diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..7f578f1 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,24 @@ +# Check http://editorconfig.org for more information +# This is the main config file for this project: +root = true + +[*] +charset = utf-8 +end_of_line = lf +insert_final_newline = true +indent_style = space +indent_size = 2 +trim_trailing_whitespace = true + +[*.{py, pyi}] +indent_style = space +indent_size = 4 + +[Makefile] +indent_style = tab + +[*.md] +trim_trailing_whitespace = false + +[*.{diff,patch}] +trim_trailing_whitespace = false diff --git a/.github/.stale.yml b/.github/.stale.yml new file mode 100644 index 0000000..dc90e5a --- /dev/null +++ b/.github/.stale.yml @@ -0,0 +1,17 @@ +# Number of days of inactivity before an issue becomes stale +daysUntilStale: 60 +# Number of days of inactivity before a stale issue is closed +daysUntilClose: 7 +# Issues with these labels will never be considered stale +exemptLabels: + - pinned + - security +# Label to use when marking an issue as stale +staleLabel: wontfix +# Comment to post when marking an issue as stale. Set to `false` to disable +markComment: > + This issue has been automatically marked as stale because it has not had + recent activity. It will be closed if no further activity occurs. Thank you + for your contributions. +# Comment to post when closing a stale issue. Set to `false` to disable +closeComment: false diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..036bffc --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,30 @@ +--- +name: ๐Ÿ› Bug report +about: If something isn't working ๐Ÿ”ง +title: "" +labels: bug +assignees: +--- + +## ๐Ÿ› Bug Report + + + +## ๐Ÿ”ฌ How To Reproduce + +Steps to reproduce the behavior: + +1. ... + +### Environment + +- OS: [e.g. Linux / Windows / macOS] +- Python version, get it with: + +```bash +python --version +``` + +## ๐Ÿ“Ž Additional context + + diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..8f2da54 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,3 @@ +# Configuration: https://help.github.com/en/github/building-a-strong-community/configuring-issue-templates-for-your-repository + +blank_issues_enabled: false diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..7ce8c12 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,15 @@ +--- +name: ๐Ÿš€ Feature request +about: Suggest an idea for this project ๐Ÿ– +title: "" +labels: enhancement +assignees: +--- + +## ๐Ÿš€ Feature Request + + + +## ๐Ÿ“Ž Additional context + + diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000..b2ca0d9 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,25 @@ +--- +name: โ“ Question +about: Ask a question about this project ๐ŸŽ“ +title: "" +labels: question +assignees: +--- + +## Checklist + + + +- [ ] I've searched the project's [`issues`](https://github.com/monologg/KoBigBird/issues?q=is%3Aissue) + +## โ“ Question + + + +How can I [...]? + +Is it possible to [...]? + +## ๐Ÿ“Ž Additional context + + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..4dab74c --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,7 @@ +## Description + + + +## Related Issue + + diff --git a/.github/images/kobigbird-logo.png b/.github/images/kobigbird-logo.png new file mode 100644 index 0000000..6244643 Binary files /dev/null and b/.github/images/kobigbird-logo.png differ diff --git a/.github/images/memory-usage.png b/.github/images/memory-usage.png new file mode 100644 index 0000000..6dc5f3b Binary files /dev/null and b/.github/images/memory-usage.png differ diff --git a/.github/images/sparse-attention.png b/.github/images/sparse-attention.png new file mode 100644 index 0000000..a2ef111 Binary files /dev/null and b/.github/images/sparse-attention.png differ diff --git a/.github/images/time-usage.png b/.github/images/time-usage.png new file mode 100644 index 0000000..7f641c4 Binary files /dev/null and b/.github/images/time-usage.png differ diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml new file mode 100644 index 0000000..411b93f --- /dev/null +++ b/.github/release-drafter.yml @@ -0,0 +1,21 @@ +name-template: "v$RESOLVED_VERSION" +tag-template: "v$RESOLVED_VERSION" + +categories: + - title: ":rocket: Features" + labels: [enhancement, feature] + - title: ":wrench: Fixes" + labels: [bug, bugfix, fix] + - title: ":toolbox: Maintenance & Refactor" + labels: [refactor, refactoring, chore] + - title: ":package: Build System & CI/CD & Test" + labels: [build, ci, testing, test] + - title: ":pencil: Documentation" + labels: [documentation] + - title: ":arrow_up: Dependencies updates" + labels: [dependencies] + +template: | + ## Whatโ€™s Changed + + $CHANGES diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml new file mode 100644 index 0000000..0018f7f --- /dev/null +++ b/.github/workflows/linter.yml @@ -0,0 +1,32 @@ +name: linter + +on: [push] + +jobs: + check-lint: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + + - name: Cache pip + uses: actions/cache@v2 + with: + # This path is specific to Ubuntu + path: ~/.cache/pip + # Look to see if there is a cache hit for the corresponding requirements file + key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }} + restore-keys: | + ${{ runner.os }}-pip- + ${{ runner.os }}- + - name: Install dependencies + run: | + python3 -m pip install --upgrade pip + - name: Check Lint (black, flake8, isort) + run: | + make quality diff --git a/.github/workflows/release-drafter.yml b/.github/workflows/release-drafter.yml new file mode 100644 index 0000000..a052646 --- /dev/null +++ b/.github/workflows/release-drafter.yml @@ -0,0 +1,20 @@ +name: Release Drafter + +on: + push: + # branches to consider in the event; optional, defaults to all + branches: + - master + # pull_request event is required only for autolabeler + pull_request: + # Only following types are handled by the action, but one can default to all as well + types: [opened, reopened, synchronize] + +jobs: + update_release_draft: + runs-on: ubuntu-latest + steps: + # Drafts your next Release notes as Pull Requests are merged into "master" + - uses: release-drafter/release-drafter@v5.15.0 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..de30ccd --- /dev/null +++ b/.gitignore @@ -0,0 +1,135 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Custom Ignore +.vscode/ +.idea/ +.DS_Store +test.ipynb diff --git a/.gitmessage b/.gitmessage new file mode 100644 index 0000000..eafd7d4 --- /dev/null +++ b/.gitmessage @@ -0,0 +1,16 @@ +# Title: Summary, imperative, don't end with a period +# No more than 50 chars. #### 50 chars is here: # + +# Remember blank line between title and body. + +# Body: Explain *what* and *why* (not *how*). +# Wrap at 72 chars. ################################## which is here: # + +# feat : ๊ธฐ๋Šฅ (์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ) +# fix : ๋ฒ„๊ทธ (๋ฒ„๊ทธ ์ˆ˜์ •) +# refactor: ๋ฆฌํŒฉํ† ๋ง +# style : ์Šคํƒ€์ผ (์ฝ”๋“œ ํ˜•์‹, ์„ธ๋ฏธ์ฝœ๋ก  ์ถ”๊ฐ€: ๋น„์ฆˆ๋‹ˆ์Šค ๋กœ์ง์— ๋ณ€๊ฒฝ ์—†์Œ) +# docs : ๋ฌธ์„œ (๋ฌธ์„œ ์ถ”๊ฐ€, ์ˆ˜์ •, ์‚ญ์ œ) +# dep : ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ์˜์กด์„ฑ (๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ์ถ”๊ฐ€, ๋ฒ„์ „ ์ˆ˜์ •) +# test : ํ…Œ์ŠคํŠธ (ํ…Œ์ŠคํŠธ ์ฝ”๋“œ ์ถ”๊ฐ€, ์ˆ˜์ •, ์‚ญ์ œ: ๋น„์ฆˆ๋‹ˆ์Šค ๋กœ์ง์— ๋ณ€๊ฒฝ ์—†์Œ) +# chore : ๊ธฐํƒ€ ๋ณ€๊ฒฝ์‚ฌํ•ญ (๋นŒ๋“œ ์Šคํฌ๋ฆฝํŠธ ์ˆ˜์ • ๋“ฑ) diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..3e6c413 --- /dev/null +++ b/Makefile @@ -0,0 +1,33 @@ +clean: clean-pyc clean-test +quality: set-style-dep check-quality +style: set-style-dep set-style + +##### basic ##### +set-git: + git config --local commit.template .gitmessage + +set-style-dep: + pip3 install isort==5.9.3 black==21.7b0 flake8==3.9.2 + +set-style: + black --config pyproject.toml . + isort --settings-path pyproject.toml . + flake8 . + +check-quality: + black --config pyproject.toml --check . + isort --settings-path pyproject.toml --check-only . + flake8 . + +##### clean ##### +clean-pyc: + find . -name '*.pyc' -exec rm -f {} + + find . -name '*.pyo' -exec rm -f {} + + find . -name '*~' -exec rm -f {} + + find . -name '__pycache__' -exec rm -fr {} + + +clean-test: + rm -f .coverage + rm -f .coverage.* + rm -rf .pytest_cache + rm -rf .mypy_cache diff --git a/README.md b/README.md new file mode 100644 index 0000000..e887d3e --- /dev/null +++ b/README.md @@ -0,0 +1,122 @@ +
+ + + +

Pretrained BigBird Model for Korean

+ +

+ What is BigBird โ€ข + How to Use โ€ข + Pretraining โ€ข + Evaluation Result โ€ข + Docs โ€ข + Citation +

+ +

+ ํ•œ๊ตญ์–ด | + English +

+ +

+ + Apache 2.0 + + + Issues + + + linter + +

+ +
+ +## What is BigBird? + + + +[BigBird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062)์—์„œ ์†Œ๊ฐœ๋œ **sparse-attention** ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ๋กœ, ์ผ๋ฐ˜์ ์ธ BERT๋ณด๋‹ค **๋” ๊ธด sequence**๋ฅผ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. + +๐Ÿฆ… **Longer Sequence** - ์ตœ๋Œ€ 512๊ฐœ์˜ token์„ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋Š” BERT์˜ 8๋ฐฐ์ธ **์ตœ๋Œ€ 4096๊ฐœ์˜ token**์„ ๋‹ค๋ฃธ + +โฑ๏ธ **Computational Efficiency** - Full attention์ด ์•„๋‹Œ **Sparse Attention**์„ ์ด์šฉํ•˜์—ฌ O(n2)์—์„œ O(n)์œผ๋กœ ๊ฐœ์„  + +## How to Use + +- ๐Ÿค— [Huggingface Hub](https://huggingface.co/monologg/kobigbird-bert-base)์— ์—…๋กœ๋“œ๋œ ๋ชจ๋ธ์„ ๊ณง๋ฐ”๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:) +- ์ผ๋ถ€ ์ด์Šˆ๊ฐ€ ํ•ด๊ฒฐ๋œ `transformers>=4.11.0` ์‚ฌ์šฉ์„ ๊ถŒ์žฅํ•ฉ๋‹ˆ๋‹ค. ([MRC ์ด์Šˆ ๊ด€๋ จ PR](https://github.com/huggingface/transformers/pull/13143)) +- **BigBirdTokenizer ๋Œ€์‹ ์— `BertTokenizer` ๋ฅผ ์‚ฌ์šฉํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. (`AutoTokenizer` ์‚ฌ์šฉ์‹œ `BertTokenizer`๊ฐ€ ๋กœ๋“œ๋ฉ๋‹ˆ๋‹ค.)** +- ์ž์„ธํ•œ ์‚ฌ์šฉ๋ฒ•์€ [BigBird Tranformers documentation](https://huggingface.co/transformers/model_doc/bigbird.html)์„ ์ฐธ๊ณ ํ•ด์ฃผ์„ธ์š”. + +```python +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained("monologg/kobigbird-bert-base") # BigBirdModel +tokenizer = AutoTokenizer.from_pretrained("monologg/kobigbird-bert-base") # BertTokenizer +``` + +## Pretraining + +> ์ž์„ธํ•œ ๋‚ด์šฉ์€ [[Pretraining BigBird]](pretrain/README.md) ์ฐธ๊ณ  + +| | Hardware | Max len | LR | Batch | Train Step | Warmup Step | +| :---------------------- | -------: | ------: | ---: | ----: | ---------: | ----------: | +| **KoBigBird-BERT-Base** | TPU v3-8 | 4096 | 1e-4 | 32 | 2M | 20k | + +- ๋ชจ๋‘์˜ ๋ง๋ญ‰์น˜, ํ•œ๊ตญ์–ด ์œ„ํ‚ค, Common Crawl, ๋‰ด์Šค ๋ฐ์ดํ„ฐ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ๋กœ ํ•™์Šต +- `ITC (Internal Transformer Construction)` ๋ชจ๋ธ๋กœ ํ•™์Šต ([ITC vs ETC](https://huggingface.co/blog/big-bird#itc-vs-etc)) + +## Evaluation Result + +### 1. Short Sequence (<=512) + +> ์ž์„ธํ•œ ๋‚ด์šฉ์€ [[Finetune on Short Sequence Dataset]](docs/short_seq_evaluation_ko.md) ์ฐธ๊ณ  + +| | NSMC
(acc) | KLUE-NLI
(acc) | KLUE-STS
(pearsonr) | Korquad 1.0
(em/f1) | KLUE MRC
(em/rouge-w) | +| :---------------------- | :-----------: | :---------------: | :--------------------: | :--------------------: | :----------------------: | +| KoELECTRA-Base-v3 | 91.13 | 86.87 | **93.14** | 85.66 / 93.94 | 59.54 / 65.64 | +| KLUE-RoBERTa-Base | 91.16 | 86.30 | 92.91 | 85.35 / 94.53 | 69.56 / 74.64 | +| **KoBigBird-BERT-Base** | **91.18** | **87.17** | 92.61 | **87.08 / 94.71** | **70.33 / 75.34** | + +### 2. Long Sequence (>=1024) + +> ์ž์„ธํ•œ ๋‚ด์šฉ์€ [[Finetune on Long Sequence Dataset]](finetune/README.md) ์ฐธ๊ณ  + +| | TyDi QA
(em/f1) | Korquad 2.1
(em/f1) | Fake News
(f1) | Modu Sentiment
(f1-macro) | +| :---------------------- | :-----------------: | :---------------------: | :----------------: | :---------------------------: | +| KLUE-RoBERTa-Base | 76.80 / 78.58 | 55.44 / 73.02 | 95.20 | 42.61 | +| **KoBigBird-BERT-Base** | **79.13 / 81.30** | **67.77 / 82.03** | **98.85** | **45.42** | + +## Docs + +- [Pretraing BigBird](pretrain/README.md) +- [Finetune on Short Sequence Dataset](docs/short_seq_evaluation_ko.md) +- [Finetune on Long Sequence Dataset](finetune/README.md) +- [Download Tensorflow v1 checkpoint](docs/download_tfv1_ckpt.md) +- [GPU Benchmark result](docs/gpu_benchmark.md) + +## Citation + +KoBigBird๋ฅผ ์‚ฌ์šฉํ•˜์‹ ๋‹ค๋ฉด ์•„๋ž˜์™€ ๊ฐ™์ด ์ธ์šฉํ•ด์ฃผ์„ธ์š”. + +```bibtex +@misc{park2021kobigbird, + author = {Jangwon Park, Donggyu Kim}, + title = {KoBigBird: Pretrained BigBird Model for Korean}, + year = {2021}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/monologg/KoBigBird}} +} +``` + +## Contributors + +[Jangwon Park](https://github.com/monologg) and [Donggyu Kim](https://github.com/donggyukimc) + +## Acknowledgements + +KoBigBird๋Š” Tensorflow Research Cloud (TFRC) ํ”„๋กœ๊ทธ๋žจ์˜ Cloud TPU ์ง€์›์œผ๋กœ ์ œ์ž‘๋˜์—ˆ์Šต๋‹ˆ๋‹ค. + +๋˜ํ•œ ๋ฉ‹์ง„ ๋กœ๊ณ ๋ฅผ ์ œ๊ณตํ•ด์ฃผ์‹  [Seyun Ahn](https://www.instagram.com/ahnsy13)๋‹˜๊ป˜ ๊ฐ์‚ฌ๋ฅผ ์ „ํ•ฉ๋‹ˆ๋‹ค. diff --git a/README_EN.md b/README_EN.md new file mode 100644 index 0000000..9d0e962 --- /dev/null +++ b/README_EN.md @@ -0,0 +1,122 @@ +
+ + + +

Pretrained BigBird Model for Korean

+ +

+ What is BigBird โ€ข + How to Use โ€ข + Pretraining โ€ข + Evaluation Result โ€ข + Docs โ€ข + Citation +

