import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Degree-normalized neighbor aggregation.

    Computes a GCN-style sparse aggregation where each incoming message is
    scaled by 1 / sqrt(deg(dst) * deg(src)). This captures a common spectral
    graph normalization pattern over CSR adjacency.
    """

    def __init__(self):
        super().__init__()

    def forward(
        self,
        row_ptr: torch.Tensor,
        col_idx: torch.Tensor,
        node_feat: torch.Tensor,
        degrees: torch.Tensor,
    ) -> torch.Tensor:
        num_nodes = row_ptr.numel() - 1
        feat_dim = node_feat.shape[1]
        out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)

        for dst in range(num_nodes):
            start = int(row_ptr[dst].item())
            end = int(row_ptr[dst + 1].item())
            if end <= start:
                continue
            src = col_idx[start:end].long()
            norm = 1.0 / torch.sqrt(degrees[dst] * degrees[src]).unsqueeze(-1)
            out[dst] = (node_feat[src] * norm).sum(dim=0)

        return out


num_nodes = 640
avg_degree = 16
feat_dim = 80




def get_inputs():
    return list(popcorn_pri.make_csr_degree_normalized_graph(num_nodes, avg_degree, feat_dim))



def get_init_inputs():
    return []

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

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

__global__ void gcn_aggregate_kernel(
    const int* __restrict__ row_ptr,
    const int* __restrict__ col_idx,
    const float* __restrict__ node_feat,
    const float* __restrict__ degrees,
    float* __restrict__ out,
    int num_nodes,
    int feat_dim
) {
    int dst = blockIdx.x;
    if (dst >= num_nodes) return;

    float deg_dst = degrees[dst];
    float inv_sqrt_deg_dst = rsqrtf(deg_dst);

    int start = row_ptr[dst];
    int end = row_ptr[dst + 1];

    for (int f = threadIdx.x; f < feat_dim; f += blockDim.x) {
        float sum = 0.0f;
        for (int i = start; i < end; ++i) {
            int src = col_idx[i];
            float deg_src = degrees[src];
            float norm = inv_sqrt_deg_dst * rsqrtf(deg_src);
            sum += node_feat[src * feat_dim + f] * norm;
        }
        out[dst * feat_dim + f] = sum;
    }
}

torch::Tensor gcn_aggregate_cuda(
    torch::Tensor row_ptr,
    torch::Tensor col_idx,
    torch::Tensor node_feat,
    torch::Tensor degrees
) {
    int num_nodes = row_ptr.size(0) - 1;
    int feat_dim = node_feat.size(1);

    auto out = torch::zeros({num_nodes, feat_dim}, node_feat.options());

    dim3 blocks(num_nodes);
    dim3 threads(32);

    gcn_aggregate_kernel<<<blocks, threads>>>(
        row_ptr.data_ptr<int>(),
        col_idx.data_ptr<int>(),
        node_feat.data_ptr<float>(),
        degrees.data_ptr<float>(),
        out.data_ptr<float>(),
        num_nodes,
        feat_dim
    );

    return out;
}
"""

cpp_source = """
torch::Tensor gcn_aggregate_cuda(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);
"""

module = load_inline(
    name="gcn_aggregate",
    cpp_sources=[cpp_source],
    cuda_sources=[cuda_source],
    functions=["gcn_aggregate_cuda"],
    verbose=False,
    extra_cuda_cflags=["-O3"]
)

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

    def forward(self, row_ptr, col_idx, node_feat, degrees):
        return self.module.gcn_aggregate_cuda(row_ptr, col_idx, node_feat, degrees)