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 Bipolar ReLU (BReLU) activation function with the following optimizations:
Vectorization: Uses float4memory operations to process 4 elements per thread, significantly increasing memory throughput by leveraging vector loads/stores.
Cache Optimization: Employs __ldg()intrinsic for read-only data to leverage GPU's texture cache and improve memory access patterns.
Memory Coalescing: Accesses contiguous memory blocks through vector operations, optimizing GPU memory bandwidth utilization.
Grid-Stride Loop: Handles arbitrary-sized tensors efficiently by having threads process multiple elements with strided indexing.
Tail Processing: Separately handles non-multiple-of-4 elements after vectorized operations to ensure complete data processing.
Fast Math Optimization: Uses --use_fast_mathcompiler flag and fmaxf/fminfintrinsics for optimized mathematical operations.
Mathematical Function: Implements Bipolar ReLU activation with alternating behavior based on element index:
Even indices: Standard ReLU - max(0, x)
Odd indices: Inverted ReLU - min(0, x)(equivalent to -ReLU(-x))
Index-Based Alternation: Uses bitwise operation (index & 1) == 0for efficient even/odd checking, providing bipolar output pattern across the tensor.
Occupancy Optimization: Configures 256 threads per block and dynamically calculates grid size based on vectorized element count (threads × 4) to maximize GPU occupancy.
Compiler Optimizations: Enabled with -O3flag for aggressive performance optimization.
Inlined Device Function: The core conditional operation is marked with __forceinline__to eliminate function call overhead within the kernel.
Efficient Index Calculation: Computes base indices for vectorized operations using bit-shifting (i << 2) for optimal performance.




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:
        original_shape = x.shape
        x_flat = x.flatten()
        D = x_flat.size(0)

        indices = torch.arange(D, device=x.device)

        mask_even = (indices % 2 == 0)

        result = torch.zeros_like(x_flat)

        result[mask_even] = F.relu(x_flat[mask_even])

        result[~mask_even] = -F.relu(-x_flat[~mask_even])

        return result.view(original_shape)


batch_size = 128
feature_dim = 512


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


def get_init_inputs():
    return []