+ +

+ ํ•œ๊ตญ์–ด | + English +

+ +

+ + Apache 2.0 + + + Issues + + + linter + +

+ +
+ +## What is BigBird? + + + +[BigBird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) is a **sparse-attention** based model that can handle **longer sequences** than a normal BERT. + +๐Ÿฆ… **Longer Sequence** - Handles **up to 4096 tokens**, 8 times the BERT, which can handle up to 512 tokens + +โฑ๏ธ **Computational Efficiency** - Improved from O(n2) to O(n) using **Sparse Attention** instead of Full Attention + +## How to Use + +- Available on the ๐Ÿค— [Huggingface Hub](https://huggingface.co/monologg/kobigbird-bert-base)! +- Recommend to use `transformers>=4.11.0`, which some issues are fixed ([PR related with MRC issue](https://github.com/huggingface/transformers/pull/13143)) +- **You have to use `BertTokenizer` instead of BigBirdTokenizer (`BertTokenizer` will be loaded if you use `AutoTokenizer`)** +- For detail guideline, see [BigBird Tranformers documentation](https://huggingface.co/transformers/model_doc/bigbird.html). + +```python +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained("monologg/kobigbird-bert-base") # BigBirdModel +tokenizer = AutoTokenizer.from_pretrained("monologg/kobigbird-bert-base") # BertTokenizer +``` + +## Pretraining + +> For more information, see [[Pretraining BigBird]](pretrain/README_EN.md) + +| | Hardware | Max len | LR | Batch | Train Step | Warmup Step | +| :---------------------- | -------: | ------: | ---: | ----: | ---------: | ----------: | +| **KoBigBird-BERT-Base** | TPU v3-8 | 4096 | 1e-4 | 32 | 2M | 20k | + +- Trained with various data such as Everyone's Corpus, Korean Wiki, Common Crawl, and news data +- Use `ITC (Internal Transformer Construction)` model for pretraining. ([ITC vs ETC](https://huggingface.co/blog/big-bird#itc-vs-etc)) + +## Evaluation Result + +### 1. Short Sequence (<=512) + +> For more information, see [[Finetune on Short Sequence Dataset]](docs/short_seq_evaluation_en.md) + +| | NSMC
(acc) | KLUE-NLI
(acc) | KLUE-STS
(pearsonr) | Korquad 1.0
(em/f1) | KLUE MRC
(em/rouge-w) | +| :---------------------- | :-----------: | :---------------: | :--------------------: | :--------------------: | :----------------------: | +| KoELECTRA-Base-v3 | 91.13 | 86.87 | **93.14** | 85.66 / 93.94 | 59.54 / 65.64 | +| KLUE-RoBERTa-Base | 91.16 | 86.30 | 92.91 | 85.35 / 94.53 | 69.56 / 74.64 | +| **KoBigBird-BERT-Base** | **91.18** | **87.17** | 92.61 | **87.08 / 94.71** | **70.33 / 75.34** | + +### 2. Long Sequence (>=1024) + +> For more information, see [[Finetune on Long Sequence Dataset]](finetune/README_EN.md) + +| | TyDi QA
(em/f1) | Korquad 2.1
(em/f1) | Fake News
(f1) | Modu Sentiment
(f1-macro) | +| :---------------------- | :-----------------: | :---------------------: | :----------------: | :---------------------------: | +| KLUE-RoBERTa-Base | 76.80 / 78.58 | 55.44 / 73.02 | 95.20 | 42.61 | +| **KoBigBird-BERT-Base** | **79.13 / 81.30** | **67.77 / 82.03** | **98.85** | **45.42** | + +## Docs + +- [Pretraing BigBird](pretrain/README_EN.md) +- [Finetune on Short Sequence Dataset](docs/short_seq_evaluation_en.md) +- [Finetune on Long Sequence Dataset](finetune/README_EN.md) +- [Download Tensorflow v1 checkpoint](docs/download_tfv1_ckpt.md) +- [GPU Benchmark result](docs/gpu_benchmark.md) + +## Citation + +If you apply KoBigBird to any project and research, please cite our code as below. + +```bibtex +@misc{park2021kobigbird, + author = {Jangwon Park, Donggyu Kim}, + title = {KoBigBird: Pretrained BigBird Model for Korean}, + year = {2021}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/monologg/KoBigBird}} +} +``` + +## Contributors + +[Jangwon Park](https://github.com/monologg) and [Donggyu Kim](https://github.com/donggyukimc) + +## Acknowledgements + +KoBigBird is built with Cloud TPU support from the Tensorflow Research Cloud (TFRC) program. + +Also, thanks to [Seyun Ahn](https://www.instagram.com/ahnsy13) for a nice logo:) diff --git a/docs/download_tfv1_ckpt.md b/docs/download_tfv1_ckpt.md new file mode 100644 index 0000000..0ebe5e0 --- /dev/null +++ b/docs/download_tfv1_ckpt.md @@ -0,0 +1,9 @@ +# Download Tensorflow v1 checkpoint + +We've upload tensorflow v1 checkpoint on [huggingface hub](https://huggingface.co/monologg/kobigbird-bert-base/tree/tfv1). + +Please download the checkpoint with the command below:) + +```bash +wget https://huggingface.co/monologg/kobigbird-bert-base/resolve/tfv1/kobigbird-bert-base-tf1.tar.gz +``` diff --git a/docs/gpu_benchmark.md b/docs/gpu_benchmark.md new file mode 100644 index 0000000..3fd886f --- /dev/null +++ b/docs/gpu_benchmark.md @@ -0,0 +1,32 @@ +# GPU Benchmark result + +`GPU Memory` & `Inference Time` check according to **sequence length** + +## Environment Info + +- transformers_version: 4.11.3 +- framework: PyTorch +- use_torchscript: False +- framework_version: 1.7.1 +- python_version: 3.6.13 +- system: Linux +- cpu: x86_64 +- architecture: 64bit +- fp16: False +- use_multiprocessing: True +- only_pretrain_model: False +- cpu_ram_mb: 15717 +- use_gpu: True +- num_gpus: 1 +- gpu: Tesla T4 +- gpu_ram_mb: 15109 +- gpu_power_watts: 70.0 +- gpu_performance_state: 0 +- use_tpu: False + +## Benchmark result + +
+ + +
diff --git a/docs/short_seq_evaluation_en.md b/docs/short_seq_evaluation_en.md new file mode 100644 index 0000000..8970009 --- /dev/null +++ b/docs/short_seq_evaluation_en.md @@ -0,0 +1,50 @@ +# Finetune on Short Sequence Dataset + +

+ ํ•œ๊ตญ์–ด | + English +

+ +## Details + +- KoBigBird performance evaluation in `max_seq_length<=512` setting + +- Evaluated with a total of **5 Datasets** + + - Single Sentence Classification: `NSMC` + - Sentence Pair Classification: `KLUE-NLI`, `KLUE-STS` + - Question Answering: `Korquad 1.0`, `KLUE-MRC` + +- **Based on the [KLUE-Baseline](https://github.com/KLUE-benchmark/KLUE-baseline) code with some modifications** + + - Add `nsmc` and `korquad 1.0` tasks + - Fix to be compatible with `transformers==4.11.3` + +- Sequence Classification is trained with a length of **128** and Question Answering with a length of **512** + + - **Full Attention** instead of Sparse Attention (Automatically changed to Full Attention with the following log) + + ```text + Attention type 'block_sparse' is not possible if sequence_length: 300 <= num global tokens: 2 * config.block_size + min. num sliding tokens: 3 * config.block_size + + config.num_random_blocks * config.block_size + additional buffer: config.num_random_blocks * config.block_size = 704 with config.block_size = 64, config.num_random_blocks = 3. + Changing attention type to 'original_full'... + ``` + +## Result + +| | NSMC
(acc) | KLUE-NLI
(acc) | KLUE-STS
(pearsonr) | Korquad 1.0
(em/f1) | KLUE MRC
(em/rouge-w) | +| :---------------------- | :-----------: | :---------------: | :--------------------: | :--------------------: | :----------------------: | +| KoELECTRA-Base-v3 | 91.13 | 86.87 | **93.14** | 85.66 / 93.94 | 59.54 / 65.64 | +| KLUE-RoBERTa-Base | 91.16 | 86.30 | 92.91 | 85.35 / 94.53 | 69.56 / 74.64 | +| **KoBigBird-BERT-Base** | **91.18** | **87.17** | 92.61 | **87.08 / 94.71** | **70.33 / 75.34** | + +- `KLUE` and `Korquad 1.0` are evaluated with **dev set**. +- For `KoELECTRA-Base-v3` and `KLUE-RoBERTa-Base`, we brought the KLUE dataset score from `A. Dev Set Results` in [KLUE Paper](https://arxiv.org/abs/2105.09680). + +## Reference + +- [NSMC](https://github.com/e9t/nsmc) +- [KLUE](https://github.com/KLUE-benchmark/KLUE) +- [Korquad 1.0](https://korquad.github.io/KorQuad%201.0/) +- [KoELECTRA-Base-v3](https://huggingface.co/monologg/koelectra-base-v3-discriminator) +- [KLUE-RoBERTa-Base](https://huggingface.co/klue/roberta-base) diff --git a/docs/short_seq_evaluation_ko.md b/docs/short_seq_evaluation_ko.md new file mode 100644 index 0000000..d9f0459 --- /dev/null +++ b/docs/short_seq_evaluation_ko.md @@ -0,0 +1,50 @@ +# Finetune on Short Sequence Dataset + +

+ ํ•œ๊ตญ์–ด | + English +

+ +## Details + +- `max_seq_length<=512` ํ™˜๊ฒฝ์—์„œ์˜ KoBigBird ์„ฑ๋Šฅ ํ‰๊ฐ€ + +- ์ด **5๊ฐœ์˜ Dataset**์œผ๋กœ ํ‰๊ฐ€ + + - Single Sentence Classification: `NSMC` + - Sentence Pair Classification: `KLUE-NLI`, `KLUE-STS` + - Question Answering: `Korquad 1.0`, `KLUE-MRC` + +- **[KLUE-Baseline](https://github.com/KLUE-benchmark/KLUE-baseline)์˜ ์ฝ”๋“œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ผ๋ถ€ ์ˆ˜์ •ํ•˜์—ฌ ํ•™์Šต** + + - `nsmc`์™€ `korquad 1.0` task ์ถ”๊ฐ€ + - `transformers==4.11.3`์— ํ˜ธํ™˜๋˜๋„๋ก ์ˆ˜์ • + +- Sequence Classification์€ **128**, Question Answering์€ **512**์˜ ๊ธธ์ด๋กœ ํ•™์Šต + + - Sparse Attention์ด ์•„๋‹Œ **Full Attention**์œผ๋กœ ์„ธํŒ… (์•„๋ž˜์˜ ๋กœ๊ทธ๊ฐ€ ๋‚˜์˜ค๋ฉด์„œ ์ž๋™์œผ๋กœ Full Attention์œผ๋กœ ๋ณ€๊ฒฝ) + + ```text + Attention type 'block_sparse' is not possible if sequence_length: 300 <= num global tokens: 2 * config.block_size + min. num sliding tokens: 3 * config.block_size + + config.num_random_blocks * config.block_size + additional buffer: config.num_random_blocks * config.block_size = 704 with config.block_size = 64, config.num_random_blocks = 3. + Changing attention type to 'original_full'... + ``` + +## Result + +| | NSMC
(acc) | KLUE-NLI
(acc) | KLUE-STS
(pearsonr) | Korquad 1.0
(em/f1) | KLUE MRC
(em/rouge-w) | +| :---------------------- | :-----------: | :---------------: | :--------------------: | :--------------------: | :----------------------: | +| KoELECTRA-Base-v3 | 91.13 | 86.87 | **93.14** | 85.66 / 93.94 | 59.54 / 65.64 | +| KLUE-RoBERTa-Base | 91.16 | 86.30 | 92.91 | 85.35 / 94.53 | 69.56 / 74.64 | +| **KoBigBird-BERT-Base** | **91.18** | **87.17** | 92.61 | **87.08 / 94.71** | **70.33 / 75.34** | + +- `KLUE`, `Korquad 1.0` ๋ชจ๋‘ **dev set**์œผ๋กœ ํ‰๊ฐ€ +- `KoELECTRA-Base-v3`์™€ `KLUE-RoBERTa-Base`์˜ KLUE dataset ๊ด€๋ จ ์ ์ˆ˜๋Š” [KLUE Paper](https://arxiv.org/abs/2105.09680)์˜ `A. Dev Set Results`์—์„œ ์ฐธ๊ณ  + +## Reference + +- [NSMC](https://github.com/e9t/nsmc) +- [KLUE](https://github.com/KLUE-benchmark/KLUE) +- [Korquad 1.0](https://korquad.github.io/KorQuad%201.0/) +- [KoELECTRA-Base-v3](https://huggingface.co/monologg/koelectra-base-v3-discriminator) +- [KLUE-RoBERTa-Base](https://huggingface.co/klue/roberta-base) diff --git a/finetune/.gitignore b/finetune/.gitignore new file mode 100644 index 0000000..6b3c714 --- /dev/null +++ b/finetune/.gitignore @@ -0,0 +1,5 @@ +cache/ +ckpt/ +output/ +seq2seq-model/ +*.out diff --git a/finetune/README.md b/finetune/README.md new file mode 100644 index 0000000..d2b985d --- /dev/null +++ b/finetune/README.md @@ -0,0 +1,78 @@ +# Finetune on Long Sequence Dataset + +

+ ํ•œ๊ตญ์–ด | + English +

+ +## About Dataset + +| Dataset | Task | Length (median) | Length (max) | +| ------------------ | ----------------------- | --------------: | -----------: | +| **TyDi QA** | Question Answering | 6,165 | 67,135 | +| **Korquad 2.1** | Question Answering | 5,777 | 486,730 | +| **Fake News** | Sequence Classification | 564 | 17,488 | +| **Modu Sentiment** | Sequence Classification | 185 | 5,245 | + +- `Length`๋Š” subword token์„ ๊ธฐ์ค€์œผ๋กœ ๊ณ„์‚ฐํ–ˆ์Šต๋‹ˆ๋‹ค. +- [TyDi QA](https://github.com/google-research-datasets/tydiqa)๋Š” ๋ณธ๋ž˜ `๋‹ค๊ตญ์–ด(multilingual) ๋ฐ์ดํ„ฐ์…‹`์ด๋ฉฐ `์˜ˆ-์•„๋‹ˆ์˜ค (BoolQA)` ๋‹ต๋ณ€ ๋ฐ์ดํ„ฐ๋ฅผ ํฌํ•จํ•ฉ๋‹ˆ๋‹ค. **๋ณธ ํ”„๋กœ์ ํŠธ์—์„œ๋Š” ํ•œ๊ตญ์–ด ๋ฐ์ดํ„ฐ์…‹๋งŒ์„ ์‚ฌ์šฉํ–ˆ์œผ๋ฉฐ, ์˜ˆ-์•„๋‹ˆ์˜ค ๋‹ต๋ณ€ ๋ฐ์ดํ„ฐ ๋˜ํ•œ ์ œ์™ธํ•˜์˜€์Šต๋‹ˆ๋‹ค.** + +## Setup + +### 1. Requirements + +```bash +pip3 install -r requirements.txt +``` + +### 2. Prepare Dataset + +#### 1) Question Answering + +```bash +bash download_qa_dataset.sh +``` + +#### 2) Sequence Classification + +- **์•„๋ž˜์˜ ๋งํฌ๋ฅผ ํ†ตํ•ด ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค์šด๋กœ๋“œ ํ›„, ๋ฐ์ดํ„ฐ๋ฅผ `--data_dir` ๊ฒฝ๋กœ์— ์œ„์น˜์‹œ์ผœ ์ฃผ์„ธ์š”.** +- `Fake news`: [Korean Fake news](https://github.com/2alive3s/Fake_news/blob/b43638105f4802de5773c21afe539157ebed6cc5/data/mission2_train.zip) (`mission2_train.csv`) +- `Modu sentiment corpus`: [๊ฐ์„ฑ ๋ถ„์„ ๋ง๋ญ‰์น˜ 2020](https://corpus.korean.go.kr) (`EXSA2002108040.json`) + +## How to Run + +- ํฐ ๊ทœ๋ชจ์˜ Long sequence ๋ฐ์ดํ„ฐ์…‹์„ ํ•™์Šต์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ **TPU ์ธ์Šคํ„ด์Šค**์—์„œ ์‹คํ–‰ํ•˜๋Š” ๊ฒƒ์„ ๊ถŒ์žฅํ•ฉ๋‹ˆ๋‹ค. +- ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋Š” ๋ชจ๋‘ [torch-xla-1.8.1](https://github.com/pytorch/xla#-consume-prebuilt-compute-vm-images) ํ™˜๊ฒฝ์—์„œ `TPU v3-8`์„ ์ด์šฉํ•˜์—ฌ ํ•™์Šต ๋ฐ ํ‰๊ฐ€ํ–ˆ์Šต๋‹ˆ๋‹ค. +- TPU๊ฐ€ ์•„๋‹Œ GPU๋กœ ํ•™์Šตํ•˜๊ณ  ์‹ถ์„ ์‹œ ์Šคํฌ๋ฆฝํŠธ ์•ˆ์˜ `--use_tpu` ์ธ์ž๋ฅผ ์ œ์™ธํ•˜๋ฉด ๋ฉ๋‹ˆ๋‹ค. + +```bash +bash scripts/run_{$TASK_NAME}.sh # kobigbird +bash scripts/run_{$TASK_NAME}_short.sh # klue roberta +``` + +```bash +bash scripts/run_tydiqa.sh # tydiqa +bash scripts/run_korquad_2.sh # korquad 2.1 +bash scripts/run_fake_news.sh # fake news +bash scripts/run_modu_sentiment.sh # modu sentiment +``` + +## Results + +- Sequence Classification์˜ ๊ฒฝ์šฐ `train:test=8:2` ๋กœ splitํ•˜์—ฌ ํ‰๊ฐ€ํ–ˆ์Šต๋‹ˆ๋‹ค. +- Korquad 2.1 ๋ฐ์ดํ„ฐ์…‹์˜ ๊ฒฝ์šฐ, ์ปดํ“จํŒ… ์ž์›์˜ ํ•œ๊ณ„๋กœ **ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹์˜ ์ผ๋ถ€๋งŒ์œผ๋กœ ๋ชจ๋ธ์„ ํ•™์Šต**ํ–ˆ์Šต๋‹ˆ๋‹ค. + - `--all_korquad_2_sample` ์ธ์ž๋ฅผ ์Šคํฌ๋ฆฝํŠธ์— ์ถ”๊ฐ€ํ•˜๋ฉด ์ „์ฒด ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•™์Šต ๊ฐ€๋Šฅ +- `KoBigBird`์˜ ๊ฒฝ์šฐ Question Answering์€ **4096**, Sequence Classification์€ **1024**์˜ ๊ธธ์ด๋กœ ํ•™์Šตํ–ˆ์Šต๋‹ˆ๋‹ค. +- `KLUE RoBERTa`๋Š” **512**์˜ ๊ธธ์ด๋กœ ํ•™์Šตํ–ˆ์Šต๋‹ˆ๋‹ค. + +| | TyDi QA
(em/f1) | Korquad 2.1
(em/f1) | Fake News
(f1) | Modu Sentiment
(f1-macro) | +| :---------------------- | :-----------------: | :---------------------: | :----------------: | :---------------------------: | +| KLUE-RoBERTa-Base | 76.80 / 78.58 | 55.44 / 73.02 | 95.20 | 42.61 | +| **KoBigBird-BERT-Base** | **79.13 / 81.30** | **67.77 / 82.03** | **98.85** | **45.42** | + +## Reference + +- [TyDi QA](https://github.com/google-research-datasets/tydiqa) +- [Korquad](https://korquad.github.io/) +- [Korean Fake news](https://github.com/2alive3s/Fake_news) +- [๋ชจ๋‘์˜ ๋ง๋ญ‰์น˜](https://corpus.korean.go.kr/) diff --git a/finetune/README_EN.md b/finetune/README_EN.md new file mode 100644 index 0000000..b7ddaf8 --- /dev/null +++ b/finetune/README_EN.md @@ -0,0 +1,78 @@ +# Finetune on Long Sequence Dataset + +

