├── .gitignore ├── .pre-commit-config.yaml ├── LICENSE ├── README.md ├── banner.png ├── data └── .gitignore ├── poetry.lock ├── pyproject.toml ├── pytorch_pqrnn ├── __init__.py ├── dataset.py ├── model.py └── utils.py ├── requirements.txt └── run.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | *logs 132 | *bz2 133 | 134 | .vscode 135 | *ckpt 136 | data/* 137 | grid.yaml 138 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | default_language_version: 2 | python: python3.8 3 | 4 | default_stages: [commit, push] 5 | 6 | repos: 7 | - repo: https://github.com/pre-commit/pre-commit-hooks 8 | rev: v2.5.0 9 | hooks: 10 | - id: check-yaml 11 | - id: end-of-file-fixer 12 | 13 | - repo: local 14 | hooks: 15 | - id: pyupgrade 16 | name: pyupgrade 17 | entry: pyupgrade --py37-plus 18 | types: [python] 19 | language: system 20 | 21 | - repo: local 22 | hooks: 23 | - id: isort 24 | name: isort 25 | entry: isort --settings-path pyproject.toml 26 | types: [python] 27 | language: system 28 | 29 | - repo: local 30 | hooks: 31 | - id: black 32 | name: black 33 | entry: black --config pyproject.toml 34 | types: [python] 35 | language: system 36 | - repo: https://github.com/Lucas-C/pre-commit-hooks-bandit 37 | rev: v1.0.4 38 | hooks: 39 | - id: python-bandit-vulnerability-check 40 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Chenghao MOU 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![banner](./banner.png) 2 | 3 | ![PyPI](https://img.shields.io/pypi/v/pytorch-pqrnn?style=plastic) ![Maintenance](https://img.shields.io/maintenance/yes/2021?style=plastic) ![PyPI - License](https://img.shields.io/pypi/l/pytorch-pqrnn?style=plastic) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4661601.svg)](https://doi.org/10.5281/zenodo.4661601) 4 | 5 | ## Installation 6 | 7 | ```bash 8 | # install with pypi 9 | pip install pytorch-pqrnn 10 | # or install locally with poetry 11 | poetry install 12 | ``` 13 | 14 | ## Environment 15 | 16 | Because of [this issue](https://github.com/salesforce/pytorch-qrnn/issues/29), `pytorch-qrnn` is no longer compatible with pytorch and it is also not actively maintained. If you want to use a QRNN layer in this model, you have install `pytorch-qrnn` with `torch <= 1.4` first. 17 | 18 | ## Usage 19 | 20 | ```python 21 | from pytorch_pqrnn.dataset import create_dataloaders 22 | from pytorch_pqrnn.model import PQRNN 23 | 24 | model = PQRNN( 25 | b=128, 26 | d=96, 27 | lr=1e-3, 28 | num_layers=2, 29 | dropout=0.5, 30 | output_size=5, 31 | rnn_type="GRU", 32 | multilabel=False, 33 | nhead=2, # used when rnn_type == "Transformer" 34 | ) 35 | 36 | # Or load the model from your checkpoint 37 | # model = PQRNN.load_from_checkpoint(checkpoint_path="example.ckpt") 38 | 39 | # Text data has to be pre-processed with DummyDataset 40 | dataset = DummyDataset( 41 | df[["text", "label"]].to_dict("records"), 42 | has_label=True, 43 | feature_size=128 * 2, 44 | add_eos_tag=True, 45 | add_bos_tag=True, 46 | max_seq_len=512, 47 | label2index={"pos": 1, "neg": 0}, 48 | ) 49 | 50 | # Explicit train/val loop 51 | # Add model.eval() when necessary 52 | dataloader = create_dataloaders(dataset) 53 | for batch in dataloader: 54 | # labels could be an empty tensor if has_label is False when creating the dataset. 55 | # To change what are included in a batch, feel free to change the collate_fn function 56 | # in dataset.py 57 | projections, lengths, labels = batch 58 | logits = model.forward(projections) 59 | 60 | # do your magic 61 | ``` 62 | 63 | ## CLI Usage 64 | 65 | ```bash 66 | Usage: run.py [OPTIONS] 67 | 68 | Options: 69 | --task [yelp2|yelp5|toxic] [default: yelp5] 70 | --b INTEGER [default: 128] 71 | --d INTEGER [default: 96] 72 | --num_layers INTEGER [default: 2] 73 | --batch_size INTEGER [default: 512] 74 | --dropout FLOAT [default: 0.5] 75 | --lr FLOAT [default: 0.001] 76 | --nhead INTEGER [default: 4] 77 | --rnn_type [LSTM|GRU|QRNN|Transformer] 78 | [default: GRU] 79 | --data_path TEXT 80 | --help Show this message and exit. 81 | ``` 82 | 83 | Datasets 84 | 85 | - yelp2(polarity): it will be downloaded w/ huggingface/datasets automatically 86 | - yelp5: [json file](https://www.kaggle.com/luisfredgs/hahnn-for-document-classification?select=yelp_reviews.json) should be downloaded to into `data_path` 87 | - toxic: [dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) should be downloaded and unzipped to into `data_path` 88 | 89 | ### Example: Yelp Polarity 90 | 91 | python -W ignore run.py --task yelp2 --b 128 --d 64 --num_layers 4 --data_path data/ 92 | 93 | ## Benchmarks(not optimized) 94 | 95 | | Model | Model Size | Yelp