diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..abc8e5a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,14 @@
+.mypy_cache
+__pycache__
+*.pyc
+.idea/
+
+results/
+kb/
+kb_embed*/
+
+slurm*
+*/model*.pt
+*/*.log
+*/*.sh
+!scripts/*.sh
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..c17a41f
--- /dev/null
+++ b/README.md
@@ -0,0 +1,209 @@
+# Latent Relation Language Models
+
+This repository contains the official PyTorch implementation of Latent Relation Language Models
+([arXiv](https://arxiv.org/abs/1908.07690)):
+
+> Hiroaki Hayashi\*, Zecong Hu\*, Chenyan Xiong, Graham Neubig<br/>
+> _Latent Relation Language Models_<br/>
+> The 34th AAAI Conference on Artificial Intelligence (AAAI 2020)
+
+![lrlm](docs/lrlm-fig.png)
+
+
+## Requirements
+
+- Python 3.6+
+- PyTorch 0.4+
+- Other packages:
+    - IPython
+    - tqdm
+    - tensorboardX
+    - [fastText@bc12859](https://github.com/facebookresearch/fastText/tree/bc1285939f1c216bd358425c3685a049dd8f56c0)
+
+
+## Usage
+
+1. Clone this repository and install dependencies:
+   ```bash
+   git clone https://github.com/neulab/lrlm.git
+   cd lrlm
+   pip install -r requirements.txt
+   ```
+   To prevent incorrect or conflicting package versions, it is recommended to install dependencies in a virtual
+   environment.
+   
+2. Download required files, including the dataset, unknown word probabilities, and (for inference only) pre-trained
+   model weights:
+   ```bash
+   # Wikitext-S,F dataset
+   wget https://github.com/neulab/lrlm/releases/download/v1.0/wikitext.tar.bz2
+
+   # Wikifacts dataset
+   wget https://github.com/neulab/lrlm/releases/download/v1.0/wikfacts.tar.bz2
+   
+   # Transformer-XL on WikiText-F model weights
+   wget https://github.com/neulab/lrlm/releases/download/v1.0/t-xl_wt-f_model17.pt
+
+   # Transformer-XL on WikiText-S model weights
+   wget https://github.com/neulab/lrlm/releases/download/v1.0/t-xl_wt-s_model17.pt
+   ```
+   Note that the list of resources above are available under [releases](https://github.com/neulab/lrlm/releases).
+
+   Please contact us for pretrained models for different configurations.
+
+   FastText model weights can be downloaded via [google drive](https://drive.google.com/file/d/1zBBMnhYEMWXAS0QK3Wg2q_fENcKLAXTE/view?usp=sharing).
+   
+3. To train a new model, use one of the scripts in the `scripts/` directory, e.g.
+   `scripts/train_lrlm_transformer_wikitext_short.sh`. This will create a directory for the experiment named
+   `lrlm-transformer-wikitext-short` under the working directory, containing the following files:
+
+   - `model<k>.pt`: The model checkpoint at the k-th epoch. Checkpoints are only saved when the validation results for
+     that epoch improves over previous epochs.
+   - `<timestamp>.txt`: The training log file.
+   - `<script>.sh`: A copy of the script used to run the experiment.
+
+   Some additional command line flags that might be useful:
+
+   - `--exp-suffix`: Add a suffix to the name of the experiment directory.
+   - `--overwrite`: Use this flag to overwrite the experiment directory. If this flag is not specified, training will
+     not proceed if the experiment directory exists.
+   - `--no-cuda`: The scripts enable CUDA by default. Use this flag to run on devices without GPU.
+   - `--debug`: Run without saving files or writing to Tensorboard.
+   - `--pdb`: Invoke the Python debugger (pdb) when an uncaught exception is encountered.
+   - `--no-cache-dataset`: Do not load cached versions of the dataset. By default the processed dataset will be cached
+     under the dataset path, and future runs with the same dataset configurations will be able to use the cached
+     version.
+
+4. To evaluate a trained model, use the same script for training, with the additional flags:
+
+   - `--mode eval`: Use evaluation mode.
+   - `--pretrained /path/to/experiment/dir`: Load pre-trained models from the given experiment directory. The best model
+     under the directory will be loaded. You can also use a path to a specific checkpoint to load that checkpoint.
+
+   ```bash
+   ./scripts/train_lrlm_transformer_wikitext_short.sh \
+       --mode eval \
+       --pretrained lrlm-transformer-wikitext-short/
+   ```
+
+5. To perform inference on a trained model, use the same script for training, with the additional flags:
+
+   - `--pretrained /path/to/experiment/dir`: Load pre-trained models from the path.
+   - `--repl`: Enter interactive mode.
+
+   This will enter an IPython interactive environment where you can play with the model. The most useful method here
+   is `sample`, which will allow you to perform sampling-based decoding on the model given a topic entity from the
+   dataset. The accepted arguments are:
+
+   - `name`: The name of the topic entity, e.g. `name="Barack Obama"`.
+   - `split` and `index`: The index of the topic entity, e.g. `split='test', index=1` gives the first entity from the
+     test set.
+   - `max_length`: Maximum number of words to generate.
+   - `n_tries`: Run sampling for multiple trials, and output the generated result with the highest probability.
+   - For other options, please see the method signature for `models.lrlm.LRLM.sampling_decode()`.
+
+   For other methods that you can use, please refer to the `repl.py` file.
+
+   Here's a demo output of running inference:
+   ```
+   > ./scripts/train_lrlm_wikitext_short.sh --repl --pretrained lrlm-transformer-wikitext-short/
+   Random seed set to 4731
+   lrlm-wikitext-short
+   Loaded model weights from lrlm-wikitext-short/model31.pt
+   Execute `sample(name="Barack Obama")` to generate samples for given entity.
+   Execute `sample(split='test', index=1)` to generate samples for specific data entry.
+   For more configurable settings, please refer to method `models.lrlm.LRLM.sampling_decode`.
+
+   Python 3.7.3 (default, Mar 27 2019, 22:11:17)
+   Type 'copyright', 'credits' or 'license' for more information
+   IPython 7.6.1 -- An enhanced Interactive Python. Type '?' for help.
+
+   In [1]: sample(name="Sonic the Hedgehog", warm_up=10)
+   Sample loss: 2.624, PPL: 13.792
+   Marginal sample loss: 2.615, PPL: 13.672
+   Relations [Generated / Annotated]: [14 / 72]
+   # of copied entities: 14
+   <s> = Sonic the Hedgehog 3 = Sonic the Hedgehog is an action @-@ adventure game_
+   [instance of] developed and published by Sega_[publisher] for the Sega Genesis_[
+   platform] . The original instalment in the Sonic the Hedgehog_[series] series , 
+   written by Takashi Iizuka_[designer] and Rafael <unk> , released on September 25
+    , 2006 ( 1983 – 2000 ) before its inclusion in the Famicom in the 2009 Mario ! 
+   series . Its predecessor , known as the Sonic_[series] 2052 , is released on Mar
+   ch 4 , 2006 , as a video game and adds 3D . The game_[instance of] includes 2.5D
+    gameplay and platforming elements , and in also the previous S&K_[followed by] 
+   and a Guinness World Tour in which the game_[instance of] hosts the future , all
+   owing the player to compete on the Sonic_[series] , visuals and omissions . The 
+   player 's player can include gameplay " gathers , skill @-@ answer " , as oppose
+   d to other scenarios , with dark levels and extreme guns . Sonic_[series] Shift 
+   was first released in Japan in September 2013 for the PlayStation Vita and Mega 
+   Drive_[platform] for the Ga$eCube . It was released in North America on Septembe
+   r 31 , 2011 in Canada , on February 3 , 2009 , in North America on March 13 , 20
+   09 , in North America on January 29 , 2011 fo$ Xbox Live Arcade . A Microsoft Wi
+   ndows_[platform] version titled Sonic_[series] Quest was released in 2016 . </s>
+   ```
+
+**Note on reproducibility:** Experiments in the paper were run using PyTorch version 0.4.1 on GPUs. Using different
+versions and/or devices might lead to slightly different results. 
+
+
+## Understanding and Extending the Code
+
+### Creating the Dataset
+
+Please see [this document](docs/data.md) for a detailed description of the dataset format, and instructions for creating
+the WikiText dataset from scratch. Similar process can be applied to creating your custom datasets.
+
+### Directory Structure
+
+```
+├── README.md
+├── data/                                .... Needs downloading from the link above.
+│   ├── found_ids/                       .... Files containing article title to Wikidata ID mapping.
+│   └── canonical_forms/                 .... Canonical forms for each entity obtained by querying to Wikipedia.
+├── dataset/                             .... Dataset definitions
+│   ├── aliaslm.py                       ....   Dataset for alias LM
+│   ├── base.py                          ....   Base class for all datasets (include dataset for vanilla LM)
+│   ├── lrlm.py                          ....   Dataset for LRLM
+│   ├── nklm.py                          ....   Dataset for NKLM
+│   ├── utils.py                         ....   Utilities for reading & batching
+│   └── vocab.py                         ....   Utilities related to vocabulary & unk-probs
+├── docs/                                .... Documentations
+│   └─── data.md                          ....   Descriptions of the processed data and data generation.
+├── models/                              .... Model definitions
+│   ├── rnns/                            ....   Sequence model definitions
+│   │   ├── base.py                      ....     Base class for all sequence models
+│   │   ├── lstm.py                      ....     LSTM model
+│   │   └── transformer.py               ....     TransformerXL model
+│   ├── aliaslm.py                       ....   Alias LM
+│   ├── base.py                          ....   Base class for all models
+│   ├── lm.py                            ....   Vanilla LM
+│   ├── lrlm.py                          ....   LRLM
+│   ├── nklm.py                          ....   NKLM
+│   ├── sample_utils.py                  ....   Utilities for sampling
+│   └── utils.py                         ....   Common modules shared among models
+├── nnlib/                               .... A tiny library for NNs
+│   ├── arguments/                       ....   A type-annotated command line argument parser
+│   └── utils/                           ....   All sorts of utility functions
+├── preprocess/                          .... Scripts for preprocessing & analysis of data
+├── scripts/                             .... Scripts for experiments
+├── stubs/                               .... Python stub files for type annotating PyTorch
+├── arguments.py                         .... Command line argument definitions
+├── repl.py                              .... Utilities for REPL mode (useful for inspecting models)
+├── run.py                               .... Program entry point
+├── run_charlm.py                        .... Train the character-level LM for unk-probs
+└── utils.py                             .... Utilities for training and checkpointing
+```
+
+
+## Reference
+
+If you use this code in your research paper, please cite:
+```bibtex
+@inproceedings{hayashi2020latent,
+  title={Latent Relation Language Models},
+  author={Hayashi, Hiroaki and Hu, Zecong and Xiong, Chenyan and Neubig, Graham},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={34},
+  year={2020}
+}
+```
diff --git a/arguments.py b/arguments.py
new file mode 100644
index 0000000..8973148
--- /dev/null
+++ b/arguments.py
@@ -0,0 +1,184 @@
+from enum import auto
+from typing import Optional
+
+from nnlib.arguments import Arguments, validator
+from nnlib.arguments.custom_types import Choices
+
+__all__ = [
+    'AliasDisamb',
+    'RNNDropoutPos',
+    'LMArguments',
+]
+
+
+class AliasDisamb(Arguments.Enum):
+    Oracle = auto()  # use the ground truth alias
+    FastText = auto()  # use FastText vectors for disambiguating between aliases
+
+
+class RNNDropoutPos(Arguments.Enum):
+    Early = auto()  # embedding dropout only
+    Late = auto()  # LSTM output dropout only
+    Both = auto()  # dropout at both positions
+
+
+class LMArguments(Arguments):
+    # General
+    seed: int = 4731
+    exp: str = None  # experiment name
+    exp_suffix: Optional[str] = None  # suffix to append to the exp
+    cuda = Arguments.Switch()
+    mode: Choices['train', 'eval'] = 'train'
+    repl = Arguments.Switch()  # enter REPL mode after loading model
+    debug = Arguments.Switch()  # enter debug mode
+    profile = Arguments.Switch()  # performance profiling
+    profile_data = Arguments.Switch()  # also profile data loader
+    profile_steps: int = 50  # number of steps to run when profiling
+    multi_gpu = Arguments.Switch()  # whether to enable multi-GPU training via DataParallel
+
+    # Logging
+    tbdir: str = "./tb/"  # path to where tensorboard logs are written
+    script: str = None  # specify the script you're running
+    logging_level: Choices['DEBUG', 'INFO', 'WARNING', 'ERROR', 'NOTSET'] = 'INFO'
+    log_interval: int = 10  # report loss every n batches
+    progress = Arguments.Switch(default=True)  # if False, do not show the progress bar
+    writer = Arguments.Switch(default=True)
+
+    # Dataset
+    path: str = None  # path to data
+    vocab_size: Optional[int] = 50000  # set to none to prune vocabulary by frequency
+    min_freq: Optional[int] = None
+    vocab_dir: str = "data/vocab/"
+    use_unk_probs = Arguments.Switch()  # whether to include back-off log-probs for UNK tokens
+    use_upp = Arguments.Switch()  # whether to compute UPP (uniform back-off for UNKs) or not
+    cache_dataset = Arguments.Switch(default=True)  # whether to cache processed batches to speedup future loads
+
+    use_anchor = Arguments.Switch()  # whether to use anchor
+    exclude_alias_disamb = Arguments.Switch()  # exclude mentions matched with aliases ambiguous for entities
+    exclude_entity_disamb = Arguments.Switch()  # exclude mentions matched with entities ambiguous for relation types
+    use_only_first_section = Arguments.Switch()  # use only the first section (summary part) of the articles
+
+    # Model
+    base_rnn: Choices['lstm', 'transformer'] = 'lstm'
+    model: Choices['VanillaLM', 'LRLM', 'NKLM', 'AliasLM'] = 'VanillaLM'
+    embed_size: int = 512  # input word embedding size
+    hidden_size: int = 1024  # RNN's hidden unit size
+    num_layers: int = 4  # number of RNN layers
+    dropout: float = 0.5  # inter-layer dropout rate
+    bptt_size: int = 140  # BPTT length
+    vocab_mlp_hidden_dim: int = -1  # vocab MLP hidden layer size
+    vocab_mlp_activation: Choices['relu', 'tanh', 'sigmoid', 'none'] = 'relu'
+    vocab_mlp_dropout: float = None  # vocab MLP dropout, `None` for same value as 'dropout'
+    use_rel_mlp: bool = False
+    rnn_dropout_pos: RNNDropoutPos = 'late'  # LSTM dropout position
+    adaptive_embed = Arguments.Switch()  # use adaptive input embeddings & softmax
+    tie_embed_weights = Arguments.Switch()  # tie input embeddings & softmax weights
+    fasttext_model_path: Optional[str] = None
+    normalize_fasttext_embeds = Arguments.Switch()
+
+    # Transformer-specific
+    num_heads: int = 10  # number of heads in multi-head attention
+    head_dim: int = 41  # per-head attention dimension
+    ffn_inner_dim: int = 2100  # fully-connected layer dimension
+    attention_dropout: float = 0.0
+    memory_size: int = 150  # maximum length of cached memory
+    pre_lnorm = Arguments.Switch()  # whether to apply layer norm before residual
+
+    # NKLM-specific
+    pos_embed_dim: int = 40  # position embedding size
+    pos_embed_count: int = 20  # number of position embeddings
+    kb_embed_dim: int = 50  # TransE embedding size
+    fact_key_mlp_hidden_dim: int = -1  # `fact_key` MLP hidden layer size, -1 for paper value
+    copy_mlp_hidden_dim: int = -1  # copy MLP hidden layer size, -1 for paper value
+    pos_mlp_hidden_dim: int = -1  # pos MLP hidden layer size
+    mask_invalid_pos = Arguments.Switch()  # mask invalid copy positions during training
+
+    # LRLM-specific
+    rel_mlp_hidden_dim: int = -1  # rel MLP hidden layer size
+    rel_mlp_activation: Choices['relu', 'tanh', 'sigmoid', 'none'] = 'relu'
+    rel_mlp_dropout: float = None  # relation MLP dropout, `None` for same value as 'dropout'
+    use_knowledge_embed = Arguments.Switch()  # use knowledge embeddings in selector & relation predictors
+    train_relation_vec: bool = Arguments.Switch()  # if True, relation embeddings are fine-tuned
+
+    # Strategies
+    unk_rels_strategy: Choices[
+        'remove',  # remove such relations
+        'unk',  # treat them as the [UNK] special relation
+        'params',  # learn separate params for each relation
+    ] = 'params'  # strategy for UNK relations
+    fact_sel_strategy: Choices[
+        'gold',  # use the gold fact from data
+        'argmax',  # use the fact with highest score
+    ] = 'gold'  # strategy for fact selection (which fact to use for wrod/pos prediction) during training
+    alias_disamb_strategy: AliasDisamb = AliasDisamb.Oracle  # strategy for selecting surface form given entity
+
+    # Optimization
+    num_epochs: int = 20
+    batch_size: int = 30
+    update_batch_size: int = 60
+    lr: float = 1e-3
+    lr_scaler: float = 0.5
+    lr_decay: float = 0.0
+    clip: Optional[float] = None
+    optimizer: Choices['adam', 'sgd'] = 'adam'
+    lr_scheduler: Choices['cosine', 'none'] = 'none'
+    optimizer_strategy: Choices[
+        'none',  # do nothing
+        'reset',  # when validation results degrade, reset model & optimizer state to previous best state
+    ] = 'reset'
+    warm_up_steps: Optional[int] = None  # no. of steps to linearly increase lr from 0 to `lr`, useful for Transformers
+
+    # Load/Save
+    save = Arguments.Switch(default=True)
+    pretrained: Optional[str] = None  # path to pretrained model
+    checkpoint_interval: int = -1  # number of iterations to run before validating, -1 for one epoch
+    overwrite = Arguments.Switch()  # overwrite the experiment log directory if it exists
+
+    dump_probs = Arguments.Switch()  # flag to dump log-prob for each example
+
+    def preprocess(self):
+        if self.vocab_mlp_dropout is None:
+            self.vocab_mlp_dropout = self.dropout
+        if self.rel_mlp_dropout is None:
+            self.rel_mlp_dropout = self.dropout
+        self.use_knowledge_embed.set_default(self.model == 'NKLM')
+        if self.vocab_mlp_hidden_dim == -1:
+            self.vocab_mlp_hidden_dim = self.hidden_size
+
+    def validate(self):
+        if self.update_batch_size % self.batch_size != 0:
+            raise ValueError("'update-batch-size' must be a multiple of 'batch-size'")
+
+        if self.vocab_size is None and self.min_freq is None:
+            raise ValueError("'vocab-size' or 'min-freq' cannot both be 'None'")
+
+        if self.tie_embed_weights and self.embed_size != self.vocab_mlp_hidden_dim:
+            raise ValueError("When tying weights embeddings and softmax, "
+                             "vocabulary MLP hidden dim (defaults to hidden size) must be equal to embedding dim.")
+
+        if self.alias_disamb_strategy is AliasDisamb.FastText and self.fasttext_model_path is None:
+            raise ValueError("'fasttext_model_path' must not be 'None' when 'alias_disamb_strategy' is 'FastText'.")
+
+        return [
+            (r'path', validator.is_path()),
+            (r'fasttext_model_path', validator.is_path(nullable=True)),
+            (r'.*dropout', validator.is_dropout()),
+            (r'.*_mlp_hidden_dim', lambda x: x == -1 or x > 0),
+        ]
+
+    def postprocess(self):
+        if self.repl:
+            self.save = False
+            self.logging_level = 'WARNING'
+
+        if self.debug:
+            self.save = False
+            self.writer = False
+            self.progress = False
+
+        # If it's just an evaluation, prefix the exp name
+        if self.mode != 'train':
+            self.exp = 'EVAL-' + self.exp
+
+        if self.exp_suffix is not None:
+            self.exp += '-' + self.exp_suffix
diff --git a/cleanup.sh b/cleanup.sh
new file mode 100755
index 0000000..7150a17
--- /dev/null
+++ b/cleanup.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+if [[ "$1" == "--dry-run" ]]; then
+    DRY_RUN=1
+    echo "Dry run"
+else
+    DRY_RUN=0
+    # Credit: https://stackoverflow.com/questions/1885525/how-do-i-prompt-a-user-for-confirmation-in-bash-script
+    read -p "Will remove intermediate model states under \"result/\". Confirm? y/[n] " -n 1 -r
+    echo
+    if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+        [[ "$0" = "$BASH_SOURCE" ]] && exit 1 || return 1
+    fi
+fi
+
+for x in `ls results`; do
+    if ! ls results/${x}/* &> /dev/null; then
+        continue
+    fi
+    if ! ls results/${x}/model?*.pt &> /dev/null; then
+        echo "Remove $x"
+        if [[ ${DRY_RUN} == 0 ]]; then
+            rm -r results/${x}
+        fi
+        continue
+    fi
+    keep=`ls results/${x}/model?*.pt | sort -V | tail -n 1`
+    echo "Keep: $keep"
+    for y in `ls results/${x}/model*.pt`; do
+        if [[ ${y} != ${keep} ]]; then
+            echo ${y}
+            if [[ ${DRY_RUN} -eq 0 ]]; then
+                rm ${y}
+            fi
+        fi
+    done
+done
diff --git a/data/canonical_forms/test_canonical_forms.txt b/data/canonical_forms/test_canonical_forms.txt
new file mode 100644
index 0000000..a2d7493
--- /dev/null
+++ b/data/canonical_forms/test_canonical_forms.txt
@@ -0,0 +1,60 @@
+Robert Boulter
+Du Fu
+Kiss You
+Ise class battleship
+Dick Rifenburg
+1933 Treasure Coast hurricane
+Second Battle of Naktong Bulge
+Hed PE
+ironclad warship
+Little Gidding
+The Portage to San Cristobal of A. H .
+Temnospondyli
+Osbert de Bayeux
+Dvorak technique
+New York State Route 31B
+Ben Amos
+Clayton Kershaw
+Josepha Petrick Kemarre
+Head VI
+Imagism
+Operation Eastern Exit
+2010 Claxton Shield
+Independiente
+2003 Pacific typhoon season
+Oxaziridine
+Battle of Dürenstein
+Brock Lesnar
+Constant k filter
+The Snowmen
+No. 20 Squadron RAAF
+Light Tank Mk VII Tetrarch
+QuackShot
+Olmec colossal jade villermosa
+Brad Stevens
+Shackleton
+American Beauty
+Christopher Gore
+Nero
+Manila
+Attalea
+The Heart of Ezra Greer
+Free Derry
+Come What ( ever ) May
+Chad at the 2008 Summer Olympics
+View of the World from 9th Avenue
+Bintulu
+Zrinski Battalion
+Weevils Wobble But They Do n't Go Down
+Temple Beth Israel
+New York State Route 93
+Operation USA
+Typhoon Krosa ( 2013 )
+Kirby 's Block Ball
+Hannah Dodd
+Commonwealth War Graves Commission
+Tatwine
+German Type UB I submarine
+military history of Gibraltar during World War II
+Nerva
+The Hustler
diff --git a/data/canonical_forms/train_canonical_forms.txt b/data/canonical_forms/train_canonical_forms.txt
new file mode 100644
index 0000000..3b42b60
--- /dev/null
+++ b/data/canonical_forms/train_canonical_forms.txt
@@ -0,0 +1,27685 @@
+Valkyria Chronicles III
+Tower Building of the Little Rock Arsenal
+Cicely Mary Barker
+Gambia women 's national football team
+Plain maskray
+2011 – 12 Columbus Blue Jackets season
+Gregorian Tower
+There 's Got to Be a Way
+Nebraska Highway 88
+USS Atlanta ( 1861 )
+Jacqueline Fernandez
+John Cullen
+SMS Erzherzog Ferdinand Max
+Ancient Egyptian deity
+South of Heaven
+General aviation in the United Kingdom
+SMS Zrínyi
+Geopyxis carbonaria
+Gold dollar
+Johnson – Corey – Chaykovsky reaction
+Treaty of Ciudad Juárez
+The Feast of the Goat
+Charles Eaton
+Tina Fey
+WASP @-@ 44
+Elephanta Caves
+Devin Townsend
+Zagreb Synagogue
+1806 Great Coastal hurricane
+Trinsey v. Pennsylvania
+Michael Jordan
+Polish culture during World War II
+Arihant @-@ class submarine
+SMS Markgraf
+Coldrum Long Barrow
+Soviet cruiser Krasnyi Kavkaz
+Rhode Island Route 4
+West End Girls
+Wrapped in Red
+Christmas 1994 nor'easter
+Sholay
+Adam Stansfield
+Saprang Kalayanamitr
+Grammy Award for Best Concept Music Video
+Hadji Ali
+Battle of Tellicherry
+Loose
+2013 – 14 York City F.C . season
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+Giving Victims a Voice
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+KFC
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+Colegio de la Preciosa Sangre
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+Walter Zinn
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+Merchants of Doubt
+1812 Louisiana hurricane
+SMS Ariadne
+Tsunkatse
+Manchester United F.C .
+New York State Route 83
+7 Things
+Kidwelly and Llanelly Canal
+Tome of the Unknown
+Battle of Ostrach
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+The Boat Race 1997
+Back to Basics
+geography of Croatia
+Cleveland
+Saint Anselm College
+Washington State Route 223
+Rockstar Games presents Table Tennis
+Thomas White
+Dude Ranch
+Joel Otto
+A Good Man Goes to War
+Northern Mockingbird
+The Hardest Part
+Thunderbirds
+Angel
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+Stand Up for Love
+Mit Fried und Freud ich fahr dahin , BWV 125
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+The Deposition
+Dwight Christmas
+The Year of the Sex Olympics
+Sviatoslav I of Kiev
+Gin Tama
+Toxic
+Home
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+Kamp Krusty
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+hydrochloric acid
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+Battle of the Strait of Otranto
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+Nest of Angels
+SMS Kronprinz Erzherzog Rudolf
+Sicilian Baroque
+Big Thunder Ski Jumping Center
+Battle of the Barracks
+Tomb of Horrors
+Amanita exitialis
+Knights of Columbus
+It 's Alright , Ma ( I 'm Only Bleeding )
+Kinzie Street railroad bridge
+Sarcófago
+That We Can Play
+15th Battalion
+Dragons of Despair
+diabetic ketoacidosis
+Mind 's Eye
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+wreck of the RMS Titanic
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+ATLiens
+Dara Torres
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+2013 Continental Cup of Curling
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+Petroleum industry in Iran
+Eastbourne manslaughter
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+M @-@ 120
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+problem of religious language
+Lyon class battleship
+First Presbyterian Church of Marcellus
+Connie Talbot 's Christmas Album
+Louis XVIII of France
+Versus
+New York State Route 92
+Mitică
+From Russia with Love
+Louvre Abu Dhabi
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+industrial music
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+Jingle All the Way
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+Hurricane Ioke
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+K @-@ 1
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+Where 's Herb ?
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+WTF ? !
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+Smoke Gets in Your Eyes
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+I 'll Be Lovin ' U Long Time
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+Walking on Air
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+Grolier Codex
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+Nausicaä of the Valley of the Wind
+Porsche RS Spyder
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+Brazilian monitor Ceará
+Anytime You Need a Friend
+Turban Head eagle
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+Crystallographic defects in diamond
+1894 – 95 Small Heath F.C . season
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+Here I Stand
+Half of Me
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+wind
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+You Are the Apple of My Eye
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+166th Aviation Brigade
+Baby
+So Yesterday
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+Rhode Island Route 37
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+Sayf al @-@ Dawla
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+lipid
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+Sticky bomb
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+insect wing
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+Ich bin vergnügt mit meinem Glücke , BWV 84
+Kumari 21F
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+Batman : The Ultimate Evil
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+Politically Inept , with Homer Simpson
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+( You Drive Me ) Crazy
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+Complete & Unbelievable : The Otis Redding Dictionary of Soul
+National Weather Service Lincoln , Illinois
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+bald eagle
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+The Boat Race 1872
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+Joseph Brittan
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+History of cricket to 1725
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+When You Believe
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+Funeral
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+Desde un Principio : From the Beginning
+1932 Freeport hurricane
+Gossip
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+Algol
+New Jersey Route 70
+Ice Cream Freeze
+gray mouse lemur
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+Skaters in the Bois de Boulogne
+SS West Nohno
+Alma @-@ class ironclad
+The Boat Race 1987
+Pokémon , I Choose You !
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+Targeted Killings : Law and Morality in an Asymmetrical World
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+Eh , Eh ( Nothing Else I Can Say )
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+Up ! Close and Personal
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+M @-@ 42
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+H
+Slow Down Your Neighbors
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+University Mall
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+John Day Fossil Beds National Monument
+demon core
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+( I Ca n't Make It ) Another Day
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+I Do n't Give A
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+Sci @-@ Fi Dine @-@ In Theater Restaurant
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+Test Track
+Street
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+White House travel office controversy
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+Seasons in the Abyss
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+war film
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+HMS Woolwich
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+Félix Trinidad
+Costello Music
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+Harris 's List of Covent Garden Ladies
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+Mercury
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+( What Is ) Love ?
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+What Was Missing
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+addition
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+Pilot
+French battleship Justice
+X @-@ Men
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+My Love
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+Malagasy hippopotamus
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+Sack of Amorium
+Fifth Test , 1948 Ashes series
+No Can Do
+Unearthed
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+Political history of medieval Karnataka
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diff --git a/data/canonical_forms/valid_canonical_forms.txt b/data/canonical_forms/valid_canonical_forms.txt
new file mode 100644
index 0000000..a8b4ccb
--- /dev/null
+++ b/data/canonical_forms/valid_canonical_forms.txt
@@ -0,0 +1,60 @@
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diff --git a/data/found_ids/test.ids.txt b/data/found_ids/test.ids.txt
new file mode 100644
index 0000000..6c842c5
--- /dev/null
+++ b/data/found_ids/test.ids.txt
@@ -0,0 +1,60 @@
+Robert Boulter	Q7342234
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+Chad at the 2008 Summer Olympics	Q140800
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diff --git a/data/found_ids/train.ids.txt b/data/found_ids/train.ids.txt
new file mode 100644
index 0000000..aef0ae6
--- /dev/null
+++ b/data/found_ids/train.ids.txt
@@ -0,0 +1,27704 @@
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+Loch Leven Castle	Q514388
+Lunar: Silver Star Story Complete	Q14948014
+Broadhurst Park	Q4858016
+Talon (roller coaster)	Q2390682
+Dare to Be Stupid	Q2399183
+Armed Forces of the Democratic Republic of the Congo	Q3076950
+Murder of Ross Parker	Q6938040
+Candice Michelle	Q233615
+God of War (comics)	Q5155154
+Ohio State Route 372	Q2492431
+Trustee Investments Act 1961	Q7848197
+The Dirty Picture	Q3090065
+Richard Barrons	Q7323979
+Harry Kane	Q969725
+Sebastian Shaw (actor)	Q532184
+1850 Atlantic hurricane season	Q17507340
+Ed Jovanovski	Q739863
+Beauty and the Beast (1991 film)	Q179673
+Henry VIII of England	Q38370
+God with Us (song)	Q5576083
+Felix Mendelssohn	Q46096
+Willamette University College of Law	Q8003310
+Shiloh (Naylor novel)	Q7496714
+Massachusetts Institute of Technology	Q49108
+Michael J. Daly	Q6831391
+Mac Marcoux	Q17489733
+Edina Müller	Q1284674
+Barmouth Bridge	Q4861279
+Evan Lysacek	Q299225
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+Princess Charlotte of Wales	Q132440
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+BR Standard Class 7	Q4836485
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+Constantine the Great	Q8413
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+Nap Lajoie	Q3335869
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+Mary Shelley	Q47152
+Enter the Dominatrix	Q17509320
+The Elder Scrolls V: Skyrim	Q323862
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+Little Annie Fanny	Q3256647
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+First Person Shooter (The X-Files)	Q1401431
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+The Wiz (film)	Q428158
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+German destroyer Z12 Erich Giese	Q136603
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+Neverwinter Nights 2: Mask of the Betrayer	Q2278874
+Enterprise (NX-01)	Q20074
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+Moving Mountains (song)	Q2569943
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+New Jersey Route 17	Q821757
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+Kid Klown in Crazy Chase	Q2751109
+Checkers speech	Q3030188
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+Zero Escape: Virtue's Last Reward	Q5931905
+Family Guy	Q5930
+A Canticle for Leibowitz	Q1659714
+George Scratchley Brown	Q464262
+U.S. Route 202 in Delaware	Q798202
+Norris Bradbury	Q322456
+Regioni-class cruiser	Q3679463
+Greatest Hits (Lost)	Q468502
+HMS Northumberland (1866)	Q5633543
+The Satanic Bible	Q9702
+Over the Edge (1999)	Q2340487
+Mississippi Highway 9W	Q2247055
+Gojira (band)	Q1011412
+Lucas North	Q6696245
+WWE 2K	Q1196256
+Mississippi Highway 198	Q2247946
+John Neild	Q6250221
+Foo Fighters (album)	Q735771
+Ladas (horse)	Q6469266
+Mingo Oak	Q6864769
+The Ash Garden	Q7714652
+Joseph Grimaldi	Q375106
+Matthew Cox	Q6790352
+Cornwallis in North America	Q5172043
+Myrmecia (ant)	Q117160
+Mike Scully	Q655207
+Boyle Roche	Q4952531
+Battle of Messines (1917)	Q1144824
+Stella Nickell	Q7607107
+Admiral-class battlecruiser	Q358956
+Fighting Tommy Riley	Q582503
+The Hunger Games (novel)	Q11678
+Viper dogfish	Q1424187
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+St Mary's Church, Bodewryd	Q7594283
+You Win or You Die	Q8057631
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+Number 1 to Infinity	Q19824814
+Bismuth	Q942
+Wood Badge	Q989467
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+Calabozos	Q2933443
+2013 Tour de France	Q1065480
+The American and the Queen	Q19864055
+ROT13	Q666455
+Hurricane Rick (2009)	Q3123924
+Tithonus poem	Q24893656
+Battle of the Îles Saint-Marcouf	Q4873048
+California State Route 174	Q493291
+Sejm of the Estates	Q7447088
+Santa María de Óvila	Q1166944
+Bruce Chatwin	Q348916
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+Cruachan Power Station	Q15212096
+New Guinea crocodile	Q504839
+Britten family	Q4971655
+Quagga	Q45969
+Liao dynasty	Q4958
+Squidward Tentacles	Q1064404
+Do the Bartman	Q906980
+Broad Ripple Park Carousel	Q930998
+Bernard Quatermass	Q2898482
+Stephen (song)	Q7608514
+Richie Powell	Q357579
+Max Bentley	Q708755
+S&M (song)	Q830734
+Major League Baseball (video game)	Q6738119
+2001 American Memorial	Q4598452
+Code of Honor (Star Trek: The Next Generation)	Q3231770
+Eric McCormack	Q436686
+Pathlight School	Q864055
+Laundry Service	Q158329
+Fancy rat	Q535498
+Mauritania at the 2012 Summer Olympics	Q141715
+The Lord of the Rings: The Battle for Middle-earth II	Q1996197
+Harry Potter and the Chamber of Secrets	Q47209
+U.S. Route 30 in New Jersey	Q811419
+Hap Holmes	Q2383100
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+Ontario Highway 59	Q7094751
+Sealyham Terrier	Q39068
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+Battle of Valcour Island	Q934626
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+USS New Jersey (BB-62)	Q507603
+French battleship Vérité	Q5501999
+Captain America: The First Avenger	Q275120
+Carcinus maenas	Q27779
+Pete Muldoon	Q741292
+Living in the Material World (song)	Q6659467
+Delaware Route 1	Q798263
+Action of 15 November 1810	Q19871518
+Lihou	Q953452
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+Silverplate	Q7516763
+James G. Blaine	Q448778
+Who Shot Mr. Burns?	Q10702
+Cool Hand Luke	Q684150
+James Fisher Robinson	Q881951
+Fakih Usman	Q5431566
+Battles of Latrun (1948)	Q2096397
+27th Battalion (Australia)	Q4632579
+Manuel Marques de Sousa, Count of Porto Alegre	Q2005413
+Dream Logic	Q5306397
+Charles McKnight	Q5080851
+Boys (Britney Spears song)	Q953383
+Klein Bikes	Q6420139
+Onefin electric ray	Q5035247
+Anarky	Q486441
+Summer Hill, New South Wales	Q2777749
+Bill Bradley	Q504025
+Al-Mu'tasim	Q284550
+Aphthous stomatitis	Q189956
+Star Wars: The Force Unleashed II	Q74539
+Our Town (The X-Files)	Q7111215
+10 (MercyMe album)	Q4546850
+French cruiser Chanzy	Q2710842
+Privy Garden of the Palace of Whitehall	Q18901051
+Prelude to a Million Years	Q17148985
+Nemesis Inferno	Q1977149
+2007 Atlantic hurricane season	Q756793
+HMS Resolution (1892)	Q5309205
+2000 Atlantic hurricane season	Q2659460
+Judah P. Benjamin	Q466115
+Gotta Be You (One Direction song)	Q2271218
+Ian McKellen	Q170510
+Denise Phua	Q5257739
+School District 53 Okanagan Similkameen	Q7432201
+It's Better If You Don't Understand	Q2382086
+Invasion of the Bane	Q55421921
+William Morris	Q182589
+Grammy Award for Best Zydeco or Cajun Music Album	Q5593909
+Goathouse Refuge	Q16840331
+Ryan Boyle	Q7383896
+You Belong with Me	Q767632
+1986–87 South-West Indian Ocean cyclone season	Q4583486
+E. Urner Goodman	Q5322182
+Andrew Wodrow	Q15100579
+The Byrds	Q211526
+Otto Becher	Q5568039
+2006 Bank of America 500	Q22953200
+Olive Trees (Van Gogh series)	Q7087213
+Peasants' Revolt	Q498871
+M-188 (Michigan highway)	Q2355050
+No. 5 Operational Training Unit RAAF	Q7043653
+Darren Bent	Q213459
+Johor Bahru	Q231318
+Virginia	Q1370
+Bound for Glory IV	Q2922184
+Choral symphony	Q2142754
+Hurricane Nate (2005)	Q3089874
+Bribery Act 2010	Q913344
+Just Like Tom Thumb's Blues	Q3122047
+Gyles v Wilcox	Q5624122
+New York State Route 286	Q2486585
+Misery (Gwen Stefani song)	Q23013730
+Byne's disease	Q3281242
+R. K. Narayan	Q334252
+Boddingtons Brewery	Q2559672
+Eyre Highway	Q824741
+Minehead	Q1020217
+Æthelnoth (archbishop of Canterbury)	Q3110969
+John Hirst (historian)	Q6239412
+Three Words (The X-Files)	Q7797929
+Tahirih Justice Center	Q3513913
+Dangerously in Love 2	Q149408
+Flight feather	Q392655
+Coprinellus micaceus	Q933206
+Frances Oldham Kelsey	Q433352
+Battle of Boroughbridge	Q2657536
+History of Minnesota	Q2984061
+The Fab Five (film)	Q7732973
+The Sweet Escape (song)	Q2256262
+Honorius of Canterbury	Q503207
+Spiderland	Q545227
+Phil Pressey	Q7182291
+Cauldron II: The Pumpkin Strikes Back	Q90985
+30 Seconds to Mars (album)	Q224160
+Action of 27 June 1798	Q4677323
+Xavier Mertz	Q124562
+Washington State Route 162	Q835408
+No Mercy (2004)	Q3296825
+Sir John Brunner, 1st Baronet	Q4097186
+Battle of Quebec (1690)	Q1708064
+1992 Cape Mendocino earthquakes	Q4587573
+Orion (mythology)	Q201261
+Yours Truly (Ariana Grande album)	Q14448491
+Russian battleship Sevastopol (1911)	Q1570972
+Sherlock (TV series)	Q192837
+By the Way	Q153475
+Grand Forks, North Dakota	Q34144
+HMS Empress of India	Q4351816
+No. 77 Wing RAAF	Q16156939
+Panic of 1907	Q844449
+Jurassic Park (song)	Q1649436
+Tropical Storm Marco (1990)	Q8350785
+Django Unchained	Q571032
+Battle of Cape St. Vincent (1780)	Q178912
+There Goes My Baby (Usher song)	Q2436385
+John Wark	Q723410
+Bomis	Q935818
+Harrie Massey	Q1586143
+Gorgosaurus	Q40752
+James Constable	Q6131659
+Ruislip	Q2379971
+History of Cornell University	Q5775762
+Grift of the Magi	Q608162
+Dive (Usher song)	Q1230492
+Justinian I	Q41866
+Tricholoma pardinum	Q1928462
+HMS Swordfish (1916)	Q3017785
+Winchester Model 1897	Q1085180
+We Remain	Q15037965
+FreeBSD	Q34236
+Tracheal intubation	Q750195
+Maryland Route 704	Q2480704
+Geet Ramayan	Q880473
+Scarborough Day School	Q7430280
+Edward Said	Q201538
+Holger Glinicki	Q16151731
+Big Man on Hippocampus	Q4035092
+Manasa	Q1518806
+Mega Mendoeng	Q14949775
+Taylor v. Beckham	Q7690202
+Bart Stops to Smell the Roosevelts	Q2981790
+SMS Hindenburg	Q698398
+Junagarh Fort	Q6311597
+Battle of Trois-Rivières	Q1247957
+Tenebrae (film)	Q1811797
+Tropical Storm Gert (2005)	Q3517890
+Gone for Goode	Q5581617
+California State Route 78	Q19183
+Strobilurus tenacellus	Q878979
+Gilbert de Clare, 8th Earl of Gloucester	Q2002601
+Romeo and Juliet	Q83186
+Reptar on Ice	Q7314335
+Keith Moon	Q211696
+CryoSat-2	Q870049
+German cruiser Admiral Scheer	Q160712
+Barrhill, New Zealand	Q19875514
+243 Ida	Q149012
+Trobairitz	Q1747339
+Ny-Ålesund Airport, Hamnerabben	Q1927160
+Louis Braille	Q93182
+Paper Hearts	Q7132631
+Retreat of glaciers since 1850	Q903816
+Livermorium	Q1304
+American white ibis	Q589171
+H-13 (Michigan county highway)	Q2350487
+Double Seven Day scuffle	Q5299875
+Freedom Planet	Q17182671
+Battle of Suoi Chau Pha	Q4872465
+Patrik Berger	Q155924
+Lodewijk van den Berg	Q719635
+Wrigley Square	Q8038337
+Homologous recombination	Q218447
+Ronnie Mann	Q7365920
+Man Enters the Cosmos	Q6746177
+Never Been Kissed (Glee)	Q2633606
+Wet season	Q3117517
+McLaren MP4-30	Q18644357
+Reed v. Town of Gilbert	Q22025353
+Zaculeu	Q139979
+Fried chicken	Q1065241
+Clare Winger Harris	Q5126278
+Richard Rennison	Q7328647
+Finite subdivision rule	Q5450407
+Ash Crimson	Q597401
+Cunard Building	Q503506
+Destination: Imagination	Q5265110
+Chevrolet Volt	Q927395
+Arthur, Prince of Wales	Q711801
+Bede	Q154938
+Treehouse of Horror III	Q2599128
+Slipknot (album)	Q743725
+Monsters University	Q641362
+British Virgin Islands at the 2008 Summer Olympics	Q141994
+HMS Neptune (1797)	Q1565111
+Crippled Summer	Q3592670
+Sweet Tooth (novel)	Q972609
+Attention deficit hyperactivity disorder	Q181923
+Chimes at Midnight	Q2529482
+Python (programming language)	Q28865
+Lisa the Vegetarian	Q1827853
+Marie Lloyd	Q6762922
+Prince Oana	Q7244184
+M-209 (Michigan highway)	Q2355183
+Eastern Area Command (RAAF)	Q21164949
+Nels Nelsen	Q6990286
+Manny Malhotra	Q1388544
+Ways and Means (The West Wing)	Q5810871
+Amir al-ʿarab	Q24883580
+Battle of Sattelberg	Q4872324
+Acquacotta	Q15630073
+The Set Up (Parks and Recreation)	Q7763402
+Great Storm of 1975	Q5600047
+Poquita Ropa	Q7229869
+1988–89 South-West Indian Ocean cyclone season	Q4584982
+Instant Karma!	Q545718
+Thanjavur	Q41496
+Order of St Patrick	Q1326038
+Colton Point State Park	Q2031902
+Call the Shots	Q2720315
+Árpád	Q210207
+James B. Longacre	Q3806459
+Philip Smith (criminal)	Q7184394
+Indian Institute of Management Lucknow	Q46023
+Sri Temasek	Q7586295
+St Nidan's Church, Llanidan	Q7594973
+The Avengers (2012 film)	Q182218
+Secrets of a Successful Marriage	Q2042199
+The Boat Race 1877	Q17512262
+Mycena cinerella	Q6946875
+The Beverly Hills Hotel	Q2021533
+The Boat Race 1992	Q17512351
+MNM (professional wrestling)	Q2183240
+New York State Route 9N	Q2485920
+The Fires of Pompeii	Q3207378
+Zennor Head	Q8069151
+Emily Ratajkowski	Q5372335
+Spafford Farm massacre	Q14714606
+I Miss You (Miley Cyrus song)	Q1655243
+55 Cancri b	Q50665
+Ridge Racer (video game)	Q15087581
+Peterborough (UK Parliament constituency)	Q1050650
+Bart's Girlfriend	Q3211498
+Delhi Metro	Q271195
+Time Enough at Last	Q3198638
+Dead Head Fred	Q3020540
+Al Wistert	Q954939
+Si Una Vez	Q3298079
+Sicklefin lemon shark	Q604191
+Flammulated flycatcher	Q1082110
+Ontario Highway 89	Q2502310
+Luke Smith (writer)	Q17048712
diff --git a/data/found_ids/valid.ids.txt b/data/found_ids/valid.ids.txt
new file mode 100644
index 0000000..cc50f21
--- /dev/null
+++ b/data/found_ids/valid.ids.txt
@@ -0,0 +1,60 @@
+Homarus gammarus	Q27690
+Frank Headlam	Q14832302
+M-82 (Michigan highway)	Q2353693
+Shikamaru Nara	Q30334
+Meridian, Mississippi	Q118152
+Papa Stour	Q1552121
+Angel of Death (Slayer song)	Q532024
+Calvin McCarty	Q5024458
+Hurricane Beatriz (2011)	Q5947191
+The Bourgeois Blues	Q7719523
+Sorry (Madonna song)	Q1646862
+High Five Interchange	Q2504328
+Tim Richmond	Q1094329
+Trout Creek Mountains	Q7846595
+Mycena inclinata	Q2181869
+Capel Lligwy	Q5035240
+2011–12 Michigan Wolverines men's basketball team	Q4623908
+Upsilon Andromedae b	Q1072024
+Daniel Radcliffe	Q38119
+...Thirteen Years Later	Q16745526
+New Jersey Route 29	Q822300
+First Mongol invasion of Burma	Q531916
+Battle of Edson's Ridge	Q1754443
+New Jersey Route 50	Q822935
+Air Rhodesia Flight 825	Q4698169
+Tikal	Q181172
+Tropical Storm Josephine (2008)	Q7846006
+Mount Elbert	Q657225
+Scientology in Germany	Q7433874
+Japanese battleship Asahi	Q711229
+Training Day (The Office)	Q7832944
+J. C. W. Beckham	Q741918
+1888–89 New Zealand Native football team	Q1983308
+Éva Gauthier	Q2041717
+Battle of Sullivan's Island	Q4071933
+Hu Zhengyan	Q15430363
+Raid on Manila (1798)	Q20050713
+B of the Bang	Q1405527
+Battle of the Hongorai River	Q4872930
+Sonic the Hedgehog (1991 video game)	Q37123
+The Same Old Story	Q7762167
+Randy Blythe manslaughter case	Q7292091
+U.S. Route 2 in Michigan	Q449983
+Butterfly World Tour	Q852369
+Thelnetham Windmill	Q7781097
+Sailor Mouth	Q7400433
+Military history of Australia	Q2585717
+Ward Churchill	Q707156
+Exit Through the Kwik-E-Mart	Q4120031
+The Boat Race 2008	Q16827708
+Fort Scott National Historic Site	Q5472003
+Slammiversary (2008)	Q978580
+Home Made Mince Pie	Q19876361
+Back Off Boogaloo	Q640295
+Where the Streets Have No Name	Q2290594
+Nazarov cyclization reaction	Q1017046
+Horrible Bosses	Q908556
+1985 Rajneeshee assassination plot	Q4582505
+Die Another Day	Q30931
+Rakie Ayola	Q16154488
diff --git a/dataset/__init__.py b/dataset/__init__.py
new file mode 100644
index 0000000..5cb0ba8
--- /dev/null
+++ b/dataset/__init__.py
@@ -0,0 +1,6 @@
+from dataset.aliaslm import *
+from dataset.base import *
+from dataset.lrlm import *
+from dataset.nklm import *
+from dataset.utils import *
+from dataset.vocab import *
diff --git a/dataset/aliaslm.py b/dataset/aliaslm.py
new file mode 100644
index 0000000..cef6a15
--- /dev/null
+++ b/dataset/aliaslm.py
@@ -0,0 +1,55 @@
+import logging
+from typing import List, Tuple
+
+import torch
+from torch import Tensor
+
+from dataset.lrlm import LRLMDataset, LRLMExample
+from dataset.utils import BatchSequence, pad
+from nnlib import utils
+
+__all__ = [
+    'AliasLMDataset',
+]
+
+LOGGER = logging.getLogger(__name__)
+
+
+class AliasLMDataset(LRLMDataset):
+
+    def create_one_batch(self, examples: List[LRLMExample], bptt_size: int) -> Tuple[Tensor, List[BatchSequence]]:
+        sentences = [x.sentence for x in examples]  # <s> accounted in construct_example method
+        max_length = max(len(s) for s in sentences)
+        n_splits = utils.ceil_div(max_length - 1, bptt_size)
+        init_batch = ([(ex.spans, ex.relations) for ex in examples], self.vocab)
+
+        mentioned_relation_ids = [
+            (ex.relations, list(set(span.rel_idx for span in ex.spans)))
+            for ex in examples
+        ]
+
+        # linearized alias surface forms whose relation actually appear in the article
+        linearized_aliases: List[List[int]] = [
+            [
+                self.word_vocab.w2i.get(word, 0)
+                for rel_idx, relation in enumerate(relations) if rel_idx in mentioned
+                for words in relation.obj_alias
+                for word in words.split(" ")
+            ]
+            for relations, mentioned in mentioned_relation_ids
+        ]
+
+        for i, aliases in enumerate(linearized_aliases):
+            if len(aliases) == 0:
+                linearized_aliases[i].append(0)  # Add an UNK as a dummy, if the seq_len is zero.
+
+        padded_aliases = torch.tensor(pad(linearized_aliases, pad_symbol=1, append=False), dtype=torch.long)
+
+        batches = []
+        for split_idx in range(n_splits):
+            interval = slice(split_idx * bptt_size, (split_idx + 1) * bptt_size + 1)
+            sentence_interval = [sent[interval] for sent in sentences]
+            batch = BatchSequence(self.word_vocab, sentence_interval)
+            batches.append(batch)
+
+        return padded_aliases, batches
diff --git a/dataset/base.py b/dataset/base.py
new file mode 100644
index 0000000..5a73f9e
--- /dev/null
+++ b/dataset/base.py
@@ -0,0 +1,457 @@
+import logging
+import pickle
+import random
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Counter as CounterType, Dict, Generic, List, NamedTuple, Optional, Set, Sized, Tuple, TypeVar
+
+import numpy as np
+import torch
+from torch import Tensor
+
+from dataset.utils import BatchSequence, Relation, Sentence
+from dataset.vocab import Vocab, prepare_unkprob
+from nnlib import utils
+from nnlib.utils import Logging
+from utils import loadpkl, savepkl
+
+__all__ = [
+    'Dataset',
+    'LMDataset',
+    'KBLMDataset',
+    'LMExample'
+]
+
+LOGGER = logging.getLogger(__name__)
+
+Example = TypeVar('Example', bound=Sized)
+
+
+class Dataset(Generic[Example]):
+    batches: Dict[str, List[Tuple[Any, List[BatchSequence]]]]
+    ntokens: Dict[str, int]
+    data: Dict[str, List[Example]]
+
+    def __init__(self, path: str, batch_size: int, vocab_dir: str, bptt_size: int,
+                 vocab_size: Optional[int], min_freq: Optional[int] = None,
+                 include_train: bool = True, create_batches: bool = True,
+                 use_only_first_section: bool = False,
+                 unk_probs_path: Optional[str] = None, use_upp: bool = False,
+                 cache_batches: bool = True, **_kwargs):
+        self.batch_size = batch_size
+        self.unk_probs = None
+        self.bptt_size = bptt_size
+        self._unk_probs_path = unk_probs_path
+        self._include_train = include_train
+        self._use_only_first_section = use_only_first_section
+        self.vocab_size = vocab_size
+        self.min_freq = min_freq
+
+        self._path = path = Path(path)
+        vocab_dir = Path(vocab_dir)
+
+        if not vocab_dir.exists():
+            vocab_dir.mkdir(parents=True)
+        vocab_file_name = 'vocab'
+        if vocab_size is not None:
+            vocab_file_name += f'.{vocab_size}'
+        if min_freq is not None:
+            vocab_file_name += f'.freq{min_freq}'
+        vocab_file_name += '.pt'
+        self.vocab_path = vocab_path = vocab_dir / vocab_file_name
+
+        loaded_batches = False
+        if cache_batches and create_batches and vocab_path.exists() and include_train:
+            # Try to load a cached version of the batches if possible; and do not load data
+            loaded_batches = self._try_load_cache(path)
+        if not loaded_batches:
+            # Failed to load cached batches
+            self.data = self.read_data(path)
+
+        if not include_train or vocab_path.exists():
+            self.word_vocab = torch.load(vocab_path)
+            LOGGER.info(f"Word Vocabulary of size {len(self.word_vocab)} loaded from {vocab_path}.")
+        else:
+            train_corpus = [w for ex in self.data['train'] for w in ex.sentence]  # type: ignore
+            self.word_vocab = Vocab.from_corpus(train_corpus,
+                                                min_freq=min_freq, max_vocab=vocab_size, lowercase=False)
+            torch.save(self.word_vocab, vocab_path)
+            LOGGER.info(f"Vocabulary of size {len(self.word_vocab)} constructed, saved to {vocab_path}.")
+        self.vocab = self.word_vocab
+
+        if unk_probs_path is not None:
+            unk_probs, total_w2i = prepare_unkprob(unk_probs_path, self.word_vocab, uniform_unk=use_upp)
+            unk_probs = torch.tensor(unk_probs, dtype=torch.float, requires_grad=False)
+            self.unk_probs = unk_probs
+            unk_probs[1] = 0  # <pad>
+            self.total_w2i = total_w2i
+
+        self._extra_init(loaded_batches)
+
+        if not loaded_batches and create_batches:
+            self.create_batches(batch_size, bptt_size)
+            if cache_batches and include_train:
+                self._save_cache(path)
+
+    def _try_load_cache(self, path: Path) -> bool:
+        r"""Try loading a cached dataset from the data directory.
+
+        :param path: The path to data directory.
+        :return: Whether loading was successful.
+        """
+        cache_dir = path / '_cached'
+        if not cache_dir.exists():
+            return False
+        params_index_path = cache_dir / 'params_index.pkl'
+        params_index: List[Dict[str, Any]] = loadpkl(params_index_path)
+        params = self._get_params()
+        index = next((idx for idx, p in enumerate(params_index)
+                      if (cache_dir / f'{idx}.pkl').exists() and self._compare_params(p, params)), -1)
+        if index != -1:
+            load_path = cache_dir / f'{index}.pkl'
+            self.batches = loadpkl(load_path)
+            self.ntokens = {
+                split: sum(batch.ntokens for _, batches in dataset for batch in batches)
+                for split, dataset in self.batches.items()
+            }
+            LOGGER.info(f"Cached dataset loaded from {load_path}, with settings: {params}")
+            # check for excluded keys and warn in case of mismatch
+            load_params = params_index[index]
+            for key in self.EXCLUDE_KEYS:
+                if key in params or key in load_params:
+                    current = params.get(key, "<does not exist>")
+                    loaded = load_params.get(key, "<does not exist>")
+                    if current != loaded:
+                        LOGGER.info(Logging.color(
+                            'red', f"Ignored data param '{key}' mismatch "
+                                   f"(current: {current}, loaded: {loaded})"))
+            return True
+        return False
+
+    def _save_cache(self, path: Path):
+        r"""Save the dataset to cache in the data directory.
+
+        :param path: The path to data directory.
+        """
+        cache_dir = path / '_cached'
+        params_index_path = cache_dir / 'params_index.pkl'
+        params = self._get_params()
+        if not cache_dir.exists():
+            cache_dir.mkdir()
+            index = 0
+            params_index = [params]
+        else:
+            params_index = loadpkl(params_index_path)
+            index = next((idx for idx in range(len(params_index))
+                          if not (cache_dir / f'{idx}.pkl').exists()), len(params_index))
+            params_index[index:(index + 1)] = [params]  # replace or append
+        savepkl(params_index, params_index_path)
+        load_path = cache_dir / f'{index}.pkl'
+        savepkl(self.batches, load_path)
+        LOGGER.info(f"Dataset cached to {load_path}, with settings: {params}")
+
+    def _get_params(self) -> Dict[str, Any]:
+        r"""Return a dictionary of data loader parameters that characterize the created batches. Used as lookup
+        key when loading from cache.
+
+        :return: The dictionary of parameters.
+        """
+        params = dict(batch_size=self.batch_size, bptt_size=self.bptt_size, path=str(self._path),
+                      vocab_path=str(self.vocab_path), vocab_size=self.vocab_size, min_freq=self.min_freq,
+                      klass=self.__class__.__name__)
+        # add the rest of boolean flags
+        for attr in dir(self):
+            if attr.startswith('_'):
+                val = getattr(self, attr)
+                if isinstance(val, bool):
+                    params[attr.lstrip('_')] = val  # remove the leading '_'
+        return params
+
+    EXCLUDE_KEYS = {'vocab_path', 'path'}  # ignore 'path' because the cache dir is stored under the path
+
+    def _compare_params(self, saved: Dict[str, Any], current: Dict[str, Any]) -> bool:
+        r"""Compare two sets of parameters. All keys except those in `EXCLUDED_KEYS` are expected to match. It is
+        allowed for saved parameters to contain
+
+        :return: Whether the two sets of parameters match.
+        """
+        for key in set(current.keys()) - self.EXCLUDE_KEYS:
+            if key not in saved or current[key] != saved[key]:
+                return False
+        return True
+
+    def _extra_init(self, loaded_batches: bool):
+        r"""Extra initialization routine performed before creation of batches.
+
+        :param loaded_batches: Whether batches are already loaded from cache. Subclass implementations should note that
+            when this is `True`, `self.data` is not accessible.
+        """
+        pass
+
+    def read_data(self, path: Path) -> Dict[str, List[Example]]:
+        r"""Read data in raw form.
+
+        :return: A dictionary mapping data splits to a list of data examples.
+        """
+        raise NotImplementedError
+
+    def create_batches(self, batch_size: int, bptt_size: int):
+        r"""A general routine to create batches of specified batch size and BPTT length.
+
+        :param batch_size: The number of examples in one batch.
+        :param bptt_size: The length for truncated-backprop, i.e. the maximum length of sentences in one batch.
+        """
+        self.batches = {}
+        self.ntokens = {}
+        for split, raw_dataset in self.data.items():
+            ntokens = 0
+            # sort the data by document length
+            parts = sorted(raw_dataset, key=len)
+            num_batches = utils.ceil_div(len(parts), batch_size)
+            all_batches = []
+            for batch_idx in utils.progress(num_batches, desc="Creating batches", ascii=True, ncols=80):
+                part = parts[(batch_idx * batch_size): ((batch_idx + 1) * batch_size)]
+                init_batch, batches = self.create_one_batch(part, bptt_size)
+                ntokens += sum(batch.ntokens for batch in batches)
+                all_batches.append((init_batch, batches))
+            self.batches[split] = all_batches
+            self.ntokens[split] = ntokens
+
+        unk_probs = self.unk_probs
+        if unk_probs is not None:
+            total_w2i = self.total_w2i
+            for split, dataset in self.batches.items():
+                dataset = utils.progress(dataset, ncols=80, desc=f"Adding unk vocab for {split} set", ascii=True)
+                for _, batches in dataset:
+                    for batch in batches:
+                        batch.add_unk_probs(unk_probs, total_w2i)
+
+    def create_one_batch(self, examples: List[Example], bptt_size: int) -> Tuple[Any, List[BatchSequence]]:
+        r"""Create one batch given a list of examples. Note that you may return multiple batches if the sentences are
+        too long.
+
+        :param examples: The examples to combine into one batch.
+        :param bptt_size: The length for truncated-backprop, i.e. the maximum length of sentences in one batch.
+        :return: A tuple of (init_batch, batches):
+            - Stuff that's the same for the entire batch, or None.
+            - A list containing: the batch cut into sequences no longer than `bptt_size`.
+        """
+        raise NotImplementedError
+
+    def get_batches(self, split: str, shuffle: bool = True) \
+            -> Optional[List[Tuple[Any, List[BatchSequence]]]]:
+        r"""Return all batches in the specified data split.
+
+        :param split: The data split.
+        :param shuffle: Whether to shuffle the batches. Note that we perform shuffle on the stored `batches`, so the
+            result of shuffle is carried.
+        :return: A list of batches, or `None` if the split does not exist.
+        """
+        if split not in self.batches:
+            return None
+        # handle shuffling here
+        if shuffle:
+            random.shuffle(self.batches[split])
+        return self.batches[split]
+
+    BOUNDARY = "="
+
+    @classmethod
+    def find_first_section(cls, tokens) -> int:
+        r"""Find the end of first section given a list of tokens. Used for WikiText dataset with option
+        `use_only_first_section`.
+
+        :param tokens: The list of tokens.
+        :return: The position to the end of first section.
+        """
+        pointer = 0
+        while pointer < len(tokens):
+            try:
+                i = tokens[pointer:].index(cls.BOUNDARY)
+            except ValueError:  # If section headers couldn't be found anymore, use full tokens.
