Repository: DeepGraphLearning/NBFNet
Branch: master
Commit: eb49ff2770d1
Files: 23
Total size: 620.1 KB

Directory structure:
gitextract_671zkorr/

├── .gitignore
├── LICENSE
├── README.md
├── config/
│   ├── homogeneous_graph/
│   │   ├── citeseer.yaml
│   │   ├── cora.yaml
│   │   └── pubmed.yaml
│   ├── inductive/
│   │   ├── fb15k237.yaml
│   │   └── wn18rr.yaml
│   └── knowledge_graph/
│       ├── fb15k237.yaml
│       ├── fb15k237_visualize.yaml
│       ├── ogbl-biokg.yaml
│       ├── ogbl-biokg_test.yaml
│       └── wn18rr.yaml
├── data/
│   └── fb15k237_entity.txt
├── nbfnet/
│   ├── __init__.py
│   ├── dataset.py
│   ├── layer.py
│   ├── model.py
│   ├── task.py
│   └── util.py
├── requirements.txt
└── script/
    ├── run.py
    └── visualize.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
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# PyCharm
/.idea

# VS Code
/.vscode

# Python
__pycache__
*.pyc

# macOS
.DS_Store

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2021 MilaGraph

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

================================================
FILE: README.md
================================================
# NBFNet: Neural Bellman-Ford Networks #

This is the official codebase of the paper

[Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction][paper]

[Zhaocheng Zhu](https://kiddozhu.github.io),
[Zuobai Zhang](https://oxer11.github.io),
[Louis-Pascal Xhonneux](https://github.com/lpxhonneux),
[Jian Tang](https://jian-tang.com)

[paper]: https://arxiv.org/pdf/2106.06935.pdf

A PyG re-implementation of NBFNet can be found [here](https://github.com/KiddoZhu/NBFNet-PyG).

## Overview ##

NBFNet is a graph neural network framework inspired by traditional path-based
methods. It enjoys the advantages of both traditional path-based methods and modern
graph neural networks, including **generalization in the inductive setting**,
**interpretability**, **high model capacity** and **scalability**. NBFNet can be
applied to solve link prediction on both homogeneous graphs and knowledge graphs.

![NBFNet](asset/nbfnet.svg)

This codebase is based on PyTorch and [TorchDrug]. It supports training and inference
with multiple GPUs or multiple machines.

[TorchDrug]: https://github.com/DeepGraphLearning/torchdrug

## Installation ##

You may install the dependencies via either conda or pip. Generally, NBFNet works
with Python 3.7/3.8 and PyTorch version >= 1.8.0.

### From Conda ###

```bash
conda install torchdrug pytorch=1.8.2 cudatoolkit=11.1 -c milagraph -c pytorch-lts -c pyg -c conda-forge
conda install ogb easydict pyyaml -c conda-forge
```

### From Pip ###

```bash
pip install torch==1.8.2+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install torchdrug
pip install ogb easydict pyyaml
```

## Reproduction ##

To reproduce the results of NBFNet, use the following command. Alternatively, you
may use `--gpus null` to run NBFNet on a CPU. All the datasets will be automatically
downloaded in the code.

```bash
python script/run.py -c config/inductive/wn18rr.yaml --gpus [0] --version v1
```

We provide the hyperparameters for each experiment in configuration files.
All the configuration files can be found in `config/*/*.yaml`.

For experiments on inductive relation prediction, you need to additionally specify
the split version with `--version v1`.

To run NBFNet with multiple GPUs or multiple machines, use the following commands

```bash
python -m torch.distributed.launch --nproc_per_node=4 script/run.py -c config/inductive/wn18rr.yaml --gpus [0,1,2,3]
```

```bash
python -m torch.distributed.launch --nnodes=4 --nproc_per_node=4 script/run.py -c config/inductive/wn18rr.yaml --gpus [0,1,2,3,0,1,2,3,0,1,2,3,0,1,2,3]
```

### Visualize Interpretations on FB15k-237 ###

Once you have models trained on FB15k237, you can visualize the path interpretations
with the following line. Please replace the checkpoint with your own path.

```bash
python script/visualize.py -c config/knowledge_graph/fb15k237_visualize.yaml --checkpoint /path/to/nbfnet/experiment/model_epoch_20.pth
```

### Evaluate ogbl-biokg ###

Due to the large size of ogbl-biokg, we only evaluate on a small portion of the
validation set during training. The following line evaluates a model on the full
validation / test sets of ogbl-biokg. Please replace the checkpoint with your own
path.

```bash
python script/run.py -c config/knowledge_graph/ogbl-biokg_test.yaml --checkpoint /path/to/nbfnet/experiment/model_epoch_10.pth
```

## Results ##

Here are the results of NBFNet on standard benchmark datasets. All the results are
obtained with 4 V100 GPUs (32GB). Note results may be slightly different if the
model is trained with 1 GPU and/or a smaller batch size.

### Knowledge Graph Completion ###

<table>
    <tr>
        <th>Dataset</th>
        <th>MR</th>
        <th>MRR</th>
        <th>HITS@1</th>
        <th>HITS@3</th>
        <th>HITS@10</th>
    </tr>
    <tr>
        <th>FB15k-237</th>
        <td>114</td>
        <td>0.415</td>
        <td>0.321</td>
        <td>0.454</td>
        <td>0.599</td>
    </tr>
    <tr>
        <th>WN18RR</th>
        <td>636</td>
        <td>0.551</td>
        <td>0.497</td>
        <td>0.573</td>
        <td>0.666</td>
    </tr>
    <tr>
        <th>ogbl-biokg</th>
        <td>-</td>
        <td>0.829</td>
        <td>0.768</td>
        <td>0.870</td>
        <td>0.946</td>
    </tr>
</table>

### Homogeneous Graph Link Prediction ###

<table>
    <tr>
        <th>Dataset</th>
        <th>AUROC</th>
        <th>AP</th>
    </tr>
    <tr>
        <th>Cora</th>
        <td>0.956</td>
        <td>0.962</td>
    </tr>
    <tr>
        <th>CiteSeer</th>
        <td>0.923</td>
        <td>0.936</td>
    </tr>
    <tr>
        <th>PubMed</th>
        <td>0.983</td>
        <td>0.982</td>
    </tr>
</table>

### Inductive Relation Prediction ###

<table>
    <tr>
        <th rowspan="2">Dataset</th>
        <th colspan="4">HITS@10 (50 sample)</th>
    </tr>
    <tr>
        <th>v1</th>
        <th>v2</th>
        <th>v3</th>
        <th>v4</th>
    </tr>
    <tr>
        <th>FB15k-237</th>
        <td>0.834</td>
        <td>0.949</td>
        <td>0.951</td>
        <td>0.960</td>
    </tr>
    <tr>
        <th>WN18RR</th>
        <td>0.948</td>
        <td>0.905</td>
        <td>0.893</td>
        <td>0.890</td>
    </tr>
</table>

Frequently Asked Questions
--------------------------

1. **The code is stuck at the beginning of epoch 0.**

   This is probably because the JIT cache is broken.
   Try `rm -r ~/.cache/torch_extensions/*` and run the code again.

Citation
--------

If you find this codebase useful in your research, please cite the following paper.

```bibtex
@article{zhu2021neural,
  title={Neural bellman-ford networks: A general graph neural network framework for link prediction},
  author={Zhu, Zhaocheng and Zhang, Zuobai and Xhonneux, Louis-Pascal and Tang, Jian},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}
```


================================================
FILE: config/homogeneous_graph/citeseer.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: CiteSeerLinkPrediction
  path: ~/datasets/homogeneous_graphs/

task:
  class: LinkPrediction
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: no
    remove_one_hop: yes
    symmetric: yes
  criterion: bce
  num_negative: 1
  strict_negative: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 64

train:
  num_epoch: 20

metric: auroc

================================================
FILE: config/homogeneous_graph/cora.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: CoraLinkPrediction
  path: ~/datasets/homogeneous_graphs/

task:
  class: LinkPrediction
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: no
    remove_one_hop: yes
    symmetric: yes
  criterion: bce
  num_negative: 1
  strict_negative: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 64

train:
  num_epoch: 20

metric: auroc

================================================
FILE: config/homogeneous_graph/pubmed.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: PubMedLinkPrediction
  path: ~/datasets/homogeneous_graphs/

task:
  class: LinkPrediction
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: no
    remove_one_hop: yes
    symmetric: yes
  criterion: bce
  num_negative: 1
  strict_negative: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 16

train:
  num_epoch: 10

metric: auroc

================================================
FILE: config/inductive/fb15k237.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: FB15k237Inductive
  path: ~/datasets/knowledge_graphs/
  version: {{ version }}

task:
  class: InductiveKnowledgeGraphCompletion
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: yes
    remove_one_hop: yes
  criterion: bce
  num_negative: 32
  strict_negative: yes
  adversarial_temperature: 0.5
  sample_weight: no
  full_batch_eval: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 64

train:
  num_epoch: 20

metric: mrr

================================================
FILE: config/inductive/wn18rr.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: WN18RRInductive
  path: ~/datasets/knowledge_graphs/
  version: {{ version }}

task:
  class: InductiveKnowledgeGraphCompletion
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: no
  criterion: bce
  num_negative: 32
  strict_negative: yes
  adversarial_temperature: 1
  sample_weight: no
  full_batch_eval: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 32

train:
  num_epoch: 20

metric: mrr

================================================
FILE: config/knowledge_graph/fb15k237.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: FB15k237
  path: ~/datasets/knowledge_graphs/

task:
  class: KnowledgeGraphCompletion
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: yes
    remove_one_hop: yes
  criterion: bce
  num_negative: 32
  strict_negative: yes
  adversarial_temperature: 0.5
  sample_weight: no
  full_batch_eval: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 64

train:
  num_epoch: 20

metric: mrr

================================================
FILE: config/knowledge_graph/fb15k237_visualize.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: FB15k237
  path: ~/datasets/knowledge_graphs/

task:
  class: KnowledgeGraphCompletion
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: yes
    remove_one_hop: yes
  criterion: bce
  num_negative: 32
  strict_negative: yes
  adversarial_temperature: 0.5
  sample_weight: no
  full_batch_eval: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: [0]
  batch_size: 64

checkpoint: {{ checkpoint }}

metric: mrr

================================================
FILE: config/knowledge_graph/ogbl-biokg.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: OGBLBioKG
  path: ~/datasets/knowledge_graphs/

task:
  class: KnowledgeGraphCompletionOGB
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: yes
    remove_one_hop: yes
  criterion: bce
  evaluator:
    class: Evaluator
    name: ogbl-biokg
  num_negative: 32
  strict_negative: yes
  heterogeneous_negative: yes
  adversarial_temperature: 0.5
  sample_weight: no

optimizer:
  class: Adam
  lr: 2.0e-4

engine:
  gpus: {{ gpus }}
  batch_size: 8
  log_interval: 10

train:
  num_epoch: 10
  batch_per_epoch: 200

metric: mrr
fast_test: 1000

================================================
FILE: config/knowledge_graph/ogbl-biokg_test.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: OGBLBioKG
  path: ~/datasets/knowledge_graphs/

task:
  class: KnowledgeGraphCompletionOGB
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: yes
    remove_one_hop: yes
  criterion: bce
  evaluator:
    class: Evaluator
    name: ogbl-biokg
  num_negative: 32
  strict_negative: yes
  heterogeneous_negative: yes
  adversarial_temperature: 0.5
  sample_weight: no