Polarity (error rate) | Yelp-5 (accuracy) | Civil Comments (mean auroc) | Command | 96 | | ------------------------ | ---------- | -------------------------- | ----------------- | --------------------------- | ---------------------------------------------------------------- | 97 | | ~~PQRNN (this repo)~~0 | ~~78K~~ | ~~6.3~~ | ~~70.4~~ | ~~TODO~~ | `--b 128 --d 64 --num_layers 4 --rnn_type QRNN` | 98 | | PRNN (this repo) | 90K | 5.5 | **70.7** | 95.57 | `--b 128 --d 64 --num_layers 1 --rnn_type GRU` | 99 | | PTransformer (this repo) | 618K | 10.8 | 68 | 92.4 | `--b 128 --d 64 --num_layers 1 --rnn_type Transformer --nhead 8` | 100 | | PRADO1 | 175K | | 65.9 | | | 101 | | BERT | 335M | **1.81** | 70.58 | **98.856**2 | | 102 | 0. Not supported with `torch >= 1.7` 103 | 1. [Paper](https://www.aclweb.org/anthology/D19-1506.pdf) 104 | 2. Best Kaggle Submission 105 | 106 | ## Credits 107 | 108 | - [original tensorflow source code for PRADO](https://github.com/tensorflow/models/tree/master/research/sequence_projection/prado) 109 | 110 | - Powered by [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning) and [grid.ai](https://www.grid.ai/) 111 | 112 | ## Citation 113 | 114 | ``` 115 | @software{chenghao_mou_2021_4661601, 116 | author = {Chenghao MOU}, 117 | title = {ChenghaoMou/pytorch-pQRNN: Add DOI}, 118 | month = apr, 119 | year = 2021, 120 | publisher = {Zenodo}, 121 | version = {0.0.3}, 122 | doi = {10.5281/zenodo.4661601}, 123 | url = {https://doi.org/10.5281/zenodo.4661601} 124 | } 125 | ``` 126 | -------------------------------------------------------------------------------- /banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChenghaoMou/pytorch-pQRNN/888d794251a483322ced54b3396d9e32214b32a6/banner.png -------------------------------------------------------------------------------- /data/.gitignore: -------------------------------------------------------------------------------- 1 | !.gitignore 2 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "absl-py" 3 | version = "0.14.1" 4 | description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." 5 | category = "main" 6 | optional = false 7 | python-versions = "*" 8 | 9 | [package.dependencies] 10 | six = "*" 11 | 12 | [[package]] 13 | name = "aiohttp" 14 | version = "3.7.4.post0" 15 | description = "Async http client/server framework (asyncio)" 16 | category = "main" 17 | optional = false 18 | python-versions = ">=3.6" 19 | 20 | [package.dependencies] 21 | async-timeout = ">=3.0,<4.0" 22 | attrs = ">=17.3.0" 23 | chardet = ">=2.0,<5.0" 24 | multidict = ">=4.5,<7.0" 25 | typing-extensions = ">=3.6.5" 26 | yarl = ">=1.0,<2.0" 27 | 28 | [package.extras] 29 | speedups = ["aiodns", "brotlipy", "cchardet"] 30 | 31 | [[package]] 32 | name = "appdirs" 33 | version = "1.4.4" 34 | description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." 35 | category = "dev" 36 | optional = false 37 | python-versions = "*" 38 | 39 | [[package]] 40 | name = "astroid" 41 | version = "2.8.2" 42 | description = "An abstract syntax tree for Python with inference support." 43 | category = "dev" 44 | optional = false 45 | python-versions = "~=3.6" 46 | 47 | [package.dependencies] 48 | lazy-object-proxy = ">=1.4.0" 49 | typed-ast = {version = ">=1.4.0,<1.5", markers = "implementation_name == \"cpython\" and python_version < \"3.8\""} 50 | typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""} 51 | wrapt = ">=1.11,<1.13" 52 | 53 | [[package]] 54 | name = "async-timeout" 55 | version = "3.0.1" 56 | description = "Timeout context manager for asyncio programs" 57 | category = "main" 58 | optional = false 59 | python-versions = ">=3.5.3" 60 | 61 | [[package]] 62 | name = "atomicwrites" 63 | version = "1.4.0" 64 | description = "Atomic file writes." 65 | category = "dev" 66 | optional = false 67 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 68 | 69 | [[package]] 70 | name = "attrs" 71 | version = "21.2.0" 72 | description = "Classes Without Boilerplate" 73 | category = "main" 74 | optional = false 75 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 76 | 77 | [package.extras] 78 | dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit"] 79 | docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] 80 | tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface"] 81 | tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins"] 82 | 83 | [[package]] 84 | name = "backports.entry-points-selectable" 85 | version = "1.1.0" 86 | description = "Compatibility shim providing selectable entry points for older implementations" 87 | category = "dev" 88 | optional = false 89 | python-versions = ">=2.7" 90 | 91 | [package.dependencies] 92 | importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} 93 | 94 | [package.extras] 95 | docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"] 96 | testing = ["pytest (>=4.6)", "pytest-flake8", "pytest-cov", "pytest-black (>=0.3.7)", "pytest-mypy", "pytest-checkdocs (>=2.4)", "pytest-enabler (>=1.0.1)"] 97 | 98 | [[package]] 99 | name = "bandit" 100 | version = "1.7.0" 101 | description = "Security oriented static analyser for python code." 102 | category = "main" 103 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[package.extras] 245 | apache-beam = ["apache-beam (>=2.26.0)"] 246 | benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.6.0)", "transformers (==3.0.2)"] 247 | dev = ["absl-py", "pytest", "pytest-xdist", "apache-beam (>=2.26.0)", "elasticsearch", "aiobotocore", "boto3", "botocore", "faiss-cpu", "fsspec", "moto[server,s3] (==2.0.4)", "rarfile (>=4.0)", "s3fs (==2021.08.1)", "tensorflow (>=2.3)", "torch", "transformers", "bs4", "conllu", "langdetect", "lxml", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "tldextract", "zstandard", "bert-score (>=0.3.6)", "rouge-score", "sacrebleu", "scipy", "seqeval", "scikit-learn", "jiwer", "sentencepiece", "toml (>=0.10.1)", "requests-file (>=1.5.1)", "tldextract (>=3.1.0)", "texttable (>=1.6.3)", "Werkzeug (>=1.0.1)", "six (>=1.15.0,<1.16.0)", "wget (>=3.2)", "pytorch-nlp (==0.5.0)", "pytorch-lightning", "fastBPE (==0.1.0)", "fairseq", "black (==21.4b0)", "flake8 (==3.7.9)", "isort", "pyyaml (>=5.3.1)", "importlib-resources"] 248 | docs = ["docutils (==0.16.0)", "recommonmark", "sphinx (==3.1.2)", "sphinx-markdown-tables", "sphinx-rtd-theme (==0.4.3)", "sphinxext-opengraph (==0.4.1)", "sphinx-copybutton", "fsspec", "s3fs", "sphinx-panels", "sphinx-inline-tabs", "myst-parser"] 249 | quality = ["black (==21.4b0)", "flake8 (==3.7.9)", "isort", "pyyaml (>=5.3.1)"] 250 | s3 = ["fsspec", "boto3", "botocore", "s3fs"] 251 | tensorflow = ["tensorflow (>=2.2.0)"] 252 | tensorflow_gpu = ["tensorflow-gpu (>=2.2.0)"] 253 | tests = ["absl-py", "pytest", "pytest-xdist", "apache-beam (>=2.26.0)", "elasticsearch", "aiobotocore", "boto3", "botocore", "faiss-cpu", "fsspec", "moto[server,s3] (==2.0.4)", "rarfile (>=4.0)", "s3fs (==2021.08.1)", "tensorflow (>=2.3)", "torch", "transformers", "bs4", "conllu", "langdetect", "lxml", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "tldextract", "zstandard", "bert-score (>=0.3.6)", "rouge-score", "sacrebleu", "scipy", "seqeval", "scikit-learn", "jiwer", "sentencepiece", "toml 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285 | pyyaml = "*" 286 | toml = "*" 287 | 288 | [package.extras] 289 | pipenv = ["pipenv"] 290 | 291 | [[package]] 292 | name = "filelock" 293 | version = "3.3.0" 294 | description = "A platform independent file lock." 295 | category = "main" 296 | optional = false 297 | python-versions = ">=3.6" 298 | 299 | [package.extras] 300 | docs = ["furo (>=2021.8.17b43)", "sphinx (>=4.1)", "sphinx-autodoc-typehints (>=1.12)"] 301 | testing = ["covdefaults (>=1.2.0)", "coverage (>=4)", "pytest (>=4)", "pytest-cov", "pytest-timeout (>=1.4.2)"] 302 | 303 | [[package]] 304 | name = "fsspec" 305 | version = "2021.10.0" 306 | description = "File-system specification" 307 | category = "main" 308 | optional = false 309 | python-versions = ">=3.6" 310 | 311 | [package.dependencies] 312 | aiohttp = {version = "*", optional = true, markers = "extra == \"http\""} 313 | requests = {version = "*", optional = true, markers = "extra == \"http\""} 314 | 315 | [package.extras] 316 | abfs = ["adlfs"] 317 | adl = 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https://python-poetry.org/docs/pyproject/ 2 | 3 | [build-system] 4 | requires = ["poetry>=1.0"] 5 | build-backend = "poetry.masonry.api" 6 | 7 | [tool.poetry] 8 | name = "pytorch-pqrnn" 9 | version = "0.1.0" 10 | description = "Pytorch implementation of pQRNN" 11 | readme = "README.md" 12 | authors = [ 13 | "pytorch-pqrnn " 14 | ] 15 | license = "MIT" 16 | repository = "https://github.com/ChenghaoMou/pytorch-pqrnn" 17 | homepage = "https://github.com/ChenghaoMou/pytorch-pqrnn" 18 | 19 | # Keywords description https://python-poetry.org/docs/pyproject/#keywords 20 | keywords = [] # Update me 21 | 22 | # Pypi classifiers: https://pypi.org/classifiers/ 23 | classifiers = [ # Update me 24 | "Development Status :: 3 - Alpha", 25 | "Intended Audience :: Developers", 26 | "Operating System :: OS Independent", 27 | "Topic :: Software Development :: Libraries :: Python Modules", 28 | ] 29 | 30 | [tool.poetry.scripts] 31 | # Entry points for the package https://python-poetry.org/docs/pyproject/#scripts 32 | "pytorch-pqrnn" = "pytorch_pqrnn.