+                pointer = len(tokens)
+                break
+            pointer += i
+            if tokens[pointer + 1] == cls.BOUNDARY:
+                break
+            else:
+                pointer += 1
+
+        return pointer
+
+
+class LMExample(NamedTuple):
+    sentence: Sentence
+
+    def __len__(self) -> int:
+        return len(self.sentence)
+
+
+class LMDataset(Dataset[LMExample]):
+    def read_data(self, path: Path) -> Dict[str, List[LMExample]]:
+        data = {}
+        for split in (['train'] if self._include_train else []) + ['valid', 'test']:
+            with (path / f'{split}.pkl').open('rb') as data_file:
+                dump = pickle.load(data_file)
+                examples = [ex[0] for ex in dump]
+                if self._use_only_first_section:
+                    examples = [ex[:self.find_first_section(ex)] for ex in examples]
+
+                data[split] = [LMExample(['<s>'] + ex + ['</s>']) for ex in examples]
+        return data
+
+    def create_one_batch(self, examples: List[LMExample], bptt_size: int) -> Tuple[None, List[BatchSequence]]:
+        sentences = [x.sentence for x in examples]
+        max_length = max(len(s) for s in sentences)
+        n_splits = utils.ceil_div(max_length - 1, bptt_size)
+
+        batches = []
+        for split_idx in range(n_splits):
+            interval = slice(split_idx * bptt_size, (split_idx + 1) * bptt_size + 1)
+            sentence_interval = [s[interval] for s in sentences]
+            batch = BatchSequence(self.word_vocab, sentence_interval)
+            batches.append(batch)
+
+        return None, batches
+
+
+class KBLMDataset(Dataset[Example]):
+    rel_vocab: Vocab
+    vocab: Dict[str, Vocab]
+    max_unkrel: int
+    entity_count_per_type: Optional[Dict[int, CounterType[int]]]
+    alias_vectors: Optional[Tensor]
+
+    def __init__(self, path: str, batch_size: int, vocab_dir: str, bptt_size: int, *args,
+                 use_anchor: bool = False, fasttext_model_path: Optional[str] = None,
+                 exclude_entity_disamb: bool = False, exclude_alias_disamb: bool = False,
+                 **kwargs):
+        if exclude_entity_disamb and not kwargs.get('include_train', False):
+            raise ValueError("`include_train` must be True, because `exclude_entity_disamb` is set to True")
+
+        self._use_anchor = use_anchor
+        self._exclude_entity_disamb = exclude_entity_disamb
+        self._exclude_alias_disamb = exclude_alias_disamb
+        self._fasttext_model_path = fasttext_model_path
+        self._use_fasttext = (fasttext_model_path is not None)
+
+        self.entity_count_per_type = None
+
+        super().__init__(path, batch_size, vocab_dir, bptt_size, *args, **kwargs)
+
+    def _extra_init(self, loaded_batches: bool):
+        self.rel_vocab = Vocab.from_dict(self._path / 'rel_names.pkl', mode='i2w')
+        self.vocab: Dict[str, Vocab] = {"word": self.word_vocab, "rel": self.rel_vocab}
+
+        self.max_unkrel = max((-rel_typ - 3 for rel_typ in self.rel_vocab.i2w if rel_typ < -3), default=0)
+
+        if self._use_fasttext:
+            def _alias_path(name):
+                path = Path(self._fasttext_model_path)
+                return path.parent / (path.name + f'.{name}')
+
+            # gather all entity aliases and compute fastText embeddings
+            alias_dict_path = _alias_path('alias_dict.pkl')
+            if alias_dict_path.exists():
+                alias_dict: Dict[str, int] = loadpkl(alias_dict_path)
+                loaded = True
+            else:
+                alias_dict = defaultdict(lambda: len(alias_dict))
+                loaded = False
+            if not loaded_batches:
+                for dataset in self.data.values():
+                    for example in dataset:
+                        for idx, rel in enumerate(example.relations):  # type: ignore
+                            example.relations[idx] = rel._replace(  # type: ignore
+                                obj_alias=[alias_dict[s] for s in rel.obj_alias])
+            if not alias_dict_path.exists():
+                alias_dict = dict(alias_dict)
+                savepkl(alias_dict, alias_dict_path)
+
+            alias_vectors_path = _alias_path('alias_vectors.pt')
+            if not alias_vectors_path.exists() or not loaded:
+                import fastText
+                ft_model = fastText.load_model(self._fasttext_model_path)
+                alias_vectors = []
+                alias_list = utils.reverse_map(alias_dict)
+                for alias in utils.progress(alias_list, desc="Building fastText vectors", ascii=True, ncols=80):
+                    vectors = [ft_model.get_word_vector(w) for w in alias.split()]
+                    vectors = np.sum(vectors, axis=0).tolist()
+                    alias_vectors.append(vectors)
+                alias_vectors = torch.tensor(alias_vectors)
+                torch.save(alias_vectors, alias_vectors_path)
+
+        if not loaded_batches and (self._exclude_entity_disamb or self._exclude_alias_disamb):
+            # no need to do this if batches are loaded
+            if self._exclude_entity_disamb:
+                # gather training set stats
+                self.entity_count_per_type = self.gather_entity_stats(self.data['train'])
+
+            for dataset in self.data.values():
+                for idx in range(len(dataset)):
+                    dataset[idx] = self.remove_ambiguity(
+                        dataset[idx], self._exclude_entity_disamb, self._exclude_alias_disamb)
+
+    def read_data(self, path: Path) -> Dict[str, List[Example]]:
+        raise NotImplementedError
+
+    def gather_entity_stats(self, dataset: List[Example]) -> Dict[int, CounterType[int]]:
+        raise NotImplementedError
+
+    def remove_ambiguity(self, ex: Example, entity=False, alias=False) -> Example:
+        raise NotImplementedError
+
+    def create_one_batch(self, examples: List[Example], bptt_size: int) \
+            -> Tuple[List[List[Relation]], List[BatchSequence]]:
+        raise NotImplementedError
+
+    @classmethod
+    def remap_rels(cls, relations: List[Relation], use_anchor=True, unk_rels_strategy='params') \
+            -> Tuple[List[Relation], List[int]]:
+        count = 0
+        new_indices = []
+        new_rels = []
+        for rel in relations:
+            if rel.rel_typ == -2 and not use_anchor:
+                new_indices.append(-1)
+            elif rel.rel_typ < -3 and unk_rels_strategy == 'remove':
+                new_indices.append(-1)
+            elif rel.rel_typ < -3 and unk_rels_strategy == 'unk':
+                new_indices.append(count)
+                count += 1
+                new_rels.append(Relation(-4, rel.obj_id, rel.obj_alias))
+            else:
+                new_indices.append(count)
+                count += 1
+                new_rels.append(rel)
+        return new_rels, new_indices
+
+    def remap_entity_ambiguous_rels(self, relations: List[Relation]) -> Tuple[Optional[List[int]], List[Relation]]:
+        assert self.entity_count_per_type is not None
+
+        rels_by_typ: Dict[int, List[int]] = defaultdict(list)
+        for idx, rel in enumerate(relations):
+            rels_by_typ[rel.rel_typ].append(idx)
+        remove_ids: Set[int] = set()
+        for rel_typ, rels in rels_by_typ.items():
+            if len(rels) == 1:
+                continue
+            counts = [self.entity_count_per_type[rel_typ].get(relations[idx].obj_id, 0) for idx in rels]
+            keep_rel_id = rels[int(np.argmax(counts))]
+            remove_ids.update(rel_id for rel_id in rels if rel_id != keep_rel_id)
+
+        if len(remove_ids) == 0:
+            return None, relations
+
+        # remap relation indices
+        count = 0
+        new_indices = []
+        new_rels = []
+        for idx, rel in enumerate(relations):
+            if idx in remove_ids:
+                new_indices.append(-1)
+            else:
+                new_indices.append(count)
+                count += 1
+                new_rels.append(rel)
+        return new_indices, new_rels
diff --git a/dataset/lrlm.py b/dataset/lrlm.py
new file mode 100644
index 0000000..94bb8d0
--- /dev/null
+++ b/dataset/lrlm.py
@@ -0,0 +1,120 @@
+import copy
+import logging
+import pickle
+from collections import Counter, defaultdict
+from pathlib import Path
+from typing import Counter as CounterType, Dict, List, NamedTuple, Tuple
+
+from dataset.base import KBLMDataset
+from dataset.utils import BatchSequence, MatchedSpan, Relation, Sentence
+from nnlib import utils
+
+__all__ = [
+    'LRLMExample',
+    'LRLMDataset',
+]
+
+LOGGER = logging.getLogger(__name__)
+
+
+class LRLMExample(NamedTuple):
+    sentence: Sentence  # sentence
+    spans: List[MatchedSpan]  # spans: matched entity mentions
+    relations: List[Relation]  # relations: all relations of the topic
+
+    def __len__(self) -> int:
+        return len(self.sentence)
+
+
+class LRLMDataset(KBLMDataset[LRLMExample]):
+    def read_data(self, path: Path) -> Dict[str, List[LRLMExample]]:
+        data: Dict[str, List[LRLMExample]] = {}
+        for split in (['train'] if self._include_train else []) + ['valid', 'test']:
+            with (path / f'{split}.pkl').open('rb') as data_file:
+                data_pkl = pickle.load(data_file)
+            with (path / f'{split}.span.pkl').open('rb') as span_file:
+                spans_pkl = pickle.load(span_file)
+
+            dataset = []
+            for ex_idx, (ex, raw_matched_spans) in enumerate(zip(data_pkl, spans_pkl)):
+                sentence = ex[0]
+                if self._use_only_first_section:
+                    end_index = self.find_first_section(ex[0])
+                    sentence = sentence[:end_index]
+                    raw_matched_spans = [s for s in raw_matched_spans if s[1] < end_index]
+                tokens = ['<s>'] + sentence + ['</s>']  # add <sos> token, spans are shifted during `make_batch`
+
+                relations: List[Relation] = [Relation(*rel) for rel in ex[2]]
+                # `unk_rels_strategy` defaults to 'params', because in LRLM we train our own embeddings anyway
+                relations, new_indices = self.remap_rels(relations, self._use_anchor, unk_rels_strategy='params')
+
+                matched_spans: List[MatchedSpan] = [MatchedSpan(*s) for s in raw_matched_spans]
+                spans = []  # shouldn't have duplicates
+                for span in matched_spans:
+                    new_rel_idx = new_indices[span.rel_idx]
+                    if new_rel_idx != -1:
+                        spans.append(span._replace(rel_idx=new_rel_idx))
+                dataset.append(LRLMExample(tokens, spans, relations))
+            data[split] = dataset
+        return data
+
+    def gather_entity_stats(self, dataset: List[LRLMExample]) -> Dict[int, CounterType[int]]:
+        entity_count_per_type: Dict[int, CounterType[int]] = defaultdict(Counter)
+        for ex in dataset:
+            rel_count = Counter(span.rel_idx for span in ex.spans)
+            for rel_id, count in rel_count.items():
+                rel = ex.relations[rel_id]
+                if rel.rel_typ not in [-1, -2, -3]:  # not TITLE or ANCHOR
+                    entity_count_per_type[rel.rel_typ].update({rel.obj_id: count})
+        return entity_count_per_type
+
+    def create_one_batch(self, examples: List[LRLMExample], bptt_size: int) \
+            -> Tuple[List[List[Relation]], List[BatchSequence]]:
+        sentences = [x.sentence for x in examples]  # <s> accounted in construct_example method
+        max_length = max(len(s) for s in sentences)
+        n_splits = utils.ceil_div(max_length - 1, bptt_size)
+        init_batch = [ex.relations for ex in examples]
+
+        split_spans: List[List[List[MatchedSpan]]] = [[[] for __ in examples] for _ in range(n_splits)]
+        for b_idx, ex in enumerate(examples):
+            for span in ex.spans:
+                # Begin index of the span should be the index before the expression begin.
+                # End index of the span points to the index of the last word in the entity
+                start = span.start % bptt_size
+                end = (span.end - 1) % bptt_size
+                # if start > end:
+                #     continue
+                split_spans[span.start // bptt_size][b_idx].append(span._replace(start=start, end=end))
+        batches = []
+        for split_idx in range(n_splits):
+            interval = slice(split_idx * bptt_size, (split_idx + 1) * bptt_size + 1)
+            rels_interval = split_spans[split_idx]
+            sentence_interval = [sent[interval] for sent in sentences]
+            batch = BatchSequence(self.word_vocab, sentence_interval, rels_interval)
+            batches.append(batch)
+        return init_batch, batches
+
+    def remove_ambiguity(self, ex: LRLMExample, entity=False, alias=False) -> LRLMExample:
+        """Takes relations and remove some of them according to the specified condition."""
+
+        if not entity and not alias:
+            return ex
+
+        relations = copy.copy(ex.relations)
+        spans = copy.copy(ex.spans)
+
+        if entity:
+            new_indices, relations = self.remap_entity_ambiguous_rels(relations)
+            if new_indices is not None:
+                new_spans = []
+                for span in spans:
+                    rel_id = span.rel_idx
+                    if new_indices[rel_id] != -1:
+                        new_spans.append(span._replace(rel_idx=new_indices[rel_id]))
+                spans = new_spans
+
+        if alias:
+            # Keep only the cases where the surface matches the canonical form
+            spans = [span for span in spans if span.surface_idx == 0]
+
+        return ex._replace(spans=spans, relations=relations)
diff --git a/dataset/nklm.py b/dataset/nklm.py
new file mode 100644
index 0000000..03ee546
--- /dev/null
+++ b/dataset/nklm.py
@@ -0,0 +1,150 @@
+import copy
+import logging
+import pickle
+from collections import Counter, defaultdict
+from pathlib import Path
+from typing import Counter as CounterType, Dict, List, NamedTuple, Tuple
+
+from dataset.base import KBLMDataset
+from dataset.utils import BatchSequence, Relation, Sentence
+from nnlib import utils
+
+__all__ = [
+    'NKLMExample',
+    'NKLMDataset',
+]
+
+LOGGER = logging.getLogger(__name__)
+
+
+class NKLMExample(NamedTuple):
+    sentence: Sentence  # sentence
+    topic_id: int  # topic_id
+    relations: List[Relation]  # relations: all relations of the topic
+    rel_ids: List[int]  # rel_ids: relation index of each word
+    copy_pos: List[int]  # copy_pos: copy position of the object for each word, -1 for NaF
+    surface_indices: List[int]  # surface_indices: surface form index of the object for each word, -1 for NaF
+
+    def __len__(self) -> int:
+        return len(self.sentence)
+
+
+class NKLMDataset(KBLMDataset[NKLMExample]):
+    """
+    Relations are represented as a sequence the length of the sentence.
+    Each time step is a tuple: (rel_id, obj_id, copy_pos).
+    For NaF, rel_id = -1. For anchor, rel_id = -2. For, rel_id = -3.
+    Additionally, topic IDs are included as another list.
+    """
+
+    def __init__(self, path, batch_size, vocab_dir, *args, unk_rels_strategy='unk', **kwargs):
+        kwargs['use_entity_vecs'] = True
+        self._unk_rels_strategy = unk_rels_strategy
+        super().__init__(path, batch_size, vocab_dir, *args, **kwargs)
+
+    def read_data(self, path: Path) -> Dict[str, List[NKLMExample]]:
+        data: Dict[str, List[NKLMExample]] = {}
+        for split in (['train'] if self._include_train else []) + ['valid', 'test']:
+            with (path / f'{split}.pkl').open('rb') as data_file:
+                dataset = []
+                for ex_idx, tup in enumerate(pickle.load(data_file)):
+                    assert len(tup) == 6
+                    # type-annotated unpacking
+                    sentence: Sentence = ['<s>'] + tup[0]
+                    topic_id: int = tup[1]
+                    # WikiFacts does not contain aliases, so we just treat it as canonical
+                    relations: List[Relation] = [Relation(*rel) for rel in tup[2]]
+                    rel_ids: List[int] = [-1] + tup[3]
+                    copy_pos: List[int] = [-1] + tup[4]
+                    surface_indices: List[int] = [-1] + tup[5]
+
+                    if self._use_only_first_section:
+                        end_index = self.find_first_section(sentence)
+                        sentence = sentence[:end_index]
+                        rel_ids = rel_ids[:end_index]
+                        copy_pos = copy_pos[:end_index]
+                        surface_indices = surface_indices[:end_index]
+
+                    # Add suffix only after finalizing the article.
+                    sentence = sentence + ['</s>']
+                    rel_ids = rel_ids + [-1]
+                    copy_pos = copy_pos + [-1]
+                    surface_indices = surface_indices + [-1]
+
+                    if (not self._use_anchor) or (self._unk_rels_strategy in ['unk', 'remove']):
+                        # remap indices
+                        relations, new_indices = self.remap_rels(relations, self._use_anchor, self._unk_rels_strategy)
+                        for idx in range(len(rel_ids)):
+                            if rel_ids[idx] != -1:
+                                new_idx = new_indices[rel_ids[idx]]
+                                if new_idx == -1:
+                                    copy_pos[idx] = rel_ids[idx] = -1
+                                else:
+                                    rel_ids[idx] = new_idx
+                    ex = NKLMExample(sentence, topic_id, relations, rel_ids, copy_pos, surface_indices)
+                    dataset.append(ex)
+                data[split] = dataset
+        return data
+
+    def gather_entity_stats(self, dataset: List[NKLMExample]) -> Dict[int, CounterType[int]]:
+        entity_count_per_type: Dict[int, CounterType[int]] = defaultdict(Counter)
+        for ex in self.data['train']:
+            rel_count = Counter(rel_id for rel_id, pos in zip(ex.rel_ids, ex.copy_pos) if pos == 0)
+            for rel_id, count in rel_count.items():
+                rel = ex.relations[rel_id]
+                if rel.rel_typ not in [-1, -2]:  # not TITLE or ANCHOR
+                    entity_count_per_type[rel.rel_typ].update({rel.obj_id: count})
+        return entity_count_per_type
+
+    def create_one_batch(self, examples: List[NKLMExample], bptt_size: int) \
+            -> Tuple[List[List[Relation]], List[BatchSequence]]:
+        sentences = [ex.sentence for ex in examples]
+        rels = [ex.relations for ex in examples]
+        rel_ids = [ex.rel_ids for ex in examples]
+        copy_pos = [ex.copy_pos for ex in examples]
+        surface_indices = [ex.surface_indices for ex in examples]
+
+        max_length = max(len(s) for s in sentences)
+        n_splits = utils.ceil_div(max_length - 1, bptt_size)  # minus 1 because there's always one extra
+
+        batches = []
+        for split_idx in range(n_splits):
+            interval = slice(split_idx * bptt_size, (split_idx + 1) * bptt_size + 1)
+            sequence = [s[interval] for s in sentences]
+            batch = BatchSequence(self.word_vocab, sequence,
+                                  rel_ids=([s[interval] for s in rel_ids], -1),
+                                  copy_pos=([s[interval] for s in copy_pos], -1),
+                                  surface_indices=([s[interval] for s in surface_indices], -1))
+            batches.append(batch)
+        return rels, batches
+
+    def remove_ambiguity(self, ex: NKLMExample, entity=False, alias=False) -> NKLMExample:
+        """Takes relations and remove some of them according to the specified condition."""
+
+        if not entity and not alias:
+            return ex
+
+        relations = copy.copy(ex.relations)
+        rel_ids = copy.copy(ex.rel_ids)
+        copy_pos = copy.copy(ex.copy_pos)
+        surface_indices = copy.copy(ex.surface_indices)
+        seq_len = len(rel_ids)
+
+        if entity:
+            new_indices, relations = self.remap_entity_ambiguous_rels(relations)
+            if new_indices is not None:
+                for idx in range(seq_len):
+                    rel_id = rel_ids[idx]
+                    if rel_id == -1:
+                        continue
+                    rel_ids[idx] = new_indices[rel_id]
+                    if new_indices[rel_id] == -1:
+                        copy_pos[idx] = surface_indices[idx] = -1
+
+        if alias:
+            # remove non-canonical indices
+            for idx in range(seq_len):
+                if surface_indices[idx] > 0:
+                    rel_ids[idx] = copy_pos[idx] = surface_indices[idx] = -1
+
+        return ex._replace(relations=relations, rel_ids=rel_ids, copy_pos=copy_pos, surface_indices=surface_indices)
diff --git a/dataset/utils.py b/dataset/utils.py
new file mode 100644
index 0000000..37c5f3b
--- /dev/null
+++ b/dataset/utils.py
@@ -0,0 +1,268 @@
+import copy
+from collections import defaultdict, deque
+from typing import Deque, Dict, List, NamedTuple, Optional, Tuple, TypeVar
+
+import numpy as np
+import torch
+from torch import LongTensor
+
+from dataset.vocab import Vocab
+
+__all__ = [
+    'BatchSequence', 'pad',
+    'Sentence', 'Span', 'Relation', 'MatchedSpan',
+    'flip_batches', 'search_paths',
+]
+
+T = TypeVar("T")
+
+Span = Tuple[int, int]
+Sentence = List[str]
+
+
+class Relation(NamedTuple):
+    rel_typ: int
+    obj_id: int
+    # `obj_alias` stores the surface forms of each alias
+    # but in case fastText vectors are used, it changes to the vector IDs of each alias
+    obj_alias: List[int]
+
+
+# The raw data loaded uses [l, r), but in our batches we use [l, r]
+class MatchedSpan(NamedTuple):
+    start: int  # start of span
+    end: int  # end of span (+1)
+    rel_typ: int  # rel-type ID
+    rel_idx: int  # index into `relations` array (relID for example)
+    surface_idx: int  # surface form's index into `alias` array
+
+
+class BatchSequence:
+    """Template for the batch of sequences with variable lengths. This class
+    handles numericalizing at the same time if needed, hence requires vocab.
+    """
+
+    def __init__(self, vocab: Vocab, sequence, spans: Optional[List[List[MatchedSpan]]] = None,
+                 **other_seqs: Tuple[List[List[T]], T]):
+        """Instantiate a batch of sequence."""
+        self.raw_sequence = sequence
+        self.spans = spans
+        self.unk_probs = None
+        self.batch_size = len(self.raw_sequence)
+
+        indices = [vocab.numericalize(s) for s in self.raw_sequence]  # TODO: Improve performance (47.582s)
+        sequence = pad_simple(indices, pad_symbol=1)
+        self.sequence = torch.from_numpy(sequence[:, :-1])
+        self.target = torch.from_numpy(sequence[:, 1:])
+
+        self.seqs: Dict[str, LongTensor] = {}
+        self.seqs_pad_symbols: Dict[str, T] = {}
+        for name, (seq, pad_symbol) in other_seqs.items():
+            self.seqs[name] = torch.from_numpy(pad_simple(seq, pad_symbol=pad_symbol))
+            self.seqs_pad_symbols[name] = pad_symbol
+
+        # don't make length -1 for empty sequences
+        self.lengths = torch.LongTensor([max(0, len(s) - 1) for s in self.raw_sequence])
+
+        # For loss calculation
+        self.ntokens = torch.sum(self.lengths).item()
+
+        self.has_article_end = vocab.w2i['</s>'] in [s[-1] for s in indices if len(s) > 0]
+
+    def __len__(self):
+        return self.batch_size
+
+    def to(self, device, persistent=False):
+        if persistent:
+            obj = self
+        else:
+            obj = copy.copy(self)
+        obj.sequence = self.sequence.to(device)
+        obj.target = self.target.to(device)
+        obj.lengths = self.lengths.to(device)
+        if self.unk_probs is not None:
+            obj.unk_probs = self.unk_probs.to(device)
+        obj.seqs = {name: seq.to(device) for name, seq in self.seqs.items()}
+        return obj
+
+    def chunk(self, n_chunks: int) -> List['BatchSequence']:
+        ret = []
+        batch_size = self.batch_size // n_chunks
+        for start_batch in range(0, self.batch_size, batch_size):
+            interval = slice(start_batch, start_batch + batch_size)
+            batch = copy.copy(self)
+            batch.batch_size = batch_size
+            batch.sequence = self.sequence[interval]
+            batch.target = self.target[interval]
+            batch.lengths = self.lengths[interval]
+            batch.ntokens = torch.sum(batch.lengths).item()
+            batch.has_article_end = '</s>' in [s[-1] for s in self.raw_sequence[interval] if len(s) > 0]
+            if self.unk_probs is not None:
+                batch.unk_probs = self.unk_probs[interval]
+            batch.seqs = {name: seq[interval] for name, seq in self.seqs.items()}
+            ret.append(batch)
+        return ret
+
+    def add_rels(self, value):
+        self.spans = value
+
+    def add_unk_probs(self, unk_probs, full_vocab):
+        # Get the label for targets
+        self.unk_probs = torch.zeros_like(self.target, dtype=torch.float, device=self.target.device)
+        for i, j in (self.target == 0).nonzero():
+            self.unk_probs[i, j] = unk_probs[full_vocab[self.raw_sequence[i][j + 1]]]
+
+    def remove_last_token(self):
+        # Pad out all the EOS tokens
+        obj = copy.deepcopy(self)
+        for idx, raw_s in enumerate(obj.raw_sequence):
+            if len(raw_s) > 1 and raw_s[-1] == '</s>':
+                end_pos = obj.lengths[idx] - 1
+                obj.sequence[idx, end_pos] = 1  # token pad_symbol
+                obj.target[idx, end_pos] = 1
+                for name in obj.seqs:
+                    pad_symbol = obj.seqs_pad_symbols[name]
+                    obj.seqs[name][idx, end_pos] = pad_symbol
+                obj.ntokens -= 1
+        return obj
+
+    @staticmethod
+    def _padded_flip(tensor: LongTensor, pad_index: int = 1) -> LongTensor:
+        """ Return a flipped and pad-aligned tensor along the second dimension.
+        e.g. if PAD = _,
+            [[1, 2, 3, 4],            [[4, 3, 2, 1],
+             [5, 6, 4, _],      =>     [4, 6, 5, _],
+             [2, _, _, _],             [2, _, _, _],
+             [2, 3, _, _]]             [3, 2, _, _]]
+        """
+        flipped_values = [[v for v in seq if v != pad_index]
+                          for seq in torch.flip(tensor, 1).tolist()]
+        return torch.LongTensor(pad(flipped_values, pad_symbol=1)).to(tensor.device)
+
+    def _flip_spans(self, rels: List[List[MatchedSpan]], lengths: LongTensor):
+
+        flipped: List[List[MatchedSpan]] = [[] for _ in range(self.batch_size)]
+        for batch, span in [(b, r) for b, rel in enumerate(rels) for r in rel]:
+            actual_len = lengths[batch]
+            # calculate flipped start and end
+            f_start, f_end = actual_len - span.end, actual_len - span.start
+            span = MatchedSpan(f_start, f_end, span.rel_typ, span.rel_idx, span.surface_idx)
+            flipped[batch].append(span)
+        return flipped
+
+    def flip(self):
+        obj = copy.copy(self)
+        trg = obj._padded_flip(obj.sequence)
+        obj.sequence = obj._padded_flip(obj.target)
+        obj.target = trg
+        obj.spans = obj._flip_spans(obj.spans, obj.lengths)
+        return obj
+
+
+def flip_batches(batches: List[BatchSequence]):
+    """flips the order of batches in an article and the sequence in each batch."""
+    new_batches = []
+    for b in batches:
+        new_batches.append(b.flip())
+    return new_batches[::-1]
+
+
+def search_paths(spans: List[MatchedSpan], begin: int, end: int):
+    """search all the possible paths spanning from begin to end, given the list of matched
+    relation spans.
+    :param spans: List of matched spans.
+    :param begin: Begin index of the range to search over.
+    :param end: End index of the range to search over.
+    """
+    # Add word transition
+    spans = spans + [MatchedSpan(i, i, -100, -100, -100) for i in range(begin, end + 1)]
+    begin2id: Dict[int, List[int]] = defaultdict(list)
+    for idx, s in enumerate(spans):
+        begin2id[s.start].append(idx)
+
+    completed = []
+    queue: Deque[List[MatchedSpan]] = deque()
+    for idx in begin2id[begin]:
+        queue.appendleft([spans[idx]])
+    # queue = [[spans[i]] for i in begin2id[begin]] + [[MatchedSpan(begin, begin, -100, -100, -100)]]
+    for path in queue:
+        if path[-1].end == end:
+            completed.append(path)
+
+    while len(queue) > 0:
+        path = queue.pop()
+        current_node = path[-1]
+        branches = [spans[i] for i in begin2id[current_node.end + 1]]
+        # branches.append(MatchedSpan(current_node.end+1, current_node.end+1, -100, -100, -100))  # Add word transition
+        for node in branches:
+            new_path = path + [node]
+            if node.end == end:
+                completed.append(new_path)
+            elif node.end < end:
+                queue.appendleft(new_path)
+    return completed
+
+
+def pad_simple(data: List[List[T]], *, pad_symbol: T) -> np.ndarray:
+    """Pad sequences with a padding index.
+    :param data: Data to be padded. This can be either text-based, or numericalized form.
+    :param pad_symbol: Padding idx or padding symbol.
+    """
+
+    batch_size = len(data)
+    max_len = max(len(x) for x in data)
+
+    padded = np.full((batch_size, max_len), pad_symbol)
+    for idx, seq in enumerate(data):
+        padded[idx, :len(seq)] = seq
+
+    return padded
+
+
+def pad(data: List[List[T]], max_len: Optional[int] = None, *, pad_symbol: T,
+        bos: Optional[T] = None, eos: Optional[T] = None, append: bool = True) -> np.ndarray:
+    """Pad sequences with a padding index.
+    :param data: Data to be padded. This can be either text-based, or numericalized form.
+    :param max_len: Maximum length of sequence. Defaults to the maximum length found in data.
+    :param pad_symbol: Padding idx or padding symbol.
+    :param bos: BOS idx or BOS symbol.
+    :param eos: EOS idx or EOS symbol.
+    :param append: Whether to append the pads or prepend pads.
+    """
+
+    batch_size = len(data)
+    if not max_len:
+        max_len = max(len(x) for x in data) + int(bos is not None) + int(eos is not None)
+    else:
+        actual_len = max_len - int(bos is not None) - int(eos is not None)
+        if actual_len <= 0:
+            raise ValueError(f"'max_len' too small (value = {max_len}, after considering bos/eos = {actual_len})")
+        data = [seq[:actual_len] for seq in data]
+
+    padded = np.full((batch_size, max_len), pad_symbol)
+    if append:
+        if bos is not None:
+            padded[:, 0] = bos
+            offset = 1
+        else:
+            offset = 0
+        for idx, seq in enumerate(data):
+            padded[idx, offset:(offset + len(seq))] = seq
+        if eos is not None:
+            for idx, seq in enumerate(data):
+                padded[idx, offset + len(seq)] = eos
+    else:
+        if eos is not None:
+            padded[:, -1] = eos
+            offset = 1
+            for idx, seq in enumerate(data):
+                padded[idx, -(len(seq) + 1):-1] = seq
+        else:
+            offset = 0
+            for idx, seq in enumerate(data):
+                padded[idx, -len(seq):] = seq
+        if bos is not None:
+            for idx, seq in enumerate(data):
+                padded[idx, -(len(seq) + offset)] = bos
+
+    return padded
diff --git a/dataset/vocab.py b/dataset/vocab.py
new file mode 100644
index 0000000..9188df1
--- /dev/null
+++ b/dataset/vocab.py
@@ -0,0 +1,172 @@
+import copy
+import logging
+from collections import Counter
+from pathlib import Path
+from typing import List, Optional, Union
+
+import numpy as np
+from tqdm import tqdm
+
+from utils import loadpkl, savepkl
+
+LOGGER = logging.getLogger(__name__)
+
+__all__ = [
+    'Vocab',
+    'prepare_unkprob',
+]
+
+
+class Vocab:
+    """Simple vocabulary class."""
+
+    def __init__(self, symbols):
+
+        self.w2i = {}
+        self.i2w = []
+        self.symbols = symbols
+
+        for sym in symbols:
+            self.i2w.append(sym)
+            self.w2i[sym] = len(self.i2w) - 1
+
+        self.min_freq = None
+        self.max_vocab = None
+        self.freq = None
+
+    @classmethod
+    def from_corpus(cls, corpus: List[str], min_freq: Optional[int] = None, max_vocab: Optional[int] = None,
+                    unk: str = '<unk>', lowercase=True,
+                    alphabetical=True, prioritized_vocab=None):
+        """read corpus (flattened list) and return vocabulary object.
+        Parameters:
+            corpus (List[str]): Flattened tokens.
+            min_freq (int): Minimum frequency of a word.
+            max_vocab (int): Maximum number of vocabulary.
+            unk (str): token representation of the unknown word.
+            lowercase (bool): Default - True.
+            alphabetical (bool): Whether sort the vocabulary in alphabetical
+                order when the frequencies are equal.
+            prioritized_vocab (List[str]): Vocabulary list to rank higher than
+                others. This may be critical when word counts tied.
+        """
+        # Register special symbols beforehand
+        vocab = Vocab(symbols=[unk, "<pad>", "<s>", "</s>"])
+
+        min_freq = min_freq or 0
+        if lowercase is True:
+            corpus = [w.lower() for w in corpus]
+
+        # Always store vocabularies with higher counts
+        counts = Counter(corpus)
+        frequencies = counts.most_common()  # list of (word, count)
+
+        # Alphabetical, descending order of frequency
+        if alphabetical:
+            frequencies = sorted(frequencies, key=lambda x: x[0])
+
+        if prioritized_vocab is not None:
+            # Words in this list will appear higher
+            freq_set = {i[0] for i in frequencies}
+            w_in_freq = list(freq_set.intersection(set(prioritized_vocab)))
+            w_out_freq = list(freq_set - set(w_in_freq))
+            # Reorder by the ones in the given vocab, followed by the ones not
+            # in the given vocab
+            frequencies = [(w, counts[w]) for w in w_in_freq + w_out_freq]
+
+        freq_vocab = sorted(frequencies, key=lambda x: x[1], reverse=True)
+
+        for word, freq in tqdm(freq_vocab, desc="Constructing vocab..", ncols=80, ascii=True):
+            if (word not in vocab.w2i) and (freq >= min_freq) \
+                    and ((max_vocab is None) or (len(vocab.i2w) < max_vocab + len(vocab.symbols))):
+                vocab.i2w.append(word)
+                vocab.w2i[word] = len(vocab.i2w) - 1
+
+        return vocab
+
+    @classmethod
+    def from_dict(cls, path: Union[str, Path], gz=False, mode=None):
+        """Load and construct dictionary into a file. No restriction about the
+        size or freq, just loads given file and set as the dictionary.
+        Parameters:
+            path (str): Path to the file.
+            gz (bool): Whether the file is gz or not.
+            mode (str or None): If 'i2w' or 'w2i', the loaded dict is used as the actual mapping of given type, and
+                the other mapping is automatically created.
+        """
+        valid_modes = ['i2w', 'w2i']
+        dic: dict = loadpkl(path, gz=gz)
+
+        vocab = Vocab(symbols=[])
+        if mode is None:
+            vocab.w2i = dic['w2i']
+            vocab.i2w = dic['i2w']
+        elif mode in valid_modes:
+            setattr(vocab, mode, dic)
+            setattr(vocab, next(m for m in valid_modes if mode != m), {v: k for k, v in dic.items()})
+        elif mode == 'w2i':
+            vocab.w2i = dic
+        else:
+            raise ValueError(f"Invalid mode '{mode}'")
+        return vocab
+
+    def numericalize(self, sequence):
+        """convert tokens to indices.
+        Parameters:
+            sequence (List[str]): Token sequence.
+        Returns:
+            List[int]
+        """
+        return [self.w2i.get(s, 0) for s in sequence]
+
+    def denumericalize(self, sequence):
+        """convert indices to tokens.
+        Parameters:
+            sequence (List[int]): Numericalized sequence.
+        Returns:
+            List[str]
+        """
+        return [self.i2w[s] for s in sequence]
+
+    def save_dict(self, path, gz=False):
+        """Save dictionary into a file.
+        Parameters:
+            path (str): Path to the file.
+            gz (bool): Whether to gz or not.
+        """
+        dic = {'w2i': self.w2i, 'i2w': self.i2w}
+        savepkl(dic, path, gz=gz)
+
+    def __len__(self):
+        return len(self.i2w)
+
+    def __repr__(self):
+        return f"Vocabulary(size={len(self.i2w)}, min_freq={self.min_freq})"
+
+
+def prepare_unkprob(path: str, vocab: Vocab, uniform_unk: bool = False):
+    res = [0.0] * len(vocab)
+    with open(path) as f:
+        word2prob = {
+            l.split('\t')[0]: float(l.split('\t')[1]) for l in f.read().strip().split('\n')
+        }
+
+    missed = 0
+    for word, idx in vocab.w2i.items():
+        if word not in word2prob:
+            LOGGER.debug(f"'{word}' was not in the dumped word probs.")
+            missed += 1
+        res[idx] = word2prob.get(word, 0.0)
+
+    unked_vocabs = list(set(word2prob.keys()) - set(vocab.w2i.keys()))
+    if uniform_unk:
+        uniform_backoff = -np.log(len(unked_vocabs))
+        res += [uniform_backoff for uv in unked_vocabs]
+    else:
+        res += [word2prob[uv] for uv in unked_vocabs]
+
+    total_w2i = copy.copy(vocab.w2i)  # make a copy!
+    total_w2i.update({v: k + len(vocab.w2i) for k, v in enumerate(unked_vocabs)})
+
+    LOGGER.info(f"{missed / len(vocab.w2i)}% vocabulary is covered by char model.")
+    return res, total_w2i
diff --git a/docs/data.md b/docs/data.md
new file mode 100644
index 0000000..e674c28
--- /dev/null
+++ b/docs/data.md
@@ -0,0 +1,131 @@
+## Dataset
+
+### Dataset Format
+
+A dataset consists of the following files:
+
+- `{train,valid,test}.pkl`: Examples in each data split, in pickled format. These files contain the sentences, topic
+  entity, and relations for each article.
+- `{train,valid,test}.span.pkl`: Span annotations for each example in data split, in pickled format. These files contain
+  spans with their matching relations for each article.
+- `entity_vec.pt` and `relation_vec.pt`: Processed knowledge base embeddings for entities and relations, in PyTorch
+  weights format.
+- `unk_probs.txt`: Word probabilities precomputed by the character-level LM.
+
+The detailed format for each file are:
+
+- `{train,valid,test}.pkl`: A list where each element corresponds to an article. Each article is an tuple consisting of
+  the following, in order:
+
+  - `sentence` (`List[str]`): A list of words representing the tokenized article.
+  - `topic_id`: A unique ID of the topic entity. This field is ignored.
+  - `relations`: A list of relations of the topic entity. Each relation is a tuple consisting of three fields:
+
+    - `rel_typ` (`int`): The ID of the type of the relation.
+    - `obj_id` (`int`): The ID of the object entity of the relation.
+    - `obj_alias` (`List[str]`): A list of surface forms (aliases) of the object entity.
+  
+  For NKLM-compatible datasets, the tuple should also contain the following fields:
+  
+  - `rel_ids` (`List[int]`): The index of the relation (index into the `relations` list) for each word of the article.
+    For words which are not a part of a entity mention, use `-1` as the index.
+  - `copy_pos` (`List[int]`): The copying position of the word (please refer to Appendix D of the paper). For words
+    which are not a part of a entity mention, use `-1` as the position. 
+  - `surface_indices` (`List[int]`): The index of the surface form (index into the `obj_alias` list of the relation
+    specified by `rel_ids`) for each word of the article. For words which are not a part of a entity mention, use `-1`
+    as the index.
+- `{train,valid,test}.span.pkl`: A list where each element corresponds to an article, in the same order as
+  `{train,valid,test}.pkl`. Each article is an list of matched spans, where each matched span is a tuple consisting of
+  5 fields:
+  
+  - `start` (`int`): The start index of the span.
+  - `end` (`int`): The end index of the span. The span is left-closed and right-open (`[start, end)`), e.g. the words
+    in the span are `sentence[start, start+1, ..., end-1]`.
+  - `rel_typ` (`int`): The ID of the type of the relation.
+  - `rel_idx` (`int`): The index of the relation (index into the `relations` list).
+  - `surface_idx` (`int`): The index of the surface form (index into the `obj_alias` list of the relation specified
+    by `rel_idx`).
+- `entity_vec.pt` and `relation_vec.pt`: A PyTorch tensor of shape `[n_vecs, vec_dim]`. Tensors should be ordered
+  according to their ID, e.g. the vector for the entity with ID `x` should be `relation_vec[x]`.
+- `unk_probs.txt`: A tab-separated file with two columns, corresponding to words and their log-probabilities.  
+
+**Notes on relation type IDs:** IDs for relation types with corresponding pre-trained embeddings are numbered from 0.
+There are also negative IDs that are used for specific purposes:
+
+- ID `-1` is used to denote that the span is not a relation (also called "not a fact").
+- ID `-2` indicates a hyperlink to another Wikipedia article (also called "anchor" relations). This ID only exists for
+  WikiFacts. Please see paper Section 4.1 and Appendix C for details.
+- ID `-3` indicates a mention of the article title (also called "title" relations).
+- IDs `-4` and below indicates relations that do not have pre-trained embeddings. We train embeddings for these
+  relations.
+
+**Notes on entity IDs:** Similar to relation type IDs, entities with corresponding pre-trained embeddings are numbered
+from 0. A special ID `-1` is used to represent entities without pre-trained embeddings (also called unknown entities).
+
+### Creating the WikiFacts Dataset
+
+To create the WikiFacts dataset from scratch:
+
+1. Download pre-trained Freebase embeddings from
+   [OpenKE](http://openke.thunlp.org/index/toolkits#pretrained-embeddings). This might take a long time.
+2. Download the original WikiFacts dataset:
+   <https://bitbucket.org/skaasj/wikifact_filmactor/src/master/>.
+3. Install `sacremoses==0.0.33`.
+4. Manually modify the path variables in `preprocess/process_wikifacts.py`:
+
+   - `DATASET_PATH` should point to the Tar archive downloaded from the WikiFacts repository.
+   - `ENTITY_VEC_PATH` and related paths should point to appropriate files downloaded from OpenKE.
+   - `SAVE_DIR` should point to the directory where the generated dataset will be stored.
+5. Run `preprocess/process_wikifacts.py`.
+6. Train a character-level language model to produce probabilities for unknown words:
+   ```bash
+   # Replace SAVE_DIR with the path specified above
+   ./scripts/run_charlm_wikitext.sh --data SAVE_DIR
+   ```
+
+### Creating the WikiText Dataset
+
+To create the WikiText-S,F dataset from scratch:
+
+1. Download pre-trained Wikidata embeddings from
+   [OpenKE](http://openke.thunlp.org/index/toolkits#pretrained-embeddings). This might take a long time.
+2. Download the original WikiText-103 character-level dataset and unzip the archive:
+   <https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip>.
+3. Download the Wikidata dump triples and unzip the archive. This takes significant space (374GB after unzipped as of 9/21/2018).
+   <https://dumps.wikimedia.org/wikidatawiki/entities/latest-truthy.nt.gz>.
+4. Install the requirements: `requests`, `tqdm`, and `nltk`.
+5. Manually modify the path variables for the two files:
+
+   - `preprocess/scripts/preprocess_wikitext.sh`:
+      - `WIKITEXT_DIR`: Path to the unzipped directory from Step 2.
+      - `WIKIDATA_DUMP_DIR`: Path to the directory containing the unzipped Wikidata dump from Step 3.
+      - `ALIGNED_DATA_DIR`: Directory to save the aligned data between Wikidata & WikiText.
+
+   - `preprocess/process_wikitext.py`:
+      - `OPENKE_PATH`: Path to the unzipped archive from Step 1.
+      - `WIKITEXT_DIR`: Path to the unzipped directory from Step 2.
+      - `WIKIDATA_DUMP_DIR`: Path to the directory containing the unzipped Wikidata dump from Step 3.
+      - `ALIGNED_DATA_DIR`: Directory to save the aligned data between Wikidata & WikiText.
+      - `SAVE_DIR` should point to the directory where the generated dataset will be stored.
+
+6. Run `preprocess/scripts/preprocess_wikitext.sh` from the root of the directory.
+7. Train fastText embeddings by running:
+   ```bash
+   # Embeddings for WT-Full
+   python preprocess/train_fasttext.py \
+       --fasttext-path /path/to/fasttext/binary \
+       --dataset-dir data/wikitext/ \
+       --output-dir data/fasttext/wt-full
+   # Embeddings for WT-Short
+   python preprocess/train_fasttext.py \
+       --fasttext-path /path/to/fasttext/binary \
+       --dataset-dir data/wikitext/ \
+       --use-only-first-section \
+       --output-dir data/fasttext/wt-short
+   ```
+6. Train a character-level language model to produce probabilities for unknown words:
+   ```bash
+   # Replace SAVE_DIR with the path specified above
+   ./scripts/run_charlm_wikitext.sh --data SAVE_DIR
+   ```
+    
diff --git a/docs/lrlm-fig.png b/docs/lrlm-fig.png
new file mode 100644
index 0000000..0e8e6e0
Binary files /dev/null and b/docs/lrlm-fig.png differ
diff --git a/models/__init__.py b/models/__init__.py
new file mode 100644
index 0000000..3fcabee
--- /dev/null
+++ b/models/__init__.py
@@ -0,0 +1,4 @@
+from models.aliaslm import *
+from models.lrlm import *
+from models.nklm import *
+from models.vanillalm import *
diff --git a/models/aliaslm.py b/models/aliaslm.py
new file mode 100644
index 0000000..e6871bb
--- /dev/null
+++ b/models/aliaslm.py
@@ -0,0 +1,87 @@
+import functools
+import math
+from typing import Optional, Dict
+
+import torch
+from torch import Tensor
+
+from arguments import LMArguments
+from dataset import LRLMExample, Vocab
+from models import sample_utils
+from models.base import HiddenState
+from models.sample_utils import SampledOutput
+from models.vanillalm import VanillaLM
+from nnlib.utils import Logging
+from utils import repackage_hidden
+
+__all__ = [
+    'AliasLM',
+]
+
+
+class AliasLM(VanillaLM):
+    def __init__(self, args: LMArguments, vocab_size: int, **_kwargs):
+        r"""A normal encoder."""
+        super().__init__(args, vocab_size)
+        self.bptt_size = args.bptt_size
+
+    def init_hidden(self, batch_size: int, init_batch: Tensor) -> HiddenState:
+        padded_aliases = init_batch
+        batched_aliases = []
+        max_len_aliases = padded_aliases.size(1)
+        for i in range((max_len_aliases - 1) // self.bptt_size + 1):
+            batched_aliases.append(padded_aliases[:, i * self.bptt_size:(i + 1) * self.bptt_size])
+
+        hidden = self.rnn.init_hidden(batch_size)
+        for batched_alias in batched_aliases:
+            batched_alias = batched_alias.to(self.device)
+            if hidden is not None:
+                hidden = repackage_hidden(hidden)
+
+            alias_embeds = self.word_embed(batched_alias)
+            _, hidden = self.rnn.forward(alias_embeds, hidden)
+
+        return hidden
+
+    @torch.no_grad()
+    def sampling_decode(self, vocab: Dict[str, Vocab], example: LRLMExample,
+                        begin_symbol: int = 2, end_symbol: int = 5,
+                        initial_hidden: Optional[HiddenState] = None, warm_up: Optional[int] = None,
+                        max_length: int = 200, greedy: bool = False, topk: Optional[int] = None,
+                        print_info: bool = True, color_outputs: bool = False, init_batch=None, **_kwargs) \
+            -> SampledOutput:
+        tensor = functools.partial(sample_utils.tensor, device=self.device)
+        sample = functools.partial(sample_utils.sample, greedy=greedy, topk=topk)
+
+        self.eval()
+        self.init_hidden(1, init_batch)
+
+        if warm_up is None:
+            inputs = [begin_symbol]
+            hidden = initial_hidden
+            total_log_prob = 0.0
+        else:
+            inputs = list(vocab["word"].numericalize(example.sentence[:warm_up]))
+            total_log_prob, hidden = self.forward(tensor(inputs[:-1]), target=tensor(inputs[1:]))
+            total_log_prob = -torch.sum(total_log_prob).item() * (len(inputs) - 1)
+
+        while len(inputs) < max_length and inputs[-1] != end_symbol:
+            # full copy of the forward pass, including dropouts. But they won't be applied due to .eval function.
+            # Run LSTM over the word
+            word_log_probs, new_hidden = self.forward(tensor(inputs[-1]), hidden)
+            word_id, word_log_prob = sample(word_log_probs)
+            inputs.append(word_id)
+            hidden = new_hidden
+            total_log_prob += word_log_prob
+
+        sample_loss = -total_log_prob / (len(inputs) - 1)
+        if print_info:
+            print(f"Sample loss: {sample_loss:.3f}, PPL: {math.exp(sample_loss):.3f}")
+
+        # Format the output
+        words = [vocab["word"].i2w[token] for token in inputs]
+        if color_outputs and warm_up is not None:
+            words[:warm_up] = [Logging.color('yellow', w) for w in words[:warm_up]]
+
+        output = SampledOutput(sentence=words, sample_loss=sample_loss, complete_copies=0, incomplete_copies=0)
+        return output
diff --git a/models/base.py b/models/base.py
new file mode 100644
index 0000000..1cd1910
--- /dev/null
+++ b/models/base.py
@@ -0,0 +1,112 @@
+from abc import ABC
+from typing import Dict, List, Optional, Tuple, Union, Any
+
+import torch
+from models.sample_utils import SampledOutput
+from torch import nn
+from torch import Tensor
+
+from arguments import LMArguments
+from dataset.utils import BatchSequence
+from models.rnns import BaseRNN, LSTM, LSTMState, TransformerXL, TransformerState
+from models.utils import AdaptiveEmbedding, MLPSoftmax, ProjectedAdaptiveLogSoftmax, Softmax
+
+__all__ = [
+    'HiddenState',
+    'BaseLM',
+]
+
+HiddenState = Union[LSTMState, TransformerState]
+
+
+class BaseLM(nn.Module, ABC):
+    model_cache: Dict[str, Any] = {}
+
+    word_embed: nn.Embedding
+    word_predictor: Softmax
+    rnn: BaseRNN
+
+    def __init__(self, args: LMArguments, vocab_size: int,
+                 input_dim: Optional[int] = None, pred_input_dim: Optional[int] = None,
+                 embed_dim: Optional[int] = None):
+        r"""Initializer for the LM base class.
+
+        ..note::
+            Please pay attention to the dimension differences between LSTM and Transformer. Transformers expect input
+            dimension to be the same as hidden size, while LSTMs can handle inputs of arbitrary dimension because it
+            does a projection of inputs anyway.
+
+            When using Transformers and `input_dim != args.embed_size`, if adaptive embeddings are used, then we add
+            projections in `AdaptiveEmbedding` by setting `proj_dim` to `input_dim`; if normal embeddings are used,
+            we add projections in `TransformerXL` by setting `embed_dim` to `input_dim`.
+
+        :param args: General model arguments.
+        :param vocab_size: Vocabulary size.
+        :param input_dim: Dimension of input to the recurrent network. If not specified, `args.embed_size` is used.
+        :param pred_input_dim: Dimension of input to the predictors. If not specified, `args.hidden_size` is used.
+        :param embed_dim: Dimension of input embeddings. If not specified, `args.embed_size` is used. This **must** be
+            specified if the RNN input is the concatenation of embeddings and other tensors.
+        """
+        super().__init__()
+
+        self.device = torch.device('cuda' if args.cuda else 'cpu')
+
+        self._vocab_size = vocab_size
+        self._embed_dim = embed_dim or args.embed_size
+        self._hidden_dim = args.hidden_size
+        self._input_dim = input_dim or args.embed_size
+        self._pred_input_dim = pred_input_dim or args.hidden_size
+
+        if args.adaptive_embed:
+            cutoffs = [20000, 40000, 200000]
+            cutoffs = [x for x in cutoffs if x < vocab_size]
+            div_val = 1
+            proj_dim = self._input_dim if args.base_rnn == 'transformer' and embed_dim is None else self._embed_dim
+            self.word_embed = AdaptiveEmbedding(
+                vocab_size, self._embed_dim, proj_dim, cutoffs, div_value=div_val)
+            # use adaptive softmax (including standard softmax)
+            self.word_predictor = ProjectedAdaptiveLogSoftmax(
+                vocab_size, args.vocab_mlp_hidden_dim, self._pred_input_dim, cutoffs, div_value=div_val)
+            if args.tie_embed_weights:
+                assert args.vocab_mlp_hidden_dim == self._embed_dim
+                # assign adaptive softmax weight to embedding,
+                # because its weight matrix is larger (head_size + n_cluster)
+                for i in range(len(self.word_predictor.out_layers)):
+                    self.word_predictor.out_layers[i].weight = self.word_embed.embed_layers[i].weight
+                if self._pred_input_dim == self._hidden_dim and div_val != 1:
+                    for i in range(len(cutoffs)):
+                        self.word_predictor.out_projs[i + 1] = self.word_embed.embed_projs[i + 1]
+        else:
+            self.word_embed = nn.Embedding(vocab_size, self._embed_dim, padding_idx=1)
+            self.word_predictor = MLPSoftmax(self._pred_input_dim,
+                                             args.vocab_mlp_hidden_dim,
+                                             vocab_size, dropout=args.vocab_mlp_dropout,
+                                             activation=args.vocab_mlp_activation)
+            if args.tie_embed_weights:
+                assert args.vocab_mlp_hidden_dim == self._embed_dim
+                self.word_predictor.layer2.weight = self.word_embed.weight.weight
+
+        if args.base_rnn == 'transformer':
+            tfm_embed_dim = self._embed_dim if args.adaptive_embed and embed_dim is None else self._input_dim
+            self.rnn = TransformerXL(
+                args.num_layers, args.num_heads, self._hidden_dim, args.head_dim,
+                args.ffn_inner_dim, args.dropout, args.attention_dropout, dropout_pos=args.rnn_dropout_pos,
+                embed_dim=tfm_embed_dim, pre_lnorm=args.pre_lnorm,
+                ext_len=0, mem_len=args.memory_size)
+        else:
+            self.rnn = LSTM(self._input_dim, self._hidden_dim, args.num_layers,
+                            dropout=args.dropout, dropout_pos=args.rnn_dropout_pos)
+
+    def calc_loss(self, batch: BatchSequence, hidden: Optional[HiddenState] = None,
+                  use_unk_probs: bool = False, dump_probs: bool = False) -> Tuple[Tensor, HiddenState]:
+        raise NotImplementedError
+
+    def init_hidden(self, batch_size: int, init_batch):
+        raise NotImplementedError
+
+    def sampling_decode(self, vocab, example,
+                        begin_symbol: int = 2, end_symbol: int = 5,
+                        initial_hidden: Optional[HiddenState] = None, warm_up: Optional[int] = None,
+                        max_length: int = 200, greedy: bool = False, topk: Optional[int] = None,
+                        print_info=True, color_outputs=False, **_kwargs) -> SampledOutput:
+        raise NotImplementedError
diff --git a/models/lrlm.py b/models/lrlm.py
new file mode 100644
index 0000000..731cde4
--- /dev/null
+++ b/models/lrlm.py
@@ -0,0 +1,542 @@
+import functools
+import math
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Dict, List, NamedTuple, Optional, Tuple
+
+from mypy_extensions import TypedDict
+import torch
+from torch import nn
+from torch import LongTensor, Tensor
+from torch.nn import functional as F
+
+from arguments import AliasDisamb, LMArguments
+from dataset import BatchSequence, LRLMExample, MatchedSpan, Relation, Vocab
+from models import sample_utils, utils
+from models.base import BaseLM, HiddenState
+from models.sample_utils import SampledOutput
+from nnlib.utils import Logging, reverse_map
+from utils import loadpkl
+
+__all__ = [
+    'LRLM',
+]
+
+SpanInfo = Tuple[int, MatchedSpan]  # (batch, span)
+
+
+class LRLM(BaseLM):
+    class CacheDict(TypedDict):
+        num_facts: LongTensor
+        batch_rels: List[List[Relation]]
+        fact_embeds: Tensor
+        knowledge_embed: Optional[Tensor]
+
+    _cache: CacheDict
+
+    def __init__(self, args: LMArguments, *, vocab_size: int, rel_vocab_size: int, max_unkrel: int):
+        self._rel_vocab_size = rel_vocab_size
+        self._max_unkrel = max_unkrel
+        self._kb_embed_dim = args.kb_embed_dim
+        self._fact_embed_dim = args.kb_embed_dim * 2
+
+        pred_input_dim = args.hidden_size + (self._fact_embed_dim if args.use_knowledge_embed else 0)
+        super().__init__(args, vocab_size, pred_input_dim=pred_input_dim)
+
+        self._num_layers = args.num_layers
+        self._use_knowledge_embed = args.use_knowledge_embed
+        self._train_relation_vec = args.train_relation_vec
+        self._alias_disamb = args.alias_disamb_strategy
+
+        self.selector = nn.Linear(self._pred_input_dim, 2)
+
+        if args.alias_disamb_strategy is AliasDisamb.FastText:
+            # Alias disambiguation using FastText.
+            def _alias_path(name):
+                path = Path(args.fasttext_model_path)
+                return path.parent / (path.name + f'.{name}')
+
+            self.alias_vec = torch.load(_alias_path('alias_vectors.pt')).to(self.device)
+            if args.normalize_fasttext_embeds:
+                self.alias_vec = self.alias_vec / torch.norm(self.alias_vec, dim=1).unsqueeze(0)
+            self.alias_list: List[str] = reverse_map(loadpkl(_alias_path('alias_dict.pkl')))
+            self._alias_vec_dim = self.alias_vec.size(1)
+            self.hid_to_alias = nn.Linear(self._pred_input_dim, self._alias_vec_dim)
+
+        # Entity disambiguation using entity embeddings.
+        self.entity_vec, relation_vec = utils.load_kb_embed(args.path, self.device)
+        if self._train_relation_vec:
+            rel_vocab_size, rel_embed_dim = relation_vec.size()
+            self.relation_embed = nn.Embedding(rel_vocab_size, rel_embed_dim)
+        else:
+            self.relation_vec = relation_vec
+
+        # (added 1) -2: anchor, -3: topic_itself, < -4: UNKs
+        self.special_rel_vecs = nn.ParameterList(
+            [nn.Parameter(torch.Tensor(self._kb_embed_dim)) for _ in range(max_unkrel + 2)])
+        self.unk_entity_vec = nn.Parameter(torch.Tensor(self._kb_embed_dim))  # -1
+
+        def _(a, b):
+            return a if a != -1 else b
+
+        # Entity prediction
+        if args.use_rel_mlp:
+            self.hid_to_fact: nn.Module = utils.MLP(self._pred_input_dim,
+                                                    _(args.fact_key_mlp_hidden_dim, self._fact_embed_dim * 2),
+                                                    self._fact_embed_dim, dropout=args.dropout)
+        else:
+            self.hid_to_fact = nn.Linear(self._pred_input_dim, self._fact_embed_dim)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for param in [self.unk_entity_vec] + list(self.special_rel_vecs.parameters()):
+            param.data.normal_(0.0, 0.02)
+
+    def _get_relation_vec(self, rel_typ: int) -> Tensor:
+        if self._train_relation_vec:
+            return self.relation_embed.weight[rel_typ]
+        return self.relation_vec[rel_typ]
+
+    @staticmethod
+    def _gather_spans(rels: List[List[MatchedSpan]], seq_len: int) -> List[List[SpanInfo]]:
+        r"""
+        Return a list of list of spans which start at each position.
+        """
+        result: List[List[SpanInfo]] = [[] for _ in range(seq_len)]
+        for batch, span in [(b, r) for b, rel in enumerate(rels) for r in rel]:
+            # relation exists at the very beginning, can't capture due to seq_len splits
+            if span.start < 0 or span.end > seq_len - 1:
+                continue
+            # A relation that span beyond batches
+            elif span.start > span.end:
+                continue
+            # relation: (bidx, relation)
+            result[span.start].append((batch, span))
+        return result
+
+    class ComputedLogProbs(NamedTuple):
+        selector: Tensor
+        word: Tensor
+        rel: Tensor
+        alias_logits: Optional[Tensor]
+
+    def _compute_log_probs(self, inputs: LongTensor, hidden: Optional[HiddenState] = None,
+                           target: Optional[LongTensor] = None) -> Tuple[ComputedLogProbs, HiddenState]:
+        batch_size, seq_len = inputs.size()
+
+        # word_embed: (batch_size, seq_len, word_embed_dim)
+        word_embed = self.word_embed(inputs)
+        # states: (batch_size, seq_len, hidden_dim)
+        states, new_hidden = self.rnn(word_embed, hidden)
+
+        if self._use_knowledge_embed:
+            knowledge_embed = self._cache['knowledge_embed']
+            assert knowledge_embed is not None
+            # predictor_input: (batch_size, seq_len, hidden_dim + fact_embed_dim)
+            predictor_input = torch.cat([
+                states, knowledge_embed.unsqueeze(1).expand(batch_size, seq_len, -1)
+            ], dim=2)
+        else:
+            # predictor_input: (batch_size, seq_len, hidden_dim)
+            predictor_input = states
+
+        selector_logits = self.selector(predictor_input)
+        # selector_log_probs: (batch_size, seq_len, 2)
+        selector_log_probs = F.log_softmax(selector_logits, dim=2)
+
+        if target is not None:
+            # target_log_probs: (batch_size, seq_len)
+            word_log_probs = -self.word_predictor(predictor_input, target)
+        else:
+            # word_log_probs: (batch_size, seq_len, vocab_size)
+            word_log_probs = self.word_predictor.log_probs(predictor_input)
+
+        fact_embeds = self._cache['fact_embeds']
+        num_facts = self._cache['num_facts']
+        # fact_key: (batch_size, seq_len, fact_embed_dim)
+        fact_key = self.hid_to_fact.forward(predictor_input)
+        # rel_logits: (batch_size, seq_len, max_facts)
+        rel_logits = torch.bmm(fact_key, fact_embeds.transpose(1, 2))
+        # rel_logits_mask: (batch_size, max_facts)
+        rel_logits_mask = utils.sequence_mask(num_facts, mask_val=0.0, default_val=-math.inf, device=self.device)
+        # rel_log_prob: (batch_size, seq_len, max_facts)
+        rel_log_probs = F.log_softmax(rel_logits + rel_logits_mask.unsqueeze(1), dim=-1)
+
+        alias_logits = None
+        if self._alias_disamb is AliasDisamb.FastText:
+            # alias_logits: (batch_size, seq_len, alias_vec_dim)
+            alias_logits = self.hid_to_alias.forward(predictor_input)
+
+        computed_log_probs = self.ComputedLogProbs(
+            selector=selector_log_probs, word=word_log_probs, rel=rel_log_probs, alias_logits=alias_logits)
+        return computed_log_probs, new_hidden
+
+    def forward(self,  # type: ignore
+                inputs: LongTensor, targets: LongTensor, lengths: LongTensor,
+                spans: List[List[MatchedSpan]],
+                hidden: Optional[HiddenState] = None,
+                unk_probs: Optional[Tensor] = None,
+                dump_posterior_probs=False) -> Tuple[Tensor, HiddenState]:
+        r"""
+        :return (per-batch loss, new hidden state, log-probs for each matched span)
+        """
+        batch_size, seq_len = inputs.size()
+
+        gathered_spans = self._gather_spans(spans, seq_len)
+
+        computed_log_probs, new_hidden = self._compute_log_probs(inputs, hidden, targets)
+
+        target_log_probs = computed_log_probs.word
+        if unk_probs is not None:
+            target_log_probs = target_log_probs + unk_probs
+
+        word_selector = computed_log_probs.selector[:, :, 0]
+        rel_selector = computed_log_probs.selector[:, :, 1]
+
+        # target_cond_log_probs: (batch_size, seq_len)
+        target_cond_log_probs = target_log_probs + word_selector
+
+        log_probs_list: List[List[Tensor]] = [[] for _ in range(seq_len)]
+        # log_probs: list(seq_len) of (batch_size)
+        log_probs: List[Tensor] = []
+
+        # posterior log-probs for each span
+        posterior_log_probs: List[Dict[MatchedSpan, Tuple[float, float]]] = [{} for _ in range(batch_size)]
+
+        batch_rels: List[List[Relation]] = self._cache['batch_rels']
+        # Slicing the input by a batch of words at each time step
+        for idx in range(seq_len):
+            if idx == 0:
+                prev_prob = torch.zeros(batch_size, dtype=torch.float, device=self.device)
+            else:
+                if len(log_probs_list[idx - 1]) == 1:
+                    prev_prob = log_probs_list[idx - 1][0]
+                else:
+                    prev_prob = torch.logsumexp(torch.stack(log_probs_list[idx - 1]), dim=0)
+                log_probs.append(prev_prob)
+
+            # Update the base selector's probability
+            # p(path_with_word) = p(ctxt) * p(selector=word|ctxt) * p(w|ctxt)
+            new_prob = prev_prob + target_cond_log_probs[:, idx]
+
+            # Duplicate the new_prob vector and mask it, update the logprob
+            # matrix' particular column
+            log_probs_list[idx].append(new_prob)
+
+            # p(path_with_rel) = p(ctxt) * p(selector=rel|ctxt) * p(rel=r|ctxt)
+            # cur_rel_log_probs: (batch_size, rel_vocab_size)
+            cur_rel_log_probs = (prev_prob.view(-1, 1) + rel_selector[:, idx].view(-1, 1) +
+                                 computed_log_probs.rel[:, idx, :])
+
+            for batch_idx, span in gathered_spans[idx]:
+                rel = batch_rels[batch_idx][span.rel_idx]
+
+                if self._alias_disamb is not AliasDisamb.FastText:
+                    alias_log_prob = 0.0
+                else:
+                    assert computed_log_probs.alias_logits is not None
+                    logits = computed_log_probs.alias_logits[batch_idx, idx]
+                    aliases = torch.tensor(rel.obj_alias, device=self.device, dtype=torch.long)
+                    alias_vecs = torch.index_select(self.alias_vec, dim=0, index=aliases)
+                    alias_log_prob = F.log_softmax(torch.mv(alias_vecs, logits), dim=0)[span.surface_idx]
+
+                rel_log_prob = cur_rel_log_probs[batch_idx][span.rel_idx] + alias_log_prob
+
+                adder = torch.full((batch_size,), -math.inf, device=self.device)
+                adder[batch_idx] = rel_log_prob
+
+                if dump_posterior_probs:
+                    rel_typ = span.rel_idx
+                    rel_posterior_log_prob = (alias_log_prob + rel_selector[batch_idx, idx] +
+                                              computed_log_probs.rel[batch_idx][idx, rel_typ])
+                    word_posterior_log_prob = torch.sum(target_cond_log_probs[batch_idx, idx:(span.end + 1)])
+                    posterior_log_probs[batch_idx][span] = (rel_posterior_log_prob.item(),
+                                                            word_posterior_log_prob.item())
+
+                log_probs_list[span.end].append(adder)
+        log_probs.append(torch.logsumexp(torch.stack(log_probs_list[-1]), dim=0))
+        assert len(log_probs) == seq_len
+
+        # stacked_log_probs: (batch_size, seq_len)
+        stacked_log_probs = torch.stack(log_probs, dim=1)
+
+        # nonzero_indices = torch.arange(batch_size)[lengths > 0]
+        nonzero_lengths = torch.max(torch.zeros(batch_size, dtype=torch.long, device=self.device), lengths - 1)
+
+        loss = -stacked_log_probs[torch.arange(batch_size), nonzero_lengths]
+
+        if dump_posterior_probs:
+            self.model_cache.update(
+                posterior_log_probs=posterior_log_probs,
+                target_cond_log_probs=target_cond_log_probs.cpu().numpy(),
+                stacked_log_probs=stacked_log_probs.cpu().numpy(),
+            )
+
+        return loss, new_hidden
+
+    def calc_loss(self, batch: BatchSequence, hidden: Optional[HiddenState] = None,
+                  use_unk_probs: bool = False, dump_probs: bool = False, dump_posterior_probs: bool = False) \
+            -> Tuple[Tensor, HiddenState]:
+        assert batch.spans is not None
+        batch_loss, new_hidden = self.forward(
+            batch.sequence,
+            batch.target,
+            batch.lengths,
+            batch.spans,
+            hidden=hidden,
+            unk_probs=(None if not use_unk_probs else batch.unk_probs),
+            dump_posterior_probs=dump_posterior_probs)
+
+        # Returns the per-token loss
+        loss = torch.sum(batch_loss) / batch.ntokens
+
+        if dump_probs:
+            log_probs = [-prob / length if length > 0 else 0.0
+                         for prob, length in zip(batch_loss.tolist(), batch.lengths.tolist())]
+            self.model_cache.update(log_probs=log_probs)
+
+        return loss, new_hidden
+
+    def init_hidden(self, batch_size, init_batch: List[List[Relation]]):
+        batch_rels = init_batch
+
+        max_facts = max([len(r) for r in batch_rels])
+        num_facts = torch.tensor([len(r) for r in batch_rels])
+
+        # fact_embeds: (batch_size, max_facts, fact_embed_dim)
+        fact_embeds = torch.stack([
+            torch.cat([torch.cat([
+                torch.stack([
+                    self._get_relation_vec(rel.rel_typ) if rel.rel_typ >= 0 else self.special_rel_vecs[rel.rel_typ + 1]
+                    for rel in relations
+                ]),
+                torch.stack([
+                    self.entity_vec[rel.obj_id] if rel.obj_id != -1 else self.unk_entity_vec
+                    for rel in relations
+                ])  # batches with fewer relations are padded to enable vectorized operations
+            ], dim=1), torch.zeros(max_facts - num_facts[idx], self._fact_embed_dim, device=self.device)])
+            for idx, relations in enumerate(batch_rels)
+        ])
+        knowledge_embed = None
+        if self._use_knowledge_embed:
+            # knowledge_embed: (batch_size, fact_embed_dim)
+            knowledge_embed = torch.stack([torch.mean(e, dim=0) for e in fact_embeds])
+
+        self._cache = {
+            'num_facts': num_facts,
+            'batch_rels': batch_rels,
+            'fact_embeds': fact_embeds,
+            'knowledge_embed': knowledge_embed,
+        }
+
+        return self.rnn.init_hidden(batch_size)
+
+    @torch.no_grad()
+    def sampling_decode(self, vocab: Dict[str, Vocab], example: LRLMExample,
+                        begin_symbol: int = 2, end_symbol: int = 5,
+                        initial_hidden: Optional[HiddenState] = None, warm_up: Optional[int] = None,
+                        max_length: int = 200, greedy: bool = False, topk: Optional[int] = None,
+                        print_info: bool = True, color_outputs: bool = False, show_rel_type: bool = True,
+                        sanity_check: bool = False, unkinfo: Optional[Tuple[Tensor, List[str]]] = None, **kwargs) \
+            -> SampledOutput:
+        r"""
+        Sampling for LRLM.