optimizer:
  class: Adam
  lr: 2.0e-4

engine:
  gpus: {{ gpus }}
  batch_size: 32
  log_interval: 10

train:
  num_epoch: 0
  batch_per_epoch: 200

checkpoint: {{ checkpoint }}
metric: mrr

================================================
FILE: config/knowledge_graph/wn18rr.yaml
================================================
output_dir: ~/experiments/

dataset:
  class: WN18RR
  path: ~/datasets/knowledge_graphs/

task:
  class: KnowledgeGraphCompletion
  model:
    class: NBFNet
    input_dim: 32
    hidden_dims: [32, 32, 32, 32, 32, 32]
    message_func: distmult
    aggregate_func: pna
    short_cut: yes
    layer_norm: yes
    dependent: no
  criterion: bce
  num_negative: 32
  strict_negative: yes
  adversarial_temperature: 1
  sample_weight: no
  full_batch_eval: yes

optimizer:
  class: Adam
  lr: 5.0e-3

engine:
  gpus: {{ gpus }}
  batch_size: 32

train:
  num_epoch: 20

metric: mrr

================================================
FILE: data/fb15k237_entity.txt
================================================
/m/027rn	languages of dominican republic (Q786)
/m/06cx9	republic (government) (Q7270)
/m/017dcd	rangers in reverse (Q513809)
/m/06v8s0	elyse floyd (Q2642437)
/m/07s9rl0	drama movie (Q130232)
/m/0170z3	danny vinyard (Q208572)
/m/01sl1q	michelle rodríguez (Q184219)
/m/044mz_	naveen andrews (Q208374)
/m/0cnk2q	australia national soccer team (Q268208)
/m/02nzb8	right winger (football) (Q193592)
/m/04nrcg	mdv (Q387312)
/m/02sdk9v	outside forward (Q280658)
/m/07nznf	bryan singer/comments (Q220751)
/m/014lc_	star trek 11 (cancelled film) (Q388319)
/m/02qyp19	bafta award for best original screenplay 1983–1999 (Q41375)
/m/02d413	philadelphia (film) (Q204057)
/m/0q9kd	daniel michael devito jr. (Q26806)
/m/0184jc	guy pearce (Q223745)
/m/03q5t	harpischord (Q81982)
/m/07y_7	violin tuning (Q8355)
/m/0gqng	academy award for best foreign language film (Q105304)
/m/073hkh	61th academy awards (Q924339)
/m/0b76d_m	anonymous (film) (Q313819)
/m/029j_	dual layer (Q5294)
/m/014_x2	city of angels (film) (Q168010)
/m/012ljv	gabriel yared (Q502957)
/m/0ds35l9	bridesmaids (2011 film) (Q837264)
/m/05r4w	republica portuguesa (Q45)
/m/015qsq	serpico (film) (Q657079)
/m/02bjrlw	history of the italian language (Q652)
/m/04bdxl	catherine keener (Q230378)
/m/02s2ft	chris cooper (actor) (Q273208)
/m/09c7w0	usofa (Q30)
/m/0rs6x	saint augustine (fl) (Q487988)
/m/079vf	stanley lee (Q181900)
/m/0d90m	x-men i (Q106182)
/m/015zyd	los angeles conservatory of music (Q1026827)
/m/05vsxz	chiwetel ejiofor (Q317343)
/m/06qgvf	carla gugino (Q228871)
/m/04ljl_l	golden raspberry award for worst supporting actor - motion picture (Q641316)
/m/03qcfvw	g.i. joe: rise of cobra (film) (Q288645)
/m/0grwj	oprah winfery (Q55800)
/m/05sxg2	theatre producer (Q1759246)
/m/0rh6k	district of columbia county, dc (Q61)
/m/01lp8	kristendom (Q5043)
/m/05d7rk	saeed jaffery (Q979990)
/m/027dtxw	bafta award for best supporting actor 2010-2034 (Q548389)
/m/05hs4r	heartland rock (Q747717)
/m/01pbxb	bob seger & the silver bullet band (Q364131)
/m/03qcq	hunter s thomson (Q219780)
/m/084w8	faulkner william (Q38392)
/m/01lxd4	avant-garde jazz (Q790188)
/m/0f0y8	jowcol music (Q7346)
/m/08815	yale model united nations (Q49112)
/m/05bnp0	james franco (Q306403)
/m/0g56t9t	frankenweenie (full-length) (Q1051023)
/m/0160w	greater bahamas (Q778)
/m/02vk52z	world bank publications (Q7164)
/m/0dbpyd	burnett, alan (Q360036)
/m/053y0s	boat drunks (Q862359)
/m/0dz3r	list of record producers (Q183945)
/m/016qtt	bill ray cyrus (Q231259)
/m/08mbj5d	/m/08mbj5d
/m/09sh8k	fantastic four (2007 film) (Q390063)
/m/09zzb8	make-up artist (Q935666)
/m/05pd94v	2010 grammies (Q1349100)
/m/01vvydl	dr.dre (Q6078)
/m/0gtsx8c	american pie 4 (Q303040)
/m/0jgd	argentinia (Q414)
/m/02r0csl	bafta award for best makeup (Q918617)
/m/0m313	viola de lesseps (Q182944)
/m/02y_lrp	love guru (Q656664)
/m/012d40	yuan-lou chen (Q36970)
/m/034qmv	around the world in 80 days (2004 film) (Q156519)
/m/07rlg	triathlon (Q10980)
/m/0b90_r	republica de méxico (Q96)
/m/0g22z	wall street 1 (Q332348)
/m/05b4l5x	golden raspberry award for worst supporting actress (Q977915)
/m/018js4	lieutenant pete mitchell (Q110138)
/m/07fq1y	rosemary ann harris (Q234195)
/m/02qgqt	phillip seymour hofman (Q180560)
/m/028q6	dave brubeck (Q108597)
/m/02581q	/m/02581q
/m/0sxg4	the dresser (1983 film) (Q620731)
/m/04xvlr	costume drama (Q7168625)
/m/0fvf9q	scott rudin productions (Q461529)
/m/0l6qt	saga louts (Q654730)
/m/0jz9f	miriam weinstein (Q465449)
/m/083shs	the cider house rules (film) (film) (Q648977)
/m/0njvn	berrien county, michigan (Q167656)
/m/09nqf	currency of the united states (Q4917)
/m/0d1tm	three-day event (Q838781)
/m/0154j	belgium (Q31)
/m/01br2w	making of the passion of the christ (Q51668)
/m/03rl1g	thirty-second congress (Q4635114)
/m/05kkh	geography of ohio (Q1397)
/m/0fbq2n	/m/0fbq2n
/m/01r3hr	scat back (Q912985)
/m/060bp	list of prime ministers and premiers (Q14212)
/m/027nb	iso 3166-1:dm (Q784)
/m/04qvl7	roger deakins (Q460277)
/m/02vxq9m	/m/02vxq9m
/m/04yywz	burt young (Q329700)
/m/0140g4	george washington duke (rocky v) (Q82949)
/m/01k7d9	h.r. walston (Q692800)
/m/0k049	history of beverly hills, california (Q127856)
/m/02p65p	donald frank "don" cheadle (Q272019)
/m/0337vz	larenz tate (Q1805906)
/m/06688p	john barrowman (Q24632)
/m/0b2v79	the untouchables (film) (Q108525)
/m/01c0cc	technische hochschule berlin-charlottenburg (Q51985)
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/m/01qszl	bandai co. (Q507269)
/m/028fjr	columnists (Q1086863)
/m/0g3bc	iwaoka (Q48320)
/m/0x2jd	the supreme court of canada (Q196615)
/m/04knq3	california state department of parks and recreation (Q2933976)
/m/0lxg6	varna, bulgaria (Q6506)
/m/050sw4	contemporary hit radio format (Q427487)
/m/02r_5vd	sports promotion (Q2378505)
/m/02pyg	alternative sport (Q309162)
/m/0gw0	anarchism/did you know/2 (Q6199)
/m/0g3cw	hiroshima, hiroshima (Q34664)
/m/01p_2p	latin jazz (Q837389)
/m/0h9vh	kwuntung (Q15175)
/m/015q02	perak darul ridzuan (Q188953)
/m/0j5nm	whodunnit! (Q1494434)
/m/01j12w	woollongong, new south wales (Q187861)
/m/026v1nw	/m/026v1nw
/m/0fr_v	parbo (Q3001)
/m/02y_3rt	/m/02y_3rt
/m/09dhj	/m/09dhj
/m/0852z	lyncanthrope (Q9410)
/m/047f9jp	jagdish raj khurana (Q6122336)
/m/0dz96	/m/0dz96
/m/0hl24	northampton county council (Q23115)
/m/038csl	l.a. unified school district (Q1320340)
/m/07sb1	tharbata (Q13972)
/m/075qbd2	no-party (Q327591)
/m/07r_p	kesoniemi (Q1770)
/m/02x0gx	ice brigade (Q1435969)
/m/08b3m	/m/08b3m
/m/01_bhs	fast food industry (Q81799)
/m/01hg2h	voyenno-vozdushnye sily (Q921432)
/m/09xw2	dixieland jazz (Q906647)
/m/09f5pp	robert hamman (Q4932675)
/m/01ftz	biotechnology companies (Q7108)
/m/0gg81w	educational tv (Q861402)
/m/01r9nk	munday swamp (Q45935)
/m/01t_1p	christian worship music (Q1339121)
/m/029tx	non-avian dinosaurs (Q430)
/m/01m59	commonist (Q6186)
/m/017ht	bhangra music (Q854844)
/m/0fr_b	panama (city) (Q3306)
/m/01dvzy	the wall street of the philippines (Q1508)
/m/01l53f	beef australia expo (Q606162)
/m/03ng8q	rich egan (Q669127)
/m/0ktds	/m/0ktds
/m/01z_lv	new haven burying ground (Q5611457)
/m/0fqpg6b	/m/0fqpg6b
/m/01gb1c	democratic unionist (Q215519)
/m/027nnh	conservative party of canada (historical) (Q909827)
/m/0b2mc	thessniki (Q17151)
/m/05j085	governor general's award for english language non-fiction (Q3405814)
/m/01w31x	century black (Q158867)
/m/09bv45	gmtv2 kids (Q1997305)
/m/01zkgw	bridgend (wales) (Q6497774)
/m/03pty	hotel room (Q27686)
/m/07cc8	mental transmission (Q170612)
/m/01pk8b	jaddah (Q374365)
/m/01fpdh	canadian reform party (Q1680589)
/m/0d8c4	lockheed (company) (Q7233)
/m/028sgq	/m/028sgq
/m/02rwmk	bosnian war (Q181533)
/m/016zxr	europop (Q517415)
/m/0ldpy	/m/0ldpy
/m/03xpx0	miami-fort lauderdale-pompano beach, fl (Q2915506)
/m/05sz6	/m/05sz6
/m/0f4r5	us bureau of land management (Q1010556)
/m/03_zv5	deep soul (Q1567032)
/m/0df9z	holy roman emperor-elect (Q181765)
/m/01f99l	general dynamics bath iron works (Q810783)
/m/01rng	co. mayo (Q178626)
/m/01q58t	notable persons born in townsville, queensland (Q190021)
/m/0jfx	antarktika (Q51)
/m/03qhnx	trevise (Q5475)
/m/08zx0s	classical crossover (Q2336819)
/m/06_y0kx	the franklin institute awards (Q817496)
/m/07vzd	united states political party structure (Q330963)
/m/01z6gc	municipal borough of penzance (Q208209)
/m/01y68z	에스비에스 (Q928831)
/m/0nk95	beauty pageant (Q2658935)
/m/01nh5h	invercargill new zealand (Q31805)
/m/03nk0g	news show (Q1358344)
/m/01qm7	community colleges (Q1336920)
/m/028sdw	real estate developments (Q695829)
/m/04bp0l	/m/04bp0l
/m/02w1b8	/m/02w1b8
/m/0fcgd	pennine alps (Q1270)
/m/074vv	snp glasgow (Q10658)
/m/018zqj	canadian defence force (Q644943)
/m/01y8kf	af canada (Q25456)
/m/07svc3	john brown shipyards (Q1699428)
/m/018zsw	royal canadian army (Q25436)
/m/053k78	metal blade records (Q756320)
/m/05r_j	petroleum (Q22656)
/m/058yv_	pricking the lites (Q1888937)
/m/0mhl6	savouè d’amont (Q12751)
/m/06pwfk	fat possum (Q1397914)
/m/0cc65	grand teton national park discovery and visitors center (Q222695)
/m/02kb_jm	foliamin (Q127060)
/m/0b1k24	napalm records (Q693194)
/m/0322c5	subiaco, w.a. (Q3976501)
/m/09b5t	chickon (Q780)
/m/050yl	military history (Q192781)
/m/0bk25	greek macedonian empire (Q83958)
/m/0p8bz	little earnock, hamilton, scotland (Q4131)
/m/09d_r	dağ (Q8502)
/m/0zz6w	erie pennsylvania (Q234061)
/m/01t3h6	scarborough, upper canada (Q1025401)
/m/05zrn	perl programming (Q42478)
/m/07pnk	/m/07pnk
/m/0237w6	/m/0237w6
/m/02fp48	unionist (united states) (Q214102)
/m/06jtx	house of romanovs (Q112707)
/m/018l5l	dramatic mezzo soprano (Q186506)
/m/05swd	propaganda in film (Q1935609)
/m/0blbx	reilig ghlas naion (Q1263215)
/m/0370vy	seattle metro (Q1114947)
/m/03ch14	spectacular (television) (Q1261214)
/m/012xsy	music in anime (Q257533)
/m/01mtt	games/cards (Q142714)
/m/02y_9cf	empire of ethiopia (Q207521)
/m/03ly1b	swan hunter shipyard (Q2061530)
/m/01z645	kettering, northamptonshire (Q984613)
/m/034wx3	brompton cemetery (Q926913)
/m/0513yzt	ballarina dancers (Q805221)
/m/06fhs	emerging renewable energy technologies (Q12705)
/m/04s84y	principal contractor (Q289612)
/m/09f5vv	aai (Q3630199)
/m/02z3cm0	ecw hardcore television (Q867288)
/m/0bwgmzd	/m/0bwgmzd
/m/02g982	west norwood cemetery (Q1290155)
/m/049c6t	elitist records (Q838544)
/m/0g9k4	the weather in swindon (Q200942)
/m/0x2hn	pc party of canada (Q1292617)
/m/0mbx4	child-star syndrome (Q970153)
/m/0dlj8q2	le monde's 100 books of the century (Q40734)
/m/0c_xl	statistician (Q2732142)
/m/05frqx	tdsb (Q1189302)
/m/09wwlj	salvador da bahia (Q36947)
/m/0q307	maghal empire (Q33296)
/m/09j2d	separates (clothing) (Q11460)
/m/0m5pn	hokkian (Q41705)
/m/0bw8r	secretary of the treasury (united states) (Q4215834)
/m/02m97n	vickers limited shipbuilding group (Q763052)
/m/022zq3	bbc three (former) (Q687427)
/m/082x5	military of nazi germany (Q128781)
/m/0gwj	autismisreal (Q38404)
/m/01pvc5	horse-racing colour (Q846750)
/m/06rkl	elementary, my dear watson (Q4653)
/m/07068	bushi (Q38142)
/m/05n5kv	knight of the elephant (Q976544)
/m/0223xd	george bancroft prize (Q724144)
/m/02656h	ea sports network (Q869106)
/m/016hjr	american continental army (Q54122)
/m/01rqxn	deputy (france) (Q193582)
/m/01wdgb	stockton-on-tees ua (Q989418)
/m/0by292	candlelight records (Q852900)
/m/027jbr	national racing museum and hall of fame (Q6974502)
/m/02070z	killed in battle (Q210392)
/m/02wxtgw	android phone (Q94)
/m/0ng8v	county kildare, ireland (Q173332)
/m/01pqx6	whiting writers award (Q10393524)
/m/01z52d	julie rose (Q725261)
/m/02h661t	/m/02h661t
/m/025spv	Q14941814
/m/06ngk	wishstar (Q523)
/m/07jqvw	perkin medal (Q1385301)
/m/0h948	voluntary poverty (Q10294)
/m/03nh9	straight people (Q1035954)
/m/02f2jv	california state senate (Q1526814)
/m/09hljw	/m/09hljw
/m/06f3l	thirteen-a-side (Q10962)
/m/01qynv	oneworld management co. ltd. (Q8787)
/m/0h9xl	people’s action party (Q371395)
/m/01803s	everlasting life (Q193942)
/m/01lys3	abuse, substance (Q4134457)
/m/01730d	chicago blues (Q1071873)
/m/057ph	mickey (disney character) (Q11934)
/m/03ykjs9	/m/03ykjs9
/m/060m4	pm of canada (Q839078)
/m/04p4r	liberal party australia (Q241149)
/m/017yzc	hanoverian britain (Q157217)
/m/021yyx	federation of bosnia & herzegovina (Q11198)
/m/01_30_	strategic simulations, inc (Q1268871)
/m/02p4pt3	the plantagenets (Q106151)
/m/09kqc	facetious (Q35874)
/m/04y41	/m/04y41
/m/01tv5c	Q13526807
/m/0gslw	te wai pounamu (Q120755)
/m/01x4x4	@skyteam (Q212282)
/m/01lk31	governor (massachusetts) (Q887117)
/m/04z288	new york state senate (Q1722258)
/m/0jq47	libyan mercenaries (Q178197)
/m/09cmq	public museum (Q33506)
/m/0dng4	daffy (Q319918)
/m/05qc_	planetary mass objects (Q634)
/m/08hbxv	kosi zone (Q9170)
/m/05hyf	nazidom (Q7310)
/m/0qb7t	friends of (Q43229)
/m/027752	history of the united national party (Q1321770)
/m/03f_jk	albert lasker clinical medical research award (Q136696)
/m/027qb1	commissioners (Q524778)
/m/01x8f6	ondp (Q827589)
/m/025x7g_	/m/025x7g_
/m/080v2	vice presidency of the united states (Q11699)
/m/037vqt	james e sullivan award (Q2720648)
/m/026y05	/m/026y05
/m/05k8m5	spinefarm records (Q1124153)
/m/0hsph	northern dancer (horse) (Q1969034)
/m/02rxd26	2009 tour de france (Q217263)
/m/07hn5	tour of france (Q33881)
/m/03tp4	Q131736
/m/04gbl3	david ward (programmer) (Q1408498)
/m/01664_	natural historian (Q484591)
/m/0171c7	ideology of the australian greens (Q781486)
/m/0zq7r	vertigo (company) (Q122669)
/m/047vnfs	football manager (Q628099)
/m/022qqh	conservator (art) (Q674426)
/m/0jvq	iceteroid (Q3863)
/m/0lyb_	pulitzer prize for music (Q1783509)
/m/027yjnv	2008 tdf (Q221530)
/m/024030	padma vibhushan (Q672392)
/m/024tv_	pakistani peoples party (Q186591)
/m/0q96	alp (Q216082)