__main__:app" 33 | 34 | [tool.poetry.dependencies] 35 | python = "^3.7" 36 | importlib_metadata = {version = "^1.6.0", python = "<3.8"} 37 | typer = {extras = ["all"], version = "^0.3.2"} 38 | rich = "^8.0.0" 39 | torch = "<=1.9.0" 40 | bitstring = "^3.1.7" 41 | pytorch-lightning = "^1.2.5" 42 | nltk = "^3.5" 43 | pandas = "^1.1.3" 44 | numpy = "^1.19.2" 45 | scikit-learn = "^0.23.2" 46 | bandit = "^1.7.0" 47 | mmh3 = "^2.5.1" 48 | datasets = "^1.4.1" 49 | grpcio = "<=1.38.1" 50 | 51 | [tool.poetry.dev-dependencies] 52 | darglint = "^1.5.4" 53 | isort = "^5.5.4" 54 | pyupgrade = "^2.7.2" 55 | black = "^20.8b1" 56 | mypy = "^0.782" 57 | bandit = "^1.6.2" 58 | safety = "^1.9.0" 59 | pytest = "^6.0.1" 60 | pylint = "^2.5.3" 61 | pydocstyle = "^5.0.2" 62 | pre-commit = "^2.7.1" 63 | 64 | [tool.black] 65 | # https://github.com/psf/black 66 | line-length = 80 67 | target-version = ["py37"] 68 | 69 | [tool.isort] 70 | # https://github.com/timothycrosley/isort/ 71 | known_typing = "typing,types,typing_extensions,mypy,mypy_extensions" 72 | sections = "FUTURE,TYPING,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER" 73 | include_trailing_comma = true 74 | default_section = "FIRSTPARTY" 75 | multi_line_output = 3 76 | indent = 4 77 | force_grid_wrap = 0 78 | use_parentheses = true 79 | line_length = 80 80 | -------------------------------------------------------------------------------- /pytorch_pqrnn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChenghaoMou/pytorch-pQRNN/888d794251a483322ced54b3396d9e32214b32a6/pytorch_pqrnn/__init__.py -------------------------------------------------------------------------------- /pytorch_pqrnn/dataset.py: -------------------------------------------------------------------------------- 1 | from typing import Any, Dict, List, Tuple, Union 2 | 3 | from pathlib import Path 4 | 5 | import numpy as np 6 | import pandas as pd 7 | import regex as re 8 | import torch 9 | from datasets import load_dataset 10 | from pytorch_pqrnn.utils import murmurhash 11 | from rich.console import Console 12 | from sklearn.model_selection import train_test_split 13 | from torch.utils.data import DataLoader, Dataset 14 | 15 | REGEX0 = re.compile(r"([\p{S}\p{P}]+)") 16 | REGEX1 = re.compile(r" +") 17 | 18 | console = Console() 19 | 20 | 21 | class DummyDataset(Dataset): 22 | def __init__( 23 | self, 24 | x: Union[pd.DataFrame, List[Dict[str, Any]]], 25 | has_label: bool = True, 26 | feature_size: int = 512, 27 | add_eos_tag: bool = True, 28 | add_bos_tag: bool = True, 29 | max_seq_len: int = 256, 30 | label2index: Dict[str, int] = None, 31 | ): 32 | """Convert the dataset into a dummy torch dataset. 33 | 34 | Parameters 35 | ---------- 36 | x : Union[pd.DataFrame, List[Dict[str, Any]]] 37 | A dataframe or a list of dicts 38 | has_label : bool, optional 39 | Whether the dataframe or each dict has a label key/column, by default True 40 | feature_size : int, optional 41 | Dimension of the hash, the output dimension would be half of the hash size(2b -> b), by default 512 42 | add_eos_tag : bool, optional 43 | Add a special eos tag, by default True 44 | add_bos_tag : bool, optional 45 | Add a special bos tag, by default True 46 | max_seq_len : int, optional 47 | Maximum sequence length, by default 256 48 | label2index : Dict[str, int], optional 49 | Mapping that converts labels into indices, by default None 50 | 51 | Examples 52 | -------- 53 | >>> data = [{'text': 'Hello world!', 'label': 'positive'}] 54 | >>> dataset = DummyDataset(data, has_label=True, feature_size=512, label2index={'positive': 1, 'negative': 0}) 55 | >>> hash, label = dataset[0] 56 | >>> assert label == 1, label 57 | >>> assert len(hash[0]) == 256, len(hash[0]) 58 | """ 59 | self.feature_size = feature_size 60 | self.add_eos_tag = add_eos_tag 61 | self.add_bos_tag = add_bos_tag 62 | self.max_seq_len = max_seq_len 63 | self.label2index = label2index 64 | self.has_label = has_label 65 | 66 | self.x = x 67 | self.eos_tag_ = 1 if add_eos_tag else 0 68 | self.bos_tag_ = 1 if add_bos_tag else 0 69 | self.eos_tag = ["EOS"] if add_eos_tag else [] 70 | self.bos_tag = ["BOS"] if add_bos_tag else [] 71 | 72 | def __len__(self): 73 | return len(self.x) 74 | 75 | def __getitem__(self, idx): 76 | line = self.x[idx] 77 | text = line["text"] 78 | if self.has_label: 79 | label = line["label"] 80 | if self.label2index: 81 | label = self.label2index[label] 82 | else: 83 | label = None 84 | 85 | tokens = ( 86 | REGEX1.sub(" ", REGEX0.sub(r" \1 ", text.lower())) 87 | .strip() 88 | .split(" ") 89 | ) 90 | 91 | curr_tokens = self.bos_tag + tokens[: self.max_seq_len] + self.eos_tag 92 | curr_hashings = [] 93 | for j in range(len(curr_tokens)): 94 | curr_hashing = murmurhash( 95 | curr_tokens[j], feature_size=self.feature_size 96 | ) 97 | curr_hashings.append(curr_hashing[: self.feature_size // 2]) 98 | if label is not None: 99 | return curr_hashings, label 100 | else: 101 | return curr_hashings 102 | 103 | 104 | def collate_fn(examples: List[Any]) -> Tuple[torch.Tensor, ...]: 105 | """Batching examples. 