+
+        Output format:
+        - Red words:       Copied from canonical form of entity.
+        - Green words:     Copied from alias form of entity.
+        - Yellow words:    Warm-up context.
+        - word_[type]:     "word" is an entity of type "type".
+        - @-@:             A dash in the original text without spaces around, e.g. M @-@ 82 => M-82.
+
+        :param vocab: Vocabulary containing id2word mapping.
+        :param example: The :class:`Example` object of the current topic.
+        :param begin_symbol: Start of sentence symbol.
+        :param end_symbol: End of sentence symbol. Sampling stops when this symbol is generated.
+        :param initial_hidden: If not specified, default hidden states returned by :meth:`init_hidden` is used.
+        :param warm_up: Number of tokens to provide as context before performing sampling.
+        :param max_length: If generated sentence exceeds specified length, sampling is force terminated.
+        :param greedy: If ``True``, use greedy decoding instead of sampling.
+        :param topk: If not ``None``, only sample from indices with top-k probabilites.
+
+        :param print_info: If ``True``, print information about sampled result.
+        :param color_outputs: If ``True``, include annotations for each output token. Tokens from entities will be
+            colored red.
+        :param show_rel_type: If ``True``, show relation types for copied entities.
+
+        :param sanity_check: If ``True``, perform sanity check on generated sample.
+
+        :param unkinfo: Precomputed unkprobs and the index-to-vocabulary mapping.
+
+        :return: A tuple of (loss_value, formatted list of words).
+        """
+        if unkinfo is not None:
+            unkprob, unki2w = unkinfo
+            unkprob = unkprob[self._vocab_size:]
+            unki2w = unki2w[self._vocab_size:]
+            normalized_unkprob = F.log_softmax(unkprob, dim=0)
+
+        # noinspection PyPep8Naming
+        UNK, INVALID, CANONICAL_IDX, WORD_PREDICTOR, REL_PREDICTOR, EPS = -100, -1, 0, 0, 1, 1e-4
+
+        self.eval()
+        self.init_hidden(1, [example.relations])
+
+        word_vocab, rel_vocab = vocab['word'], vocab['rel']
+
+        tensor = functools.partial(sample_utils.tensor, device=self.device)
+        sample = functools.partial(sample_utils.sample, greedy=greedy, topk=topk)
+        np_sample = functools.partial(sample_utils.np_sample, greedy=greedy, topk=topk)
+
+        # noinspection PyShadowingNames
+        def compute_loss(inputs: List[int], spans: List[MatchedSpan],
+                         hidden: Optional[HiddenState] = None) -> Tuple[float, HiddenState]:
+            batch = SimpleNamespace(
+                sequence=tensor(inputs[:-1]),
+                target=tensor(inputs[1:]),
+                spans=[spans],
+                unkprob=None,
+                lengths=torch.tensor([len(inputs) - 1], device=self.device),
+                ntokens=len(inputs) - 1,
+            )
+            loss, next_hidden = self.calc_loss(batch, hidden=hidden)  # type: ignore
+            return loss.item(), next_hidden
+
+        if warm_up is None:
+            inputs = [begin_symbol]
+            rel_ids = [INVALID]
+            surface_indices = [INVALID]
+            spans: List[MatchedSpan] = []
+            total_log_prob = 0.0
+            marginal_log_prob = 0.0
+            hidden = initial_hidden
+        else:
+            inputs = list(word_vocab.numericalize(example.sentence[:warm_up]))
+            rel_ids = [INVALID] * len(inputs)  # assume everything is generated from vocabulary
+            surface_indices = [INVALID] * len(inputs)
+            spans = [span for span in example.spans if span.end < warm_up]
+            loss, hidden = compute_loss(inputs, spans, initial_hidden)
+            total_log_prob = -loss * (len(inputs) - 1)
+            marginal_log_prob = -loss * (len(inputs) - 1)
+
+        while len(inputs) < max_length and inputs[-1] != end_symbol:
+            computed_log_probs, new_hidden = self._compute_log_probs(tensor(inputs[-1]), hidden)
+            predictor, selector_loss = sample(computed_log_probs.selector)
+
+            if predictor == REL_PREDICTOR:
+                rel_id, rel_loss = sample(computed_log_probs.rel[0])
+
+                if self._alias_disamb is AliasDisamb.FastText:
+                    assert computed_log_probs.alias_logits is not None
+                    aliases = example.relations[rel_id].obj_alias
+                    alias_vecs = self.alias_vec[aliases]
+                    surface_log_prob = F.log_softmax(
+                        torch.mv(alias_vecs, computed_log_probs.alias_logits.flatten()), dim=0)
+                    surface_idx, alias_loss = sample(surface_log_prob)
+                    alias = self.alias_list[aliases[surface_idx]]
+                else:
+                    # can't tell which one under oracle, use the canonical (first) alias
+                    surface_idx = 0
+                    alias_loss = 0.0
+                    alias = example.relations[rel_id].obj_alias[0]  # type: ignore
+
+                # forward the hidden state according to the generated in-vocab tokens
+                raw_tokens: List[str] = alias.split()
+                token_ids: List[int] = word_vocab.numericalize(raw_tokens)
+                if len(raw_tokens) > 1:
+                    _, new_hidden = self._compute_log_probs(tensor(token_ids[:-1]), new_hidden)
+
+                # compute marginal probability for current span
+                span_inputs = tensor([inputs[-1]] + token_ids[:-1])
+                span_computed_log_probs, _ = self._compute_log_probs(span_inputs, hidden)
+                word_gen_loss = torch.sum(
+                    span_computed_log_probs.selector[0, :, WORD_PREDICTOR] +
+                    torch.gather(span_computed_log_probs.word, index=tensor(token_ids).unsqueeze(-1), dim=2).flatten()
+                ).item()
+                marginal_log_prob += torch.logsumexp(
+                    tensor([selector_loss + rel_loss + alias_loss, word_gen_loss]), dim=1
+                ).item()
+
+                spans.append(MatchedSpan(len(inputs) - 1, len(inputs) + len(token_ids) - 1,
+                                         example.relations[rel_id].rel_typ, rel_id, surface_idx))
+                inputs.extend(token_ids)
+                rel_ids.extend([rel_id] + [INVALID] * (len(token_ids) - 1))
+                surface_indices.extend([surface_idx] + [INVALID] * (len(token_ids) - 1))
+
+                total_log_prob += selector_loss + rel_loss + alias_loss
+            elif predictor == WORD_PREDICTOR:
+                word, word_loss = sample(computed_log_probs.word)
+                total_log_prob += selector_loss + word_loss
+                marginal_log_prob += selector_loss + word_loss
+
+                if word == 0 and unkinfo is not None:  # unk
+                    unk_idx, unk_loss = np_sample(normalized_unkprob)
+                    total_log_prob += unk_loss
+                    marginal_log_prob += unk_loss
+                    # Ugly multi-purpose use of variables.
+                    surface_indices.append(unk_idx)  # Record unk word index in surface_indices.
+                    rel_ids.append(UNK)  # Record UNK in rel_ids.
+                else:
+                    rel_ids.append(INVALID)
+                    surface_indices.append(INVALID)
+
+                inputs.append(word)
+            else:
+                raise ValueError
+
+            hidden = new_hidden
+
+        sample_loss = -total_log_prob / (len(inputs) - 1)
+        marginal_loss = -marginal_log_prob / (len(inputs) - 1)
+        if print_info:
+            print(f"Sample loss: {sample_loss:.3f}, PPL: {math.exp(sample_loss):.3f}")
+            print(f"Marginal sample loss: {marginal_loss:.3f}, PPL: {math.exp(marginal_loss):.3f}")
+        # Sanity checks
+        if sanity_check:
+            # noinspection PyTypeChecker
+            loss_val, gold_hidden = compute_loss(inputs, spans, initial_hidden)
+            assert hidden is not None
+            hidden_state_diff = max(torch.max(torch.abs(g - h)).item() for g, h in zip(gold_hidden, hidden))
+            if hidden_state_diff > EPS:
+                Logging.warn(f"Hidden states do not match. Difference: {hidden_state_diff}")
+            if abs(marginal_loss - loss_val) > EPS:
+                Logging.warn(f"Marginal loss values do not match. "
+                             f"Forward loss: {loss_val}, difference: {abs(marginal_loss - loss_val)}")
+
+        num_rels_generated = sum(int(rel_id != INVALID) for rel_id in rel_ids)
+        if print_info:
+            print(f"Relations [Generated / Annotated]: "
+                  f"[{num_rels_generated} / {len([s for s in example.spans if s.end < max_length])}]")
+
+        words = []
+        idx = 0
+        copy_count = 0
+        while idx < len(inputs):
+            is_warm_up = (warm_up is not None and idx < warm_up)
+            token_id, rel_id, surface_idx = inputs[idx], rel_ids[idx], surface_indices[idx]
+            if rel_id == INVALID:
+                token = word_vocab.i2w[token_id]
+                idx += 1
+            elif rel_id == UNK:
+                token = Logging.color('blue', unki2w[surface_idx])
+                idx += 1
+            else:
+                copy_count += 1
+                word_id = example.relations[rel_id].obj_alias[surface_idx]  # multiple words
+                token = self.alias_list[word_id]
+                idx += len(token.split())
+                if show_rel_type:
+                    token = f"{token}_[{rel_vocab.i2w[example.relations[rel_id].rel_typ]}]"
+                if color_outputs and not is_warm_up:
+                    token = Logging.color('red' if surface_idx == CANONICAL_IDX else 'green', token)
+            if color_outputs and is_warm_up:
+                token = Logging.color('yellow', token)
+            words.append(token)
+
+        if print_info:
+            print(f"# of copied entities: {copy_count}")
+
+        output = SampledOutput(sentence=words, sample_loss=sample_loss,
+                               complete_copies=copy_count, incomplete_copies=0)
+        return output
diff --git a/models/nklm.py b/models/nklm.py
new file mode 100644
index 0000000..d630b46
--- /dev/null
+++ b/models/nklm.py
@@ -0,0 +1,709 @@
+import functools
+import math
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Dict, List, NamedTuple, Optional, Tuple
+
+from mypy_extensions import TypedDict
+import torch
+from torch import nn
+from torch import LongTensor, Tensor
+from torch.nn import functional as F
+
+from arguments import AliasDisamb, LMArguments
+from dataset import BatchSequence, NKLMExample, Relation, Vocab
+from models import sample_utils, utils
+from models.base import BaseLM, HiddenState
+from models.sample_utils import SampledOutput
+from nnlib.utils import Logging, reverse_map
+from utils import loadpkl
+
+__all__ = [
+    'NKLM',
+]
+
+
+class NKLM(BaseLM):
+    class CacheDict(TypedDict):
+        num_facts: Tensor
+        fact_embeds: Tensor
+        knowledge_embed: Optional[Tensor]
+        alias_word_cnt: Optional[List[List[List[int]]]]
+        alias_vecs: Optional[List[Tensor]]
+        alias_masks: Optional[List[Tensor]]
+
+    _cache: CacheDict
+
+    def __init__(self, args: LMArguments, *, vocab_size: int, rel_vocab_size: int, max_unkrel: int):
+        self._word_embed_dim: int = args.embed_size
+        self._hidden_dim: int = args.hidden_size
+        self._rel_vocab_size = rel_vocab_size
+        self._position_embed_dim: int = args.pos_embed_dim
+        self._position_size: int = args.pos_embed_count
+        self._kb_embed_dim: int = args.kb_embed_dim
+        self._num_layers: int = args.num_layers
+        self._dropout: float = args.dropout
+        self._use_anchor_rels: bool = args.use_anchor
+        self._alias_disamb = args.alias_disamb_strategy
+
+        self._fact_embed_dim: int = self._kb_embed_dim * 2  # concat of TransE embeds for relation & object
+
+        input_dim = self._fact_embed_dim + self._word_embed_dim + self._position_embed_dim
+        pred_input_dim = self._hidden_dim + self._fact_embed_dim
+        super().__init__(args, vocab_size, input_dim=input_dim, pred_input_dim=pred_input_dim,
+                         embed_dim=self._word_embed_dim)
+
+        self._fact_sel_strategy = args.fact_sel_strategy
+        self._mask_invalid_pos = args.mask_invalid_pos
+        self._use_knowledge_embed = args.use_knowledge_embed
+
+        def _(a, b):
+            return a if a != -1 else b
+
+        # All MLP hidden dim specs are copied from the original code
+        # Sec 3.2.2 Fact Extraction
+        self.fact_key_mlp = utils.MLP(self._hidden_dim + (self._fact_embed_dim if self._use_knowledge_embed else 0),
+                                      _(args.fact_key_mlp_hidden_dim, self._fact_embed_dim * 2),
+                                      self._fact_embed_dim, dropout=self._dropout)
+        # Sec 3.2.3 Selecting Word Generation Source
+        self.copy_predictor = utils.MLP(self._pred_input_dim,
+                                        _(args.copy_mlp_hidden_dim, self._hidden_dim),
+                                        1, dropout=self._dropout)
+        self.pos_predictor = utils.MLP(self._pred_input_dim,
+                                       _(args.pos_mlp_hidden_dim, self._position_embed_dim * 2),
+                                       self._position_size, dropout=self._dropout)
+
+        if args.alias_disamb_strategy is AliasDisamb.FastText:
+            def _alias_path(name):
+                path = Path(args.fasttext_model_path)
+                return path.parent / (path.name + f'.{name}')
+
+            self.alias_vec = torch.load(_alias_path('alias_vectors.pt')).to(self.device)
+            if args.normalize_fasttext_embeds:
+                self.alias_vec = self.alias_vec / torch.norm(self.alias_vec, dim=1).unsqueeze(0)
+            self.alias_list: List[str] = reverse_map(loadpkl(_alias_path('alias_dict.pkl')))
+            self._alias_vec_dim = self.alias_vec.size(1)
+            self.hid_to_alias = nn.Linear(self._pred_input_dim, self._alias_vec_dim)
+
+        # Embeddings
+        self.position_embed = nn.Embedding(self._position_size, self._position_embed_dim)
+
+        # KB related
+        self.entity_vec, self.relation_vec = utils.load_kb_embed(args.path, self.device)
+        self.naf_vec = nn.Parameter(torch.Tensor(self._fact_embed_dim))  # -1
+        # (added 1) -2: anchor, -3: topic_itself, < -4: UNKs
+        self.special_rel_vecs = nn.ParameterList([nn.Parameter(torch.Tensor(self._kb_embed_dim))
+                                                  for _ in range(max_unkrel + 2)])
+        self.unk_entity_vec = nn.Parameter(torch.Tensor(self._kb_embed_dim))  # -1
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for param in [self.naf_vec, self.unk_entity_vec] + list(self.special_rel_vecs.parameters()):
+            param.data.normal_(0.0, 0.02)
+
+    @staticmethod
+    def _pick_embed(embedding: nn.Embedding, indices: LongTensor, mask: torch.ByteTensor):
+        indices = indices.clone()
+        indices[~mask] = 0
+        embed = embedding.forward(indices)
+        embed *= mask.to(dtype=torch.float).unsqueeze(-1).expand_as(embed)
+        return embed
+
+    def _get_fact_embeds(self, rel_ids: LongTensor) -> Tensor:
+        fact_embeds = self._cache['fact_embeds']
+        gold_fact_embed = torch.gather(fact_embeds, dim=1,
+                                       index=(rel_ids + 1).unsqueeze(-1).expand(*rel_ids.size(), fact_embeds.size(-1)))
+        return gold_fact_embed
+
+    def _compute_fact_log_probs(self, inputs: LongTensor, rel_ids: LongTensor, copy_pos: LongTensor,
+                                hidden: Optional[HiddenState]) \
+            -> Tuple[List[Tensor], Tensor, Tensor, HiddenState]:
+        """
+        Compute the log probability of selecting each fact.
+
+        .. note::
+            Different to the :meth:`forward` method, the final element should be removed from tensors ``rel_ids`` and
+            ``copy_pos``. As a result, both tensors should have the same size as ``inputs``.
+
+        :return: A tuple of 4 elements:
+            - fact_log_probs  : list(batch_size) of (seq_len, num_facts + 1), log probabilities of selecting each fact.
+            - output          : (batch_size, seq_len, hidden_dim), output of LSTM/Transformer.
+            - gold_fact_embed : Embeddings of the gold facts selected at each time step. Can be reused later.
+            - next_hidden     : The next hidden state.
+        """
+        batch_size, seq_len = inputs.size()
+
+        """ Fact Embeddings """
+        # Fact embeddings are calculated in `init_hidden`
+        fact_embeds = self._cache['fact_embeds']
+
+        """ Input """
+        # word_embed: (batch_size, seq_len, word_embed_dim)
+        # pos_embed: (batch_size, seq_len, pos_embed_dim)
+        word_embed: Tensor = self._pick_embed(self.word_embed, inputs, mask=(rel_ids == -1))
+        pos_embed: Tensor = self._pick_embed(self.position_embed, copy_pos, mask=(copy_pos != -1))
+        # gold_fact_embed: (batch_size, seq_len, fact_embed_dim)
+        gold_fact_embed: Tensor = self._get_fact_embeds(rel_ids)
+        # input_embed: (batch_size, seq_len, input_dim)
+        input_embed = torch.cat([gold_fact_embed, word_embed, pos_embed], dim=2)
+
+        """ LSTM """
+        # output: (batch_size, seq_len, hidden_dim)
+        # next_hidden: tuple[2] of (num_layers, batch_size, hidden_dim)
+        output, next_hidden = self.rnn.forward(input_embed, hidden)
+
+        """ Fact Extraction """
+        if self._use_knowledge_embed:
+            knowledge_embed = self._cache['knowledge_embed']
+            assert knowledge_embed is not None
+            # predictor_input: (batch_size, seq_len, hidden_dim + fact_embed_dim)
+            predictor_input = torch.cat([
+                knowledge_embed.unsqueeze(1).expand(batch_size, seq_len, -1), output
+            ], dim=2)
+        else:
+            # predictor_input: (batch_size, seq_len, hidden_dim)
+            predictor_input = output
+        # fact_queries: (batch_size, seq_len, fact_embed_dim)
+        fact_queries = self.fact_key_mlp.forward(predictor_input)
+        # Calculate log-prob per batch example, because there could be different number of facts
+        fact_log_probs = []
+        num_facts = self._cache['num_facts']
+        all_fact_logits = torch.bmm(fact_queries, fact_embeds.transpose(1, 2))
+        for batch in range(batch_size):
+            # cur_fact_log_probs: (seq_len, num_facts + 1)
+            cur_fact_log_probs = F.log_softmax(all_fact_logits[batch, :, :(num_facts[batch] + 1)], dim=1)
+            fact_log_probs.append(cur_fact_log_probs)
+
+        return fact_log_probs, output, gold_fact_embed, next_hidden
+
+    def _compute_generate_log_probs(self, lstm_output: Tensor, next_fact_embed: Tensor, rel_ids: LongTensor,
+                                    target: Optional[LongTensor] = None) \
+            -> Tuple[Tensor, Optional[List[Tensor]], Tensor, Tensor]:
+        """
+        Compute the log probability of generating each token.
+
+        :return: A tuple of 4 elements, each representing different log-probabilities:
+            - copy_indicator  : (batch_size, seq_len)
+            - alias_log_probs : list(batch_size) of (seq_len, max_facts + 1), None when alias disamb. is not FastText.
+            - pos_log_probs   : (batch_size, seq_len, position_size)
+            - vocab_log_probs : (batch_size, seq_len, vocab_size)
+          Or, if `target` is specified:
+            - vocab_loss      : (batch_size, seq_len)
+        """
+        # queries_input: (batch_size, seq_len, hidden_dim + fact_embed_dim)
+        queries_input = torch.cat([lstm_output, next_fact_embed], dim=2)
+
+        # copy_indicator: (batch_size, seq_len)
+        copy_indicator = torch.sigmoid(self.copy_predictor.forward(queries_input).squeeze(-1))
+
+        alias_log_probs = None
+        if self._alias_disamb is AliasDisamb.FastText:
+            batch_size, seq_len, _ = lstm_output.size()
+            alias_vecs = self._cache['alias_vecs']
+            alias_masks = self._cache['alias_masks']
+            assert alias_vecs is not None and alias_masks is not None
+            # alias_input: (batch_size, seq_len, alias_vec_dim)
+            alias_input = self.hid_to_alias.forward(queries_input)
+            # alias_log_probs: list(batch_size) of (seq_len, max_facts + 1)
+            # note that `alias_log_probs` will be masked where `rel_ids == -1`, so we can fill in garbage values
+            alias_log_probs = []
+            for b in range(batch_size):
+                num_aliases = alias_masks[b].size(1)
+                # cur_rel_ids: (seq_len)
+                cur_rel_ids = rel_ids[b, 1:]
+                # nonzero_*: (valid_seq_len)
+                valid_indices = (cur_rel_ids != -1).nonzero()
+                if valid_indices.size(0) == 0:
+                    alias_log_probs.append(torch.empty(seq_len, num_aliases, device=self.device))
+                    continue
+                else:
+                    valid_indices = valid_indices.squeeze(1)
+                valid_rel_ids = cur_rel_ids[valid_indices]
+                # cur_alias_vecs: (valid_seq_len, max_aliases + 1, alias_vec_dim)
+                cur_alias_vecs = torch.index_select(alias_vecs[b], dim=0, index=valid_rel_ids)
+                # cur_alias_logits: (valid_seq_len, max_aliases + 1)
+                cur_alias_logits = torch.bmm(cur_alias_vecs, alias_input[b, valid_indices].unsqueeze(2)).squeeze(2)
+                # valid_alias_log_probs: (valid_seq_len, max_aliases + 1)
+                valid_alias_log_probs = F.log_softmax(cur_alias_logits + alias_masks[b][valid_rel_ids], dim=1)
+                # cur_alias_log_probs: (valid_seq_len, max_aliases + 1)
+                cur_alias_log_probs = torch.empty(seq_len, num_aliases, device=self.device)
+                cur_alias_log_probs.index_copy_(dim=0, index=valid_indices, source=valid_alias_log_probs)
+                alias_log_probs.append(cur_alias_log_probs)
+
+        # pos_log_probs: (batch_size, seq_len, position_size)
+        pos_log_probs = F.log_softmax(self.pos_predictor.forward(queries_input), dim=2)
+        if target is not None:
+            # vocab_loss: (batch_size, seq_len)
+            vocab_loss = self.word_predictor.forward(queries_input, target)
+            return copy_indicator, alias_log_probs, pos_log_probs, vocab_loss
+        else:
+            # vocab_log_probs: (batch_size, seq_len, vocab_size)
+            vocab_log_probs = self.word_predictor.log_probs(queries_input)
+            return copy_indicator, alias_log_probs, pos_log_probs, vocab_log_probs
+
+    class ComputedLogProbs(NamedTuple):
+        fact: List[Tensor]
+        copy_indicator: Tensor
+        alias: Optional[List[Tensor]]
+        vocab: Tensor
+        pos: Tensor
+
+    def _compute_log_probs(self, inputs: LongTensor, rel_ids: LongTensor, copy_pos: LongTensor,
+                           hidden: Optional[HiddenState], target: Optional[LongTensor] = None,
+                           use_argmax_facts=False) -> Tuple[ComputedLogProbs, HiddenState]:
+        """
+        Compute the log probability for each loss component.
+        """
+
+        fact_log_probs, output, gold_fact_embed, next_hidden = \
+            self._compute_fact_log_probs(inputs, rel_ids[:, :-1], copy_pos[:, :-1], hidden)
+
+        # next_fact_embed: (batch_size, seq_len, fact_embed_dim)
+        if use_argmax_facts:
+            argmax_facts = torch.tensor([torch.argmax(log_probs, dim=1) - 1 for log_probs in fact_log_probs],
+                                        device=self.device)
+            next_fact_embed = self._get_fact_embeds(argmax_facts)
+        else:
+            # final_gold_fact_embed: (batch_size, fact_embed_dim)
+            final_gold_fact_embed: Tensor = self._get_fact_embeds(rel_ids[:, -1:])
+            next_fact_embed = torch.cat([gold_fact_embed[:, 1:], final_gold_fact_embed], dim=1)
+
+        copy_indicator, alias_log_probs, pos_log_probs, vocab_log_probs = \
+            self._compute_generate_log_probs(output, next_fact_embed, rel_ids, target)
+
+        computed_log_probs = self.ComputedLogProbs(
+            fact=fact_log_probs, copy_indicator=copy_indicator, alias=alias_log_probs,
+            vocab=vocab_log_probs, pos=pos_log_probs)
+        return computed_log_probs, next_hidden
+
+    def forward(self,  # type: ignore
+                inputs: LongTensor, targets: LongTensor, masks: Tensor,
+                rel_ids: LongTensor, copy_pos: LongTensor, surface_indices: LongTensor,
+                hidden: Optional[HiddenState],
+                pos_mask: Optional[Tensor] = None,
+                unk_probs: Optional[Tensor] = None) -> Tuple[Tensor, HiddenState]:
+        """
+        :param inputs: (batch_size, seq_len), includes </s>.
+        :param targets: (batch_size, seq_len), includes </s>.
+        :param masks: (batch_size, seq_len), sequence masks.
+
+        :param rel_ids: (batch_size, seq_len + 1), relation index (of `rels` list) at each time step. -1 for NaF.
+        :param copy_pos: (batch_size, seq_len + 1), copy position at each time step, -1 for NaF.
+        :param surface_indices: (batch_size, seq_len + 1), index of surface form for each target token.
+
+        :param hidden: Initial hidden state.
+
+        :param pos_mask: (batch_size, seq_len, position_size), used for masking invalid copy positions.
+
+        :param unk_probs: (batch_size, seq_len), UNK-probs for each target token (to be added to prob of choosing UNK).
+
+        :return (per-batch loss, new hidden state, log-probs for each element)
+        """
+        batch_size, seq_len = inputs.size()
+
+        computed_log_probs, state = self._compute_log_probs(
+            inputs, rel_ids, copy_pos, hidden, target=targets,
+            use_argmax_facts=(not self.training and self._fact_sel_strategy == 'argmax'))
+
+        # fact_loss: (batch_size, seq_len)
+        fact_loss = torch.stack([
+            F.nll_loss(log_probs, rel_ids[batch, 1:] + 1, reduction='none')  # add one to rel_ids
+            for batch, log_probs in enumerate(computed_log_probs.fact)
+        ])
+
+        # indicator_loss: (batch_size, seq_len), 1 for copy, 0 for not
+        indicator_loss = F.binary_cross_entropy(
+            computed_log_probs.copy_indicator,
+            (rel_ids[:, 1:] != -1).to(dtype=torch.float), reduction='none')
+
+        surface_loss = 0
+        if self._alias_disamb is AliasDisamb.FastText:
+            assert computed_log_probs.alias is not None
+            surface_loss = torch.stack([
+                F.nll_loss(log_probs, surface_indices[b, 1:], ignore_index=-1, reduction='none')
+                for b, log_probs in enumerate(computed_log_probs.alias)
+            ])
+
+        vocab_loss = computed_log_probs.vocab
+        if unk_probs is not None:
+            vocab_loss = vocab_loss - unk_probs
+
+        pos_log_probs = computed_log_probs.pos
+        if self._mask_invalid_pos:
+            assert pos_mask is not None
+            # re-normalize the log-probs
+            pos_log_probs = pos_log_probs * pos_mask + (-math.inf) * (1.0 - pos_mask)
+            pos_log_probs_sum = torch.logsumexp(pos_log_probs, dim=2, keepdim=True)
+            # we mask `pos_log_probs` again, because there could be invalid copies in data
+            # (e.g. WikiFacts has no alias info while mentions are annotated for aliases)
+            pos_log_probs = (pos_log_probs - pos_log_probs_sum) * pos_mask
+        # pos_loss: (batch_size, seq_len)
+        pos_loss = F.nll_loss(
+            pos_log_probs.view(batch_size * seq_len, -1),
+            copy_pos[:, 1:].contiguous().view(-1), ignore_index=-1, reduction='none'
+        ).view(batch_size, seq_len)
+
+        """ Final Loss """
+        # *loss: (batch_size, seq_len)
+        generate_loss = torch.where(rel_ids[:, 1:] == -1, vocab_loss,
+                                    pos_loss + surface_loss)  # surface_loss only added for relations
+        if not self.training and self._fact_sel_strategy == 'argmax':
+            total_loss = generate_loss
+        else:
+            total_loss = fact_loss + indicator_loss + generate_loss
+        total_loss = total_loss * masks
+        loss = torch.sum(total_loss, dim=1)
+
+        return loss, state
+
+    def calc_loss(self, batch: BatchSequence, hidden: Optional[HiddenState] = None,
+                  use_unk_probs=False, dump_probs=False) -> Tuple[Tensor, HiddenState]:
+        sequence, targets = batch.sequence, batch.target
+
+        masks = (targets != 1).to(dtype=torch.float)  # pad symbol is 1
+
+        rel_ids, copy_pos, surface_indices = \
+            [batch.seqs[name] for name in ['rel_ids', 'copy_pos', 'surface_indices']]
+
+        pos_mask = None
+        if self._mask_invalid_pos:
+            alias_word_cnt = self._cache['alias_word_cnt']
+            assert alias_word_cnt is not None
+            pos_mask = torch.zeros(*targets.size(), self._position_size, dtype=torch.float, device=self.device)
+            batch_size = targets.size(0)
+            for b in range(batch_size):
+                for idx, (rel_id, surface_idx) in enumerate(zip(rel_ids[b, 1:], surface_indices[b, 1:])):
+                    if rel_id != -1:
+                        pos_mask[b, idx, :alias_word_cnt[b][rel_id][surface_idx]] = 1.0
+
+        assert (sequence.shape == targets.shape and rel_ids.shape == copy_pos.shape and
+                sequence.shape[0] == rel_ids.shape[0] and sequence.shape[1] + 1 == rel_ids.shape[1])
+
+        # def if_then(condition: torch.ByteTensor, statement: torch.ByteTensor) -> bool:
+        #     return (~condition | statement).all().item()  # P→Q => ¬P ∨ Q
+        #
+        # assert if_then(rel_ids == -1, copy_pos == -1) and if_then(copy_pos != -1, rel_ids != -1)
+
+        assert torch.sum(masks) == batch.ntokens
+
+        batch_loss, next_state = self.forward(sequence, targets, masks,
+                                              rel_ids, copy_pos, surface_indices,
+                                              hidden,
+                                              pos_mask=pos_mask,
+                                              unk_probs=(None if not use_unk_probs else batch.unk_probs))
+        # Returns the per-token loss
+        loss = torch.sum(batch_loss) / batch.ntokens
+
+        if dump_probs:
+            log_probs = [-prob / length if length > 0 else 0.0
+                         for prob, length in zip(batch_loss.tolist(), batch.lengths.tolist())]
+            self.model_cache.update(log_probs=log_probs)
+
+        return loss, next_state
+
+    def init_hidden(self, batch_size: int, init_batch: List[List[Relation]]) -> HiddenState:
+        """
+        Initialize stuff shared within whole batch, and return initial hidden states.
+
+        topics: (batch_size), topic ID of each example.
+        batch_rels: facts for each topic. Each a list of tuples (rel_id, obj_id, alias).
+            - rel_id: {-1: NaF, -2: anchor, -3: topic_itself}
+            - obj_id: -1: NaF / UNK
+            - obj_alias: list of aliases for the relation.
+        """
+        batch_rels = init_batch
+
+        """ Fact Embeddings """
+        max_facts = max([len(r) for r in batch_rels])
+        num_facts = torch.tensor([len(r) for r in batch_rels])
+
+        # insert NaF as the first vector, and add 1 to all rel_ids
+        # this is because we need to compute the CE loss including NaF, and -1 can't be used as target
+        # fact_embeds: (batch_size, max_facts + 1, fact_embed_dim)
+        fact_embeds = torch.stack([
+            torch.cat([torch.cat([
+                torch.stack([self.naf_vec[:self._kb_embed_dim]] + [
+                    self.relation_vec[rel.rel_typ] if rel.rel_typ >= 0 else self.special_rel_vecs[rel.rel_typ + 1]
+                    for rel in relations
+                ]),
+                torch.stack([self.naf_vec[self._kb_embed_dim:]] + [
+                    self.entity_vec[rel.obj_id] if rel.obj_id != -1 else self.unk_entity_vec
+                    for rel in relations
+                ])  # batches with fewer relations are padded to enable vectorized operations
+            ], dim=1), torch.zeros(max_facts + 1 - num_facts[idx], self._fact_embed_dim, device=self.device)])
+            for idx, relations in enumerate(batch_rels)
+        ])
+
+        knowledge_embed = None
+        if self._use_knowledge_embed:
+            # knowledge_embed: (batch_size, fact_embed_dim)
+            knowledge_embed = torch.stack([torch.mean(e, dim=0) for e in fact_embeds])
+
+        alias_word_cnt = None
+        if self._mask_invalid_pos:
+            # alias_word_cnt: list(batch_size) of list(num_facts) of n_alias
+            alias_word_cnt = [[
+                [len(self.alias_list[alias].split()) for alias in rel.obj_alias]
+                for rel in relations
+            ] for relations in batch_rels]
+
+        alias_vecs = alias_masks = None
+        if self._alias_disamb is AliasDisamb.FastText:
+            max_aliases = [max(len(rel.obj_alias) for rel in relations) for relations in batch_rels]
+            # alias_vecs: list(batch_size) of (max_facts + 1, max_aliases, alias_vec_dim)
+            # alias_masks: list(batch_size) of (max_facts + 1, max_aliases)
+            # the masks set invalid positions to -inf, used during log_softmax
+            alias_vecs = []
+            alias_masks = []
+            for b, relations in enumerate(batch_rels):
+                aliases = torch.zeros(len(relations), max_aliases[b] + 1, device=self.device, dtype=torch.long)
+                mask = torch.full((len(relations), max_aliases[b] + 1), -math.inf, device=self.device)
+                for idx, rel in enumerate(relations):
+                    aliases[idx, :len(rel.obj_alias)] = torch.tensor(rel.obj_alias, device=self.device)
+                    mask[idx, :len(rel.obj_alias)] = 0
+                vectors = F.embedding(aliases, self.alias_vec)
+                alias_vecs.append(vectors)
+                alias_masks.append(mask)
+
+        self._cache = {
+            'num_facts': num_facts,
+            'fact_embeds': fact_embeds,
+            'knowledge_embed': knowledge_embed,
+            'alias_word_cnt': alias_word_cnt,
+            'alias_vecs': alias_vecs,
+            'alias_masks': alias_masks,
+        }
+
+        return self.rnn.init_hidden(batch_size)
+
+    @torch.no_grad()
+    def sampling_decode(self, vocab: Dict[str, Vocab], example: NKLMExample,
+                        begin_symbol: int = 2, end_symbol: int = 5,
+                        initial_hidden: Optional[HiddenState] = None, warm_up: Optional[int] = None,
+                        max_length: int = 200, greedy=False, topk=None,
+                        fill_incomplete=False, allow_invalid_pos=False,
+                        print_info=True, color_outputs=False, color_incomplete=True,
+                        show_ellipses=True, show_rel_type=True, show_copy_pos=False,
+                        sanity_check=False, unkinfo: Optional[Tuple[Tensor, List[str]]] = None, **kwargs) \
+            -> SampledOutput:
+        """
+        Sampling for NKLM.
+
+        Output format:
+        - Red words:       Copied from canonical form of entity.
+        - Green words:     Copied from alias form of entity.
+        - Yellow words:    Warm-up context.
+        - word_[type]:     "word" is an entity of type "type".
+        - word...(a_b_c):  "word" is a partially copied entity with remaining suffix "a b c".
+        - (a_b_c)...word:  "word" is a partially copied entity with remaining prefix "a b c".
+        - @-@:             A dash in the original text without spaces around, e.g. M @-@ 82 => M-82.
+        - <X>:             A token copied from an invalid position of an entity.
+
+        :param vocab: Vocabulary containing id2word mapping.
+        :param example: The :class:`Example` object of the current topic.
+        :param begin_symbol: Start of sentence symbol.
+        :param end_symbol: End of sentence symbol. Sampling stops when this symbol is generated.
+        :param initial_hidden: If not specified, default hidden states returned by :meth:`init_hidden` is used.
+        :param warm_up: Number of tokens to provide as context before performing sampling.
+        :param max_length: If generated sentence exceeds specified length, sampling is force terminated.
+        :param greedy: If ``True``, use greedy decoding instead of sampling.
+        :param topk: If not ``None``, only sample from indices with top-k probabilites.
+        :param fill_incomplete: If ``True``, entities that are partially copied will be completed.
+        :param allow_invalid_pos: If ``True``, allowing copying from invalid positions, and use <unk> as input.
+
+        :param print_info: If ``True``, print information about sampled result.
+        :param color_outputs: If ``True``, include annotations for each output token. Tokens from entities will be
+            colored red.
+        :param color_incomplete: If ``True`` and ``color_outputs`` is ``True``, also color partially copied entities.
+        :param show_ellipses: If ``True``, show ellipses at beginning or end of partially copied entities.
+        :param show_rel_type: If ``True``, show relation types for copied entities.
+        :param show_copy_pos: If ``True``, show the position from which the entity tokens are copied.
+
+        :param sanity_check: If ``True``, perform sanity check on generated sample.
+
+        :return: A tuple of (loss_value, formatted list of words).
+        """
+
+        if unkinfo is not None:
+            unkprob, unki2w = unkinfo
+            unkprob = unkprob[self._vocab_size:]
+            unki2w = unki2w[self._vocab_size:]
+            normalized_unkprob = F.log_softmax(unkprob, dim=0)
+
+        # noinspection PyPep8Naming
+        UNK, INVALID, UNK_TOKEN, CANONICAL_IDX, EPS = -100, -1, 0, 0, 1e-4
+
+        self.eval()
+        self.init_hidden(1, [example.relations])
+
+        word_vocab, rel_vocab = vocab['word'], vocab['rel']
+
+        tensor = functools.partial(sample_utils.tensor, device=self.device)
+        randint = sample_utils.randint
+        sample = functools.partial(sample_utils.sample, greedy=greedy, topk=topk)
+        np_sample = functools.partial(sample_utils.np_sample, greedy=greedy, topk=topk)
+
+        # noinspection PyShadowingNames
+        def compute_loss(inputs: List[int], rel_ids: List[int], copy_pos: List[int], surface_indices: List[int],
+                         hidden: Optional[HiddenState] = None) -> Tuple[float, HiddenState]:
+            batch = SimpleNamespace(
+                sequence=tensor(inputs[:-1]),
+                target=tensor(inputs[1:]),
+                unkprob=None,
+                seqs={'rel_ids': tensor(rel_ids),
+                      'copy_pos': tensor(copy_pos),
+                      'surface_indices': tensor(surface_indices)},
+                ntokens=len(inputs) - 1,
+            )
+            loss, next_hidden = self.calc_loss(batch, hidden=hidden)  # type: ignore
+            return loss.item(), next_hidden
+
+        # Initialization
+        if warm_up is None:
+            inputs = [begin_symbol]
+            rel_ids = [INVALID]
+            copy_pos = [INVALID]
+            surface_indices = [INVALID]
+            total_log_prob = 0.0
+            hidden = initial_hidden
+        else:
+            inputs = list(word_vocab.numericalize(example.sentence[:warm_up]))
+            rel_ids = list(example.rel_ids[:warm_up])
+            copy_pos = list(example.copy_pos[:warm_up])
+            surface_indices = list(example.surface_indices[:warm_up])
+            total_log_prob, hidden = compute_loss(inputs, rel_ids, copy_pos, surface_indices, initial_hidden)
+            total_log_prob = -total_log_prob * (len(inputs) - 1)
+
+        # Sampling procedure
+        while len(inputs) < max_length and inputs[-1] != end_symbol:
+            fact_log_probs, output, _, next_hidden = \
+                self._compute_fact_log_probs(tensor(inputs[-1]), tensor(rel_ids[-1]), tensor(copy_pos[-1]), hidden)
+            rel_id, fact_loss = sample(fact_log_probs[0])
+            rel_id -= 1
+            total_log_prob += fact_loss
+
+            # next_fact_embed: (1, 1, fact_embed_dim)
+            next_fact_embed = self._get_fact_embeds(tensor(rel_id))
+            copy_indicator, alias_log_probs, pos_log_probs, vocab_log_probs = \
+                self._compute_generate_log_probs(output, next_fact_embed, tensor([rel_ids[-1], rel_id]))
+            if torch.bernoulli(copy_indicator).item():
+                total_log_prob += torch.log(copy_indicator).item()
+                assert rel_id != -1
+                # copy entity
+                aliases = example.relations[rel_id].obj_alias
+                if self._alias_disamb is AliasDisamb.FastText:
+                    assert alias_log_probs is not None
+                    surface_idx, surface_loss = sample(alias_log_probs[0])
+                else:
+                    surface_idx, surface_loss = 0, 0.0
+                alias = self.alias_list[aliases[surface_idx]]
+
+                entity: List[str] = alias.split()
+                # normalization not required
+                # TODO: keep consistent with _mask_invalid_pos setting
+                pos, pos_loss = sample(pos_log_probs if allow_invalid_pos else pos_log_probs.squeeze()[:len(entity)])
+                if self._mask_invalid_pos:
+                    pos_loss -= torch.logsumexp(pos_log_probs.squeeze()[:len(entity)], dim=0).item()
+                total_log_prob += surface_loss + pos_loss
+                token = UNK_TOKEN if pos >= len(entity) else word_vocab.w2i.get(entity[pos], UNK_TOKEN)
+            else:
+                total_log_prob += torch.log(1.0 - copy_indicator).item()
+                assert rel_id == -1
+                # generate word
+                token, token_loss = sample(vocab_log_probs)
+                total_log_prob += token_loss
+                pos = INVALID
+                surface_idx = INVALID
+
+                if token == 0 and unkinfo is not None:  # unk
+                    unk_idx, unk_loss = np_sample(normalized_unkprob)
+                    total_log_prob += unk_loss
+                    # Ugly multi-purpose use of variables.
+                    surface_idx = unk_idx  # Record unk word index in surface_indices.
+                    rel_id = UNK  # Record UNK in rel_ids.
+
+            inputs.append(token)
+            rel_ids.append(rel_id)
+            copy_pos.append(pos)
+            surface_indices.append(surface_idx)
+            hidden = next_hidden
+
+        sample_loss = -total_log_prob / (len(inputs) - 1)
+        if print_info:
+            print(f"Sample loss: {sample_loss:.3f}, PPL: {math.exp(sample_loss):.3f}")
+        # Sanity checks
+        if sanity_check:
+            loss_val, gold_hidden = compute_loss(inputs, rel_ids, copy_pos, surface_indices, initial_hidden)
+            assert hidden is not None
+            hidden_state_diff = max(torch.max(torch.abs(g - h)).item() for g, h in zip(gold_hidden, hidden))
+            if hidden_state_diff > EPS:
+                Logging.warn(f"Hidden states do not match. Difference: {hidden_state_diff}")
+            if abs(sample_loss - loss_val) > EPS:
+                Logging.warn(f"Loss values do not match. "
+                             f"Forward loss: {loss_val}, difference: {abs(sample_loss - loss_val)}")
+
+        # Format the output
+        sentence = list(zip(inputs, rel_ids, copy_pos, surface_indices))
+        words = []
+        copy_count = 0
+        complete_count = 0
+        last_entity = None
+        entity_continuing = False
+        for idx, (token, rel_id, pos, surface_idx) in enumerate(sentence):
+            is_warm_up = (warm_up is not None and idx < warm_up)
+            if rel_id == INVALID:
+                word = word_vocab.i2w[token]
+            elif rel_id == UNK:
+                word = Logging.color('blue', unki2w[surface_idx])
+            else:
+                copy_count += 1
+                entity_id = example.relations[rel_id].obj_alias[surface_idx]
+                entity = self.alias_list[entity_id].split()
+                if pos >= len(entity):
+                    word = "<X>"
+                else:
+                    word = entity[pos]
+                if show_copy_pos:
+                    word = f"{pos}_{rel_id}_{surface_idx}_{word}"
+                is_last_word_in_entity = (idx == len(sentence) - 1 or
+                                          sentence[idx + 1][1:] != (rel_id, pos + 1, surface_idx))
+                is_first_word_in_entity = (idx == 0 or sentence[idx - 1][1:] != (rel_id, pos - 1, surface_idx))
+                # add entity tag after the last word
+                if show_rel_type and is_last_word_in_entity:
+                    word = f"{word}_[{rel_vocab.i2w[example.relations[rel_id].rel_typ]}]"
+
+                # check whether fully copied
+                if show_ellipses:
+                    if pos < len(entity) - 1 and is_last_word_in_entity:
+                        word = word + '...' + (f"({'_'.join(entity[(pos + 1):])})" if fill_incomplete else "")
+                    if pos > 0 and is_first_word_in_entity:
+                        word = (f"({'_'.join(entity[:pos])})" if fill_incomplete else "") + '...' + word
+
+                if entity_continuing:
+                    if last_entity == (rel_id, surface_idx, pos - 1):  # Continuing
+                        last_entity = (rel_id, surface_idx, pos)
+                    else:
+                        entity_continuing = False
+                        last_entity = None
+
+                if pos == 0 and not entity_continuing:  # reset
+                    entity_continuing = True
+                    last_entity = (rel_id, surface_idx, 0)
+
+                if color_outputs and not is_warm_up and (color_incomplete or entity_continuing):
+                    word = Logging.color('red' if surface_idx == 0 else 'green', word)
+
+                if pos == len(entity) - 1 and entity_continuing:  # commit
+                    entity_continuing = False
+                    complete_count += 1
+
+            if color_outputs and is_warm_up:
+                word = Logging.color('yellow', word)
+            words.append(word)
+
+        if print_info:
+            print(f"Copied, Completed: {copy_count}, {complete_count}")
+        sampled_output = SampledOutput(sentence=words, sample_loss=sample_loss,
+                                       complete_copies=complete_count, incomplete_copies=copy_count)
+        return sampled_output
diff --git a/models/rnns/__init__.py b/models/rnns/__init__.py
new file mode 100644
index 0000000..bb70e62
--- /dev/null
+++ b/models/rnns/__init__.py
@@ -0,0 +1,3 @@
+from models.rnns.base import *
+from models.rnns.lstm import *
+from models.rnns.transformer import *
diff --git a/models/rnns/base.py b/models/rnns/base.py
new file mode 100644
index 0000000..5906736
--- /dev/null
+++ b/models/rnns/base.py
@@ -0,0 +1,22 @@
+from abc import ABC
+from typing import Generic, TypeVar
+
+from torch import nn
+
+from arguments import RNNDropoutPos
+
+__all__ = [
+    'BaseRNN',
+]
+
+HiddenState = TypeVar('HiddenState')
+
+
+class BaseRNN(nn.Module, Generic[HiddenState], ABC):
+    def __init__(self, dropout: float, dropout_pos: RNNDropoutPos):
+        super().__init__()
+        self.dropout = nn.Dropout(dropout)
+        self.dropout_pos = dropout_pos
+
+    def init_hidden(self, batch_size: int) -> HiddenState:
+        raise NotImplementedError
diff --git a/models/rnns/lstm.py b/models/rnns/lstm.py
new file mode 100644
index 0000000..0681725
--- /dev/null
+++ b/models/rnns/lstm.py
@@ -0,0 +1,37 @@
+from typing import Optional, Tuple
+
+from torch import nn
+from torch import Tensor
+
+from arguments import RNNDropoutPos
+from models.rnns.base import BaseRNN
+
+__all__ = [
+    'LSTM',
+    'LSTMState',
+]
+
+LSTMState = Tuple[Tensor, Tensor]
+
+
+class LSTM(BaseRNN[LSTMState]):
+    def __init__(self, embed_dim: int, hidden_dim: int, num_layers: int,
+                 *, dropout: float, dropout_pos: RNNDropoutPos):
+        super().__init__(dropout, dropout_pos)
+        self.lstm = nn.LSTM(embed_dim, hidden_dim, num_layers, dropout=dropout, batch_first=True)
+
+    def forward(self,  # type: ignore
+                input: Tensor, hidden: Optional[LSTMState] = None) -> Tuple[Tensor, LSTMState]:
+        if self.dropout_pos in [RNNDropoutPos.Early, RNNDropoutPos.Both]:
+            input = self.dropout(input)
+        output, hidden = self.lstm.forward(input, hidden)
+        if self.dropout_pos in [RNNDropoutPos.Late, RNNDropoutPos.Both]:
+            output = self.dropout(output)
+        return output, hidden
+
+    def init_hidden(self, batch_size) -> LSTMState:
+        weight = next(self.parameters())
+        return (
+            weight.new_zeros(self.lstm.num_layers, batch_size, self.lstm.hidden_size),
+            weight.new_zeros(self.lstm.num_layers, batch_size, self.lstm.hidden_size),
+        )
diff --git a/models/rnns/transformer.py b/models/rnns/transformer.py
new file mode 100644
index 0000000..2d9c7e6
--- /dev/null
+++ b/models/rnns/transformer.py
@@ -0,0 +1,344 @@
+# Credit: https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py
+#   Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov
+#   Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
+import math
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from torch import Tensor
+from torch.nn import functional as F
+
+from arguments import RNNDropoutPos
+from models.rnns.base import BaseRNN
+
+__all__ = [
+    'TransformerXL',
+    'TransformerState',
+]
+
+Memory = List[Tensor]
+TransformerState = Memory
+
+
+class PositionalEmbedding(nn.Module):
+    inv_freq: Tensor
+
+    def __init__(self, embed_dim: int):
+        super().__init__()
+
+        inv_freq = 1 / (10000 ** (torch.arange(0.0, embed_dim, 2.0) / embed_dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, pos_seq: Tensor, batch_size: Optional[int] = None) -> Tensor:  # type: ignore
+        sinusoid = torch.ger(pos_seq, self.inv_freq)
+        pos_embed = torch.cat([sinusoid.sin(), sinusoid.cos()], dim=-1)[:, None, :]
+
+        if batch_size is not None:
+            pos_embed = pos_embed.expand(-1, batch_size, -1)
+        return pos_embed
+
+
+class PositionWiseFF(nn.Module):
+    def __init__(self, hidden_dim: int, ffn_inner_dim: int, dropout: float, pre_lnorm=False):
+        super().__init__()
+
+        self.core_net = nn.Sequential(
+            nn.Linear(hidden_dim, ffn_inner_dim),
+            nn.ReLU(inplace=True),
+            nn.Dropout(dropout),
+            nn.Linear(ffn_inner_dim, hidden_dim),
+            nn.Dropout(dropout),
+        )
+
+        self.layer_norm = nn.LayerNorm(hidden_dim)
+        self.pre_lnorm = pre_lnorm
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.layer_norm.weight, 1.0, 0.02)
+
+    def forward(self, input: Tensor) -> Tensor:  # type: ignore
+        orig_input = input
+        if self.pre_lnorm:
+            input = self.layer_norm(input)
+
+        # position-wise feed-forward
+        output = self.core_net(input)
+        # residual connection
+        output = orig_input + output
+
+        if not self.pre_lnorm:
+            output = self.layer_norm(output)
+        return output
+
+
+class RelPartialLearnableMultiHeadAttn(nn.Module):
+    def __init__(self, num_heads: int, hidden_dim: int, head_dim: int, dropout: float,
+                 dropout_attn: float = 0.0, pre_lnorm=False):
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.qkv_net = nn.Linear(hidden_dim, 3 * num_heads * head_dim, bias=False)
+
+        self.dropout = nn.Dropout(dropout)
+        self.dropout_attn = nn.Dropout(dropout_attn)
+        self.o_net = nn.Linear(num_heads * head_dim, hidden_dim, bias=False)
+
+        self.layer_norm = nn.LayerNorm(hidden_dim)
+        self.pre_lnorm = pre_lnorm
+
+        self.r_net = nn.Linear(hidden_dim, num_heads * head_dim, bias=False)
+
+        self.scale = 1 / (head_dim ** 0.5)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.layer_norm.weight, 1.0, 0.02)
+
+    @staticmethod
+    def _rel_shift(x: Tensor, zero_triu=False) -> Tensor:
+        zero_pad = x.new_zeros(x.size(0), 1, *x.size()[2:])
+        x_padded = torch.cat([zero_pad, x], dim=1)
+        x_padded = x_padded.view(x.size(1) + 1, x.size(0), *x.size()[2:])
+
+        x = x_padded[1:].view_as(x)
+        if zero_triu:
+            ones = torch.ones(x.size(0), x.size(1))
+            x = x * torch.tril(ones, x.size(1) - x.size(0))[:, :, None, None]
+        return x
+
+    def forward(self,  # type: ignore
+                input: Tensor, pos_embed: Tensor, r_w_bias: Tensor, r_r_bias: Tensor,
+                attn_mask: Optional[Tensor] = None, memory: Optional[Tensor] = None) -> Tensor:
+        seq_len, rlen, batch_size = input.size(0), pos_embed.size(0), input.size(1)
+
+        orig_input = input
+        if memory is not None:
+            input = torch.cat([memory, input], dim=0)
+        if self.pre_lnorm:
+            input = self.layer_norm(input)
+        w_heads = self.qkv_net(input)
+        r_head_k = self.r_net(pos_embed)
+
+        w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
+        w_head_q = w_head_q[-seq_len:]
+
+        tot_len = w_head_k.size(0)
+
+        # w_head_*: (seq_len, batch_size, num_heads, head_dim)
+        w_head_q = w_head_q.view(seq_len, batch_size, self.num_heads, self.head_dim)
+        w_head_k = w_head_k.view(tot_len, batch_size, self.num_heads, self.head_dim)
+        w_head_v = w_head_v.view(tot_len, batch_size, self.num_heads, self.head_dim)
+
+        r_head_k = r_head_k.view(rlen, self.num_heads, self.head_dim)  # seq_len x n_head x d_head
+
+        # compute attention score
+        rw_head_q = w_head_q + r_w_bias
+        # attn_ac = torch.einsum('ibnd,jbnd->ijbn', (rw_head_q, w_head_k))  # seq_len x tot_len x batch_size x n_head
+        dim_i, dim_b, dim_n, dim_d = rw_head_q.size()
+        dim_j = w_head_k.size(0)
+        attn_ac = torch.bmm(
+            rw_head_q.contiguous().view(dim_i, dim_b * dim_n, dim_d).transpose(1, 0),
+            w_head_k.contiguous().view(dim_j, dim_b * dim_n, dim_d).permute(1, 2, 0)
+        ).permute(1, 2, 0).view(dim_i, dim_j, dim_b, dim_n)
+
+        rr_head_q = w_head_q + r_r_bias
+        # attn_bd = torch.einsum('ibnd,jnd->ijbn', (rr_head_q, r_head_k))  # seq_len x tot_len x batch_size x n_head
+        dim_i, dim_b, dim_n, dim_d = rr_head_q.size()
+        dim_j = r_head_k.size(0)
+        attn_bd = torch.bmm(
+            rr_head_q.contiguous().view(dim_i * dim_b, dim_n, dim_d).transpose(1, 0),
+            r_head_k.contiguous().permute(1, 2, 0)
+        ).view(dim_n, dim_i, dim_b, dim_j).permute(1, 3, 2, 0)
+        attn_bd = self._rel_shift(attn_bd)
+
+        # [seq_len x tot_len x batch_size x n_head]
+        attn_score = attn_ac + attn_bd
+        attn_score.mul_(self.scale)
+
+        # compute attention probability
+        if attn_mask is not None:
+            if attn_mask.dim() == 2:
+                attn_mask = attn_mask[None, :, :, None]
+            elif attn_mask.dim() == 3:
+                attn_mask = attn_mask[:, :, :, None]
+            attn_score = attn_score.float().masked_fill(attn_mask, -math.inf).type_as(attn_score)
+
+        # [seq_len x tot_len x batch_size x n_head]
+        attn_prob = F.softmax(attn_score, dim=1)
+        attn_prob = self.dropout_attn(attn_prob)
+
+        # compute attention vector
+        # attn_vec = torch.einsum('ijbn,jbnd->ibnd', (attn_prob, w_head_v))
+        dim_i, dim_j, dim_b, dim_n = attn_prob.size()
+        dim_d = w_head_v.size(3)
+        attn_vec = torch.bmm(
+            attn_prob.contiguous().view(dim_i, dim_j, dim_b * dim_n).permute(2, 0, 1),
+            w_head_v.contiguous().view(dim_j, dim_b * dim_n, dim_d).permute(1, 0, 2)
+        ).view(dim_b, dim_n, dim_i, dim_d).permute(2, 0, 1, 3)
+
+        # [seq_len x batch_size x n_head x d_head]
+        attn_vec = attn_vec.contiguous().view(attn_vec.size(0), attn_vec.size(1), self.num_heads * self.head_dim)
+
+        # linear projection
+        attn_out = self.o_net(attn_vec)
+        attn_out = self.dropout(attn_out)
+
+        # residual connection
+        output = orig_input + attn_out
+        if not self.pre_lnorm:
+            output = self.layer_norm(output)
+
+        return output
+
+
+class RelPartialLearnableDecoderLayer(nn.Module):
+    def __init__(self, num_heads: int, hidden_dim: int, head_dim: int, ffn_inner_dim: int,
+                 dropout: float, dropout_attn: float, pre_lnorm=False):
+        super().__init__()
+
+        self.dec_attn = RelPartialLearnableMultiHeadAttn(
+            num_heads, hidden_dim, head_dim, dropout, dropout_attn=dropout_attn, pre_lnorm=pre_lnorm)
+        self.pos_ff = PositionWiseFF(hidden_dim, ffn_inner_dim, dropout, pre_lnorm=pre_lnorm)
+
+    def forward(self,  # type: ignore
+                input: Tensor, pos_embed: Tensor, r_w_bias: Tensor, r_r_bias: Tensor,
+                dec_attn_mask: Optional[Tensor] = None, memory: Optional[Memory] = None) -> Tensor:
+        output = self.dec_attn(input, pos_embed, r_w_bias, r_r_bias, attn_mask=dec_attn_mask, memory=memory)
+        output = self.pos_ff(output)
+        return output
+
+
+class TransformerXL(BaseRNN[Memory]):
+    def __init__(self, num_layers: int, num_heads: int, hidden_dim: int, head_dim: int, ffn_inner_dim: int,
+                 dropout: float, dropout_attn: float, dropout_pos: RNNDropoutPos,
+                 embed_dim: Optional[int] = None, pre_lnorm=False,
+                 ext_len: int = 0, mem_len: int = 0):
+        super().__init__(dropout, dropout_pos)
+
+        self.num_layers = num_layers
+
+        self.mem_len = mem_len
+        self.ext_len = ext_len
+
+        embed_dim = hidden_dim if embed_dim is None else embed_dim
+        self.proj_input = None
+        if embed_dim != hidden_dim:
+            self.proj_input = nn.Linear(embed_dim, hidden_dim)
+
+        self.layers: 'nn.ModuleList[RelPartialLearnableDecoderLayer]' = nn.ModuleList()
+        for i in range(num_layers):
+            self.layers.append(RelPartialLearnableDecoderLayer(
+                num_heads, hidden_dim, head_dim, ffn_inner_dim, dropout, dropout_attn=dropout_attn,
+                pre_lnorm=pre_lnorm))
+
+        self.pos_embed = PositionalEmbedding(hidden_dim)
+        self.r_w_bias = nn.Parameter(torch.Tensor(num_heads, head_dim))
+        self.r_r_bias = nn.Parameter(torch.Tensor(num_heads, head_dim))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.r_w_bias, 0.0, 0.02)
+        nn.init.normal_(self.r_r_bias, 0.0, 0.02)
+
+    def set_lengths(self, ext_len: int, mem_len: int):
+        self.mem_len = mem_len
+        self.ext_len = ext_len
+
+    def init_memory(self) -> Optional[Memory]:
+        if self.mem_len == 0:
+            return None
+
+        param: nn.Parameter = next(self.parameters())
+        memory = [param.new_empty(0) for _ in range(self.num_layers + 1)]
+        return memory
+
+    def _update_memory(self, states: List[Tensor], memory: Optional[Memory],
+                       seq_len: int, mem_len: int) -> Optional[Memory]:
+        # does not deal with None
+        if memory is None:
+            return None
+
+        assert len(states) == len(memory)
+
+        # There are `seq_len + mem_len` steps that can be cached into memories.
