import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Multi-head sparse aggregation with per-edge attention weights.

    Each edge carries one weight per head, and source node features are arranged
    as `(num_nodes, num_heads, head_dim)`. The output aggregates weighted
    source features into `(num_nodes, num_heads, head_dim)`.
    """

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

    def forward(
        self,
        row_ptr: torch.Tensor,
        col_idx: torch.Tensor,
        edge_weight: torch.Tensor,
        node_feat: torch.Tensor,
    ) -> torch.Tensor:
        num_nodes = row_ptr.numel() - 1
        num_heads = node_feat.shape[1]
        head_dim = node_feat.shape[2]
        out = torch.zeros(num_nodes, num_heads, head_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:
                src = col_idx[start:end].long()
                weights = edge_weight[start:end].unsqueeze(-1)
                out[dst] = (node_feat[src] * weights).sum(dim=0)

        return out


num_nodes = 320
avg_degree = 14
num_heads = 8
head_dim = 32


def get_inputs():
    return list(
        popcorn_pri.make_csr_multihead_spmm_graph(
            num_nodes, avg_degree, num_heads, head_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 sparse_agg_kernel(
    const int* __restrict__ row_ptr,
    const int* __restrict__ col_idx,
    const float* __restrict__ edge_weight,
    const float* __restrict__ node_feat,
    float* __restrict__ out,
    int num_nodes,
    int num_heads,
    int head_dim
) {
    int dst = blockIdx.x;
    if (dst >= num_nodes) return;

    int tid = threadIdx.x;
    int total_dim = num_heads * head_dim;
    if (tid >= total_dim) return;

    int h = tid / head_dim;
    int d = tid % head_dim;

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

    float sum = 0.0f;
    for (int e = start; e < end; ++e) {
        int src = col_idx[e];
        float w = edge_weight[e * num_heads + h];
        float feat = node_feat[src * total_dim + h * head_dim + d];
        sum += w * feat;
    }

    out[dst * total_dim + tid] = sum;
}

torch::Tensor sparse_agg_cuda(
    torch::Tensor row_ptr,
    torch::Tensor col_idx,
    torch::Tensor edge_weight,
    torch::Tensor node_feat
) {
    int num_nodes = row_ptr.numel() - 1;
    int num_heads = node_feat.size(1);
    int head_dim = node_feat.size(2);

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

    int threads = num_heads * head_dim;
    dim3 blocks(num_nodes);

    sparse_agg_kernel<<<blocks, threads>>>(
        row_ptr.data_ptr<int>(),
        col_idx.data_ptr<int>(),
        edge_weight.data_ptr<float>(),
        node_feat.data_ptr<float>(),
        out.data_ptr<float>(),
        num_nodes,
        num_heads,
        head_dim
    );

    return out;
}
"""

cpp_source = """
torch::Tensor sparse_agg_cuda(
    torch::Tensor row_ptr,
    torch::Tensor col_idx,
    torch::Tensor edge_weight,
    torch::Tensor node_feat
);
"""

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

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

    def forward(self, row_ptr, col_idx, edge_weight, node_feat):
        return self.module.sparse_agg_cuda(row_ptr, col_idx, edge_weight, node_feat)