================================================
FILE: nbfnet/__init__.py
================================================


================================================
FILE: nbfnet/dataset.py
================================================
import os
import csv
import glob
from tqdm import tqdm
from ogb import linkproppred

import torch
from torch.utils import data as torch_data

from torchdrug import data, datasets, utils
from torchdrug.core import Registry as R


class InductiveKnowledgeGraphDataset(data.KnowledgeGraphDataset):

    def load_inductive_tsvs(self, train_files, test_files, verbose=0):
        assert len(train_files) == len(test_files) == 2
        inv_train_entity_vocab = {}
        inv_test_entity_vocab = {}
        inv_relation_vocab = {}
        triplets = []
        num_samples = []

        for txt_file in train_files:
            with open(txt_file, "r") as fin:
                reader = csv.reader(fin, delimiter="\t")
                if verbose:
                    reader = tqdm(reader, "Loading %s" % txt_file, utils.get_line_count(txt_file))

                num_sample = 0
                for tokens in reader:
                    h_token, r_token, t_token = tokens
                    if h_token not in inv_train_entity_vocab:
                        inv_train_entity_vocab[h_token] = len(inv_train_entity_vocab)
                    h = inv_train_entity_vocab[h_token]
                    if r_token not in inv_relation_vocab:
                        inv_relation_vocab[r_token] = len(inv_relation_vocab)
                    r = inv_relation_vocab[r_token]
                    if t_token not in inv_train_entity_vocab:
                        inv_train_entity_vocab[t_token] = len(inv_train_entity_vocab)
                    t = inv_train_entity_vocab[t_token]
                    triplets.append((h, t, r))
                    num_sample += 1
            num_samples.append(num_sample)

        for txt_file in test_files:
            with open(txt_file, "r") as fin:
                reader = csv.reader(fin, delimiter="\t")
                if verbose:
                    reader = tqdm(reader, "Loading %s" % txt_file, utils.get_line_count(txt_file))

                num_sample = 0
                for tokens in reader:
                    h_token, r_token, t_token = tokens
                    if h_token not in inv_test_entity_vocab:
                        inv_test_entity_vocab[h_token] = len(inv_test_entity_vocab)
                    h = inv_test_entity_vocab[h_token]
                    assert r_token in inv_relation_vocab
                    r = inv_relation_vocab[r_token]
                    if t_token not in inv_test_entity_vocab:
                        inv_test_entity_vocab[t_token] = len(inv_test_entity_vocab)
                    t = inv_test_entity_vocab[t_token]
                    triplets.append((h, t, r))
                    num_sample += 1
            num_samples.append(num_sample)

        train_entity_vocab, inv_train_entity_vocab = self._standarize_vocab(None, inv_train_entity_vocab)
        test_entity_vocab, inv_test_entity_vocab = self._standarize_vocab(None, inv_test_entity_vocab)
        relation_vocab, inv_relation_vocab = self._standarize_vocab(None, inv_relation_vocab)

        self.train_graph = data.Graph(triplets[:num_samples[0]],
                                      num_node=len(train_entity_vocab), num_relation=len(relation_vocab))
        self.valid_graph = self.train_graph
        self.test_graph = data.Graph(triplets[sum(num_samples[:2]): sum(num_samples[:3])],
                                     num_node=len(test_entity_vocab), num_relation=len(relation_vocab))
        self.graph = self.train_graph
        self.triplets = torch.tensor(triplets[:sum(num_samples[:2])] + triplets[sum(num_samples[:3]):])
        self.num_samples = num_samples[:2] + num_samples[3:]
        self.train_entity_vocab = train_entity_vocab
        self.test_entity_vocab = test_entity_vocab
        self.relation_vocab = relation_vocab
        self.inv_train_entity_vocab = inv_train_entity_vocab
        self.inv_test_entity_vocab = inv_test_entity_vocab
        self.inv_relation_vocab = inv_relation_vocab

    def __getitem__(self, index):
        return self.triplets[index]

    def split(self):
        offset = 0
        splits = []
        for num_sample in self.num_samples:
            split = torch_data.Subset(self, range(offset, offset + num_sample))
            splits.append(split)
            offset += num_sample
        return splits


