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 activation function (GCU - x * cos(x)) with the following optimizations:
Grid-Stride Loop: Uses strided indexing to handle arbitrary tensor sizes efficiently, ensuring good GPU utilization regardless of input dimensions.
Memory Access Optimization: Employs __restrict__qualifiers and contiguous memory tensors to enable better compiler optimizations and reduce memory bank conflicts.
Fast Math Intrinsics: Uses __cosf()intrinsic for faster cosine calculations compared to standard math library functions.
Occupancy Optimization: Configures 256 threads per block and dynamically calculates grid size (up to 65535 blocks) to maximize GPU occupancy.
Compiler Optimizations: Enabled with -O3flag for aggressive performance optimization of the generated code.
Inlined Device Function: The core mathematical operation is marked with __forceinline__to eliminate function call overhead within the kernel.


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:
        return x * torch.cos(x)


batch_size = 64
feature_dim = 256


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


def get_init_inputs():
    # GCU 不需要特殊的初始化输入
    return []