+        # For the next step, the last `ext_len` of the `seq_len` tokens will
+        # be used as the extended context. Hence, we only cache the tokens
+        # from `mem_len + seq_len - self.ext_len - self.mem_len`
+        # to `mem_len + seq_len - self.ext_len`.
+        with torch.no_grad():
+            new_memory = []
+            end = mem_len + max(0, seq_len - self.ext_len)
+            start = max(0, end - self.mem_len)
+            for layer_idx in range(len(states)):
+                if start >= self.mem_len:
+                    total_steps = states[layer_idx]
+                    start -= self.mem_len
+                    end -= self.mem_len
+                else:
+                    total_steps = torch.cat([memory[layer_idx], states[layer_idx]], dim=0)
+                new_memory.append(total_steps[start:end].detach())
+
+        return new_memory
+
+    def _forward(self, word_embed: Tensor, memory: Optional[Memory] = None) -> Tuple[Tensor, Optional[Memory]]:
+        word_embed = word_embed.transpose(0, 1)  # word_embed is batch first
+        seq_len, batch_size = word_embed.size(0), word_embed.size(1)
+
+        if self.proj_input is not None:
+            word_embed = self.proj_input(word_embed)
+
+        mem_len = memory[0].size(0) if memory is not None else 0
+        tot_len = mem_len + seq_len
+        dec_attn_mask = torch.triu(word_embed.new_ones(seq_len, tot_len),
+                                   diagonal=1 + mem_len).byte()[:, :, None]
+
+        states = []
+        output = word_embed
+
+        pos_seq = torch.arange(tot_len - 1, -1, -1.0, device=word_embed.device, dtype=word_embed.dtype)
+        pos_embed = self.pos_embed(pos_seq)
+
+        if self.dropout_pos in [RNNDropoutPos.Early, RNNDropoutPos.Both]:
+            output = self.dropout(output)
+            pos_embed = self.dropout(pos_embed)
+
+        states.append(output)
+        for idx, layer in enumerate(self.layers):
+            layer_mem = None if memory is None else memory[idx]
+            output = layer(output, pos_embed, self.r_w_bias, self.r_r_bias,
+                           dec_attn_mask=dec_attn_mask, memory=layer_mem)
+            states.append(output)
+
+        if self.dropout_pos in [RNNDropoutPos.Late, RNNDropoutPos.Both]:
+            output = self.dropout(output)
+        output = output.transpose(0, 1).contiguous()  # convert back to batch-first
+
+        new_memory = self._update_memory(states, memory, mem_len, seq_len)
+        if new_memory is not None:
+            new_memory = [m.transpose(0, 1) for m in new_memory if m.dim() >= 2]
+
+        return output, new_memory
+
+    def forward(self,  # type: ignore
+                input: Tensor, memory: Optional[Memory]) -> Tuple[Tensor, Optional[Memory]]:
+        # nn.DataParallel does not allow size(0) tensors to be broadcasted.
+        # So, have to initialize size(0) memories inside the model forward.
+        # Moreover, have to return new_memory to allow nn.DataParallel to piece
+        # them together.
+        if memory is None:
+            memory = self.init_memory()
+        else:
+            # switch from batch first to seq_len first
+            memory = [m.transpose(0, 1) for m in memory if m.dim() >= 2]
+
+        output, new_memory = self._forward(input, memory=memory)
+        return output, new_memory
+
+    def init_hidden(self, batch_size: int):
+        return None  # self.init_memory()
diff --git a/models/sample_utils.py b/models/sample_utils.py
new file mode 100644
index 0000000..0a82d37
--- /dev/null
+++ b/models/sample_utils.py
@@ -0,0 +1,60 @@
+import random
+from typing import List, NamedTuple, Optional, Tuple, Union
+
+import torch
+from torch import LongTensor, Tensor
+
+__all__ = [
+    'SampledOutput',
+    'tensor',
+    'randint',
+    'sample',
+    'np_sample',
+]
+
+
+class SampledOutput(NamedTuple):
+    sentence: List[str]
+    sample_loss: float
+    complete_copies: int
+    incomplete_copies: int
+
+
+def tensor(x: Union[int, float, List[int]], device: Optional[torch.device] = None) -> LongTensor:
+    if not isinstance(x, list):
+        x = [x]  # type: ignore
+    return torch.tensor(x, device=device).unsqueeze(0)  # (batch, seq_len)
+
+
+def randint(low: int, high: int) -> int:
+    return random.randint(low, high - 1)  # do not use torch.randint so as to preserve random state?
+
+
+def sample(t: Tensor, greedy: bool = False, topk: Optional[int] = None) -> Tuple[int, float]:
+    """ Return one sample based on log probs, and the corresponding log prob. """
+    t = t.flatten()
+    if greedy:
+        i = torch.argmax(t).item()
+    elif topk is not None:
+        values, indices = torch.topk(t, min(topk, t.numel()))
+        x = torch.multinomial(torch.exp(values), 1).item()
+        i = indices[x]
+    else:
+        i = torch.multinomial(torch.exp(t), 1).item()
+    return i, t[i].item()
+
+
+def np_sample(t: Tensor, greedy: bool = False, topk: Optional[int] = None) -> Tuple[int, float]:
+    import numpy as np
+    t = t.flatten()
+    if greedy:
+        i = torch.argmax(t).item()
+    elif topk is not None:
+        values, indices = torch.topk(t, min(topk, t.numel()))
+        prob = torch.exp(values).cpu().numpy()
+        x = np.random.choice(len(prob), 1, p=prob)[0]
+        i = indices[x]
+    else:
+        prob = torch.exp(t).cpu().numpy()
+        i = np.random.choice(len(prob), 1, p=prob)[0]
+    return i, t[i].item()
diff --git a/models/utils.py b/models/utils.py
new file mode 100644
index 0000000..a44e5c4
--- /dev/null
+++ b/models/utils.py
@@ -0,0 +1,332 @@
+from abc import ABC
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from torch import LongTensor, Tensor
+from torch.nn import functional as F
+
+__all__ = [
+    'load_kb_embed',
+    'sequence_mask',
+    'linear_weight_init',
+    'MLP',
+    'Softmax',
+    'MLPSoftmax',
+    'ProjectedAdaptiveLogSoftmax',
+    'AdaptiveEmbedding',
+]
+
+
+def load_kb_embed(path: str, device: Optional[torch.device]) -> Tuple[Tensor, Tensor]:
+    path = Path(path)
+    entity_embed = torch.load(path / 'entity_vec.pt')
+    rel_embed = torch.load(path / 'relation_vec.pt')
+    if device is not None:
+        entity_embed = entity_embed.to(device=device)
+        rel_embed = rel_embed.to(device=device)
+    return entity_embed, rel_embed
+
+
+def sequence_mask(lengths: List[int], max_len: Optional[int] = None, mask_val=1.0, default_val=0.0,
+                  dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> Tensor:
+    batch_size = len(lengths)
+    max_len = max_len or max(lengths)
+    mask = torch.full((batch_size, max_len), mask_val, dtype=dtype, device=device)
+    for b, length in enumerate(lengths):
+        mask[b, length:] = default_val
+    return mask
+
+
+def linear_weight_init(weight: nn.Parameter, bias: Optional[nn.Parameter] = None, dim: Optional[int] = None):
+    # stdv = 1. / math.sqrt(dim or weight.size(1))
+    # weight.data.uniform_(-stdv, stdv)
+    weight.data.normal_(0.0, 0.02)
+    if bias is not None:
+        # bias.data.uniform_(-stdv, stdv)
+        bias.data.zero_()
+
+
+class MLP(nn.Module):
+    r"""Wrapper for a two-layer MLP."""
+
+    _ACTIVATION: Dict[str, Callable[[Tensor], Tensor]] = {
+        'relu': torch.relu,
+        'sigmoid': torch.sigmoid,
+        'tanh': torch.tanh,
+        'none': lambda x: x,
+    }
+
+    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, activation: str = 'relu',
+                 dropout: Optional[float] = None):
+        super().__init__()
+        if hidden_dim == -1:
+            hidden_dim = output_dim
+        self.layer1 = nn.Linear(input_dim, hidden_dim)
+        self.activation = self._ACTIVATION[activation]
+        self.dropout = nn.Dropout(dropout) if dropout is not None else None
+        self.layer2 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, input: Tensor) -> Tensor:  # type: ignore
+        hidden = self.layer1.forward(input)
+        hidden = self.activation(hidden)
+        if self.dropout is not None:
+            hidden = self.dropout.forward(hidden)
+        hidden = self.layer2.forward(hidden)
+        return hidden
+
+
+class Softmax(nn.Module, ABC):
+    def forward(self, input: Tensor, target: LongTensor) -> Tensor:  # type: ignore
+        raise NotImplementedError
+
+    def log_probs(self, input: Tensor) -> Tensor:
+        raise NotImplementedError
+
+
+class MLPSoftmax(MLP, Softmax):
+    def forward(self, input: Tensor, target: LongTensor) -> Tensor:  # type: ignore
+        input_shape = input.size()
+        logits = super().forward(input)
+        nll = F.cross_entropy(logits.view(-1, logits.size(-1)), target.view(-1), reduction='none')
+        return nll.view(input_shape[:-1])
+
+    def log_probs(self, input: Tensor) -> Tensor:
+        return F.log_softmax(super().forward(input), dim=-1)
+
+
+class ProjectedAdaptiveLogSoftmax(Softmax):
+    def __init__(self, vocab_size: int, embed_dim: int, proj_dim: int,
+                 cutoffs: List[int], div_value: int = 1):
+        super().__init__()
+
+        self.cutoffs = cutoffs + [vocab_size]
+        self.cutoff_ends = [0] + self.cutoffs
+        self.div_value = div_value
+        self.vocab_size = vocab_size
+
+        self.shortlist_size = self.cutoffs[0]
+        self.n_clusters = len(self.cutoffs) - 1
+        self.head_size = self.shortlist_size + self.n_clusters
+
+        if self.n_clusters > 0:
+            self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, embed_dim))
+            self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
+
+        self.out_layers: 'nn.ModuleList[nn.Linear]' = nn.ModuleList()
+        self.out_projs = nn.ParameterList()
+
+        if div_value == 1:
+            if proj_dim != embed_dim:
+                for i in range(len(self.cutoffs)):
+                    self.out_projs.append(nn.Parameter(torch.Tensor(proj_dim, embed_dim)))
+            self.out_layers.append(nn.Linear(embed_dim, vocab_size))
+        else:
+            for i in range(len(self.cutoffs)):
+                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
+                part_embed_dim = embed_dim // (div_value ** i)
+                self.out_projs.append(nn.Parameter(torch.Tensor(proj_dim, part_embed_dim)))
+                self.out_layers.append(nn.Linear(part_embed_dim, r_idx - l_idx))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.n_clusters > 0:
+            nn.init.normal_(self.cluster_weight, 0.0, 0.02)
+            nn.init.zeros_(self.cluster_bias)
+        for param in self.out_projs:
+            # nn.init.normal_(param, 0.0, 0.01)
+            linear_weight_init(param)
+
+    @staticmethod
+    def _compute_logits(hidden: Tensor, weight: nn.Parameter,
+                        bias: nn.Parameter, proj: Optional[nn.Parameter]) -> Tensor:
+        if proj is None:
+            logits = F.linear(hidden, weight, bias=bias)
+        else:
+            proj_hid = F.linear(hidden, proj.t().contiguous())
+            logits = F.linear(proj_hid, weight, bias=bias)
+
+        return logits
+
+    def _construct_weights(self) -> Tuple[List[Tensor], List[Tensor]]:
+        # construct weights and biases
+        weights, biases = [], []
+        for i in range(len(self.cutoffs)):
+            if self.div_value == 1:
+                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
+                weight_i = self.out_layers[0].weight[l_idx:r_idx]
+                bias_i = self.out_layers[0].bias[l_idx:r_idx]
+            else:
+                weight_i = self.out_layers[i].weight
+                bias_i = self.out_layers[i].bias
+
+            if i == 0:
+                weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
+                bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)
+
+            weights.append(weight_i)
+            biases.append(bias_i)
+        return weights, biases
+
+    def forward(self, input: Tensor, target: LongTensor) -> Tensor:  # type: ignore
+        """
+        hidden :: [len*bsz x d_proj]
+        target :: [len*bsz]
+        """
+        input_shape = input.size()
+        input = input.contiguous().view(-1, input_shape[-1])
+        target = target.contiguous().view(-1)
+
+        if input.size(0) != target.size(0):
+            raise RuntimeError('Input and target should have the same size '
+                               'in the batch dimension.')
+
+        if self.n_clusters == 0:
+            logits = self._compute_logits(input, self.out_layers[0].weight,
+                                          self.out_layers[0].bias, self.out_projs[0])
+            nll = F.nll_loss(logits, target, reduction='none')
+        else:
+            weights, biases = self._construct_weights()
+
+            head_weight, head_bias = weights[0], biases[0]
+            head_proj = self.out_projs[0] if len(self.out_projs) > 0 else None
+
+            head_logits = self._compute_logits(input, head_weight, head_bias, head_proj)
+            head_log_probs = F.log_softmax(head_logits, dim=1)
+
+            nonzero_indices: List[torch.ByteTensor] = [
+                ((target >= l) & (target < r)).nonzero().squeeze()
+                for l, r in zip(self.cutoffs[:-1], self.cutoffs[1:])
+            ]
+            head_indices: LongTensor = target.clone()
+            for idx, indices in enumerate(nonzero_indices):
+                if indices.numel() == 0:
+                    continue
+                index = self.shortlist_size + self.n_clusters - 1 - idx
+                head_indices.index_fill_(0, indices, index)
+
+            head_nll = F.nll_loss(head_log_probs, head_indices, reduction='none')
+
+            for idx, indices in enumerate(nonzero_indices):
+                if indices.numel() == 0:
+                    continue
+
+                weight_i, bias_i = weights[idx + 1], biases[idx + 1]
+                proj_i = self.out_projs[idx + 1] if len(self.out_projs) > idx + 1 else None
+
+                cluster_hidden = input.index_select(0, indices)
+                cluster_target = target.index_select(0, indices) - self.cutoffs[idx]
+
+                cluster_logits = self._compute_logits(cluster_hidden, weight_i, bias_i, proj_i)
+                cluster_nll = F.cross_entropy(cluster_logits, cluster_target, reduction='none')
+
+                tail_nll = torch.zeros_like(head_nll)
+                tail_nll.index_copy_(0, indices, cluster_nll)
+                head_nll = head_nll + tail_nll
+
+            nll = head_nll
+
+        nll = nll.view(input_shape[:-1])
+        return nll
+
+    def log_probs(self, input: Tensor) -> Tensor:
+        input_shape = input.size()
+        input = input.contiguous().view(-1, input_shape[-1])
+
+        if self.n_clusters == 0:
+            logits = self._compute_logits(input, self.out_layers[0].weight,
+                                          self.out_layers[0].bias, self.out_projs[0])
+            log_probs = F.log_softmax(logits, dim=-1)
+        else:
+            weights, biases = self._construct_weights()
+
+            head_weight, head_bias = weights[0], biases[0]
+            head_proj = self.out_projs[0] if len(self.out_projs) > 0 else None
+
+            head_logits = self._compute_logits(input, head_weight, head_bias, head_proj)
+            head_log_probs: Tensor = F.log_softmax(head_logits, dim=-1)
+            all_log_probs = [head_log_probs[:, :self.shortlist_size]]
+
+            for idx, (l, r) in enumerate(zip(self.cutoffs[:-1], self.cutoffs[1:])):
+                weight_i, bias_i = weights[idx + 1], biases[idx + 1]
+                proj_i = self.out_projs[idx + 1] if len(self.out_projs) > idx + 1 else None
+
+                tail_logits = self._compute_logits(input, weight_i, bias_i, proj_i)
+                tail_log_probs = F.log_softmax(tail_logits, dim=-1)
+                assert tail_log_probs.size(-1) == r - l
+                index = self.shortlist_size + self.n_clusters - 1 - idx
+                cluster_log_probs = head_log_probs[:, index].unsqueeze(-1) + tail_log_probs
+                all_log_probs.append(cluster_log_probs)
+
+            log_probs = torch.cat(all_log_probs, dim=-1)
+
+        log_probs = log_probs.view(*input_shape[:-1], self.vocab_size)
+        return log_probs
+
+
+class AdaptiveEmbedding(nn.Embedding):
+    def __init__(self, vocab_size: int, embed_dim: int, proj_dim: int, cutoffs: List[int], div_value: int = 1):
+        nn.Module.__init__(self)  # skip initialization for nn.Embedding
+
+        self.vocab_size = vocab_size
+        self.cutoffs = cutoffs + [vocab_size]
+        self.cutoff_ends = [0] + self.cutoffs
+        self.div_value = div_value
+
+        self.embed_dim = embed_dim
+        self.proj_dim = proj_dim
+        self.embed_scale = proj_dim ** 0.5
+
+        self.embed_layers: 'nn.ModuleList[nn.Embedding]' = nn.ModuleList()
+        self.embed_projs = nn.ParameterList()
+        if div_value == 1:
+            self.embed_layers.append(nn.Embedding(vocab_size, embed_dim))
+            if proj_dim != embed_dim:
+                self.embed_projs.append(nn.Parameter(torch.Tensor(proj_dim, embed_dim)))
+        else:
+            for i in range(len(self.cutoffs)):
+                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
+                d_emb_i = embed_dim // (div_value ** i)
+                self.embed_layers.append(nn.Embedding(r_idx - l_idx, d_emb_i))
+                self.embed_projs.append(nn.Parameter(torch.Tensor(proj_dim, d_emb_i)))
+
+        self.reset_parameters()
+
+    def extra_repr(self) -> str:
+        return f''
+
+    def reset_parameters(self):
+        for param in self.embed_projs:
+            # nn.init.normal_(param, 0.0, 0.01)
+            linear_weight_init(param)
+
+    def forward(self, input: LongTensor) -> Tensor:  # type: ignore
+        if self.div_value == 1:
+            embed = self.embed_layers[0](input)
+            if len(self.embed_projs) > 0:
+                embed = F.linear(embed, self.embed_projs[0])
+        else:
+            param = next(self.parameters())
+            input_flat = input.view(-1)
+            embed_flat = param.new_zeros((input_flat.size(0), self.proj_dim))
+            for i in range(len(self.cutoffs)):
+                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
+
+                mask_i: torch.ByteTensor = (input_flat >= l_idx) & (input_flat < r_idx)
+                indices_i = mask_i.nonzero().squeeze()
+
+                if indices_i.numel() == 0:
+                    continue
+
+                input_i = input_flat.index_select(0, indices_i) - l_idx
+                embed_i = self.embed_layers[i](input_i)
+                embed_i = F.linear(embed_i, self.embed_projs[i])
+
+                embed_flat.index_copy_(0, indices_i, embed_i)
+
+            embed = embed_flat.view(*input.size(), self.proj_dim)
+
+        embed.mul_(self.embed_scale)
+        return embed
diff --git a/models/vanillalm.py b/models/vanillalm.py
new file mode 100644
index 0000000..90f015e
--- /dev/null
+++ b/models/vanillalm.py
@@ -0,0 +1,116 @@
+import functools
+import math
+from typing import Optional, Tuple
+
+import torch
+from torch import LongTensor, Tensor
+
+from arguments import LMArguments
+from dataset import BatchSequence, LMExample, Vocab
+from models import sample_utils
+from models.base import BaseLM, HiddenState
+from models.sample_utils import SampledOutput
+from nnlib.utils import Logging
+
+__all__ = [
+    'VanillaLM',
+]
+
+
+class VanillaLM(BaseLM):
+    def __init__(self, args: LMArguments, vocab_size: int, **_kwargs):
+        r"""A normal encoder."""
+        super().__init__(args, vocab_size)
+
+    def forward(self,  # type: ignore
+                inputs: LongTensor, hidden: Optional[HiddenState] = None,
+                target: Optional[LongTensor] = None) -> Tuple[Tensor, HiddenState]:
+        r"""A RNN module wrapper.
+        Parameters:
+            inputs: (B x N x D) (sorted) batch of tensor.
+            hidden: hidden state.
+            target: (B x N)
+        Returns:
+        """
+        emb = self.word_embed(inputs)
+        outputs, hidden = self.rnn(emb, hidden)
+
+        if target is not None:
+            # batch_size x seq_len
+            loss = self.word_predictor(outputs, target)
+            return loss, hidden
+        else:
+            # batch_size x seq_len x vocab_size
+            log_probs = self.word_predictor.log_probs(outputs)
+            return log_probs, hidden
+
+    def calc_loss(self, batch: BatchSequence, hidden: Optional[HiddenState] = None,
+                  use_unk_probs: bool = False, dump_probs: bool = False) -> Tuple[Tensor, HiddenState]:
+        sequence = batch.sequence
+        target = batch.target
+        unk_probs = batch.unk_probs
+
+        masks = (target != 1).to(dtype=torch.float)  # pad symbol is 1
+        loss, new_hidden = self.forward(sequence, hidden, target)
+
+        if use_unk_probs and unk_probs is not None:
+            loss = loss - unk_probs
+
+        # Sum of all the real elements
+        batch_loss = torch.sum(loss * masks, dim=1)
+        loss = torch.sum(batch_loss) / batch.ntokens
+
+        assert batch.ntokens == torch.sum(masks)
+
+        if dump_probs:
+            log_probs = [-prob / length if length > 0 else 0.0
+                         for prob, length in zip(batch_loss.tolist(), batch.lengths.tolist())]
+            self.model_cache.update(log_probs=log_probs)
+
+        return loss, new_hidden
+
+    def init_hidden(self, batch_size: int, init_batch) -> HiddenState:
+        return self.rnn.init_hidden(batch_size)
+
+    @torch.no_grad()
+    def sampling_decode(self, vocab: Vocab, example: LMExample,
+                        begin_symbol: int = 2, end_symbol: int = 5,
+                        initial_hidden: Optional[HiddenState] = None, warm_up: Optional[int] = None,
+                        max_length: int = 200, greedy: bool = False, topk: Optional[int] = None,
+                        print_info: bool = True, color_outputs: bool = False, **_kwargs) \
+            -> SampledOutput:
+        tensor = functools.partial(sample_utils.tensor, device=self.device)
+        sample = functools.partial(sample_utils.sample, greedy=greedy, topk=topk)
+
+        self.eval()
+        self.init_hidden(1, None)
+
+        if warm_up is None:
+            inputs = [begin_symbol]
+            hidden = initial_hidden
+            total_log_prob = 0.0
+        else:
+            inputs = list(vocab.numericalize(example.sentence[:warm_up]))
+            total_log_prob, hidden = self.forward(tensor(inputs[:-1]), target=tensor(inputs[1:]))
+            total_log_prob = -torch.sum(total_log_prob).item() * (len(inputs) - 1)
+
+        while len(inputs) < max_length and inputs[-1] != end_symbol:
+            # full copy of the forward pass, including dropouts. But they won't be applied due to .eval function.
+            # Run LSTM over the word
+            word_log_probs, new_hidden = self.forward(tensor(inputs[-1]), hidden)
+            word_id, word_log_prob = sample(word_log_probs)
+            inputs.append(word_id)
+            hidden = new_hidden
+            total_log_prob += word_log_prob
+
+        sample_loss = -total_log_prob / (len(inputs) - 1)
+        if print_info:
+            print(f"Sample loss: {sample_loss:.3f}, PPL: {math.exp(sample_loss):.3f}")
+
+        # Format the output
+        words = [vocab.i2w[token] for token in inputs]
+        if color_outputs and warm_up is not None:
+            words[:warm_up] = [Logging.color('yellow', w) for w in words[:warm_up]]
+
+        output = SampledOutput(sentence=words, sample_loss=sample_loss, complete_copies=0, incomplete_copies=0)
+        return output
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 0000000..280dfd1
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,16 @@
+[mypy]
+warn_unused_ignores = True
+warn_unused_configs = True
+warn_redundant_casts = True
+no_implicit_optional = True
+follow_imports = silent
+ignore_missing_imports = True
+mypy_path = ./, ./stubs/
+allow_redefinition = True
+
+[mypy-torch]
+# Newer versions of PyTorch deprecated certain functions.
+warn_unused_ignores = False
+
+[mypy-numpy]
+follow_imports = skip
diff --git a/nnlib/arguments/__init__.py b/nnlib/arguments/__init__.py
new file mode 100644
index 0000000..4a358e2
--- /dev/null
+++ b/nnlib/arguments/__init__.py
@@ -0,0 +1 @@
+from nnlib.arguments.arguments import Arguments
diff --git a/nnlib/arguments/arguments.py b/nnlib/arguments/arguments.py
new file mode 100644
index 0000000..d124a7f
--- /dev/null
+++ b/nnlib/arguments/arguments.py
@@ -0,0 +1,378 @@
+import ast
+import enum
+import inspect
+import re
+import sys
+from pathlib import Path
+from typing import Any, Dict, List, NamedTuple, Tuple, Union
+
+from nnlib.arguments import custom_types
+from nnlib.arguments.custom_types import NoneType, is_choices
+from nnlib.arguments.validator import ValidationError, Validator
+from nnlib.utils import Logging
+
+
+class ArgumentError(Exception):
+    pass
+
+
+class Arguments:
+    r"""
+
+    """
+
+    _reserved_keys_cls = ['switch', 'enum']
+    _reserved_keys_method = ['validate', 'preprocess', 'postprocess', 'to_string']
+    _reserved_keys_other = ['help']
+    _reserved_keys = _reserved_keys_cls + _reserved_keys_method + _reserved_keys_other
+
+    class Enum(enum.Enum):
+        # noinspection PyMethodParameters
+        def _generate_next_value_(name, start, count, last_values):
+            # support return-type-polymorphism:   a, b, c = auto()
+            return name.lower()
+
+        def __eq__(self, other):
+            return self.value == other or super().__eq__(other)
+
+    class Switch:
+        r"""
+        A flag argument that takes no values. Similar to the :attr:`action='store_true'` setting of :mod:`argparse`.
+        """
+
+        def __init__(self, default: bool = False):
+            self._default = default
+            self._value = None
+
+        def set_default(self, default: bool):
+            self._default = default
+
+        def __bool__(self):
+            return self._value if self._value is not None else self._default
+
+        def __repr__(self):
+            return f"(Switch) {self._value}"
+
+    pdb = Switch()
+
+    def _check_types(self):
+        pass
+
+    class _ArgTypeSpec(NamedTuple):
+        typ: type
+        nullable: bool
+        required: bool
+        default: Any
+
+    @classmethod
+    def _check_reserved(cls, arg_name) -> bool:
+        """
+        Check whether an argument name is reserved.
+        """
+        key = arg_name.lower()
+        if key not in Arguments._reserved_keys:
+            return False
+        if key in Arguments._reserved_keys_other:
+            return True
+        if key in Arguments._reserved_keys_cls and getattr(cls, arg_name) is not getattr(Arguments, arg_name):
+            return True
+        if key in Arguments._reserved_keys_method and not inspect.ismethod(getattr(cls, arg_name)):
+            return True
+        return False
+
+    # TODO
+    @classmethod
+    def _parse_type_spec(cls) -> Dict[str, _ArgTypeSpec]:
+        """
+        :return: A dict mapping argument names to their type-specs
+        """
+        _attr_name = '__type_dict__'
+        if hasattr(cls, _attr_name):
+            return getattr(cls, _attr_name)
+
+        type_dict = {}
+
+        # get annotations from the current class and all its base classes as well
+        annotations = {}
+        for base in reversed(cls.__mro__):
+            if base not in [object, Arguments]:
+                annotations.update(base.__dict__.get('__annotations__', {}))
+
+        bad_names = []
+        warn_names = []
+
+        def check_name_conventions(name):
+            if name.startswith('_') or name.endswith('_'):
+                # names should not start or begin with underscores
+                bad_names.append(name)
+            if name != name.lower() or any(ord(c) >= 128 for c in name):
+                # names are recommended to contain non-uppercase ASCII characters only
+                warn_names.append(name)
+
+        # check that all attributes are annotated, except for `Switch`es
+        for arg_name in dir(cls):
+            if arg_name.startswith('__') or cls._check_reserved(arg_name):  # magic stuff
+                continue
+            arg_val = getattr(cls, arg_name)
+            if isinstance(arg_val, Arguments.Switch):
+                check_name_conventions(arg_name)
+                # noinspection PyProtectedMember,PyCallByClass
+                type_dict[arg_name.lower()] = Arguments._ArgTypeSpec(
+                    Arguments.Switch, nullable=False, required=False, default=arg_val._default)
+            elif arg_name not in cls.__annotations__:
+                raise ArgumentError(f"Type is not specified for argument '{arg_name}'. "
+                                    f"Type annotation can omitted only when argument is a `Switch`.")
+
+        # iterate over annotated values and generate type-specs
+        for arg_name, arg_typ in annotations.items():
+            if cls._check_reserved(arg_name):
+                raise ArgumentError(f"'{arg_name}' cannot be used as argument name because it is reserved.")
+
+            check_name_conventions(arg_name)
+
+            nullable = False
+            # hacky check of whether `arg_typ` is `Optional`: `Optional` is `Union` with `type(None)`
+            if getattr(arg_typ, '__origin__', None) is Union and NoneType in arg_typ.__args__:
+                nullable = True
+                # extract the type wrapped inside `Optional`
+                arg_typ = next(t for t in arg_typ.__args__ if not isinstance(t, NoneType))  # type: ignore
+
+            arg_val = getattr(cls, arg_name, None)
+            required = not hasattr(cls, arg_name) or (arg_val is None and not nullable)
+            type_dict[arg_name] = Arguments._ArgTypeSpec(
+                arg_typ, nullable=nullable, required=required, default=arg_val)
+
+        if len(bad_names) > 0:
+            bad_names_str = ', '.join(f"'{s}'" for s in bad_names)
+            raise ArgumentError(f"Invalid argument names: {bad_names_str}. "
+                                f"Names cannot begin or end with underscores.")
+        if len(warn_names) > 0:
+            warn_names_str = ', '.join(f"'{s}'" for s in warn_names)
+            Logging.warn(f"Consider changing these argument names: {warn_names_str}. "
+                         f"Names are recommended to contain non-uppercase ASCII characters only.")
+
+        setattr(cls, _attr_name, type_dict)
+        return type_dict
+
+    @classmethod
+    def _parse_help(cls) -> Dict[str, str]:
+        pass
+
+    def _get_arg_type(self, argname: str) -> Tuple[bool, type]:
+        attr = getattr(self, argname)
+        typ = self.__annotations__.get(argname, type(attr))
+        # TODO: hacks here
+        if hasattr(typ, '__origin__') and typ.__origin__ == Union and type(None) in typ.__args__:
+            # hacky check of whether `typ` is `Optional`
+            typ = next(t for t in typ.__args__ if not isinstance(t, custom_types.NoneType))  # type: ignore
+            return True, typ
+        return False, typ
+
+    def __init__(self, *args, **kwargs) -> None:
+        self._check_types()
+
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+
+        # TODO: Add non-null checks
+        # TODO: Add "no-" prefix stuff for switches
+        # TODO: Generate help by inspecting comments
+
+        if len(args) == 0:
+            argv = sys.argv
+        elif len(args) == 1:
+            argv = args[0]
+        else:
+            raise ValueError(f"Argument class takes zero or one positional arguments but {len(args)} were given")
+        i = 1
+        while i < len(argv):
+            arg: str = argv[i]
+            if arg.startswith('--'):
+                argname = arg[2:].replace('-', '_')
+                if argname.startswith('no_') and not hasattr(self, argname) and hasattr(self, argname[3:]):
+                    attr = getattr(self, argname[3:])
+                    if isinstance(attr, Arguments.Switch):
+                        attr._value = False
+                        i += 1
+                        continue
+
+                if hasattr(self, argname):
+                    attr = getattr(self, argname)
+                    if isinstance(attr, Arguments.Switch):
+                        attr._value = True
+                        i += 1
+                        continue
+
+                    nullable, typ = self._get_arg_type(argname)
+                    argval: str = argv[i + 1]
+                    if argval.lower() == 'none':
+                        if nullable:
+                            val = None
+                        else:
+                            assert typ is str or is_choices(typ), \
+                                f"Cannot assign None to non-nullable, non-str argument '{argname}'"
+                            val = argval
+                    elif isinstance(typ, custom_types.NoneType):  # type: ignore
+                        val = None  # just to suppress "ref before assign" warning
+                        try:
+                            # priority: low -> high
+                            for target_typ in [str, float, int]:
+                                val = target_typ(argval)
+                        except ValueError:
+                            pass
+                    elif typ is str:
+                        val = argval
+                    elif isinstance(typ, custom_types.Path) or typ is custom_types.Path:
+                        val = Path(argval)
+                        if isinstance(typ, custom_types.Path) and typ.exists:
+                            assert val.exists(), ValueError(f"Argument '{argname}' requires an existing path, "
+                                                            f"but '{argval}' does not exist")
+                    elif is_choices(typ):
+                        val = argval
+                        assert val in typ.__values__, f"Invalid value '{val}' for argument '{arg}', " \
+                                                      f"available choices are: {typ.__values__}"
+                    elif issubclass(typ, Arguments.Enum):
+                        # experimental support for custom enum
+                        try:
+                            # noinspection PyCallingNonCallable
+                            val = typ(argval)
+                        except ValueError:
+                            valid_args = {x.value for x in typ}
+                            raise ValueError(f"Invalid value '{argval}' for argument '{argname}', "
+                                             f"available choices are: {valid_args}") from None
+
+                    elif typ is bool:
+                        val = argval in ['true', '1', 'True', 'y', 'yes']
+                    else:
+                        try:
+                            val = ast.literal_eval(argval)
+                        except ValueError:
+                            raise ValueError(f"Invalid value '{argval}' for argument '{argname}'") from None
+                    setattr(self, argname, val)
+                    i += 2
+                else:
+                    raise ValueError(f"Invalid argument: '{arg}'")
+            else:
+                Logging.warn(f"Unrecognized command line argument: '{arg}'")
+                i += 1
+
+        if self.pdb:
+            # enter IPython debugger on exception
+            from IPython.core import ultratb
+            ipython_hook = ultratb.FormattedTB(mode='Context', color_scheme='Linux', call_pdb=1)
+
+            def excepthook(type, value, traceback):
+                if type is KeyboardInterrupt:
+                    # don't capture keyboard interrupts (Ctrl+C)
+                    sys.__excepthook__(type, value, traceback)
+                else:
+                    ipython_hook(type, value, traceback)
+
+            sys.excepthook = excepthook
+
+        self.preprocess()
+
+        # check whether non-optional attributes are none
+        for arg in dir(self):
+            if not arg.startswith('_') and arg not in self._reserved_keys:
+                attr = getattr(self, arg)
+                nullable, _ = self._get_arg_type(arg)
+                if attr is None and not nullable:
+                    raise ValueError(f"argument '{arg}' cannot be none")
+
+        self._validate()
+        self.postprocess()
+
+        # convert switches to bool
+        for arg in dir(self):
+            if not arg.startswith('_') and arg not in self._reserved_keys:
+                attr = getattr(self, arg)
+                typ = self.__annotations__.get(arg, None)
+                if isinstance(attr, Arguments.Switch):
+                    # noinspection PyProtectedMember
+                    setattr(self, arg, bool(attr))
+                if isinstance(typ, type) and issubclass(typ, Path) and isinstance(attr, str):
+                    setattr(self, arg, Path(attr))
+
+    def _validate(self):
+        rules = self.validate() or []
+        for pattern, validator in rules:
+            regex = re.compile(rf"^{pattern}$")
+            matches = 0
+            for k in dir(self):
+                if not k.startswith('_') and regex.match(k):
+                    matches += 1
+                    v = getattr(self, k)
+                    try:
+                        result = validator(v)
+                    except Exception:
+                        raise ValidationError(k, v, validator.__name__)
+                    else:
+                        if not result:
+                            raise ValidationError(k, v, validator.__name__)
+            if matches == 0:
+                Logging.warn(f"regex \"{pattern}\" did not match any arguments")
+
+    def preprocess(self) -> None:
+        """
+        Postprocessing of the arguments. This will be called before validation.
+        """
+        pass
+
+    def validate(self) -> List[Tuple[str, Validator]]:
+        r"""
+        Return a list of validation rules. Each validation rule is a tuple of (pattern, validator), where:
+
+        - ``pattern``: A regular expression string. The validation rule is applied to all arguments whose name is
+          fully-matched (i.e. ``^$``) by the pattern.
+        - ``validator``: A validator instance from ``arguments.validator``.
+
+        Example::
+
+            def validate(self):
+                return [
+                    ('.*_data_path', validator.is_path()),
+                    ('pretrained_embedding_type', validator.is_embedding_type()),
+                    ('pretrained_embedding_path', validator.is_path(nullable=True)),
+                    ('.*_lstm_dropout', validator.is_dropout()),
+                ]
+
+        :return: List of (pattern, validator).
+        """
+        pass
+
+    def postprocess(self) -> None:
+        """
+        Postprocessing of the arguments. This will be called after validation.
+        """
+        pass
+
+    # Credit: https://github.com/eaplatanios/symphony-mt/
+    def to_string(self, max_width=None) -> str:
+        k_col = "Arguments"
+        v_col = "Values"
+        valid_keys = [k for k in dir(self) if not (k.startswith('_') or k.lower() in self._reserved_keys)]
+        # valid_keys = list(self._get_arg_names())
+        valid_vals = [repr(getattr(self, k)) for k in valid_keys]
+        max_key = max(len(k_col), max(len(k) for k in valid_keys))
+        max_val = max(len(v_col), max(len(v) for v in valid_vals))
+        if max_width is not None:
+            max_val = min(max_val, max_width - max_key - 7)
+
+        def get_row(k: str, v: str) -> str:
+            if len(v) > max_val:
+                v = v[:((max_val - 5) // 2)] + ' ... ' + v[-((max_val - 4) // 2):]
+                assert len(v) == max_val
+            return f"║ {k.ljust(max_key)} │ {v.ljust(max_val)} ║\n"
+
+        s = repr(self.__class__) + '\n'
+        s += f"╔═{'═' * max_key}═╤═{'═' * max_val}═╗\n"
+        s += get_row(k_col, v_col)
+        s += f"╠═{'═' * max_key}═╪═{'═' * max_val}═╣\n"
+        for k, v in zip(valid_keys, valid_vals):
+            s += get_row(k, v)
+        s += f"╚═{'═' * max_key}═╧═{'═' * max_val}═╝\n"
+        return s
+
+    def __str__(self):
+        return self.to_string()
diff --git a/nnlib/arguments/custom_types.py b/nnlib/arguments/custom_types.py
new file mode 100644
index 0000000..e089b0f
--- /dev/null
+++ b/nnlib/arguments/custom_types.py
@@ -0,0 +1,46 @@
+# TODO: lot's to be done here
+import typing
+from pathlib import Path
+
+from nnlib.arguments.validator import ValidationError
+
+__all__ = ['NoneType', 'Path', 'Choices', 'is_choices']
+
+NoneType = type(None)
+
+
+class ArgType:
+    def __call__(self, value):
+        try:
+            ret = super().__new__(value)
+            ret.__init__(value)
+            return ret
+        except ValueError:
+            raise ValidationError(f"Invalid value \"{value}\" for type ") from None
+
+
+@typing.no_type_check
+class _Choices:
+    def __new__(cls, values=None):
+        self = super().__new__(cls)
+        self.__values__ = values
+        return self
+
+    def __getitem__(self, values):
+        if values == ():
+            raise TypeError("Choices must contain at least one string")
+        if not isinstance(values, tuple):
+            values = (values,)
+        values = tuple(values)
+        return self.__class__(values)
+
+
+Choices = _Choices()
+
+
+def is_choices(typ) -> bool:
+    """
+    Check whether a type is a choices type. This cannot be checked using traditional methods,
+    since Choices is a metaclass.
+    """
+    return type(typ) is type(Choices)
diff --git a/nnlib/arguments/validator.py b/nnlib/arguments/validator.py
new file mode 100644
index 0000000..d1c10b7
--- /dev/null
+++ b/nnlib/arguments/validator.py
@@ -0,0 +1,66 @@
+import functools
+from typing import Any, Callable
+
+Validator = Callable[[Any], bool]
+
+
+class ValidationError(ValueError):
+    def __init__(self, arg, val, vtor):
+        super().__init__(f"Argument '{arg}' failed validator '{vtor}' with value: {val!r}")
+
+
+def validator(nullable=False, default=None):  # TODO: confusing naming
+    def decorator(f):
+        @functools.wraps(f)
+        def wrapped(**kwargs):
+            if not nullable and 'nullable' in kwargs:
+                raise ValueError(f"Validator \"{f.__name__}\" is not nullable.")
+            this_nullable = nullable and kwargs.get('nullable', default)
+            if this_nullable is None:
+                raise ValueError(f"You must specify whether validator \"{f.__name__}\" is nullable or not.")
+
+            @functools.wraps(f)
+            def call(value, **kw):
+                if value is None:
+                    return this_nullable
+                return f(value, **kw)
+
+            if nullable:
+                call.__name__ += f'(nullable={this_nullable})'
+
+            return call
+
+        return wrapped
+
+    return decorator
+
+
+@validator(nullable=True, default=False)
+def is_path(s):
+    import os
+    return os.path.exists(s)
+
+
+@validator(nullable=True, default=True)
+def is_embedding_type(s):
+    return s in ['word2vec', 'fasttext']
+
+
+@validator()
+def is_dropout(s):
+    return 0 <= s < 1
+
+
+@validator()
+def is_lstm_dropout(s):
+    return len(s) == 2 and all(map(is_dropout(), s))
+
+
+@validator()
+def is_activation(s):
+    return s in ['relu', 'sigmoid', 'tanh', 'id']
+
+
+@validator()
+def is_optimizer(s):
+    return s in ['adam', 'sgd', 'adamax']
diff --git a/nnlib/utils/__init__.py b/nnlib/utils/__init__.py
new file mode 100644
index 0000000..b3bbf49
--- /dev/null
+++ b/nnlib/utils/__init__.py
@@ -0,0 +1,8 @@
+from nnlib.utils.io import *
+from nnlib.utils.logging import *
+from nnlib.utils.math import *
+from nnlib.utils.misc import *
+from nnlib.utils.timing import *
+from nnlib.utils.values import *
+
+__magic__ = 82632319
diff --git a/nnlib/utils/io.py b/nnlib/utils/io.py
new file mode 100644
index 0000000..c0b10c3
--- /dev/null
+++ b/nnlib/utils/io.py
@@ -0,0 +1,160 @@
+import contextlib
+import os
+from pathlib import Path
+from typing import IO, TextIO, Union
+
+from nnlib.utils.misc import progress
+
+__all__ = ['shut_up', 'FileProgressWrapper', 'reverse_open']
+
+
+@contextlib.contextmanager
+def shut_up(stderr=True, stdout=False):
+    """
+    Suppress output (probably generated by external script or badly-written libraries) for stderr or stdout.
+    :param stderr: If true, suppress output from stderr.
+    :param stdout: If true, suppress output from stdout.
+    """
+    # redirect output to /dev/null
+    fds = ([1] if stdout else []) + ([2] if stderr else [])
+    null_fds = [os.open(os.devnull, os.O_RDWR) for _ in fds]
+    output_fds = [os.dup(fd) for fd in fds]
+    for null_fd, fd in zip(null_fds, fds):
+        os.dup2(null_fd, fd)
+    yield
+    # restore normal stderr
+    for null_fd, output_fd, fd in zip(null_fds, output_fds, fds):
+        os.dup2(output_fd, fd)
+        os.close(null_fd)
+
+
+class FileProgressWrapper:
+    def __new__(cls, f: TextIO, *, verbose=True, **kwargs):
+        if not verbose:
+            return f
+        return super().__new__(cls)
+
+    def __init__(self, f: TextIO, *, encoding='utf-8', **kwargs):
+        self.f = f
+        self.encoding = encoding
+        self.file_size = os.fstat(f.fileno()).st_size
+        self.progress_bar = progress(total=self.file_size, **kwargs)
+        self.size_read = 0
+        self._next_tick = 1
+        self._next_size = self.file_size // 100
+        self._accum_size = 0
+
+    def __iter__(self):
+        return self
+
+    def _update(self, line: str):
+        size = len(line.encode(self.encoding))
+        self._accum_size += size
+        if self.size_read + self._accum_size >= self._next_size:  # do a bulk update
+            self.progress_bar.update(self._accum_size, )
+            self.size_read += self._accum_size
+            self._accum_size = 0
+            while self.size_read >= self._next_size:
+                self._next_tick += 1
+                self._next_size = self.file_size * self._next_tick // 100
+
+    def __next__(self) -> str:
+        line = next(self.f)
+        self._update(line)
+        return line
+
+    def readline(self, *args) -> str:
+        line = self.f.readline(*args)
+        self._update(line)
+        return line
+
+    def __enter__(self):
+        self.f.__enter__()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.f.__exit__(exc_type, exc_val, exc_tb)
+
+
+class _ReverseReadlineFile:
+    MAX_CHAR_BYTES = 4  # Maximum length of byte sequences for any character in target encoding
+
+    @staticmethod
+    def generator(fp, *, encoding='ansi', allow_empty_lines=False, buf_size=8192):
+        segment = None
+        offset = 0
+
+        fp.seek(0, os.SEEK_END)
+        file_size = remaining_size = fp.tell()
+        while remaining_size > 0:
+            cur_buf_size = buf_size
+            offset = min(file_size, offset + cur_buf_size)
+            fp.seek(file_size - offset)
+            buffer_bytes = fp.read(min(remaining_size, cur_buf_size))
+
+            trials = 0
+            while True:
+                trials += 1
+                try:
+                    buffer = buffer_bytes.decode(encoding)
+                    break
+                except UnicodeDecodeError:
+                    if trials >= _ReverseReadlineFile.MAX_CHAR_BYTES:
+                        raise
+                    buffer_bytes = buffer_bytes[1:]
+                    cur_buf_size -= 1
+                    offset -= 1
+            fp.seek(file_size - offset)
+
+            remaining_size -= cur_buf_size
+            lines = buffer.split('\n')
+            # the first line of the buffer is probably not a complete line so
+            # we'll save it and append it to the last line of the next buffer
+            # we read
+            if segment is not None:
+                # if the previous chunk starts right from the beginning of line
+                # do not concat the segment to the last line of new chunk
+                # instead, yield the segment first
+                if buffer[-1] != '\n':
+                    lines[-1] += segment
+                else:
+                    yield segment
+            segment = lines[0]
+            for index in range(len(lines) - 1, 0, -1):
+                if allow_empty_lines or len(lines[index]):
+                    yield lines[index]
+        # Don't yield None if the file was empty
+        if segment is not None:
+            yield segment
+
+    def __init__(self, fp: IO, gen):
+        self.fp = fp
+        self.gen = gen
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        return next(self.gen)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.fp.close()
+
+    def readline(self):
+        return next(self.gen) + '\n'
+
+    def close(self):
+        self.fp.close()
+
+
+@contextlib.contextmanager
+def reverse_open(path: Union[str, Path], *, encoding='ansi', allow_empty_lines=False, buf_size=8192):
+    # Credits: https://stackoverflow.com/questions/2301789/read-a-file-in-reverse-order-using-python
+    """a generator that returns the lines of a file in reverse order"""
+    with Path(path).open('rb') as fp:
+        gen = _ReverseReadlineFile.generator(fp, encoding=encoding, allow_empty_lines=allow_empty_lines,
+                                             buf_size=buf_size)
+        yield _ReverseReadlineFile(fp, gen)
diff --git a/nnlib/utils/logging.py b/nnlib/utils/logging.py
new file mode 100644
index 0000000..19d4402
--- /dev/null
+++ b/nnlib/utils/logging.py
@@ -0,0 +1,104 @@
+import sys
+import time
+import warnings
+from typing import List, TextIO
+
+__all__ = ['Logging']
+
+
+class Logging:
+    CLEAR_LINE = '\033[2K\r'
+    RESET_CODE = '\033[0m'
+    COLOR_CODE = {
+        'red': '\033[31m',
+        'green': '\033[32m',
+        'yellow': '\033[33m',
+        'blue': '\033[94m',
+        'magenta': '\033[35m',
+        'cyan': '\033[36m',
+        'gray': '\033[37m',
+        'grey': '\033[37m'
+    }
+
+    ERROR = 0
+    WARNING = DEFAULT = 1
+    INFO = VERBOSE = 2
+    DEBUG = 3
+    verbosity_level = DEFAULT
+
+    _global_files: List[TextIO] = []
+
+    def __init__(self, level=DEFAULT, file: TextIO = sys.stderr):
+        self._level = level
+        self._file = file
+
+    @classmethod
+    def tee(cls, file):
+        cls._global_files.append(file)
+
+    @classmethod
+    def _format(cls, *args, **kwargs):
+        prefix = kwargs.get('prefix', None)
+        color = kwargs.get('color', None)
+        prefix_color = kwargs.get('prefix_color', color)
+        end = kwargs.get('end', '\n')
+        sep = kwargs.get('sep', ' ')
+        args = list(args)
+        if prefix is not None:
+            if prefix_color is not None:
+                prefix = cls.COLOR_CODE[prefix_color.lower()] + prefix + cls.RESET_CODE
+            prefix += sep
+        else:
+            prefix = ''
+        if kwargs.get('timestamp', False):
+            args = [f"[{time.strftime('%Y-%m-%d %H:%M:%S')}]"] + args
+
+        s = sep.join(str(x) for x in args)
+        if color is not None:
+            s = cls.COLOR_CODE[color.lower()] + s + cls.RESET_CODE
+        s = prefix + s + end
+        return s
+
+    def _log(self, *args, **kwargs):
+        if Logging.verbosity_level >= self._level:
+            s = self._format(*args, **kwargs)
+            for file in self._global_files + [self._file]:
+                file.write(s)
+                file.flush()
+
+    def log(self, *args, **kwargs):
+        self._log(*args, **kwargs)
+
+    @classmethod
+    def warn(cls, *args, category=None, stacklevel=1, **kwargs):
+        if cls.verbosity_level >= cls.WARNING:
+            ignored = category in [DeprecationWarning, PendingDeprecationWarning, ImportWarning, ResourceWarning]
+            if ignored:
+                warnings.simplefilter(action='default', category=category)
+            warnings.warn(cls._format(*args, **kwargs), category=category, stacklevel=stacklevel + 1)
+            if ignored:
+                warnings.filters.pop(0)
+
+    @classmethod
+    def info(cls, *args, **kwargs):
+        cls(cls.INFO, file=sys.stderr)._log(*args, prefix="INFO:", prefix_color='green', **kwargs)
+
+    @classmethod
+    def verbose(cls, *args, **kwargs):
+        cls(cls.VERBOSE, file=sys.stderr)._log(*args, **kwargs)
+
+    @classmethod
+    def color(cls, col, s):
+        return cls.COLOR_CODE[col.lower()] + s + cls.RESET_CODE
+
+
+# monkey-patch built-in warnings.showwarning
+# noinspection PyUnusedLocal
+def showwarning(message, category, filename, lineno, file=None, line=None):
+    if file is None:
+        file = sys.stderr
+    text = f"{filename}:{lineno}: {Logging.COLOR_CODE['red']}{category.__name__}{Logging.RESET_CODE}: {message}\n"
+    file.write(text)  # we don't deal with situations when built-in stuff broke
+
+
+warnings.showwarning = showwarning
diff --git a/nnlib/utils/math.py b/nnlib/utils/math.py
new file mode 100644
index 0000000..9224551
--- /dev/null
+++ b/nnlib/utils/math.py
@@ -0,0 +1,12 @@
+"""
+Math Utilities
+"""
+
+__all__ = ['ceil_div']
+
+
+def ceil_div(a, b):
+    """
+    Integer division that rounds up.
+    """
+    return (a + b - 1) // b
diff --git a/nnlib/utils/misc.py b/nnlib/utils/misc.py
new file mode 100644
index 0000000..9c4410c
--- /dev/null
+++ b/nnlib/utils/misc.py
@@ -0,0 +1,128 @@
+"""
+Miscellaneous Functions
+"""
+import functools
+import io
+import warnings
+from typing import Dict, Iterable, List, TypeVar, Union, overload
+
+__all__ = ['progress', 'deprecated', 'map_to_list', 'memoization', 'reverse_map']
+
+T = TypeVar('T')
+
+
+@overload
+def progress(iterable: Iterable[T], verbose=True, **kwargs) -> Iterable[T]:
+    ...
+
+
+@overload
+def progress(iterable: int, verbose=True, **kwargs) -> Iterable[int]:
+    ...
+
+
+try:
+    # noinspection PyUnresolvedReferences
+    import tqdm
+
+
+    @overload
+    def progress(iterable=None, verbose=True, **kwargs) -> tqdm.tqdm:
+        ...
+
+
+    class _DummyTqdm:
+        """ Ignores everything """
+
+        @staticmethod
+        def nop(*args, **kwargs):
+            pass
+
+        def __getattr__(self, item):
+            return _DummyTqdm.nop
+
+
+    def progress(iterable=None, verbose=True, **kwargs):
+        if not verbose:
+            if iterable is None:
+                return _DummyTqdm()
+            if isinstance(iterable, int):
+                return range(iterable)
+            return iterable
+        # if 'bar_format' not in kwargs:
+        #     kwargs['bar_format'] = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed} ~{remaining}]'
+        if isinstance(iterable, int):
+            return tqdm.trange(iterable, **kwargs)
+        if iterable is None:
+            return tqdm.tqdm(**kwargs)
+        if isinstance(iterable, io.IOBase):
+            from nnlib.utils.io import FileProgressWrapper  # avoid circular import
+            return FileProgressWrapper(iterable, verbose=verbose, **kwargs)
+        return tqdm.tqdm(iterable, **kwargs)
+
+except ImportError:
+    # noinspection PyUnusedLocal
+    def progress(iterable=None, verbose=True, **_kwargs):
+        if not verbose:
+            if isinstance(iterable, int):
+                return range(iterable)
+            return iterable
+        warnings.warn("`tqdm` package is not installed, no progress bar is shown.", category=ImportWarning)
+        if isinstance(iterable, int):
+            return range(iterable)
+        return iterable
+
+
+def deprecated(new_func=None):
+    def decorator(func):
+        warn_msg = f"{func.__name__} is deprecated."
+        if new_func is not None:
+            warn_msg += f" Use {new_func.__name__} instead."
+
+        def wrapped(*args, **kwargs):
+            warnings.warn(warn_msg, category=DeprecationWarning)
+            return func(*args, **kwargs)
+
+        return wrapped
+
+    return decorator
+
+
+def map_to_list(d: Dict[int, T]) -> List[T]:
+    """
+    Given a dict mapping indices (continuous indices starting from 0) to values, convert it into a list.
+
+    :type d: dict
+    """
+    return [d[idx] for idx in range(len(d))]
+
+
+def memoization(f):
+    @functools.wraps(f)
+    def wrapped(*args):
+        key = tuple(args)
+        if key in wrapped.__states__:
+            return wrapped.__states__[key]
+        ret = f(*args)
+        wrapped.__states__[key] = ret
+        return ret
+
+    wrapped.__states__ = {}
+    return wrapped
+
+
+def reverse_map(d: Union[Dict[T, int], List[T]]) -> List[T]:
+    """
+    Given a dict containing pairs of (`item`, `id`), return a list where the `id`-th element is `item`.
+
+    Or, given a list containing a permutation, return its reverse.
+
+    :type d: dict | list | np.ndarray
+    """
+    if isinstance(d, dict):
+        return [k for k, _ in sorted(d.items(), key=lambda xs: xs[1])]
+
+    rev = [0] * len(d)
+    for idx, x in enumerate(d):
+        rev[x] = idx
+    return rev
diff --git a/nnlib/utils/timing.py b/nnlib/utils/timing.py
new file mode 100644
index 0000000..1187355
--- /dev/null
+++ b/nnlib/utils/timing.py
@@ -0,0 +1,59 @@
+"""
+Timing Functions
+"""
+import contextlib
+import time
+from functools import wraps
+from typing import Dict
+
+from nnlib.utils.logging import Logging
+
+__all__ = ['work_in_progress', 'tic', 'toc', 'report_timing', 'tic_toc']
+
+
+@contextlib.contextmanager
+def work_in_progress(msg):
+    begin_time = time.time()
+    Logging.verbose(msg + "... ", end='')
+    yield
+    Logging.verbose(f"done. ({time.time() - begin_time:.2f}s)")
+
+
+class _TimingHelperClass:
+    ticks = 0
+    time_records: Dict[str, float] = {}
+
+
+def tic():
+    _TimingHelperClass.ticks = time.time()
+
+
+def toc(key=None):
+    ticks = time.time() - _TimingHelperClass.ticks
+
+    if key not in _TimingHelperClass.time_records:
+        _TimingHelperClass.time_records[key] = ticks
+    else:
+        _TimingHelperClass.time_records[key] += ticks
+
+
+def report_timing(level=Logging.VERBOSE):
+    Logging(level).log("Time consumed:")
+    for k in sorted(_TimingHelperClass.time_records.keys()):
+        v = _TimingHelperClass.time_records[k]
+        Logging(level).log(f"> {k}: {v:f}")
+    Logging(level).log("------")
+    _TimingHelperClass.time_records = {}
+
+
+def tic_toc(f):
+    func_name = f.__module__ + '.' + f.__name__
+
+    @wraps(f)
+    def func(*args, **kwargs):
+        tic()
+        result = f(*args, **kwargs)
+        toc(func_name)
+        return result
+
+    return func
diff --git a/nnlib/utils/values.py b/nnlib/utils/values.py
new file mode 100644
index 0000000..2c5abb3
--- /dev/null
+++ b/nnlib/utils/values.py
@@ -0,0 +1,91 @@
+"""
+Values and Counter Utilities
+"""
+import math
+from typing import List
+
+__all__ = ['Average', 'MovingAverage', 'WeightedAverage', 'SimpleAverage']
+
+
+class Average:
+    def add(self, value: float):
+        raise NotImplementedError
+
+    def value(self) -> float:
+        raise NotImplementedError
+
+    def clear(self) -> None:
+        raise NotImplementedError
+
+
+class MovingAverage(Average):
+    def __init__(self, length: int, plateau_threshold: int = 5):
+        # Assume optimization has reached plateau if moving average does not decrease for 5 consecutive iterations
+        self.length = length
+        self.values: List[float] = []
+        self.sum = 0.0
+        self.previous_best = math.inf
+        self.plateau_iters = 0
+        self.plateau_threshold = plateau_threshold
+
+    def add(self, value: float):
+        self.values.append(value)
+        self.sum += value
+        if len(self.values) > self.length:
+            self.sum -= self.values.pop(0)
+
+        val = self.value()
+        if val < self.previous_best:
+            self.previous_best = val
+            self.plateau_iters = 0
+        else:
+            self.plateau_iters += 1
+
+    def value(self) -> float:
+        return float(self.sum) / min(len(self.values), self.length)
+
+    def clear(self) -> None:
+        self.values = []
+        self.sum = 0
+        self.previous_best = math.inf
+        self.plateau_iters = 0
+
+    def decreasing(self) -> bool:
+        return self.plateau_iters <= self.plateau_threshold
+
+    def reset_stats(self) -> None:
+        self.plateau_iters = 0
+
+
+class WeightedAverage(Average):
+    def __init__(self):
+        self.sum = 0.0
+        self.count = 0.0
+
+    def add(self, value: float, count: float = 1.0):
+        self.sum += value * count
+        self.count += count
+
+    def value(self) -> float:
+        return self.sum / self.count
+
+    def clear(self) -> None:
+        self.sum = 0.0
+        self.count = 0.0
+
+
+class SimpleAverage(Average):
+    def __init__(self):
+        self.sum = 0.0
+        self.count = 0
+
+    def add(self, value: float):
+        self.sum += value
+        self.count += 1
+
+    def value(self) -> float:
+        return self.sum / self.count
+
+    def clear(self) -> None:
+        self.sum = 0.0
+        self.count = 0
diff --git a/preprocess/analyze_probs.py b/preprocess/analyze_probs.py
new file mode 100644
index 0000000..8028c1b
--- /dev/null
+++ b/preprocess/analyze_probs.py
@@ -0,0 +1,291 @@
+import argparse
+import math
+import os
+import random
+import re
+import sys
+from typing import List, Tuple, Dict
+
+import numpy as np
+
+from dataset import LRLMDataset
+from dataset.utils import BatchSequence, Relation
+from nnlib.utils import Logging
+
+
+class Average:
+    def __init__(self):
+        self._value = 0
+        self._count = 0
+
+    def add(self, val, cnt=1):
+        self._value += val * cnt
+        self._count += cnt
+
+    def value(self):
+        return self._value / self._count if self._count > 0 else 0.0
+
+    def clear(self):
+        self._value = 0
+        self._count = 0
+
+
+def create_bins(counts, max_value, n_bins=10):
+    bin_idx = []
+    bins = []
+    total_counts = sum(counts)
+    n_bin = 0
+    cumsum = 0
+    prev_idx = 0
+    for idx, cnt in enumerate(counts):
+        cumsum += cnt
+        bin_idx.append(n_bin)
+        if cumsum / total_counts > (n_bin + 1) / n_bins:
+            bins.append((prev_idx, idx + 1))
+            prev_idx = idx + 1
+            n_bin += 1
+    if cumsum > 0:
+        n_bin += 1
+        bins.append((prev_idx, max_value))
+
+    return bins, bin_idx
+
+
+def get_diff(results):
+    (k1, m1), (k2, m2) = results.items()
+    if 'lrlm' in results:
+        if k1 == 'lrlm': diff = m1 - m2
+        else: diff = m2 - m1
+    elif 'nklm' in results:
+        if k1 == 'nklm': diff = m1 - m2
+        else: diff = m2 - m1
+    else:
+        diff = m1 - m2
+
+    return diff
+
+
+def dump_bin_probs(examples_per_criterion, model_probs_per_criterion, bins, n_articles):
+    for seg_idx, bin_range in enumerate(bins):
+        results = {model: probs[seg_idx].value() for model, probs in model_probs_per_criterion.items()}
+        best_result = max(results.values())
+        results_str = {model: f'{val:8.4f}' for model, val in results.items()}
+        diff = get_diff(results)
+        if best_result == 0.0:
+            continue
+        print(f"{seg_idx:3d} ({bin_range[0]:4d},{bin_range[1]:4d}):"
+              f"  examples = {sum(examples_per_criterion[slice(*bin_range)]) / n_articles:.4f}",
+              '  '.join(f"{model} = {Logging.color('red', val) if results[model] == best_result else val}"
+                        for model, val in results_str.items()),
+              f"  diff = {diff:8.4f}")
+
+
+def aggregate_model_loss(criterion, all_batches, model_probs, n_bins, criterion2bin_idx):
+    def get_criterion(criterion):
+        if criterion == "nbatches":
+            return seg_idx
+        elif criterion == "nrels":
+            return len(init_batch[ex_offset])
+        elif criterion == "nspans":
+            return sum(len(batch.spans[ex_offset]) for batch in batches)
+        else:
+            raise ValueError(f"{criterion} not known.")
+
+    model_prob_per_criterion = {model: [Average() for _ in range(n_bins)] for model in model_probs}
+    for model, probs in model_probs.items():
+        start_idx = 0
+        for init_batch, batches in all_batches:
+            for ex_offset in range(len(init_batch)):
+                cur_loss = 0.0
+                ntokens = 0
+                for seg_idx, batch in enumerate(batches):
+                    if (batch.lengths[ex_offset].item() == 0) or (len(probs[start_idx + ex_offset]) <= seg_idx):
+                        break
+                    cur_loss += probs[start_idx + ex_offset][seg_idx] * batch.lengths[ex_offset].item()
+                    ntokens += batch.lengths[ex_offset].item()
+                else:
+                    seg_idx = len(batches)
+
+                cur_loss /= ntokens
+                crit_val = get_criterion(criterion)
+                model_prob_per_criterion[model][criterion2bin_idx[crit_val]].add(cur_loss)
+            start_idx += len(init_batch)
+
+    return model_prob_per_criterion
+
+
+def bin_probs_by_nsegments(all_batches, model_probs, n_bins=10):
+    """bin by the number of segments (i.e. batches/article) in each article."""
