import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Edge-only sampled dense-dense matmul.

    For each graph edge (src, dst), compute a dot product between source and
    destination node embeddings. This pattern appears in link prediction,
    sampled attention, and graph contrastive learning.
    """

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

    def forward(
        self,
        src_idx: torch.Tensor,
        dst_idx: torch.Tensor,
        src_feat: torch.Tensor,
        dst_feat: torch.Tensor,
    ) -> torch.Tensor:
        src = src_feat[src_idx.long()]
        dst = dst_feat[dst_idx.long()]
        return (src * dst).sum(dim=-1)


num_src_nodes = 1024
num_dst_nodes = 768
num_edges = 16384
feat_dim = 128


def get_inputs():
    p = popcorn_pri
    n_edges = p.jitter_int(num_edges)
    fd = p.jitter_int(feat_dim, align=8)
    ns = p.jitter_int(num_src_nodes)
    nd = p.jitter_int(num_dst_nodes)
    src_idx = torch.randint(0, ns, (n_edges,), dtype=torch.int32)
    dst_idx = torch.randint(0, nd, (n_edges,), dtype=torch.int32)
    src_feat = torch.randn(ns, fd, dtype=torch.float32)
    dst_feat = torch.randn(nd, fd, dtype=torch.float32)
    return [src_idx, dst_idx, src_feat, dst_feat]





def get_init_inputs():
    return []

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

class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()
        cuda_source = """
#include <torch/extension.h>
#include <cstdint>

__global__ void edge_dot_product(
    const int32_t* __restrict__ src_idx,
    const int32_t* __restrict__ dst_idx,
    const float* __restrict__ src_feat,
    const float* __restrict__ dst_feat,
    float* __restrict__ output,
    int num_edges,
    int feat_dim
) {
    int edge = blockIdx.x * blockDim.x + threadIdx.x;
    if (edge < num_edges) {
        int32_t src = src_idx[edge];
        int32_t dst = dst_idx[edge];
        float sum = 0.0f;
        for (int d = 0; d < feat_dim; d++) {
            sum += src_feat[src * feat_dim + d] * dst_feat[dst * feat_dim + d];
        }
        output[edge] = sum;
    }
}

torch::Tensor edge_dot(
    torch::Tensor src_idx,
    torch::Tensor dst_idx,
    torch::Tensor src_feat,
    torch::Tensor dst_feat
) {
    src_idx = src_idx.contiguous().to(torch::kInt32);
    dst_idx = dst_idx.contiguous().to(torch::kInt32);
    src_feat = src_feat.contiguous();
    dst_feat = dst_feat.contiguous();
    int num_edges = src_idx.size(0);
    int feat_dim = src_feat.size(1);
    auto output = torch::empty({num_edges}, src_feat.options());
    dim3 block(256);
    dim3 grid((num_edges + 255) / 256);
    edge_dot_product<<<grid, block>>>(
        src_idx.data_ptr<int32_t>(),
        dst_idx.data_ptr<int32_t>(),
        src_feat.data_ptr<float>(),
        dst_feat.data_ptr<float>(),
        output.data_ptr<float>(),
        num_edges,
        feat_dim
    );
    return output;
}
"""
        cpp_source = """
torch::Tensor edge_dot(torch::Tensor src_idx, torch::Tensor dst_idx, torch::Tensor src_feat, torch::Tensor dst_feat);
"""
        self.module = load_inline(
            name='edge_dot_module',
            cpp_sources=[cpp_source],
            cuda_sources=[cuda_source],
            functions=['edge_dot'],
            verbose=False
        )

    def forward(self, src_idx, dst_idx, src_feat, dst_feat):
        return self.module.edge_dot(src_idx, dst_idx, src_feat, dst_feat)