+ ํ•œ๊ตญ์–ด | + English +

+ +## About Dataset + +| Dataset | Task | Length (median) | Length (max) | +| ------------------ | ----------------------- | --------------: | -----------: | +| **TyDi QA** | Question Answering | 6,165 | 67,135 | +| **Korquad 2.1** | Question Answering | 5,777 | 486,730 | +| **Fake News** | Sequence Classification | 564 | 17,488 | +| **Modu Sentiment** | Sequence Classification | 185 | 5,245 | + +- `Length` is calculated based on subword token. +- [TyDi QA](https://github.com/google-research-datasets/tydiqa) is originally `multilingual` and contains `BoolQA` cases. **We only use korean samples and skip BoolQA samples.** + +## Setup + +### 1. Requirements + +```bash +pip3 install -r requirements.txt +``` + +### 2. Prepare Dataset + +#### 1) Question Answering + +```bash +bash download_qa_dataset.sh +``` + +#### 2) Sequence Classification + +- **After downloading the data through the link below, place the data in the `--data_dir` path.** +- `Fake news`: [Korean Fake news](https://github.com/2alive3s/Fake_news/blob/b43638105f4802de5773c21afe539157ebed6cc5/data/mission2_train.zip) (`mission2_train.csv`) +- `Modu sentiment corpus`: [๊ฐ์„ฑ ๋ถ„์„ ๋ง๋ญ‰์น˜ 2020](https://corpus.korean.go.kr) (`EXSA2002108040.json`) + +## How to Run + +- We highly recommend to run the scripts on **TPU instance** in order to train and evaluate large and long-sequence datasets. +- We trained and evaluated the models on the [torch-xla-1.8.1](https://github.com/pytorch/xla#-consume-prebuilt-compute-vm-images) environment with `TPU v3-8`. +- Disable `--use_tpu` argument for GPU training. + +```bash +bash scripts/run_{$TASK_NAME}.sh # kobigbird +bash scripts/run_{$TASK_NAME}_short.sh # klue roberta +``` + +```bash +bash scripts/run_tydiqa.sh # tydiqa +bash scripts/run_korquad_2.sh # korquad 2.1 +bash scripts/run_fake_news.sh # fake news +bash scripts/run_modu_sentiment.sh # modu sentiment +``` + +## Results + +- In the case of sequence classification, it was evaluated by splitting `train:test=8:2`. +- For `korquad 2.1`, we **only use the subset of the train dataset** because of limited computational resources. + - Enable `--all_korquad_2_sample` argument in order to use full train dataset. +- In the case of `KoBigBird`, question answering was trained with a length of **4096** and sequence classification was trained with a length of **1024**. +- `KLUE RoBERTa` was trained with a length of **512**. + +| | TyDi QA
(em/f1) | Korquad 2.1
(em/f1) | Fake News
(f1) | Modu Sentiment
(f1-macro) | +| :---------------------- | :-----------------: | :---------------------: | :----------------: | :---------------------------: | +| KLUE-RoBERTa-Base | 76.80 / 78.58 | 55.44 / 73.02 | 95.20 | 42.61 | +| **KoBigBird-BERT-Base** | **79.13 / 81.30** | **67.77 / 82.03** | **98.85** | **45.42** | + +## Reference + +- [TyDi QA](https://github.com/google-research-datasets/tydiqa) +- [Korquad](https://korquad.github.io/) +- [Korean Fake news](https://github.com/2alive3s/Fake_news) +- [๋ชจ๋‘์˜ ๋ง๋ญ‰์น˜](https://corpus.korean.go.kr/) diff --git a/finetune/data/__init__.py b/finetune/data/__init__.py new file mode 100644 index 0000000..dc52b1c --- /dev/null +++ b/finetune/data/__init__.py @@ -0,0 +1 @@ +from data.common import get_data # noqa diff --git a/finetune/data/cls.py b/finetune/data/cls.py new file mode 100644 index 0000000..30c7c97 --- /dev/null +++ b/finetune/data/cls.py @@ -0,0 +1,147 @@ +import json + +import numpy as np +import pandas as pd +import torch +from datasets import load_dataset +from sklearn.model_selection import train_test_split + + +def sample_writer(data, config, tokenizer, is_train): + feature = tokenizer( + data["text"], + max_length=config.max_seq_length, + padding="max_length", + truncation=True, + add_special_tokens=True, + ) + write_data = { + "input_ids": feature["input_ids"], + "attention_mask": feature["attention_mask"], + "labels": data["label"], + } + return write_data + + +def make_label_map(labels): + unique_labels = sorted(list(set(labels))) + label2id = dict() + for i, label in enumerate(unique_labels): + label2id[label] = i + return label2id + + +def postprocess(): + def decorator(fn): + def wrapped(config, data_file, is_train, **kwargs): + get_label_map = kwargs.get("get_label_map", False) + texts, labels = fn(config, data_file, is_train) + + try: + label2id = config.label2id + except Exception: + label2id = label2id = make_label_map(labels) + + labels = [label2id[label] for label in labels] + + if get_label_map: + return label2id + + data = [{"text": text, "label": label} for text, label in zip(texts, labels)] + pd.DataFrame(data).to_csv( + "{}_{}_{}.csv".format(data_file, config.dataset, "train" if is_train else "valid"), + index=False, + encoding="utf-8-sig", + ) + if is_train: + pd.DataFrame(list(label2id.items()), columns=["label", "id"]).to_csv( + "{}_{}_label2id.csv".format(data_file, config.dataset), index=False, encoding="utf-8-sig" + ) + + return data + + return wrapped + + return decorator + + +def train_split(config, texts, labels, is_train): + x_train, y_train, x_label, y_label = train_test_split( + texts, labels, test_size=0.2, random_state=config.seed, stratify=labels + ) + if is_train: + texts, labels = x_train, x_label + else: + texts, labels = y_train, y_label + return texts, labels + + +@postprocess() +def process_fake_news_cls(config, data_file, is_train): + df = pd.read_csv(data_file) + try: + labels = df["Label"].astype(str).values.tolist() + except Exception: + labels = df["label"].astype(str).values.tolist() + texts = [ + title + " " + content + for title, content in zip(df["title"].astype(str).values.tolist(), df["content"].astype(str).values.tolist()) + ] + texts, labels = train_split(config, texts, labels, is_train) + return texts, labels + + +@postprocess() +def process_aihub_sentiment(config, data_file, is_train): + with open(data_file) as handle: + data = json.load(handle) + texts = [" ".join([str(v) for _, v in datum["talk"]["content"].items()]) for datum in data] + labels = [datum["profile"]["emotion"]["type"] for datum in data] + return texts, labels + + +@postprocess() +def process_modu_sentiment(config, data_file, is_train): + with open(data_file) as handle: + data = json.load(handle)["document"] + texts, labels = [], [] + for datum in data: + texts.append(" ".join(paragraph["paragraph_form"] for paragraph in datum["paragraph"])) + labels.append(datum["document_score"]) + texts, labels = train_split(config, texts, labels, is_train) + return texts, labels + + +@postprocess() +def process_nsmc(config, data_file, is_train): + data = load_dataset("nsmc", cache_dir=config.cache_dir) + data = data[("train" if is_train else "test")] + texts, labels = [], [] + for datum in data: + labels.append(datum.pop("label")) + texts.append(datum.pop("document")) + return texts, labels + + +process_map = { + "fake_news": process_fake_news_cls, + "aihub_sentiment": process_aihub_sentiment, + "modu_sentiment": process_modu_sentiment, + "nsmc": process_nsmc, +} + + +def collate_fn(features): + input_ids = [sample["input_ids"] for sample in features] + attention_mask = [sample["attention_mask"] for sample in features] + labels = [sample["labels"] for sample in features] + + input_ids = torch.tensor(np.array(input_ids).astype(np.int64), dtype=torch.long) + attention_mask = torch.tensor(np.array(attention_mask).astype(np.int8), dtype=torch.long) + labels = torch.tensor(np.array(labels).astype(np.int64), dtype=torch.long) + inputs = { + "input_ids": input_ids, + "attention_mask": attention_mask, + "labels": labels, + } + return inputs diff --git a/finetune/data/common.py b/finetune/data/common.py new file mode 100644 index 0000000..d19d203 --- /dev/null +++ b/finetune/data/common.py @@ -0,0 +1,188 @@ +import json +import logging +import multiprocessing +import os + +import torch +import torch.utils.data as torch_data +from tqdm import tqdm + +logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, +) + +logger = logging.getLogger(__name__) + +from data import cls as cls_data +from data import qa as qa_data +from datasets import load_dataset + +DATA_PROCESSOR = {"cls": cls_data, "qa": qa_data} + + +def get_data(config, tokenizer, is_train=True, overwrite=False): + if is_train: + data_file = config.train_file + else: + data_file = config.predict_file + + data_path = config.data_dir + if data_file is not None: + data_path = os.path.join(data_path, data_file) + else: + data_path += "/" + + data_processor = DATA_PROCESSOR.get(config.task, None) + if data_processor is None: + raise Exception(f"Invalid data task {config.task}!") + + processor = data_processor.process_map.get(config.dataset, None) + if processor is None: + raise Exception(f"Invalid task dataset {config.dataset}!") + + comps = [ + data_path, + config.dataset, + config.model_name_or_path.replace("/", "_"), + config.max_seq_length, + "train" if is_train else "dev", + "dataset.txt", + ] + dataset_file = "_".join([str(comp) for comp in comps]) + + if not os.path.exists(dataset_file) or overwrite: + with open(dataset_file, "w", encoding="utf-8") as writer_file: + if data_file is None or not os.path.isdir(data_path): + data = processor(config, data_path, is_train) + cnt = write_samples( + config, tokenizer, is_train, data_processor, writer_file, data, workers=config.threads + ) + else: + cnt = 0 + for filename in sorted([f for f in os.listdir(data_path) if f.endswith(".json")]): + data = processor(config, os.path.join(data_path, filename), is_train) + cnt += write_samples( + config, tokenizer, is_train, data_processor, writer_file, data, workers=config.threads + ) + logger.info(f"{cnt} features processed from {data_path}") + + dataset = load_dataset("text", data_files=dataset_file)["train"] + dataset = dataset.map(lambda x: json.loads(x["text"]), batched=False) + + if not is_train: + # for valid datasets, we pad datasets so that no sample will be skiped in multi-device settings + dataset = IterableDatasetPad( + dataset=dataset, + batch_size=config.train_batch_size if is_train else config.eval_batch_size, + num_devices=config.world_size, + seed=config.seed, + ) + + dataloader = torch_data.DataLoader( + dataset, + sampler=torch_data.RandomSampler(dataset) if is_train else None, + drop_last=False, + batch_size=config.train_batch_size if is_train else config.eval_batch_size, + collate_fn=(data_processor.collate_fn), + ) + + return dataloader + + +config = None +tokenizer = None +is_train = None +writer = None + + +def init_sample_writer(_config, _tokenizer, _is_train, _writer): + global config + global tokenizer + global is_train + global writer + config = _config + tokenizer = _tokenizer + is_train = _is_train + writer = _writer + + +def sample_writer(data): + global config + global tokenizer + global is_train + global writer + return writer(data, config, tokenizer, is_train) + + +def write_samples(config, tokenizer, is_train, processor, writer_file, data, workers=4): + write_cnt = 0 + with multiprocessing.Pool( + processes=workers, + initializer=init_sample_writer, + initargs=(config, tokenizer, is_train, processor.sample_writer), + ) as pool: + for write_data in tqdm( + pool.imap(sample_writer, data), total=len(data), dynamic_ncols=True, desc="writing samples..." + ): + if isinstance(write_data, list): + for datum in write_data: + writer_file.write(json.dumps(datum) + "\n") + write_cnt += len(write_data) + else: + writer_file.write(json.dumps(write_data) + "\n") + write_cnt += 1 + return write_cnt + + +class IterableDatasetPad(torch_data.IterableDataset): + def __init__( + self, + dataset: torch_data.IterableDataset, + batch_size: int = 1, + num_devices: int = 1, + seed: int = 0, + ): + self.dataset = dataset + self.batch_size = batch_size + self.seed = seed + self.num_examples = 0 + + chunk_size = self.batch_size * num_devices + length = len(dataset) + self.length = length + (chunk_size - length % chunk_size) + + def __len__(self): + return self.length + + def __iter__(self): + self.num_examples = 0 + if ( + not hasattr(self.dataset, "set_epoch") + and hasattr(self.dataset, "generator") + and isinstance(self.dataset.generator, torch.Generator) + ): + self.dataset.generator.manual_seed(self.seed + self.epoch) + + first_batch = None + current_batch = [] + for element in self.dataset: + self.num_examples += 1 + current_batch.append(element) + # Wait to have a full batch before yielding elements. + if len(current_batch) == self.batch_size: + for batch in current_batch: + yield batch + if first_batch is None: + first_batch = batch.copy() + current_batch = [] + + # pad the last batch with elements from the beginning. + while self.num_examples < self.length: + add_num = self.batch_size - len(current_batch) + self.num_examples += add_num + current_batch += [first_batch] * add_num + for batch in current_batch: + yield batch + current_batch = [] diff --git a/finetune/data/qa.py b/finetune/data/qa.py new file mode 100644 index 0000000..fcb352b --- /dev/null +++ b/finetune/data/qa.py @@ -0,0 +1,581 @@ +import copy +import json +import re + +import numpy as np +import torch +import transformers.data.processors.squad as squad +from tqdm import tqdm + + +class SquadExample: + """ + A single training/test example for the Squad dataset, as loaded from disk. + + Args: + qas_id: The example's unique identifier + question_text: The question string + context_text: The context string + answer_text: The answer string + start_position_character: The character position of the start of the answer + title: The title of the example + answers: None by default, this is used during evaluation. Holds answers as well as their start positions. + is_impossible: False by default, set to True if the example has no possible answer. + """ + + def __init__( + self, + qas_id, + question_text, + context_text, + answer_text, + start_position_character, + title, + answers=[], + is_impossible=False, + doc_tokens=None, + char_to_word_offset=None, + ): + self.qas_id = qas_id + self.question_text = question_text + self.context_text = context_text + self.answer_text = answer_text + self.title = title + self.is_impossible = is_impossible + self.answers = answers + + self.start_position, self.end_position = 0, 0 + + if doc_tokens is None: + doc_tokens = [] + char_to_word_offset = [] + prev_is_whitespace = True + + # Split on whitespace so that different tokens may be attributed to their original position. + for c in self.context_text: + if squad._is_whitespace(c): + prev_is_whitespace = True + else: + if prev_is_whitespace: + doc_tokens.append(c) + else: + doc_tokens[-1] += c + prev_is_whitespace = False + char_to_word_offset.append(len(doc_tokens) - 1) + + self.doc_tokens = doc_tokens + self.char_to_word_offset = char_to_word_offset + + # Start and end positions only has a value during evaluation. + if start_position_character is not None and not is_impossible: + self.start_position = char_to_word_offset[start_position_character] + self.end_position = char_to_word_offset[ + min(start_position_character + len(answer_text) - 1, len(char_to_word_offset) - 1) + ] + + +def squad_convert_example_to_features( + example, + tokenizer, + max_seq_length, + doc_stride, + max_query_length, + padding_strategy, + is_training, + tok_to_orig_index=None, + orig_to_tok_index=None, + all_doc_tokens=None, +): + features = [] + if is_training and not example.is_impossible: + # Get start and end position + start_position = example.start_position + end_position = example.end_position + + # If the answer cannot be found in the text, then skip this example. + actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)]) + cleaned_answer_text = " ".join(squad.whitespace_tokenize(example.answer_text)) + if actual_text.find(cleaned_answer_text) == -1: + return [], None, None, None + + if tok_to_orig_index is None: + tok_to_orig_index = [] + orig_to_tok_index = [] + all_doc_tokens = [] + for (i, token) in enumerate(example.doc_tokens): + orig_to_tok_index.append(len(all_doc_tokens)) + if tokenizer.__class__.__name__ in [ + "RobertaTokenizer", + "LongformerTokenizer", + "BartTokenizer", + "RobertaTokenizerFast", + "LongformerTokenizerFast", + "BartTokenizerFast", + ]: + sub_tokens = tokenizer.tokenize(token, add_prefix_space=True) + else: + sub_tokens = tokenizer.tokenize(token) + for sub_token in sub_tokens: + tok_to_orig_index.append(i) + all_doc_tokens.append(sub_token) + + if is_training and not example.is_impossible: + tok_start_position = orig_to_tok_index[example.start_position] + if example.end_position < len(example.doc_tokens) - 1: + tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 + else: + tok_end_position = len(all_doc_tokens) - 1 + + (tok_start_position, tok_end_position) = squad._improve_answer_span( + all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text + ) + + spans = [] + + truncated_query = tokenizer.encode( + example.question_text, add_special_tokens=False, truncation=True, max_length=max_query_length + ) + + # Tokenizers who insert 2 SEP tokens in-between & need to have special handling + # in the way they compute mask of added tokens. + tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower() + sequence_added_tokens = ( + tokenizer.model_max_length - tokenizer.max_len_single_sentence + 1 + if tokenizer_type in squad.MULTI_SEP_TOKENS_TOKENIZERS_SET + else tokenizer.model_max_length - tokenizer.max_len_single_sentence + ) + sequence_pair_added_tokens = tokenizer.model_max_length - tokenizer.max_len_sentences_pair + + span_doc_tokens = all_doc_tokens + while len(spans) * doc_stride < len(all_doc_tokens): + + # Define the side we want to truncate / pad and the text/pair sorting + if tokenizer.padding_side == "right": + texts = truncated_query + pairs = span_doc_tokens + truncation = squad.TruncationStrategy.ONLY_SECOND.value + else: + texts = span_doc_tokens + pairs = truncated_query + truncation = squad.TruncationStrategy.ONLY_FIRST.value + + encoded_dict = tokenizer.encode_plus( # TODO(thom) update this logic + texts, + pairs, + truncation=truncation, + padding=padding_strategy, + max_length=max_seq_length, + return_overflowing_tokens=True, + stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens, + return_token_type_ids=True, + ) + + paragraph_len = min( + len(all_doc_tokens) - len(spans) * doc_stride, + max_seq_length - len(truncated_query) - sequence_pair_added_tokens, + ) + + if tokenizer.pad_token_id in encoded_dict["input_ids"]: + if tokenizer.padding_side == "right": + non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)] + else: + last_padding_id_position = ( + len(encoded_dict["input_ids"]) - 1 - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id) + ) + non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :] + + else: + non_padded_ids = encoded_dict["input_ids"] + + tokens = tokenizer.convert_ids_to_tokens(non_padded_ids) + + token_to_orig_map = {} + for i in range(paragraph_len): + index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i + token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i] + + encoded_dict["paragraph_len"] = paragraph_len + encoded_dict["tokens"] = tokens + encoded_dict["token_to_orig_map"] = token_to_orig_map + encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens + encoded_dict["token_is_max_context"] = {} + encoded_dict["start"] = len(spans) * doc_stride + encoded_dict["length"] = paragraph_len + + spans.append(encoded_dict) + + if "overflowing_tokens" not in encoded_dict or ( + "overflowing_tokens" in encoded_dict and len(encoded_dict["overflowing_tokens"]) == 0 + ): + break + span_doc_tokens = encoded_dict["overflowing_tokens"] + + for doc_span_index in range(len(spans)): + for j in range(spans[doc_span_index]["paragraph_len"]): + is_max_context = squad._new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j) + index = ( + j + if tokenizer.padding_side == "left" + else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j + ) + spans[doc_span_index]["token_is_max_context"][index] = is_max_context + + for span in spans: + # Identify the position of the CLS token + cls_index = span["input_ids"].index(tokenizer.cls_token_id) + + p_mask = np.array([]) + + # # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer) + # # Original TF implementation also keep the classification token (set to 0) + # p_mask = np.ones_like(span["token_type_ids"]) + # if tokenizer.padding_side == "right": + # p_mask[len(truncated_query) + sequence_added_tokens :] = 0 + # else: + # p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0 + + # pad_token_indices = np.where(span["input_ids"] == tokenizer.pad_token_id) + # special_token_indices = np.asarray( + # tokenizer.get_special_tokens_mask(span["input_ids"], already_has_special_tokens=True) + # ).nonzero() + + # p_mask[pad_token_indices] = 1 + # p_mask[special_token_indices] = 1 + + # # Set the cls index to 0: the CLS index can be used for impossible answers + # p_mask[cls_index] = 0 + + span_is_impossible = example.is_impossible + start_position = 0 + end_position = 0 + if is_training and not span_is_impossible: + # For training, if our document chunk does not contain an annotation + # we throw it out, since there is nothing to predict. + doc_start = span["start"] + doc_end = span["start"] + span["length"] - 1 + out_of_span = False + + if not (tok_start_position >= doc_start and tok_end_position <= doc_end): + out_of_span = True + + if out_of_span: + start_position = cls_index + end_position = cls_index + span_is_impossible = True + else: + if tokenizer.padding_side == "left": + doc_offset = 0 + else: + doc_offset = len(truncated_query) + sequence_added_tokens + + start_position = tok_start_position - doc_start + doc_offset + end_position = tok_end_position - doc_start + doc_offset + + features.append( + squad.SquadFeatures( + span["input_ids"], + span["attention_mask"], + span["token_type_ids"], + cls_index, + p_mask.tolist(), + example_index=0, # Can not set unique_id and example_index here. They will be set after multiple processing. + unique_id=0, + paragraph_len=span["paragraph_len"], + token_is_max_context=span["token_is_max_context"], + tokens=span["tokens"], + token_to_orig_map=span["token_to_orig_map"], + start_position=start_position, + end_position=end_position, + is_impossible=span_is_impossible, + qas_id=example.qas_id, + ) + ) + return features, tok_to_orig_index, orig_to_tok_index, all_doc_tokens + + +def sample_writer(data, config, tokenizer, is_train): + """process and write single 'paragraph-context' from squad-formed QA dataset""" + context = data["context"] + example = None + tok_to_orig_index = None + orig_to_tok_index = None + all_doc_tokens = None + write_data = [] + for qas in data["qas"]: + is_impossible = qas.get("is_impossible", False) + example = SquadExample( + qas_id=qas["id"], + question_text=qas["question"], + context_text=context, + answer_text="" if is_impossible else qas["answers"][0]["text"], + start_position_character=0 if is_impossible else qas["answers"][0]["answer_start"], + title="", + answers=qas["answers"], + is_impossible=is_impossible, + doc_tokens=(None if example is None else example.doc_tokens), + char_to_word_offset=(None if example is None else example.char_to_word_offset), + ) + features, tok_to_orig_index, orig_to_tok_index, all_doc_tokens = squad_convert_example_to_features( + example=example, + tokenizer=tokenizer, + max_seq_length=config.max_seq_length, + doc_stride=config.doc_stride, + max_query_length=config.max_query_length, + padding_strategy="max_length", + is_training=is_train, + tok_to_orig_index=tok_to_orig_index, + orig_to_tok_index=orig_to_tok_index, + all_doc_tokens=all_doc_tokens, + ) + for i, feature in enumerate(features): + write_datum = { + "input_ids": feature.input_ids, + "attention_mask": feature.attention_mask, + "token_type_ids": feature.token_type_ids, + "start_position": feature.start_position, + "end_position": feature.end_position, + } + if not is_train: + write_datum["id"] = feature.qas_id + write_datum["unique_id"] = "{}_{}".format(feature.qas_id, i) + write_datum["tokens"] = feature.tokens + write_datum["token_to_orig_map"] = feature.token_to_orig_map + write_datum["paragraph_len"] = feature.paragraph_len + write_datum["token_is_max_context"] = feature.token_is_max_context + if i == 0: # only store example data for the first feature from the example + write_datum["is_impossible"] = example.is_impossible + write_datum["answers"] = example.answers + write_datum["doc_tokens"] = example.doc_tokens + else: + write_datum["is_impossible"] = None + write_datum["answers"] = None + write_datum["doc_tokens"] = None + write_data.append(write_datum) + + return write_data + + +def process_korquad_1(config, data_file, train): + with open(data_file) as handle: + load_data = json.load(handle)["data"] + data = [] + for datum in load_data: + data.extend(datum["paragraphs"]) + + return data + + +def process_korquad_2(config, data_file, train): + TAGS = [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "
", + "
", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "

", + "

", + "", + "", + "", + "", + "
", + "
", + "

", + "

", + "

", + "

", + "

", + "

", + "
", + "
", + "", + "", + "", + "", + "", + "", + "", + "", + "[ํŽธ์ง‘]", + ] + + def remove_tags(text): + # only retain meaningful html tags to extract answers + # tags like
  • will remain + text = re.sub(".*?", " ", text) + text = text.replace('rowspan="', "r") # summarize attribute name for col, row spans + text = text.replace('colspan="', "c") # which indicate merged table cells + for tag in TAGS: + text = text.replace(tag, "") + text = re.sub(" +", " ", text) + text = re.sub("\n+", "\n", text) + return text + + with open(data_file) as handle: + data = json.load(handle)["data"] + + qas_num = [] + for datum in data: + context = datum["context"] + datum["context"] = remove_tags(context) + del datum["raw_html"] + if train and not config.all_korquad_2_sample: # only use first sample from context for train split + datum["qas"] = datum["qas"][:1] + for qas in datum["qas"]: + del qas["answer"]["html_answer_text"] + del qas["answer"]["html_answer_start"] + # qas["id"] = "{}-{}".format(i, qas["id"]) + answer_prev_text = context[: qas["answer"]["answer_start"]] + answer_prev_len = len(answer_prev_text) + remove_tag_text = remove_tags(answer_prev_text) + # adjust span position according to text without tags + qas["answer"]["answer_start"] -= answer_prev_len - len(remove_tag_text) + qas["answer"]["text"] = remove_tags(qas["answer"]["text"]) + qas["answers"] = [qas.pop("answer")] + qas_num.append(len(datum["qas"])) + # some paragraph in korquad_2 have too many question samples + # it will slow down the progress of multiprocessing job + # so, i split them + limit_qas_num = int(np.percentile(np.array(qas_num), 50)) + flat_data = [] + for datum in data: + qas_num = len(datum["qas"]) + if qas_num > limit_qas_num: + for j in range(0, qas_num, limit_qas_num): + _datum = copy.deepcopy(datum) + _datum["qas"] = datum["qas"][j : j + limit_qas_num] + flat_data.append(_datum) + else: + flat_data.append(datum) + data = flat_data + + return data + + +def process_kluemrc(config, data_file, train): + data = [] + with open(data_file) as handle: + for datum in json.load(handle)["data"]: + datum = datum["paragraphs"] + for qas in datum: + for q in qas["qas"]: + q["id"] = q.pop("guid") + data.extend(datum) + return data + + +def process_tydiqa(config, data_file, train): + data = [] + total = sum([1 for _ in open(data_file)]) + for line in tqdm(open(data_file), total=total, dynamic_ncols=True): + datum = json.loads(line) + + if datum["language"].lower().strip() != "korean": + continue + + span_byte_map = {} + prev_bytes = 0 + for i, char in enumerate(datum["document_plaintext"]): + byte_len = len(char.encode("utf-8")) + for j in range(byte_len): + span_byte_map[prev_bytes + j] = i + prev_bytes += byte_len + + answers = [] + bool_answers = [] + is_impossible = False + for annot in datum["annotations"]: + spans = annot["minimal_answer"] + start = spans["plaintext_start_byte"] + end = spans["plaintext_end_byte"] + + yesno = None if annot["yes_no_answer"] == "NONE" else annot["yes_no_answer"] + if yesno is not None: + bool_answers.append(yesno) + continue + + if spans["plaintext_start_byte"] == -1: + is_impossible = True + else: + start = span_byte_map[start] + end = span_byte_map[end] + answers.append( + { + "text": datum["document_plaintext"][start:end], + "answer_start": start, + } + ) + + # skip boolqa samples + if len(bool_answers) != 0: + continue + + if len(answers) != 0: + is_impossible = False + else: + is_impossible = True + + data.append( + { + "context": datum["document_plaintext"], + "qas": [ + { + "id": len(data), + "question": datum["question_text"], + "is_impossible": is_impossible, + "answers": answers, + } + ], + } + ) + + return data + + +process_map = { + "korquad_1": process_korquad_1, + "korquad_2": process_korquad_2, + "tydiqa": process_tydiqa, + "kluemrc": process_kluemrc, +} + + +def collate_fn(features): + input_ids = [sample["input_ids"] for sample in features] + attention_mask = [sample["attention_mask"] for sample in features] + token_type_ids = [sample["token_type_ids"] for sample in features] + start_position = [sample["start_position"] for sample in features] + end_position = [sample["end_position"] for sample in features] + + input_ids = torch.tensor(np.array(input_ids).astype(np.int64), dtype=torch.long) + attention_mask = torch.tensor(np.array(attention_mask).astype(np.int8), dtype=torch.long) + token_type_ids = torch.tensor(np.array(token_type_ids).astype(np.int8), dtype=torch.long) + start_position = torch.tensor(np.array(start_position).astype(np.int64), dtype=torch.long) + end_position = torch.tensor(np.array(end_position).astype(np.int64), dtype=torch.long) + inputs = { + "input_ids": input_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + "start_positions": start_position, + "end_positions": end_position, + } + if "unique_id" in features[0]: + inputs["unique_id"] = [sample["unique_id"] for sample in features] + return inputs diff --git a/finetune/download_qa_dataset.sh b/finetune/download_qa_dataset.sh new file mode 100755 index 0000000..7f95a7d --- /dev/null +++ b/finetune/download_qa_dataset.sh @@ -0,0 +1,48 @@ +#!/bin/bash + +mkdir cache +cd cache + +# Download qa datasets +## 1. tydiqa +mkdir -p tydiqa +wget -P tydiqa https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-train.jsonl.gz +wget -P tydiqa https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-dev.jsonl.gz +gzip -d tydiqa/tydiqa-v1.0-train.jsonl.gz +gzip -d tydiqa/tydiqa-v1.0-dev.jsonl.gz + +## 2. korquad 2.1 +mkdir -p korquad_2/train +for var in {0..12} +do + var=$(printf %02d $var) + wget -P korquad_2/train https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_2.1/train/KorQuAD_2.1_train_${var}.zip +done + +mkdir -p korquad_2/dev +for var in {0..1} +do + var=$(printf %02d $var) + wget -P korquad_2/dev https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_2.1/dev/KorQuAD_2.1_dev_${var}.zip +done + +cd korquad_2 +cd train +unzip '*.zip' +rm *.zip +cd .. + +cd dev +unzip '*.zip' +rm *.zip +cd .. + +## 3. korquad 1.0 +# mkdir korquad_1 +# wget -P korquad_1 https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_v1.0_train.json +# wget -P korquad_1 https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_v1.0_dev.json + +## 4. klue mrc +# mkdir -p kluemrc +# wget -P kluemrc https://raw.githubusercontent.com/KLUE-benchmark/KLUE/v1.1.0/klue_benchmark/klue-mrc-v1.1/klue-mrc-v1.1_train.json +# wget -P kluemrc https://raw.githubusercontent.com/KLUE-benchmark/KLUE/v1.1.0/klue_benchmark/klue-mrc-v1.1/klue-mrc-v1.1_dev.json diff --git a/finetune/evaluate/__init__.py b/finetune/evaluate/__init__.py new file mode 100644 index 0000000..84d6a28 --- /dev/null +++ b/finetune/evaluate/__init__.py @@ -0,0 +1,4 @@ +from evaluate.cls import eval_cls +from evaluate.qa import eval_qa + +EVAL_FUNC_MAP = {"cls": eval_cls, "qa": eval_qa} diff --git a/finetune/evaluate/cls.py b/finetune/evaluate/cls.py new file mode 100644 index 0000000..e10e272 --- /dev/null +++ b/finetune/evaluate/cls.py @@ -0,0 +1,33 @@ +from functools import partial + +import numpy as np +import sklearn.metrics as sklearn_metrics + +binary_metrics = { + "accuracy": sklearn_metrics.accuracy_score, + "precision": sklearn_metrics.precision_score, + "recall": sklearn_metrics.recall_score, + "f1": sklearn_metrics.f1_score, + "matthews_corrcoef": sklearn_metrics.matthews_corrcoef, + "roc_auc": sklearn_metrics.roc_auc_score, +} + + +metrics = { + "accuracy": sklearn_metrics.accuracy_score, + "f1-macro": partial(sklearn_metrics.f1_score, average="macro"), +} + + +def eval_cls(results, **kwargs): + predictions = np.array([result["prediction"] for result in results]) + labels = np.array([result["label"] for result in results]) + is_binary = len(set(labels.tolist())) < 3 + results = { + metric: round(f(labels, predictions) * 100, 2) + for metric, f in (binary_metrics.items() if is_binary else metrics.items()) + } + return { + "results": results, + "best_score": results["f1" if is_binary else "f1-macro"], + } diff --git a/finetune/evaluate/qa.py b/finetune/evaluate/qa.py new file mode 100644 index 0000000..b1a103b --- /dev/null +++ b/finetune/evaluate/qa.py @@ -0,0 +1,593 @@ +import collections +import json +import os +import re +import shutil +import string +from collections import Counter + +from bs4 import BeautifulSoup +from tqdm import tqdm +from transformers.data.metrics.squad_metrics import _compute_softmax, _get_best_indexes, get_final_text +from transformers.data.processors.squad import SquadFeatures, SquadResult + +"""KorQuAD 2.0์— ๋Œ€ํ•œ ๊ณต์‹ ํ‰๊ฐ€ ์Šคํฌ๋ฆฝํŠธ """ +"""๋ณธ ์Šคํฌ๋ฆฝํŠธ๋Š” SQuAD v1.1 ํ‰๊ฐ€ ์Šคํฌ๋ฆฝํŠธ https://rajpurkar.github.io/SQuAD-explorer/ ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ž‘์„ฑ๋จ.""" + + +def normalize_answer(s): + def tag_clean(t): + return BeautifulSoup(t, features="lxml").get_text() + + def remove_(text): + """๋ถˆํ•„์š”ํ•œ ๊ธฐํ˜ธ ์ œ๊ฑฐ""" + text = re.sub("'", " ", text) + text = re.sub('"', " ", text) + text = re.sub("ใ€Š", " ", text) + text = re.sub("ใ€‹", " ", text) + text = re.sub("<", " ", text) + text = re.sub(">", " ", text) + text = re.sub("ใ€ˆ", " ", text) + text = re.sub("ใ€‰", " ", text) + text = re.sub(r"\(", " ", text) + text = re.sub(r"\)", " ", text) + text = re.sub("โ€˜", " ", text) + text = re.sub("โ€™", " ", text) + return text + + def white_space_fix(text): + return " ".join(text.split()).replace("\n", "").replace("\t", "").replace(" ", "") + + def remove_punc(text): + exclude = set(string.punctuation) + return "".join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_punc(lower(remove_(tag_clean(s))))) + + +def f1_score(prediction, ground_truth): + prediction_tokens = normalize_answer(prediction).split() + ground_truth_tokens = normalize_answer(ground_truth).split() + + # F1 by character + prediction_Char = [] + for tok in prediction_tokens: + now = [a for a in tok] + prediction_Char.extend(now) + + ground_truth_Char = [] + for tok in ground_truth_tokens: + now = [a for a in tok] + ground_truth_Char.extend(now) + + common = Counter(prediction_Char) & Counter(ground_truth_Char) + num_same = sum(common.values()) + if num_same == 0: + return 0 + + precision = 1.0 * num_same / len(prediction_Char) + recall = 1.0 * num_same / len(ground_truth_Char) + f1 = (2 * precision * recall) / (precision + recall) + + return f1 + + +def exact_match_score(prediction, ground_truth): + return normalize_answer(prediction) == normalize_answer(ground_truth) + + +def get_raw_scores(examples, preds): + """ + Computes the exact and f1 scores from the examples and the model predictions + """ + exact_scores = {} + f1_scores = {} + + for example in examples: + qas_id = example.qas_id + gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])] + + if not gold_answers: + # For unanswerable questions, only correct answer is empty string + gold_answers = [""] + + if qas_id not in preds: + print("Missing prediction for %s" % qas_id) + continue + + prediction = preds[qas_id] + + exact_scores[qas_id] = max(exact_match_score(a, prediction) for a in gold_answers) + f1_scores[qas_id] = max(f1_score(a, prediction) for a in gold_answers) + + return exact_scores, f1_scores + + +def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh): + new_scores = {} + for qid, s in scores.items(): + pred_na = na_probs[qid] > na_prob_thresh + if pred_na: + new_scores[qid] = float(not qid_to_has_ans[qid]) + else: + new_scores[qid] = s + return new_scores + + +def make_eval_dict(exact_scores, f1_scores, qid_list=None): + if not qid_list: + total = len(exact_scores) + return collections.OrderedDict( + [ + ("exact", 100.0 * sum(exact_scores.values()) / total), + ("f1", 100.0 * sum(f1_scores.values()) / total), + ("total", total), + ] + ) + else: + total = len(qid_list) + return collections.OrderedDict( + [ + ("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total), + ("f1", 100.0 * sum(f1_scores[k] for k in qid_list) / total), + ("total", total), + ] + ) + + +def merge_eval(main_eval, new_eval, prefix): + for k in new_eval: + main_eval["%s_%s" % (prefix, k)] = new_eval[k] + + +def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans): + num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) + cur_score = num_no_ans + best_score = cur_score + best_thresh = 0.0 + qid_list = sorted(na_probs, key=lambda k: na_probs[k]) + for i, qid in enumerate(qid_list): + if qid not in scores: + continue + if qid_to_has_ans[qid]: + diff = scores[qid] + else: + if preds[qid]: + diff = -1 + else: + diff = 0 + cur_score += diff + if cur_score > best_score: + best_score = cur_score + best_thresh = na_probs[qid] + + has_ans_score, has_ans_cnt = 0, 0 + for qid in qid_list: + if not qid_to_has_ans[qid]: + continue + has_ans_cnt += 1 + + if qid not in scores: + continue + has_ans_score += scores[qid] + + return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt + + +def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans): + best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans) + best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans) + main_eval["best_exact"] = best_exact + main_eval["best_exact_thresh"] = exact_thresh + main_eval["best_f1"] = best_f1 + main_eval["best_f1_thresh"] = f1_thresh + main_eval["has_ans_exact"] = has_ans_exact + main_eval["has_ans_f1"] = has_ans_f1 + + +def find_best_thresh(preds, scores, na_probs, qid_to_has_ans): + num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) + cur_score = num_no_ans + best_score = cur_score + best_thresh = 0.0 + qid_list = sorted(na_probs, key=lambda k: na_probs[k]) + for _, qid in enumerate(qid_list): + if qid not in scores: + continue + if qid_to_has_ans[qid]: + diff = scores[qid] + else: + if preds[qid]: + diff = -1 + else: + diff = 0 + cur_score += diff + if cur_score > best_score: + best_score = cur_score + best_thresh = na_probs[qid] + return 100.0 * best_score / len(scores), best_thresh + + +def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans): + best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans) + best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans) + + main_eval["best_exact"] = best_exact + main_eval["best_exact_thresh"] = exact_thresh + main_eval["best_f1"] = best_f1 + main_eval["best_f1_thresh"] = f1_thresh + + +def compute_predictions_logits( + all_examples, + all_features, + all_results, + n_best_size, + max_answer_length, + do_lower_case, + output_prediction_file, + output_nbest_file, + output_null_log_odds_file, + verbose_logging, + version_2_with_negative, + null_score_diff_threshold, + tokenizer, +): + """Write final predictions to the json file and log-odds of null if needed.""" + + example_index_to_features = collections.defaultdict(list) + for feature in all_features: + example_index_to_features[feature.example_index].append(feature) + + unique_id_to_result = {} + for result in all_results: + unique_id_to_result[result.unique_id] = result + + _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name + "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"] + ) + + all_predictions = collections.OrderedDict() + # all_nbest_json = collections.OrderedDict() + scores_diff_json = collections.OrderedDict() + + for (example_index, example) in enumerate(all_examples): + features = example_index_to_features[example_index] + + prelim_predictions = [] + # keep track of the minimum score of null start+end of position 0 + score_null = 1000000 # large and positive + min_null_feature_index = 0 # the paragraph slice with min null score + null_start_logit = 0 # the start logit at the slice with min null score + null_end_logit = 0 # the end logit at the slice with min null score + for (feature_index, feature) in enumerate(features): + result = unique_id_to_result[feature.unique_id] + start_indexes = _get_best_indexes(result.start_logits, n_best_size) + end_indexes = _get_best_indexes(result.end_logits, n_best_size) + # if we could have irrelevant answers, get the min score of irrelevant + if version_2_with_negative: + feature_null_score = result.start_logits[0] + result.end_logits[0] + if feature_null_score < score_null: + score_null = feature_null_score + min_null_feature_index = feature_index + null_start_logit = result.start_logits[0] + null_end_logit = result.end_logits[0] + for start_index in start_indexes: + for end_index in end_indexes: + # We could hypothetically create invalid predictions, e.g., predict + # that the start of the span is in the question. We throw out all + # invalid predictions. + if start_index >= len(feature.tokens): + continue + if end_index >= len(feature.tokens): + continue + if start_index not in feature.token_to_orig_map: + continue + if end_index not in feature.token_to_orig_map: + continue + if not feature.token_is_max_context.get(start_index, False): + continue + if end_index < start_index: + continue + length = end_index - start_index + 1 + if length > max_answer_length: + continue + prelim_predictions.append( + _PrelimPrediction( + feature_index=feature_index, + start_index=start_index, + end_index=end_index, + start_logit=result.start_logits[start_index], + end_logit=result.end_logits[end_index], + ) + ) + if version_2_with_negative: + prelim_predictions.append( + _PrelimPrediction( + feature_index=min_null_feature_index, + start_index=0, + end_index=0, + start_logit=null_start_logit, + end_logit=null_end_logit, + ) + ) + prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True) + + _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name + "NbestPrediction", ["text", "start_logit", "end_logit", "tok_text", "orig_text"] + ) + + seen_predictions = {} + nbest = [] + for pred in prelim_predictions: + if len(nbest) >= n_best_size: + break + feature = features[pred.feature_index] + if pred.start_index > 0: # this is a non-null prediction + tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)] + orig_doc_start = feature.token_to_orig_map[pred.start_index] + orig_doc_end = feature.token_to_orig_map[pred.end_index] + orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)] + + tok_text = tokenizer.convert_tokens_to_string(tok_tokens) + + # tok_text = " ".join(tok_tokens) + # + # # De-tokenize WordPieces that have been split off. + # tok_text = tok_text.replace(" ##", "") + # tok_text = tok_text.replace("##", "") + + # Clean whitespace + tok_text = tok_text.strip() + tok_text = " ".join(tok_text.split()) + orig_text = " ".join(orig_tokens) + + final_text = orig_text + # final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging) + if final_text in seen_predictions: + continue + + seen_predictions[final_text] = True + else: + tok_text = "" + orig_text = "" + final_text = "" + seen_predictions[final_text] = True + + nbest.append( + _NbestPrediction( + text=final_text, + start_logit=pred.start_logit, + end_logit=pred.end_logit, + tok_text=tok_text, + orig_text=orig_text, + ) + ) + # if we didn't include the empty option in the n-best, include it + if version_2_with_negative: + if "" not in seen_predictions: + nbest.append( + _NbestPrediction( + text="", start_logit=null_start_logit, end_logit=null_end_logit, tok_text="", orig_text="" + ) + ) + + # In very rare edge cases we could only have single null prediction. + # So we just create a nonce prediction in this case to avoid failure. + if len(nbest) == 1: + nbest.insert( + 0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0, tok_text="", orig_text="") + ) + + # In very rare edge cases we could have no valid predictions. So we + # just create a nonce prediction in this case to avoid failure. + if not nbest: + nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0, tok_text="", orig_text="")) + + assert len(nbest) >= 1, "No valid predictions" + + total_scores = [] + best_non_null_entry = None + for entry in nbest: + total_scores.append(entry.start_logit + entry.end_logit) + if not best_non_null_entry: + if entry.text: + best_non_null_entry = entry + + probs = _compute_softmax(total_scores) + + nbest_json = [] + for (i, entry) in enumerate(nbest): + output = collections.OrderedDict() + output["text"] = entry.text + output["probability"] = probs[i] + output["start_logit"] = entry.start_logit + output["end_logit"] = entry.end_logit + output["tok_text"] = entry.tok_text + output["orig_text"] = entry.orig_text + nbest_json.append(output) + + assert len(nbest_json) >= 1, "No valid predictions" + + if not version_2_with_negative: + # all_predictions[example.qas_id] = nbest_json[0]["text"] + all_predictions[example.qas_id] = get_final_text( + nbest_json[0]["tok_text"], nbest_json[0]["orig_text"], do_lower_case, verbose_logging + ) + else: + # predict "" iff the null score - the score of best non-null > threshold + score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit) + scores_diff_json[example.qas_id] = score_diff + if score_diff > null_score_diff_threshold: + all_predictions[example.qas_id] = "" + else: + # all_predictions[example.qas_id] = best_non_null_entry.text + all_predictions[example.qas_id] = get_final_text( + best_non_null_entry.tok_text, best_non_null_entry.orig_text, do_lower_case, verbose_logging + ) + + return all_predictions + + +def squad_evaluate(examples, preds, no_answer_probs=None, no_answer_probability_threshold=1.0): + qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples} + has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer] + no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer] + + if no_answer_probs is None: + no_answer_probs = {k: 0.0 for k in preds} + + exact, f1 = get_raw_scores(examples, preds) + + exact_threshold = apply_no_ans_threshold( + exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold + ) + f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold) + + evaluation = make_eval_dict(exact_threshold, f1_threshold) + + if has_answer_qids: + has_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=has_answer_qids) + merge_eval(evaluation, has_ans_eval, "HasAns") + + if no_answer_qids: + no_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=no_answer_qids) + merge_eval(evaluation, no_ans_eval, "NoAns") + + if no_answer_probs: + find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs, qas_id_to_has_answer) + + return evaluation, (exact, f1) + + +class Example: + def __init__(self, qas_id, answers, is_impossible, doc_tokens): + self.qas_id = qas_id + self.answers = answers + self.is_impossible = is_impossible + self.doc_tokens = doc_tokens + + def __str__(self): + text = "" + text += str(self.qas_id) + "\n" + text += str(self.answers) + "\n" + text += str(self.is_impossible) + "\n" + return text + + +def eval_qa(config, loader, tokenizer, results, **kwargs): + pred_dir = os.path.join(config.output_dir, config.task, "predictions") + batch_size = 10 + total_feature_num = int(len(loader) * config.eval_batch_size) + + examples, features, results, predictions = {}, {}, {}, {} + for i, feature in tqdm( + enumerate(loader.dataset), total=total_feature_num, dynamic_ncols=True, desc="compute scores..." + ): + qas_id = feature["id"] + + if qas_id not in examples and feature.get("answers", None) is not None: + examples[qas_id] = Example( + qas_id=feature["id"], + answers=feature["answers"], + is_impossible=feature["is_impossible"], + doc_tokens=feature["doc_tokens"], + ) + + if qas_id not in features: + features[qas_id] = [] + features[qas_id].append( + SquadFeatures( + input_ids=None, + attention_mask=None, + token_type_ids=None, + cls_index=None, + p_mask=None, + example_index=None, + unique_id=feature["unique_id"], + paragraph_len=feature["paragraph_len"], + token_is_max_context={int(k): v for k, v in feature["token_is_max_context"].items() if v is not None}, + tokens=feature["tokens"], + token_to_orig_map={int(k): v for k, v in feature["token_to_orig_map"].items() if v is not None}, + start_position=feature["start_position"], + end_position=feature["end_position"], + is_impossible=False, + qas_id=feature["id"], + ) + ) + + result_path = os.path.join(pred_dir, str(qas_id)) + if qas_id not in results and os.path.exists(result_path): + load_results = [json.loads(result) for result in open(result_path)] + results[qas_id] = [ + SquadResult(result["unique_id"], result["start_logits"], result["end_logits"]) + for result in load_results + ] + + # check how many examples loaded completely(feature num == result num). + all_loaded_ids = [] + incomplete_cnt = 0 + for k, v in results.items(): + if len(v) == len(features[k]): + all_loaded_ids.append(k) + else: + incomplete_cnt += 1 + + if len(all_loaded_ids) >= batch_size or (i == total_feature_num - 1 and len(all_loaded_ids) > 0): + + batch_examples, batch_features, batch_results = [], [], [] + for qas_id in all_loaded_ids: + for feature in features.pop(qas_id): + feature.example_index = len(batch_examples) + batch_features.append(feature) + batch_examples.append(examples[qas_id]) + batch_results.extend(results.pop(qas_id)) + + predictions.update( + compute_predictions_logits( + batch_examples, + batch_features, + batch_results, + config.n_best_size, + config.max_answer_length, + config.do_lower_case, + None, # output_prediction_file, + None, # output_nbest_file, + None, # output_null_log_odds_file, + False, + config.version_2_with_negative, + config.null_score_diff_threshold, + tokenizer, + ) + ) + + del batch_examples, batch_features, batch_results + + eval_result, _ = squad_evaluate([v for k, v in examples.items()], predictions) + + eval_keys = ["HasAns_total", "NoAns_total", "best_exact", "best_f1"] + if config.version_2_with_negative: + eval_keys.extend( + [ + "HasAns_exact", + "HasAns_f1", + "NoAns_exact", + ] + ) + + results = {} + for k in eval_keys: + if k not in eval_result: + continue + results[k] = round(eval_result[k], 2) + + shutil.rmtree(pred_dir) + os.makedirs(pred_dir) + + return { + "results": results, + "best_score": results["best_f1"], + } diff --git a/finetune/model/__init__.py b/finetune/model/__init__.py new file mode 100644 index 0000000..c0298e2 --- /dev/null +++ b/finetune/model/__init__.py @@ -0,0 +1,4 @@ +from model.cls import ClsModel +from model.qa import QAModel + +MODEL_CLASS_MAP = {"cls": ClsModel, "qa": QAModel} diff --git a/finetune/model/base.py b/finetune/model/base.py new file mode 100644 index 0000000..7198023 --- /dev/null +++ b/finetune/model/base.py @@ -0,0 +1,87 @@ +import torch +from transformers import AdamW, get_linear_schedule_with_warmup + + +class BaseModel(torch.nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.from_pretrained() + + def save_pretrained(self, save_dir): + self.model.save_pretrained(save_dir) + for key in ["special_tokens_map_file", "tokenizer_file"]: + self.tokenizer.init_kwargs.pop(key, None) + self.tokenizer.save_pretrained(save_dir) + + def from_pretrained(self): + raise NotImplementedError + + def forward(self, inputs): + return self.model(**inputs) + + def eval_step(self, outputs): + raise NotImplementedError + + @staticmethod + def add_args(parser): + parser.add_argument( + "--model_name_or_path", + default=None, + type=str, + required=True, + ) + parser.add_argument("--data_dir", default="cache", type=str) + parser.add_argument("--train_file", default=None, type=str) + parser.add_argument("--predict_file", default=None, type=str) + + parser.add_argument("--do_lower_case", default=False, type=bool) + parser.add_argument("--max_seq_length", default=512, type=int) + parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") + parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") + parser.add_argument("--max_grad_norm", default=1.0, type=float) + parser.add_argument("--num_train_epochs", default=10, type=int) + parser.add_argument("--train_batch_size", default=8, type=int) + parser.add_argument("--eval_batch_size", default=16, type=int) + parser.add_argument("--learning_rate", default=3e-5, type=float) + parser.add_argument("--gradient_accumulation_steps", default=1, type=int) + parser.add_argument("--warmup_proportion", default=0.0, type=float) + + return parser + + def get_optimizer(self): + """Prepare optimizer""" + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": self.config.weight_decay, + }, + { + "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": self.config.weight_decay, + }, + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=self.config.learning_rate, eps=self.config.adam_epsilon) + return optimizer + + def get_scheduler(self, batch_num, optimizer): + """Prepare scheduler""" + if self.config.warmup_proportion == 0.0: + return None + + t_total = batch_num // self.config.gradient_accumulation_steps * self.config.num_train_epochs + + scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=int(t_total * self.config.warmup_proportion), + num_training_steps=t_total, + ) + + return scheduler + + def tensor_to_array(self, tensor): + return tensor.detach().cpu().numpy() + + def tensor_to_list(self, tensor): + return self.tensor_to_array(tensor).tolist() diff --git a/finetune/model/cls.py b/finetune/model/cls.py new file mode 100644 index 0000000..b4b2c07 --- /dev/null +++ b/finetune/model/cls.py @@ -0,0 +1,47 @@ +import os + +import torch +from data.cls import process_map +from model.base import BaseModel +from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer + + +class ClsModel(BaseModel): + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + config.label2id = self.config.label2id + + def from_pretrained(self): + data_file = os.path.join(self.config.data_dir, str(self.config.train_file)) + self.config.label2id = process_map[self.config.dataset](self.config, data_file, True, get_label_map=True) + num_labels = len(self.config.label2id) + if num_labels != self.config.num_labels: + print( + f"given args num_labels({self.config.num_labels}) is not same with num_labels({num_labels}) from dataset." + ) + print(f"switch num_labels {self.config.num_labels} -> {num_labels}") + self.config.num_labels = num_labels + model_config = AutoConfig.from_pretrained(self.config.model_name_or_path, num_labels=self.config.num_labels) + model_config.label2id = self.config.label2id + model_config.id2label = {int(v): k for k, v in model_config.label2id.items()} + self.model = AutoModelForSequenceClassification.from_pretrained( + self.config.model_name_or_path, config=model_config, cache_dir=self.config.cache_dir + ) + self.tokenizer = AutoTokenizer.from_pretrained(self.config.model_name_or_path, cache_dir=self.config.cache_dir) + + def forward(self, inputs): + outputs = self.model(**inputs) + return outputs + + def eval_step(self, inputs, outputs): + logits = outputs.logits.detach().cpu() + predictions = self.tensor_to_list(torch.argmax(logits, dim=-1)) + labels = self.tensor_to_list(inputs["labels"]) + results = [{"prediction": prediction, "label": label} for prediction, label in zip(predictions, labels)] + return results + + @staticmethod + def add_args(parser): + parser = BaseModel.add_args(parser) + parser.add_argument("--num_labels", default=2, type=int) + return parser diff --git a/finetune/model/qa.py b/finetune/model/qa.py new file mode 100644 index 0000000..5ea2c7e --- /dev/null +++ b/finetune/model/qa.py @@ -0,0 +1,77 @@ +import json +import os +import shutil + +from model.base import BaseModel +from transformers import AutoModelForQuestionAnswering, AutoTokenizer + + +class QAModel(BaseModel): + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + self.pred_dir = os.path.join(self.config.output_dir, self.config.task, "predictions") + if os.path.exists(self.pred_dir): + shutil.rmtree(self.pred_dir) + os.makedirs(self.pred_dir) + + def from_pretrained(self): + self.model = AutoModelForQuestionAnswering.from_pretrained( + self.config.model_name_or_path, + cache_dir=self.config.cache_dir, + ) + self.tokenizer = AutoTokenizer.from_pretrained( + self.config.model_name_or_path, cache_dir=self.config.cache_dir, use_fast=False + ) + + def forward(self, inputs): + if self.model.config.model_type in ["roberta"]: + inputs.pop("token_type_ids", None) + unique_id = inputs.pop("unique_id", None) + outputs = self.model(**inputs) + inputs["unique_id"] = unique_id + return outputs + + def eval_step(self, inputs, outputs): + start_logits, end_logits = outputs.start_logits, outputs.end_logits + start_logits = self.tensor_to_list(start_logits) + end_logits = self.tensor_to_list(end_logits) + sample_num = len(inputs["unique_id"]) + for i in range(sample_num): + qas_id = "_".join(inputs["unique_id"][i].split("_")[:-1]) + writer = open(os.path.join(self.pred_dir, str(qas_id)), "a") + write_data = { + "unique_id": inputs["unique_id"][i], + "start_logits": start_logits[i], + "end_logits": end_logits[i], + } + writer.write(json.dumps(write_data) + "\n") + writer.close() + return [None] * sample_num + + @staticmethod + def add_args(parser): + parser = BaseModel.add_args(parser) + parser.add_argument("--version_2_with_negative", action="store_true") + parser.add_argument("--null_score_diff_threshold", default=0.0, type=float) + parser.add_argument("--doc_stride", default=384, type=int) + parser.add_argument("--max_query_length", default=64, type=int) + parser.add_argument( + "--n_best_size", + default=20, + type=int, + help="The total number of n-best predictions to generate in the nbest_predictions.json output file.", + ) + parser.add_argument( + "--max_answer_length", + default=32, + type=int, + help="The maximum length of an answer that can be generated. This is needed because the start " + "and end predictions are not conditioned on one another.", + ) + parser.add_argument( + "--all_korquad_2_sample", + action="store_true", + help="Use all training samples from korquad2 or not. Do not use all samples by default " + "because of the limitation on computational resources", + ) + return parser diff --git a/finetune/requirements.txt b/finetune/requirements.txt new file mode 100644 index 0000000..3c93700 --- /dev/null +++ b/finetune/requirements.txt @@ -0,0 +1,7 @@ +tqdm==4.62.3 +torch==1.8.1 +transformers==4.11.3 +datasets==1.13.3 +scikit-learn==0.24.2 +beautifulsoup4==4.6.0 +lxml==4.6.3 diff --git a/finetune/run.py b/finetune/run.py new file mode 100644 index 0000000..581e997 --- /dev/null +++ b/finetune/run.py @@ -0,0 +1,259 @@ +import argparse +import copy +import json +import logging +import os + +import numpy as np +import torch +import transformers +from data import get_data +from evaluate import EVAL_FUNC_MAP +from model import MODEL_CLASS_MAP +from tqdm import tqdm + +logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, +) + +logger = logging.getLogger(__name__) + + +try: + import torch_xla.core.xla_model as xm + import torch_xla.distributed.data_parallel as xla_dp +except ImportError as e: + logger.error(f"Failed to import XLA. {e}") + + +def set_seed(seed): + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + + +def cal_running_avg_loss(loss, running_avg_loss, decay=0.99): + if running_avg_loss == 0: + return loss + running_avg_loss = running_avg_loss * decay + (1 - decay) * loss + return running_avg_loss + + +def _run_epoch(model, loader, device=None, context=None, **kwargs): + config = kwargs["config"] + is_train = kwargs["is_train"] + + avg_loss = 0 + results = [] + batch_num = len(loader) + + if is_train: + model.train() + if config.use_tpu: + optimizer = context.getattr_or( + "optimizer", + lambda: model.get_optimizer(), + ) + scheduler = context.getattr_or( + "scheduler", + lambda: model.get_scheduler(batch_num, optimizer), + ) + else: + optimizer = kwargs["optimizer"] + scheduler = kwargs["scheduler"] + else: + model.eval() + + is_master = True + if config.use_tpu: + is_master = xm.is_master_ordinal() + + pbar = tqdm(enumerate(loader), total=batch_num, disable=not is_master, dynamic_ncols=True) + for i, inputs in pbar: + + if not config.use_tpu: + for k, v in inputs.items(): + if isinstance(v, torch.Tensor): + inputs[k] = v.to(device) + + outputs = model(inputs) + loss = outputs.loss.mean() + avg_loss = cal_running_avg_loss(loss.item(), avg_loss) + loss /= config.gradient_accumulation_steps + + if is_train: + loss.backward() + if i % config.gradient_accumulation_steps == 0 or i == batch_num - 1: + + if config.max_grad_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), config.max_grad_norm) + + if config.use_tpu: + xm.optimizer_step(optimizer) + else: + optimizer.step() + optimizer.zero_grad() + + if scheduler is not None: + scheduler.step() + else: + result = (model.module if hasattr(model, "module") else model).eval_step(inputs, outputs) + results.extend(result) + + if is_master: + pbar.set_description( + f"epoch: {kwargs['epoch'] + 1}, {('train' if is_train else 'valid')} loss: {min(100, round(avg_loss, 4))}" + ) + + return { + "loss": avg_loss, + "result": results, + } + + +def run_epoch(**kwargs): + model = kwargs.pop("model") + if kwargs["config"].use_tpu: + results = model(_run_epoch, **kwargs) + else: + results = _run_epoch(model, **kwargs) + + if isinstance(results, list): + loss = sum([result["loss"] for result in results]) / len(results) + result = [] + for res in results: + result.extend(res["result"]) + results = {"loss": loss, "result": result} + + return results + + +def run(parser): + # NOTE Remove redundant bigbird logs + transformers.logging.get_logger("transformers.models.big_bird.modeling_big_bird").setLevel(logging.ERROR) + + args, _ = parser.parse_known_args() + + model = MODEL_CLASS_MAP.get(args.task, None) + if model is None: + raise Exception(f"Invalid model task {args.task}") + + parser = model.add_args(parser) + config = parser.parse_args() + + set_seed(config.seed) + + model = model(config) + + logger.info(f"configuration: {str(config)}") + + if config.use_tpu: + devices = xm.get_xla_supported_devices() + model_dp = xla_dp.DataParallel(model, device_ids=devices) + else: + if torch.cuda.is_available(): + gpu_count = torch.cuda.device_count() + logger.info(f"{gpu_count} GPU device detected") + devices = ["cuda:{}".format(i) for i in range(gpu_count)] + model_dp = torch.nn.DataParallel(model, device_ids=devices) + model.to(devices[0]) + else: + devices = ["cpu"] + model_dp = model + + config.world_size = len(devices) + if config.do_train: + train_loader = get_data(config, tokenizer=model.tokenizer) + valid_loader = get_data(config, tokenizer=model.tokenizer, is_train=False) + + optimizer = None + scheduler = None + if not config.use_tpu and config.do_train: + optimizer = model.get_optimizer() + scheduler = model.get_scheduler(len(train_loader), optimizer) + + params = { + "config": config, + "model": model_dp, + "optimizer": optimizer, + "scheduler": scheduler, + } + if not config.use_tpu: + params["device"] = devices[0] + + def do_eval(epoch): + with torch.no_grad(): + results = run_epoch(loader=valid_loader, epoch=epoch, is_train=False, **params)["result"] + results = EVAL_FUNC_MAP[config.task]( + config=config, + model=model, + loader=valid_loader, + tokenizer=model.tokenizer, + results=results, + ) + + logger.info("Eval results.") + for k, v in results["results"].items(): + logger.info(f"{k} : {v}") + + return results["best_score"] + + if config.do_train: + best_score = 0 + for epoch in range(config.num_train_epochs): + run_epoch(loader=train_loader, epoch=epoch, is_train=True, **params) + + score = 0 + if config.do_eval_during_train: + score = do_eval(epoch) + + if score >= best_score: + best_score = score + output_dir = os.path.join(config.output_dir, config.task, config.dataset, f"{epoch}-{best_score}-ckpt") + copy.deepcopy( + model_dp.module + if hasattr(model_dp, "module") + else model_dp._models[0] + if hasattr(model_dp, "_models") + else model_dp + ).cpu().save_pretrained(output_dir) + with open(os.path.join(output_dir, "finetune_config.json"), "w") as save_config: + json.dump(vars(config), save_config, sort_keys=True, indent=4) + logger.info(f"Checkpoint {output_dir} saved.") + + if config.do_eval: + do_eval(-1) + + +def main(parser): + args, _ = parser.parse_known_args() + + if not os.path.exists(args.cache_dir): + os.makedirs(args.cache_dir) + + output_dir = os.path.join(args.output_dir, args.task, args.dataset) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + run(parser) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--task", default=None, type=str, required=True) + parser.add_argument("--dataset", default=None, type=str, required=True) + parser.add_argument("--cache_dir", default="cache", type=str) + parser.add_argument("--output_dir", default="output", type=str) + parser.add_argument("--do_train", action="store_true") + parser.add_argument("--do_eval_during_train", action="store_true") + parser.add_argument("--do_eval", action="store_true") + parser.add_argument("--use_tpu", action="store_true") + parser.add_argument("--threads", default=4, type=int) + parser.add_argument("--seed", default=42, type=int) + + main(parser) diff --git a/finetune/scripts/run_fake_news.sh b/finetune/scripts/run_fake_news.sh new file mode 100644 index 0000000..99bae5a --- /dev/null +++ b/finetune/scripts/run_fake_news.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +python3 run.py \ +--task cls \ +--dataset fake_news \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path monologg/kobigbird-bert-base \ +--data_dir cache/fake_news_data \ +--train_file mission2_train.csv \ +--predict_file mission2_train.csv \ +--max_seq_length 1024 \ +--train_batch_size 4 \ +--eval_batch_size 2 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 2 \ +--num_labels 2 \ +--num_train_epochs 10 diff --git a/finetune/scripts/run_fake_news_short.sh b/finetune/scripts/run_fake_news_short.sh new file mode 100644 index 0000000..1cd4b84 --- /dev/null +++ b/finetune/scripts/run_fake_news_short.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +python3 run.py \ +--task cls \ +--dataset fake_news \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path klue/roberta-base \ +--data_dir cache/fake_news_data \ +--train_file mission2_train.csv \ +--predict_file mission2_train.csv \ +--max_seq_length 512 \ +--train_batch_size 8 \ +--eval_batch_size 8 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 1 \ +--num_labels 2 \ +--num_train_epochs 10 diff --git a/finetune/scripts/run_korquad_2.sh b/finetune/scripts/run_korquad_2.sh new file mode 100644 index 0000000..c4d6cd0 --- /dev/null +++ b/finetune/scripts/run_korquad_2.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +python3 run.py \ +--task qa \ +--dataset korquad_2 \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path monologg/kobigbird-bert-base \ +--data_dir cache/korquad_2 \ +--train_file train \ +--predict_file dev \ +--max_seq_length 4096 \ +--doc_stride 3072 \ +--max_answer_length 4096 \ +--train_batch_size 2 \ +--eval_batch_size 1 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 4 \ +--num_train_epochs 5 diff --git a/finetune/scripts/run_korquad_2_short.sh b/finetune/scripts/run_korquad_2_short.sh new file mode 100644 index 0000000..fef40de --- /dev/null +++ b/finetune/scripts/run_korquad_2_short.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +python3 run.py \ +--task qa \ +--dataset korquad_2 \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path klue/roberta-base \ +--data_dir cache/korquad_2 \ +--train_file train \ +--predict_file dev \ +--max_seq_length 512 \ +--doc_stride 384 \ +--max_answer_length 512 \ +--train_batch_size 8 \ +--eval_batch_size 8 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 1 \ +--num_train_epochs 5 diff --git a/finetune/scripts/run_modu_sentiment.sh b/finetune/scripts/run_modu_sentiment.sh new file mode 100644 index 0000000..3316ee3 --- /dev/null +++ b/finetune/scripts/run_modu_sentiment.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +python3 run.py \ +--task cls \ +--dataset modu_sentiment \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path monologg/kobigbird-bert-base \ +--data_dir cache/modu-corpus/sentiment-analysis \ +--train_file EXSA2002108040.json \ +--predict_file EXSA2002108040.json \ +--max_seq_length 1024 \ +--train_batch_size 4 \ +--eval_batch_size 2 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 2 \ +--num_labels 2 \ +--num_train_epochs 20 diff --git a/finetune/scripts/run_modu_sentiment_short.sh b/finetune/scripts/run_modu_sentiment_short.sh new file mode 100644 index 0000000..44a00a3 --- /dev/null +++ b/finetune/scripts/run_modu_sentiment_short.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +python3 run.py \ +--task cls \ +--dataset modu_sentiment \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path klue/roberta-base \ +--data_dir cache/modu-corpus/sentiment-analysis \ +--train_file EXSA2002108040.json \ +--predict_file EXSA2002108040.json \ +--max_seq_length 512 \ +--train_batch_size 8 \ +--eval_batch_size 8 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 1 \ +--num_labels 2 \ +--num_train_epochs 20 diff --git a/finetune/scripts/run_tydiqa.sh b/finetune/scripts/run_tydiqa.sh new file mode 100644 index 0000000..60adb7c --- /dev/null +++ b/finetune/scripts/run_tydiqa.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +python3 run.py \ +--task qa \ +--dataset tydiqa \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path monologg/kobigbird-bert-base \ +--data_dir cache/tydiqa \ +--train_file tydiqa-v1.0-train.jsonl \ +--predict_file tydiqa-v1.0-dev.jsonl \ +--max_seq_length 4096 \ +--doc_stride 3072 \ +--max_answer_length 32 \ +--version_2_with_negative \ +--train_batch_size 2 \ +--eval_batch_size 1 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 4 \ +--num_train_epochs 5 diff --git a/finetune/scripts/run_tydiqa_short.sh b/finetune/scripts/run_tydiqa_short.sh new file mode 100644 index 0000000..59ad063 --- /dev/null +++ b/finetune/scripts/run_tydiqa_short.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +python3 run.py \ +--task qa \ +--dataset tydiqa \ +--do_train \ +--do_eval_during_train \ +--do_eval \ +--use_tpu \ +--model_name_or_path klue/roberta-base \ +--data_dir cache/tydiqa \ +--train_file tydiqa-v1.0-train.jsonl \ +--predict_file tydiqa-v1.0-dev.jsonl \ +--max_seq_length 512 \ +--doc_stride 384 \ +--max_answer_length 32 \ +--version_2_with_negative \ +--train_batch_size 8 \ +--eval_batch_size 8 \ +--learning_rate 3e-5 \ +--gradient_accumulation_steps 1 \ +--num_train_epochs 5 diff --git a/pretrain/.gitignore b/pretrain/.gitignore new file mode 100644 index 0000000..60490f0 --- /dev/null +++ b/pretrain/.gitignore @@ -0,0 +1,6 @@ +data/ +pretrain_tfrecords/ +*wiki*.txt + +init_checkpoint/ +kobigbird-bert-base*/ diff --git a/pretrain/README.md b/pretrain/README.md new file mode 100644 index 0000000..092369c --- /dev/null +++ b/pretrain/README.md @@ -0,0 +1,156 @@ +# Pretraining BigBird + +