+    n_batches = sum(len(init_batch) for init_batch, _ in all_batches)
+    max_nbatches = max(len(batches) for _, batches in all_batches)
+    print(n_batches, max_nbatches)
+
+    examples_per_length = [0 for _ in range(max_nbatches + 1)]
+    rel_per_length = [Average() for _ in range(max_nbatches + 1)]
+    for init_batch, batches in all_batches:
+        for b_idx in range(len(init_batch)):
+            seg_idx = next((idx for idx, batch in enumerate(batches) if batch.lengths[b_idx].item() == 0), len(batches))
+            examples_per_length[seg_idx] += 1
+            rel_per_length[seg_idx].add(len(init_batch[b_idx]))
+    rel_per_length = [avg.value() for avg in rel_per_length]
+
+    bins, len2bin_idx = create_bins(examples_per_length, max_nbatches, n_bins)
+
+    model_prob_per_length = aggregate_model_loss("nbatches", all_batches, model_probs, len(bins), len2bin_idx)
+    dump_bin_probs(examples_per_length, model_prob_per_length, bins, n_batches)
+    # dump_bin_probs(rel_per_length, model_prob_per_length, bins, n_batches)
+
+
+def bin_probs_by_n_rels(all_batches, model_probs, n_bins=10):
+    """bin by the number of relations in each article."""
+    n_batches = sum(len(init_batch) for init_batch, _ in all_batches)
+    max_nrels = max(len(rels) for init_batch, _ in all_batches for rels in init_batch)
+    print(n_batches, max_nrels)
+
+    examples_per_nrels = [0 for _ in range(max_nrels + 1)]
+    for init_batch, batches in all_batches:
+        for b_idx in range(len(init_batch)):
+            nrels = len(init_batch[b_idx])
+            examples_per_nrels[nrels] += 1
+
+    bins, val2bin_idx = create_bins(examples_per_nrels, max_nrels, n_bins)
+
+    model_prob_per_nrels = aggregate_model_loss("nrels", all_batches, model_probs, len(bins), val2bin_idx)
+    dump_bin_probs(examples_per_nrels, model_prob_per_nrels, bins, n_batches)
+
+
+def bin_probs_by_n_spans(all_batches, model_probs, bins=10):
+    """bin by the number of spans in each article."""
+    n_batches = sum(len(init_batch) for init_batch, _ in all_batches)
+    max_nspans = max(sum(len(batch.spans[b_idx]) for batch in batches)
+                     for init_batch, batches in all_batches for b_idx in range(len(init_batch)))
+    print(n_batches, max_nspans)
+
+    examples_per_nspans = [0 for _ in range(max_nspans + 1)]
+    for init_batch, batches in all_batches:
+        for b_idx in range(len(init_batch)):
+            nspans = sum(len(batch.spans[b_idx]) for batch in batches)
+            examples_per_nspans[nspans] += 1
+
+    bins, span2bin_idx = create_bins(examples_per_nspans, max_nspans, n_bins)
+
+    model_prob_per_nspans = aggregate_model_loss("nspans", all_batches, model_probs, len(bins), span2bin_idx)
+    dump_bin_probs(examples_per_nspans, model_prob_per_nspans, bins, n_batches)
+
+
+def main():
+    random.seed(4731)
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--exp", nargs="+", required=True)
+    parser.add_argument("--stat-test", action="store_true")
+    parser.add_argument("--bin-probs", type=str, choices=["nrels", "nsegments", "nspans"],
+                        default="nrels")
+    parser.add_argument("--num-bins", type=int, default=10)
+    args = parser.parse_args()
+
+    paths = args.exp
+    print(paths)
+
+    if "wikitext-short" in paths[0]:
+        dataset = LRLMDataset(path='data/lm', batch_size=15, bptt_size=150, vocab_size=None,
+                              min_freq=3, vocab_dir='data/vocab/analyze-wt-short',
+                              use_only_first_section=True)
+
+    elif "wikitext" in paths[0]:  # wt-full
+        dataset = LRLMDataset(path='data/lm', batch_size=30, bptt_size=150, vocab_size=None,
+                              min_freq=3, vocab_dir='data/vocab/analyze-wt-full')
+
+    elif "wikifacts" in paths[0]:
+        dataset = LRLMDataset(path='data/wikifacts_orig', batch_size=30, bptt_size=150,
+                              vocab_size=40000, vocab_dir='data/vocab/analyze-wf')
+
+    paths = {re.search(r"EVAL-(.*?)-", p).group(1): p for p in paths}
+    # sanity check
+    for k, v in paths.items():
+        assert k.lower() in v.lower(), f"{k} not in {v}"
+
+    split_model_probs = {}
+    for split in ['train', 'train_sample', 'test', 'valid']:
+        model_probs = {}
+        for model, path in paths.items():
+            path = os.path.join(path, f'prob_dump_{split}.txt')
+            if not os.path.exists(path):
+                break
+            cur_probs = []
+            with open(path, 'r') as f:
+                for line in f:
+                    cur_probs.append([float(x) for x in line.split()])
+            model_probs[model] = cur_probs
+            print(f"{model} {split}: {len(cur_probs)} articles")
+        if len(model_probs) > 0:
+            split_model_probs[split] = model_probs
+
+    # do it twice to simulate effect of add unk probs
+    all_splits: Dict[str, List[Tuple[List[List[Relation]], List[BatchSequence]]]] = {}
+    for _ in range(2):
+        all_splits = {}
+        all_splits['train'] = dataset.get_batches("train", shuffle=False)
+        all_splits['train_sample'] = all_splits['train'][:5]
+        all_splits['valid'] = dataset.get_batches("valid", shuffle=False)
+        all_splits['test'] = dataset.get_batches("test", shuffle=False)
+
+    split_probs_per_example = {}
+    # sanity check: compute valid and test PPL
+    for split in ['valid', 'test']:
+        split_probs_per_example[split] = {}
+        if split not in split_model_probs:
+            continue
+        for model, probs in split_model_probs[split].items():
+            split_probs_per_example[split][model] = []
+            start_idx = 0
+            total_loss = 0.0
+            ntokens = 0
+            for init_batch, batches in all_splits[split]:
+                for ex_offset in range(len(init_batch)):
+                    cur_loss = 0.0
+                    cur_ntokens = 0
+                    for seg_idx, batch in enumerate(batches):
+                        if batch.lengths[ex_offset] == 0:
+                            break
+                        cur_loss += probs[start_idx + ex_offset][seg_idx] * batch.lengths[ex_offset].item()
+                        cur_ntokens += batch.lengths[ex_offset].item()
+                    split_probs_per_example[split][model].append(cur_loss / cur_ntokens)
+                    total_loss += cur_loss
+                    ntokens += cur_ntokens
+                start_idx += len(init_batch)
+            split_probs_per_example[split][model] = np.asarray(split_probs_per_example[split][model])
+            total_loss /= ntokens
+            print(f"{model} on {split} set: loss = {total_loss:.4f}, PPL = {math.exp(-total_loss):.4f}")
+
+    if args.stat_test:
+        try:
+            # Use Wilcoxon signed-rank test following: https://arxiv.org/pdf/1809.01448.pdf
+            from scipy.stats import wilcoxon
+            for split in ["valid", "test"]:
+                model_probs_per_example = split_probs_per_example[split]
+                assert len(model_probs_per_example) == 2, "Can only compare exactly two models."
+
+                diff_array = get_diff(model_probs_per_example)
+                print(f"p-value for {split}: {wilcoxon(diff_array).pvalue}")
+            print()
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError("Install scipy.stats to run statistical testing.")
+
+    for split in split_model_probs:
+        print(split)
+        if args.bin_probs == "nrels":
+            bin_probs_by_n_rels(all_splits[split], split_model_probs[split])
+        elif args.bin_probs == "nsegments":
+            bin_probs_by_nsegments(all_splits[split], split_model_probs[split])
+        elif args.bin_probs == "nspans":
+            bin_probs_by_n_spans(all_splits[split], split_model_probs[split])
+
+
+if __name__ == '__main__':
+    if '--ipdb' in sys.argv:
+        from IPython.core import ultratb
+
+        sys.excepthook = ultratb.FormattedTB(mode="Context", color_scheme="Linux", call_pdb=1)
+        sys.argv.remove('--ipdb')
+
+    main()
diff --git a/preprocess/process_wikifacts.py b/preprocess/process_wikifacts.py
new file mode 100644
index 0000000..ba6f380
--- /dev/null
+++ b/preprocess/process_wikifacts.py
@@ -0,0 +1,462 @@
+import pickle
+import tarfile
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, NewType, Set, Tuple, TypeVar
+
+import numpy as np
+import torch
+import sacremoses
+
+from nnlib import utils
+
+
+####################### MODIFY THESE ACCORDING TO YOUR PATHS ##########################
+DATASET_PATH = Path('../wikifact_filmactor/tagged_film_actor_v0.1.tar.gz')
+
+# Downloaded and unzipped OpenKE *Freebase* directory from Step 1
+OPENKE_DIR = Path('./openke/Freebase')
+
+SAVE_DIR = Path('./processed_wikifacts/')
+#######################################################################################
+
+VEC_DIM = 50
+ENTITY_VEC_PATH = OPENKE_DIR / '/embeddings/dimension_50/transe/entity2vec.bin'
+RELATION_VEC_PATH = OPENKE_DIR / 'embeddings/dimension_50/transe/relation2vec.bin'
+ENTITY_MAPPING_PATH = OPENKE_DIR / 'knowledge graphs/entity2id.txt'
+RELATION_MAPPING_PATH = OPENKE_DIR / 'knowledge graphs/relation2id.txt'
+
+SAVE_DATASET_PATH = lambda split: SAVE_DIR / f'{split}.pkl'
+SAVE_MATCHED_SPANS_PATH = lambda split: SAVE_DIR / f'{split}.span.pkl'
+SAVE_ENTITY_VEC_PATH = SAVE_DIR / 'entity_vec.pt'
+SAVE_RELATION_VEC_PATH = SAVE_DIR / 'relation_vec.pt'
+
+T = TypeVar('T')
+
+# Type definitions
+FreebaseID = NewType('FreebaseID', str)
+Relation = Tuple[FreebaseID, FreebaseID]
+Sentence = List[str]
+
+# There are no aliases for WikiFacts, but we add surface form related stuff for consistency
+Example = Tuple[
+    Sentence,  # sentence
+    int,  # topic_id
+    # List[Tuple[int, int]],  # relations: all relations of the topic
+    List[Tuple[int, int, List[str]]],  # relations: all relations of the topic
+    List[int],  # rel_ids: relation index of each word
+    List[int],  # copy_pos: copy position of the object for each word, -1 for NaF
+    List[int],  # surface_idx: surface form index of the object for each word, -1 for NaF
+]
+MatchedSpan = Tuple[
+    int,  # start of span
+    int,  # end of span (+1)
+    int,  # rel-type ID
+    int,  # index into `relations` array (relID for example)
+    int,  # surface form's index into `alias` array
+]
+
+# Constant definitions
+NAF = -1
+ANCHOR = -2
+TOPIC_ITSELF = -3
+UNK_ENTITY = -1
+
+
+def extract_id(path: tarfile.TarInfo) -> FreebaseID:
+    # format: [topic_id].[freebase_id...].[extension]
+    # e.g.: 4693.m.0k3jp_.sm
+    return FreebaseID('.'.join(path.name.split('/')[-1].split('.')[1:-1]))
+
+
+def remove_prefix(s: str) -> FreebaseID:
+    # remove the '/ns/' prefix
+    return FreebaseID(s[4:])
+
+
+def extract_vector(vec: np.ndarray, idx: int) -> np.ndarray:
+    return vec[(idx * VEC_DIM):((idx + 1) * VEC_DIM)]
+
+
+class Vocabulary(defaultdict):
+    def __init__(self, default_factory=None):
+        if default_factory is None:
+            default_factory = self.__len__
+        super().__init__(default_factory=default_factory)
+
+
+def load_wikifacts():
+    """ Load the WikiFacts dataset """
+    id_name: Dict[FreebaseID, str] = {}
+    id_summary: Dict[FreebaseID, str] = {}
+    id_relations: Dict[FreebaseID, List[Relation]] = {}
+    relation_types: Set[FreebaseID] = set()
+
+    def add_or_check(fid: FreebaseID, name: str):
+        name = name.lower()
+        if fid in id_name:
+            assert name == id_name[fid]
+        else:
+            id_name[fid] = name
+
+    tar = tarfile.open(DATASET_PATH)
+
+    id_fb_raw: Dict[FreebaseID, str] = {}
+    id_en_raw: Dict[FreebaseID, str] = {}
+
+    # Annotated Wikipedia summary (.sm)
+    for summary_file in utils.progress(tar.getmembers(), desc='Reading summary (.sm)'):
+        if not summary_file.name.endswith('.sm'):
+            continue
+        freebase_id = extract_id(summary_file)
+        with tar.extractfile(summary_file) as f:
+            id_summary[freebase_id] = f.read().decode('utf-8')
+
+    # Cache Freebase Topic files (.fb, .en)
+    for file in utils.progress(tar.getmembers(), desc='Caching topic files'):
+        freebase_id = extract_id(file)
+        if file.name.endswith('.fb'):
+            with tar.extractfile(file) as f:
+                id_fb_raw[freebase_id] = f.read().decode('utf-8')
+        elif file.name.endswith('en'):
+            with tar.extractfile(file) as f:
+                id_en_raw[freebase_id] = f.read().decode('utf-8')
+
+    # Freebase Topic (.fb, .en)
+    for freebase_id in utils.progress(id_fb_raw, desc='Extracting relations'):
+        relations = []
+        f_fb = id_fb_raw[freebase_id].split('\n')
+        f_en = id_en_raw[freebase_id].split('\n')
+        for rel_line, name_line in zip(f_fb, f_en):
+            parts = rel_line.split()
+            if len(parts) == 0:  # empty line
+                continue
+            rel_name = name_line.strip().split(' ')  # do not split on non-breaking or unicode spaces
+            assert len(parts) == len(rel_name)
+            if len(parts) == 5:  # composite value type
+                if parts[0].startswith('['):  # subject is CVT
+                    continue
+                parts = [parts[0], parts[3], parts[4].rstrip(']')]
+                if parts[0] == parts[-1]:  # don't include a relation with itself
+                    continue
+                # remove the final ']', but do not use `.rstrip` because the name could contain ']'
+                rel_name = [rel_name[0], rel_name[3], rel_name[4][:-1]]
+            elif len(parts) == 3:  # simple relation
+                pass
+            else:
+                raise ValueError  # malformed data
+            rel = [remove_prefix(r) for r in parts]
+            r_sub, r_rel, r_obj = rel
+            if r_sub != freebase_id:  # only keep relations whose subject matches topic
+                continue
+            add_or_check(r_sub, rel_name[0])
+            add_or_check(r_obj, rel_name[2])
+            relations.append((r_rel, r_obj))
+            relation_types.add(r_rel)
+        id_relations[freebase_id] = relations
+
+    # # Freebase to Wikidata mappings
+    # freebase_wikidata_map = {}
+    # with utils.progress(open(FREEBASE_MAPPING_PATH), desc='Reading fb2w') as f:
+    #     for line in f:
+    #         if line.startswith('#') or line.strip() == '':
+    #             continue
+    #         parts = line.split()
+    #         freebase_id = parts[0].split('/')[-1][:-1]
+    #         wikidata_id = parts[2].split('/')[-1][:-1]
+    #         freebase_wikidata_map[freebase_id] = wikidata_id
+    #
+    # # Check coverage
+    # print(f"id_name coverage in fb2w: "
+    #       f"{sum(int(fid in freebase_wikidata_map) for fid in id_name)}/{len(id_name)}")
+    # print(f"id_summary coverage in fb2w: "
+    #       f"{sum(int(fid in freebase_wikidata_map) for fid in id_summary)}/{len(id_summary)}")
+
+    return id_name, id_summary, id_relations, relation_types
+
+
+def load_openke_embeddings():
+    """ Load OpenKE pretrained embeddings """
+
+    entity_index: Dict[FreebaseID, int] = {}
+    relation_index: Dict[FreebaseID, int] = {}
+
+    # Entity to OpenKE ID mapping
+    with utils.work_in_progress("Reading entity2id"), ENTITY_MAPPING_PATH.open() as f:
+        entity_count = int(f.readline())
+        for line in f:
+            parts = line.split()
+            freebase_id = FreebaseID(parts[0])
+            entity_index[freebase_id] = int(parts[1])
+        assert len(entity_index) == entity_count
+
+    # Relation to OpenKE ID mapping
+    with utils.work_in_progress("Reading relation2id"), RELATION_MAPPING_PATH.open() as f:
+        relation_count = int(f.readline())
+        for line in f:
+            parts = line.split()
+            freebase_id = FreebaseID(parts[0])
+            relation_index[freebase_id] = int(parts[1])
+        assert len(relation_index) == relation_count
+
+    # Load binary vectors
+    entity_vec = np.memmap(ENTITY_VEC_PATH, dtype=np.float32, mode='r')
+    relation_vec = np.memmap(RELATION_VEC_PATH, dtype=np.float32, mode='r')
+
+    return entity_index, relation_index, entity_vec, relation_vec
+
+
+def main():
+    replace_canonical = any(arg.startswith('--replace') for arg in sys.argv)
+    if not replace_canonical:
+        # global SAVE_DIR
+        # SAVE_DIR = Path('./data/wikifacts_orig/')
+        pass
+    else:
+        print("Arguments: Will replace the canonical forms.")
+    print(f"Output directory: {SAVE_DIR}")
+
+    skip_embeddings = any(arg.startswith('--skip') for arg in sys.argv)
+    if skip_embeddings:
+        print("Arguments: Will skip embedding generation.")
+
+    id_name, id_summary, id_relations, relation_types = load_wikifacts()
+
+    entity_index, relation_index, entity_vec, relation_vec = load_openke_embeddings()
+
+    # Check OpenKE coverage
+    print(f"Entity coverage in OpenKE: "
+          f"{sum(int(fid in entity_index) for fid in id_name)}/{len(id_name)}")
+    print(f"Relation coverage in OpenKE: "
+          f"{sum(int(fid in relation_index) for fid in relation_types)}/{len(relation_types)}")
+
+    """ Match entity positions and generate pickled dataset """
+
+    # Remap entities and rel-types and store the mapping
+    entity_map: Dict[FreebaseID, int] = {}
+    relation_map: Dict[FreebaseID, int] = {}
+    mapped_entity_vecs: List[np.ndarray] = []
+    mapped_relation_vecs: List[np.ndarray] = []
+
+    # noinspection PyShadowingNames
+    def get_relation_id(rel: FreebaseID) -> int:
+        rel_id = relation_map.get(rel, None)
+        if rel_id is None:
+            rel_id = relation_map[rel] = len(relation_map)
+            mapped_relation_vecs.append(extract_vector(relation_vec, relation_index[rel]))
+        return rel_id
+
+    # noinspection PyShadowingNames
+    def get_entity_id(entity: FreebaseID) -> int:
+        ent_id = entity_map.get(entity, None)
+        if ent_id is None:
+            if entity not in entity_index:  # not all covered
+                return UNK_ENTITY
+            ent_id = entity_map[entity] = len(entity_map)
+            mapped_entity_vecs.append(extract_vector(entity_vec, entity_index[entity]))
+        return ent_id
+
+    # Create the dataset
+    dataset: List[Example] = []  # mapping of topic ID to data example
+    dataset_matched_spans: List[List[MatchedSpan]] = []  # mapping of topic ID to matched spans
+
+    # noinspection PyShadowingNames
+    def find_relation(rels: List[Tuple[FreebaseID, FreebaseID]], obj: FreebaseID) -> List[FreebaseID]:
+        # when there are multiple matches, just find the first one
+        matched_rels = []
+        for r_rel, r_obj in rels:
+            if r_obj == obj:
+                matched_rels.append(r_rel)
+        return matched_rels
+
+    # noinspection PyShadowingNames
+    def match_positions(tokens: List[str], name: List[str]) -> List[int]:
+        matched = [False] * len(name)
+        positions = []
+        for idx, token in enumerate(tokens):
+            for match_idx, match_token in enumerate(name):
+                if matched[match_idx] or match_token != token:
+                    continue
+                positions.append(match_idx)
+                matched[match_idx] = True
+                break
+            else:
+                return []
+        return positions
+
+    tokenizer = sacremoses.MosesTokenizer(lang='en')
+    position_stats = defaultdict(int)
+    for freebase_id in utils.progress(id_summary, desc='Creating dataset'):
+        topic_id = get_entity_id(freebase_id)
+        summary = id_summary[freebase_id].strip().split(' ')
+        raw_relations = id_relations[freebase_id]
+        relations = defaultdict(lambda: len(relations))
+        rel_obj_names: Dict[int, str] = {}
+        for r_rel, r_obj in raw_relations:
+            rel_obj_names[relations[(get_relation_id(r_rel), get_entity_id(r_obj))]] = id_name[r_obj]
+        topic_name = summary[0][2:summary[0].index('/')] if '/' in summary[0] else "<unknown topic name>"
+        rel_obj_names[relations[(TOPIC_ITSELF, topic_id)]] = topic_name  # topic_itself
+        sentence, rel_ids, copy_pos, surface_indices = [], [], [], []
+        matched_spans: List[MatchedSpan] = []
+
+        def add_words(s: str):
+            tokens = tokenizer.tokenize(s, escape=False)
+            sentence.extend(tokens)
+            rel_ids.extend([NAF] * len(tokens))
+            copy_pos.extend([-1] * len(tokens))
+            surface_indices.extend([-1] * len(tokens))
+
+        for word in summary:
+            if '@@' in word:
+                start_pos = word.find('@@')
+                end_pos = word.find('@@', start_pos + 2)
+
+                if start_pos > 0:
+                    add_words(word[:start_pos])  # leading stuff
+
+                entity_desc = word[(start_pos + 2):end_pos].split('/')  # there could be punctuation following '@@'
+                assert len(entity_desc) >= 4  # entity name could contain '/'
+                trailing = word[(end_pos + 2):]  # trailing stuff
+                entity_name = '/'.join(entity_desc[:-3]).split('_')
+                r_obj = FreebaseID(entity_desc[-1])
+                obj_id = get_entity_id(r_obj)
+                if entity_desc[-3] == 'f':
+                    if r_obj == freebase_id:  # topic_itself
+                        rel_id = TOPIC_ITSELF
+                        matched_rels = [TOPIC_ITSELF]
+                    else:
+                        matched_rels = [get_relation_id(r_rel) for r_rel in find_relation(raw_relations, r_obj)]
+                        # the relation might not exist
+                        rel_id = None if len(matched_rels) == 0 else matched_rels[0]
+                else:
+                    rel_id = ANCHOR
+                    matched_rels = [ANCHOR]  # include anchors anyway and filter them out if we don't use them
+                    # matched_rels = []  # don't include anchors because we're only using them for our model
+
+                if rel_id is None:  # not anymore, we allow unknown stuff: if obj_id is None or rel_id is None:
+                    # no embedding for word, convert to normal token
+                    add_words(' '.join(entity_name) + trailing)
+                else:
+                    position_stats['all'] += 1
+
+                    if replace_canonical and r_obj in id_name:  # basically, everything except anchors
+                        # just replace the entity_name, position will be in order
+                        canonical_name = id_name[r_obj].split('_')
+                        if canonical_name != entity_name:
+                            position_stats['not_canonical'] += 1
+                        entity_name = canonical_name
+
+                    if entity_desc[-3] == 'f' and r_obj != freebase_id:
+                        positions = match_positions(entity_name, id_name[r_obj].split('_'))
+                        if len(positions) == 0:  # cannot match, resort to replacing
+                            entity_name = id_name[r_obj].split('_')
+                            positions = list(range(len(entity_name)))
+                            position_stats['order'] += 1
+                        else:
+                            position_stats['match'] += 1
+                            if positions == list(range(len(entity_name))):
+                                position_stats['order'] += 1
+                                position_stats['match_order'] += 1
+                            elif positions == list(range(len(positions))):
+                                position_stats['match_prefix'] += 1
+                            elif positions == list(range(positions[0], positions[-1] + 1)):
+                                position_stats['match_sub'] += 1
+                    else:
+                        # we don't replace canonical names for anchors (because we don't have them)
+                        # anyway, these can't be used by our model
+                        if entity_desc[-3] == 'a':
+                            position_stats['anchor'] += 1
+                            # add the anchor relation
+                            rel_obj_names[relations[(ANCHOR, obj_id)]] = ' '.join(entity_name)
+                        positions = list(range(len(entity_name)))  # these may not be in `id_name`, use as is
+                        position_stats['order'] += 1
+
+                    assert (rel_id, obj_id) in relations
+                    rel_idx = relations[(rel_id, obj_id)]  # must exist
+                    for rel_typ in matched_rels:
+                        matched_spans.append((len(sentence), len(sentence) + len(entity_name), rel_typ, rel_idx, 0))
+                    sentence.extend(entity_name)
+                    rel_ids.extend([rel_idx] * len(entity_name))
+                    copy_pos.extend(positions)
+                    surface_indices.extend([0] * len(entity_name))
+
+                    add_words(trailing)
+            else:
+                add_words(word)
+
+        # we assume everything's canonical, so just add a pseudo canonical form
+        rel_rev_map = {idx: rel for rel, idx in relations.items()}
+        rel_list = []  # just in case `list(relations)` doesn't do as we expect
+        for idx in range(len(rel_rev_map)):
+            rel_id, obj_id = rel_rev_map[idx]
+            rel_list.append((rel_id, obj_id, [rel_obj_names[idx].replace('_', ' ')]))
+        example = (sentence, topic_id, rel_list, rel_ids, copy_pos, surface_indices)
+        dataset.append(example)
+        dataset_matched_spans.append(matched_spans)
+    print(f"Position stats: {position_stats}")
+
+    if replace_canonical:
+        assert position_stats['all'] == position_stats['order']
+
+    # Save them
+    directory: Path = SAVE_DATASET_PATH('train').parent
+    if not directory.exists():
+        directory.mkdir(parents=True)
+
+    # noinspection PyShadowingNames
+    def split_dataset(dataset: List[T], splits: List[Tuple[int, int]]) -> List[List[T]]:
+        dataset_size = len(dataset)
+        dataset_splits = []
+        for l, r in splits:
+            start = int(dataset_size * (l / 100))
+            end = int(dataset_size * (r / 100))
+            dataset_splits.append(dataset[start:end])
+        return dataset_splits
+
+    splits = [(0, 80), (80, 90), (90, 100)]
+    dataset_splits = split_dataset(dataset, splits)
+    matched_spans_splits = split_dataset(dataset_matched_spans, splits)
+
+    print(f"{len(dataset)} examples")
+    for split, data, spans in zip(['train', 'valid', 'test'], dataset_splits, matched_spans_splits):
+        path = SAVE_DATASET_PATH(split)
+        with path.open('wb') as f:
+            pickle.dump(data, f)
+        print(f"Dataset split '{split}' saved to {path}, {len(data)} examples")
+
+        path = SAVE_MATCHED_SPANS_PATH(split)
+        with path.open('wb') as f:
+            pickle.dump(spans, f)
+        print(f"Matched spans split '{split}' saved to {path}")
+
+    # save relation type names for use during generation
+    rel_names: Dict[int, str] = {NAF: 'Not-A-Fact', ANCHOR: 'ANCHOR', TOPIC_ITSELF: 'TITLE'}
+    for r_rel, rel_id in relation_map.items():
+        rel_names[rel_id] = r_rel
+    with (SAVE_DIR / 'rel_names.pkl').open('wb') as f:
+        pickle.dump(rel_names, f)
+    print("Relation names saved.")
+
+    if not skip_embeddings:
+        with utils.work_in_progress("Saving entity vecs"):
+            stacked_entity_vecs = torch.from_numpy(np.stack(mapped_entity_vecs))
+            torch.save(stacked_entity_vecs, SAVE_ENTITY_VEC_PATH)
+        print(f"Entity vecs saved to {SAVE_ENTITY_VEC_PATH}, {len(stacked_entity_vecs)} vectors in total")
+
+        with utils.work_in_progress("Saving relation vecs"):
+            stacked_relation_vecs = torch.from_numpy(np.stack(mapped_relation_vecs))
+            torch.save(stacked_relation_vecs, SAVE_RELATION_VEC_PATH)
+        print(f"Relation vecs saved to {SAVE_RELATION_VEC_PATH}, {len(stacked_relation_vecs)} vectors in total")
+    else:
+        print("Embedding updates skipped.")
+
+    print("Processing done.")
+
+
+if __name__ == '__main__':
+    from IPython.core import ultratb
+    import sys
+
+    sys.excepthook = ultratb.FormattedTB(mode="Context", color_scheme="Linux", call_pdb=1)
+
+    main()
diff --git a/preprocess/process_wikitext.py b/preprocess/process_wikitext.py
new file mode 100644
index 0000000..d39c070
--- /dev/null
+++ b/preprocess/process_wikitext.py
@@ -0,0 +1,424 @@
+import json
+import logging
+import pickle
+import pprint
+import re
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, NamedTuple, NewType, Optional, Set, Tuple, TypeVar
+
+import numpy as np
+import torch
+from torch import Tensor
+
+from nnlib import utils
+from nnlib.utils import Logging
+
+LOGGER = logging.getLogger(__name__)
+
+
+####################### MODIFY THESE ACCORDING TO YOUR PATHS ##########################
+# For the step numbers, please follow docs/data.md
+
+# Downloaded and unzipped OpenKE *Wikidata* directory from Step 1
+OPENKE_DIR = Path('./openke/Wikidata')
+# Result of Step 3
+WIKIDATA_DUMP_DIR = Path('./wikidata/')
+# The same path as specified in `preprocess/scripts/preprocess_wikitext.sh`
+ALIGNED_DATA_DIR = Path('./dataset')
+# Path to the unzipped WikiText-103-raw from Step 2
+WIKITEXT_DIR = Path('./wikitext-raw')
+
+# Target directory
+SAVE_DIR = Path('./processed/')
+#######################################################################################
+
+
+VEC_DIM = 100
+ENTITY_VEC_PATH = OPENKE_DIR / '/embeddings/dimension_100/transe/entity2vec.bin'
+RELATION_VEC_PATH = OPENKE_DIR / '/embeddings/dimension_100/transe/relation2vec.bin'
+ENTITY_MAPPING_PATH = OPENKE_DIR / '/Wikidata/knowledge graphs/entity2id.txt'
+RELATION_MAPPING_PATH = OPENKE_DIR / '/knowledge graphs/relation2id.txt'
+
+TOPIC_JSON_PATH = lambda split: (WIKITEXT_DIR / f'{split}.json')
+
+# Type definitions
+K = TypeVar('K')
+V = TypeVar('V')
+T = TypeVar('T')
+
+WikidataID = NewType('WikidataID', str)
+Span = Tuple[int, int]
+Sentence = List[str]
+
+
+class EntityMention(NamedTuple):
+    span: Span
+    surface: str
+    rel_idx: int  # index to the unique relations of a topic
+
+
+class RelationWikiID(NamedTuple):
+    rel_typ: WikidataID
+    obj_id: WikidataID
+    obj_alias: List[str]  # canonical form is the first element
+
+
+class RawExampleWikiID(NamedTuple):
+    topic_id: WikidataID
+    sentence: Sentence
+    relations: List[RelationWikiID]
+    mentions: List[EntityMention]
+
+
+RawDump = Tuple[
+    Sentence,          # sentence
+    List[              # List of matched spans
+        Tuple[
+            str,       # Wikidata ID for the object entity
+            str,       # Wikidata ID for the subject entity (topic entity)
+            Span,      # span
+            Tuple[     # List of relations that connects subject and object
+                List[str],  # Relations in the form of WikidataID
+                List[str]   # Relations in the form of text
+            ],
+            str,       # matched surface form
+            List[str]  # List of aliases
+        ]
+    ]
+]
+
+
+# do not use a named tuple for objects to dump
+Example = Tuple[
+    Sentence,  # sentence
+    int,  # topic_id
+    List[Tuple[int, int, List[str]]],  # relations: all relations of the topic, in format like RelationWikiID
+    List[int],  # rel_ids: relation index of each word
+    List[int],  # copy_pos: copy position of the object for each word, -1 for NaF
+    List[int],  # surface_indices: surface form index of the object for each word, -1 for NaF
+]
+
+
+MatchedSpan = Tuple[
+    int,  # start of span
+    int,  # end of span (+1)
+    int,  # rel-type ID
+    int,  # index into `relations` array (relID for example)
+    int,  # surface form's index into `alias` array
+]
+
+# Constant definitions
+NAF = WikidataID('__NaF__')
+ANCHOR = WikidataID('__anchor__')
+TOPIC_ITSELF = WikidataID('__topic_itself__')
+UNK_ENTITY = WikidataID('__none__')
+
+
+def preprocess(t: str) -> str:
+    t = re.sub(r"\s@-@\s", "-", t)
+    t = re.sub(r"\(\s(.+)\s\)", r"(\1)", t)
+    t = re.sub(r"\s\'(\w+)", r"'\1", t)
+    t = t.replace("What (ever)", "What(ever)")
+    t = t.replace(" ; ", ";")
+    t = t.replace(" – ", "–")  # something called "em dash" present in some titles
+    t = t.replace("... ", "...")
+    t = t.replace(" ...", "...")
+    t = t.replace(" , ", ", ")
+    t = t.replace(" ' ", "' ")
+    t = re.sub(r"\s?'\s?([a-z]{,2})\s", r"'\1 ", t)
+    t = t.replace(" !", "!")
+    t = t.replace(" ?", "?")
+    t = t.replace(" :", ":")
+    # Only reduce spaces when single characters are ampersand'ed.
+    t = re.sub(r"(^|\s)(\w)\s&\s(\w)(\s|$)", r"\1\2&\3\4", t)
+    return t
+
+
+def load_id2str(id2str_path: Path, keys: Optional[Set[WikidataID]] = None, skip_header=False) \
+        -> Dict[WikidataID, str]:
+    id2str: Dict[WikidataID, str] = {}
+    with id2str_path.open() as f:
+        if skip_header:
+            _ = f.readline()
+        for line in f:
+            if line.strip() == "":
+                continue
+            wiki_id, *rel_names = line.strip().split('\t')
+            rel_name = rel_names[0]  # get rid of AKAs
+            wikidata_id = WikidataID(wiki_id)
+            if keys is not None and wikidata_id not in keys:
+                continue  # save memory
+            id2str[wikidata_id] = rel_name
+    return id2str
+
+
+def load_id2vec(id2vec_path: Path, keys: Optional[Set[WikidataID]] = None) -> Dict[WikidataID, int]:
+    id2vec: Dict[WikidataID, int] = {}
+    with id2vec_path.open() as f:
+        _ = f.readline()  # always skip first line
+        for line in f:
+            if line.strip() == "":
+                continue
+            wiki_id, vec_id = line.split()
+            wikidata_id = WikidataID(wiki_id)
+            if keys is not None and wikidata_id not in keys:
+                continue  # save memory
+            id2vec[wikidata_id] = int(vec_id)
+    return id2vec
+
+
+def _load_vecs(all_ids: Set[WikidataID], id2vec_path: Path, vec_path: Path,
+               vec_dim: int) -> Tuple[Dict[WikidataID, int], Tensor]:
+    id2vec = load_id2vec(id2vec_path, keys=all_ids)
+
+    # check coverage
+    if len(all_ids) != len(id2vec):
+        missing = all_ids - set(id2vec.keys())
+        print(f"Missing id2vec items ({len(missing)})")
+
+    all_vecs = np.memmap(vec_path, dtype=np.float32, mode='r')
+    extracted_vecs = [all_vecs[(idx * vec_dim):((idx + 1) * vec_dim)]
+                      for idx in sorted(id2vec.values())]
+    vecs = torch.from_numpy(np.stack(extracted_vecs))
+    # map to vec idx
+    final_map = {r: idx for idx, (r, _) in enumerate(sorted(id2vec.items(), key=lambda x: x[1]))}
+    return final_map, vecs
+
+
+def load_entities(all_entities: Set[WikidataID]) -> Dict[WikidataID, int]:
+    entity_vec_path = SAVE_DIR / 'entity_vec.pt'
+    entity_map, entity_vec = _load_vecs(
+        all_entities,
+        id2vec_path=ENTITY_MAPPING_PATH,
+        vec_path=ENTITY_VEC_PATH,
+        vec_dim=VEC_DIM
+    )
+    torch.save(entity_vec, entity_vec_path)
+
+    return entity_map
+
+
+def load_relations(all_rels: Set[WikidataID]) -> Dict[WikidataID, int]:
+    rel_vec_path = SAVE_DIR / 'relation_vec.pt'
+    rel_map, rel_vec = _load_vecs(
+        all_rels,
+        id2vec_path=RELATION_MAPPING_PATH,
+        vec_path=RELATION_VEC_PATH,
+        vec_dim=VEC_DIM
+    )
+    torch.save(rel_vec, rel_vec_path)
+
+    return rel_map
+
+
+def numericalize_rel(data: List[RawExampleWikiID],
+                     rel_map: Dict[WikidataID, int], entity_map: Dict[WikidataID, int]) \
+        -> Tuple[List[Example], List[List[MatchedSpan]]]:
+    processed_data = []
+    processed_spans = []
+    for example in data:
+        # We store the surface forms for mentions because there might be subtle differences with sentence tokens.
+        # We also rely on the model to compute the probabilities (either on-the-fly or by per-batch preprocessing).
+        sent = example.sentence
+        topic_id = entity_map.get(example.topic_id, -1)
+        rel_ids = [-1] * len(sent)  # NaF
+        copy_pos = [-1] * len(sent)
+        surface_indices = [-1] * len(sent)
+        matched_spans = []
+
+        # noinspection PyShadowingNames
+        def sort_key(mention: EntityMention):
+            l, r = mention.span
+            is_canonical = (example.relations[mention.rel_idx].obj_alias[0] == mention.surface)
+            return (
+                -(r - l),  # span length (desc)
+                l,  # span position (incr)
+                not is_canonical,  # canonical mentions first
+                mention.rel_idx,  # break ties by relation ID
+                mention.surface,  # break further ties by surface form
+            )
+
+        # Sort mentions in reverse order of span length.
+        # In case of tie, put earlier spans in the front. Break further ties by relation ID to keep consistency.
+        # E.g. sentence = [a, b, c, d, e]. Prefer [a, b, c, d] to [a, b, c]; prefer [a, b, c] to [b, c, d].
+        for mention in sorted(example.mentions, key=sort_key):
+            l, r = mention.span
+            # simple overwrite the spans if they overlap
+            if l == -1 or r == -1:
+                continue  # non-existing
+            l = max(l, 0)
+            r = min(r, len(sent))
+            if l >= r:
+                continue
+
+            rel_idx = mention.rel_idx
+            rel = example.relations[rel_idx]
+            try:
+                surface_idx = next(i for i, x in enumerate(rel.obj_alias) if x == mention.surface)  # must exist
+            except StopIteration:
+                continue  # skip this mention
+            # spans information is only used by LRLM, so we keep these anyway
+            matched_spans.append((l, r, rel_map[rel.rel_typ], rel_idx, surface_idx))
+
+            if any(x != -1 for x in rel_ids[l:r]):
+                # if any token in span is already annotated, skip it to avoid inconsistent annotations
+                continue
+
+            rel_ids[l:r] = [mention.rel_idx] * (r - l)
+            copy_pos[l:r] = range(r - l)
+            surface_indices[l:r] = [surface_idx] * (r - l)
+
+        processed_spans.append(matched_spans)
+        processed_data.append((
+            sent,
+            topic_id,
+            [(rel_map[rel.rel_typ], entity_map.get(rel.obj_id, -1), rel.obj_alias)
+             for rel in example.relations],
+            rel_ids,
+            copy_pos,
+            surface_indices,
+        ))
+    return processed_data, processed_spans
+
+
+def read_data(path: Path) -> Dict[str, List[RawExampleWikiID]]:
+    bad_examples: List[Tuple[str, int, str]] = []
+    data = {}
+    for split in ['train', 'valid', 'test']:
+        with (path / f'{split}.pkl').open('rb') as f:
+            # relation tuple: (span, rel_type_desc, name, canonical_name)
+            with utils.work_in_progress(f"Loading {split} set"):
+                dump: List[RawDump] = pickle.load(f)
+
+            examples = []
+            for idx, (sent, rels) in enumerate(utils.progress(dump, desc='Reading data')):
+                # map (rel_typ, canonical) to list of aliases, since lists aren't hashable
+                rel_to_alias: Dict[Tuple[str, str], List[str]] = \
+                    {(rel[0][0], obj_id): alias for obj_id, _, _, rel, _, alias in rels}
+
+                # sort it so the order is consistent
+                relations: List[RelationWikiID] = sorted([
+                    RelationWikiID(WikidataID(rel_id), WikidataID(obj_id), obj_alias)
+                    for (rel_id, obj_id), obj_alias in rel_to_alias.items()
+                ])
+                rel_to_id: Dict[Tuple[str, str], int] = {
+                    (rel_id, obj_id): idx
+                    for idx, (rel_id, obj_id, obj_alias) in enumerate(relations)
+                }
+                # dedup to remove duplicate (-1, -1)
+                mentions: List[EntityMention] = list(set(
+                    EntityMention(span, surface, rel_to_id[(rel_info[0][0], obj_id)])
+                    for obj_id, head_id, span, rel_info, surface, _ in rels
+                ))
+                try:
+                    # must exist - head id with the relation: @TITLE@ is the topic WikidataID
+                    topic_id = next(head_id for _, head_id, _, rel_info, surface, alias in rels if rel_info[0][0] == "@TITLE@")
+                except StopIteration:
+                    bad_examples.append((split, idx, ' '.join(sent)[:100]))
+                    continue
+
+                converted_relations = []
+                for r in relations:
+                    converted_relations.append(
+                        RelationWikiID(
+                            TOPIC_ITSELF if r.rel_typ == "@TITLE@" else r.rel_typ,
+                            r.obj,
+                            r.obj_alias
+                        )
+                    )
+
+                example = RawExampleWikiID(WikidataID(topic_id), sent, converted_relations, mentions)
+                examples.append(example)
+            data[split] = examples
+
+    if len(bad_examples) > 0:
+        Logging.warn(f"{len(bad_examples)} bad examples:\n"
+                     f"{pprint.pformat(bad_examples)}")
+    else:
+        Logging.verbose("All examples are good")
+
+    return data
+
+
+def main():
+    Logging.verbosity_level = Logging.VERBOSE
+
+    Logging.warn("This program requires lots of memory (preferably >= 30GB).")
+
+    if not SAVE_DIR.exists():
+        SAVE_DIR.mkdir(parents=True)
+
+    # Read the Wikimedia IDs for each article, and filter the relations
+    topic_ids: Set[WikidataID] = set()
+    split_title_id: Dict[str, List[Tuple[str, WikidataID]]] = {}
+    for split in ['train', 'valid', 'test']:
+        with utils.work_in_progress(f"Loading {split} set titles"), \
+             open(TOPIC_JSON_PATH(split=split)) as f:
+            j = json.load(f)
+        split_title_id[split] = [(article['title'], WikidataID(article['id'])) for article in j]
+        topic_ids.update([wid for _, wid in split_title_id[split]])
+        del j
+
+    with utils.work_in_progress("Loading Wikidata ID mapping"):
+        id2rel = load_id2str(WIKIDATA_DUMP_DIR / 'properties.txt')
+
+    # Match the relations
+    matched_dataset = read_data(ALIGNED_DATA_DIR)
+
+    # Gather entities & relation vectors
+    found_entities = set()
+    found_rels = set()
+    for split in matched_dataset:
+        for example in matched_dataset[split]:
+            found_entities.add(example.topic_id)
+            for rel in example.relations:
+                found_entities.add(rel.obj_id)
+                found_rels.add(rel.rel_typ)
+    found_entities -= {UNK_ENTITY}
+    found_rels -= {NAF, ANCHOR, TOPIC_ITSELF}
+    with utils.work_in_progress("Building rel vecs"):
+        rel_map = load_relations(found_rels)
+        rel_map.update({NAF: -1, ANCHOR: -2, TOPIC_ITSELF: -3})
+        unk_rels = found_rels.difference(rel_map)
+        # NOTE: unk_rels is a set, its order is undetermined, so we sort it to make sure it's consistent between runs
+        for idx, rel in enumerate(sorted(unk_rels)):
+            rel_map[rel] = -idx - 4  # starting from -4, going towards -inf
+    with utils.work_in_progress("Building entity vecs"):
+        entity_map = load_entities(found_entities)
+        entity_map.update({UNK_ENTITY: -1})
+        print(f"Topic ID coverage: {len(topic_ids.intersection(entity_map))}/{len(topic_ids)}")
+
+    # save relation type names for use during generation
+    id_to_rel_name = dict(id2rel)
+    id_to_rel_name.update({NAF: 'Not-A-Fact', ANCHOR: 'ANCHOR', TOPIC_ITSELF: 'TITLE'})
+    rel_names: Dict[int, str] = {}
+    for r_rel, rel_id in rel_map.items():
+        rel_names[rel_id] = id_to_rel_name[r_rel]
+    with (SAVE_DIR / 'rel_names.pkl').open('wb') as f:
+        pickle.dump(rel_names, f)
+        print(f"Relation names saved to {(SAVE_DIR / 'rel_names.pkl')}")
+
+    # Convert into numbers to create the final dataset
+    for split in matched_dataset:
+        with utils.work_in_progress(f"Converting {split} set"):
+            dataset, matched_spans = numericalize_rel(matched_dataset[split], rel_map, entity_map)
+
+        path = SAVE_DIR / f'{split}.pkl'
+        with path.open('wb') as f:
+            pickle.dump(dataset, f)
+        print(f"Dataset split '{split}' saved to {path}, {len(dataset)} examples")
+
+        path = SAVE_DIR / f'{split}.span.pkl'
+        with path.open('wb') as f:
+            pickle.dump(matched_spans, f)
+        print(f"Matched spans split '{split}' saved to {path}")
+
+
+if __name__ == '__main__':
+    from IPython.core import ultratb
+    import sys
+
+    sys.excepthook = ultratb.FormattedTB(mode="Context", color_scheme="Linux", call_pdb=1)
+
+    main()
diff --git a/preprocess/scripts/preprocess_wikitext.sh b/preprocess/scripts/preprocess_wikitext.sh
new file mode 100644
index 0000000..7dda34b
--- /dev/null
+++ b/preprocess/scripts/preprocess_wikitext.sh
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+############################ MODIFY THESE ACCORDINGLY #################################
+WIKITEXT_DIR=                 # Path to the unzipped wikitext-raw dataset.
+WIKIDATA_DUMP_DIR=            # Path to the directory containing `latest-truthy.nt` file.
+ALIGNED_DATA_DIR='./dataset'  # Target path to dump the aligned results between Wikidata and WikiText
+
+# Additionally, don't forget to modify the path in `preprocess/process_wikitext.py`.
+#######################################################################################
+
+# Split wikitext into articles.
+python preprocess/wikitext/split_wikitext.py \
+    --wikitext-dir $WIKITEXT_DIR \
+    --ids-dir data/found_ids \
+    --output-dir $WIKITEXT_DIR
+
+echo "Articles are splitted into $WIKITEXT_DIR" >&2
+
+# Process raw Wikidata dump into usable format.
+python preprocess/wikitext/extract_wikidata.py \
+    --source-path $WIKIDATA_DUMP_DIR/latest-truthy.nt \
+    --target-dir $WIKIDATA_DUMP_DIR \
+    --lang en \
+
+echo "Wikdata KG is extracted into $WIKIDATA_DUMP_DIR" >&2
+
+# Attempt surface matching between Wikidata and the splitted articles.
+python preprocess/wikitext/match_wikidata.py \
+    --wikitext-dir $WIKITEXT_DIR \
+    --canonical-forms-dir data/canonical_forms/ \
+    --wikidata-dir $WIKIDATA_DUMP_DIR \
+    --output-dir $ALIGNED_DATA_DIR \
+    --lower \
+
+echo "Wikdata and Wikitext articles are aligned and stored at $ALIGNED_DATA_DIR" >&2
+
+# Preprocess the data
+# NOTE: Check the following paths in the file!
+#     OPENKE_DIR        ... Directory to the unzipped OpenKE Wikidata archive.
+#     WIKIDATA_DUMP_DIR ... same as $WIKIDATA_DUMP_DIR in this file.
+#     WIKITEXT_DIR      ... same as $WIKITEXT_DIR in this file.
+#     ALIGNED_DATA_DIR  ... same as $ALIGNED_DATA_DIR in this file.
+#     SAVE_DIR          ... Directory to save the processed Wikitext-S,F data.
+python preprocess/process_wikitext.py
+
+echo "Dataset is created and ready for training." >&2
diff --git a/preprocess/train_fasttext.py b/preprocess/train_fasttext.py
new file mode 100644
index 0000000..bd8217f
--- /dev/null
+++ b/preprocess/train_fasttext.py
@@ -0,0 +1,67 @@
+import argparse
+import pickle
+import subprocess
+import sys
+from pathlib import Path
+
+from tqdm import tqdm
+
+sys.path.append(".")
+from dataset import Dataset
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--fasttext-path", type=Path, help="Path to compiled fastText binary")
+parser.add_argument("--dataset-dir", type=Path, help="Path to generated dataset")
+parser.add_argument("--use-only-first-section", default=False, action='store_true')
+parser.add_argument("--output-dir", type=Path, help="Path to save fastText embeddings")
+args = parser.parse_args()
+
+
+def run_fasttext_training(fasttext_path: Path, input_path: Path, output_path: Path):
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    FASTTEXT_ARGS = {
+        "input": input_path.absolute(),
+        "output": output_path.absolute(),
+        "lr": 0.025,
+        "dim": 300,
+        "ws": 5,
+        "epoch": 3,
+        "minCount": 5,
+        "neg": 5,
+        "loss": "ns",
+        "bucket": 2000000,
+        "minn": 3,
+        "maxn": 6,
+        "thread": 4,
+        "t": 1e-4,
+        "lrUpdateRate": 100,
+    }
+
+    args = [str(fasttext_path.absolute()), "skipgram"]
+    for k, v in FASTTEXT_ARGS.items():
+        args.extend([f"-{k}", str(v)])
+    print(args)
+    subprocess.run(args, check=True)
+
+
+def dump_training_data(dataset_dir: Path, use_only_first_section=False) -> Path:
+    with (dataset_dir / "train.pkl").open("rb") as f:
+        train_data = pickle.load(f)
+    file_name = "train.txt" if not use_only_first_section else "train-short.txt"
+    data_path = dataset_dir / file_name
+    with data_path.open("w") as f:
+        for ex in tqdm(train_data, desc="Dumping training data"):
+            sentence = ex[0]
+            if use_only_first_section:
+                sentence = sentence[:Dataset.find_first_section(sentence)]
+            f.write(' '.join(sentence) + '\n')
+    return data_path
+
+
+def main():
+    input_path = dump_training_data(args.dataset_dir, args.use_only_first_section)
+    run_fasttext_training(args.fasttext_path, input_path, args.output_dir)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/preprocess/unkprob/README.md b/preprocess/unkprob/README.md
new file mode 100644
index 0000000..1045ade
--- /dev/null
+++ b/preprocess/unkprob/README.md
@@ -0,0 +1,13 @@
+## Preprocess data to obtain required data for the character model
+
+This directory contains the following file:
+
+- `prepare_charlm_data.py`  is the script to extract unique vocabulary over the given dataset and form
+another dataset for the character model to train. Use like below:
+
+```sh
+python prepare_charlm_data.py \
+    --data-dir data/lm \
+    --split 60 20 20 \  # Proportion of train/valid/test splits
+    --output-dir data/char
+```
diff --git a/preprocess/unkprob/prepare_charlm_data.py b/preprocess/unkprob/prepare_charlm_data.py
new file mode 100644
index 0000000..c92b02c
--- /dev/null
+++ b/preprocess/unkprob/prepare_charlm_data.py
@@ -0,0 +1,62 @@
+import argparse
+import pickle
+import random
+from pathlib import Path
+
+
+def load_unique_tokens(path):
+    """Load the pickled dataset (after preprocess_wikitext.py). """
+    tokens = []
+    for m in ["train", "valid", "test"]:
+        with (path / f"{m}.pkl").open("rb") as f:
+            data = pickle.load(f)
+            tokens += [w for ex in data for w in ex[0]]
+    return list(set(tokens))
+
+
+def split_data(data, splits):
+    """Split the tokens into charlm dataset splits."""
+    total = len(data)
+    split = [i / sum(splits) for i in splits]
+    split = [int(total * i) for i in split] + [0.]
+    split = [0] + [sum(split[:i]) for i in range(1, len(split)-1)] + [total]
+    slices = [slice(split[idx], split[idx+1]) for idx in range(len(split)-1)]
+
+    print(f"Number of tokens: {len(data)}, split into {slices}")
+
+    random.shuffle(data)
+    data_splits = [data[sl] for sl in slices]
+    return data_splits
+
+
+def main(args):
+    data_dir = Path(args.data_dir)
+    output_dir = Path(args.output_dir)
+    if not output_dir.exists():
+        output_dir.mkdir()
+    else:
+        assert not (output_dir / "train.pkl").exists(), "Some files with same name exist. Abort."
+        assert not (output_dir / "valid.pkl").exists(), "Some files with same name exist. Abort."
+        assert not (output_dir / "test.pkl").exists(), "Some files with same name exist. Abort."
+
+    unique_tokens = load_unique_tokens(data_dir)
+    data_splits = split_data(unique_tokens, args.split)
+
+    mode = ["train", "valid", "test"]
+    for m, spl in zip(mode, data_splits):
+        with (output_dir / f"{m}.pkl").open("wb") as f:
+            # Compatible format as LMDataset.
+            spl = [(list(s), ) for s in spl] 
+            pickle.dump(spl, f)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--seed", type=int, default=4731)
+    parser.add_argument("--data-dir", type=str, required=True, help="Path to the original dataset.")
+    parser.add_argument("--split", nargs="+", type=int, required=True, help="Ratio of splits.")
+    parser.add_argument("--output-dir", type=str, required=True)
+    args = parser.parse_args()
+
+    random.seed(args.seed)
+    main(args)
diff --git a/preprocess/wikifacts_preprocess.md b/preprocess/wikifacts_preprocess.md
new file mode 100644
index 0000000..031c442
--- /dev/null
+++ b/preprocess/wikifacts_preprocess.md
@@ -0,0 +1,103 @@
+# Preprocessing Routine for WikiFacts Dataset
+
+## Dataset Format
+
+The dataset can be downloaded from <https://bitbucket.org/skaasj/wikifact_filmactor/overview>. The link also contains
+descriptions of the data format. Simply said, the dataset contains three files for each topic (Freebase topic, i.e.
+Wikipedia article title):
+
+- `.sm`: Wikipedia summary annotated with entities.
+- `.fb`: Freebase relations related to the topic. A relation is a triplet `(subject, relation_type, object)`. Each
+  relation is given as a triplet of Freebase IDs.
+- `.en`: The same relations, given in their English names.
+
+For instance, the topic `Louise_Allbritton` is Freebase ID `m.0k3jp_`. An excerpt of the `.fb` file is as follows:
+
+```
+/ns/m.0k3jp_ /ns/people.person.spouse_s [/ns/m.06638z7 /ns/people.marriage.spouse /ns/m.03wl63]
+/ns/m.0k3jp_ /ns/people.person.gender /ns/m.02zsn
+```
+
+And the corresponding `.en` file:
+
+```
+Louise_Allbritton Spouse_(or_domestic_partner) [NO_EN Spouse Charles_Collingwood]
+Louise_Allbritton Gender Female
+```
+
+The stuff in `[]` is called a "composite value type" (CVT), which is a relation triplet in another triplet. This can be
+understood as relation between an entity and a `namedtuple` of entities, and Freebase stores the relation as multiple
+relations between the entity and each entity in the tuple, where the tuple entity is represented as another relation
+between itself and the tuple ID.
+
+Here is an excerpt from the `.sm` file:
+
+```
+@@louise_allbritton/f/ns/m.0k3jp_@@ (3 july 1920–16 february 1979) was an american @@actress/f/ns/m.02hrh1q@@ born in
+@@oklahoma_city_oklahoma/f/ns/m.0fvzg@@.
+```
+
+Entity mentions are tagged as `@@[words]/[type]/ns/[freebase_id]@@`, where `type` can be either `f` or `a`, denoting
+relations in Freebase and Wikipedia anchors. `a` type mentions are links in the Wikipedia page but does not exist
+relations in Freebase.
+
+## Processing Relations
+
+For topic relations, we only use those whose subject is the topic _(the `.fb` file also contains relations where the
+topic is the object)_. For relations with CVT as object, we replace it with a simple relation using the following rule:
+
+```
+topic rel_type [tuple_id tuple_rel_type object]
+ =>
+topic tuple_rel_type object
+```
+
+This is assumed from the heat map in the appendix of the paper.
+
+Note that the object in CVT could also be the topic. Such relations are also removed.
+
+### Indexing Relations
+
+Relations are indexed starting from 0. Negative indices are used for special relations:
+
+- Index -1 (Not-a-fact, NaF): As the name.
+- Index -2 (Anchor): An entity in Freebase but is not has no relation with the topic. Including these actually gives the
+  model an unfair advantage (it'll know these are bound to appear once in the text), but they're still included for
+  completeness.
+- Index -3 (Topic-itself): A self-relation. In WikiText, this is called `TITLE`.
+
+### Indexing Entities
+
+Entities are indexed starting from 0. Negative indices are used for special entities:
+
+- Index -1 (UNK): An entity that is not in the pretrained embeddings.
+
+## Processing Text
+
+### Assigning Copying Positions
+
+Another problem is that the surface form of words in an extracted entity may not exactly match the canonical entity
+name (the first name in the list of aliases). Freebase, like Wikidata, contains an `aka` attribute which lists
+alternative names, but unfortunately is not contained in the WikiFacts dataset. Since the Freebase dump is way too big
+to process, we have two substitution strategies to consider instead:
+
+- Ignore the differences and just assign copying positions sequentially.
+- Replace all entity mentions with their canonical forms. This will be done for non-anchor relations only since there
+  are no canonical forms for anchors.
+
+We use the "ignore" strategy in experiments since it does not change the original text. Alternatively, you can test
+the "replace" strategy by running the `process_wikifacts.py` script with the `--replace` flag.
+
+Finally, we perform additional tokenization using Moses after replacement.
+
+### Anchor Mentions
+
+A post-hoc analysis of matched positions show that:
+
+- Of all 167,287 mentions, 71,728 are anchors.
+- Among non-anchor mentions, 50,073 of them are not in canonical form.
+
+### Disambiguating Relations
+
+When matching relations to objects, if there are multiple relation matches, simply pick the first one. This is the
+paper's strategy as the authors had answered on OpenReview.
diff --git a/preprocess/wikitext/extract_wikidata.py b/preprocess/wikitext/extract_wikidata.py
new file mode 100644
index 0000000..ab51f96
--- /dev/null
+++ b/preprocess/wikitext/extract_wikidata.py
@@ -0,0 +1,243 @@
+"""Preprocessing script for latest-truthy.nt, which is obtainable from
+https://dumps.wikimedia.org/wikidatawiki/entities/
+
+The original script is authored by Shoetsu @ University of Tokyo and modified by
+Hiroaki Hayashi.
+
+Usage:
+
+    python extract_wikidata.py \
+        --source-path /path/to/latest-truthy.nt \
+        --target-dir /path/to/target_dir \
+        --lang LANG \
+        --required-value-types VALUE_TYPES \
+        --exclude-value-types VALUE_TYPES \
+
+* VALUE_TYPES can be specified from the following: name, aka, desc.
+
+* Specify LANG to harvest surface forms only in the language.
+
+"""
+import argparse
+import bz2
+import codecs
+import gzip
+import pickle
+import re
+import sys
+from pathlib import Path
+from collections import defaultdict
+
+from tqdm import tqdm
+
+
+# for important columns that have a particular value or string.
+url2type = {
+    "<http://schema.org/name>": "name",
+    "<http://schema.org/description>": "desc",
+    "<http://www.w3.org/2004/02/skos/core#altLabel>": "aka",
+    "<http://www.wikidata.org/prop/direct/P569>": "dob",
+    "<http://www.wikidata.org/prop/direct/P570>": "dod",
+}
+
+# the values of name, description, and aka are followed by a lang symbol. (e.g. "green plants"@en)
+VALUE_TEMPLATE = r"\"(.+)\"\s*@({})"
+
+
+def create_entity_template():
+    return {"name": "", "desc": "", "aka": []}
+
+
+def is_an_edge(triple):
+    pattern = "[PQ][0-9]+"
+    ids = [re.match(pattern, x.split("/")[-1][:-1]) for x in triple]
+    if not None in ids:
+        return [m.group(0) for m in ids]
+    else:
+        return None
+
+
+def is_a_value(triple):
+    rel = triple[1]
+    return rel in url2type.keys()
+
+
+def process_data(args):
+
+    """reads `latest-truthy.nt` line by line and extract both entities and triples
+    informatoin.
+
+    Entities are stored in the form of gigantic dictionaries.
+    Triples are dumped in this function as the function processes each line.
+    """
+    f_triple = (args.target_dir / "triples.txt").open("w")
+
+    counts = {"item": 0, "triple": 0}
+    entities = {}
+    value_regx = VALUE_TEMPLATE.format(args.lang)
+    date_regx = r"\"(.+)\"\^\^.+"
+
+    if args.source_path.name.endswith("gz"):
+        open_func = gzip.open
+        is_byte = True
+    elif args.source_path.name.endswith("bz2"):
+        open_func = bz2.open
+        is_byte = True
+    else:
+        open_func = open
+        is_byte = False
+
+    # Approximate number of lines, as of the dump from 20180921.
+    for i, line in tqdm(
+        enumerate(open_func(args.source_path, "r")), ncols=80, total=3127159097, ascii=True
+    ):
+        if is_byte:
+            line = line.decode("utf8")
+        line_splits = line.split(" ")
+        subj_url, rel_url, obj_url = (
+            line_splits[0],
+            line_splits[1],
+            " ".join(line_splits[2:-1]),
+        )
+
+        # Extract only (entity, prop, entity) or (entity | prop, type, value) triples.
+        triple = is_an_edge((subj_url, rel_url, obj_url))
+        if triple:
+            print("\t".join(triple), file=f_triple)
+            counts["triple"] += 1
+
+        # Extract values (name, description, aka.)
+        elif is_a_value((subj_url, rel_url, obj_url)):
+            v_type = url2type[rel_url]
+
+            subj = subj_url.split("/")[-1][:-1]  # "<.+/Q[0-9+]>" -> "Q[0-9+]"
+
+            if v_type in ["dob", "dod"]:
+                m = re.match(date_regx, obj_url)
+            else:
+                m = re.match(value_regx, obj_url)
+
+            if m:  # values must be written in target lang.
+                v = m.group(1)  # value
+                v = " ".join([x for x in v.split() if x]).strip()
+                v = codecs.decode(v, "unicode-escape")
+            else:
+                continue
+
+            if subj not in entities:
+                entities[subj] = create_entity_template()
+                counts["item"] += 1
+            if v_type == "aka":
+                entities[subj][v_type].append(v)
+            else:
+                entities[subj][v_type] = v
+
+        if args.debug and i > 10000000:
+            break
+
+    f_triple.close()
+
+    return entities, counts
+
+
+def filter_items(entities, required_value_types=[], exclude_value_types=[]):
+    def is_complete(entity):
+        if False not in [
+            True if entity[v_type] else False for v_type in required_value_types
+        ]:
+            return True
+        else:
+            return False
+
+    if required_value_types:
+        # Remove incomplete (no name) entities due to lack of data
+        e_complete = {k: v for k, v in entities.items() if is_complete(v)}
+    else:
+        e_complete = entities
+
+    if exclude_value_types:
+        # Remove fields from entities
+        for vt in exclude_value_types:
+            for k, e in e_complete.items():
+                if vt in e:
+                    del e_complete[k][vt]
+
+    items = {k: v for k, v in e_complete.items() if k[0] == "Q"}
+    item_with_dates = {k: v for k, v in items.items() if "dob" in v or "dod" in v}
+    props = {k: v for k, v in e_complete.items() if k[0] == "P"}
+    return items, item_with_dates, props
+
+
+def dump_dict(data, path):
+    with path.open("w") as f:
+        if isinstance(data, dict):  # items, props
+            for k, values in list(data.items()):
+                name = values["name"]
+                aka = values["aka"]
+                if type(aka) == list:
+                    aka = "||".join([x for x in aka])
+                columns = [k, name, aka]
+                if "desc" in values:
+                    columns.append(values["desc"])
+                print("\t".join(columns), file=f)
+        else:
+            raise Exception
+
+
+def main(args):
+    args.target_dir.mkdir(exist_ok=True)
+
+    f_items = args.target_dir / "items.bin"
+    f_item_with_dates = args.target_dir / "item_with_dates.bin"
+    f_props = args.target_dir / "properties.bin"
+
+    entities, counts = process_data(args)  # Triples are already dumped
+    items, iwds, props = filter_items(
+        entities, args.required_value_types, args.exclude_value_types
+    )
+    print(f"items, props, triples = ({len(items)}, {len(props)}, {counts['triple']})")
+
+    # Additionally dump items and props line by line.