@R.register("datasets.CoraLinkPrediction")
class CoraLinkPrediction(datasets.Cora):

    def __init__(self, **kwargs):
        super(CoraLinkPrediction, self).__init__(**kwargs)
        self.transform = None

    def __getitem__(self, index):
        return self.graph.edge_list[index]

    def __len__(self):
        return self.graph.num_edge

    def split(self, ratios=(85, 5, 10)):
        length = self.graph.num_edge
        norm = sum(ratios)
        lengths = [int(r / norm * length) for r in ratios]
        lengths[-1] = length - sum(lengths[:-1])

        g = torch.Generator()
        g.manual_seed(0)
        return torch_data.random_split(self, lengths, generator=g)


@R.register("datasets.CiteSeerLinkPrediction")
class CiteSeerLinkPrediction(datasets.CiteSeer):

    def __init__(self, **kwargs):
        super(CiteSeerLinkPrediction, self).__init__(**kwargs)
        self.transform = None

    def __getitem__(self, index):
        return self.graph.edge_list[index]

    def __len__(self):
        return self.graph.num_edge

    def split(self, ratios=(85, 5, 10)):
        length = self.graph.num_edge
        norm = sum(ratios)
        lengths = [int(r / norm * length) for r in ratios]
        lengths[-1] = length - sum(lengths[:-1])

        g = torch.Generator()
        g.manual_seed(0)
        return torch_data.random_split(self, lengths, generator=g)


@R.register("datasets.PubMedLinkPrediction")
class PubMedLinkPrediction(datasets.PubMed):

    def __init__(self, **kwargs):
        super(PubMedLinkPrediction, self).__init__(**kwargs)
        self.transform = None

    def __getitem__(self, index):
        return self.graph.edge_list[index]

    def __len__(self):
        return self.graph.num_edge

    def split(self, ratios=(85, 5, 10)):
        length = self.graph.num_edge
        norm = sum(ratios)
        lengths = [int(r / norm * length) for r in ratios]
        lengths[-1] = length - sum(lengths[:-1])

        g = torch.Generator()
        g.manual_seed(0)
        return torch_data.random_split(self, lengths, generator=g)


@R.register("datasets.FB15k237Inductive")
class FB15k237Inductive(InductiveKnowledgeGraphDataset):

    train_urls = [
        "https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s/train.txt",
        "https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s/valid.txt",
    ]

    test_urls = [
        "https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s_ind/train.txt",
        "https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s_ind/test.txt",
    ]

    def __init__(self, path, version="v1", verbose=1):
        path = os.path.expanduser(path)
        if not os.path.exists(path):
            os.makedirs(path)
        self.path = path

        train_files = []
        for url in self.train_urls:
            url = url % version
            save_file = "fb15k237_%s_%s" % (version, os.path.basename(url))
            txt_file = os.path.join(path, save_file)
            if not os.path.exists(txt_file):
                txt_file = utils.download(url, self.path, save_file=save_file)
            train_files.append(txt_file)
        test_files = []
        for url in self.test_urls:
            url = url % version
            save_file = "fb15k237_%s_ind_%s" % (version, os.path.basename(url))
            txt_file = os.path.join(path, save_file)
            if not os.path.exists(txt_file):
                txt_file = utils.download(url, self.path, save_file=save_file)
            test_files.append(txt_file)

        self.load_inductive_tsvs(train_files, test_files, verbose=verbose)


@R.register("datasets.WN18RRInductive")
class WN18RRInductive(InductiveKnowledgeGraphDataset):

    train_urls = [
        "https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s/train.txt",
        "https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s/valid.txt",
    ]

    test_urls = [
        "https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s_ind/train.txt",
        "https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s_ind/test.txt",
    ]

    def __init__(self, path, version="v1", verbose=1):
        path = os.path.expanduser(path)
        if not os.path.exists(path):
            os.makedirs(path)
        self.path = path

        train_files = []
        for url in self.train_urls:
            url = url % version
            save_file = "wn18rr_%s_%s" % (version, os.path.basename(url))
            txt_file = os.path.join(path, save_file)
            if not os.path.exists(txt_file):
                txt_file = utils.download(url, self.path, save_file=save_file)
            train_files.append(txt_file)
        test_files = []
        for url in self.test_urls:
            url = url % version
            save_file = "wn18rr_%s_ind_%s" % (version, os.path.basename(url))
            txt_file = os.path.join(path, save_file)
            if not os.path.exists(txt_file):
                txt_file = utils.download(url, self.path, save_file=save_file)
            test_files.append(txt_file)

        self.load_inductive_tsvs(train_files, test_files, verbose=verbose)


@R.register("datasets.OGBLBioKG")
class OGBLBioKG(data.KnowledgeGraphDataset):

    def __init__(self, path, verbose=1):
        path = os.path.expanduser(path)
        self.path = path

        dataset = linkproppred.LinkPropPredDataset("ogbl-biokg", path)
        self.load_ogb(dataset, verbose=verbose)

    def load_ogb(self, dataset, verbose=1):
        entity_vocab = []
        relation_vocab = []
        entity_type_vocab = []
        inv_entity_type_offset = {}
        entity_type2num = []

        zip_files = glob.glob(os.path.join(dataset.root, "mapping/*.gz"))
        for zip_file in zip_files:
            csv_file = utils.extract(zip_file)
            type = os.path.basename(csv_file).split("_")[0]
            with open(csv_file, "r") as fin:
                reader = csv.reader(fin)
                if verbose:
                    reader = iter(tqdm(reader, "Loading %s" % csv_file, utils.get_line_count(csv_file)))
                fields = next(reader)
                if "relidx" in csv_file:
                    for index, token in reader:
                        relation_vocab.append(token)
                else:
                    entity_type_vocab.append(type)
                    inv_entity_type_offset[type] = len(entity_vocab)
                    num_entity = 0
                    for index, token in reader:
                        entity_vocab.append("%s (%s)" % (type, token))
                        num_entity += 1
                    entity_type2num.append(num_entity)

        edge_split = dataset.get_edge_split()
        triplets = []
        num_samples = []
        num_samples_with_neg = []
        negative_heads = []
        negative_tails = []
        for key in ["train", "valid", "test"]:
            split_dict = edge_split[key]
            h = torch.as_tensor(split_dict["head"])
            t = torch.as_tensor(split_dict["tail"])
            r = torch.as_tensor(split_dict["relation"])
            h_type = torch.tensor([inv_entity_type_offset[h] for h in split_dict["head_type"]])
            t_type = torch.tensor([inv_entity_type_offset[t] for t in split_dict["tail_type"]])

            h = h + h_type
            t = t + t_type
            triplet = torch.stack([h, t, r], dim=-1)
            triplets.append(triplet)
            num_samples.append(len(h))
            if "head_neg" in split_dict:
                neg_h = torch.as_tensor(split_dict["head_neg"])
                neg_t = torch.as_tensor(split_dict["tail_neg"])
                neg_h = neg_h + h_type.unsqueeze(-1)
                neg_t = neg_t + t_type.unsqueeze(-1)
                negative_heads.append(neg_h)
                negative_tails.append(neg_t)
                num_samples_with_neg.append(len(h))
            else:
                num_samples_with_neg.append(0)
        triplets = torch.cat(triplets)

        self.load_triplet(triplets, entity_vocab=entity_vocab, relation_vocab=relation_vocab)
        entity_type_vocab, inv_entity_type_vocab = self._standarize_vocab(entity_type_vocab, None)
        self.entity_type_vocab = entity_type_vocab
        self.inv_entity_type_vocab = inv_entity_type_vocab
        self.num_samples = num_samples
        self.num_samples_with_neg = num_samples_with_neg
        self.negative_heads = torch.cat(negative_heads)
        self.negative_tails = torch.cat(negative_tails)

        node_type = []
        for i, num_entity in enumerate(entity_type2num):
            node_type += [i] * num_entity
        with self.graph.node():
            self.graph.node_type = torch.tensor(node_type)

    def split(self, test_negative=True):
        offset = 0
        neg_offset = 0
        splits = []
        for num_sample, num_sample_with_neg in zip(self.num_samples, self.num_samples_with_neg):
            if test_negative and num_sample_with_neg:
                pos_h, pos_t, pos_r = self[offset: offset + num_sample].t()
                neg_h = self.negative_heads[neg_offset: neg_offset + num_sample_with_neg]
                neg_t = self.negative_tails[neg_offset: neg_offset + num_sample_with_neg]
                num_negative = neg_h.shape[-1]
                h = pos_h.unsqueeze(-1).repeat(2, num_negative + 1)
                t = pos_t.unsqueeze(-1).repeat(2, num_negative + 1)
                r = pos_r.unsqueeze(-1).repeat(2, num_negative + 1)
                t[:num_sample_with_neg, 1:] = neg_t
                h[num_sample_with_neg:, 1:] = neg_h
                split = torch.stack([h, t, r], dim=-1)
            else:
                split = torch_data.Subset(self, range(offset, offset + num_sample))
            splits.append(split)
            offset += num_sample
            neg_offset += num_sample_with_neg
        return splits


================================================
FILE: nbfnet/layer.py
================================================
import torch
from torch import nn
from torch.nn import functional as F

from torch_scatter import scatter_add, scatter_mean, scatter_max, scatter_min

from torchdrug import layers
from torchdrug.layers import functional


class GeneralizedRelationalConv(layers.MessagePassingBase):

    eps = 1e-6

    message2mul = {
        "transe": "add",
        "distmult": "mul",
    }

    def __init__(self, input_dim, output_dim, num_relation, query_input_dim, message_func="distmult",
                 aggregate_func="pna", layer_norm=False, activation="relu", dependent=True):
        super(GeneralizedRelationalConv, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.num_relation = num_relation
        self.query_input_dim = query_input_dim
        self.message_func = message_func
        self.aggregate_func = aggregate_func
        self.dependent = dependent

        if layer_norm:
            self.layer_norm = nn.LayerNorm(output_dim)
        else:
            self.layer_norm = None
        if isinstance(activation, str):
            self.activation = getattr(F, activation)
        else:
            self.activation = activation

        if self.aggregate_func == "pna":
            self.linear = nn.Linear(input_dim * 13, output_dim)
        else:
            self.linear = nn.Linear(input_dim * 2, output_dim)
        if dependent:
            self.relation_linear = nn.Linear(query_input_dim, num_relation * input_dim)
        else:
            self.relation = nn.Embedding(num_relation, input_dim)

    def message(self, graph, input):
        assert graph.num_relation == self.num_relation

        batch_size = len(graph.query)
        node_in, node_out, relation = graph.edge_list.t()
        if self.dependent:
            relation_input = self.relation_linear(graph.query).view(batch_size, self.num_relation, self.input_dim)
        else:
            relation_input = self.relation.weight.expand(batch_size, -1, -1)
        relation_input = relation_input.transpose(0, 1)
        node_input = input[node_in]
        edge_input = relation_input[relation]

        if self.message_func == "transe":
            message = edge_input + node_input
        elif self.message_func == "distmult":
            message = edge_input * node_input
        elif self.message_func == "rotate":
            node_re, node_im = node_input.chunk(2, dim=-1)
            edge_re, edge_im = edge_input.chunk(2, dim=-1)
            message_re = node_re * edge_re - node_im * edge_im
            message_im = node_re * edge_im + node_im * edge_re
            message = torch.cat([message_re, message_im], dim=-1)
        else:
            raise ValueError("Unknown message function `%s`" % self.message_func)
        message = torch.cat([message, graph.boundary])