106 | 107 | Parameters 108 | ---------- 109 | examples : List[Any] 110 | List of examples 111 | 112 | Returns 113 | ------- 114 | Tuple[torch.Tensor, ...] 115 | Tuple of hash tensor, length tensor, and label tensor 116 | """ 117 | 118 | projection = [] 119 | labels = [] 120 | 121 | for example in examples: 122 | if not isinstance(example, tuple): 123 | projection.append(np.asarray(example)) 124 | else: 125 | projection.append(np.asarray(example[0])) 126 | labels.append(example[1]) 127 | lengths = torch.from_numpy(np.asarray(list(map(len, examples)))).long() 128 | projection_tensor = np.zeros( 129 | (len(projection), max(map(len, projection)), len(projection[0][0])) 130 | ) 131 | for i, doc in enumerate(projection): 132 | projection_tensor[i, : len(doc), :] = doc 133 | 134 | return ( 135 | torch.from_numpy(projection_tensor).float(), 136 | lengths, 137 | torch.from_numpy(np.asarray(labels)), 138 | ) 139 | 140 | 141 | def create_dataloaders( 142 | task: str = "yelp2", 143 | batch_size: int = 32, 144 | feature_size: int = 128, 145 | add_eos_tag: bool = True, 146 | add_bos_tag: bool = True, 147 | max_seq_len: int = 256, 148 | label2index: Dict[str, int] = None, 149 | data_path: Union[str, Path] = "data", 150 | ) -> Tuple[DataLoader, DataLoader]: 151 | """Create train and eval dataloaders. 152 | 153 | Parameters 154 | ---------- 155 | task : str, optional 156 | Name from predefined tasks, by default "yelp2" 157 | batch_size : int, optional 158 | Size of the batch, by default 32 159 | feature_size : int, optional 160 | Dimension of the features, by default 128 161 | add_eos_tag : bool, optional 162 | Add a special eos tag, by default True 163 | add_bos_tag : bool, optional 164 | Add a special bos tag, by default True 165 | max_seq_len : int, optional 166 | Maximum sequence length, by default 256 167 | label2index : Dict[str, int], optional 168 | Mapping that converts labels to indices, by default None 169 | data_path: str, optional 170 | Path to the data files 171 | 172 | Returns 173 | ------- 174 | Tuple[DataLoader, DataLoader] 175 | Train and eval dataloaders 176 | 177 | Raises 178 | ------ 179 | Exception 180 | Unsupported task 181 | """ 182 | 183 | data_path = Path(data_path) 184 | 185 | if task == "yelp2": 186 | dataset = load_dataset("yelp_polarity") 187 | elif task == "yelp5": 188 | data = pd.read_json(data_path / "yelp_reviews.json", lines=True) 189 | data["label"] = data["stars"] - 1 190 | data["label"] = data["label"].astype(int) 191 | train, val = train_test_split( 192 | data, test_size=0.1, stratify=data["label"] 193 | ) 194 | dataset = { 195 | "train": train.to_dict("records"), 196 | "test": val.to_dict("records"), 197 | } 198 | elif task == "toxic": 199 | train = pd.read_csv(data_path / "train.csv") 200 | labels = pd.read_csv(data_path / "test_labels.csv") 201 | test = pd.read_csv(data_path / "test.csv") 202 | labels["id"] = labels["id"].astype(str) 203 | test["id"] = test["id"].astype(str) 204 | test = test.merge(labels) 205 | test = test[test["toxic"] != -1] 206 | 207 | train["text"] = train["comment_text"] 208 | train["label"] = train[ 209 | [ 210 | "toxic", 211 | "severe_toxic", 212 | "obscene", 213 | "threat", 214 | "insult", 215 | "identity_hate", 216 | ] 217 | ].values.tolist() 218 | 219 | test["text"] = test["comment_text"] 220 | test["label"] = test[ 221 | [ 222 | "toxic", 223 | "severe_toxic", 224 | "obscene", 225 | "threat", 226 | "insult", 227 | "identity_hate", 228 | ] 229 | ].values.tolist() 230 | 231 | dataset = { 232 | "train": train[["text", "label"]].to_dict("records"), 233 | "test": test[["text", "label"]].to_dict("records"), 234 | } 235 | else: 236 | raise Exception(f"Unsupported task: {task} VS. {{yelp2, yelp5}}") 237 | 238 | train_dataset = DummyDataset( 239 | dataset["train"], 240 | feature_size=feature_size, 241 | add_eos_tag=add_eos_tag, 242 | add_bos_tag=add_bos_tag, 243 | max_seq_len=max_seq_len, 244 | label2index=label2index, 245 | ) 246 | 247 | val_dataset = DummyDataset( 248 | dataset["test"], 249 | feature_size=feature_size, 250 | add_eos_tag=add_eos_tag, 251 | add_bos_tag=add_bos_tag, 252 | max_seq_len=max_seq_len, 253 | label2index=label2index, 254 | ) 255 | 256 | return ( 257 | DataLoader( 258 | train_dataset, 259 | batch_size=batch_size, 260 | collate_fn=collate_fn, 261 | num_workers=4, 262 | ), 263 | DataLoader( 264 | val_dataset, 265 | batch_size=batch_size, 266 | collate_fn=collate_fn, 267 | num_workers=4, 268 | ), 269 | ) 270 | -------------------------------------------------------------------------------- /pytorch_pqrnn/model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # @Date : 2021-01-01 16:18:28 3 | # @Author : Chenghao Mou (mouchenghao@gmail.com) 4 | # @Description : pQRNN 5 | 6 | from typing import Any, Dict, List 7 | 8 | import math 9 | import warnings 10 | from functools import partial 11 | 12 | with warnings.catch_warnings(): 13 | warnings.filterwarnings("ignore") 14 | import numpy as np 15 | import pytorch_lightning as pl 16 | import torch 17 | import torch.nn as nn 18 | from pytorch_lightning.metrics.functional import accuracy, auroc 19 | from pytorch_lightning.metrics.functional import f1 as f1_score 20 | from torch.nn import TransformerEncoder, TransformerEncoderLayer 21 | from torch.optim.lr_scheduler import ReduceLROnPlateau 22 | 23 | try: 24 | from torchqrnn import QRNN 25 | except ImportError: 26 | from torch.nn import LSTM as QRNN 27 | 28 | 29 | class PositionalEncoding(nn.Module): 30 | def __init__(self, d_model, dropout=0.1, max_len=5000): 31 | super().__init__() 32 | self.dropout = nn.Dropout(p=dropout) 33 | 34 | pe = torch.zeros(max_len, d_model) 35 | position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) 36 | div_term = torch.exp( 37 | torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model) 38 | ) 39 | pe[:, 0::2] = torch.sin(position * div_term) 40 | pe[:, 1::2] = torch.cos(position * div_term) 41 | pe = pe.unsqueeze(0).transpose(0, 1) 42 | self.register_buffer("pe", pe) 43 | 44 | def forward(self, x): 45 | x = x + self.pe[: x.size(0), :] 46 | return self.dropout(x) 47 | 48 | 49 | class PQRNN(pl.LightningModule): 50 | def __init__( 51 | self, 52 | b: int = 512, 53 | d: int = 64, 54 | num_layers: int = 4, 55 | fc_sizes: List[int] = None, 56 | output_size: int = 2, 57 | lr: float = 0.025, 58 | dropout: float = 0.5, 59 | rnn_type: str = "LSTM", 60 | multilabel: bool = False, 61 | nhead: int = 8, 62 | ): 63 | super().__init__() 64 | if fc_sizes is None: 65 | fc_sizes = [128, 64] 66 | 67 | self.hparams: Dict[str, Any] = { 68 | "b": b, 69 | "d": d, 70 | "fc_size": fc_sizes, 71 | "lr": lr, 72 | "output_size": output_size, 73 | "dropout": dropout, 74 | "rnn_type": rnn_type.upper(), 75 | "multilabel": multilabel, 76 | "nhead": nhead, 77 | } 78 | 79 | layers: List[nn.Module] = [] 80 | for x, y in zip([d] + fc_sizes, fc_sizes + [output_size]): 81 | layers.append(nn.ReLU()) 82 | layers.append(nn.Linear(x, y)) 83 | 84 | self.tanh = nn.Hardtanh() 85 | if self.hparams["rnn_type"] in {"LSTM", "GRU", "QRNN"}: 86 | self.hidden = { 87 | "LSTM": partial(nn.LSTM, bidirectional=True), 88 | "GRU": partial(nn.GRU, bidirectional=True), 89 | "QRNN": QRNN, 90 | }[self.hparams["rnn_type"]]( 91 | b, d, num_layers=num_layers, dropout=dropout 92 | ) 93 | else: 94 | self.pos_encoder = PositionalEncoding(d_model=b, dropout=dropout) 95 | encoder_layers = TransformerEncoderLayer( 96 | d_model=b, nhead=nhead, dropout=dropout 97 | ) 98 | self.hidden = TransformerEncoder( 99 | encoder_layers, num_layers=num_layers 100 | ) 101 | self.linear = nn.Linear(b, d) 102 | 103 | self.output = nn.ModuleList(layers) 104 | self.loss = ( 105 | nn.CrossEntropyLoss() 106 | if not self.hparams["multilabel"] 107 | else nn.BCEWithLogitsLoss() 108 | ) 109 | 110 | def generate_square_subsequent_mask(self, sz): 111 | mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) 112 | mask = ( 113 | mask.float() 114 | .masked_fill(mask == 0, float("-inf")) 115 | .masked_fill(mask == 1, float(0.0)) 116 | ) 117 | return mask 118 | 119 | def forward(self, projection): 120 | features = self.tanh(projection) 121 | features = features.transpose(0, 1) 122 | if self.hparams["rnn_type"] in {"LSTM", "GRU", "QRNN"}: 123 | output, _ = self.hidden(features) 124 | if self.hparams["rnn_type"] != "QRNN": 125 | output = ( 126 | output[..., : output.shape[-1] // 2] 127 | + output[..., output.shape[-1] // 2 :] 128 | ) 129 | else: 130 | features = features * math.sqrt(self.hparams["b"]) 131 | features = self.pos_encoder(features) 132 | output = self.hidden( 133 | features, 134 | self.generate_square_subsequent_mask(features.size(0)).to( 135 | features.device 136 | ), 137 | ) 138 | output = self.linear(output) 139 | output = output.transpose(0, 1) 140 | logits = torch.mean(output, dim=1) 141 | for layer in self.output: 142 | logits = layer(logits) 143 | return logits 144 | 145 | def training_step(self, batch, batch_idx): 146 | projection, _, labels = batch 147 | logits = self.forward(projection) 148 | self.log( 149 | "loss", 150 | self.loss( 151 | logits, 152 | labels.type( 153 | logits.dtype if self.hparams["multilabel"] else labels.dtype 154 | ), 155 | ) 156 | .detach() 157 | .cpu() 158 | .item(), 159 | ) 160 | return { 161 | "loss": self.loss( 162 | logits, 163 | labels.type( 164 | logits.dtype if self.hparams["multilabel"] else labels.dtype 165 | ), 166 | ) 167 | } 168 | 169 | def validation_step(self, batch, batch_idx): 170 | projection, _, labels = batch 171 | logits = self.forward(projection) 172 | 173 | return {"logits": logits, "labels": labels} 174 | 175 | def validation_epoch_end(self, outputs): 176 | 177 | logits = torch.cat([o["logits"] for o in outputs], dim=0) 178 | labels = torch.cat([o["labels"] for o in outputs], dim=0) 179 | if self.hparams["multilabel"]: 180 | self.log( 181 | "val_auroc", 182 | np.mean( 183 | [ 184 | auroc( 185 | torch.sigmoid(logits[:, i]), 186 | labels[:, i], 187 | pos_label=1, 188 | ) 189 | .detach() 190 | .cpu() 191 | .item() 192 | for i in range(logits.shape[1]) 193 | ] 194 | ), 195 | prog_bar=True, 196 | ) 197 | else: 198 | self.log( 199 | "val_f1", 200 | f1_score( 201 | torch.argmax(logits, dim=1), 202 | labels, 203 | num_classes=self.hparams["output_size"], 204 | class_reduction="macro", 205 | ) 206 | .detach() 207 | .cpu() 208 | .item(), 209 | prog_bar=True, 210 | ) 211 | self.log( 212 | "val_acc", 213 | accuracy(torch.argmax(logits, dim=1), labels) 214 | .detach() 215 | .cpu() 216 | .item(), 217 | prog_bar=True, 218 | ) 219 | self.log( 220 | "val_loss", 221 | self.loss( 222 | logits, 223 | labels.type( 224 | logits.dtype if self.hparams["multilabel"] else labels.dtype 225 | ), 226 | ) 227 | .detach() 228 | .cpu() 229 | .item(), 230 | prog_bar=True, 231 | ) 232 | 233 | def configure_optimizers(self): 234 | optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams["lr"]) 235 | scheduler = ReduceLROnPlateau(optimizer, "min") 236 | return { 237 | "optimizer": optimizer, 238 | "lr_scheduler": scheduler, 239 | "monitor": "val_loss", 240 | } 241 | -------------------------------------------------------------------------------- /pytorch_pqrnn/utils.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | import mmh3 4 | 5 | kMul: int = 0xC6A4A7935BD1E995 6 | kMul2: int = 0x9E3779B97F4A7835 7 | 8 | kMappingTable: List[int] = [0, 1, -1, 0] 9 | 10 | 11 | def shift_mix(val): 12 | return val ^ (val >> 47) 13 | 14 | 15 | def get_more_bits(hash1, hash2): 16 | 17 | hash1 = shift_mix(hash1) * kMul 18 | hash2 ^= hash1 19 | newhigh = shift_mix(hash1) 20 | newlow = shift_mix(hash2 * kMul2) * kMul2 21 | 22 | return newlow, newhigh 23 | 24 | 25 | def murmurhash(token: str, feature_size: int = 512): 26 | 27 | hash_low = 0 28 | hash_high = 0 29 | hash_codes = [] 30 | 31 | for i in range(0, feature_size, 64): 32 | if i == 0: 33 | hash_low, hash_high = mmh3.hash64(token, signed=False) 34 | else: 35 | hash_low, hash_high = get_more_bits(hash_low, hash_high) 36 | hash_codes.append(hash_low) 37 | hash_codes.append(hash_high) 38 | 39 | projection: List[int] = [] 40 | for code in hash_codes: 41 | while code: 42 | if len(projection) >= feature_size // 2: 43 | break 44 | projection.append(kMappingTable[code & 3]) 45 | code = code >> 2 46 | if len(projection) >= feature_size // 2: 47 | break 48 | return projection[: feature_size // 2] 49 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.12.0 2 | aiohttp==3.7.4.post0 3 | async-timeout==3.0.1 4 | attrs==20.3.0 5 | bandit==1.7.0 6 | bitstring==3.1.7 7 | cachetools==4.2.1 8 | certifi==2020.12.5 9 | chardet==4.0.0 10 | click==7.1.2 11 | colorama==0.4.4 12 | colorama==0.4.4; platform_system == "Windows" 13 | commonmark==0.9.1 14 | datasets==1.5.0 15 | dill==0.3.3 16 | filelock==3.0.12 17 | fsspec==0.8.7 18 | future==0.18.2 19 | gitdb==4.0.7 20 | gitpython==3.1.14 21 | google-auth-oauthlib==0.4.3 22 | google-auth==1.28.0 23 | grpcio==1.36.1 24 | huggingface-hub==0.0.7 25 | idna==2.10 26 | importlib-metadata==1.7.0; python_version < "3.8" 27 | joblib==1.0.1 28 | markdown==3.3.4 29 | mmh3==2.5.1 30 | multidict==5.1.0 31 | multiprocess==0.70.11.1 32 | nltk==3.5 33 | numpy==1.20.2 34 | oauthlib==3.1.0 35 | pandas==1.1.5 36 | pbr==5.5.1 37 | protobuf==3.15.6 38 | pyarrow==3.0.0 39 | pyasn1-modules==0.2.8 40 | pyasn1==0.4.8 41 | pygments==2.8.1 42 | pyhash==0.9.3 43 | python-dateutil==2.8.1 44 | pytorch-lightning==1.2.5 45 | pytz==2021.1 46 | regex==2021.3.17 47 | requests-oauthlib==1.3.0 48 | requests==2.25.1 49 | rich==8.0.0 50 | rsa==4.7.2; python_version >= "3.6" 51 | scikit-learn==0.23.2 52 | scipy==1.6.1 53 | shellingham==1.4.0 54 | six==1.15.0 55 | smmap==4.0.0 56 | stevedore==3.3.0 57 | tensorboard-plugin-wit==1.8.0 58 | tensorboard==2.4.1 59 | threadpoolctl==2.1.0 60 | torch==1.7.1 61 | torchmetrics==0.2.0 62 | tqdm==4.49.0 63 | typer==0.3.2 64 | typing-extensions==3.7.4.3 65 | typing-extensions==3.7.4.3; python_version < "3.8" 66 | urllib3==1.26.4 67 | werkzeug==1.0.1 68 | xxhash==2.0.0 69 | yarl==1.6.3 70 | zipp==3.4.1; python_version < "3.8" 71 | -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | import click 2 | import pytorch_lightning as pl 3 | import torch 4 | from pytorch_lightning import loggers as pl_loggers 5 | from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint 6 | from pytorch_lightning.plugins import DeepSpeedPlugin 7 | from pytorch_pqrnn.dataset import create_dataloaders 8 | from pytorch_pqrnn.model import PQRNN 9 | from rich.console import Console 10 | 11 | console = Console() 12 | 13 | 14 | @click.command() 15 | @click.option( 16 | "--task", 17 | type=click.Choice(["yelp2", "yelp5", "toxic"], case_sensitive=False), 18 | default="yelp5", 19 | show_default=True, 20 | ) 21 | @click.option("--b", type=int, default=128, show_default=True) 22 | @click.option("--d", type=int, default=96, show_default=True) 23 | @click.option("--num_layers", type=int, default=2, show_default=True) 24 | @click.option("--batch_size", type=int, default=512, show_default=True) 25 | @click.option("--dropout", type=float, default=0.5, show_default=True) 26 | @click.option("--lr", type=float, default=1e-3, show_default=True) 27 | @click.option("--nhead", type=int, default=4, show_default=True) 28 | @click.option( 29 | "--rnn_type", 30 | type=click.Choice( 31 | ["LSTM", "GRU", "QRNN", "Transformer"], case_sensitive=False 32 | ), 33 | default="GRU", 34 | show_default=True, 35 | ) 36 | @click.option("--data_path", type=str, default="data") 37 | def train( 38 | task: str, 39 | b: int, 40 | d: int, 41 | num_layers: int, 42 | batch_size: int, 43 | dropout: float, 44 | lr: float, 45 | nhead: int, 46 | rnn_type: str, 47 | data_path: str, 48 | ): 49 | 50 | deepspeed_config = { 51 | "zero_allow_untested_optimizer": True, 52 | "optimizer": { 53 | "type": "Adam", 54 | "params": { 55 | "lr": lr, 56 | "betas": [0.998, 0.999], 57 | "eps": 1e-5, 58 | "weight_decay": 1e-9, 59 | }, 60 | }, 61 | "scheduler": { 62 | "type": "WarmupLR", 63 | "params": { 64 | "last_batch_iteration": -1, 65 | "warmup_min_lr": 0, 66 | "warmup_max_lr": 3e-5, 67 | "warmup_num_steps": 100, 68 | }, 69 | }, 70 | "zero_optimization": { 71 | "stage": 2, # Enable Stage 2 ZeRO (Optimizer/Gradient state partitioning) 72 | "cpu_offload": True, # Enable Offloading optimizer state/calculation to the host CPU 73 | "contiguous_gradients": True, # Reduce gradient fragmentation. 74 | "overlap_comm": True, # Overlap reduce/backward operation of gradients for speed. 75 | "allgather_bucket_size": 2e8, # Number of elements to all gather at once. 76 | "reduce_bucket_size": 2e8, # Number of elements we reduce/allreduce at once. 77 | }, 78 | } 79 | 80 | train_dataloader, dev_dataloader = create_dataloaders( 81 | task, 82 | batch_size=batch_size, 83 | feature_size=b * 2, 84 | label2index=None, 85 | data_path=data_path, 86 | ) 87 | num_classes = {"yelp2": 2, "yelp5": 5, "toxic": 6}.get(task, 2) 88 | 89 | model = PQRNN( 90 | b=b, 91 | d=d, 92 | lr=lr, 93 | num_layers=num_layers, 94 | dropout=dropout, 95 | output_size=num_classes, 96 | rnn_type=rnn_type, 97 | multilabel=task == "toxic", 98 | nhead=nhead, 99 | ) 100 | 101 | trainer = pl.Trainer( 102 | logger=pl_loggers.TensorBoardLogger("lightning_logs", log_graph=False), 103 | callbacks=[EarlyStopping(monitor="val_loss", patience=5)], 104 | checkpoint_callback=ModelCheckpoint( 105 | "./checkpoints/", monitor="val_loss" 106 | ), 107 | min_epochs=2, 108 | deterministic=True, 109 | val_check_interval=0.2, 110 | gpus=list(range(torch.cuda.device_count())) 111 | if torch.cuda.is_available() 112 | else None, 113 | gradient_clip_val=1.0, 114 | accelerator="ddp" if torch.cuda.is_available() else None, 115 | precision=16 if torch.cuda.is_available() else 32, 116 | accumulate_grad_batches=2 if rnn_type == "Transformer" else 1, 117 | ) 118 | 119 | trainer.fit(model, train_dataloader, dev_dataloader) 120 | 121 | 122 | if __name__ == "__main__": 123 | 124 | train() 125 | --------------------------------------------------------------------------------