+    dump_dict(items, f_items.with_suffix(".txt"))
+    dump_dict(props, f_props.with_suffix(".txt"))
+
+    if not f_items.exists():
+        with f_items.open("wb") as f:
+            pickle.dump(items, f)
+        del items
+
+    if not f_item_with_dates.exists():
+        with f_item_with_dates.open("wb") as f:
+            pickle.dump(iwds, f)
+        del iwds
+
+    if not f_props.exists():
+        with f_props.open("wb") as f:
+            pickle.dump(props, f)
+        del props
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Wikidata dump parser.")
+    parser.add_argument("--source-path", type=Path, required=True)
+    parser.add_argument("--target-dir", type=Path, default="./extracted")
+    parser.add_argument("--lang", default="en")
+    parser.add_argument(
+        "--required_value_types",
+        nargs="+",
+        default=["name"],
+        help="Required value types which an *entity* must have.",
+    )
+    parser.add_argument(
+        "--exclude-value-types",
+        nargs="+",
+        default=[],
+        help="Excluded value types which an *entity* must omit.",
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Debug mode; only a small amount is extracted.",
+    )
+    args = parser.parse_args()
+    main(args)
diff --git a/preprocess/wikitext/match_wikidata.py b/preprocess/wikitext/match_wikidata.py
new file mode 100644
index 0000000..cba16f2
--- /dev/null
+++ b/preprocess/wikitext/match_wikidata.py
@@ -0,0 +1,576 @@
+import argparse
+import json
+import logging
+import pickle
+import re
+from collections import defaultdict, deque
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import tqdm
+
+import numpy as np
+from nltk.corpus import stopwords
+
+LOGGER = logging.getLogger(__name__)
+STOPWORDS = set(stopwords.words("english") + [".", ",", '"'])
+PAREN = re.compile(r"([^\(\s]+)\s\([^\)]+\)")  # Parenthesis removal
+
+
+class Node:
+    def __init__(
+        self,
+        id: str,
+        relations: List[str],
+        depth: int,
+        head_id: str,
+        surfaces: List[str] = [],
+    ):
+        self.id = id
+        self.relations = relations
+        self.depth = depth
+        self.head_id = head_id
+        self.surfaces = surfaces
+
+    def add_surfaces(self, surfaces):
+        self.surfaces = surfaces
+
+
+class Wikidata:
+
+    """A class object containing the full wikidata KB."""
+
+    def __init__(self, id2name, id2nb, id2date):
+        self.id2name = id2name
+        self.id2date = id2date
+        self.id2nb = id2nb
+
+    def retrieve_neighbors(self, query: Node, distance: int = 1) -> List[Node]:
+        """get a neighbor of an entity with BFS
+
+        :param query: query node.
+        :param distance: k-hop neighbors.
+        """
+        assert distance > 0
+        depth_limit = query.depth + distance
+        retrieved = [
+            self.get_surface_forms(Node(n, [rel], query.depth + 1, query.id))
+            for rel, n in self.id2nb.get(query.id, [])
+        ]
+
+        if distance == 1:
+            return retrieved
+
+        q = deque(retrieved)
+        while len(q) > 0:
+            node = q.popleft()
+            if node.depth > depth_limit:
+                break
+
+            retrieved.append(node)
+            for r, n in self.id2nb.get(node.id, []):
+                # Construct a new node and register surface forms for the node
+                neighbor = self.get_surface_forms(
+                    Node(n, node.relations + [r], node.depth + 1, node.id)
+                )
+                q.append(neighbor)
+
+        return retrieved
+
+    def get_surface_forms(self, node: Node) -> Node:
+        """get surface forms of a given node and return the same node."""
+        surface_forms = []
+        expressions = self.id2name.get(node.id)
+        if expressions is not None:
+            canonical = re.sub(PAREN, r"\1", expressions[0])
+            aliases = list(
+                set(re.sub(PAREN, r"\1", e.strip()) for e in expressions[1:])
+            )  # remove duplicates
+            node.add_surfaces([canonical] + aliases)
+
+        return node
+
+
+class SubGraph:
+
+    """A subgraph for an article."""
+
+    def __init__(self, root: Node):
+        self.root = root
+        self.heads: Dict[str, Node] = {root.id: root}
+        self.entities = []
+
+    def expand(self, ent_id: str, kg: Wikidata):
+        """retrieve one-hop neighbors from a given query wikidata ID.
+
+            ent_id: Wikidata ID.
+            kg: Wikidata.
+        """
+        # Search the graph from the title entity if ent_id is not given.
+        if ent_id is None:
+            ent_id = self.root.id
+            query_node = self.root
+        elif ent_id in self.heads:
+            query_node = self.heads[ent_id]
+
+        retrieved = kg.retrieve_neighbors(query_node, distance=1)
+        head_candidates = set([n.id for n in retrieved])
+        for hid in head_candidates:
+            if hid not in self.heads:
+                self.heads[hid] = next(n for n in retrieved if n.id == hid)
+
+        self.entities += retrieved
+
+
+def load_corpus(wikitext_path: Path):
+    """reads the dataset preprocessed by the split_wikitext.py."""
+    dataset, skipped = {}, {}
+    for split in ["train", "valid", "test"]:
+        with (wikitext_path / f"{split}.json").open("r") as f:
+            data = json.load(f)
+
+        loaded_articles, skipped_articles = [], []
+        for i, article in enumerate(data):
+            if article["id"] == "":
+                skipped_articles[split].append(article)
+
+            # Join section text into single string
+            full_body = f"= {article['title'].strip()} = "
+            for head, sec in article["sections"]:
+                head = head.strip()
+                if head != "":
+                    head = f"= = {head.strip()} = = "
+                sec = sec.strip() + " "
+                if sec.strip() == "":
+                    sec = ""
+                full_body += head + re.sub(r"\s+", " ", sec)
+
+            data[i]["tokens"] = full_body
+            loaded_articles.append(article)
+
+        dataset[split] = data
+        skipped[split] = skipped_articles
+
+    return dataset, skipped
+
+
+def find_surface_matches(
+    article: str, token_array: np.ndarray, entities: List[Node], match_stopwords, lower
+):
+    """looks for surface matches for given entities over an article.
+
+    :param article: Article text in a single string.
+    :param token_array: Token-split numpy array of the article.
+    :param entities: List of entity nodes to match the surfaces against.
+    :param match_stopwords: A flag for whether or not to include stopwords.
+    :param lower: A flag for whether or not to match lowercased.
+    """
+    found_spans = []
+    for entity in entities:
+
+        for surface in entity.surfaces:
+            if len(surface.strip()) == 0:
+                continue
+
+            # single word entity
+            if len(surface.split(" ")) == 1:
+                if not match_stopwords and surface in STOPWORDS:
+                    continue
+
+                match_indices = np.asarray(
+                    token_array == (surface.lower() if lower else surface)
+                ).nonzero()[0]
+                if match_indices.size > 0:  # Couldn't be found.
+                    found_spans += [
+                        (
+                            entity.id,
+                            entity.head_id,
+                            (i, i + 1),
+                            entity.relations,
+                            surface,
+                            entity.surfaces,
+                        )
+                        for i in match_indices
+                    ]
+
+            # multi-word entity: concatenate it and match, and then split the string back
+            else:
+                # multi-word entity matching
+                concat_surface = surface.replace(" ", "####")
+                if lower:
+                    concat_surface = concat_surface.lower()
+
+                try:
+                    # Insert concat chars in the original article, and split again if there's any merged.
+                    subbed_article, sub_count = re.subn(
+                        re.escape(surface.lower() if lower else surface),
+                        concat_surface,
+                        article,
+                    )
+                    if sub_count > 0:
+                        phrase_len = len(surface.split(" "))
+                        subbed_token_array = np.array(subbed_article.split(" "))
+                        match_indices = np.asarray(
+                            subbed_token_array == concat_surface
+                        ).nonzero()[0]
+                        # token-level matched indices if it were not concat'd
+                        match_token_indices = [
+                            range(
+                                pos + (phrase_len - 1) * i,
+                                pos + (phrase_len - 1) * i + phrase_len,
+                            )
+                            for i, pos in enumerate(match_indices)
+                        ]
+                        found_spans += [
+                            (
+                                entity.id,
+                                entity.head_id,
+                                (i.start, i.stop),
+                                entity.relations,
+                                surface,
+                                entity.surfaces,
+                            )
+                            for i in match_token_indices
+                        ]
+
+                # TODO: more specific exception handling.
+                except Exception:
+                    LOGGER.info(f"Bad name : {surface}")
+
+    # sort by the starting position
+    found_spans = sorted(found_spans, key=lambda x: x[2][0])
+
+    return found_spans
+
+
+def annotate_articles(
+    dataset,
+    canonical_forms,
+    kg: Wikidata,
+    match_title: bool = True,
+    match_date: bool = True,
+    match_stopwords: bool = False,
+    lower: bool = False,
+):
+    """annotates entity matches in each article.
+
+    :param dataset: Dictionary of list of instances.
+    :param kg: KG object containing the loaded wikidata dictionaries.
+    :param match_title: A flag for whether or not to match the title itself.
+    :param match_date: A flag for whether or not to match the date info if present.
+    :param match_stopwords: A flag for whether or not to match the stopwords.
+    :param lower: A flag for whether or not to match lowercased.
+
+    """
+
+    def remove_duplicate_occurrence(matches) -> Dict[str, int]:
+        """finds the earliest occurrences of matched entities."""
+        earliest_span = {}
+        for ent_id, _, (bg, ed), _, _, _ in matches:
+            if ent_id != "" and (
+                ent_id not in earliest_span or bg < earliest_span[ent_id][0]
+            ):
+                earliest_span[ent_id] = (bg, ed)
+        return earliest_span
+
+    spans = []
+
+    for idx, article in tqdm.tqdm(
+        enumerate(dataset), ncols=80, desc="Searching over dataset", ascii=True
+    ):
+        title, title_id, article_body = (
+            article["title"],
+            article["id"],
+            article["tokens"],
+        )
+
+        # prepare a graph with the title entity
+        graph = SubGraph(Node(title_id, [], 0, ""))
+
+        if lower:
+            article_body = article_body.lower()
+
+        tokens = article_body.strip().split(" ")
+
+        token_array = np.array(tokens)
+        n_tokens = len(token_array)
+
+        ent_id = graph.heads[article["id"]].id  # first entity to check
+        graph.expand(None, kg)  # expand and get the new surfaces
+        entities_tobe_searched = graph.entities
+        if article["id"] == "Q2353693":
+            import pdb
+
+            pdb.set_trace()
+
+        if match_title:
+            # ALWAYS put canonical forms at the first index of the surface forms.
+            canonical_form = canonical_forms[idx]
+            if canonical_form == title:
+                surfaces = [title]
+            else:
+                surfaces = [canonical_form, title]
+
+            entities_tobe_searched += [
+                Node(
+                    id="",
+                    relations=["@TITLE@"],
+                    depth=1,
+                    head_id=ent_id,
+                    surfaces=surfaces,
+                )
+            ]
+
+        if match_date:
+            if ent_id in kg.id2date and kg.id2date[ent_id][1] == 0:
+                dates = kg.id2date.get(ent_id)[0]
+                kg.id2date[ent_id][1] = 1
+            else:
+                dates = []
+
+            entities_tobe_searched += [
+                Node(id="", relations=d[1], depth=d[2], head_id=ent_id, surfaces=d[0])
+                for d in dates
+            ]
+
+        found = find_surface_matches(
+            article_body,
+            token_array,
+            entities=entities_tobe_searched,
+            match_stopwords=match_stopwords,
+            lower=lower,
+        )
+        spans.append(found)
+
+    return spans
+
+
+def expand_date(date):
+    """Get multiple surface form date expressions from a date object."""
+
+    def convert_datetime(date_string):
+        year = "%Y"
+        if date_string.startswith("-"):
+            year = "BC%Y"
+            date_string = date_string[1:]
+
+        # add more formats here if more variations are necessary
+        fmts = [f"%-d %B {year}", f"%B %-d , {year}"]
+
+        date_obj = datetime.strptime(date_string, "%Y-%m-%dT00:00:00Z")
+        s = [datetime.strftime(date_obj, f) for f in fmts]
+        return s
+
+    name2propid = {"dob": "P569", "dod": "P570"}
+    expanded_dates = []
+    for k, v in date.items():
+        if k in name2propid:
+            try:
+                expanded_dates += [[convert_datetime(v), [name2propid[k]], 1]]
+            except ValueError:
+                LOGGER.info(f"Date expression: {v} was not parsed.")
+
+    return expanded_dates
+
+
+def convert_ids(data, prop2name):
+    """Convert relation IDs into actual surface forms."""
+    converted = []
+    for matches in data:
+        named = [
+            (
+                ent_id,
+                head_id,
+                (int(span[0]), int(span[1])),
+                (relations, [prop2name.get(r, "NO_REL") for r in relations]),
+                surface,
+                surfaces,
+            )
+            for ent_id, head_id, span, relations, surface, surfaces in matches
+        ]
+        converted.append(named)
+
+    return converted
+
+
+def main(args):
+
+    args.output_dir.mkdir(exist_ok=True)
+    LOGGER.info(f"Save directory is {args.output_dir}")
+
+    dataset, _ = load_corpus(args.wikitext_dir)
+    LOGGER.info("Loaded dataset.")
+
+    # Load canonical_forms
+    canonical_forms = {}
+    for mode in ["train", "valid", "test"]:
+        with (args.canonical_forms_dir / f"{mode}_canonical_forms.txt").open("r") as f:
+            canonical_forms[mode] = [l.strip() for l in f]
+            assert len(canonical_forms[mode]) == len(dataset[mode])
+    LOGGER.info("Loaded canonical forms.")
+
+    # Assuming that triples are separated in multiple files
+    id2name: Dict[str, List[str]] = {}
+    with (args.wikidata_dir / "items.txt").open("r") as f:
+        for line in tqdm.tqdm(
+            f, ncols=80, desc="Loading entities", ascii=True, total=37899556
+        ):
+            line = line[:-1].split("\t")
+            if len(line) == 3:
+                k, name, aliases = line
+            else:
+                k, name, aliases, _ = line
+
+            id2name[k] = [name] + aliases.split("||")
+
+    # only items with date info found
+    id2date = {}
+    with (args.wikidata_dir / "item_with_dates.bin").open("rb") as f:
+        item_with_dates = pickle.load(f)
+        for k, v in tqdm.tqdm(
+            item_with_dates.items(),
+            ncols=80,
+            desc="Loading entities w/ dates",
+            ascii=True,
+        ):
+            # item_with_dates contain dob/dod info under "n/a" key
+            # Second element to keep track of whether the date info is considered or not
+            id2date[k] = [expand_date(v), 0]
+
+    LOGGER.info(f"Loaded names and dates.")
+
+    # load triples
+    id2nb: Dict[str, List[Tuple[str, str]]] = defaultdict(list)
+    with (args.wikidata_dir / f"triples.txt").open("r") as f:
+        for line in tqdm.tqdm(
+            f, ncols=80, desc="Loading triples", ascii=True, total=270306417
+        ):
+            subj, rel, obj = line[:-1].split("\t")
+            if obj.startswith("Q"):
+                id2nb[subj].append((rel, obj))
+
+    # load properties
+    with (args.wikidata_dir / "properties.bin").open("rb") as f:
+        props = pickle.load(f)
+        prop2name = {k: v["name"] for k, v in props.items()}
+        prop2name["@TITLE@"] = "TITLE"
+
+    wikidata_kg = Wikidata(id2name, id2nb, id2date)
+
+    for mode in tqdm.tqdm(dataset, ncols=80, total=3, ascii=True):
+        if args.use_shards:
+            shards = len(dataset[mode]) // args.shard_size
+            for shard in tqdm.trange(shards + 1, ncols=80, desc="Shard", ascii=True):
+                slice_ = slice(
+                    args.shard_size * shard,
+                    min(args.shard_size * (shard + 1), len(dataset[mode])),
+                )
+                matches = annotate_articles(
+                    dataset[mode][slice_],
+                    canonical_forms[mode][slice_],
+                    wikidata_kg,
+                    match_title=True,
+                    match_date=True,
+                    match_stopwords=False,
+                    lower=args.lower,
+                )
+
+                # Insert show_example function here.
+                named_matches = convert_ids(matches, prop2name)
+
+                output = []
+                for article, matched_spans in tqdm.tqdm(
+                    list(zip(dataset[mode][slice_], named_matches)),
+                    ncols=80,
+                    ascii=True,
+                ):
+                    # save the triple annotations with the tokens
+                    output.append((article["tokens"].strip().split(" "), matched_spans))
+
+                # Dump the dataset splits with relation annotations
+                with (args.output_dir / f"{mode}_{shard:02d}.pkl").open("wb") as f:
+                    pickle.dump(output, f)
+
+        else:
+            matches = annotate_articles(
+                dataset[mode],
+                canonical_forms[mode],
+                wikidata_kg,
+                match_title=True,
+                match_date=True,
+                match_stopwords=False,
+                lower=args.lower,
+            )
+            # Insert show_example function here.
+            named_matches = convert_ids(matches, prop2name)
+
+            output = []
+            for article, relations in tqdm.tqdm(
+                list(zip(dataset[mode], named_matches)),
+                ncols=80,
+                ascii=True,
+                desc="Dumping",
+            ):
+                # save the triple annotations with the tokens
+                output.append((article["tokens"].strip().split(" "), relations))
+
+            # Dump the dataset splits with relation annotations
+            with (args.output_dir / f"{mode}.pkl").open("wb") as f:
+                pickle.dump(output, f)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        "Perform surface matching on text using Wikidata dump."
+    )
+    parser.add_argument(
+        "--wikitext-dir",
+        type=Path,
+        help="Path to Article-split WikiText-103 JSON files.",
+        required=True,
+    )
+    parser.add_argument(
+        "--wikidata-dir",
+        type=Path,
+        help="Path to the extrated Wikidata dump.",
+        required=True,
+    )
+    parser.add_argument(
+        "--canonical-forms-dir", type=Path, help="Path to canonincal forms."
+    )
+    parser.add_argument(
+        "--output-dir", type=Path, help="Path to the output directory.", required=True
+    )
+    parser.add_argument(
+        "--use-shards",
+        action="store_true",
+        help="Whether to split the result files into shards.",
+    )
+    parser.add_argument(
+        "--shard-size", type=int, help="# of examples in one shard.", default=1000
+    )
+    parser.add_argument(
+        "--lower", action="store_true", help="If matching lowercased or not."
+    )
+    parser.add_argument(
+        "--logging-level",
+        type=str,
+        default="INFO",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
+    )
+    args = parser.parse_args()
+
+    logfile = args.output_dir / f"{str(datetime.now()).replace(' ', '_')}.log"
+
+    if not args.output_dir.exists():
+        args.output_dir.mkdir()
+    LOGGER.info(f"Saving into {args.output_dir}.")
+
+    logging.basicConfig(
+        datefmt="%m-%d %H:%M:%S",
+        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+        level=logging.getLevelName(args.logging_level),
+        filename=logfile,
+    )
+    main(args)
diff --git a/preprocess/wikitext/split_wikitext.py b/preprocess/wikitext/split_wikitext.py
new file mode 100644
index 0000000..b2161f8
--- /dev/null
+++ b/preprocess/wikitext/split_wikitext.py
@@ -0,0 +1,237 @@
+""" split_wikitext.py: Split WikiText=103 at article-level."""
+import argparse
+import json
+import re
+from pathlib import Path
+from typing import Dict, List
+
+import requests
+from tqdm import tqdm
+
+WIKI_URL = "https://en.wikipedia.org/w/api.php"
+
+TITLE_PATTERN = re.compile(r"^= ([^=]+) =$")
+SECTION_PATTERN = re.compile(r"^(= ){2,}([^=]+)(= ){2,}$")
+
+
+def split_into_articles(path: Path, found_ids):
+    """Reads the WikiText line by line and split into articles.
+
+        path: Path to WikiText-103.
+        found_ids: title to ID mapping.
+    """
+    docs = []
+    with path.open("r") as f:
+        doc = {}
+        buffer = ""
+        no_record = False
+        data = f.read().strip().split("\n")
+        missed = []
+
+        for idx, line in tqdm(enumerate(data), ncols=80, total=len(data), ascii=True):
+            line = line.strip()
+
+            # Only perform search when there's a symbol
+            matched_title = (
+                re.search(TITLE_PATTERN, line) if line.startswith("=") else None
+            )
+
+            if matched_title is None and no_record:
+                continue
+
+            # When a title is detected, the next line is a new line
+            elif matched_title is not None and data[idx + 1].strip() == "":
+                if "sections" in doc and len(doc["sections"]) > 0:
+                    if len(buffer) > 0:
+                        doc["sections"][-1][1] = buffer
+                        buffer = ""
+
+                    # Commit document
+                    docs.append(doc)
+                    doc = {}
+
+                processed_title = preprocess([matched_title.group(1)])[0]
+
+                # Ignore the whole document until next valid title is found
+                if found_ids is not None and processed_title not in found_ids:
+                    no_record = True
+                    doc["id"] = ""
+                else:
+                    no_record = False
+                    doc["title"] = matched_title.group(1)
+                    doc["sections"] = [["", ""]]
+                    doc["id"] = found_ids[processed_title]
+
+            elif matched_title is not None:
+                missed.append(matched_title.group(1))
+                buffer += line + " "
+
+            # Not a title, but with heading
+            elif line.startswith("= ="):
+                # Commit buffer
+                if len(buffer) > 0:
+                    doc["sections"][-1][1] = buffer
+                    buffer = ""
+
+                line += " "
+                section_match = re.search(SECTION_PATTERN, line)
+                if section_match is None:
+                    section_header = ""
+                else:
+                    section_header = section_match.group(2)
+                doc["sections"].append([section_header, ""])
+
+            else:
+                buffer += line + " "
+
+        if len(buffer) > 0:
+            doc["sections"][-1][1] = buffer
+        if len(doc.keys()) > 0:
+            docs.append(doc)
+
+    return docs
+
+
+def preprocess(titles: List[str]) -> List[str]:
+    """Handmade text normalization."""
+    preprocessed = []
+    for t in titles:
+        t = re.sub(r"\s@-@\s", "-", t)
+        t = re.sub(r"\(\s(.+)\s\)", r"(\1)", t)
+        t = re.sub(r"\s\'(\w+)", r"'\1", t)
+        t = t.replace("What (ever)", "What(ever)")
+        t = t.replace(" ; ", ";")
+        t = t.replace(" – ", "–")  # "em dash" present in some titles
+        t = t.replace("... ", "...")
+        t = t.replace(" ...", "...")
+        t = t.replace(" , ", ", ")
+        t = t.replace(" ' ", "' ")
+        t = re.sub(r"\s?'\s?([a-z]{,2})\s", r"'\1 ", t)
+        t = t.replace(" !", "!")
+        t = t.replace(" ?", "?")
+        t = t.replace(" :", ":")
+        # Only reduce spaces when single characters are ampasanded
+        t = re.sub(r"(^|\s)(\w)\s\&\s(\w)(\s|$)", r"\1\2&\3\4", t)
+
+        preprocessed.append(t)
+    return preprocessed
+
+
+def query_wikidata_id(titles: List[str], dest: Path) -> Dict[str, str]:
+    """Queries Wikipedia (slowly) and obtain the *Wikidata* ID given the title strings.
+
+        titles: List of title strings.
+        dest: Output file name to store the found IDs, if specified.
+    """
+
+    def get_id(title: str) -> str:
+        """Issues a query to obtain Wikidata ID for a given title string.
+        Returns an empty string if not found.
+        """
+        query = {
+            "action": "query",
+            "prop": "pageprops",
+            "titles": title,
+            "format": "json",
+            "maxlag": 5,
+        }
+        try:
+            r = requests.get(WIKI_URL, params=query)
+            page = json.loads(r.content.decode(r.encoding))["query"]["pages"]
+            page_id = list(page.keys())[0]
+            return page[page_id]["pageprops"]["wikibase_item"]
+        except KeyError:  # When the title is not found for any reason
+            return ""
+
+    titles = preprocess(titles)
+    # Get ids by tagging missing ones as empty string
+    with tqdm(titles, ncols=80, ascii=True, desc="Querying Wikidata") as pbar:
+        ids = {t: get_id(t) for t in pbar}
+
+    notfound_count = len([k for k, v in ids.items() if len(v) == 0])
+
+    with dest.open("w") as f, dest.with_suffix(".missing").open("w") as fnot:
+        for t, id_ in ids.items():
+            if len(id_) > 0:
+                print(t, id_, file=f, sep="\t")
+            else:
+                print(t, "", file=fnot, sep="\t")
+                print(t, "", file=f, sep="\t")
+
+    print(
+        f"Wikidata IDs for {notfound_count}/{len(titles)} articles were not found.\n"
+        "Resolve this by visitng *.ids.missing and query by yourself and fill them"
+        " into *.ids.txt."
+    )
+    assert len(titles) == len(ids)
+    return ids
+
+
+def main(args):
+
+    args.output_dir.mkdir(exist_ok=True)
+
+    # Considers the raw WikiText-103, which is not unk-ed
+    wikitext_paths = args.wikitext_dir.glob("*.raw")
+
+    dataset = {}
+    for wpath in wikitext_paths:
+        splits = wpath.with_suffix("").name.split(".")[1]
+
+        ids_path = args.ids_dir / f"{splits}.ids.txt"
+        if ids_path.exists():
+            with ids_path.open("r") as f:
+                found_ids: Dict[str, str] = {}
+                for l in f.read().strip().split("\n"):
+                    name, id_ = tuple(l.strip().split("\t"))
+                    found_ids[name] = id_
+        else:
+            found_ids = None
+
+        dataset[splits] = split_into_articles(wpath, found_ids)
+
+        # Make found_ids, they have to be constructed based on the split articles
+        if found_ids is None:
+            titles = [d["title"] for d in dataset[splits]]
+            found_ids = query_wikidata_id(titles, ids_path)
+
+            for d in dataset[splits]:
+                d["id"] = found_ids[preprocess([d["title"]])[0]]
+
+        if args.drop_missing_ids:
+            dataset[splits] = [d for d in dataset[splits] if len(d["id"]) > 0]
+
+        print(f"{splits}: {len(dataset[splits])} articles.")
+
+        with (Path(args.output_dir) / f"{splits}.json").open("w") as fout:
+            json.dump(dataset[splits], fout, ensure_ascii=False, indent=2)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Preprocess WikiText-103 into a list of articles.")
+    parser.add_argument(
+        "--wikitext-dir",
+        type=Path,
+        help="Directory to WikiText raw files.",
+        required=True,
+    )
+    parser.add_argument(
+        "--ids-dir",
+        type=Path,
+        help="Directory to title-id mapping files, if exists.",
+        default="",
+    )
+    parser.add_argument(
+        "--output-dir", type=Path, help="Where the articles are dumped.", default=None
+    )
+    parser.add_argument(
+        "--drop-missing-ids",
+        action="store_true",
+        help="Whether to keep articles with missing IDs.",
+    )
+    args = parser.parse_args()
+
+    if args.output_dir is None:
+        args.output_dir = args.wikitext_dir
+
+    main(args)
diff --git a/repl.py b/repl.py
new file mode 100644
index 0000000..ad9ef94
--- /dev/null
+++ b/repl.py
@@ -0,0 +1,358 @@
+import functools
+import math
+import sys
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+from torch import Tensor
+
+from dataset import LRLMExample
+from dataset.base import KBLMDataset
+from dataset.utils import BatchSequence, MatchedSpan, Relation
+from models.base import BaseLM
+from models.sample_utils import SampledOutput
+from nnlib import utils
+from nnlib.utils import Logging
+
+
+def repl(dataset: KBLMDataset, model: BaseLM):
+    import re
+    from run import compute_batch_loss  # avoid circular import
+
+    print(Logging.color(
+        s="Execute `sample(name=\"Barack Obama\")` to generate samples for given entity.\n"
+          "Execute `sample(split='test', index=1)` to generate samples for specific data entry.\n"
+          "For more configurable settings, please refer to method `models.lrlm.LRLM.sampling_decode`.\n",
+        col='green'))
+
+    def get_example(func):
+        def find_name(name: str) -> Tuple[str, int]:
+            for split in dataset.data:
+                try:
+                    index = next(idx for idx, ex in enumerate(dataset.data[split])
+                                 if name in ' '.join(ex.sentence[:10]))
+                    return split, index
+                except StopIteration:
+                    continue
+            else:
+                raise ValueError("Name not found!")
+
+        @functools.wraps(func)
+        def wrapped(name: Optional[str] = None, split: str = 'test', index: int = 1,
+                    start_symbol: str = '<s>', **kwargs):
+            if name is not None:
+                try:
+                    split, index = find_name(f"= {name} =")
+                except ValueError:
+                    split, index = find_name(name)
+            if start_symbol is None:
+                start_symbol = '<s>'
+            start_symbol = dataset.word_vocab.w2i[start_symbol] if isinstance(start_symbol, str) else start_symbol
+            example = dataset.data[split][index]
+            try:
+                name = ' '.join(example.sentence[2:example.sentence.index('=', 2)])
+            except ValueError:
+                # probably WikiFacts
+                pos = min((example.sentence.index(token) for token in ['is', 'was', '(']
+                           if token in example.sentence), default=3)
+                name = ' '.join(example.sentence[1:pos])
+            if "file" not in kwargs:
+                file = sys.stdout
+            else:
+                file = kwargs["file"]
+            print(f"Data split {split}, index {index}, name \"{name}\"", file=file)
+            func(example, start_symbol, **kwargs)
+
+        return wrapped
+
+    end_symbol = dataset.word_vocab.w2i['</s>']
+
+    @get_example
+    def sample(example, start_symbol, max_length=500, n_tries=1, bptt_size=150, **kwargs):
+        (init_batch, batches) = dataset.create_one_batch([example], bptt_size)
+        if kwargs.get('generate_unk', False):
+            fulli2w = [w for w, i in sorted(dataset.total_w2i.items(), key=lambda x: x[1])]
+            unkinfo = (dataset.unk_probs, fulli2w)
+            del kwargs["generate_unk"]
+        else:
+            unkinfo = None
+
+        best_output = None
+        for _ in range(n_tries):
+            print_info = kwargs.get('print_info', n_tries == 1)
+            output: SampledOutput = model.sampling_decode(
+                dataset.vocab,
+                example,
+                begin_symbol=start_symbol,  # this is the actual start symbol in all articles
+                end_symbol=end_symbol,
+                max_length=max_length,
+                color_outputs=True,  # generate colored outputs in terminal
+                print_info=print_info,
+                unkinfo=unkinfo,
+                init_batch=init_batch,
+                **kwargs)
+            if best_output is None or output.sample_loss < best_output.sample_loss:
+                best_output = output
+
+        # format title & subtitles
+        if n_tries > 1:
+            print(f"Sample loss: {best_output.sample_loss:.3f}, PPL: {math.exp(best_output.sample_loss):.3f}")
+            print(f"Complete / incomplete entities: {best_output.complete_copies} / {best_output.incomplete_copies}")
+        sentence = re.sub(r'= = (.*?) = = ', r'\n\n== \1 ==\n    ', ' '.join(best_output.sentence))
+        sentence = re.sub(r'= (.*?) = ', r'= \1 =\n    ', sentence)
+        print(sentence)
+
+    @get_example
+    def average_copies(example, start_symbol, max_length=200, n_tries=10, progress=False,
+                       show_samples=False, **kwargs):
+        complete_copies = utils.SimpleAverage()
+        incomplete_copies = utils.SimpleAverage()
+
+        if show_samples:
+            sample_kwargs = dict(print_info=True, color_outputs=True, color_incomplete=False, **kwargs)
+        else:
+            sample_kwargs = dict(print_info=False, color_outputs=False, **kwargs)
+
+        for _ in utils.progress(n_tries, verbose=progress):
+            output: SampledOutput = model.sampling_decode(
+                dataset.vocab, example, begin_symbol=start_symbol, end_symbol=end_symbol,
+                max_length=max_length, **sample_kwargs)
+            complete_copies.add(output.complete_copies)
+            incomplete_copies.add(output.incomplete_copies)
+            if show_samples:
+                print(' '.join(output.sentence))
+        if isinstance(example, LRLMExample):
+            n_gold_rels = sum(int(span.end < max_length) for span in example.spans)
+            print(f"Complete / Gold entities: {complete_copies.value()} / {n_gold_rels}")
+        else:
+            print(f"Complete / Incomplete entities: {complete_copies.value()} / {incomplete_copies.value()}")
+
+    @get_example
+    def logprob_article(example, _start_symbol, bptt_size=150, **_kwargs):
+        (init_batch, batches) = dataset.create_one_batch([example], bptt_size)
+
+        # log-prob calculation
+        def callback(loss: Tensor, batch: BatchSequence, *_extra):
+            if batch.spans is None:
+                # NKLM
+                rel_ids = np.concatenate([[-1], batch.seqs['rel_ids'][0].cpu().numpy(), [-1]])
+                diff = np.ediff1d(rel_ids)
+                n_rels = np.count_nonzero(np.cumsum(diff[diff != 0]))
+            else:
+                n_rels = len(batch.spans[0])
+            print(f"{n_rels:2d}\t{math.exp(loss.item()):2.3f}")
+
+        print("#rels\tPPL")
+        with torch.no_grad():
+            model.eval()
+            total_loss = compute_batch_loss(model, init_batch, batches, use_unk_probs=True,
+                                            callback=callback, evaluate=True)
+        total_loss /= sum(batch.ntokens for batch in batches)
+        print(f"Total PPL: {math.exp(total_loss)}")
+
+    @get_example
+    def posterior_log_probs(example, _, bptt_size=140, n_context=5, max_segments=-1,
+                            ignore_single_relation=False, file=sys.stdout):
+        from collections import defaultdict
+        from dataset.utils import flip_batches, search_paths
+
+        (init_batch, batches) = dataset.create_one_batch([example], bptt_size)
+        flipped_batches = flip_batches(batches)
+
+        word_prob: Dict[str, List[np.ndarray]] = {k: [] for k in ["forward", "backward"]}
+        marginal_prob: Dict[str, List[np.ndarray]] = {k: [] for k in ["forward", "backward"]}
+        posterior_probs: List[Dict[MatchedSpan, Tuple[float, float]]] = []  # list(n_batches) of list(n_spans)
+        seq_loss: List[float] = []
+
+        def callback(loss: Tensor, _batch: BatchSequence):
+            posterior_probs.append(model.model_cache['posterior_log_probs'][0])
+            word_prob['forward'].append(model.model_cache['target_cond_log_probs'][0])
+            marginal_prob['forward'].append(model.model_cache['stacked_log_probs'][0])
+            seq_loss.append(loss.item())
+
+        def callback_flip(_loss: Tensor, _batch: BatchSequence):
+            marginal_prob['backward'].append(model.model_cache['stacked_log_probs'][0])
+
+        with torch.no_grad():
+            model.eval()
+            compute_batch_loss(model, init_batch, batches, use_unk_probs=True,
+                               callback=callback, evaluate=True,
+                               calc_loss_kwargs={'dump_posterior_probs': True})
+
+            compute_batch_loss(model, init_batch, flipped_batches, use_unk_probs=True,
+                               callback=callback_flip, evaluate=True,
+                               calc_loss_kwargs={'dump_posterior_probs': True})
+
+        # credit: http://bayesjumping.net/log-sum-exp-trick/ for without using scipy
+        def log_sum_exp(ns: List[int]):
+            max_ = np.max(ns)
+            sum_exp = np.exp(ns - max_).sum()
+            return max_ + np.log(sum_exp)
+
+        n_words = 0
+        for seq_idx, (batch, probs_dict) in enumerate(zip(batches, posterior_probs)):
+            if max_segments != -1 and seq_idx >= max_segments:
+                break
+
+            tokens = batch.raw_sequence[0][1:]
+            spans = batch.spans[0]
+            if len(spans) == 0:
+                continue
+            overlap_group = defaultdict(list)
+            sorted_spans = sorted(spans, key=lambda x: (x.start, x.end))
+            latest = (sorted_spans[0].start, sorted_spans[0].end)  # The most recent span group
+            overlap_group[latest] = [sorted_spans[0]]
+            for sp in sorted_spans[1:]:
+                if sp.start > sp.end or sp.end >= batch.ntokens:
+                    continue
+                if sp.start <= latest[1]:
+                    grp = overlap_group[latest]
+                    del overlap_group[latest]
+                    latest = (latest[0], max(latest[1], sp.end))
+                    overlap_group[latest] = grp + [sp]
+                else:
+                    latest = (sp.start, sp.end)
+                    overlap_group[latest] = [sp]
+
+            if np.any([len(g) > 1 for g in overlap_group.values()]) or not ignore_single_relation:
+                print(Logging.color('green',
+                                    f"Segment #{seq_idx}: "
+                                    f"words {n_words} - {n_words + batch.ntokens}, "
+                                    f"ppl = {math.exp(seq_loss[seq_idx]):.4f}"),
+                      file=file)
+                n_words += batch.ntokens
+
+            for span, group in overlap_group.items():
+                if ignore_single_relation and len(group) == 1:
+                    continue
+
+                alpha = marginal_prob["forward"][seq_idx][span[0] - 1]
+                beta = marginal_prob["backward"][::-1][seq_idx][batch.lengths[0] - (span[1] + 1) - 1]
+                # Enumerate all the paths
+                paths = search_paths(group, span[0], span[1])
+                log_probs = []
+                annotations = []
+                delimiters = []
+                for path in paths:
+                    path_anno = []
+                    path_delims = []
+                    logprob = alpha + beta
+                    for hop in path:
+                        if hop.rel_typ == -100:  # dummy relation - word transition
+                            logprob += word_prob["forward"][seq_idx][span[0]]
+                            path_anno.append("word")
+                        else:
+                            logprob += probs_dict[hop][0]
+                            path_anno.append(dataset.rel_vocab.i2w[hop.rel_typ])
+                        path_delims += ["   "] * (hop.end - hop.start)
+                        if hop.end < span[1]:
+                            path_delims.append(" | ")
+
+                    delimiters.append(path_delims)
+                    log_probs.append(logprob)
+                    annotations.append(path_anno)
+                log_denom = log_sum_exp(log_probs)
+                normalized_probs = [np.exp(log_prob - log_denom) for log_prob in log_probs]
+
+                l = max(0, span[0] - n_context)
+                r = min(batch.ntokens, span[1] + n_context)
+                token_string = " ".join([
+                    '    ',
+                    '... ' if l > 0 else '',
+                    ' '.join(tokens[idx] for idx in range(l, span[0])),
+                    '|',
+                    '   '.join(Logging.color('red', tokens[idx]) for idx in range(span[0], span[1] + 1)),
+                    '|',
+                    ' '.join(tokens[idx] for idx in range(span[1] + 1, r)),
+                    ' ...' if r < batch.ntokens else ''])
+                print(token_string, file=file)
+
+                annotation_strings = [" => ".join(a) for a in annotations]
+                max_anno_len = max([len(a) for a in annotation_strings])
+                max_score_idx = np.argmax(normalized_probs)
+
+                for idx, (delim, anno, prob) in enumerate(zip(delimiters, annotation_strings, normalized_probs)):
+                    matched_span_tokens = (" " * token_string.index(" | ") + " | " +
+                                           ' _ '.join(tokens[idx] for idx in range(span[0], span[1] + 1)) + " | ")
+                    delim_positions = re.finditer(r" _ ", matched_span_tokens)
+                    for d, match in zip(delim, delim_positions):
+                        pos = match.start(0)
+                        matched_span_tokens = matched_span_tokens[:pos] + d + matched_span_tokens[(pos + 3):]
+                    score = Logging.color("green", f"  {prob:1.4f}") if idx == max_score_idx else f"  {prob:1.4f}"
+                    matched_span_tokens += "  " + f"{anno}{' ' * (max_anno_len - len(anno))}" + score
+                    print(matched_span_tokens, file=file)
+
+                print(file=file)
+
+    @get_example
+    def span_log_probs(example, _, bptt_size=140, ppl_threshold=200.0,
+                       split_len=20, n_context=5, max_segments=-1):
+        (init_batch, batches) = dataset.create_one_batch([example], bptt_size)
+        rels: List[Relation] = init_batch[0]
+
+        posterior_probs: List[List[Optional[Tuple[float, float]]]] = []  # list(n_batches) of list(n_spans)
+        seq_loss: List[float] = []
+
+        # noinspection PyShadowingNames
+        def callback(loss: Tensor, batch: BatchSequence) -> None:
+            assert batch.spans is not None
+            probs_dict: Dict[MatchedSpan, Tuple[float, float]] = model.model_cache['posterior_log_probs'][0]
+            probs = [probs_dict.get(span, None) for span in batch.spans[0]]
+            posterior_probs.append(probs)
+            seq_loss.append(loss.item())
+
+        def color_if_less(val: float, threshold: float, format_str: str = '{:.4f}', color: str = 'yellow'):
+            s = format_str.format(val)
+            return Logging.color(color, s) if val < threshold else s
+
+        with torch.no_grad():
+            model.eval()
+            compute_batch_loss(model, init_batch, batches, use_unk_probs=True,
+                               callback=callback, evaluate=True, calc_loss_kwargs={'dump_posterior_probs': True})
+
+        n_words = 0
+        for seq_idx, (batch, probs) in enumerate(zip(batches, posterior_probs)):
+            if max_segments != -1 and seq_idx >= max_segments:
+                break
+            print(Logging.color('green',
+                                f"Segment #{seq_idx}: "
+                                f"words {n_words} - {n_words + batch.ntokens}, "
+                                f"ppl = {math.exp(seq_loss[seq_idx]):.4f}"))
+            n_words += batch.ntokens
+
+            tokens = batch.raw_sequence[0][1:]
+            spans = batch.spans[0]
+            is_in_span = [False] * batch.ntokens
+            for span in spans:
+                if span.start > span.end or span.end >= batch.ntokens:
+                    continue
+                is_in_span[span.start:(span.end + 1)] = [True] * (span.end - span.start + 1)
+            for idx in range(0, batch.ntokens, split_len):
+                print(f'{idx:3d}:', ' '.join(
+                    Logging.color('red', w) if in_span else w
+                    for w, in_span in zip(tokens[idx:(idx + split_len)], is_in_span[idx:(idx + split_len)])))
+            print()
+
+            for span, prob in sorted(zip(spans, probs)):
+                if prob is None:
+                    continue
+                rel_prob, word_prob = prob
+                l = max(0, span.start - n_context)
+                r = min(batch.ntokens, span.end + 1 + n_context)
+                print(f"[{span.start}, {span.end}]"
+                      f" <{dataset.rel_vocab.i2w[span.rel_typ]}>"
+                      f" {Logging.color('red', rels[span.rel_idx].obj_alias[span.surface_idx])}"
+                      f"{' (alias)' if span.surface_idx > 0 else ''}"
+                      f": rel = {color_if_less(math.exp(-rel_prob), ppl_threshold)}"
+                      f", word = {color_if_less(math.exp(-word_prob), ppl_threshold)}")
+                print('    ',
+                      '... ' if l > 0 else '',
+                      ' '.join(Logging.color('red', tokens[idx])
+                               if span.start <= idx <= span.end else tokens[idx]
+                               for idx in range(l, r)),
+                      ' ...' if r < batch.ntokens else '')
+            print()
+
+    from IPython import embed
+    embed()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..e5be215
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+git+git://github.com/facebookresearch/fasttext.git@bc1285939f1c216bd358425c3685a049dd8f56c0#egg=fastText
+IPython
+tqdm
+tensorboardX
+torch>=0.4
+numpy
diff --git a/run.py b/run.py
new file mode 100644
index 0000000..5f093aa
--- /dev/null
+++ b/run.py
@@ -0,0 +1,439 @@
+import logging
+import math
+import os
+import random
+import shutil
+import sys
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type
+
+import numpy as np
+from tensorboardX import SummaryWriter
+import torch
+from torch import nn
+from torch import Tensor
+from tqdm import trange
+
+import utils
+from arguments import LMArguments
+from dataset import BatchSequence, AliasLMDataset, Dataset, LMDataset, LRLMDataset, NKLMDataset, Relation
+from models import AliasLM, LRLM, NKLM, VanillaLM
+from models.base import BaseLM
+from models.utils import linear_weight_init
+from nnlib.utils import Logging, WeightedAverage
+
+LOGGER = logging.getLogger(__name__)
+
+
+def create_dataset(args: LMArguments):
+    dataset_kwargs = dict(
+        use_anchor=args.use_anchor,
+        include_train=(args.mode == 'train'),
+        exclude_entity_disamb=args.exclude_entity_disamb,
+        exclude_alias_disamb=args.exclude_alias_disamb,
+        create_batches=not args.repl,
+        use_only_first_section=args.use_only_first_section,
+        cache_batches=args.cache_dataset,
+        fasttext_model_path=args.fasttext_model_path,
+    )
+    if args.use_unk_probs:
+        dataset_kwargs.update(
+            unk_probs_path=os.path.join(args.path, 'unk_probs.txt'),
+            use_upp=args.use_upp,
+        )
+    dataset_class: Type[Dataset]
+    if args.model == 'VanillaLM':
+        dataset_class = LMDataset
+    elif args.model == 'LRLM':
+        dataset_class = LRLMDataset
+    elif args.model == 'AliasLM':
+        dataset_class = AliasLMDataset
+    elif args.model == 'NKLM':
+        dataset_class = NKLMDataset
+        dataset_kwargs.update(unk_rels_strategy=args.unk_rels_strategy)
+    else:
+        raise ValueError(f"Invalid model choice '{args.model}'")
+    dataset = dataset_class(args.path, args.batch_size, args.vocab_dir, args.bptt_size,
+                            vocab_size=args.vocab_size, min_freq=args.min_freq, **dataset_kwargs)
+    return dataset
+
+
+def create_model_and_optimizer(args: LMArguments, dataset) -> Tuple[BaseLM, Optional[torch.optim.Optimizer]]:
+    model: BaseLM
+    if args.model == 'VanillaLM':
+        model = VanillaLM(args, vocab_size=len(dataset.word_vocab))
+    elif args.model == 'LRLM':
+        model = LRLM(args, vocab_size=len(dataset.word_vocab), rel_vocab_size=len(dataset.rel_vocab),
+                     max_unkrel=dataset.max_unkrel)
+    elif args.model == 'NKLM':
+        model = NKLM(args, vocab_size=len(dataset.word_vocab), rel_vocab_size=len(dataset.rel_vocab),
+                     max_unkrel=dataset.max_unkrel)
+    elif args.model == 'AliasLM':
+        model = AliasLM(args, vocab_size=len(dataset.word_vocab))
+    else:
+        raise ValueError(f"Invalid model choice '{args.model}'")
+
+    if args.multi_gpu:
+        model = nn.DataParallel(model)  # type: ignore
+
+    optimizer: Optional[torch.optim.Optimizer] = None
+    if args.mode == 'train' and not args.repl:
+        if args.optimizer == 'adam':
+            optimizer = torch.optim.Adam(model.parameters(), args.lr)
+        elif args.optimizer == 'sgd':
+            optimizer = torch.optim.SGD(model.parameters(), args.lr)
+        else:
+            raise ValueError(f"Invalid optimizer setting {args.optimizer}")
+
+    if args.repl and args.pretrained is None:
+        print(f"Pretrained model path not specified, using default experiment directory.")
+        args.pretrained = args.exp
+    if args.pretrained is not None:
+        if os.path.isdir(args.pretrained):
+            path, _ = utils.get_best_model(args.pretrained)
+            assert path is not None
+        else:
+            path = args.pretrained
+        states = torch.load(path, map_location='cpu')
+        incompatible_keys = model.load_state_dict(states['model'], strict=False)
+        if incompatible_keys is not None and (incompatible_keys.missing_keys or incompatible_keys.unexpected_keys):
+            print(repr(incompatible_keys))
+        model.to(model.device)
+        if optimizer is not None:
+            optimizer.load_state_dict(states['optimizer'])
+        LOGGER.info(f"Loaded model weights from {path}")
+        print(f"Loaded model weights from {path}")
+    else:
+        def weights_init(m):
+            if type(m) is nn.Linear:
+                linear_weight_init(m.weight, m.bias)
+            elif type(m) is nn.Embedding:
+                m.weight.data.normal_(0.0, 0.02)
+                if m.padding_idx is not None:
+                    m.weight.data[m.padding_idx].fill_(0)
+
+        model.to(model.device)
+        model.apply(weights_init)
+
+    return model, optimizer
+
+
+Callback = Callable[[Tensor, BatchSequence], None]
+
+
+def compute_batch_loss(model: BaseLM, init_batch: List[List[Relation]], batches: List[BatchSequence],
+                       progress_bar=None, use_unk_probs: bool = False,
+                       calc_loss_kwargs: Optional[Dict[str, Any]] = None,
+                       callback: Optional[Callback] = None,
+                       evaluate: bool = False) -> float:
+    hidden = model.init_hidden(batches[0].batch_size, init_batch)
+    total_loss = 0.0
+
+    if calc_loss_kwargs is None:
+        calc_loss_kwargs = {}
+
+    for batch in batches:
+        # The loss is used to calculate PPL. Remove <EOS>
+        if evaluate and batch.has_article_end:
+            batch = batch.remove_last_token()
+        if batch.ntokens == 0:
+            continue  # this could happen when the entire batch are all <EOS> tokens
+        batch = batch.to(model.device, persistent=False)
+        loss, hidden = model.calc_loss(batch, hidden, use_unk_probs, **calc_loss_kwargs)
+        hidden = utils.repackage_hidden(hidden)  # do it ASAP
+        loss_val = loss.item()
+        total_loss += loss_val * batch.ntokens
+
+        if callback is not None:
+            callback(loss, batch)
+        del loss
+
+        if progress_bar is not None:
+            progress_bar.update(1)
+    return total_loss
+
+
+def train_model(model, dataset, optimizer, args: LMArguments, writer: Optional[SummaryWriter] = None,
+                max_steps: Optional[int] = None):
+    LOGGER.info("Training starts..")
+
+    update_frequency = args.update_batch_size // args.batch_size
+
+    best_checkpoint = None
+    steps_since_checkpoint = 0
+    interval = args.checkpoint_interval
+    # by default, validation happens every epoch
+    if args.checkpoint_interval == -1:
+        interval = sum(len(b) for _, b in dataset.get_batches('train')) // update_frequency
+
+    train_steps = 0
+    scheduler = None
+    if args.lr_scheduler == 'cosine':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200000, eta_min=0.0)
+    model.train()
+    for eph in trange(1, args.num_epochs + 1, ncols=80, desc="Epochs", ascii=True):
+        eph_loss = 0.0
+        num_backwards = 0  # Track the number of gradient calc
+        learn_batch_loss = WeightedAverage()  # Cumulative batch loss for one logging interval
+
+        def grad_callback(loss: Tensor, batch: BatchSequence, *_):
+            nonlocal num_backwards, steps_since_checkpoint, train_steps
+            learn_batch_loss.add(loss.item(), batch.ntokens)
+            scaled = loss / update_frequency
+            scaled.backward()
+            # gc.collect()  # forced GC to free unused parts of the graph
+            num_backwards += 1
+
+            # clip if needed
+            if args.clip is not None:
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
+
+            # Update the parameters every update_batch_size
+            if num_backwards % update_frequency == 0:
+                optimizer.step()
+                optimizer.zero_grad()
+                steps_since_checkpoint += 1
+                train_steps += 1
+
+                if args.warm_up_steps is not None and train_steps < args.warm_up_steps:
+                    lr = args.lr * train_steps / args.warm_up_steps
+                    optimizer.param_groups[0]['lr'] = lr
+                elif scheduler is not None:
+                    scheduler.step(train_steps)
+
+                if args.logging_level != -1 and train_steps % args.log_interval == 0:
+                    LOGGER.info(f"Epoch {eph:2d}"
+                                f" | Step {train_steps:5d}"
+                                f" | LR {optimizer.param_groups[0]['lr']:1.5f}"
+                                f" | LOSS {learn_batch_loss.value():7.4f}"
+                                f" | PPL {math.exp(learn_batch_loss.value()):9.3f}")
+                    learn_batch_loss.clear()
+
+        train_batches = dataset.get_batches('train')
+        progress_bar = utils.get_progress_bar(train_batches, verbose=args.progress)
+
+        for init_batch, batches in train_batches:
+            model.train()
+            eph_loss += compute_batch_loss(
+                model, init_batch, batches, progress_bar=progress_bar,
+                use_unk_probs=args.use_unk_probs, callback=grad_callback)
+
+            # Evaluate on validation set if needed.
+            if steps_since_checkpoint >= interval:
+                steps_since_checkpoint = 0
+
+                model.eval()
+                val_loss = 0.0
+                valid_batches = dataset.get_batches('valid')
+                valid_progress_bar = utils.get_progress_bar(valid_batches, desc="Validating", verbose=args.progress)
+                with torch.no_grad():
+                    for eval_init_batch, eval_batches in valid_batches:
+                        val_loss += compute_batch_loss(
+                            model, eval_init_batch, eval_batches, progress_bar=valid_progress_bar,
+                            use_unk_probs=args.use_unk_probs, evaluate=True)
+                valid_progress_bar.close()
+                val_loss /= dataset.ntokens['valid']  # per char or word
+                LOGGER.info("VALID |                 "
+                            f" | LOSS {val_loss:3.4f} | BPC {(val_loss / math.log(2)):3.4f}"
+                            f" | PPL {math.exp(val_loss):3.4f}")
+
+                # Save current checkpoint because it's the best
+                if best_checkpoint is None or val_loss < best_checkpoint.val_loss:
+                    best_checkpoint = utils.Checkpoint(
+                        eph, val_loss,
+                        utils.cpu_state_dict(model.state_dict()),
+                        utils.cpu_state_dict(optimizer.state_dict()))
+
+                    # Also save the model
+                    if args.save:
+                        state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict()}
+                        torch.save(state, os.path.join(args.exp, f'model{eph}.pt'))
+                        LOGGER.info(f"      | Best model achieved at eph {eph}, saved.")
+
+                # Otherwise revert to the previous best checkpoint
+                elif args.optimizer_strategy == 'reset':
+                    # Reset both
+                    new_lr = optimizer.param_groups[0]['lr'] * args.lr_scaler
+                    model.load_state_dict(best_checkpoint.model_state)
+                    optimizer.load_state_dict(best_checkpoint.optim_state)
+                    optimizer.param_groups[0]['lr'] = new_lr
+                    LOGGER.info(f"      | Loaded best model at epoch {best_checkpoint.epoch}.")
+
+                if writer is not None:
+                    writer.add_scalar('valid/ephloss', val_loss, eph)
+                    writer.add_scalar('valid/perplexity', math.exp(val_loss), eph)
+
+            if max_steps is not None and train_steps >= max_steps:
+                progress_bar.close()
+                return
+
+        progress_bar.close()
+
+        eph_loss /= dataset.ntokens['train']  # per token
+        if writer is not None:
+            writer.add_scalar('train/ephloss', eph_loss, eph)
+            writer.add_scalar('train/perplexity', math.exp(eph_loss), eph)
+        LOGGER.info("TRAIN"
+                    f" | Eph {eph:2d} | LR {optimizer.param_groups[0]['lr']:1.5f}"
+                    f" | LOSS {eph_loss:3.4f} | BPC {(eph_loss / math.log(2)):3.4f}"
+                    f" | PPL {math.exp(eph_loss):3.4f}")
+        if args.lr_decay > 0.0:
+            optimizer.param_groups[0]['lr'] *= 1.0 - args.lr_decay
+
+
+def evaluate_model(model, dataset, args: LMArguments, split, writer=None):
+    LOGGER.info(f"Evaluation starts. Running model on {split} set.")
+
+    model.eval()
+    total_loss = 0.0
+    data_length = 0
+
+    callback: Optional[Callback] = None
+    if args.dump_probs:
+        batch_log_probs: List[List[float]] = []  # list(seq_len) of list(batch_size)
+        split_log_probs: List[List[float]] = []  # list(n_examples) of list(seq_len)
+
+        def callback(_loss: Tensor, _batch: BatchSequence):
+            batch_log_probs.append(model.model_cache['log_probs'])
+
+    with torch.no_grad():
+        if split == 'train_sample':
+            all_batches = dataset.get_batches('train')[:5]
+        else:
+            all_batches = dataset.get_batches(split, shuffle=False)
+        data_length += sum(sum(b.ntokens for b in bs) for _, bs in all_batches)
+        progress_bar = utils.get_progress_bar(all_batches, verbose=args.progress, desc=f"{split} batches", leave=True)
+        for init_batch, batches in all_batches:
+            total_loss += compute_batch_loss(
+                model, init_batch, batches, progress_bar=progress_bar,
+                use_unk_probs=args.use_unk_probs, calc_loss_kwargs={'dump_probs': args.dump_probs},
+                callback=callback, evaluate=True)
+            if args.dump_probs:
+                for b in range(len(batch_log_probs[0])):
+                    split_log_probs.append([log_probs[b] for log_probs in batch_log_probs])
+                batch_log_probs = []
+        progress_bar.close()
+
+    if args.dump_probs:
+        with open(os.path.join(args.exp, f'prob_dump_{split}.txt'), 'w') as f:
+            # noinspection PyUnboundLocalVariable
+            for log_probs in split_log_probs:
+                f.write(' '.join(str(p) for p in log_probs) + '\n')
+
+    # All the lengths
+    total_loss /= data_length  # per char
+    if writer is not None:
+        writer.add_scalar(f'{split}/ephloss', total_loss, 1)
+        writer.add_scalar(f'{split}/perplexity', math.exp(total_loss), 1)
+
+    LOGGER.info(f"{split.upper()} |                 "
+                f" | LOSS {total_loss:3.4f} | BPC {(total_loss / math.log(2)):3.4f}"
+                f" | PPL {math.exp(total_loss):3.4f}")
+
+
+def run():
+    args = LMArguments()
+
+    # Seed RNGs for reproducibility
+    if args.seed > 0:
+        print(f"Random seed set to {args.seed}")
+        random.seed(args.seed)
+        np.random.seed(args.seed)
+        torch.manual_seed(args.seed)
+        if args.cuda:
+            torch.cuda.manual_seed(args.seed)
+
+    # Configure logging
+    if args.save:
+        logfile = utils.create_exp_dir(args.exp, args.script, overwrite=args.overwrite)
+    else:
+        logfile = None
+
+    # must init logging before SummaryWriter, otherwise it adds handler to root logger so basicConfig does not work
+    logging.basicConfig(
+        datefmt="%m-%d %H:%M:%S",
+        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+        level=logging.getLevelName(args.logging_level),
+        filename=logfile,
+    )
+
+    if args.writer:
+        writer_path = os.path.join(args.tbdir, args.exp)
+        if os.path.exists(writer_path):
+            shutil.rmtree(writer_path)
+        writer = SummaryWriter(writer_path)
+    else:
+        writer = None
+
+    # Print out all the arguments set.
+    LOGGER.info("Arguments passed: " + args.to_string(max_width=80))
+    print(args.exp, flush=True)
+
+    if args.profile:
+        LOGGER.info(Logging.color(
+            col='yellow',
+            s=f"Profiling performance{' (including data loading)' if args.profile_data else ''}, "
+              f"running the model for {args.profile_steps} steps..."))
+        import cProfile
+        profiler = cProfile.Profile()
+    else:
+        profiler = None
+
+    n_gpus = torch.cuda.device_count()
+    LOGGER.info("Running the model on " + (f"CUDA with {n_gpus} GPU(s)" if args.cuda else "CPU"))
+    for device in range(n_gpus):
+        props = torch.cuda.get_device_properties(device)
+        LOGGER.info(f"GPU ({device}) name: {props.name}, CUDA version {props.major}.{props.minor}, "
+                    f"available memory: {props.total_memory / 1024 / 1024:.2f}MB.")
+
+    if args.profile and args.profile_data:
+        profiler.enable()
+    # Create dataset
+    dataset = create_dataset(args)
+
+    # Create model
+    model, optimizer = create_model_and_optimizer(args, dataset)
+
+    # Print model parameter info
+    n_params = sum(p.nelement() for p in model.parameters())
+    LOGGER.info(f"Model parameters: {n_params}")
+    LOGGER.info(f"Model structure:\n{utils.repr_module(model)}")
+
+    if args.repl:
+        # REPL mode
+        from repl import repl
+        repl(dataset, model)
+        sys.exit(0)
+
+    if args.profile and not args.profile_data:
+        profiler.enable()
+    if args.mode == 'train':
+        # Training mode
+        try:
+            train_model(model, dataset, optimizer, args, writer,
+                        max_steps=args.profile_steps if args.profile else None)
+        except KeyboardInterrupt:
+            LOGGER.info("Training halted.")
+
+        if not args.profile:
+            # load best model
+            best_path, best_epoch = utils.get_best_model(args.exp)
+            if best_path is not None:
+                model.load_state_dict(torch.load(best_path)['model'])
+                LOGGER.info(f"Loaded best model (epoch {best_epoch})")
+                evaluate_model(model, dataset, args, split='test', writer=writer)
+            else:
+                LOGGER.info(Logging.color('red', "No saved checkpoints, skipping evaluation"))
+
+    else:
+        # Evaluation mode
+        for split in ['valid', 'test']:
+            evaluate_model(model, dataset, args, split=split, writer=writer)
+
+    if args.profile:
+        import pstats
+        profiler.disable()
+        pstats.Stats(profiler).sort_stats('cumulative').print_stats()
+
+
+if __name__ == '__main__':
+    run()
diff --git a/run_charlm.py b/run_charlm.py
new file mode 100644
index 0000000..a0fc697
--- /dev/null
+++ b/run_charlm.py
@@ -0,0 +1,111 @@
+import logging
+import random
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+
+import utils
+from arguments import LMArguments
+from repl import repl
+from run import create_dataset, create_model_and_optimizer, evaluate_model, train_model
+
+LOGGER = logging.getLogger(__name__)
+
+
+def postprocess_probdump(dataset, exp_dir):
+    all_logprobs = []
+    for split_ in ["train", "valid", "test"]:
+        with (Path(exp_dir) / f"prob_dump_{split_}.txt").open("r") as f:
+            logprobs = f.read().strip().split("\n")
+        all_batches = dataset.get_batches(split_, shuffle=False)
+        tokens = ["".join(ex[1:-1]) for batches in all_batches for b in batches[1] for ex in b.raw_sequence]
+        assert len(tokens) == len(logprobs)
+
+        all_logprobs += list(zip(tokens, logprobs))
+
+    with (Path(exp_dir) / "unk_probs.txt").open("w") as f:
+        for ex in all_logprobs:
+            print("\t".join(ex), file=f)
+
+
+def run():
+    args = LMArguments()
+
+    # Seed RNGs for reproducibility
+    if args.seed > 0:
+        print(f"Random seed set to {args.seed}")
+        random.seed(args.seed)
+        np.random.seed(args.seed)
+        torch.manual_seed(args.seed)
+        if args.cuda:
+            torch.cuda.manual_seed(args.seed)
+
+    # Configure logging
+    if args.save:
+        logfile = utils.create_exp_dir(args.exp, args.script, overwrite=args.overwrite)
+    else:
+        logfile = None
+
+    # must init logging before SummaryWriter, otherwise it adds handler to root logger so basicConfig does not work
+    logging.basicConfig(
+        datefmt="%m-%d %H:%M:%S",
+        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+        level=logging.getLevelName(args.logging_level),
+        filename=logfile,
+    )
+
+    # Print out all the arguments set.
+    LOGGER.info("Arguments passed: " + args.to_string(max_width=80))
+    print(args.exp, flush=True)
+
+    LOGGER.info(f"Running the model on {'GPU (CUDA)' if args.cuda else 'CPU'}")
+    if args.cuda:
+        props = torch.cuda.get_device_properties(0)
+        LOGGER.info(f"GPU name: {props.name}, CUDA version {props.major}.{props.minor}, "
+                    f"available memory: {props.total_memory / 1024 / 1024:.2f}MB.")