        return message

    def aggregate(self, graph, message):
        node_out = graph.edge_list[:, 1]
        node_out = torch.cat([node_out, torch.arange(graph.num_node, device=graph.device)])
        edge_weight = torch.cat([graph.edge_weight, torch.ones(graph.num_node, device=graph.device)])
        edge_weight = edge_weight.unsqueeze(-1).unsqueeze(-1)
        degree_out = graph.degree_out.unsqueeze(-1).unsqueeze(-1) + 1

        if self.aggregate_func == "sum":
            update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)
        elif self.aggregate_func == "mean":
            update = scatter_mean(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)
        elif self.aggregate_func == "max":
            update = scatter_max(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)[0]
        elif self.aggregate_func == "pna":
            mean = scatter_mean(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)
            sq_mean = scatter_mean(message ** 2 * edge_weight, node_out, dim=0, dim_size=graph.num_node)
            max = scatter_max(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)[0]
            min = scatter_min(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)[0]
            std = (sq_mean - mean ** 2).clamp(min=self.eps).sqrt()
            features = torch.cat([mean.unsqueeze(-1), max.unsqueeze(-1), min.unsqueeze(-1), std.unsqueeze(-1)], dim=-1)
            features = features.flatten(-2)
            scale = degree_out.log()
            scale = scale / scale.mean()
            scales = torch.cat([torch.ones_like(scale), scale, 1 / scale.clamp(min=1e-2)], dim=-1)
            update = (features.unsqueeze(-1) * scales.unsqueeze(-2)).flatten(-2)
        else:
            raise ValueError("Unknown aggregation function `%s`" % self.aggregate_func)

        return update

    def message_and_aggregate(self, graph, input):
        if graph.requires_grad or self.message_func == "rotate":
            return super(GeneralizedRelationalConv, self).message_and_aggregate(graph, input)

        assert graph.num_relation == self.num_relation

        batch_size = len(graph.query)
        input = input.flatten(1)
        boundary = graph.boundary.flatten(1)

        degree_out = graph.degree_out.unsqueeze(-1) + 1
        if self.dependent:
            relation_input = self.relation_linear(graph.query).view(batch_size, self.num_relation, self.input_dim)
            relation_input = relation_input.transpose(0, 1).flatten(1)
        else:
            relation_input = self.relation.weight.repeat(1, batch_size)
        adjacency = graph.adjacency.transpose(0, 1)

        if self.message_func in self.message2mul:
            mul = self.message2mul[self.message_func]
        else:
            raise ValueError("Unknown message function `%s`" % self.message_func)
        if self.aggregate_func == "sum":
            update = functional.generalized_rspmm(adjacency, relation_input, input, sum="add", mul=mul)
            update = update + boundary
        elif self.aggregate_func == "mean":
            update = functional.generalized_rspmm(adjacency, relation_input, input, sum="add", mul=mul)
            update = (update + boundary) / degree_out
        elif self.aggregate_func == "max":
            update = functional.generalized_rspmm(adjacency, relation_input, input, sum="max", mul=mul)
            update = torch.max(update, boundary)
        elif self.aggregate_func == "pna":
            sum = functional.generalized_rspmm(adjacency, relation_input, input, sum="add", mul=mul)
            sq_sum = functional.generalized_rspmm(adjacency, relation_input ** 2, input ** 2, sum="add", mul=mul)
            max = functional.generalized_rspmm(adjacency, relation_input, input, sum="max", mul=mul)
            min = functional.generalized_rspmm(adjacency, relation_input, input, sum="min", mul=mul)
            mean = (sum + boundary) / degree_out
            sq_mean = (sq_sum + boundary ** 2) / degree_out
            max = torch.max(max, boundary)
            min = torch.min(min, boundary)
            std = (sq_mean - mean ** 2).clamp(min=self.eps).sqrt()
            features = torch.cat([mean.unsqueeze(-1), max.unsqueeze(-1), min.unsqueeze(-1), std.unsqueeze(-1)], dim=-1)
            features = features.flatten(-2)
            scale = degree_out.log()
            scale = scale / scale.mean()
            scales = torch.cat([torch.ones_like(scale), scale, 1 / scale.clamp(min=1e-2)], dim=-1)
            update = (features.unsqueeze(-1) * scales.unsqueeze(-2)).flatten(-2)
        else:
            raise ValueError("Unknown aggregation function `%s`" % self.aggregate_func)

        return update.view(len(update), batch_size, -1)

    def combine(self, input, update):
        output = self.linear(torch.cat([input, update], dim=-1))
        if self.layer_norm:
            output = self.layer_norm(output)
        if self.activation:
            output = self.activation(output)
        return output

================================================
FILE: nbfnet/model.py
================================================
from collections.abc import Sequence

import torch
from torch import nn
from torch import autograd

from torch_scatter import scatter_add

from torchdrug import core, layers
from torchdrug.layers import functional
from torchdrug.core import Registry as R

from . import layer


@R.register("model.NBFNet")
class NeuralBellmanFordNetwork(nn.Module, core.Configurable):

    def __init__(self, input_dim, hidden_dims, num_relation=None, symmetric=False,
                 message_func="distmult", aggregate_func="pna", short_cut=False, layer_norm=False, activation="relu",
                 concat_hidden=False, num_mlp_layer=2, dependent=True, remove_one_hop=False,
                 num_beam=10, path_topk=10):
        super(NeuralBellmanFordNetwork, self).__init__()

        if not isinstance(hidden_dims, Sequence):
            hidden_dims = [hidden_dims]
        if num_relation is None:
            double_relation = 1
        else:
            num_relation = int(num_relation)
            double_relation = num_relation * 2
        self.dims = [input_dim] + list(hidden_dims)
        self.num_relation = num_relation
        self.symmetric = symmetric
        self.short_cut = short_cut
        self.concat_hidden = concat_hidden
        self.remove_one_hop = remove_one_hop
        self.num_beam = num_beam
        self.path_topk = path_topk

        self.layers = nn.ModuleList()
        for i in range(len(self.dims) - 1):
            self.layers.append(layer.GeneralizedRelationalConv(self.dims[i], self.dims[i + 1], double_relation,
                                                               self.dims[0], message_func, aggregate_func, layer_norm,
                                                               activation, dependent))

        feature_dim = hidden_dims[-1] * (len(hidden_dims) if concat_hidden else 1) + input_dim
        self.query = nn.Embedding(double_relation, input_dim)
        self.mlp = layers.MLP(feature_dim, [feature_dim] * (num_mlp_layer - 1) + [1])

    def remove_easy_edges(self, graph, h_index, t_index, r_index=None):
        if self.remove_one_hop:
            h_index_ext = torch.cat([h_index, t_index], dim=-1)
            t_index_ext = torch.cat([t_index, h_index], dim=-1)
            if r_index is not None:
                any = -torch.ones_like(h_index_ext)
                pattern = torch.stack([h_index_ext, t_index_ext, any], dim=-1)
            else:
                pattern = torch.stack([h_index_ext, t_index_ext], dim=-1)
        else:
            if r_index is not None:
                pattern = torch.stack([h_index, t_index, r_index], dim=-1)
            else:
                pattern = torch.stack([h_index, t_index], dim=-1)
        pattern = pattern.flatten(0, -2)
        edge_index = graph.match(pattern)[0]
        edge_mask = ~functional.as_mask(edge_index, graph.num_edge)
        return graph.edge_mask(edge_mask)

    def negative_sample_to_tail(self, h_index, t_index, r_index):
        # convert p(h | t, r) to p(t' | h', r')
        # h' = t, r' = r^{-1}, t' = h
        is_t_neg = (h_index == h_index[:, [0]]).all(dim=-1, keepdim=True)
        new_h_index = torch.where(is_t_neg, h_index, t_index)
        new_t_index = torch.where(is_t_neg, t_index, h_index)
        new_r_index = torch.where(is_t_neg, r_index, r_index + self.num_relation)
        return new_h_index, new_t_index, new_r_index

    def as_relational_graph(self, graph, self_loop=True):
        # add self loop
        # convert homogeneous graphs to knowledge graphs with 1 relation
        edge_list = graph.edge_list
        edge_weight = graph.edge_weight
        if self_loop:
            node_in = node_out = torch.arange(graph.num_node, device=self.device)
            loop = torch.stack([node_in, node_out], dim=-1)
            edge_list = torch.cat([edge_list, loop])
            edge_weight = torch.cat([edge_weight, torch.ones(graph.num_node, device=self.device)])
        relation = torch.zeros(len(edge_list), 1, dtype=torch.long, device=self.device)
        edge_list = torch.cat([edge_list, relation], dim=-1)
        graph = type(graph)(edge_list, edge_weight=edge_weight, num_node=graph.num_node,
                            num_relation=1, meta_dict=graph.meta_dict, **graph.data_dict)
        return graph

    def bellmanford(self, graph, h_index, r_index, separate_grad=False):
        query = self.query(r_index)
        index = h_index.unsqueeze(-1).expand_as(query)
        boundary = torch.zeros(graph.num_node, *query.shape, device=self.device)
        boundary.scatter_add_(0, index.unsqueeze(0), query.unsqueeze(0))
        with graph.graph():
            graph.query = query
        with graph.node():
            graph.boundary = boundary

        hiddens = []
        step_graphs = []
        layer_input = boundary

        for layer in self.layers:
            if separate_grad:
                step_graph = graph.clone().requires_grad_()
            else:
                step_graph = graph
            hidden = layer(step_graph, layer_input)
            if self.short_cut and hidden.shape == layer_input.shape:
                hidden = hidden + layer_input
            hiddens.append(hidden)
            step_graphs.append(step_graph)
            layer_input = hidden

        node_query = query.expand(graph.num_node, -1, -1)
        if self.concat_hidden:
            output = torch.cat(hiddens + [node_query], dim=-1)
        else:
            output = torch.cat([hiddens[-1], node_query], dim=-1)

        return {
            "node_feature": output,
            "step_graphs": step_graphs,
        }

    def forward(self, graph, h_index, t_index, r_index=None, all_loss=None, metric=None):
        if all_loss is not None:
            graph = self.remove_easy_edges(graph, h_index, t_index, r_index)

        shape = h_index.shape
        if graph.num_relation:
            graph = graph.undirected(add_inverse=True)
            h_index, t_index, r_index = self.negative_sample_to_tail(h_index, t_index, r_index)
        else:
            graph = self.as_relational_graph(graph)
            h_index = h_index.view(-1, 1)
            t_index = t_index.view(-1, 1)
            r_index = torch.zeros_like(h_index)

        assert (h_index[:, [0]] == h_index).all()
        assert (r_index[:, [0]] == r_index).all()
        output = self.bellmanford(graph, h_index[:, 0], r_index[:, 0])
        feature = output["node_feature"].transpose(0, 1)
        index = t_index.unsqueeze(-1).expand(-1, -1, feature.shape[-1])
        feature = feature.gather(1, index)

        if self.symmetric:
            assert (t_index[:, [0]] == t_index).all()
            output = self.bellmanford(graph, t_index[:, 0], r_index[:, 0])
            inv_feature = output["node_feature"].transpose(0, 1)
            index = h_index.unsqueeze(-1).expand(-1, -1, inv_feature.shape[-1])
            inv_feature = inv_feature.gather(1, index)
            feature = (feature + inv_feature) / 2

        score = self.mlp(feature).squeeze(-1)
        return score.view(shape)

    def visualize(self, graph, h_index, t_index, r_index):
        assert h_index.numel() == 1 and h_index.ndim == 1
        graph = graph.undirected(add_inverse=True)

        output = self.bellmanford(graph, h_index, r_index, separate_grad=True)
        feature = output["node_feature"]
        step_graphs = output["step_graphs"]