    + ํ•œ๊ตญ์–ด | + English +

    + +## ๊ธฐ์กด BigBird ์ฝ”๋“œ์™€์˜ ์ฐจ์ด์  ๋ฐ ๊ฐœ์„ ์  + +> NOTE: ์›๋ณธ BigBird ์ฝ”๋“œ๋Š” [original bigbird github](https://github.com/google-research/bigbird) ์ฐธ๊ณ  + +- **RoBERTa๊ฐ€ ์•„๋‹Œ BERT๋ฅผ ์ด์šฉํ•˜์—ฌ warm start** + + - ์ž์ฒด์ ์œผ๋กœ ์ƒˆ๋กœ ํ•™์Šตํ•œ BERT๋ฅผ ์ด์šฉ + +- **BERT checkpoint loading ๊ด€๋ จ ์ด์Šˆ ํ•ด๊ฒฐ** + + - tf variable name์„ ์ˆ˜์ •ํ•˜์—ฌ LMHead๊ฐ€ ์ •์ƒ์ ์œผ๋กœ ๋กœ๋”ฉ๋˜์ง€ ์•Š๋Š” ์ด์Šˆ ํ•ด๊ฒฐ (e.g. `transform` -> `transform/dense`) + - ๊ธฐ์กด์˜ 512์ธ position embeddings์„ ๋กœ๋”ฉํ•˜์ง€ ์•Š๋„๋ก ์ฒ˜๋ฆฌ + +- **Hard Coding ์ด์Šˆ ํ•ด๊ฒฐ** + + - `MAX_SEQ_LEN=4096`์œผ๋กœ ๊ฐ•์ œ๋˜์–ด ์žˆ๋Š” ๋ถ€๋ถ„ + - Sentencepiece tokenizer ์‚ฌ์šฉ์ด ๊ฐ•์ œ๋˜์–ด ์žˆ๋Š” ๋ถ€๋ถ„ + - RoBERTa Vocab์— ๋งž์ถฐ token id๊ฐ€ ํ•˜๋“œ์ฝ”๋”ฉ๋œ ๋ถ€๋ถ„ + +- **`Tensorflow Datasets (tfds)`๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋„๋ก ๋ณ€๊ฒฝ** + + - ์—ฌ๋Ÿฌ ๋ฒ„์ „์œผ๋กœ ํ…Œ์ŠคํŠธํ•ด๋ณด์•˜์ง€๋งŒ ์ •์ƒ ์ž‘๋™ํ•˜์ง€ ์•Š๋Š” ์ด์Šˆ ์กด์žฌ + - ๊ทธ ๋Œ€์‹  TFRecord Builder ์ฝ”๋“œ ์ถ”๊ฐ€ ([`create_pretraining_data.py`](./create_pretraining_data.py)) + +## How to Pretrain + +### 0. (Optional) Prepare your own BERT + +- Warm Start๋ฅผ ํ•˜์ง€ ์•Š๋Š”๋‹ค๋ฉด BERT๊ฐ€ ์—†์–ด๋„ ์ƒ๊ด€์—†์Šต๋‹ˆ๋‹ค. +- BERT๋ฅผ ์ง์ ‘ ๋งŒ๋“ค๊ณ  ์‹ถ์œผ๋ฉด [BERT Github](https://github.com/google-research/bert)๋ฅผ ์ฐธ๊ณ  +- ์•„๋ž˜์™€ ๊ฐ™์€ ํ˜•ํƒœ๋กœ **tensorflow v1 checkpoint**๋ฅผ ์ค€๋น„ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. + +```text +init_checkpoint +โ”œโ”€โ”€ bert_config.json +โ”œโ”€โ”€ checkpoint +โ”œโ”€โ”€ graph.pbtxt +โ”œโ”€โ”€ model.ckpt-0.data-00000-of-00001 +โ”œโ”€โ”€ model.ckpt-0.index +โ””โ”€โ”€ model.ckpt-0.meta +``` + +### 1. Prepare Tokenizer + +- `Huggingface Transformers`์— ํ˜ธํ™˜๋˜๋Š” tokenizer ์ค€๋น„ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. (KoBigBird์˜ [tokenizer](./tokenizer)๋ฅผ sample๋กœ ์—…๋กœ๋“œ) +- `BigBirdTokenizer`์™€์˜ ํ˜ธํ™˜์„ ์œ„ํ•ด `BertTokenizer`์— `bos token(=)`, `eos token(=)`์„ ์ถ”๊ฐ€ + +### 2. Create TFRecord + +```bash +bash scripts/build_tfrecord.sh +``` + +#### Prepare Corpus + +[`ko_lm_dataformat`](https://github.com/monologg/ko_lm_dataformat) ํ˜น์€ `txt` ํŒŒ์ผ ์‚ฌ์šฉ ๊ฐ€๋Šฅ + +- `ko_lm_dataformat`์€ sentence split๋œ document ๋‹จ์œ„๋กœ ๋“ค์–ด์˜จ๋‹ค๊ณ  ๊ฐ€์ • + +```python +for data in rdr.stream_data(): + print(data) + +# data +['์ œ์ž„์Šค ์–ผ ์นดํ„ฐ ์ฃผ๋‹ˆ์–ด(1924๋…„ 10์›” 1์ผ ~)๋Š” ๋ฏผ์ฃผ๋‹น ์ถœ์‹  ๋ฏธ๊ตญ 39๋Œ€ ๋Œ€ํ†ต๋ น (1977๋…„ ~ 1981๋…„)์ด๋‹ค.', + '์ง€๋ฏธ ์นดํ„ฐ๋Š” ์กฐ์ง€์•„์ฃผ ์„ฌํ„ฐ ์นด์šดํ‹ฐ ํ”Œ๋ ˆ์ธ์Šค ๋งˆ์„์—์„œ ํƒœ์–ด๋‚ฌ๋‹ค.', + '๊ทธ ํ›„ ํ•ด๊ตฐ์— ๋“ค์–ด๊ฐ€ ์ „ํ•จยท์›์ž๋ ฅยท์ž ์ˆ˜ํ•จ์˜ ์Šน๋ฌด์›์œผ๋กœ ์ผํ•˜์˜€๋‹ค'., + ...] +``` + +- `txt`์˜ ๊ฒฝ์šฐ document ์‚ฌ์ด์— newline์ด ์žˆ๋‹ค๊ณ  ๊ฐ€์ • + +```text +์ œ์ž„์Šค ์–ผ ์นดํ„ฐ ์ฃผ๋‹ˆ์–ด(1924๋…„ 10์›” 1์ผ ~)๋Š” ๋ฏผ์ฃผ๋‹น ์ถœ์‹  ๋ฏธ๊ตญ 39๋Œ€ ๋Œ€ํ†ต๋ น (1977๋…„ ~ 1981๋…„)์ด๋‹ค. +์ง€๋ฏธ ์นดํ„ฐ๋Š” ์กฐ์ง€์•„์ฃผ ์„ฌํ„ฐ ์นด์šดํ‹ฐ ํ”Œ๋ ˆ์ธ์Šค ๋งˆ์„์—์„œ ํƒœ์–ด๋‚ฌ๋‹ค. + +์ˆ˜ํ•™(ๆ•ธๅญธ)์€ ์ˆ˜, ์–‘, ๊ตฌ์กฐ, ๊ณต๊ฐ„, ๋ณ€ํ™” ๋“ฑ์˜ ๊ฐœ๋…์„ ๋‹ค๋ฃจ๋Š” ํ•™๋ฌธ์ด๋‹ค. +๋„๋ฆฌ ๋ฐ›์•„๋“ค์—ฌ์ง€๋Š” ๋ช…ํ™•ํ•œ ์ •์˜๋Š” ์—†์œผ๋‚˜ ํ˜„๋Œ€ ์ˆ˜ํ•™์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์—„๋ฐ€ํ•œ ๋…ผ๋ฆฌ์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ถ”์ƒ์  ๋Œ€์ƒ์„ ํƒ๊ตฌํ•˜๋ฉฐ, ์ด๋Š” ๊ทœ์น™์˜ ๋ฐœ๊ฒฌ๊ณผ ๋ฌธ์ œ์˜ ์ œ์‹œ ๋ฐ ํ•ด๊ฒฐ์˜ ๊ณผ์ •์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. + +๋ฌธํ•™(ๆ–‡ๅญธ)์€ ์–ธ์–ด๋ฅผ ์˜ˆ์ˆ ์  ํ‘œํ˜„์˜ ์ œ์žฌ๋กœ ์‚ผ์•„ ์ƒˆ๋กœ์šด ์˜๋ฏธ๋ฅผ ์ฐฝ์ถœํ•˜์—ฌ, ์ธ๊ฐ„๊ณผ ์‚ฌํšŒ๋ฅผ ์ง„์‹ค๋˜๊ฒŒ ๋ฌ˜์‚ฌํ•˜๋Š” ์˜ˆ์ˆ ์˜ ํ•˜์œ„๋ถ„์•ผ์ด๋‹ค. +๊ฐ„๋‹จํ•˜๊ฒŒ ์„ค๋ช…ํ•˜๋ฉด, ์–ธ์–ด๋ฅผ ํ†ตํ•ด ์ธ๊ฐ„์˜ ์‚ถ์„ ๋ฏธ์ (็พŽ็š„)์œผ๋กœ ํ˜•์ƒํ™”ํ•œ ๊ฒƒ์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. +``` + +#### Details + +- `BertTokenizer` ์‚ฌ์šฉ์„ ๊ฐ€์ •ํ•˜๊ณ  ์ฝ”๋“œ ์ž‘์„ฑ (๋งŒ์ผ ๋‹ค๋ฅธ tokenizer๋ฅผ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ ์ฝ”๋“œ๋ฅผ ์ง์ ‘ ์ˆ˜์ •ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค) +- `whole word masking`, `max ngram masking` ์ ์šฉ ๊ฐ€๋Šฅ +- `static masking` (dupe_factor๋ฅผ ํ†ตํ•ด ๋‹ค๋ฅธ ๋งˆ์Šคํ‚น์œผ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ๋ช‡ ๋ฐฐ๋กœ ๋งŒ๋“ค์ง€ ๊ฒฐ์ •) +- `long_seq_threshold`๋ฅผ ํ†ตํ•ด example์„ ๋งŒ๋“ค ๋•Œ ๋„ˆ๋ฌด ๊ธด ๋ฌธ์žฅ์˜ ๊ฒฝ์šฐ ์—ฌ๋Ÿฌ ๊ฐœ์˜ example๋กœ ๋‚˜๋ˆ ์คŒ +- `RoBERTa`์—์„œ ์‚ฌ์šฉํ•œ **full sentences** ๋ฐฉ์‹์œผ๋กœ example ์ƒ์„ฑ + +### 3. Pretraining with TPU + +- **TPU์˜ tensorflow version์€ `2.3.1`์„ ๊ถŒ์žฅ** +- ๊ธฐ๋ณธ์ ์œผ๋กœ BERT์—์„œ Warm start ํ–ˆ์œผ๋ฉฐ, `position embedding (size 4096)`๋งŒ random initialize ์ฒ˜๋ฆฌ +- `max_predictions_per_seq=640` ์œผ๋กœ ์„ค์ • (์› ๋…ผ๋ฌธ์˜ 600๋ณด๋‹ค ํฌ๊ฒŒ ์„ค์ •) +- `tokenizer`, `pretrain_config.json` ๋ชจ๋‘ `output_dir`์— ์ €์žฅํ•˜๋„๋ก ์ฒ˜๋ฆฌ (์ถ”ํ›„ huggingface transformers ํฌ๋งท์œผ๋กœ ๋ณ€ํ™˜ํ•  ๋•Œ ์‚ฌ์šฉ) + +#### How to run + +- Google Storage์— `tfrecord` ์™€ `BERT checkpoint`(optional) ์—…๋กœ๋“œ + +```bash +gsutil -m cp -r pretrain_tfrecords gs://{$BUCKET_NAME} # tfrecord +gsutil -m cp -r init_checkpoint gs://{$BUCKET_NAME} # BERT tf v1 ckpt +``` + +- GCP cloud shell์—์„œ ์•„๋ž˜์˜ ๋ช…๋ น์–ด๋กœ instance์™€ TPU ์„ธํŒ… + +```bash +ctpu up --zone=europe-west4-a --tf-version=2.3.1 --tpu-size=v3-8 --machine-type=n1-standard-1 --disk-size-gb=20 --name={$GCP_NAME} --project={$PROJECT_NAME} +``` + +- Instance์—์„œ ํ•™์Šต ์ง„ํ–‰ + + - BERT tf v1 checkpoint๋ฅผ warm start์— ์‚ฌ์šฉํ•  ์‹œ ์Šคํฌ๋ฆฝํŠธ์— ์•„๋ž˜์™€ ๊ฐ™์ด ์ธ์ž๋ฅผ ์ถ”๊ฐ€ํ•ด์ฃผ์„ธ์š” + + ```bash + --init_checkpoint=gs://$BUCKET_NAME/init_checkpoint/model.ckpt-0 + ``` + +```bash +cd pretrain +pip3 install -r requirements.txt +bash scripts/base_size_tpu.sh +``` + +## Convert Tensorflow checkpoint to Huggingface Transformers format + +```bash +python3 convert_bigbird_tf_to_pt.py \ + --checkpoint_dir $ORIG_TF_BERT_CKPT \ + --big_bird_config_file $BIGBIRD_CONFIG_PATH \ + --output_dir $PT_OUTPUT_DIR \ + --tokenizer_dir $TOKENIZER_DIR \ + --step_on_output +``` + +- `--big_bird_config_file`๋ฅผ ๋ช…์‹œํ•˜์ง€ ์•Š์œผ๋ฉด, script๊ฐ€ ์ž๋™์œผ๋กœ tensorflow checkpoint ์•ˆ์˜ `pretrain_config.json`์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. +- `--tokenizer_dir`๋ฅผ ๋ช…์‹œํ•˜์ง€ ์•Š์œผ๋ฉด, script๊ฐ€ ์ž๋™์œผ๋กœ tensorflow checkpoint ์•ˆ์˜ `tokenizer`๋ฅผ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. + +## Reference + +- [Original BigBird implementation](https://github.com/google-research/bigbird) +- [BERT tensorflow v1 implementation](https://github.com/google-research/bert) +- [BERT tensorflow v2 implementation](https://github.com/tensorflow/models/tree/d4c5f8975a7b89f01421101882bc8922642c2314/official/nlp/bert) +- [ELECTRA implementation](https://github.com/google-research/electra) +- [Huggingface Transformers Documentation](https://huggingface.co/transformers/) +- [ko-lm-dataformat](https://github.com/monologg/ko_lm_dataformat) diff --git a/pretrain/README_EN.md b/pretrain/README_EN.md new file mode 100644 index 0000000..8798df7 --- /dev/null +++ b/pretrain/README_EN.md @@ -0,0 +1,156 @@ +# Pretraining BigBird + +