+
+    # Create dataset
+    dataset = create_dataset(args)
+
+    # Create model
+    model, optimizer = create_model_and_optimizer(args, dataset)
+
+    # Print model parameter info
+    n_params = sum(p.nelement() for p in model.parameters())
+    LOGGER.info(f"Model parameters: {n_params}")
+    LOGGER.info(f"Model structure:\n{str(model)}")
+
+    if args.repl:
+        # REPL mode
+        repl(dataset, model)
+        sys.exit(0)
+
+    if args.mode == "train":
+        # Training mode
+        try:
+            train_model(model, dataset, optimizer, args, writer=None)
+        except KeyboardInterrupt:
+            LOGGER.info("Training halted.")
+
+        # load best model
+        best_path, best_epoch = utils.get_best_model(args.exp)
+        model.load_state_dict(torch.load(best_path)['model'])
+        LOGGER.info(f"Loaded best model (epoch {best_epoch})")
+
+        # Evaluate AND dump the avg logprobs of each word
+        evaluate_model(model, dataset, args, split="train", writer=None)
+        evaluate_model(model, dataset, args, split="valid", writer=None)
+        evaluate_model(model, dataset, args, split="test", writer=None)
+
+        postprocess_probdump(dataset, args.exp)
+
+    else:
+        # Evaluation mode
+        for split in ['train', 'valid', 'test']:
+            evaluate_model(model, dataset, args, split=split, writer=None)
+
+
+if __name__ == '__main__':
+    run()
diff --git a/scripts/run_charlm_wikitext.sh b/scripts/run_charlm_wikitext.sh
new file mode 100755
index 0000000..90983fb
--- /dev/null
+++ b/scripts/run_charlm_wikitext.sh
@@ -0,0 +1,34 @@
+#!/usr/bin/env bash
+# This script trains a character model AND evaluate the whole dataset to dump word probabilities.
+# Exactly the same hyperparameters for both WT and WF.
+# Move the <exp_dir>/unk_probs.txt to the data location after this script is finished.
+
+python run_charlm.py \
+    --exp charlm-wikitext \
+    --script $0 \
+    --lr 5e-4 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 140 \
+    --num-epochs 20 \
+    \
+    --num-layers 2 \
+    --embed-size 256 \
+    --hidden-size 512 \
+    --dropout 0.1 \
+    --vocab-mlp-activation none \
+    --vocab-mlp-hidden-dim 256 \
+    --vocab-mlp-dropout 0.1 \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model VanillaLM \
+    --vocab-dir data/vocab/charlm-wikitext/ \
+    --logging-level DEBUG \
+    --no-use-unk-probs \
+    --tbdir ~/tb/ \
+    --dump-probs \
+    \
+    $@
diff --git a/scripts/train_aliaslm_transformer_wikifacts.sh b/scripts/train_aliaslm_transformer_wikifacts.sh
new file mode 100755
index 0000000..8a9ac1a
--- /dev/null
+++ b/scripts/train_aliaslm_transformer_wikifacts.sh
@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+
+python run.py \
+    --exp aliaslm-transformer-wikifacts \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 50 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --path data/wikifacts \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model AliasLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/aliaslm-transformer-wikifacts \
+    --no-use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_aliaslm_transformer_wikitext_full.sh b/scripts/train_aliaslm_transformer_wikitext_full.sh
new file mode 100755
index 0000000..1f09590
--- /dev/null
+++ b/scripts/train_aliaslm_transformer_wikitext_full.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp aliaslm-transformer-wikitext-full \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 50 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --kb-embed-dim 100 \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model AliasLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/aliaslm-transformer-wikitext-full \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_aliaslm_transformer_wikitext_short.sh b/scripts/train_aliaslm_transformer_wikitext_short.sh
new file mode 100755
index 0000000..4872b67
--- /dev/null
+++ b/scripts/train_aliaslm_transformer_wikitext_short.sh
@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp aliaslm-transformer-wikitext-short \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 50 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --kb-embed-dim 100 \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model AliasLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/aliaslm-transformer-wikitext-short \
+    --use-unk-probs \
+    --use-only-first-section \
+    \
+    $@
diff --git a/scripts/train_aliaslm_wikifacts.sh b/scripts/train_aliaslm_wikifacts.sh
new file mode 100755
index 0000000..abf4e2c
--- /dev/null
+++ b/scripts/train_aliaslm_wikifacts.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp aliaslm-wikifacts \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --vocab-mlp-hidden-dim 1000 \
+    --vocab-mlp-dropout 0.5 \
+    --dropout 0.5 \
+    --rnn-dropout-pos both \
+    \
+    --kb-embed-dim 50 \
+    \
+    --path data/wikifacts/ \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model AliasLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --vocab-dir data/vocab/aliaslm-wikifacts \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_aliaslm_wikitext_full.sh b/scripts/train_aliaslm_wikitext_full.sh
new file mode 100755
index 0000000..f00697e
--- /dev/null
+++ b/scripts/train_aliaslm_wikitext_full.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp aliaslm-wikitext-full \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 4 \
+    --embed-size 512 \
+    --hidden-size 1024 \
+    --dropout 0.1 \
+    --kb-embed-dim 100 \
+    --vocab-mlp-dropout 0.1 \
+    --vocab-mlp-hidden-dim 500 \
+    \
+    --adaptive-embed \
+    --rnn-dropout-pos both \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model AliasLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/aliaslm-wikitext-full/ \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
diff --git a/scripts/train_aliaslm_wikitext_short.sh b/scripts/train_aliaslm_wikitext_short.sh
new file mode 100755
index 0000000..1509070
--- /dev/null
+++ b/scripts/train_aliaslm_wikitext_short.sh
@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp aliaslm-wikitext-short \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 4 \
+    --embed-size 512 \
+    --hidden-size 1024 \
+    --dropout 0.1 \
+    --kb-embed-dim 100 \
+    --vocab-mlp-dropout 0.1 \
+    --vocab-mlp-hidden-dim 500 \
+    \
+    --adaptive-embed \
+    --rnn-dropout-pos both \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model AliasLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/aliaslm-wikitext-full/ \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    --use-only-first-section \
+    \
diff --git a/scripts/train_lrlm_transformer_wikifacts.sh b/scripts/train_lrlm_transformer_wikifacts.sh
new file mode 100755
index 0000000..c54ec8f
--- /dev/null
+++ b/scripts/train_lrlm_transformer_wikifacts.sh
@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp lrlm-transformer-wikifacts \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    --alias-disamb-strategy oracle \
+    \
+    --kb-embed-dim 50 \
+    --use-rel-mlp True \
+    --rel-mlp-hidden-dim 500 \
+    --rel-mlp-dropout 0.1 \
+    --rel-mlp-activation none \
+    \
+    --path data/wikifacts \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model LRLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --min-freq 0 \
+    --vocab-dir data/vocab/lrlm-transformer-wikifacts \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_lrlm_transformer_wikitext_full.sh b/scripts/train_lrlm_transformer_wikitext_full.sh
new file mode 100755
index 0000000..28f7023
--- /dev/null
+++ b/scripts/train_lrlm_transformer_wikitext_full.sh
@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp lrlm-transformer-wikitext-full \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --kb-embed-dim 100 \
+    --use-rel-mlp True \
+    --rel-mlp-hidden-dim 500 \
+    --rel-mlp-dropout 0.1 \
+    --rel-mlp-activation none \
+    --alias-disamb-strategy fasttext \
+    --fasttext-model-path data/fasttext/wt-full.bin \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model LRLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/lrlm-transformer-wikitext-full \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_lrlm_transformer_wikitext_short.sh b/scripts/train_lrlm_transformer_wikitext_short.sh
new file mode 100755
index 0000000..f6a12e7
--- /dev/null
+++ b/scripts/train_lrlm_transformer_wikitext_short.sh
@@ -0,0 +1,55 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp lrlm-transformer-wikitext-short \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --kb-embed-dim 100 \
+    --use-rel-mlp True \
+    --rel-mlp-hidden-dim 500 \
+    --rel-mlp-dropout 0.1 \
+    --rel-mlp-activation none \
+    --alias-disamb-strategy fasttext \
+    --fasttext-model-path data/fasttext/wt-short.bin \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model LRLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/lrlm-transformer-wikitext-short \
+    --use-unk-probs \
+    --use-only-first-section \
+    \
+    $@
diff --git a/scripts/train_lrlm_wikifacts.sh b/scripts/train_lrlm_wikifacts.sh
new file mode 100755
index 0000000..0c68b77
--- /dev/null
+++ b/scripts/train_lrlm_wikifacts.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp lrlm-wikifacts \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --dropout 0.5 \
+    --kb-embed-dim 50 \
+    --alias-disamb-strategy oracle \
+    \
+    --vocab-mlp-hidden-dim 800 \
+    --vocab-mlp-dropout 0.5 \
+    --vocab-mlp-activation none \
+    --use-rel-mlp True \
+    --rel-mlp-hidden-dim 800 \
+    --rel-mlp-dropout 0.5 \
+    --rel-mlp-activation none \
+    --rnn-dropout-pos both \
+    \
+    --path data/wikifacts \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model LRLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --vocab-dir data/vocab/lrlm-wikifacts \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_lrlm_wikitext_full.sh b/scripts/train_lrlm_wikitext_full.sh
new file mode 100755
index 0000000..fbb875d
--- /dev/null
+++ b/scripts/train_lrlm_wikitext_full.sh
@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp lrlm-wikitext-full \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 4 \
+    --embed-size 512 \
+    --hidden-size 1024 \
+    --dropout 0.1 \
+    --kb-embed-dim 100 \
+    --alias-disamb-strategy fasttext \
+    \
+    --vocab-mlp-hidden-dim 500 \
+    --vocab-mlp-dropout 0.1 \
+    --vocab-mlp-activation none \
+    --use-rel-mlp True \
+    --rel-mlp-hidden-dim 500 \
+    --rel-mlp-dropout 0.1 \
+    --rel-mlp-activation none \
+    --rnn-dropout-pos both \
+    --adaptive-embed \
+    --fasttext-model-path data/fasttext/wt-full.bin \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model LRLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/lrlm-wikitext-full/ \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_lrlm_wikitext_short.sh b/scripts/train_lrlm_wikitext_short.sh
new file mode 100755
index 0000000..4dca653
--- /dev/null
+++ b/scripts/train_lrlm_wikitext_short.sh
@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp lrlm-wikitext-short \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --dropout 0.5 \
+    --kb-embed-dim 100 \
+    --alias-disamb-strategy fasttext \
+    \
+    --vocab-mlp-hidden-dim 800 \
+    --vocab-mlp-dropout 0.5 \
+    --vocab-mlp-activation none \
+    --use-rel-mlp True \
+    --rel-mlp-hidden-dim 800 \
+    --rel-mlp-dropout 0.5 \
+    --rel-mlp-activation none \
+    --rnn-dropout-pos both \
+    --adaptive-embed \
+    --fasttext-model-path data/fasttext/wt-short.bin \
+    \
+    --use-only-first-section \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model LRLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/lrlm-wikitext-short/ \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_nklm_orig.sh b/scripts/train_nklm_orig.sh
new file mode 100755
index 0000000..608a560
--- /dev/null
+++ b/scripts/train_nklm_orig.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-orig-wikifacts \
+    --script $0 \
+    --lr 0.5 \
+    --lr-decay 0.02 \
+    --lr-scaler 1.0 \
+    --optimizer sgd \
+    --optimizer-strategy none \
+    --clip 5.0 \
+    --batch-size 50 \
+    --bptt-size 30 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --dropout 0.5 \
+    --pos-embed-dim 40 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 50 \
+    \
+    --entity-disamb-strategy entvec \
+    --alias-disamb-strategy oracle \
+    --vocab-mlp-activation relu \
+    --rnn-dropout-pos both \
+    \
+    --use-anchor \
+    --path data/wikifacts_orig \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --vocab-dir data/vocab/nklm-orig-wikifacts \
+    --tbdir ~/tb/ \
+    \
+    $@
diff --git a/scripts/train_nklm_transformer_wikifacts.sh b/scripts/train_nklm_transformer_wikifacts.sh
new file mode 100755
index 0000000..d3fa8a4
--- /dev/null
+++ b/scripts/train_nklm_transformer_wikifacts.sh
@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-transformer-wikifacts \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --pos-embed-dim 50 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 50 \
+    --copy-mlp-hidden-dim 500 \
+    --vocab-mlp-activation none \
+    \
+    --path data/wikifacts \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --min-freq 0 \
+    --vocab-dir data/vocab/nklm-transformer-wikifacts \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_nklm_transformer_wikitext_full.sh b/scripts/train_nklm_transformer_wikitext_full.sh
new file mode 100755
index 0000000..8cbe4de
--- /dev/null
+++ b/scripts/train_nklm_transformer_wikitext_full.sh
@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-transformer-wikitext-full \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --pos-embed-dim 50 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 100 \
+    --copy-mlp-hidden-dim 500 \
+    --vocab-mlp-activation none \
+    --alias-disamb-strategy fasttext \
+    --fasttext-model-path data/fasttext/wt-full.bin \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/nklm-transformer-wikitext-full \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_nklm_transformer_wikitext_short.sh b/scripts/train_nklm_transformer_wikitext_short.sh
new file mode 100755
index 0000000..132d880
--- /dev/null
+++ b/scripts/train_nklm_transformer_wikitext_short.sh
@@ -0,0 +1,55 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-transformer-wikitext-short \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --pos-embed-dim 50 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 100 \
+    --copy-mlp-hidden-dim 500 \
+    --vocab-mlp-activation none \
+    --alias-disamb-strategy fasttext \
+    --fasttext-model-path data/fasttext/wt-short.bin \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/nklm-transformer-wikitext-short \
+    --use-unk-probs \
+    --use-only-first-section \
+    \
+    $@
diff --git a/scripts/train_nklm_wikifacts.sh b/scripts/train_nklm_wikifacts.sh
new file mode 100755
index 0000000..3544ba0
--- /dev/null
+++ b/scripts/train_nklm_wikifacts.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-wikifacts \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --dropout 0.5 \
+    --pos-embed-dim 40 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 50 \
+    \
+    --vocab-mlp-activation none \
+    --rnn-dropout-pos both \
+    \
+    --path data/wikifacts \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --vocab-dir data/vocab/nklm-wikifacts \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_nklm_wikitext_full.sh b/scripts/train_nklm_wikitext_full.sh
new file mode 100755
index 0000000..970fb3a
--- /dev/null
+++ b/scripts/train_nklm_wikitext_full.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-wikitext-full \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 4 \
+    --embed-size 512 \
+    --hidden-size 1024 \
+    --dropout 0.1 \
+    --pos-embed-dim 50 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 100 \
+    --copy-mlp-hidden-dim 500 \
+    --vocab-mlp-hidden-dim 500 \
+    \
+    --vocab-mlp-activation none \
+    --rnn-dropout-pos both \
+    --alias-disamb-strategy fasttext \
+    --fasttext-model-path data/fasttext/wt-full.bin \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/nklm-wikitext-full \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_nklm_wikitext_short.sh b/scripts/train_nklm_wikitext_short.sh
new file mode 100755
index 0000000..e976605
--- /dev/null
+++ b/scripts/train_nklm_wikitext_short.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp nklm-wikitext-short \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --dropout 0.5 \
+    --pos-embed-dim 40 \
+    --pos-embed-count 20 \
+    --kb-embed-dim 100 \
+    --adaptive-embed \
+    \
+    --vocab-mlp-activation none \
+    --rnn-dropout-pos both \
+    --alias-disamb-strategy fasttext \
+    --fasttext-model-path data/fasttext/wt-short.bin \
+    \
+    --use-only-first-section \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --model NKLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/nklm-wikitext-short \
+    --tbdir ~/tb/ \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_vanillalm_transformer_wikifacts.sh b/scripts/train_vanillalm_transformer_wikifacts.sh
new file mode 100755
index 0000000..136fdb2
--- /dev/null
+++ b/scripts/train_vanillalm_transformer_wikifacts.sh
@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp vanillalm-transformer-wikifacts \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 20 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 30 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --path data/wikifacts \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model VanillaLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --vocab-dir data/vocab/vanillalm-transformer-wikifacts \
+    --no-use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_vanillalm_transformer_wikitext_full.sh b/scripts/train_vanillalm_transformer_wikitext_full.sh
new file mode 100755
index 0000000..d2dadd7
--- /dev/null
+++ b/scripts/train_vanillalm_transformer_wikitext_full.sh
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+# note: you must have `vocab.1.pt` under the vocab-dir
+
+python run.py \
+    --exp vanillalm-transformer-wikitext-full \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 50 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model VanillaLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/vanillalm-transformer-wikitext-full \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_vanillalm_transformer_wikitext_short.sh b/scripts/train_vanillalm_transformer_wikitext_short.sh
new file mode 100755
index 0000000..8effc3f
--- /dev/null
+++ b/scripts/train_vanillalm_transformer_wikitext_short.sh
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp vanillalm-transformer-wikitext-short \
+    --script $0 \
+    --lr 0.00025 \
+    --lr-scaler 0.9 \
+    --batch-size 15 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --memory-size 150 \
+    --num-epochs 20 \
+    --clip 0.25 \
+    --optimizer-strategy none \
+    --lr-scheduler cosine \
+    --warm-up-steps 6000 \
+    \
+    --base-rnn transformer \
+    --num-layers 16 \
+    --embed-size 410 \
+    --hidden-size 410 \
+    --num-heads 10 \
+    --head-dim 41 \
+    --ffn-inner-dim 2100 \
+    --attention-dropout 0.0 \
+    --dropout 0.1 \
+    --adaptive-embed \
+    --tie-embed-weights \
+    --rnn-dropout-pos both \
+    --no-pre-lnorm \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model VanillaLM \
+    --logging-level DEBUG \
+    --vocab-size none \
+    --min-freq 3 \
+    --vocab-dir data/vocab/vanillalm-transformer-wikitext-short \
+    --use-unk-probs \
+    --use-only-first-section \
+    \
+    $@
diff --git a/scripts/train_vanillalm_wikifacts.sh b/scripts/train_vanillalm_wikifacts.sh
new file mode 100755
index 0000000..b607829
--- /dev/null
+++ b/scripts/train_vanillalm_wikifacts.sh
@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp vanillalm-wikifacts \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --dropout 0.5 \
+    \
+    --path data/wikifacts/ \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model VanillaLM \
+    --logging-level DEBUG \
+    --vocab-size 40000 \
+    --vocab-dir data/vocab/vanillalm-wikifacts \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_vanillalm_wikitext_full.sh b/scripts/train_vanillalm_wikitext_full.sh
new file mode 100755
index 0000000..7b3ed48
--- /dev/null
+++ b/scripts/train_vanillalm_wikitext_full.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp vanillalm-wikitext-full \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 4 \
+    --embed-size 512 \
+    --hidden-size 1024 \
+    --dropout 0.1 \
+    --vocab-mlp-dropout 0.1 \
+    --vocab-mlp-hidden-dim 500 \
+    \
+    --adaptive-embed \
+    \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model VanillaLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/vanillalm-wikitext-full \
+    --use-unk-probs \
+    \
+    $@
diff --git a/scripts/train_vanillalm_wikitext_short.sh b/scripts/train_vanillalm_wikitext_short.sh
new file mode 100755
index 0000000..9a43e30
--- /dev/null
+++ b/scripts/train_vanillalm_wikitext_short.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+
+SCRIPT_NAME=`basename "$0"`
+
+python run.py \
+    --exp vanillalm-wikitext-short \
+    --script $0 \
+    --lr 1e-3 \
+    --lr-scaler 0.9 \
+    --batch-size 30 \
+    --update-batch-size 60 \
+    --bptt-size 150 \
+    --num-epochs 50 \
+    \
+    --num-layers 2 \
+    --embed-size 400 \
+    --hidden-size 1000 \
+    --vocab-mlp-hidden-dim 1000 \
+    --vocab-mlp-dropout 0.5 \
+    --dropout 0.5 \
+    \
+    --adaptive-embed \
+    \
+    --use-only-first-section \
+    --path data/wikitext \
+    --mode train \
+    --cuda \
+    --checkpoint-interval -1 \
+    --tbdir ~/tb/ \
+    --model VanillaLM \
+    --logging-level DEBUG \
+    --vocab-size None \
+    --min-freq 3 \
+    --vocab-dir data/vocab/vanillalm-wikitext-short \
+    --use-unk-probs \
+    \
+    $@
diff --git a/stubs/torch/__init__.pyi b/stubs/torch/__init__.pyi
new file mode 100644
index 0000000..fa5672a
--- /dev/null
+++ b/stubs/torch/__init__.pyi
@@ -0,0 +1,2861 @@
+import builtins
+import pickle
+from pathlib import Path
+from typing import Any, Callable, Dict, IO, Iterator, List, Optional, Sequence, Tuple, Type, TypeVar, Union, overload
+
+import numpy as np
+from ._tensor_str import set_printoptions
+from .random import get_rng_state, initial_seed, set_rng_state
+from .storage import _StorageBase
+from .tensor import Tensor as TensorBase
+from .utils.hooks import RemovableHandle
+
+from . import cuda as cuda
+from . import optim as optim
+
+
+def manual_seed(seed: builtins.int): ...
+
+
+def load(f: Union[str, Path, IO],
+         map_location: Optional[Union[Dict[str, str], str, device, Callable[[str, str], str]]] = None,
+         pickle_module=pickle, **pickle_load_args): ...
+
+
+def save(obj: Any, f: Union[str, Path, IO], pickle_module=pickle, pickle_protocol=pickle.DEFAULT_PROTOCOL): ...
+
+
+no_grad: Any = ...
+enable_grad: Any = ...
+set_grad_enabled: Any = ...
+
+
+class device:
+    def __init__(self, device: Union[builtins.int, builtins.str]): ...
+
+
+class finfo:
+
+    def __init__(self, type: dtype): ...
+
+    @property
+    def bits(self) -> builtins.int: ...
+
+    @property
+    def eps(self) -> builtins.float: ...
+
+    @property
+    def max(self) -> builtins.float: ...
+
+    @property
+    def min(self) -> builtins.float: ...
+
+    @property
+    def tiny(self) -> builtins.float: ...
+
+
+class layout: ...
+
+
+class dtype: ...
+
+
+class Generator: ...
+
+
+class Size(Tuple[builtins.int, ...]):
+    def __init__(self, xs: Optional[Sequence[builtins.int]] = None): ...
+
+
+T = TypeVar('T')
+MaybeTuple = Union[T, Tuple[T, ...]]
+MaybeList = Union[T, List[T]]
+
+
+class _float16(dtype): ...
+
+
+class _float32(dtype): ...
+
+
+class _float64(dtype): ...
+
+
+class _uint8(dtype): ...
+
+
+class _int8(dtype): ...
+
+
+class _int16(dtype): ...
+
+
+class _int32(dtype): ...
+
+
+class _int64(dtype): ...
+
+
+half = float16 = _float16()
+double = float64 = _float64()
+short = int16 = _int16()
+long = int64 = _int64()
+uint8 = _uint8()
+float = float32 = _float32()
+int = int32 = _int32()
+
+
+class StorageBase(_StorageBase):
+    @classmethod
+    def from_buffer(cls, buffer: bytes) -> StorageBase: ...
+
+
+class ByteStorage(StorageBase): ...
+
+
+class FloatStorage(StorageBase): ...
+
+
+class DoubleStorage(StorageBase): ...
+
+
+class HalfStorage(StorageBase): ...
+
+
+class IntStorage(StorageBase): ...
+
+
+class LongStorage(StorageBase): ...
+
+
+class ShortStorage(StorageBase): ...
+
+
+class strided(layout): pass
+
+
+class sparse_coo(layout): pass
+
+
+# type aliases for use in `Tensor` class, to avoid conflicts with attributes of the same name.
+_dtype = dtype
+_device = device
+
+
+class Tensor(TensorBase):
+    dtype: _dtype
+    device: _device
+
+    @overload
+    def __init__(self, *size: builtins.int): ...
+
+    @overload
+    def __init__(self, elems: list): ...
+
+    @overload
+    def __init__(self, storage: _StorageBase): ...
+
+    def __add__(self, other) -> Tensor: ...
+
+    def __radd__(self, other) -> Tensor: ...
+
+    def __iadd__(self, other) -> Tensor: ...
+
+    def __sub__(self, other) -> Tensor: ...
+
+    def __rsub__(self, other) -> Tensor: ...
+
+    def __isub__(self, other) -> Tensor: ...
+
+    def __mul__(self, other) -> Tensor: ...
+
+    def __rmul__(self, other) -> Tensor: ...
+
+    def __imul__(self, other) -> Tensor: ...
+
+    def __rdiv__(self, other) -> Tensor: ...
+
+    def __idiv__(self, other) -> Tensor: ...
+
+    def __truediv__(self, other) -> Tensor: ...
+
+    def __rtruediv__(self, other) -> Tensor: ...  # type: ignore
+
+    def __itruediv__(self, other) -> Tensor: ...
+
+    def __pow__(self, power, modulo=None) -> Tensor: ...
+
+    def __rpow__(self, other) -> Tensor: ...
+
+    def __mod__(self, other) -> Tensor: ...
+
+    def __rmod__(self, other) -> Tensor: ...
+
+    def __imod__(self, other) -> Tensor: ...
+
+    def __matmul__(self, other) -> Tensor: ...
+
+    def __rmatmul__(self, other) -> Tensor: ...
+
+    def __imatmul__(self, other) -> Tensor: ...
+
+    def __neg__(self) -> Tensor: ...
+
+    def __eq__(self, other) -> ByteTensor: ...
+
+    def __ne__(self, other) -> ByteTensor: ...
+
+    def __lt__(self, other) -> ByteTensor: ...
+
+    def __le__(self, other) -> ByteTensor: ...
+
+    def __gt__(self, other) -> ByteTensor: ...
+
+    def __ge__(self, other) -> ByteTensor: ...
+
+    def __and__(self, other) -> ByteTensor: ...
+
+    def __iand__(self, other) -> ByteTensor: ...
+
+    def __rand__(self, other) -> ByteTensor: ...
+
+    def __or__(self, other) -> ByteTensor: ...
+
+    def __ior__(self, other) -> ByteTensor: ...
+
+    def __ror__(self, other) -> ByteTensor: ...
+
+    def __invert__(self) -> ByteTensor: ...
+
+    def __xor__(self, other) -> ByteTensor: ...
+
+    def __ixor__(self, other) -> ByteTensor: ...
+
+    def __rxor__(self, other) -> ByteTensor: ...
+
+    def __len__(self) -> builtins.int: ...
+
+    def __iter__(self) -> Iterator[Tensor]: ...
+
+    def __abs__(self) -> Tensor: ...
+
+    def __reversed__(self) -> Tensor: ...
+
+    def __getitem__(self, *item) -> Tensor: ...
+
+    def abs(self) -> Tensor: ...
+
+    def abs_(self) -> Tensor: ...
+
+    def acos(self) -> Tensor: ...
+
+    def acos_(self) -> Tensor: ...
+
+    @overload
+    def add(self, alpha: builtins.float, other: Tensor) -> Tensor: ...
+
+    @overload
+    def add(self, other: Tensor, *, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def add_(self, alpha: builtins.float, other: Tensor) -> Tensor: ...
+
+    @overload
+    def add_(self, other: Tensor, *, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addbmm(self, beta: builtins.float, alpha: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def addbmm(self, beta: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def addbmm(self, batch1: Tensor, batch2: Tensor, *, beta: builtins.float = 1,
+               alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addbmm_(self, beta: builtins.float, alpha: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def addbmm_(self, beta: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def addbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: builtins.float = 1,
+                alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addcdiv(self, value: builtins.float, tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
+
+    @overload
+    def addcdiv(self, tensor1: Tensor, tensor2: Tensor, *, value: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addcdiv_(self, value: builtins.float, tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
+
+    @overload
+    def addcdiv_(self, tensor1: Tensor, tensor2: Tensor, *, value: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addcmul(self, value: builtins.float, tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
+
+    @overload
+    def addcmul(self, tensor1: Tensor, tensor2: Tensor, *, value: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addcmul_(self, value: builtins.float, tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
+
+    @overload
+    def addcmul_(self, tensor1: Tensor, tensor2: Tensor, *, value: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addmm(self, beta: builtins.float, alpha: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+    @overload
+    def addmm(self, beta: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+    @overload
+    def addmm(self, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addmm_(self, beta: builtins.float, alpha: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+    @overload
+    def addmm_(self, beta: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+    @overload
+    def addmm_(self, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addmv(self, beta: builtins.float, alpha: builtins.float, mat: Tensor, vec: Tensor) -> Tensor: ...
+
+    @overload
+    def addmv(self, beta: builtins.float, mat: Tensor, vec: Tensor) -> Tensor: ...
+
+    @overload
+    def addmv(self, mat: Tensor, vec: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addmv_(self, beta: builtins.float, alpha: builtins.float, mat: Tensor, vec: Tensor) -> Tensor: ...
+
+    @overload
+    def addmv_(self, beta: builtins.float, mat: Tensor, vec: Tensor) -> Tensor: ...
+
+    @overload
+    def addmv_(self, mat: Tensor, vec: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addr(self, beta: builtins.float, alpha: builtins.float, vec1: Tensor, vec2: Tensor) -> Tensor: ...
+
+    @overload
+    def addr(self, beta: builtins.float, vec1: Tensor, vec2: Tensor) -> Tensor: ...
+
+    @overload
+    def addr(self, vec1: Tensor, vec2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def addr_(self, beta: builtins.float, alpha: builtins.float, vec1: Tensor, vec2: Tensor) -> Tensor: ...
+
+    @overload
+    def addr_(self, beta: builtins.float, vec1: Tensor, vec2: Tensor) -> Tensor: ...
+
+    @overload
+    def addr_(self, vec1: Tensor, vec2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def all(self) -> Tensor: ...
+
+    @overload
+    def all(self, dim: builtins.int, keepdim: bool = False) -> Tensor: ...
+
+    def allclose(self, other: Tensor, rtol: builtins.float = 1e-05, atol: builtins.float = 1e-08,
+                 equal_nan: bool = False) -> bool: ...
+
+    @overload
+    def any(self) -> Tensor: ...
+
+    @overload
+    def any(self, dim: builtins.int, keepdim: bool = False) -> Tensor: ...
+
+    @overload
+    def argmax(self) -> LongTensor: ...
+
+    @overload
+    def argmax(self, dim: builtins.int, keepdim: bool = False) -> LongTensor: ...
+
+    @overload
+    def argmin(self) -> LongTensor: ...
+
+    @overload
+    def argmin(self, dim: builtins.int, keepdim: bool = False) -> LongTensor: ...
+
+    def argsort(self, dim: builtins.int = -1, descending: bool = False) -> LongTensor: ...
+
+    @overload
+    def as_strided(self, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int]) -> Tensor: ...
+
+    @overload
+    def as_strided(self, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int],
+                   storage_offset: Optional[builtins.int] = None) -> Tensor: ...
+
+    @overload
+    def as_strided_(self, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int]) -> Tensor: ...
+
+    @overload
+    def as_strided_(self, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int],
+                    storage_offset: Optional[builtins.int] = None) -> Tensor: ...
+
+    def asin(self) -> Tensor: ...
+
+    def asin_(self) -> Tensor: ...
+
+    def atan(self) -> Tensor: ...
+
+    def atan2(self, other: Tensor) -> Tensor: ...
+
+    def atan2_(self, other: Tensor) -> Tensor: ...
+
+    def atan_(self) -> Tensor: ...
+
+    def backward(self, gradient: Optional[Tensor] = None, retain_graph: Optional[bool] = None,
+                 create_graph: bool = False): ...
+
+    @overload
+    def baddbmm(self, beta: builtins.float, alpha: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def baddbmm(self, beta: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def baddbmm(self, batch1: Tensor, batch2: Tensor, *, beta: builtins.float = 1,
+                alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def baddbmm_(self, beta: builtins.float, alpha: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def baddbmm_(self, beta: builtins.float, batch1: Tensor, batch2: Tensor) -> Tensor: ...
+
+    @overload
+    def baddbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: builtins.float = 1,
+                 alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def bernoulli(self, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    @overload
+    def bernoulli(self, p: builtins.float, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    @overload
+    def bernoulli_(self, p: Tensor, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    @overload
+    def bernoulli_(self, p: builtins.float = 0.5, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    def bincount(self, weights: Optional[Tensor] = None, minlength: builtins.int = 0) -> Tensor: ...
+
+    def bmm(self, mat2: Tensor) -> Tensor: ...
+
+    def btrifact(self, info: Optional[IntTensor] = None,  # type: ignore
+                 pivot: bool = True) -> Tuple[Tensor, IntTensor]: ...
+
+    def btrifact_with_info(self, *, pivot: bool = True) -> Tuple[Tensor, IntTensor, IntTensor]: ...  # type: ignore
+
+    def btrisolve(self, LU_data: Tensor, LU_pivots: Tensor) -> Tensor: ...
+
+    def cauchy_(self, median: builtins.float = 0, sigma: builtins.float = 1, *,
+                generator: Optional[Generator] = None) -> Tensor: ...
+
+    def ceil(self) -> Tensor: ...
+
+    def ceil_(self) -> Tensor: ...
+
+    def chunk(self, chunks: builtins.int, dim: builtins.int = 0) -> MaybeTuple[Tensor]: ...
+
+    def clamp(self, min: Optional[builtins.float] = None, max: Optional[builtins.float] = None) -> Tensor: ...
+
+    def clamp_(self, min: Optional[builtins.float] = None, max: Optional[builtins.float] = None) -> Tensor: ...
+
+    def clamp_max(self, max: builtins.float) -> Tensor: ...
+
+    def clamp_max_(self, max: builtins.float) -> Tensor: ...
+
+    def clamp_min(self, min: builtins.float) -> Tensor: ...
+
+    def clamp_min_(self, min: builtins.float) -> Tensor: ...
+
+    def clone(self) -> Tensor: ...
+
+    def coalesce(self) -> Tensor: ...
+
+    def contiguous(self) -> Tensor: ...
+
+    def cos(self) -> Tensor: ...
+
+    def cos_(self) -> Tensor: ...
+
+    def cosh(self) -> Tensor: ...
+
+    def cosh_(self) -> Tensor: ...
+
+    def cross(self, other: Tensor, dim: builtins.int = -1) -> Tensor: ...
+
+    def cumprod(self, dim: builtins.int, *, dtype: Optional[_dtype] = None) -> Tensor: ...
+
+    def cumsum(self, dim: builtins.int, *, dtype: Optional[_dtype] = None) -> Tensor: ...
+
+    def data_ptr(self) -> builtins.int: ...
+
+    def det(self) -> Tensor: ...
+
+    def detach(self) -> Tensor: ...
+
+    def detach_(self) -> Tensor: ...
+
+    def diag(self, diagonal: builtins.int = 0) -> Tensor: ...
+
+    def diagflat(self, offset: builtins.int = 0) -> Tensor: ...
+
+    def diagonal(self, offset: builtins.int = 0, dim1: builtins.int = 0, dim2: builtins.int = 1) -> Tensor: ...
+
+    def digamma(self) -> Tensor: ...
+
+    def digamma_(self) -> Tensor: ...
+
+    def dim(self) -> builtins.int: ...
+
+    def dist(self, other: Tensor, p: builtins.float = 2) -> Tensor: ...
+
+    def div(self, other: Union[builtins.float, Tensor]) -> Tensor: ...
+
+    def div_(self, other: Union[builtins.float, Tensor]) -> Tensor: ...
+
+    def dot(self, tensor: Tensor) -> Tensor: ...
+
+    def double(self) -> DoubleTensor: ...
+
+    def eig(self, eigenvectors: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    @overload
+    def eq(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def eq(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def eq_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def eq_(self, other: builtins.float) -> Tensor: ...
+
+    def equal(self, other: Tensor) -> bool: ...
+
+    def erf(self) -> Tensor: ...
+
+    def erf_(self) -> Tensor: ...
+
+    def erfc(self) -> Tensor: ...
+
+    def erfc_(self) -> Tensor: ...
+
+    def erfinv(self) -> Tensor: ...
+
+    def erfinv_(self) -> Tensor: ...
+
+    def exp(self) -> Tensor: ...
+
+    def exp_(self) -> Tensor: ...
+
+    @overload
+    def expand(self, *size: builtins.int, implicit: bool = False) -> Tensor: ...
+
+    @overload
+    def expand(self, size: MaybeTuple[builtins.int], implicit: bool = False) -> Tensor: ...
+
+    def expand_as(self, other: Tensor) -> Tensor: ...
+
+    def expm1(self) -> Tensor: ...
+
+    def expm1_(self) -> Tensor: ...
+
+    def exponential_(self, lambd: builtins.float = 1, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    def fft(self, signal_ndim: builtins.int, normalized: bool = False) -> Tensor: ...
+
+    @overload
+    def fill_(self, value: Tensor) -> Tensor: ...
+
+    @overload
+    def fill_(self, value: builtins.float) -> Tensor: ...
+
+    def flatten(self, start_dim: builtins.int = 0, end_dim: builtins.int = -1) -> Tensor: ...
+
+    def flip(self, dims: MaybeTuple[builtins.int]) -> Tensor: ...
+
+    def float(self) -> FloatTensor: ...
+
+    def floor(self) -> Tensor: ...
+
+    def floor_(self) -> Tensor: ...
+
+    @overload
+    def fmod(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def fmod(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def fmod_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def fmod_(self, other: builtins.float) -> Tensor: ...
+
+    def frac(self) -> Tensor: ...
+
+    def frac_(self) -> Tensor: ...
+
+    def gather(self, dim: builtins.int, index: Tensor) -> Tensor: ...
+
+    @overload
+    def ge(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def ge(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def ge_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def ge_(self, other: builtins.float) -> Tensor: ...
+
+    def gels(self, A: Tensor) -> Tuple[Tensor, Tensor]: ...
+
+    def geometric_(self, p: builtins.float, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    def geqrf(self) -> Tuple[Tensor, Tensor]: ...
+
+    def ger(self, vec2: Tensor) -> Tensor: ...
+
+    def gesv(self, A: Tensor) -> Tuple[Tensor, Tensor]: ...
+
+    def get_device(self) -> builtins.int: ...
+
+    @overload
+    def gt(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def gt(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def gt_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def gt_(self, other: builtins.float) -> Tensor: ...
+
+    def half(self) -> HalfTensor: ...
+
+    def hardshrink(self, lambd: builtins.float = 0.5) -> Tensor: ...
+
+    def histc(self, bins: builtins.int = 100, min: builtins.float = 0, max: builtins.float = 0) -> Tensor: ...
+
+    def ifft(self, signal_ndim: builtins.int, normalized: bool = False) -> Tensor: ...
+
+    def index_add(self, dim: builtins.int, index: Tensor, source: Tensor) -> Tensor: ...
+
+    def index_add_(self, dim: builtins.int, index: Tensor, source: Tensor) -> Tensor: ...
+
+    def index_copy(self, dim: builtins.int, index: Tensor, source: Tensor) -> Tensor: ...
+
+    def index_copy_(self, dim: builtins.int, index: Tensor, source: Tensor) -> Tensor: ...
+
+    def index_fill(self, dim: builtins.int, index: Tensor, value: Union[Tensor, builtins.float]) -> Tensor: ...
+
+    def index_fill_(self, dim: builtins.int, index: Tensor, value: Union[Tensor, builtins.float]) -> Tensor: ...
+
+    def index_put(self, indices: MaybeTuple[Tensor], values: Tensor) -> Tensor: ...
+
+    def index_put_(self, indices: MaybeTuple[Tensor], values: Tensor) -> Tensor: ...
+
+    def index_select(self, dim: builtins.int, index: Tensor) -> Tensor: ...
+
+    def int(self) -> IntTensor: ...
+
+    def inverse(self) -> Tensor: ...
+
+    def irfft(self, signal_ndim: builtins.int, normalized: bool = False, onesided: bool = True,
+              signal_sizes: Optional[MaybeTuple[builtins.int]] = None) -> Tensor: ...
+
+    def is_coalesced(self) -> bool: ...
+
+    def is_complex(self) -> bool: ...
+
+    def is_contiguous(self) -> bool: ...
+
+    def is_distributed(self) -> bool: ...
+
+    def is_floating_point(self) -> bool: ...
+
+    def is_nonzero(self) -> bool: ...
+
+    def is_pinned(self) -> bool: ...
+
+    def is_same_size(self, other: Tensor) -> bool: ...
+
+    def is_set_to(self, tensor: Tensor) -> bool: ...
+
+    def is_shared(self) -> bool: ...
+
+    def is_signed(self) -> bool: ...
+
+    def isclose(self, other: Tensor, rtol: builtins.float = 1e-05, atol: builtins.float = 1e-08,
+                equal_nan: bool = False) -> Tensor: ...
+
+    def kthvalue(self, k: builtins.int, dim: builtins.int = -1, keepdim: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    @overload
+    def le(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def le(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def le_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def le_(self, other: builtins.float) -> Tensor: ...
+
+    def lerp(self, end: Tensor, weight: builtins.float) -> Tensor: ...
+
+    def lerp_(self, end: Tensor, weight: builtins.float) -> Tensor: ...
+
+    def lgamma(self) -> Tensor: ...
+
+    def lgamma_(self) -> Tensor: ...
+
+    def log(self) -> Tensor: ...
+
+    def log10(self) -> Tensor: ...
+
+    def log10_(self) -> Tensor: ...
+
+    def log1p(self) -> Tensor: ...
+
+    def log1p_(self) -> Tensor: ...
+
+    def log2(self) -> Tensor: ...
+
+    def log2_(self) -> Tensor: ...
+
+    def log_(self) -> Tensor: ...
+
+    def log_normal_(self, mean: builtins.float = 1, std: builtins.float = 2, *,
+                    generator: Optional[Generator] = None) -> Tensor: ...
+
+    def log_softmax(self, dim: builtins.int) -> Tensor: ...
+
+    def logdet(self) -> Tensor: ...
+
+    def logsumexp(self, dim: builtins.int, keepdim: bool = False) -> Tensor: ...
+
+    def long(self) -> LongTensor: ...
+
+    @overload
+    def lt(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def lt(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def lt_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def lt_(self, other: builtins.float) -> Tensor: ...
+
+    def masked_fill(self, mask: Tensor, value: Union[builtins.float, Tensor]) -> Tensor: ...
+
+    def masked_fill_(self, mask: Tensor, value: Union[builtins.float, Tensor]) -> Tensor: ...
+
+    def masked_scatter(self, mask: Tensor, source: Tensor) -> Tensor: ...
+
+    def masked_scatter_(self, mask: Tensor, source: Tensor) -> Tensor: ...
+
+    def masked_select(self, mask: Tensor) -> Tensor: ...
+
+    def matmul(self, other: Tensor) -> Tensor: ...
+
+    def matrix_power(self, n: builtins.int) -> Tensor: ...
+
+    @overload
+    def max(self) -> Tensor: ...
+
+    @overload
+    def max(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def max(self, dim: builtins.int, keepdim: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    @overload
+    def mean(self) -> Tensor: ...
+
+    @overload
+    def mean(self, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def mean(self, dim: builtins.int, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def mean(self, dim: builtins.int, keepdim: bool, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def mean(self, dim: builtins.int, keepdim: bool = False) -> Tensor: ...
+
+    @overload
+    def median(self) -> Tensor: ...
+
+    @overload
+    def median(self, dim: builtins.int, keepdim: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    @overload
+    def min(self) -> Tensor: ...
+
+    @overload
+    def min(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def min(self, dim: builtins.int, keepdim: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    def mm(self, mat2: Tensor) -> Tensor: ...
+
+    def mode(self, dim: builtins.int = -1, keepdim: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    def mul(self, other: Union[builtins.float, Tensor]) -> Tensor: ...
+
+    def mul_(self, other: Union[builtins.float, Tensor]) -> Tensor: ...
+
+    def multinomial(self, num_samples: builtins.int, replacement: bool = False, *,
+                    generator: Optional[Generator] = None) -> Tensor: ...
+
+    def mv(self, vec: Tensor) -> Tensor: ...
+
+    def mvlgamma(self, p: builtins.int) -> Tensor: ...
+
+    def mvlgamma_(self, p: builtins.int) -> Tensor: ...
+
+    def narrow(self, dim: builtins.int, start: builtins.int, length: builtins.int) -> Tensor: ...
+
+    @overload
+    def ne(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def ne(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def ne_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def ne_(self, other: builtins.float) -> Tensor: ...
+
+    def neg(self) -> Tensor: ...
+
+    def neg_(self) -> Tensor: ...
+
+    def new_tensor(self, data, dtype: Optional[_dtype] = None, device: Union[_device, str, None] = None,
+                   requires_grad: bool = False) -> Tensor: ...
+
+    def new_full(self, size: MaybeTuple[builtins.int], fill_value: builtins.float, dtype: Optional[_dtype] = None,
+                 device: Union[_device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+    @overload
+    def new_empty(self, size: MaybeTuple[builtins.int], dtype: Optional[_dtype] = None,
+                  device: Union[_device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+    @overload
+    def new_empty(self, *size: builtins.int, dtype: Optional[_dtype] = None, device: Union[_device, str, None] = None,
+                  requires_grad: bool = False) -> Tensor: ...
+
+    @overload
+    def new_ones(self, size: MaybeTuple[builtins.int], dtype: Optional[_dtype] = None,
+                 device: Union[_device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+    @overload
+    def new_ones(self, *size: builtins.int, dtype: Optional[_dtype] = None, device: Union[_device, str, None] = None,
+                 requires_grad: bool = False) -> Tensor: ...
+
+    @overload
+    def new_zeros(self, size: MaybeTuple[builtins.int], dtype: Optional[_dtype] = None,
+                  device: Union[_device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+    @overload
+    def new_zeros(self, *size: builtins.int, dtype: Optional[_dtype] = None, device: Union[_device, str, None] = None,
+                  requires_grad: bool = False) -> Tensor: ...
+
+    def nonzero(self) -> Tensor: ...
+
+    def norm(self, p: Union[builtins.float, str, None] = 'fro', dim: Optional[builtins.int] = None,  # type: ignore
+             keepdim: bool = False) -> Tensor: ...
+
+    def normal_(self, mean: builtins.float = 0, std: builtins.float = 1, *,
+                generator: Optional[Generator] = None) -> Tensor: ...
+
+    def numel(self) -> builtins.int: ...
+
+    def orgqr(self, input2: Tensor) -> Tensor: ...
+
+    def ormqr(self, input2: Tensor, input3: Tensor, left: bool = True, transpose: bool = False) -> Tensor: ...
+
+    @overload
+    def permute(self, *dims: builtins.int) -> Tensor: ...
+
+    @overload
+    def permute(self, dims: MaybeTuple[builtins.int]) -> Tensor: ...
+
+    def pin_memory(self) -> Tensor: ...
+
+    def pinverse(self, rcond: builtins.float = 1e-15) -> Tensor: ...
+
+    def polygamma(self, n: builtins.int, ) -> Tensor: ...
+
+    def polygamma_(self, n: builtins.int) -> Tensor: ...
+
+    def potrf(self, upper: bool = True) -> Tensor: ...
+
+    def potri(self, upper: bool = True) -> Tensor: ...
+
+    def potrs(self, input2: Tensor, upper: bool = True) -> Tensor: ...
+
+    @overload
+    def pow(self, exponent: Tensor) -> Tensor: ...
+
+    @overload
+    def pow(self, exponent: builtins.float) -> Tensor: ...
+
+    @overload
+    def pow_(self, exponent: Tensor) -> Tensor: ...
+
+    @overload
+    def pow_(self, exponent: builtins.float) -> Tensor: ...
+
+    def prelu(self, weight: Tensor) -> Tensor: ...
+
+    @overload
+    def prod(self) -> Tensor: ...
+
+    @overload
+    def prod(self, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def prod(self, dim: builtins.int, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def prod(self, dim: builtins.int, keepdim: bool, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def prod(self, dim: builtins.int, keepdim: bool = False) -> Tensor: ...
+
+    def pstrf(self, upper: bool = True, tol: builtins.float = -1) -> Tuple[Tensor, Tensor]: ...
+
+    def put_(self, index: Tensor, source: Tensor, accumulate: bool = False) -> Tensor: ...
+
+    def qr(self) -> Tuple[Tensor, Tensor]: ...
+
+    @overload
+    def random_(self, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    @overload
+    def random_(self, from_: builtins.int, to: builtins.int, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    @overload
+    def random_(self, to: builtins.int, *, generator: Optional[Generator] = None) -> Tensor: ...
+
+    def reciprocal(self) -> Tensor: ...
+
+    def reciprocal_(self) -> Tensor: ...
+
+    def register_hook(self, hook: Callable[[Tensor], Optional[Tensor]]) -> RemovableHandle: ...
+
+    def relu(self) -> Tensor: ...
+
+    def relu_(self) -> Tensor: ...
+
+    @overload
+    def remainder(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def remainder(self, other: builtins.float) -> Tensor: ...
+
+    @overload
+    def remainder_(self, other: Tensor) -> Tensor: ...
+
+    @overload
+    def remainder_(self, other: builtins.float) -> Tensor: ...
+
+    def renorm(self, p: builtins.float, dim: builtins.int, maxnorm: builtins.float) -> Tensor: ...
+
+    def renorm_(self, p: builtins.float, dim: builtins.int, maxnorm: builtins.float) -> Tensor: ...
+
+    @overload
+    def repeat(self, *repeats: builtins.int) -> Tensor: ...
+
+    @overload
+    def repeat(self, repeats: Tuple[builtins.int, ...]) -> Tensor: ...
+
+    @overload
+    def reshape(self, *shape: builtins.int) -> Tensor: ...
+
+    @overload
+    def reshape(self, shape: Tuple[builtins.int, ...]) -> Tensor: ...
+
+    def reshape_as(self, other: Tensor) -> Tensor: ...
+
+    def resize_(self, *size: builtins.int) -> Tensor: ...
+
+    def resize_as_(self, the_template: Tensor) -> Tensor: ...
+
+    def retain_grad(self) -> None: ...
+
+    def rfft(self, signal_ndim: builtins.int, normalized: bool = False, onesided: bool = True) -> Tensor: ...
+
+    def round(self) -> Tensor: ...
+
+    def round_(self) -> Tensor: ...
+
+    def rsqrt(self) -> Tensor: ...
+
+    def rsqrt_(self) -> Tensor: ...
+
+    def scatter(self, dim: builtins.int, index: LongTensor, value: Union[Tensor, builtins.float]) -> Tensor: ...
+
+    def scatter_(self, dim: builtins.int, index: LongTensor, value: Union[Tensor, builtins.float]) -> Tensor: ...
+
+    def scatter_add(self, dim: builtins.int, index: LongTensor, src: Tensor) -> Tensor: ...
+
+    def scatter_add_(self, dim: builtins.int, index: LongTensor, src: Tensor) -> Tensor: ...
+
+    def select(self, dim: builtins.int, index: builtins.int) -> Tensor: ...
+
+    @overload
+    def set_(self) -> Tensor: ...
+
+    @overload
+    def set_(self, source: Union[Tensor, _StorageBase]) -> Tensor: ...
+
+    @overload
+    def set_(self, source: _StorageBase, storage_offset: builtins.int, size: MaybeTuple[builtins.int],
+             stride: Optional[MaybeTuple[builtins.int]] = None) -> Tensor: ...
+
+    def share_memory_(self) -> Tensor: ...
+
+    @property
+    def shape(self) -> Size: ...
+
+    def sigmoid(self) -> Tensor: ...
+
+    def sigmoid_(self) -> Tensor: ...
+
+    def sign(self) -> Tensor: ...
+
+    def sign_(self) -> Tensor: ...
+
+    def sin(self) -> Tensor: ...
+
+    def sin_(self) -> Tensor: ...
+
+    def sinh(self) -> Tensor: ...
+
+    def sinh_(self) -> Tensor: ...
+
+    @overload
+    def size(self) -> Size: ...
+
+    @overload
+    def size(self, dim: builtins.int) -> builtins.int: ...
+
+    def slogdet(self) -> Tuple[Tensor, Tensor]: ...
+
+    def smm(self, mat2: Tensor) -> Tensor: ...
+
+    def softmax(self, dim: builtins.int) -> Tensor: ...
+
+    def sort(self, dim: builtins.int = -1, descending: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    def sparse_mask(self, mask: Tensor) -> Tensor: ...
+
+    def sparse_resize_(self, size: MaybeTuple[builtins.int], sparseDims: builtins.int,
+                       denseDims: builtins.int) -> Tensor: ...
+
+    def sparse_resize_and_clear_(self, size: MaybeTuple[builtins.int], sparseDims: builtins.int,
+                                 denseDims: builtins.int) -> Tensor: ...
+
+    def split(self, split_size_or_sections: MaybeList[builtins.int], dim: builtins.int = 0) -> List[Tensor]: ...
+
+    def sqrt(self) -> Tensor: ...
+
+    def sqrt_(self) -> Tensor: ...
+
+    @overload
+    def squeeze(self) -> Tensor: ...
+
+    @overload
+    def squeeze(self, dim: builtins.int) -> Tensor: ...
+
+    @overload
+    def squeeze_(self) -> Tensor: ...
+
+    @overload
+    def squeeze_(self, dim: builtins.int) -> Tensor: ...
+
+    @overload
+    def sspaddmm(self, beta: builtins.float, alpha: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+    @overload
+    def sspaddmm(self, beta: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+    @overload
+    def sspaddmm(self, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1,
+                 alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def std(self, unbiased: bool = True) -> Tensor: ...
+
+    @overload
+    def std(self, dim: builtins.int, unbiased: bool = True, keepdim: bool = False) -> Tensor: ...
+
+    def stft(self, n_fft: builtins.int, hop_length: Optional[builtins.int] = None,
+             win_length: Optional[builtins.int] = None, window: Optional[Tensor] = None, center: bool = True,
+             pad_mode: str = 'reflect', normalized: bool = False, onesided: bool = True) -> Tensor: ...
+
+    def storage_offset(self) -> builtins.int: ...
+
+    @overload
+    def sub(self, alpha: builtins.float, other: Tensor) -> Tensor: ...
+
+    @overload
+    def sub(self, other: Tensor, *, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def sub_(self, alpha: builtins.float, other: Tensor) -> Tensor: ...
+
+    @overload
+    def sub_(self, other: Tensor, *, alpha: builtins.float = 1) -> Tensor: ...
+
+    @overload
+    def sum(self) -> Tensor: ...
+
+    @overload
+    def sum(self, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def sum(self, dim: MaybeTuple[builtins.int], *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def sum(self, dim: MaybeTuple[builtins.int], keepdim: bool, *, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def sum(self, dim: MaybeTuple[builtins.int], keepdim: bool = False) -> Tensor: ...
+
+    def svd(self, some: bool = True) -> Tuple[Tensor, Tensor, Tensor]: ...
+
+    def symeig(self, eigenvectors: bool = False, upper: bool = True) -> Tuple[Tensor, Tensor]: ...
+
+    def t(self) -> Tensor: ...
+
+    def t_(self) -> Tensor: ...
+
+    def take(self, index: Tensor) -> Tensor: ...
+
+    def tan(self) -> Tensor: ...
+
+    def tan_(self) -> Tensor: ...
+
+    def tanh(self) -> Tensor: ...
+
+    def tanh_(self) -> Tensor: ...
+
+    @overload
+    def to(self, dtype: _dtype) -> Tensor: ...
+
+    @overload
+    def to(self, device: Union[_device, str]) -> Tensor: ...
+
+    @overload
+    def to(self, device: Union[_device, str, None] = None, dtype: Optional[_dtype] = None,
+           non_blocking: bool = False) -> Tensor: ...
+
+    @overload
+    def to(self, other: Tensor, non_blocking: bool = False) -> Tensor: ...
+
+    def to_dense(self) -> Tensor: ...
+
+    def topk(self, k: builtins.int, dim: builtins.int = -1, largest: bool = True, sorted: bool = True) -> Tuple[
+        Tensor, Tensor]: ...
+
+    def trace(self) -> Tensor: ...
+
+    def transpose(self, dim0: builtins.int, dim1: builtins.int) -> Tensor: ...
+
+    def transpose_(self, dim0: builtins.int, dim1: builtins.int) -> Tensor: ...
+
+    def tril(self, diagonal: builtins.int = 0) -> Tensor: ...
+
+    def tril_(self, diagonal: builtins.int = 0) -> Tensor: ...
+
+    def triu(self, diagonal: builtins.int = 0) -> Tensor: ...
+
+    def triu_(self, diagonal: builtins.int = 0) -> Tensor: ...
+
+    def trtrs(self, A: Tensor, upper: bool = True, transpose: bool = False,
+              unitriangular: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+    def trunc(self) -> Tensor: ...
+
+    def trunc_(self) -> Tensor: ...
+
+    def type(self, dtype: _dtype) -> Tensor: ...
+
+    def type_as(self, other: Tensor) -> Tensor: ...
+
+    def unbind(self, dim: builtins.int = 0) -> MaybeTuple[Tensor]: ...
+
+    def unfold(self, dimension: builtins.int, size: builtins.int, step: builtins.int) -> Tensor: ...
+
+    def uniform_(self, from_: builtins.float = 0, to: builtins.float = 1, *,
+                 generator: Optional[Generator] = None) -> Tensor: ...
+
+    def unsqueeze(self, dim: builtins.int) -> Tensor: ...
+
+    def unsqueeze_(self, dim: builtins.int) -> Tensor: ...
+
+    @overload
+    def var(self, unbiased: bool = True) -> Tensor: ...
+
+    @overload
+    def var(self, dim: builtins.int, unbiased: bool = True, keepdim: bool = False) -> Tensor: ...
+
+    @overload
+    def view(self, *size: builtins.int) -> Tensor: ...
+
+    @overload
+    def view(self, size: MaybeTuple[builtins.int]) -> Tensor: ...
+
+    def view_as(self, other: Tensor) -> Tensor: ...
+
+    def where(self, condition: Tensor, other: Tensor) -> Tensor: ...
+
+    def zero_(self) -> Tensor: ...
+
+
+FloatTensor = Tensor
+
+
+class DoubleTensor(Tensor): ...
+
+
+class HalfTensor(Tensor): ...
+
+
+class ByteTensor(Tensor): ...
+
+
+class ShortTensor(Tensor): ...
+
+
+class IntTensor(Tensor): ...
+
+
+class LongTensor(Tensor): ...
+
+
+def tensor(data, dtype: Optional[dtype] = None, device: Union[device, str, None] = None,
+           requires_grad: bool = False) -> Tensor: ...
+
+
+def abs(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def abs_(input: Tensor) -> Tensor: ...
+
+
+def acos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def acos_(input: Tensor) -> Tensor: ...
+
+
+def adaptive_avg_pool1d(input: Tensor, output_size: MaybeTuple[builtins.int]) -> Tensor: ...
+
+
+def adaptive_max_pool1d(input: Tensor, output_size: MaybeTuple[builtins.int]) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def add(input: Tensor, alpha: builtins.float, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def add(input: Tensor, other: Tensor, *, alpha: builtins.float = 1, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addbmm(beta: builtins.float, self: Tensor, alpha: builtins.float, batch1: Tensor, batch2: Tensor, *,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addbmm(beta: builtins.float, self: Tensor, batch1: Tensor, batch2: Tensor, *,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addcdiv(input: Tensor, value: builtins.float, tensor1: Tensor, tensor2: Tensor, *,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addcdiv(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: builtins.float = 1,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addcmul(input: Tensor, value: builtins.float, tensor1: Tensor, tensor2: Tensor, *,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addcmul(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: builtins.float = 1,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmm(beta: builtins.float, self: Tensor, alpha: builtins.float, mat1: Tensor, mat2: Tensor, *,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmm(beta: builtins.float, self: Tensor, mat1: Tensor, mat2: Tensor, *,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmv(beta: builtins.float, self: Tensor, alpha: builtins.float, mat: Tensor, vec: Tensor, *,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmv(beta: builtins.float, self: Tensor, mat: Tensor, vec: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmv(input: Tensor, mat: Tensor, vec: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addmv_(beta: builtins.float, self: Tensor, alpha: builtins.float, mat: Tensor, vec: Tensor) -> Tensor: ...
+
+
+@overload
+def addmv_(beta: builtins.float, self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: ...
+
+
+@overload
+def addmv_(input: Tensor, mat: Tensor, vec: Tensor, *, beta: builtins.float = 1,
+           alpha: builtins.float = 1) -> Tensor: ...
+
+
+@overload
+def addr(beta: builtins.float, self: Tensor, alpha: builtins.float, vec1: Tensor, vec2: Tensor, *,
+         out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addr(beta: builtins.float, self: Tensor, vec1: Tensor, vec2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def addr(input: Tensor, vec1: Tensor, vec2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+         out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def all(input: Tensor) -> Tensor: ...
+
+
+@overload
+def all(input: Tensor, dim: builtins.int, keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def allclose(input: Tensor, other: Tensor, rtol: builtins.float = 1e-05, atol: builtins.float = 1e-08,
+             equal_nan: bool = False) -> bool: ...
+
+
+def alpha_dropout(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def alpha_dropout_(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+@overload
+def any(input: Tensor) -> Tensor: ...
+
+
+@overload
+def any(input: Tensor, dim: builtins.int, keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def arange(start: Union[builtins.int, builtins.float], end: Union[builtins.int, builtins.float],
+           step: Union[builtins.int, builtins.float] = 1, out: Optional[Tensor] = None,
+           dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+           requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def arange(end: Union[builtins.int, builtins.float], step: Union[builtins.int, builtins.float] = 1,
+           out: Optional[Tensor] = None, dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+           device: Union[_device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def argmax(input: Tensor) -> LongTensor: ...
+
+
+@overload
+def argmax(input: Tensor, dim: builtins.int, keepdim: bool = False) -> LongTensor: ...
+
+
+@overload
+def argmin(input: Tensor) -> LongTensor: ...
+
+
+@overload
+def argmin(input: Tensor, dim: builtins.int, keepdim: bool = False) -> LongTensor: ...
+
+
+@overload
+def as_strided(input: Tensor, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int]) -> Tensor: ...
+
+
+@overload
+def as_strided(input: Tensor, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int],
+               storage_offset: Optional[builtins.int] = None) -> Tensor: ...
+
+
+@overload
+def as_strided_(input: Tensor, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int]) -> Tensor: ...
+
+
+@overload
+def as_strided_(input: Tensor, size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int],
+                storage_offset: Optional[builtins.int] = None) -> Tensor: ...
+
+
+def asin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def asin_(input: Tensor) -> Tensor: ...
+
+
+def atan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def atan2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def atan_(input: Tensor) -> Tensor: ...
+
+
+def avg_pool1d(input: Tensor, kernel_size: Optional[MaybeTuple[builtins.int]],
+               stride: Optional[MaybeTuple[builtins.int]] = None, padding: Optional[MaybeTuple[builtins.int]] = 0,
+               ceil_mode: bool = False, count_include_pad: bool = True) -> Tensor: ...
+
+
+@overload
+def baddbmm(beta: builtins.float, self: Tensor, alpha: builtins.float, batch1: Tensor, batch2: Tensor, *,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def baddbmm(beta: builtins.float, self: Tensor, batch1: Tensor, batch2: Tensor, *,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def baddbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+            out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def bartlett_window(window_length: builtins.int, *, dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+                    device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def bartlett_window(window_length: builtins.int, periodic: bool, *, dtype: Optional[dtype] = None,
+                    layout: Type[layout] = strided, device: Union[device, str, None] = None,
+                    requires_grad: bool = False) -> Tensor: ...
+
+
+def batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor],
+               running_var: Optional[Tensor], training: bool, momentum: builtins.float, eps: builtins.float,
+               cudnn_enabled: bool) -> Tensor: ...
+
+
+def bernoulli(input: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: ...
+
+
+def bincount(input: Tensor, weights: Optional[Tensor] = None, minlength: builtins.int = 0) -> Tensor: ...
+
+
+@overload
+def blackman_window(window_length: builtins.int, *, dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+                    device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def blackman_window(window_length: builtins.int, periodic: bool, *, dtype: Optional[dtype] = None,
+                    layout: Type[layout] = strided, device: Union[device, str, None] = None,
+                    requires_grad: bool = False) -> Tensor: ...
+
+
+def bmm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def broadcast_tensors(tensors: Sequence[Tensor]) -> MaybeTuple[Tensor]: ...
+
+
+def btrifact(input: Tensor, *, pivot: bool = True, out: Optional[Tuple[Tensor, IntTensor]]) \
+        -> Tuple[Tensor, IntTensor]: ...
+
+
+def btrifact_with_info(input: Tensor, *, pivot: bool = True,
+                       out: Optional[Tuple[Tensor, IntTensor, IntTensor]]) -> Tuple[Tensor, IntTensor, IntTensor]: ...