        index = t_index.unsqueeze(0).unsqueeze(-1).expand(-1, -1, feature.shape[-1])
        feature = feature.gather(0, index).squeeze(0)
        score = self.mlp(feature).squeeze(-1)

        edge_weights = [graph.edge_weight for graph in step_graphs]
        edge_grads = autograd.grad(score, edge_weights)
        for graph, edge_grad in zip(step_graphs, edge_grads):
            with graph.edge():
                graph.edge_grad = edge_grad
        distances, back_edges = self.beam_search_distance(step_graphs, h_index, t_index, self.num_beam)
        paths, weights = self.topk_average_length(distances, back_edges, t_index, self.path_topk)

        return paths, weights

    @torch.no_grad()
    def beam_search_distance(self, graphs, h_index, t_index, num_beam=10):
        num_node = graphs[0].num_node
        input = torch.full((num_node, num_beam), float("-inf"), device=self.device)
        input[h_index, 0] = 0

        distances = []
        back_edges = []
        for graph in graphs:
            graph = graph.edge_mask(graph.edge_list[:, 0] != t_index)
            node_in, node_out = graph.edge_list.t()[:2]

            message = input[node_in] + graph.edge_grad.unsqueeze(-1)
            msg_source = graph.edge_list.unsqueeze(1).expand(-1, num_beam, -1)

            is_duplicate = torch.isclose(message.unsqueeze(-1), message.unsqueeze(-2)) & \
                           (msg_source.unsqueeze(-2) == msg_source.unsqueeze(-3)).all(dim=-1)
            is_duplicate = is_duplicate.float() - \
                           torch.arange(num_beam, dtype=torch.float, device=self.device) / (num_beam + 1)
            # pick the first occurrence as the previous state
            prev_rank = is_duplicate.argmax(dim=-1, keepdim=True)
            msg_source = torch.cat([msg_source, prev_rank], dim=-1)

            node_out, order = node_out.sort()
            node_out_set = torch.unique(node_out)
            # sort message w.r.t. node_out
            message = message[order].flatten()
            msg_source = msg_source[order].flatten(0, -2)
            size = scatter_add(torch.ones_like(node_out), node_out, dim_size=num_node)
            msg2out = torch.repeat_interleave(size[node_out_set] * num_beam)
            # deduplicate
            is_duplicate = (msg_source[1:] == msg_source[:-1]).all(dim=-1)
            is_duplicate = torch.cat([torch.zeros(1, dtype=torch.bool, device=self.device), is_duplicate])
            message = message[~is_duplicate]
            msg_source = msg_source[~is_duplicate]
            msg2out = msg2out[~is_duplicate]
            size = scatter_add(torch.ones_like(msg2out), msg2out, dim_size=len(node_out_set))

            if not torch.isinf(message).all():
                distance, rel_index = functional.variadic_topk(message, size, k=num_beam)
                abs_index = rel_index + (size.cumsum(0) - size).unsqueeze(-1)
                back_edge = msg_source[abs_index]
                distance = distance.view(len(node_out_set), num_beam)
                back_edge = back_edge.view(len(node_out_set), num_beam, 4)
                distance = scatter_add(distance, node_out_set, dim=0, dim_size=num_node)
                back_edge = scatter_add(back_edge, node_out_set, dim=0, dim_size=num_node)
            else:
                distance = torch.full((num_node, num_beam), float("-inf"), device=self.device)
                back_edge = torch.zeros(num_node, num_beam, 4, dtype=torch.long, device=self.device)

            distances.append(distance)
            back_edges.append(back_edge)
            input = distance

        return distances, back_edges

    def topk_average_length(self, distances, back_edges, t_index, k=10):
        paths = []
        average_lengths = []

        for i in range(len(distances)):
            distance, order = distances[i][t_index].flatten(0, -1).sort(descending=True)
            back_edge = back_edges[i][t_index].flatten(0, -2)[order]
            for d, (h, t, r, prev_rank) in zip(distance[:k].tolist(), back_edge[:k].tolist()):
                if d == float("-inf"):
                    break
                path = [(h, t, r)]
                for j in range(i - 1, -1, -1):
                    h, t, r, prev_rank = back_edges[j][h, prev_rank].tolist()
                    path.append((h, t, r))
                paths.append(path[::-1])
                average_lengths.append(d / len(path))

        if paths:
            average_lengths, paths = zip(*sorted(zip(average_lengths, paths), reverse=True)[:k])

        return paths, average_lengths

================================================
FILE: nbfnet/task.py
================================================
import math

import torch
from torch.nn import functional as F
from torch.utils import data as torch_data

from ogb import linkproppred

from torchdrug import core, tasks, metrics
from torchdrug.layers import functional
from torchdrug.core import Registry as R


Evaluator = core.make_configurable(linkproppred.Evaluator)
Evaluator = R.register("ogb.linkproppred.Evaluator")(Evaluator)
setattr(linkproppred, "Evaluator", Evaluator)


@R.register("tasks.KnowledgeGraphCompletionExt")
class KnowledgeGraphCompletionExt(tasks.KnowledgeGraphCompletion, core.Configurable):

    def __init__(self, model, criterion="bce",
                 metric=("mr", "mrr", "hits@1", "hits@3", "hits@10", "1-to-1", "1-to-n", "n-to-1", "n-to-n"),
                 num_negative=128, margin=6, adversarial_temperature=0, strict_negative=True, fact_ratio=None,
                 sample_weight=True, filtered_ranking=True, full_batch_eval=False):
        super(KnowledgeGraphCompletionExt, self).__init__(
            model, criterion, metric, num_negative, margin, adversarial_temperature, strict_negative, fact_ratio,
            sample_weight, filtered_ranking, full_batch_eval)

    def preprocess(self, train_set, valid_set, test_set):
        super(KnowledgeGraphCompletionExt, self).preprocess(train_set, valid_set, test_set)

        degree_hr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)
        degree_tr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)
        for h, t, r in train_set:
            degree_hr[h, r] += 1
            degree_tr[t, r] += 1

        has_category = False
        for _metric in self.metric:
            if _metric in ["1-to-1", "1-to-n", "n-to-1", "n-to-n"]:
                has_category = True
        if has_category:
            is_to_one = degree_hr.sum(dim=0).float() / (degree_hr > 0).sum(dim=0) < 1.5
            is_one_to = degree_tr.sum(dim=0).float() / (degree_tr > 0).sum(dim=0) < 1.5
            self.register_buffer("is_one_to_one", is_one_to & is_to_one)
            self.register_buffer("is_one_to_many", is_one_to & ~is_to_one)
            self.register_buffer("is_many_to_one", ~is_one_to & is_to_one)
            self.register_buffer("is_many_to_many", ~is_one_to & ~is_to_one)
            assert self.is_one_to_one.sum() + self.is_one_to_many.sum() + \
                   self.is_many_to_one.sum() + self.is_many_to_many.sum() == self.num_relation
            assert (self.is_one_to_one | self.is_one_to_many | self.is_many_to_one | self.is_many_to_many).all()

    def target(self, batch):
        mask, target = super(KnowledgeGraphCompletionExt, self).target(batch)
        relation = batch[:, 2]
        # in case of GPU OOM
        return mask, target, relation.cpu()

    def evaluate(self, pred, target):
        mask, target, relation = target

        pos_pred = pred.gather(-1, target.unsqueeze(-1))
        if self.filtered_ranking:
            ranking = torch.sum((pos_pred <= pred) & mask, dim=-1) + 1
        else:
            ranking = torch.sum(pos_pred <= pred, dim=-1) + 1

        metric = {}
        for _metric in self.metric:
            if _metric == "mr":
                score = ranking.float().mean()
            elif _metric == "mrr":
                score = (1 / ranking.float()).mean()
            elif _metric.startswith("hits@"):
                threshold = int(_metric[5:])
                score = (ranking <= threshold).float().mean()
            elif _metric == "1-to-1":
                score = (1 / ranking[self.is_one_to_one[relation]].float()).mean()
                metric["1-to-1 tail"] = (1 / ranking[self.is_one_to_one[relation]].float()).mean(dim=0)[0]
                metric["1-to-1 head"] = (1 / ranking[self.is_one_to_one[relation]].float()).mean(dim=0)[1]
            elif _metric == "1-to-n":
                score = (1 / ranking[self.is_one_to_many[relation]].float()).mean()
                metric["1-to-n tail"] = (1 / ranking[self.is_one_to_many[relation]].float()).mean(dim=0)[0]
                metric["1-to-n head"] = (1 / ranking[self.is_one_to_many[relation]].float()).mean(dim=0)[1]
            elif _metric == "n-to-1":
                score = (1 / ranking[self.is_many_to_one[relation]].float()).mean()
                metric["n-to-1 tail"] = (1 / ranking[self.is_many_to_one[relation]].float()).mean(dim=0)[0]
                metric["n-to-1 head"] = (1 / ranking[self.is_many_to_one[relation]].float()).mean(dim=0)[1]
            elif _metric == "n-to-n":
                score = (1 / ranking[self.is_many_to_many[relation]].float()).mean()
                metric["n-to-n tail"] = (1 / ranking[self.is_many_to_many[relation]].float()).mean(dim=0)[0]
                metric["n-to-n head"] = (1 / ranking[self.is_many_to_many[relation]].float()).mean(dim=0)[1]
            else:
                raise ValueError("Unknown metric `%s`" % _metric)

            name = tasks._get_metric_name(_metric)
            metric[name] = score

        return metric


@R.register("tasks.LinkPrediction")
class LinkPrediction(tasks.Task, core.Configurable):

    _option_members = ["criterion", "metric"]

    def __init__(self, model, criterion="bce", metric=("auroc", "ap"), num_negative=128, strict_negative=True):
        super(LinkPrediction, self).__init__()
        self.model = model
        self.criterion = criterion
        self.metric = metric
        self.num_negative = num_negative
        self.strict_negative = strict_negative

    def preprocess(self, train_set, valid_set, test_set):
        if isinstance(train_set, torch_data.Subset):
            dataset = train_set.dataset
        else:
            dataset = train_set
        self.num_node = dataset.num_node
        train_mask = train_set.indices
        valid_mask = train_set.indices + valid_set.indices
        train_graph = dataset.graph.edge_mask(train_mask)
        valid_graph = dataset.graph.edge_mask(valid_mask)
        self.register_buffer("train_graph", train_graph.undirected())
        self.register_buffer("valid_graph", valid_graph.undirected())
        self.register_buffer("test_graph", dataset.graph.undirected())

    def forward(self, batch):
        all_loss = torch.tensor(0, dtype=torch.float32, device=self.device)
        metric = {}

        pred, target = self.predict_and_target(batch, all_loss, metric)
        metric.update(self.evaluate(pred, target))

        for criterion, weight in self.criterion.items():
            if criterion == "bce":
                loss = F.binary_cross_entropy_with_logits(pred, target, reduction="none")
                neg_weight = torch.ones_like(pred)
                neg_weight[:, 1:] = 1 / self.num_negative
                loss = (loss * neg_weight).sum(dim=-1) / neg_weight.sum(dim=-1)
            else:
                raise ValueError("Unknown criterion `%s`" % criterion)

            loss = loss.mean()
            name = tasks._get_criterion_name(criterion)
            metric[name] = loss
            all_loss += loss * weight

        return all_loss, metric

    @torch.no_grad()
    def _strict_negative(self, count, split="train"):
        graph = getattr(self, "%s_graph" % split)

        node_in = graph.edge_list[:, 0]
        degree_in = torch.bincount(node_in, minlength=self.num_node)
        prob = (graph.num_node - degree_in - 1).float()

