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.

Key optimization techniques used in this implementation:

1. **Operator Fusion**: Fused chunk + gelu + elementwise multiplication into a single kernel
2. **Shared Memory Optimization**: Utilizes shared memory for cooperative data loading and reuse
3. **Dynamic Workload Balancing**: Adapts workload per block based on total elements
4. **Memory Access Coalescing**: Organized memory access patterns for better bandwidth utilization
5. **Exact GELU Implementation**: Maintains numerical precision with erf-based GELU

The custom kernel eliminates intermediate tensor allocations and reduces global memory traffic by processing the entire GeGLU operation in a single fused kernel with optimized memory hierarchy usage.
"""
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):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        GeGLU(x) = GELU(gate) * act
        """
        gate, act = x.chunk(2, dim=-1)

        return F.gelu(gate) * act


batch_size = 4096
feature_dim = 4096


def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return []