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**: NVIDIA GPU parallel computing
- **C++**: Kernel implementation with math.h

## CUDA Components
- **CUDA kernel**: `robust_scale_huber_kernel`
- **CUDA math functions**: `fabsf()` for absolute value
- **Element-wise parallelism**: One thread per element
- **Conditional branching**: Huber loss piecewise logic

## Mathematical Operations
1. **Robust standardization**: `(x - center) / scale`
2. **Huber loss function**: Piecewise quadratic/linear
   - Quadratic: `0.5 * y²` when |y| ≤ delta
   - Linear: `delta * (|y| - 0.5*delta)` when |y| > delta
3. **Absolute value**: `fabsf(y)` for condition check

## Architecture
- **Standard 1D grid**: Simple block/grid configuration
- **Element-wise computation**: Independent processing per element
- **Branching logic**: Condition based on delta threshold

## Robust Statistics Features
- **Outlier resistance**: Huber loss reduces influence of outliers
- **Parameterized**: User-defined center, scale, and delta
- **Piecewise behavior**: Smooth transition at delta boundary
- **Scale invariance**: Sensitive to scale parameter

## Performance Features
- **GPU acceleration**: Parallel computation across all elements
- **Simple operations**: Basic arithmetic and conditional logic
- **Memory efficiency**: Direct input-output mapping
- **Low computational cost**: Moderate operations per element

## Numerical Considerations
- **Scale requirement**: scale ≠ 0 (no protection in code)
- **Delta parameter**: Controls quadratic/linear transition point
- **Continuity**: Huber loss is C¹ continuous at |y| = delta
- **Absolute value**: Uses `fabsf()` for efficiency

## Use Case Applications
- **Robust regression**: Loss function resistant to outliers
- **Error metric**: Combines L1 and L2 loss properties
- **Standardized input**: Pre-scales data before loss computation
- **Custom loss function**: Parameterized Huber loss implementation

## Mathematical Properties
- **Convex**: Huber loss is convex for optimization
- **Differentiable**: Smooth derivative everywhere
- **Robustness**: Less sensitive to outliers than pure L2 loss
- **Parameter tuning**: Delta controls robustness vs sensitivity



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, center, scale, delta):
        super(Model, self).__init__()
        self.center = center
        self.scale = scale
        self.delta = delta

    def forward(self, x):
        y = (x - self.center) / self.scale
        abs_y = torch.abs(y)

        quad = 0.5 * y * y
        linear = self.delta * (abs_y - 0.5 * self.delta)

        return torch.where(abs_y <= self.delta, quad, linear)


batch_size = 1024
dim = 1024


def get_inputs():
    x = torch.randn(batch_size, dim) * 10.0
    return [x]


def get_init_inputs():
    return [0.0, 1.0, 1.0]