        neg_h_index = functional.multinomial(prob, count, replacement=True)
        any = -torch.ones_like(neg_h_index)
        pattern = torch.stack([neg_h_index, any], dim=-1)
        edge_index, num_t_truth = graph.match(pattern)
        t_truth_index = graph.edge_list[edge_index, 1]
        pos_index = torch.repeat_interleave(num_t_truth)
        t_mask = torch.ones(count, self.num_node, dtype=torch.bool, device=self.device)
        t_mask[pos_index, t_truth_index] = 0
        t_mask.scatter_(1, neg_h_index.unsqueeze(-1), 0)
        neg_t_candidate = t_mask.nonzero()[:, 1]
        num_t_candidate = t_mask.sum(dim=-1)
        neg_t_index = functional.variadic_sample(neg_t_candidate, num_t_candidate, 1).squeeze(-1)

        return neg_h_index, neg_t_index

    def predict_and_target(self, batch, all_loss=None, metric=None):
        batch_size = len(batch)
        pos_h_index, pos_t_index = batch.t()

        if self.split == "train":
            num_negative = self.num_negative
        else:
            num_negative = 1
        if self.strict_negative or self.split != "train":
            neg_h_index, neg_t_index = self._strict_negative(batch_size * num_negative, self.split)
        else:
            neg_h_index, neg_t_index = torch.randint(self.num_node, (2, batch_size * num_negative), device=self.device)
        neg_h_index = neg_h_index.view(batch_size, num_negative)
        neg_t_index = neg_t_index.view(batch_size, num_negative)
        h_index = pos_h_index.unsqueeze(-1).repeat(1, num_negative + 1)
        t_index = pos_t_index.unsqueeze(-1).repeat(1, num_negative + 1)
        h_index[:, 1:] = neg_h_index
        t_index[:, 1:] = neg_t_index

        pred = self.model(self.train_graph, h_index, t_index, all_loss=all_loss, metric=metric)
        target = torch.zeros_like(pred)
        target[:, 0] = 1
        return pred, target

    def evaluate(self, pred, target):
        pred = pred.flatten()
        target = target.flatten()

        metric = {}
        for _metric in self.metric:
            if _metric == "auroc":
                score = metrics.area_under_roc(pred, target)
            elif _metric == "ap":
                score = metrics.area_under_prc(pred, target)
            else:
                raise ValueError("Unknown metric `%s`" % _metric)

            name = tasks._get_metric_name(_metric)
            metric[name] = score

        return metric


@R.register("tasks.InductiveKnowledgeGraphCompletion")
class InductiveKnowledgeGraphCompletion(tasks.KnowledgeGraphCompletion, core.Configurable):

    def __init__(self, model, criterion="bce", metric=("mr", "mrr", "hits@1", "hits@3", "hits@10", "hits@10_50"),
                 num_negative=128, margin=6, adversarial_temperature=0, strict_negative=True, sample_weight=True,
                 full_batch_eval=False):
        super(InductiveKnowledgeGraphCompletion, self).__init__(
            model, criterion, metric, num_negative, margin, adversarial_temperature, strict_negative,
            sample_weight=sample_weight, filtered_ranking=True, full_batch_eval=full_batch_eval)

    def preprocess(self, train_set, valid_set, test_set):
        if isinstance(train_set, torch_data.Subset):
            dataset = train_set.dataset
        else:
            dataset = train_set
        self.num_entity = dataset.num_entity
        self.num_relation = dataset.num_relation
        self.register_buffer("fact_graph", dataset.graph)

        if self.sample_weight:
            degree_hr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)
            degree_tr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)
            for h, t, r in train_set:
                degree_hr[h, r] += 1
                degree_tr[t, r] += 1
            self.register_buffer("degree_hr", degree_hr)
            self.register_buffer("degree_tr", degree_tr)

        self.register_buffer("train_graph", dataset.train_graph)
        self.register_buffer("valid_graph", dataset.valid_graph)
        self.register_buffer("test_graph", dataset.test_graph)

        return train_set, valid_set, test_set

    def predict(self, batch, all_loss=None, metric=None):
        pos_h_index, pos_t_index, pos_r_index = batch.t()
        batch_size = len(batch)
        graph = getattr(self, "%s_graph" % self.split)

        if all_loss is None:
            # test
            all_index = torch.arange(graph.num_node, device=self.device)
            t_preds = []
            h_preds = []
            num_negative = graph.num_node if self.full_batch_eval else self.num_negative
            for neg_index in all_index.split(num_negative):
                r_index = pos_r_index.unsqueeze(-1).expand(-1, len(neg_index))
                h_index, t_index = torch.meshgrid(pos_h_index, neg_index)
                t_pred = self.model(graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)
                t_preds.append(t_pred)
            t_pred = torch.cat(t_preds, dim=-1)
            for neg_index in all_index.split(num_negative):
                r_index = pos_r_index.unsqueeze(-1).expand(-1, len(neg_index))
                t_index, h_index = torch.meshgrid(pos_t_index, neg_index)
                h_pred = self.model(graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)
                h_preds.append(h_pred)
            h_pred = torch.cat(h_preds, dim=-1)
            pred = torch.stack([t_pred, h_pred], dim=1)
            # in case of GPU OOM
            pred = pred.cpu()
        else:
            # train
            if self.strict_negative:
                neg_index = self._strict_negative(pos_h_index, pos_t_index, pos_r_index)
            else:
                neg_index = torch.randint(self.num_entity, (batch_size, self.num_negative), device=self.device)
            h_index = pos_h_index.unsqueeze(-1).repeat(1, self.num_negative + 1)
            t_index = pos_t_index.unsqueeze(-1).repeat(1, self.num_negative + 1)
            r_index = pos_r_index.unsqueeze(-1).repeat(1, self.num_negative + 1)
            t_index[:batch_size // 2, 1:] = neg_index[:batch_size // 2]
            h_index[batch_size // 2:, 1:] = neg_index[batch_size // 2:]
            pred = self.model(graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)

        return pred

    def target(self, batch):
        # test target
        batch_size = len(batch)
        graph = getattr(self, "%s_graph" % self.split)
        pos_h_index, pos_t_index, pos_r_index = batch.t()
        any = -torch.ones_like(pos_h_index)

        pattern = torch.stack([pos_h_index, any, pos_r_index], dim=-1)
        edge_index, num_t_truth = graph.match(pattern)
        t_truth_index = graph.edge_list[edge_index, 1]
        pos_index = torch.repeat_interleave(num_t_truth)
        t_mask = torch.ones(batch_size, graph.num_node, dtype=torch.bool, device=self.device)
        t_mask[pos_index, t_truth_index] = 0

        pattern = torch.stack([any, pos_t_index, pos_r_index], dim=-1)
        edge_index, num_h_truth = graph.match(pattern)
        h_truth_index = graph.edge_list[edge_index, 0]
        pos_index = torch.repeat_interleave(num_h_truth)
        h_mask = torch.ones(batch_size, graph.num_node, dtype=torch.bool, device=self.device)
        h_mask[pos_index, h_truth_index] = 0

        mask = torch.stack([t_mask, h_mask], dim=1)
        target = torch.stack([pos_t_index, pos_h_index], dim=1)

        # in case of GPU OOM
        return mask.cpu(), target.cpu()

    def evaluate(self, pred, target):
        mask, target = target

        pos_pred = pred.gather(-1, target.unsqueeze(-1))
        ranking = torch.sum((pos_pred <= pred) & mask, dim=-1) + 1

        metric = {}
        for _metric in self.metric:
            if _metric == "mr":
                score = ranking.float().mean()
            elif _metric == "mrr":
                score = (1 / ranking.float()).mean()
            elif _metric.startswith("hits@"):
                values = _metric[5:].split("_")
                threshold = int(values[0])
                if len(values) > 1:
                    num_sample = int(values[1])
                    # unbiased estimation
                    fp_rate = (ranking - 1).float() / mask.sum(dim=-1)
                    score = 0
                    for i in range(threshold):
                        # choose i false positive from num_sample negatives
                        num_comb = math.factorial(num_sample) / math.factorial(i) / math.factorial(num_sample - i)
                        score += num_comb * (fp_rate ** i) * ((1 - fp_rate) ** (num_sample - i))
                    score = score.mean()
                else:
                    score = (ranking <= threshold).float().mean()
            else:
                raise ValueError("Unknown metric `%s`" % _metric)

            name = tasks._get_metric_name(_metric)
            metric[name] = score

        return metric


@R.register("tasks.KnowledgeGraphCompletionOGB")
class KnowledgeGraphCompletionOGB(tasks.KnowledgeGraphCompletion, core.Configurable):

    def __init__(self, model, criterion="bce", evaluator=None, num_negative=128, margin=6, adversarial_temperature=0,
                 strict_negative=True, heterogeneous_negative=False, fact_ratio=None, sample_weight=True):
        super(KnowledgeGraphCompletionOGB, self).__init__(
            model, criterion, None, num_negative, margin, adversarial_temperature, strict_negative, fact_ratio,
            sample_weight)

        self.evaluator = evaluator
        self.heterogeneous_negative = heterogeneous_negative

    def preprocess(self, train_set, valid_set, test_set):
        if isinstance(train_set, torch_data.Subset):
            dataset = train_set.dataset
        else:
            dataset = train_set
        self.num_entity = dataset.num_entity
        self.num_relation = dataset.num_relation
        self.register_buffer("graph", dataset.graph)
        fact_mask = torch.zeros(len(dataset), dtype=torch.bool)
        fact_mask[train_set.indices] = 1
        if self.fact_ratio:
            length = int(len(train_set) * self.fact_ratio)
            index = torch.randperm(len(train_set))[length:]
            train_indices = torch.tensor(train_set.indices)
            fact_mask[train_indices[index]] = 0
            train_set = torch_data.Subset(train_set, index)
        self.register_buffer("fact_graph", dataset.graph.edge_mask(fact_mask))

        if self.sample_weight:
            degree_hr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)
            degree_tr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)
            for h, t, r in train_set:
                degree_hr[h, r] += 1
                degree_tr[t, r] += 1
            self.register_buffer("degree_hr", degree_hr)
            self.register_buffer("degree_tr", degree_tr)

        return train_set, valid_set, test_set

    @torch.no_grad()
    def _strict_negative(self, pos_h_index, pos_t_index, pos_r_index):
        batch_size = len(pos_h_index)
        any = -torch.ones_like(pos_h_index)
        node_type = self.fact_graph.node_type

        pattern = torch.stack([pos_h_index, any, pos_r_index], dim=-1)
        pattern = pattern[:batch_size // 2]
        edge_index, num_t_truth = self.fact_graph.match(pattern)
        t_truth_index = self.fact_graph.edge_list[edge_index, 1]
        pos_index = torch.repeat_interleave(num_t_truth)
        if self.heterogeneous_negative:
            pos_t_type = node_type[pos_t_index[:batch_size // 2]]
            t_mask = pos_t_type.unsqueeze(-1) == node_type.unsqueeze(0)
        else:
            t_mask = torch.ones(len(pattern), self.num_entity, dtype=torch.bool, device=self.device)
        t_mask[pos_index, t_truth_index] = 0
        neg_t_candidate = t_mask.nonzero()[:, 1]
        num_t_candidate = t_mask.sum(dim=-1)
        neg_t_index = functional.variadic_sample(neg_t_candidate, num_t_candidate, self.num_negative)

