward memory access pattern

Performance Features
Massive Parallelization: GPU acceleration for activation function

Minimal Memory Traffic: In-place style computation

Low Computational Cost: Simple arithmetic operations

Numerical Stability: No complex numerical issues

Unique Implementation Aspects
Composite Activation: Combination of three different operations

Parameterized Design: Five tunable hyperparameters

Element-Wise Independence: No inter-element dependencies

Deterministic Output: Simple, predictable computation





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, scale, shift, negative_slope, min_val, max_val):
        super(Model, self).__init__()
        self.scale = scale
        self.shift = shift
        self.negative_slope = negative_slope
        self.min_val = min_val
        self.max_val = max_val

    def forward(self, x):
        x = x * self.scale + self.shift
        x = F.leaky_relu(x, negative_slope=self.negative_slope)
        return torch.clamp(x, self.min_val, self.max_val)

batch_size = 1024
dim = 1024

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

def get_init_inputs():
    return [2.0, 0.5, 0.1, -1.0, 1.0]