import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

cuda_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>

__global__ void sqrt_reciprocal_rsqrt_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int size
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        float x = input[idx];

        float y = sqrtf(x);
        float z = 1.0f / y;

        output[idx] = rsqrtf(z);
    }
}

torch::Tensor sqrt_reciprocal_rsqrt_cuda(torch::Tensor input) {
    auto output = torch::empty_like(input);
    int size = input.numel();

    const int block_size = 256;
    int num_blocks = (size + block_size - 1) / block_size;

    sqrt_reciprocal_rsqrt_kernel<<<num_blocks, block_size>>>(
        input.data_ptr<float>(),
        output.data_ptr<float>(),
        size
    );

    return output;
}
"""

cpp_source = """
torch::Tensor sqrt_reciprocal_rsqrt_cuda(torch::Tensor input);
"""

module = load_inline(
    name="sqrt_reciprocal_rsqrt",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["sqrt_reciprocal_rsqrt_cuda"],
    verbose=True
)


class ModelNew(nn.Module):
    def __init__(self):
        super(ModelNew, self).__init__()
        self.module = module

    def forward(self, x):
        return self.module.sqrt_reciprocal_rsqrt_cuda(x)