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.

This CUDA kernel implements a custom Hard ELiSH activation function with the following optimizations:
Tiled Kernel Design: Uses a block-based tiling approach where each thread processes elements within its assigned block, improving memory locality and cache efficiency compared to naive grid-stride loops.
Branch Prediction Optimization: Implements conditional execution (x ≥ 0 vs x < 0) at the thread level, allowing warp-level parallelism to maintain efficiency despite the branch divergence.
Fast Math Operations: Uses CUDA's fmaxf, fminf, and expfintrinsics for optimized mathematical computations without sacrificing readability.
Memory Access Optimization: Employs __restrict__qualifiers and contiguous memory tensors to enable better compiler optimizations and reduce memory bank conflicts.
Compiler Optimizations: Enabled with -O3flag for aggressive performance optimization of the generated machine code.
Occupancy Optimization: Configures 256 threads per block and dynamically calculates grid size (up to 65535 blocks) to maximize GPU occupancy and resource utilization.
Inlined Device Functions: Both the core activation function (hard_elish_op) and helper gate function (gate_op) are marked with __forceinline__to eliminate function call overhead within the kernel.
Numerical Stability: Carefully implements the Hard ELiSH function with proper handling of the exponential term for negative inputs to maintain numerical precision.



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:
        gate = torch.clamp(x / 2.0 + 0.5, min=0.0, max=1.0)

        positive_mask = x >= 0

        output = torch.empty_like(x)

        # x >= 0: x * gate
        output_pos = x.masked_select(positive_mask) * gate.masked_select(positive_mask)
        output.masked_scatter_(positive_mask, output_pos)

        # x < 0: (exp(x) - 1) * gate
        x_neg = x.masked_select(~positive_mask)
        gate_neg = gate.masked_select(~positive_mask)
        output_neg = (torch.exp(x_neg) - 1.0) * gate_neg
        output.masked_scatter_(~positive_mask, output_neg)

        return output


batch_size = 512
feature_dim = 1024


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


def get_init_inputs():
    return []