+
+
+def btrisolve(input: Tensor, LU_data: Tensor, LU_pivots: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def cat(tensors: Sequence[Tensor], dim: builtins.int = 0, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def ceil(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def ceil_(input: Tensor) -> Tensor: ...
+
+
+def celu(input: Tensor, alpha: builtins.float = 1.0) -> Tensor: ...
+
+
+def celu_(input: Tensor, alpha: builtins.float = 1.0) -> Tensor: ...
+
+
+def chunk(input: Tensor, chunks: builtins.int, dim: builtins.int = 0) -> MaybeTuple[Tensor]: ...
+
+
+def clamp(input: Tensor, min: Optional[builtins.float] = None, max: Optional[builtins.float] = None, *,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def clamp_(input: Tensor, min: Optional[builtins.float] = None, max: Optional[builtins.float] = None) -> Tensor: ...
+
+
+def clamp_max(input: Tensor, max: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def clamp_max_(input: Tensor, max: builtins.float) -> Tensor: ...
+
+
+def clamp_min(input: Tensor, min: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def clamp_min_(input: Tensor, min: builtins.float) -> Tensor: ...
+
+
+def clone(input: Tensor) -> Tensor: ...
+
+
+def conv1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: MaybeTuple[builtins.int] = 1,
+           padding: MaybeTuple[builtins.int] = 0, dilation: MaybeTuple[builtins.int] = 1,
+           groups: builtins.int = 1) -> Tensor: ...
+
+
+def conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: MaybeTuple[builtins.int] = 1,
+           padding: MaybeTuple[builtins.int] = 0, dilation: MaybeTuple[builtins.int] = 1,
+           groups: builtins.int = 1) -> Tensor: ...
+
+
+def conv3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: MaybeTuple[builtins.int] = 1,
+           padding: MaybeTuple[builtins.int] = 0, dilation: MaybeTuple[builtins.int] = 1,
+           groups: builtins.int = 1) -> Tensor: ...
+
+
+def conv_tbc(input: Tensor, weight: Tensor, bias: Tensor, pad: builtins.int = 0) -> Tensor: ...
+
+
+def conv_transpose1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: MaybeTuple[builtins.int] = 1,
+                     padding: MaybeTuple[builtins.int] = 0, output_padding: MaybeTuple[builtins.int] = 0,
+                     groups: builtins.int = 1, dilation: MaybeTuple[builtins.int] = 1) -> Tensor: ...
+
+
+def conv_transpose2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: MaybeTuple[builtins.int] = 1,
+                     padding: MaybeTuple[builtins.int] = 0, output_padding: MaybeTuple[builtins.int] = 0,
+                     groups: builtins.int = 1, dilation: MaybeTuple[builtins.int] = 1) -> Tensor: ...
+
+
+def conv_transpose3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: MaybeTuple[builtins.int] = 1,
+                     padding: MaybeTuple[builtins.int] = 0, output_padding: MaybeTuple[builtins.int] = 0,
+                     groups: builtins.int = 1, dilation: MaybeTuple[builtins.int] = 1) -> Tensor: ...
+
+
+def convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: MaybeTuple[builtins.int],
+                padding: MaybeTuple[builtins.int], dilation: MaybeTuple[builtins.int], transposed: bool,
+                output_padding: MaybeTuple[builtins.int], groups: builtins.int) -> Tensor: ...
+
+
+def cos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def cos_(input: Tensor) -> Tensor: ...
+
+
+def cosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def cosh_(input: Tensor) -> Tensor: ...
+
+
+def cosine_embedding_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: builtins.float = 0.0,
+                          reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+def cross(input: Tensor, other: Tensor, dim: builtins.int = -1, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: MaybeTuple[builtins.int],
+             target_lengths: MaybeTuple[builtins.int], blank: builtins.int = 0,
+             reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+@overload
+def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: builtins.int = 0,
+             reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+def cudnn_affine_grid_generator(theta: Tensor, N: builtins.int, C: builtins.int, H: builtins.int,
+                                W: builtins.int) -> Tensor: ...
+
+
+def cudnn_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor],
+                     running_var: Optional[Tensor], training: bool, exponential_average_factor: builtins.float,
+                     epsilon: builtins.float) -> Tuple[Tensor, Tensor, Tensor]: ...
+
+
+def cudnn_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: MaybeTuple[builtins.int],
+                      stride: MaybeTuple[builtins.int], dilation: MaybeTuple[builtins.int], groups: builtins.int,
+                      benchmark: bool, deterministic: bool) -> Tensor: ...
+
+
+def cudnn_convolution_transpose(input: Tensor, weight: Tensor, bias: Optional[Tensor],
+                                padding: MaybeTuple[builtins.int], output_padding: MaybeTuple[builtins.int],
+                                stride: MaybeTuple[builtins.int], dilation: MaybeTuple[builtins.int],
+                                groups: builtins.int, benchmark: bool, deterministic: bool) -> Tensor: ...
+
+
+def cudnn_grid_sampler(input: Tensor, grid: Tensor) -> Tensor: ...
+
+
+def cudnn_is_acceptable(input: Tensor) -> bool: ...
+
+
+@overload
+def cumprod(input: Tensor, dim: builtins.int, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def cumprod(input: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def cumsum(input: Tensor, dim: builtins.int, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def cumsum(input: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def det(input: Tensor) -> Tensor: ...
+
+
+def detach(input: Tensor) -> Tensor: ...
+
+
+def detach_(input: Tensor) -> Tensor: ...
+
+
+def diag(input: Tensor, diagonal: builtins.int = 0, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def diagflat(input: Tensor, offset: builtins.int = 0) -> Tensor: ...
+
+
+def diagonal(input: Tensor, offset: builtins.int = 0, dim1: builtins.int = 0, dim2: builtins.int = 1) -> Tensor: ...
+
+
+def digamma(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def dist(input: Tensor, other: Tensor, p: builtins.float = 2) -> Tensor: ...
+
+
+def div(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def dot(input: Tensor, tensor: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def dropout(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def dropout_(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def eig(input: Tensor, eigenvectors: bool = False, *,
+        out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def einsum(equation: str, tensors: Sequence[Tensor]) -> Tensor: ...
+
+
+def embedding(weight: Tensor, indices: LongTensor, padding_idx: builtins.int = -1, scale_grad_by_freq: bool = False,
+              sparse: bool = False) -> Tensor: ...
+
+
+def embedding_bag(weight: Tensor, indices: LongTensor, offsets: Tensor, scale_grad_by_freq: bool = False,
+                  mode: builtins.int = 0, sparse: bool = False) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
+
+
+def embedding_renorm_(input: Tensor, indices: LongTensor, max_norm: builtins.float,
+                      norm_type: builtins.float) -> Tensor: ...
+
+
+@overload
+def empty(size: MaybeTuple[builtins.int], out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+          layout: Type[layout] = strided, device: Union[device, str, None] = None,
+          requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def empty(*size: builtins.int, out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+          layout: Type[layout] = strided, device: Union[device, str, None] = None,
+          requires_grad: bool = False) -> Tensor: ...
+
+
+def empty_like(input: Tensor, *, dtype: Optional[dtype] = None, layout: Optional[Type[layout]] = None,
+               device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+def empty_strided(size: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int], *, dtype: Optional[dtype] = None,
+                  layout: Type[layout] = strided, device: Union[device, str, None] = None,
+                  requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def eq(input: Tensor, other: Tensor, *, out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def eq(input: Tensor, other: Union[builtins.int, builtins.float], *,
+       out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+def equal(input: Tensor, other: Tensor) -> bool: ...
+
+
+def erf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def erf_(input: Tensor) -> Tensor: ...
+
+
+def erfc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def erfc_(input: Tensor) -> Tensor: ...
+
+
+def erfinv(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def exp(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def exp_(input: Tensor) -> Tensor: ...
+
+
+def expm1(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def expm1_(input: Tensor) -> Tensor: ...
+
+
+@overload
+def eye(n: builtins.int, *, out: Optional[Tensor] = None, dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+        device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def eye(n: builtins.int, m: builtins.int, *, out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+        layout: Type[layout] = strided, device: Union[device, str, None] = None,
+        requires_grad: bool = False) -> Tensor: ...
+
+
+def feature_alpha_dropout(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def feature_alpha_dropout_(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def feature_dropout(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def feature_dropout_(input: Tensor, p: builtins.float, train: bool) -> Tensor: ...
+
+
+def fft(input: Tensor, signal_ndim: builtins.int, normalized: bool = False) -> Tensor: ...
+
+
+@overload
+def fill_(input: Tensor, value: Tensor) -> Tensor: ...
+
+
+@overload
+def fill_(input: Tensor, value: builtins.float) -> Tensor: ...
+
+
+def flatten(input: Tensor, start_dim: builtins.int = 0, end_dim: builtins.int = -1) -> Tensor: ...
+
+
+def flip(input: Tensor, dims: MaybeTuple[builtins.int]) -> Tensor: ...
+
+
+def floor(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def floor_(input: Tensor) -> Tensor: ...
+
+
+@overload
+def fmod(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def fmod(input: Tensor, other: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def frac(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def frobenius_norm(input: Tensor) -> Tensor: ...
+
+
+@overload
+def frobenius_norm(input: Tensor, dim: MaybeTuple[builtins.int], keepdim: bool = False, *,
+                   out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def full(size: MaybeTuple[builtins.int], fill_value: builtins.float, *, out: Optional[Tensor] = None,
+         dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+         requires_grad: bool = False) -> Tensor: ...
+
+
+def full_like(input: Tensor, fill_value: builtins.float, *, dtype: Optional[dtype] = None,
+              layout: Optional[Type[layout]] = None, device: Union[device, str, None] = None,
+              requires_grad: bool = False) -> Tensor: ...
+
+
+def gather(input: Tensor, dim: builtins.int, index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def ge(input: Tensor, other: Tensor, *, out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def ge(input: Tensor, other: Union[builtins.int, builtins.float], *,
+       out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+def gels(input: Tensor, A: Tensor, *, out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def geqrf(input: Tensor, *, out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def ger(input: Tensor, vec2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def gesv(input: Tensor, A: Tensor, *, out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def get_device(input: Tensor) -> builtins.int: ...
+
+
+def grid_sampler(input: Tensor, grid: Tensor, interpolation_mode: builtins.int,
+                 padding_mode: builtins.int) -> Tensor: ...
+
+
+def grid_sampler_2d(input: Tensor, grid: Tensor, interpolation_mode: builtins.int,
+                    padding_mode: builtins.int) -> Tensor: ...
+
+
+def grid_sampler_3d(input: Tensor, grid: Tensor, interpolation_mode: builtins.int,
+                    padding_mode: builtins.int) -> Tensor: ...
+
+
+def group_norm(input: Tensor, num_groups: builtins.int, weight: Optional[Tensor] = None, bias: Optional[Tensor] = None,
+               eps: builtins.float = 1e-05, cudnn_enabled: bool = True) -> Tensor: ...
+
+
+@overload
+def gru(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: MaybeTuple[Tensor], has_biases: bool,
+        num_layers: builtins.int, dropout: builtins.float, train: bool,
+        bidirectional: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def gru(input: Tensor, hx: Tensor, params: MaybeTuple[Tensor], has_biases: bool, num_layers: builtins.int,
+        dropout: builtins.float, train: bool, bidirectional: bool, batch_first: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+def gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None,
+             b_hh: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def gt(input: Tensor, other: Tensor, *, out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def gt(input: Tensor, other: Union[builtins.int, builtins.float], *,
+       out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def hamming_window(window_length: builtins.int, *, dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+                   device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def hamming_window(window_length: builtins.int, periodic: bool, *, dtype: Optional[dtype] = None,
+                   layout: Type[layout] = strided, device: Union[device, str, None] = None,
+                   requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def hamming_window(window_length: builtins.int, periodic: bool, alpha: builtins.float, *, dtype: Optional[dtype] = None,
+                   layout: Type[layout] = strided, device: Union[device, str, None] = None,
+                   requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def hamming_window(window_length: builtins.int, periodic: bool, alpha: builtins.float, beta: builtins.float, *,
+                   dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+                   device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def hann_window(window_length: builtins.int, *, dtype: Optional[dtype] = None, layout: Type[layout] = strided,
+                device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def hann_window(window_length: builtins.int, periodic: bool, *, dtype: Optional[dtype] = None,
+                layout: Type[layout] = strided, device: Union[device, str, None] = None,
+                requires_grad: bool = False) -> Tensor: ...
+
+
+def hardshrink(input: Tensor, lambd: builtins.float = 0.5) -> Tensor: ...
+
+
+def hinge_embedding_loss(input: Tensor, target: Tensor, margin: builtins.float = 1.0,
+                         reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+def histc(input: Tensor, bins: builtins.int = 100, min: builtins.float = 0, max: builtins.float = 0, *,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def hspmm(mat1: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def ifft(input: Tensor, signal_ndim: builtins.int, normalized: bool = False) -> Tensor: ...
+
+
+def index_put(input: Tensor, indices: Sequence[LongTensor], values: Tensor) -> Tensor: ...
+
+
+def index_put_(input: Tensor, indices: Sequence[LongTensor], values: Tensor) -> Tensor: ...
+
+
+def index_select(input: Tensor, dim: builtins.int, index: LongTensor, *,
+                 out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def instance_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor],
+                  running_var: Optional[Tensor], use_input_stats: bool, momentum: builtins.float, eps: builtins.float,
+                  cudnn_enabled: bool) -> Tensor: ...
+
+
+def inverse(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def irfft(input: Tensor, signal_ndim: builtins.int, normalized: bool = False, onesided: bool = True,
+          signal_sizes: Optional[MaybeTuple[builtins.int]] = None) -> Tensor: ...
+
+
+def is_complex(input: Tensor) -> bool: ...
+
+
+def is_distributed(input: Tensor) -> bool: ...
+
+
+def is_floating_point(input: Tensor) -> bool: ...
+
+
+def is_nonzero(input: Tensor) -> bool: ...
+
+
+def is_same_size(input: Tensor, other: Tensor) -> bool: ...
+
+
+def is_signed(input: Tensor) -> bool: ...
+
+
+def is_tensor(input) -> bool: ...
+
+
+def isclose(input: Tensor, other: Tensor, rtol: builtins.float = 1e-05, atol: builtins.float = 1e-08,
+            equal_nan: bool = False) -> Tensor: ...
+
+
+def kl_div(input: Tensor, target: Tensor, reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+def kthvalue(input: Tensor, k: builtins.int, dim: builtins.int = -1, keepdim: bool = False, *,
+             out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def layer_norm(input: Tensor, normalized_shape: MaybeTuple[builtins.int], weight: Optional[Tensor] = None,
+               bias: Optional[Tensor] = None, eps: builtins.float = 1e-05, cudnn_enable: bool = True) -> Tensor: ...
+
+
+@overload
+def le(input: Tensor, other: Tensor, *, out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def le(input: Tensor, other: Union[builtins.int, builtins.float], *,
+       out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+def lerp(input: Tensor, end: Tensor, weight: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def lgamma(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def linspace(start: builtins.float, end: builtins.float, *, out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+             layout: Type[layout] = strided, device: Union[device, str, None] = None,
+             requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def linspace(start: builtins.float, end: builtins.float, steps: builtins.int, *, out: Optional[Tensor] = None,
+             dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+             requires_grad: bool = False) -> Tensor: ...
+
+
+def log(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def log10(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def log10_(input: Tensor) -> Tensor: ...
+
+
+def log1p(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def log1p_(input: Tensor) -> Tensor: ...
+
+
+def log2(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def log2_(input: Tensor) -> Tensor: ...
+
+
+def log_(input: Tensor) -> Tensor: ...
+
+
+def log_softmax(input: Tensor, dim: builtins.int) -> Tensor: ...
+
+
+def log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: builtins.int, self: Tensor) -> Tensor: ...
+
+
+def logdet(input: Tensor) -> Tensor: ...
+
+
+@overload
+def logspace(start: builtins.float, end: builtins.float, *, out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+             layout: Type[layout] = strided, device: Union[device, str, None] = None,
+             requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def logspace(start: builtins.float, end: builtins.float, steps: builtins.int, *, out: Optional[Tensor] = None,
+             dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+             requires_grad: bool = False) -> Tensor: ...
+
+
+def logsumexp(input: Tensor, dim: builtins.int, keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def lstm(data: Tensor, batch_sizes: Tensor, hx: MaybeTuple[Tensor], params: MaybeTuple[Tensor], has_biases: bool,
+         num_layers: builtins.int, dropout: builtins.float, train: bool,
+         bidirectional: bool) -> Tuple[Tensor, Tensor, Tensor]: ...
+
+
+@overload
+def lstm(input: Tensor, hx: MaybeTuple[Tensor], params: MaybeTuple[Tensor], has_biases: bool, num_layers: builtins.int,
+         dropout: builtins.float, train: bool, bidirectional: bool,
+         batch_first: bool) -> Tuple[Tensor, Tensor, Tensor]: ...
+
+
+def lstm_cell(input: Tensor, hx: MaybeTuple[Tensor], w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None,
+              b_hh: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def lt(input: Tensor, other: Tensor, *, out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def lt(input: Tensor, other: Union[builtins.int, builtins.float], *,
+       out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+def margin_ranking_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: builtins.float = 0.0,
+                        reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+def masked_select(input: Tensor, mask: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def matmul(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def matrix_power(input: Tensor, n: builtins.int) -> Tensor: ...
+
+
+@overload
+def matrix_rank(input: Tensor, symmetric: bool = False) -> Tensor: ...
+
+
+@overload
+def matrix_rank(input: Tensor, tol: builtins.float, symmetric: bool = False) -> Tensor: ...
+
+
+@overload
+def max(input: Tensor) -> Tensor: ...
+
+
+@overload
+def max(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def max(input: Tensor, dim: builtins.int, keepdim: bool = False, *,
+        out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def max_pool1d_with_indices(input: Tensor, kernel_size: MaybeTuple[builtins.int],
+                            stride: Optional[MaybeTuple[builtins.int]] = None, padding: MaybeTuple[builtins.int] = 0,
+                            dilation: MaybeTuple[builtins.int] = 1,
+                            ceil_mode: bool = False) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def mean(input: Tensor) -> Tensor: ...
+
+
+@overload
+def mean(input: Tensor, *, dtype: _dtype) -> Tensor: ...
+
+
+@overload
+def mean(input: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def mean(input: Tensor, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def mean(input: Tensor, dim: MaybeTuple[builtins.int], keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def median(input: Tensor) -> Tensor: ...
+
+
+@overload
+def median(input: Tensor, dim: builtins.int, keepdim: bool = False, *,
+           out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def meshgrid(tensors: Sequence[Tensor]) -> MaybeTuple[Tensor]: ...
+
+
+@overload
+def min(input: Tensor) -> Tensor: ...
+
+
+@overload
+def min(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def min(input: Tensor, dim: builtins.int, keepdim: bool = False, *,
+        out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def miopen_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor],
+                      running_var: Optional[Tensor], training: bool, exponential_average_factor: builtins.float,
+                      epsilon: builtins.float) -> Tuple[Tensor, Tensor, Tensor]: ...
+
+
+def miopen_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: MaybeTuple[builtins.int],
+                       stride: MaybeTuple[builtins.int], dilation: MaybeTuple[builtins.int], groups: builtins.int,
+                       benchmark: bool, deterministic: bool) -> Tensor: ...
+
+
+def miopen_convolution_transpose(input: Tensor, weight: Tensor, bias: Optional[Tensor],
+                                 padding: MaybeTuple[builtins.int],
+                                 output_padding: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int],
+                                 dilation: MaybeTuple[builtins.int], groups: builtins.int, benchmark: bool,
+                                 deterministic: bool) -> Tensor: ...
+
+
+def mkldnn_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: MaybeTuple[builtins.int],
+                       stride: MaybeTuple[builtins.int], dilation: MaybeTuple[builtins.int],
+                       groups: builtins.int) -> Tensor: ...
+
+
+def mkldnn_convolution_backward_weights(weight_size: MaybeTuple[builtins.int], grad_output: Tensor, self: Tensor,
+                                        padding: MaybeTuple[builtins.int], stride: MaybeTuple[builtins.int],
+                                        dilation: MaybeTuple[builtins.int], groups: builtins.int,
+                                        bias_defined: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+def mm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def mode(input: Tensor, dim: builtins.int = -1, keepdim: bool = False, *,
+         out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def mul(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def multinomial(input: Tensor, num_samples: builtins.int, replacement: bool = False, *,
+                generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def mv(input: Tensor, vec: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def mvlgamma(input: Tensor, p: builtins.int) -> Tensor: ...
+
+
+def narrow(input: Tensor, dim: builtins.int, start: builtins.int, length: builtins.int) -> Tensor: ...
+
+
+def native_clone(input: Tensor) -> Tensor: ...
+
+
+def native_get_device(input: Tensor) -> builtins.int: ...
+
+
+def native_norm(input: Tensor, p: builtins.float = 2) -> Tensor: ...
+
+
+def native_pow(input: Tensor, exponent: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def native_resize_as_(input: Tensor, the_template: Tensor) -> Tensor: ...
+
+
+def native_zero_(input: Tensor) -> Tensor: ...
+
+
+@overload
+def ne(input: Tensor, other: Tensor, *, out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+@overload
+def ne(input: Tensor, other: Union[builtins.int, builtins.float], *,
+       out: Optional[ByteTensor] = None) -> ByteTensor: ...
+
+
+def neg(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def nonzero(input: Tensor, *, out: Optional[LongTensor] = None) -> LongTensor: ...
+
+
+def norm(input: Tensor, p: Union[builtins.float, str, None] = 'fro', dim: Optional[builtins.int] = None,
+         keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def norm_except_dim(v: Tensor, pow: builtins.int = 2, dim: builtins.int = 0) -> Tensor: ...
+
+
+@overload
+def normal(mean: Tensor, std: Tensor, *, generator: Optional[Generator] = None,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def normal(mean: Tensor, std: builtins.float = 1, *, generator: Optional[Generator] = None,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def normal(mean: builtins.float, std: Tensor, *, generator: Optional[Generator] = None,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def nuclear_norm(input: Tensor, keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def numel(input: Tensor) -> builtins.int: ...
+
+
+@overload
+def ones(size: MaybeTuple[builtins.int], out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+         layout: Type[layout] = strided, device: Union[device, str, None] = None,
+         requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def ones(*size: builtins.int, out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+         layout: Type[layout] = strided, device: Union[device, str, None] = None,
+         requires_grad: bool = False) -> Tensor: ...
+
+
+def ones_like(input: Tensor, *, dtype: Optional[dtype] = None, layout: Optional[Type[layout]] = None,
+              device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+def orgqr(input: Tensor, input2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def ormqr(input: Tensor, input2: Tensor, input3: Tensor, left: bool = True, transpose: bool = False, *,
+          out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def pairwise_distance(x1: Tensor, x2: Tensor, p: builtins.float = 2, eps: builtins.float = 1e-06,
+                      keepdim: bool = False) -> Tensor: ...
+
+
+def pdist(input: Tensor, p: builtins.float = 2) -> Tensor: ...
+
+
+def pin_memory(input: Tensor) -> Tensor: ...
+
+
+def pinverse(input: Tensor, rcond: builtins.float = 1e-15) -> Tensor: ...
+
+
+def pixel_shuffle(input: Tensor, upscale_factor: builtins.int) -> Tensor: ...
+
+
+def poisson(input: Tensor, generator: Optional[Generator] = None) -> Tensor: ...
+
+
+def polygamma(n: builtins.int, self: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def potrf(input: Tensor, upper: bool = True, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def potri(input: Tensor, upper: bool = True, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def potrs(input: Tensor, input2: Tensor, upper: bool = True, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def pow(input: Tensor, exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def pow(input: Tensor, exponent: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def prelu(input: Tensor, weight: Tensor) -> Tensor: ...
+
+
+@overload
+def prod(input: Tensor) -> Tensor: ...
+
+
+@overload
+def prod(input: Tensor, *, dtype: _dtype) -> Tensor: ...
+
+
+@overload
+def prod(input: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def prod(input: Tensor, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def prod(input: Tensor, dim: builtins.int, keepdim: bool = False, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def pstrf(input: Tensor, upper: bool = True, tol: builtins.float = -1, *,
+          out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def qr(input: Tensor, *, out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def rand(*size: builtins.int, generator: Optional[Generator] = None, out: Optional[Tensor] = None,
+         dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+         requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def rand(sizes: Tuple[builtins.int, ...], generator: Optional[Generator] = None, out: Optional[Tensor] = None,
+         dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+         requires_grad: bool = False) -> Tensor: ...
+
+
+def rand_like(input: Tensor, *, dtype: Optional[dtype] = None, layout: Optional[Type[layout]] = None,
+              device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def randint_like(input: Tensor, high: builtins.int, *, dtype: Optional[dtype] = None,
+                 layout: Optional[Type[layout]] = None, device: Union[device, str, None] = None,
+                 requires_grad: bool = False) -> LongTensor: ...
+
+
+@overload
+def randint_like(input: Tensor, low: builtins.int, high: builtins.int, *, dtype: Optional[dtype] = None,
+                 layout: Optional[Type[layout]] = None, device: Union[device, str, None] = None,
+                 requires_grad: bool = False) -> LongTensor: ...
+
+
+@overload
+def randint(high: builtins.int, size: MaybeTuple[builtins.int], *, dtype: Optional[dtype] = None,
+            layout: Optional[Type[layout]] = None, device: Union[device, str, None] = None,
+            requires_grad: bool = False) -> LongTensor: ...
+
+
+@overload
+def randint(low: builtins.int, high: builtins.int, size: MaybeTuple[builtins.int], *, dtype: Optional[dtype] = None,
+            layout: Optional[Type[layout]] = None, device: Union[device, str, None] = None,
+            requires_grad: bool = False) -> LongTensor: ...
+
+
+@overload
+def randn(*size: builtins.int, generator: Optional[Generator] = None, out: Optional[Tensor] = None,
+          dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+          requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def randn(sizes: Tuple[builtins.int, ...], generator: Optional[Generator] = None, out: Optional[Tensor] = None,
+          dtype: Optional[dtype] = None, layout: Type[layout] = strided, device: Union[device, str, None] = None,
+          requires_grad: bool = False) -> Tensor: ...
+
+
+def randn_like(input: Tensor, *, dtype: Optional[dtype] = None, layout: Optional[Type[layout]] = None,
+               device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def randperm(n: builtins.int, *, generator: Generator, out: Optional[Tensor] = None, dtype: _dtype = int64,
+             layout: Type[layout] = strided, device: Union[device, str, None] = None,
+             requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def randperm(n: builtins.int, *, out: Optional[Tensor] = None, dtype: _dtype = int64, layout: Type[layout] = strided,
+             device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+def raw_copy_sparse_(input: Tensor, src: Tensor) -> Tensor: ...
+
+
+def reciprocal(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def relu(input: Tensor) -> Tensor: ...
+
+
+def relu_(input: Tensor) -> Tensor: ...
+
+
+@overload
+def remainder(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def remainder(input: Tensor, other: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def renorm(input: Tensor, p: builtins.float, dim: builtins.int, maxnorm: builtins.float, *,
+           out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def reshape(input: Tensor, shape: MaybeTuple[builtins.int]) -> Tensor: ...
+
+
+def resize_as_(input: Tensor, the_template: Tensor) -> Tensor: ...
+
+
+def rfft(input: Tensor, signal_ndim: builtins.int, normalized: bool = False, onesided: bool = True) -> Tensor: ...
+
+
+@overload
+def rnn_relu(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: MaybeTuple[Tensor], has_biases: bool,
+             num_layers: builtins.int, dropout: builtins.float, train: bool,
+             bidirectional: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def rnn_relu(input: Tensor, hx: Tensor, params: MaybeTuple[Tensor], has_biases: bool, num_layers: builtins.int,
+             dropout: builtins.float, train: bool, bidirectional: bool, batch_first: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+def rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None,
+                  b_hh: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def rnn_tanh(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: MaybeTuple[Tensor], has_biases: bool,
+             num_layers: builtins.int, dropout: builtins.float, train: bool,
+             bidirectional: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+@overload
+def rnn_tanh(input: Tensor, hx: Tensor, params: MaybeTuple[Tensor], has_biases: bool, num_layers: builtins.int,
+             dropout: builtins.float, train: bool, bidirectional: bool, batch_first: bool) -> Tuple[Tensor, Tensor]: ...
+
+
+def rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None,
+                  b_hh: Optional[Tensor] = None) -> Tensor: ...
+
+
+def round(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def round_(input: Tensor) -> Tensor: ...
+
+
+def rrelu(input: Tensor, lower: builtins.float = 0.125, upper: builtins.float = 0.3333333333333333,
+          training: bool = False, generator: Optional[Generator] = None) -> Tensor: ...
+
+
+def rrelu_(input: Tensor, lower: builtins.float = 0.125, upper: builtins.float = 0.3333333333333333,
+           training: bool = False, generator: Optional[Generator] = None) -> Tensor: ...
+
+
+def rsqrt(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def rsqrt_(input: Tensor) -> Tensor: ...
+
+
+def s_native_addmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+                   out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def s_native_addmm_(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1,
+                    alpha: builtins.float = 1) -> Tensor: ...
+
+
+def select(input: Tensor, dim: builtins.int, index: builtins.int) -> Tensor: ...
+
+
+def selu(input: Tensor) -> Tensor: ...
+
+
+def selu_(input: Tensor) -> Tensor: ...
+
+
+def sigmoid(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def sigmoid_(input: Tensor) -> Tensor: ...
+
+
+def sign(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def sin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def sin_(input: Tensor) -> Tensor: ...
+
+
+def sinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def sinh_(input: Tensor) -> Tensor: ...
+
+
+def slogdet(input: Tensor) -> Tuple[Tensor, Tensor]: ...
+
+
+def smm(input: Tensor, mat2: Tensor) -> Tensor: ...
+
+
+def softmax(input: Tensor, dim: builtins.int) -> Tensor: ...
+
+
+def softmax_backward_data(grad_output: Tensor, output: Tensor, dim: builtins.int, self: Tensor) -> Tensor: ...
+
+
+def sort(input: Tensor, dim: builtins.int = -1, descending: bool = False, *,
+         out: Optional[Tuple[Tensor, LongTensor]] = None) -> Tuple[Tensor, LongTensor]: ...
+
+
+def split(input: Tensor, split_size_or_sections: MaybeList[builtins.int],
+          dim: builtins.int = 0) -> List[Tensor]: ...
+
+
+def sqrt(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def sqrt_(input: Tensor) -> Tensor: ...
+
+
+@overload
+def squeeze(input: Tensor) -> Tensor: ...
+
+
+@overload
+def squeeze(input: Tensor, dim: builtins.int) -> Tensor: ...
+
+
+@overload
+def sspaddmm(beta: builtins.float, self: Tensor, alpha: builtins.float, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+
+@overload
+def sspaddmm(beta: builtins.float, self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
+
+
+@overload
+def sspaddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+             out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def stack(tensors: Sequence[Tensor], dim: builtins.int = 0, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def std(input: Tensor, unbiased: bool = True) -> Tensor: ...
+
+
+@overload
+def std(input: Tensor, dim: MaybeTuple[builtins.int], unbiased: bool = True, keepdim: bool = False, *,
+        out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def stft(input: Tensor, n_fft: builtins.int, hop_length: Optional[builtins.int] = None,
+         win_length: Optional[builtins.int] = None, window: Optional[Tensor] = None, normalized: bool = False,
+         onesided: bool = True) -> Tensor: ...
+
+
+@overload
+def sub(input: Tensor, alpha: builtins.float, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def sub(input: Tensor, other: Tensor, *, alpha: builtins.float = 1, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def sum(input: Tensor) -> Tensor: ...
+
+
+@overload
+def sum(input: Tensor, *, dtype: _dtype) -> Tensor: ...
+
+
+@overload
+def sum(input: Tensor, dim: MaybeTuple[builtins.int], *, dtype: _dtype, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def sum(input: Tensor, dim: MaybeTuple[builtins.int], keepdim: bool, *, dtype: _dtype,
+        out: Optional[Tensor] = None) -> Tensor: ...
+
+
+@overload
+def sum(input: Tensor, dim: MaybeTuple[builtins.int], keepdim: bool = False, *,
+        out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def svd(input: Tensor, some: bool = True, *,
+        out: Optional[Tuple[Tensor, Tensor, Tensor]]) -> Tuple[Tensor, Tensor, Tensor]: ...
+
+
+def symeig(input: Tensor, eigenvectors: bool = False, upper: bool = True, *,
+           out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def t(input: Tensor) -> Tensor: ...
+
+
+def take(input: Tensor, index: LongTensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def tan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def tan_(input: Tensor) -> Tensor: ...
+
+
+def tanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def tanh_(input: Tensor) -> Tensor: ...
+
+
+def tensordot(input: Tensor, other: Tensor, dims_self: MaybeTuple[builtins.int],
+              dims_other: MaybeTuple[builtins.int]) -> Tensor: ...
+
+
+def th_addmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1, alpha: builtins.float = 1,
+             out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def th_addmm_(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: builtins.float = 1,
+              alpha: builtins.float = 1) -> Tensor: ...
+
+
+def th_clone(input: Tensor) -> Tensor: ...
+
+
+def th_norm(input: Tensor, p: builtins.float = 2) -> Tensor: ...
+
+
+def th_pow(input: Tensor, exponent: builtins.float, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def th_resize_as_(input: Tensor, the_template: Tensor) -> Tensor: ...
+
+
+def th_zero_(input: Tensor) -> Tensor: ...
+
+
+def topk(input: Tensor, k: builtins.int, dim: builtins.int = -1, largest: bool = True, sorted: bool = True, *,
+         out: Optional[Tuple[Tensor, LongTensor]] = None) -> Tuple[Tensor, LongTensor]: ...
+
+
+def trace(input: Tensor) -> Tensor: ...
+
+
+def transpose(input: Tensor, dim0: builtins.int, dim1: builtins.int) -> Tensor: ...
+
+
+def tril(input: Tensor, diagonal: builtins.int = 0, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def triplet_margin_loss(anchor: Tensor, positive: Tensor, negative: Tensor, margin: builtins.float = 1.0,
+                        p: builtins.float = 2, eps: builtins.float = 1e-06, swap: bool = False,
+                        reduction: str = 'elementwise_mean') -> Tensor: ...
+
+
+def triu(input: Tensor, diagonal: builtins.int = 0, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def trtrs(input: Tensor, A: Tensor, upper: bool = True, transpose: bool = False, unitriangular: bool = False, *,
+          out: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]: ...
+
+
+def trunc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def trunc_(input: Tensor) -> Tensor: ...
+
+
+def unbind(input: Tensor, dim: builtins.int = 0) -> MaybeTuple[Tensor]: ...
+
+
+def unsqueeze(input: Tensor, dim: builtins.int) -> Tensor: ...
+
+
+@overload
+def var(input: Tensor, unbiased: bool = True) -> Tensor: ...
+
+
+@overload
+def var(input: Tensor, dim: builtins.int, unbiased: bool = True, keepdim: bool = False, *,
+        out: Optional[Tensor] = None) -> Tensor: ...
+
+
+def where(condition: ByteTensor, self: Tensor, other: Tensor) -> Tensor: ...
+
+
+def zero_(input: Tensor) -> Tensor: ...
+
+
+@overload
+def zeros(size: MaybeTuple[builtins.int], out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+          layout: Type[layout] = strided, device: Union[device, str, None] = None,
+          requires_grad: bool = False) -> Tensor: ...
+
+
+@overload
+def zeros(*size: builtins.int, out: Optional[Tensor] = None, dtype: Optional[dtype] = None,
+          layout: Type[layout] = strided, device: Union[device, str, None] = None,
+          requires_grad: bool = False) -> Tensor: ...
+
+
+def zeros_like(input: Tensor, *, dtype: Optional[dtype] = None, layout: Optional[Type[layout]] = None,
+               device: Union[device, str, None] = None, requires_grad: bool = False) -> Tensor: ...
+
+
+def from_numpy(data: np.ndarray) -> Tensor: ...
+
+
+def as_tensor(data: np.ndarray, dtype: Optional[dtype] = None, device: Union[device, str, None] = None) -> Tensor: ...
diff --git a/stubs/torch/nn/__init__.pyi b/stubs/torch/nn/__init__.pyi
new file mode 100644
index 0000000..cba91f4
--- /dev/null
+++ b/stubs/torch/nn/__init__.pyi
@@ -0,0 +1,6 @@
+from .modules import *
+from .parameter import Parameter as Parameter
+from .parallel import DataParallel as DataParallel
+from . import init as init
+from . import utils as utils
+from . import functional as functional
diff --git a/stubs/torch/nn/modules/__init__.pyi b/stubs/torch/nn/modules/__init__.pyi
new file mode 100644
index 0000000..fe59591
--- /dev/null
+++ b/stubs/torch/nn/modules/__init__.pyi
@@ -0,0 +1,46 @@
+from .module import Module as Module
+from .activation import CELU as CELU, ELU as ELU, GLU as GLU, Hardshrink as Hardshrink, Hardtanh as Hardtanh, \
+    LeakyReLU as LeakyReLU, LogSigmoid as LogSigmoid, LogSoftmax as LogSoftmax, PReLU as PReLU, RReLU as RReLU, \
+    ReLU as ReLU, ReLU6 as ReLU6, SELU as SELU, Sigmoid as Sigmoid, Softmax as Softmax, Softmax2d as Softmax2d, \
+    Softmin as Softmin, Softplus as Softplus, Softshrink as Softshrink, Softsign as Softsign, Tanh as Tanh, \
+    Tanhshrink as Tanhshrink, Threshold as Threshold
+from .adaptive import AdaptiveLogSoftmaxWithLoss as AdaptiveLogSoftmaxWithLoss
+from .batchnorm import BatchNorm1d as BatchNorm1d, BatchNorm2d as BatchNorm2d, BatchNorm3d as BatchNorm3d, \
+    SyncBatchNorm as SyncBatchNorm
+from .container import Container as Container, ModuleDict as ModuleDict, ModuleList as ModuleList, \
+    ParameterDict as ParameterDict, ParameterList as ParameterList, Sequential as Sequential
+from .conv import Conv1d as Conv1d, Conv2d as Conv2d, Conv3d as Conv3d, ConvTranspose1d as ConvTranspose1d, \
+    ConvTranspose2d as ConvTranspose2d, ConvTranspose3d as ConvTranspose3d
+from .distance import CosineSimilarity as CosineSimilarity, PairwiseDistance as PairwiseDistance
+from .dropout import AlphaDropout as AlphaDropout, Dropout as Dropout, Dropout2d as Dropout2d, Dropout3d as Dropout3d, \
+    FeatureAlphaDropout as FeatureAlphaDropout
+from .fold import Fold as Fold, Unfold as Unfold
+from .instancenorm import InstanceNorm1d as InstanceNorm1d, InstanceNorm2d as InstanceNorm2d, \
+    InstanceNorm3d as InstanceNorm3d
+from .linear import Bilinear as Bilinear, Linear as Linear
+from .loss import BCELoss as BCELoss, BCEWithLogitsLoss as BCEWithLogitsLoss, CTCLoss as CTCLoss, \
+    CosineEmbeddingLoss as CosineEmbeddingLoss, CrossEntropyLoss as CrossEntropyLoss, \
+    HingeEmbeddingLoss as HingeEmbeddingLoss, KLDivLoss as KLDivLoss, L1Loss as L1Loss, MSELoss as MSELoss, \
+    MarginRankingLoss as MarginRankingLoss, MultiLabelMarginLoss as MultiLabelMarginLoss, \
+    MultiLabelSoftMarginLoss as MultiLabelSoftMarginLoss, MultiMarginLoss as MultiMarginLoss, NLLLoss as NLLLoss, \
+    NLLLoss2d as NLLLoss2d, PoissonNLLLoss as PoissonNLLLoss, SmoothL1Loss as SmoothL1Loss, \
+    SoftMarginLoss as SoftMarginLoss, TripletMarginLoss as TripletMarginLoss
+from .module import Module as Module
+from .normalization import CrossMapLRN2d as CrossMapLRN2d, GroupNorm as GroupNorm, LayerNorm as LayerNorm, \
+    LocalResponseNorm as LocalResponseNorm
+from .padding import ConstantPad1d as ConstantPad1d, ConstantPad2d as ConstantPad2d, ConstantPad3d as ConstantPad3d, \
+    ReflectionPad1d as ReflectionPad1d, ReflectionPad2d as ReflectionPad2d, ReplicationPad1d as ReplicationPad1d, \
+    ReplicationPad2d as ReplicationPad2d, ReplicationPad3d as ReplicationPad3d, ZeroPad2d as ZeroPad2d
+from .pixelshuffle import PixelShuffle as PixelShuffle
+from .pooling import AdaptiveAvgPool1d as AdaptiveAvgPool1d, AdaptiveAvgPool2d as AdaptiveAvgPool2d, \
+    AdaptiveAvgPool3d as AdaptiveAvgPool3d, AdaptiveMaxPool1d as AdaptiveMaxPool1d, \
+    AdaptiveMaxPool2d as AdaptiveMaxPool2d, AdaptiveMaxPool3d as AdaptiveMaxPool3d, AvgPool1d as AvgPool1d, \
+    AvgPool2d as AvgPool2d, AvgPool3d as AvgPool3d, FractionalMaxPool2d as FractionalMaxPool2d, \
+    FractionalMaxPool3d as FractionalMaxPool3d, LPPool1d as LPPool1d, LPPool2d as LPPool2d, MaxPool1d as MaxPool1d, \
+    MaxPool2d as MaxPool2d, MaxPool3d as MaxPool3d, MaxUnpool1d as MaxUnpool1d, MaxUnpool2d as MaxUnpool2d, \
+    MaxUnpool3d as MaxUnpool3d
+from .rnn import GRU as GRU, GRUCell as GRUCell, LSTM as LSTM, LSTMCell as LSTMCell, RNN as RNN, RNNBase as RNNBase, \
+    RNNCell as RNNCell, RNNCellBase as RNNCellBase
+from .sparse import Embedding as Embedding, EmbeddingBag as EmbeddingBag
+from .upsampling import Upsample as Upsample, UpsamplingBilinear2d as UpsamplingBilinear2d, \
+    UpsamplingNearest2d as UpsamplingNearest2d
diff --git a/stubs/torch/nn/modules/container.pyi b/stubs/torch/nn/modules/container.pyi
new file mode 100644
index 0000000..b83eeae
--- /dev/null
+++ b/stubs/torch/nn/modules/container.pyi
@@ -0,0 +1,146 @@
+from .module import Module
+from typing import Any, Optional, Union, overload, TypeVar, Iterable, Tuple, Mapping, Iterator, KeysView, ValuesView, \
+    ItemsView, Sequence, Generic
+from collections import OrderedDict
+from ... import Tensor
+from .. import Parameter
+
+
+class Container(Module):
+    def __init__(self, **kwargs: Any) -> None: ...
+
+
+T = TypeVar('T')
+Mod = TypeVar('Mod', bound=Module)
+
+
+class Sequential(Module):
+    @overload
+    def __init__(self, *args: Module) -> None: ...
+
+    @overload
+    def __init__(self, arg: OrderedDict[str, Module]) -> None: ...
+
+    @overload
+    def __getitem__(self, idx: int) -> Module: ...
+
+    @overload
+    def __getitem__(self: T, idx: slice) -> T: ...
+
+    def __setitem__(self, idx: Union[int], module: Module) -> None: ...
+
+    def __delitem__(self, idx: Union[slice, int]) -> None: ...
+
+    def __len__(self) -> int: ...
+
+    def forward(self, input: Tensor) -> Tensor: ...  # type: ignore
+
+    def __call__(self, input: Tensor) -> Tensor: ...  # type: ignore
+
+
+class ModuleList(Module, Generic[Mod], Sequence[Mod]):
+    def __init__(self, modules: Optional[Iterable[Mod]] = ...) -> None: ...
+
+    @overload
+    def __getitem__(self, idx: int) -> Mod: ...
+
+    @overload
+    def __getitem__(self: T, idx: slice) -> T: ...
+
+    def __setitem__(self, idx: int, module: Mod) -> None: ...
+
+    def __delitem__(self, idx: Union[int, slice]) -> None: ...
+
+    def __len__(self) -> int: ...
+
+    def __iter__(self) -> Iterator[Mod]: ...
+
+    def __iadd__(self: T, modules: Iterable[Mod]) -> T: ...
+
+    def insert(self, index: int, module: Mod) -> None: ...
+
+    def append(self: T, module: Mod) -> T: ...
+
+    def extend(self: T, modules: Iterable[Mod]) -> T: ...
+
+
+class ModuleDict(Module, Generic[Mod]):
+    def __init__(self, modules: Optional[Mapping[str, Mod]] = ...) -> None: ...
+
+    def __getitem__(self, key: str): ...
+
+    def __setitem__(self, key: str, module: Mod) -> None: ...
+
+    def __delitem__(self, key: str) -> None: ...
+
+    def __len__(self) -> int: ...
+
+    def __iter__(self) -> Iterator[str]: ...
+
+    def __contains__(self, key: str) -> bool: ...
+
+    def clear(self) -> None: ...
+
+    def pop(self, key: str): ...
+
+    def keys(self) -> KeysView[str]: ...
+
+    def items(self) -> ItemsView[str, Mod]: ...
+
+    def values(self) -> ValuesView[Mod]: ...
+
+    def update(self, modules: Mapping[str, Mod]) -> None: ...
+
+
+class ParameterList(Module, Sequence[Parameter]):
+    def __init__(self, parameters: Optional[Iterable[Parameter]] = ...) -> None: ...
+
+    @overload
+    def __getitem__(self, idx: int) -> Parameter: ...
+
+    @overload
+    def __getitem__(self: T, idx: slice) -> T: ...
+
+    def __setitem__(self, idx: int, param: Parameter) -> None: ...
+
+    def __delitem__(self, idx: Union[int, slice]) -> None: ...
+
+    def __len__(self) -> int: ...
+
+    def __iter__(self) -> Iterator[Parameter]: ...
+
+    def __iadd__(self: T, parameters: Iterable[Parameter]) -> T: ...
+
+    def insert(self, index: int, parameter: Parameter) -> None: ...
+
+    def append(self: T, parameter: Parameter) -> T: ...
+
+    def extend(self: T, parameters: Iterable[Parameter]) -> T: ...
+
+
+class ParameterDict(Module):
+    def __init__(self, parameters: Optional[Mapping[str, Parameter]] = ...) -> None: ...
+
+    def __getitem__(self, key: str): ...
+
+    def __setitem__(self, key: str, param: Parameter) -> None: ...
+
+    def __delitem__(self, key: str) -> None: ...
+
+    def __len__(self) -> int: ...
+
+    def __iter__(self) -> Iterator[str]: ...
+
+    def __contains__(self, key: str) -> bool: ...
+
+    def clear(self) -> None: ...
+
+    def pop(self, key: str): ...
+
+    def keys(self) -> KeysView[str]: ...
+
+    def items(self) -> ItemsView[str, Parameter]: ...
+
+    def values(self) -> ValuesView[Parameter]: ...
+
+    def update(self, parameters: Mapping[str, Parameter]) -> None: ...
diff --git a/stubs/torch/nn/modules/module.pyi b/stubs/torch/nn/modules/module.pyi
new file mode 100644
index 0000000..871a8e5
--- /dev/null
+++ b/stubs/torch/nn/modules/module.pyi
@@ -0,0 +1,100 @@
+from ... import Tensor, device, dtype
+from ..parameter import Parameter
+from typing import Union, Tuple, Any, Callable, Iterator, Set, Optional, overload, TypeVar, Mapping, Dict, Generic
+from collections import OrderedDict
+from ...utils.hooks import RemovableHandle
+
+_grad_t = Union[Tuple[Tensor, ...], Tensor]
+# See https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self for the use
+# of `T` to annotate `self`. Many methods of `Module` return `self` and we want those return values to be
+# the type of the subclass, not the looser type of `Module`.
+T = TypeVar('T')
+# We parameter modules by the return type of its `forward` (and therefore `__call__`) method. This allows
+# type inference to infer that the return value of calling a module in the canonical way (via `__call__)` is the
+# same as the custom `forward` function of the submodule. Submodules tha wish to opt in this functionality be
+# defined as eg class ReturnsTwoTensors(Module[Tuple[Tensor, Tensor]]): ...
+T_co = TypeVar('T_co', covariant=True)
+
+
+class Module(Generic[T_co]):
+    training: bool = ...
+
+    def forward(self, *input: Any, **kwargs: Any) -> T_co: ...
+
+    def __call__(self, *input: Any, **kwargs: Any) -> T_co: ...
+
+    def register_buffer(self, name: str, tensor: Tensor) -> None: ...
+
+    def register_parameter(self, name: str, param: Parameter) -> None: ...
+
+    def add_module(self, name: str, module: 'Module') -> None: ...
+
+    def apply(self: T, fn: Callable[['Module'], None]) -> T: ...
+
+    def cuda(self: T, device: Union[int, device]) -> T: ...
+
+    def cpu(self: T) -> T: ...
+
+    def type(self: T, dst_type: Union[dtype, str]) -> T: ...
+
+    def float(self: T) -> T: ...
+
+    def double(self: T) -> T: ...
+
+    def half(self: T) -> T: ...
+
+    @overload
+    def to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ...,
+           non_blocking: bool = ...) -> T: ...
+
+    @overload
+    def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T: ...
+
+    @overload
+    def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T: ...
+
+    def register_backward_hook(self, hook: Callable[
+        ['Module', _grad_t, _grad_t], Union[None, Tensor]]) -> RemovableHandle: ...
+
+    # The hook takes a module as a first argument and variadic arguments after that, but there is no way to express that
+    def register_forward_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle: ...
+
+    def register_forward_hook(self, hook: Callable[..., None]) -> RemovableHandle: ...
+
+    # The user can pass an optional arbitrary mappable object to `state_dict`, in which case `state_dict` returns
+    # back that same object. But if they pass nothing, an `OrederedDict` is created and returned.
+    T_destination = TypeVar('T_destination', bound=Mapping[str, Tensor])
+
+    @overload
+    def state_dict(self, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination: ...
+
+    @overload
+    def state_dict(self, prefix: str = ..., keep_vars: bool = ...) -> OrderedDict[str, Tensor]: ...
+
+    def load_state_dict(self, state_dict: Union[Dict[str, Tensor], OrderedDict[str, Tensor]], strict: bool = ...): ...
+
+    def parameters(self, recurse: bool = ...) -> Iterator[Parameter]: ...
+
+    def named_parameters(self, prefix: str = ..., recurse: bool = ...) -> Iterator[Tuple[str, Parameter]]: ...
+
+    def buffers(self, recurse: bool = ...) -> Iterator[Tensor]: ...
+
+    def named_buffers(self, prefix: str = ..., recurse: bool = ...) -> Iterator[Tuple[str, Tensor]]: ...
+
+    def children(self) -> Iterator['Module']: ...
+
+    def named_children(self) -> Iterator[Tuple[str, 'Module']]: ...
+
+    def modules(self) -> Iterator['Module']: ...
+
+    def named_modules(self, memo: Optional[Set['Module']] = ..., prefix: str = ...) -> Iterator[Tuple[str, 'Module']]: ...
+
+    def train(self: T, mode: bool = ...) -> T: ...
+
+    def eval(self: T) -> T: ...
+
+    def zero_grad(self) -> None: ...
+
+    def share_memory(self: T) -> T: ...
+
+    def extra_repr(self) -> str: ...
diff --git a/stubs/torch/nn/parameter.pyi b/stubs/torch/nn/parameter.pyi
new file mode 100644
index 0000000..03d5885
--- /dev/null
+++ b/stubs/torch/nn/parameter.pyi
@@ -0,0 +1,7 @@
+from typing import Any, Optional
+
+from .. import Tensor
+
+
+class Parameter(Tensor):
+    def __new__(cls, data: Optional[Any] = None, requires_grad:bool = True): ...
diff --git a/stubs/torch/optim/__init__.pyi b/stubs/torch/optim/__init__.pyi
new file mode 100644
index 0000000..48356ea
--- /dev/null
+++ b/stubs/torch/optim/__init__.pyi
@@ -0,0 +1,4 @@
+from .sgd import SGD as SGD
+from .adam import Adam as Adam
+from . import lr_scheduler as lr_scheduler
+from .optimizer import Optimizer as Optimizer
diff --git a/stubs/torch/optim/lr_scheduler.pyi b/stubs/torch/optim/lr_scheduler.pyi
new file mode 100644
index 0000000..f64d207
--- /dev/null
+++ b/stubs/torch/optim/lr_scheduler.pyi
@@ -0,0 +1,32 @@
+from typing import Iterable, Any, Optional, Callable
+from .optimizer import Optimizer
+
+class _LRScheduler:
+    def __init__(self, optimizer: Optimizer, last_epoch: int=...) -> None: ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, state_dict: dict) -> None: ...
+    def get_lr(self) -> float: ...
+    def step(self, epoch: Optional[int] = None) -> None: ...
+
+class LambdaLR(_LRScheduler):
+    def __init__(self, optimizer: Optimizer, lr_lambda: Callable[[int], float], last_epoch: int=...) -> None: ...
+
+class StepLR(_LRScheduler):
+    def __init__(self, optimizer: Optimizer, step_size: int, gamma: float=..., last_epoch: int=...) -> None:...
+
+class MultiStepLR(_LRScheduler):
+    def __init__(self, optimizer: Optimizer, milestones: Iterable[int], gamma: float=..., last_epoch: int=...) -> None: ...
+
+class ExponentialLR(_LRScheduler):
+    def __init__(self, optimizer: Optimizer, gamma: float, last_epoch: int=...) -> None: ...
+
+class CosineAnnealingLR(_LRScheduler):
+    def __init__(self, optimizer: Optimizer, T_max: int, eta_min: float, last_epoch: int=...) -> None: ...
+
+class ReduceLROnPlateau:
+    in_cooldown: bool
+
+    def __init__(self, optimizer: Optimizer, mode: str=..., factor: float=..., patience: int=..., verbose: bool=..., threshold: float=..., threshold_mode: str=..., cooldown: int=..., min_lr: float=..., eps: float=...) -> None: ...
+    def step(self, metrics: Any, epoch: Optional[int]=...) -> None: ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, state_dict: dict): ...
diff --git a/stubs/torch/optim/optimizer.pyi b/stubs/torch/optim/optimizer.pyi
new file mode 100644
index 0000000..7ede3da
--- /dev/null
+++ b/stubs/torch/optim/optimizer.pyi
@@ -0,0 +1,14 @@
+from typing import Iterable, Union, Callable, Optional, List
+from .. import Tensor
+
+_params_t = Union[Iterable[Tensor], Iterable[dict]]
+
+class Optimizer:
+    param_groups: List[dict]
+
+    def __init__(self, params: _params_t) -> None: ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, state_dict: dict) -> None: ...
+    def zero_grad(self) -> None: ...
+    def step(self, closure: Optional[Callable[[], float]]=...) -> None: ...
+    def add_param_group(self, param_group: dict) -> None: ...
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..157b79b
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,292 @@
+import gc
+import gzip
+import logging
+import os
+import pickle
+import shutil
+import sys
+from collections import OrderedDict
+from datetime import datetime
+from pathlib import Path
+from typing import Iterator, List, NamedTuple, Optional, Tuple, Union, overload
+
+import torch
+from torch import nn
+from torch import Tensor
+
+import nnlib
+from nnlib.utils import Logging
+
+__all__ = [
+    'Checkpoint',
+    'cpu_state_dict',
+    'repackage_hidden',
+    'loadpkl',
+    'savepkl',
+    'get_best_model',
+    'get_progress_bar',
+    'create_exp_dir',
+    'repr_module',
+]
+
+LOGGER = logging.getLogger(__name__)
+
+
+class Checkpoint(NamedTuple):
+    epoch: int
+    val_loss: float
+    model_state: OrderedDict
+    optim_state: dict
+
+
+def cpu_state_dict(state_dict):
+    res = {}
+    for k, v in state_dict.items():
+        if isinstance(v, Tensor):
+            res[k] = v.to('cpu')
+        elif isinstance(v, dict):
+            res[k] = cpu_state_dict(v)
+        else:
+            res[k] = v
+    return res
+
+
+@overload
+def repackage_hidden(h: Tensor) -> Tensor: ...
+
+
+@overload
+def repackage_hidden(h: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]: ...
+
+
+# From AWD-LSTM
+def repackage_hidden(h):
+    """Wraps hidden states in new Tensors,
+    to detach them from their history."""
+    if isinstance(h, Tensor):
+        return h.detach()
+    else:
+        return tuple(repackage_hidden(v) for v in h)
+
+
+def loadpkl(file_name: Union[Path, str], gz: bool = False):
+    """A wrapper for loading function with pickle.
+    Parameters:
+        file_name (str): File path to load a pickle file from.
+        gz (bool): Whether to open file with gz or not.
+    Returns:
+        An object loaded from the file_name.
+    """
+
+    if gz:
+        with gzip.open(file_name, 'rb') as f:
+            return pickle.load(f)
+    else:
+        with open(file_name, 'rb') as f:
+            return pickle.load(f)
+
+
+def savepkl(obj, file_name: Union[Path, str], gz: bool = False):
+    """A wrapper for saving function with pickle.
+    Parameters:
+        file_name (str): File path to save a pickle file to.
+        obj: The object to save.
+        gz (bool): Whether to save file with gz or not. Automatically append
+            ".gz" to the provided filename if True.
+    Returns:
+        None
+    """
+    if gz:
+        with gzip.open(f'{file_name}.gz', 'wb') as f:
+            pickle.dump(obj, f, protocol=4)
+    else:
+        with open(file_name, 'wb') as f:
+            pickle.dump(obj, f, protocol=4)
+    return
+
+
+# https://gist.github.com/Stonesjtu/368ddf5d9eb56669269ecdf9b0d21cbe
+def mem_report():
+    """Report the memory usage of the tensor.storage in pytorch
+    Both on CPUs and GPUs are reported"""
+
+    def _mem_report(tensors, mem_type):
+        """Print the selected tensors of type
+        There are two major storage types in our major concern:
+            - GPU: tensors transferred to CUDA devices
+            - CPU: tensors remaining on the system memory (usually unimportant)
+        Args:
+            - tensors: the tensors of specified type
+            - mem_type: 'CPU' or 'GPU' in current implementation """
+        print("Storage on %s" % (mem_type))
+        print("-" * LEN)
+        total_numel = 0
+        total_mem = 0
+        visited_data = []
+        for tensor in tensors:
+            if tensor.is_sparse:
+                continue
+            # a data_ptr indicates a memory block allocated
+            data_ptr = tensor.storage().data_ptr()
+            if data_ptr in visited_data:
+                continue
+            visited_data.append(data_ptr)
+
+            numel = tensor.storage().size()
+            total_numel += numel
+            element_size = tensor.storage().element_size()
+            mem = numel * element_size / 1024 / 1024  # 32bit=4Byte, MByte
+            total_mem += mem
+            element_type = type(tensor).__name__
+            size = tuple(tensor.size())
+
+            print("%s\t\t%s\t\t%.2f" % (element_type, size, mem))
+        print("-" * LEN)
+        print("Total Tensors: %d \tUsed Memory Space: %.2f MBytes" % (total_numel, total_mem))
+        print("-" * LEN)
+
+    LEN = 65
+    print("=" * LEN)
+    objects = gc.get_objects()
+    print("%s\t%s\t\t\t%s" % ("Element type", "Size", "Used MEM(MBytes)"))
+    tensors = [obj for obj in objects if isinstance(obj, Tensor)]
+    cuda_tensors = [t for t in tensors if t.is_cuda]
+    host_tensors = [t for t in tensors if not t.is_cuda]
+    _mem_report(cuda_tensors, "GPU")
+    _mem_report(host_tensors, "CPU")
+    print("=" * LEN)
+
+
+def get_best_model(path: str) -> Tuple[Optional[str], int]:
+    try:
+        best_epoch = max((int(p[5:-3]) for p in os.listdir(path)
+                          if p.startswith('model') and p.endswith('.pt')), default=-1)
+        best_model_path = os.path.join(path, f'model{best_epoch}.pt') if best_epoch != -1 else None
+        return best_model_path, best_epoch
+    except FileNotFoundError:
+        return None, -1
+
+
+def get_progress_bar(batches, verbose=True, desc=None, leave=False):
+    return nnlib.utils.progress(verbose=verbose, ncols=80, desc=desc or "Batches",
+                                ascii=True, total=sum(len(b) for _, b in batches), leave=leave)
+
+
+def create_exp_dir(path: str, script_path: str, overwrite=False) -> str:
+    """
+    Create experiment directory, and return path to log file.
+    """
+    if os.path.exists(path):
+        if not overwrite:
+            print(Logging.color(col='red', s=f"The experiment name: {path} already exists. "
+                                             f"Training will not proceed without the `--overwrite` flag."))
+            sys.exit(1)
+        else:
+            # Radical
+            print(Logging.color(col='green', s=f"Overwriting the experiment: {path} ..."))
+            shutil.rmtree(path)
+
+    os.mkdir(path)
+    shutil.copy(script_path, path)
+    logfile = os.path.join(path, f"{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.log")
+    return logfile
+
+
+def _add_indent(s_, n_spaces):
+    s = s_.split('\n')
+    # don't do anything for single-line stuff
+    if len(s) == 1:
+        return s_
+    first = s.pop(0)
+    s = [(n_spaces * ' ') + line for line in s]
+    s = '\n'.join(s)
+    s = first + '\n' + s
+    return s
+
+
+def _convert_id(keys: List[str]) -> Iterator[str]:
+    start = end = None
+    for key in keys:
+        if key.isnumeric() and end == int(key) - 1:
+            end = int(key)
+        else:
+            if start is not None:
+                if start == end:
+                    yield f"id {start}"
+                else:
+                    yield f"ids {start}-{end}"
+            if key.isnumeric():
+                start = end = int(key)
+            else:
+                start = end = None
+                yield key
+    if start is not None:
+        if start == end:
+            yield f"id {start}"
+        else:
+            yield f"ids {start}-{end}"
+
+
+def _compress_reprs(reprs: List[Tuple[str, str]]) -> List[str]:
+    lines = []
+    prev_mod_str = None
+    keys: List[str] = []
+    for key, mod_str in reprs:
+        if prev_mod_str is None or prev_mod_str != mod_str:
+            if prev_mod_str is not None:
+                for name in _convert_id(keys):
+                    lines.append(f"({name}): {prev_mod_str}")
+            prev_mod_str = mod_str
+            keys = [key]
+        else:
+            keys.append(key)
+    if len(keys) > 0:
+        for name in _convert_id(keys):
+            lines.append(f"({name}): {prev_mod_str}")
+    return lines
+
+
+def repr_module(module: nn.Module) -> str:
+    r"""Create a compressed representation by combining identical modules in
+    `nn.ModuleList`s and `nn.ParameterList`s.
+    """
+    # We treat the extra repr like the sub-module, one item per line
+    extra_lines: List[str] = []
+    extra_repr = module.extra_repr()
+    # empty string will be split into list ['']
+    if extra_repr:
+        extra_lines = extra_repr.split('\n')
+    child_reprs = [(key, _add_indent(repr_module(submodule), 2))
+                   for key, submodule in module.named_children()]
+    child_lines = _compress_reprs(child_reprs)
+    lines = extra_lines + child_lines
+
+    main_str = module.__class__.__name__ + '('
+    if lines:
+        # simple one-liner info, which most builtin Modules will use
+        if len(extra_lines) == 1 and not child_lines:
+            main_str += extra_lines[0]
+        else:
+            main_str += '\n  ' + '\n  '.join(lines) + '\n'
+
+    main_str += ')'
+    return main_str
+
+
+def _ParameterList_extra_repr(self):
+    param_reprs = []
+    for key, param in self._parameters.items():
+        if param is not None:
+            size_str = 'x'.join(str(size) for size in param.size())
+            device_str = '' if not param.is_cuda else ' (GPU {})'.format(param.get_device())
+            para_str = f'Parameter containing: [{torch.typename(param.data)} of size {size_str}{device_str}]'
+        else:
+            para_str = 'None'
+        param_reprs.append((key, para_str))
+    child_lines = _compress_reprs(param_reprs)
+    main_str = '\n'.join(child_lines)
+    return main_str
+
+
+# monkey-patch the output for ParameterList
+nn.ParameterList.extra_repr = _ParameterList_extra_repr  # type: ignore