        pattern = torch.stack([any, pos_t_index, pos_r_index], dim=-1)
        pattern = pattern[batch_size // 2:]
        edge_index, num_h_truth = self.fact_graph.match(pattern)
        h_truth_index = self.fact_graph.edge_list[edge_index, 0]
        pos_index = torch.repeat_interleave(num_h_truth)
        if self.heterogeneous_negative:
            pos_h_type = node_type[pos_h_index[batch_size // 2:]]
            h_mask = pos_h_type.unsqueeze(-1) == node_type.unsqueeze(0)
        else:
            h_mask = torch.ones(len(pattern), self.num_entity, dtype=torch.bool, device=self.device)
        h_mask[pos_index, h_truth_index] = 0
        neg_h_candidate = h_mask.nonzero()[:, 1]
        num_h_candidate = h_mask.sum(dim=-1)
        neg_h_index = functional.variadic_sample(neg_h_candidate, num_h_candidate, self.num_negative)

        neg_index = torch.cat([neg_t_index, neg_h_index])

        return neg_index

    def predict(self, batch, all_loss=None, metric=None):
        batch_size = len(batch)

        if all_loss is None:
            # test
            h_index, t_index, r_index = batch.unbind(-1)
            pattern = batch[:, 0, :]
            num_match = self.fact_graph.match(pattern)[1]
            assert (num_match == 0).all()
            pred = self.model(self.fact_graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)
            # in case of GPU OOM
            pred = pred.cpu()
        else:
            # train
            pos_h_index, pos_t_index, pos_r_index = batch.t()
            if self.strict_negative:
                neg_index = self._strict_negative(pos_h_index, pos_t_index, pos_r_index)
            else:
                neg_index = torch.randint(self.num_entity, (batch_size, self.num_negative), device=self.device)
            h_index = pos_h_index.unsqueeze(-1).repeat(1, self.num_negative + 1)
            t_index = pos_t_index.unsqueeze(-1).repeat(1, self.num_negative + 1)
            r_index = pos_r_index.unsqueeze(-1).repeat(1, self.num_negative + 1)
            t_index[:batch_size // 2, 1:] = neg_index[:batch_size // 2]
            h_index[batch_size // 2:, 1:] = neg_index[batch_size // 2:]
            pred = self.model(self.fact_graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)

        return pred

    def target(self, batch):
        # test target
        batch_size = len(batch)
        target = torch.zeros(batch_size, dtype=torch.long, device=self.device)
        # in case of GPU OOM
        return target.cpu()

    def evaluate(self, pred, target):
        is_positive = torch.zeros(pred.shape, dtype=torch.bool)
        is_positive.scatter_(-1, target.unsqueeze(-1), 1)
        pos_pred = pred[is_positive]
        neg_pred = pred[~is_positive].view(len(pos_pred), -1)
        metric = self.evaluator.eval({"y_pred_pos": pos_pred, "y_pred_neg": neg_pred})

        new_metric = {}
        for key in metric:
            new_key = key.split("_")[0]
            new_metric[new_key] = metric[key].mean()

        return new_metric

================================================
FILE: nbfnet/util.py
================================================
import os
import time
import logging
import argparse

import yaml
import jinja2
from jinja2 import meta
import easydict

import torch
from torch.utils import data as torch_data
from torch import distributed as dist

from torchdrug import core, utils
from torchdrug.utils import comm


logger = logging.getLogger(__file__)


def get_root_logger(file=True):
    logger = logging.getLogger("")
    logger.setLevel(logging.INFO)
    format = logging.Formatter("%(asctime)-10s %(message)s", "%H:%M:%S")

    if file:
        handler = logging.FileHandler("log.txt")
        handler.setFormatter(format)
        logger.addHandler(handler)

    return logger


def create_working_directory(cfg):
    file_name = "working_dir.tmp"
    world_size = comm.get_world_size()
    if world_size > 1 and not dist.is_initialized():
        comm.init_process_group("nccl", init_method="env://")

    working_dir = os.path.join(os.path.expanduser(cfg.output_dir),
                               cfg.task["class"], cfg.dataset["class"], cfg.task.model["class"],
                               time.strftime("%Y-%m-%d-%H-%M-%S"))

    # synchronize working directory
    if comm.get_rank() == 0:
        with open(file_name, "w") as fout:
            fout.write(working_dir)
        os.makedirs(working_dir)
    comm.synchronize()
    if comm.get_rank() != 0:
        with open(file_name, "r") as fin:
            working_dir = fin.read()
    comm.synchronize()
    if comm.get_rank() == 0:
        os.remove(file_name)

    os.chdir(working_dir)
    return working_dir


def detect_variables(cfg_file):
    with open(cfg_file, "r") as fin:
        raw = fin.read()
    env = jinja2.Environment()
    ast = env.parse(raw)
    vars = meta.find_undeclared_variables(ast)
    return vars


def load_config(cfg_file, context=None):
    with open(cfg_file, "r") as fin:
        raw = fin.read()
    template = jinja2.Template(raw)
    instance = template.render(context)
    cfg = yaml.safe_load(instance)
    cfg = easydict.EasyDict(cfg)
    return cfg


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("-c", "--config", help="yaml configuration file", required=True)
    parser.add_argument("-s", "--seed", help="random seed for PyTorch", type=int, default=1024)

    args, unparsed = parser.parse_known_args()
    # get dynamic arguments defined in the config file
    vars = detect_variables(args.config)
    parser = argparse.ArgumentParser()
    for var in vars:
        parser.add_argument("--%s" % var, required=True)
    vars = parser.parse_known_args(unparsed)[0]
    vars = {k: utils.literal_eval(v) for k, v in vars._get_kwargs()}

    return args, vars


def build_solver(cfg, dataset):
    train_set, valid_set, test_set = dataset.split()
    if comm.get_rank() == 0:
        logger.warning(dataset)
        logger.warning("#train: %d, #valid: %d, #test: %d" % (len(train_set), len(valid_set), len(test_set)))

    if "fast_test" in cfg:
        if comm.get_rank() == 0:
            logger.warning("Quick test mode on. Only evaluate on %d samples for valid / test." % cfg.fast_test)
        g = torch.Generator()
        g.manual_seed(1024)
        valid_set = torch_data.random_split(valid_set, [cfg.fast_test, len(valid_set) - cfg.fast_test], generator=g)[0]
        test_set = torch_data.random_split(test_set, [cfg.fast_test, len(test_set) - cfg.fast_test], generator=g)[0]
    if hasattr(dataset, "num_relation"):
        cfg.task.model.num_relation = dataset.num_relation

    task = core.Configurable.load_config_dict(cfg.task)
    cfg.optimizer.params = task.parameters()
    optimizer = core.Configurable.load_config_dict(cfg.optimizer)
    solver = core.Engine(task, train_set, valid_set, test_set, optimizer, **cfg.engine)

    if "checkpoint" in cfg:
        solver.load(cfg.checkpoint)

    return solver

================================================
FILE: requirements.txt
================================================
torch>=1.8.0
torch-scatter>=2.0.8
torchdrug>=0.1.2
ogb>=1.3.0
easydict
pyyaml

================================================
FILE: script/run.py
================================================
import os
import sys
import math
import pprint

import torch

from torchdrug import core
from torchdrug.utils import comm

sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from nbfnet import dataset, layer, model, task, util


def train_and_validate(cfg, solver):
    if cfg.train.num_epoch == 0:
        return

    step = math.ceil(cfg.train.num_epoch / 10)
    best_result = float("-inf")
    best_epoch = -1

    for i in range(0, cfg.train.num_epoch, step):
        kwargs = cfg.train.copy()
        kwargs["num_epoch"] = min(step, cfg.train.num_epoch - i)
        solver.model.split = "train"
        solver.train(**kwargs)
        solver.save("model_epoch_%d.pth" % solver.epoch)
        solver.model.split = "valid"
        metric = solver.evaluate("valid")
        result = metric[cfg.metric]
        if result > best_result:
            best_result = result
            best_epoch = solver.epoch

    solver.load("model_epoch_%d.pth" % best_epoch)
    return solver


def test(cfg, solver):
    solver.model.split = "valid"
    solver.evaluate("valid")
    solver.model.split = "test"
    solver.evaluate("test")


if __name__ == "__main__":
    args, vars = util.parse_args()
    cfg = util.load_config(args.config, context=vars)
    working_dir = util.create_working_directory(cfg)

    torch.manual_seed(args.seed + comm.get_rank())

    logger = util.get_root_logger()
    if comm.get_rank() == 0:
        logger.warning("Config file: %s" % args.config)
        logger.warning(pprint.pformat(cfg))

    dataset = core.Configurable.load_config_dict(cfg.dataset)
    solver = util.build_solver(cfg, dataset)

    train_and_validate(cfg, solver)
    test(cfg, solver)


================================================
FILE: script/visualize.py
================================================
import os
import sys
import pprint

import torch

from torchdrug import core
from torchdrug.utils import comm

sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from nbfnet import dataset, layer, model, task, util


vocab_file = os.path.join(os.path.dirname(__file__), "../data/fb15k237_entity.txt")
vocab_file = os.path.abspath(vocab_file)


def load_vocab(dataset):
    entity_mapping = {}
    with open(vocab_file, "r") as fin:
        for line in fin:
            k, v = line.strip().split("\t")
            entity_mapping[k] = v
    entity_vocab = [entity_mapping[t] for t in dataset.entity_vocab]
    relation_vocab = ["%s (%d)" % (t[t.rfind("/") + 1:].replace("_", " "), i)
                      for i, t in enumerate(dataset.relation_vocab)]

    return entity_vocab, relation_vocab


def visualize_path(solver, triplet, entity_vocab, relation_vocab):
    num_relation = len(relation_vocab)
    h, t, r = triplet.tolist()
    triplet = torch.as_tensor([[h, t, r]], device=solver.device)
    inverse = torch.as_tensor([[t, h, r + num_relation]], device=solver.device)
    solver.model.eval()
    pred, (mask, target) = solver.model.predict_and_target(triplet)
    pos_pred = pred.gather(-1, target.unsqueeze(-1))
    rankings = torch.sum((pos_pred <= pred) & mask, dim=-1) + 1
    rankings = rankings.squeeze(0)

    logger.warning("")
    samples = (triplet, inverse)
    for sample, ranking in zip(samples, rankings):
        h, t, r = sample.squeeze(0).tolist()
        h_name = entity_vocab[h]
        t_name = entity_vocab[t]
        r_name = relation_vocab[r % num_relation]
        if r >= num_relation:
            r_name += "^(-1)"
        logger.warning(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
        logger.warning("rank(%s | %s, %s) = %g" % (t_name, h_name, r_name, ranking))

        paths, weights = solver.model.visualize(sample)
        for path, weight in zip(paths, weights):
            triplets = []
            for h, t, r in path:
                h_name = entity_vocab[h]
                t_name = entity_vocab[t]
                r_name = relation_vocab[r % num_relation]
                if r >= num_relation:
                    r_name += "^(-1)"
                triplets.append("<%s, %s, %s>" % (h_name, r_name, t_name))
            logger.warning("weight: %g\n\t%s" % (weight, " ->\n\t".join(triplets)))


if __name__ == "__main__":
    args, vars = util.parse_args()
    cfg = util.load_config(args.config, context=vars)
    working_dir = util.create_working_directory(cfg)

    torch.manual_seed(args.seed + comm.get_rank())

    logger = util.get_root_logger()
    logger.warning("Config file: %s" % args.config)
    logger.warning(pprint.pformat(cfg))

    if cfg.dataset["class"] != "FB15k237":
        raise ValueError("Visualization is only implemented for FB15k237")

    dataset = core.Configurable.load_config_dict(cfg.dataset)
    solver = util.build_solver(cfg, dataset)

    entity_vocab, relation_vocab = load_vocab(dataset)

    for i in range(500):
        visualize_path(solver, solver.test_set[i], entity_vocab, relation_vocab)