    + ํ•œ๊ตญ์–ด | + English +

    + +## Differences & Improvements from existing BigBird code + +> NOTE: Refer [original bigbird github](https://github.com/google-research/bigbird) for the original BigBird code + +- **Warm start using BERT instead of RoBERTa** + + - Use newly pretrained BERT for warm start + +- **Fix BERT checkpoint loading issues** + + - Fix the issue where LMHead doesn't load properly by modifying tf variable name (e.g. `transform` -> `transform/dense`) + - Prevent not to load existing 512 position embeddings + +- **Fix Hard Coding issues** + + - Forced `MAX_SEQ_LEN=4096` + - Forced to use Sentencepiece tokenizer + - Token ids which are hard-coded according to the RoBERTa Vocab + +- **Change not to use `Tensorflow Datasets (tfds)`** + + - We've tested with several versions, but doesn't work properly. + - Instead, we add TFRecord builder code ([`create_pretraining_data.py`](./create_pretraining_data.py)) + +## How to Pretrain + +### 0. (Optional) Prepare your own BERT + +- It doesn't matter if you don't have a BERT if you don't use warm starting. +- Refer [BERT Github](https://github.com/google-research/bert) if you want to make BERT by yourself. +- Prepare **tensorflow v1 checkpoint** as below. + +```text +init_checkpoint +โ”œโ”€โ”€ bert_config.json +โ”œโ”€โ”€ checkpoint +โ”œโ”€โ”€ graph.pbtxt +โ”œโ”€โ”€ model.ckpt-0.data-00000-of-00001 +โ”œโ”€โ”€ model.ckpt-0.index +โ””โ”€โ”€ model.ckpt-0.meta +``` + +### 1. Prepare Tokenizer + +- Prepare the tokenizer witch is compatible with `Huggingface Transformers`. (Uploaded KoBigBird [tokenizer](./tokenizer) as sample) +- Added `bos token(=)`and `eos token(=)` in `BertTokenizer` for compatibility with `BigBirdTokenizer` + +### 2. Create TFRecord + +```bash +bash scripts/build_tfrecord.sh +``` + +#### Prepare Corpus + +You can use either [`ko_lm_dataformat`](https://github.com/monologg/ko_lm_dataformat) or `txt` file. + +- Assume that `ko_lm_dataformat` comes in sentence-splitted documents + +```python +for data in rdr.stream_data(): + print(data) + +# data +['์ œ์ž„์Šค ์–ผ ์นดํ„ฐ ์ฃผ๋‹ˆ์–ด(1924๋…„ 10์›” 1์ผ ~)๋Š” ๋ฏผ์ฃผ๋‹น ์ถœ์‹  ๋ฏธ๊ตญ 39๋Œ€ ๋Œ€ํ†ต๋ น (1977๋…„ ~ 1981๋…„)์ด๋‹ค.', + '์ง€๋ฏธ ์นดํ„ฐ๋Š” ์กฐ์ง€์•„์ฃผ ์„ฌํ„ฐ ์นด์šดํ‹ฐ ํ”Œ๋ ˆ์ธ์Šค ๋งˆ์„์—์„œ ํƒœ์–ด๋‚ฌ๋‹ค.', + '๊ทธ ํ›„ ํ•ด๊ตฐ์— ๋“ค์–ด๊ฐ€ ์ „ํ•จยท์›์ž๋ ฅยท์ž ์ˆ˜ํ•จ์˜ ์Šน๋ฌด์›์œผ๋กœ ์ผํ•˜์˜€๋‹ค'., + ...] +``` + +- For `txt`, assume that there is a newline between documents + +```text +์ œ์ž„์Šค ์–ผ ์นดํ„ฐ ์ฃผ๋‹ˆ์–ด(1924๋…„ 10์›” 1์ผ ~)๋Š” ๋ฏผ์ฃผ๋‹น ์ถœ์‹  ๋ฏธ๊ตญ 39๋Œ€ ๋Œ€ํ†ต๋ น (1977๋…„ ~ 1981๋…„)์ด๋‹ค. +์ง€๋ฏธ ์นดํ„ฐ๋Š” ์กฐ์ง€์•„์ฃผ ์„ฌํ„ฐ ์นด์šดํ‹ฐ ํ”Œ๋ ˆ์ธ์Šค ๋งˆ์„์—์„œ ํƒœ์–ด๋‚ฌ๋‹ค. + +์ˆ˜ํ•™(ๆ•ธๅญธ)์€ ์ˆ˜, ์–‘, ๊ตฌ์กฐ, ๊ณต๊ฐ„, ๋ณ€ํ™” ๋“ฑ์˜ ๊ฐœ๋…์„ ๋‹ค๋ฃจ๋Š” ํ•™๋ฌธ์ด๋‹ค. +๋„๋ฆฌ ๋ฐ›์•„๋“ค์—ฌ์ง€๋Š” ๋ช…ํ™•ํ•œ ์ •์˜๋Š” ์—†์œผ๋‚˜ ํ˜„๋Œ€ ์ˆ˜ํ•™์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์—„๋ฐ€ํ•œ ๋…ผ๋ฆฌ์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ถ”์ƒ์  ๋Œ€์ƒ์„ ํƒ๊ตฌํ•˜๋ฉฐ, ์ด๋Š” ๊ทœ์น™์˜ ๋ฐœ๊ฒฌ๊ณผ ๋ฌธ์ œ์˜ ์ œ์‹œ ๋ฐ ํ•ด๊ฒฐ์˜ ๊ณผ์ •์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. + +๋ฌธํ•™(ๆ–‡ๅญธ)์€ ์–ธ์–ด๋ฅผ ์˜ˆ์ˆ ์  ํ‘œํ˜„์˜ ์ œ์žฌ๋กœ ์‚ผ์•„ ์ƒˆ๋กœ์šด ์˜๋ฏธ๋ฅผ ์ฐฝ์ถœํ•˜์—ฌ, ์ธ๊ฐ„๊ณผ ์‚ฌํšŒ๋ฅผ ์ง„์‹ค๋˜๊ฒŒ ๋ฌ˜์‚ฌํ•˜๋Š” ์˜ˆ์ˆ ์˜ ํ•˜์œ„๋ถ„์•ผ์ด๋‹ค. +๊ฐ„๋‹จํ•˜๊ฒŒ ์„ค๋ช…ํ•˜๋ฉด, ์–ธ์–ด๋ฅผ ํ†ตํ•ด ์ธ๊ฐ„์˜ ์‚ถ์„ ๋ฏธ์ (็พŽ็š„)์œผ๋กœ ํ˜•์ƒํ™”ํ•œ ๊ฒƒ์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. +``` + +#### Details + +- Code assumes you are using `BertTokenizer`. (If you use a different tokenizer, you need to modify the code by yourself) +- `whole word masking` and `max ngram masking` are available. +- `static masking` (Decide how many times to double the data with different masking via dupe_factor) +- When creating an example through `long_seq_threshold`, a sentence that is too long is divided into multiple examples. +- Create examples with **full sentences**, according to the method used in `RoBERTa` + +### 3. Pretraining with TPU + +- **Strongly recommend to use `tensorflow==2.3.1` for TPU.** +- Warm started from BERT, and only `position embedding (size 4096)` was random initialized. +- `max_predictions_per_seq=640` (set greater than 600 of the original paper) +- Both `tokenizer` and `pretrain_config.json` will be saved in `output_dir` (Will be used when converting to huggingface transformers format) + +#### How to run + +- Upload `tfrecord` and `BERT checkpoint` (optional) to Google Storage + +```bash +gsutil -m cp -r pretrain_tfrecords gs://{$BUCKET_NAME} # tfrecord +gsutil -m cp -r init_checkpoint gs://{$BUCKET_NAME} # BERT tf v1 ckpt +``` + +- In GCP cloud shell, set the instance and TPU with the following command + +```bash +ctpu up --zone=europe-west4-a --tf-version=2.3.1 --tpu-size=v3-8 --machine-type=n1-standard-1 --disk-size-gb=20 --name={$GCP_NAME} --project={$PROJECT_NAME} +``` + +- Run training on instance + + - When using BERT tf v1 checkpoint for warm start, please add the following argument to the script. + + ```bash + --init_checkpoint=gs://$BUCKET_NAME/init_checkpoint/model.ckpt-0 + ``` + +```bash +cd pretrain +pip3 install -r requirements.txt +bash scripts/base_size_tpu.sh +``` + +## Convert Tensorflow checkpoint to Huggingface Transformers format + +```bash +python3 convert_bigbird_tf_to_pt.py \ + --checkpoint_dir $ORIG_TF_BERT_CKPT \ + --big_bird_config_file $BIGBIRD_CONFIG_PATH \ + --output_dir $PT_OUTPUT_DIR \ + --tokenizer_dir $TOKENIZER_DIR \ + --step_on_output +``` + +- If you don't specify `--big_bird_config_file`, the script will automatically use `pretrain_config.json` in tensorflow checkpoint. +- If `--tokenizer_dir` is not specified, the script will automatically use the `tokenizer` in tensorflow checkpoint. + +## Reference + +- [Original BigBird implementation](https://github.com/google-research/bigbird) +- [BERT tensorflow v1 implementation](https://github.com/google-research/bert) +- [BERT tensorflow v2 implementation](https://github.com/tensorflow/models/tree/d4c5f8975a7b89f01421101882bc8922642c2314/official/nlp/bert) +- [ELECTRA implementation](https://github.com/google-research/electra) +- [Huggingface Transformers Documentation](https://huggingface.co/transformers/) +- [ko-lm-dataformat](https://github.com/monologg/ko_lm_dataformat) diff --git a/pretrain/convert_bigbird_tf_to_pt.py b/pretrain/convert_bigbird_tf_to_pt.py new file mode 100644 index 0000000..dc4ee7e --- /dev/null +++ b/pretrain/convert_bigbird_tf_to_pt.py @@ -0,0 +1,161 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert BigBird checkpoint.""" + + +import argparse +import json +import os + +import torch +import transformers +from transformers import AutoTokenizer, BigBirdConfig, BigBirdForPreTraining, load_tf_weights_in_big_bird +from transformers.utils import check_min_version, logging +from transformers.utils.versions import require_version + +logger = transformers.logging.get_logger() +logging.set_verbosity_info() +transformers.logging.enable_default_handler() +transformers.logging.enable_explicit_format() + +# NOTE check library version +require_version("torch>=1.6.0", "To fix: pip install torch>=1.6.0") +check_min_version("4.11.3") + +logger.warning("This script is tested on `transformers==4.11.3`. Might not work properly on other version!") + + +def load_bigbird_config(config_filepath): + """Load bigbird config from original tf checkpoint. (pretrain_config.json)""" + config_key_lst = [ + "attention_probs_dropout_prob", + "attention_type", + "block_size", + "pad_token_id", + "bos_token_id", + "eos_token_id", + "sep_token_id", + "gradient_checkpointing", # Originally `use_gradient_checkpointing` + "hidden_dropout_prob", + "hidden_size", + "initializer_range", + "intermediate_size", + "max_position_embeddings", + "num_attention_heads", + "num_hidden_layers", + "num_random_blocks", + "pad_token_id", + "rescale_embeddings", + "type_vocab_size", + "use_bias", + "vocab_size", + ] + + config = { + "position_embedding_type": "absolute", + "tokenizer_class": "BertTokenizer", # NOTE Remove this one if you use other tokenizer. + } + + with open(config_filepath, "r", encoding="utf-8") as f: + tf_config = json.load(f) + + for config_key in config_key_lst: + if config_key in tf_config: + config[config_key] = tf_config[config_key] + else: + if config_key == "gradient_checkpointing": + config[config_key] = tf_config["use_gradient_checkpointing"] + elif config_key == "num_random_blocks": + config[config_key] = tf_config["num_rand_blocks"] + elif config_key == "rescale_embeddings": + config[config_key] = tf_config["rescale_embedding"] + else: + raise KeyError(f"{config_key} not in tensorflow config!!") + + return dict(sorted(config.items())) + + +def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, output_dir, tokenizer_path): + # Initialize PyTorch model + config = BigBirdConfig.from_dict(load_bigbird_config(os.path.join(tf_checkpoint_path, big_bird_config_file))) + print(f"Building PyTorch model from configuration: {config}") + + model = BigBirdForPreTraining(config) + + # Load weights from tf checkpoint + load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=False) + + # Save pytorch-model + print(f"Save PyTorch model to {output_dir}") + model.save_pretrained(output_dir) + + # NOTE Convert model which is compatible for torch<1.5 + pytorch_model = torch.load(os.path.join(output_dir, "pytorch_model.bin")) + torch.save( + pytorch_model, + os.path.join(args.output_dir, "pytorch_model.bin"), + _use_new_zipfile_serialization=False, + ) + + # Save tokenizer + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + tokenizer.model_max_length = config.max_position_embeddings # 1024, 2048, 4096 + tokenizer.save_pretrained(output_dir) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--checkpoint_dir", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." + ) + parser.add_argument( + "--big_bird_config_file", + default="pretrain_config.json", + type=str, + help="The config json file corresponding to the pre-trained BigBird model. \n" + "This specifies the model architecture.", + ) + parser.add_argument("--output_dir", default=None, type=str, required=True, help="Path to the output PyTorch model.") + parser.add_argument( + "--tokenizer_dir", + default=None, + type=str, + help="Tokenizer path (include vocab.txt, tokenizer_config.json, special_tokens_map.json)\n" + "If not specified, converter will check tokenizer dir in tensorflow checkpoint", + ) + parser.add_argument( + "--step_on_output", + action="store_true", + help="Whether to write step on pytorch output\ne.g. kobigbird-bert-base-200k, kobigbird-bert-base-600k", + ) + args = parser.parse_args() + + # Write step on output_dir + if args.step_on_output: + with open(os.path.join(args.checkpoint_dir, "checkpoint"), "r", encoding="utf-8") as f: + line = f.readline() # read only first line & check with step of checkpoint to convert + step = line.split("-")[-1][:-1] + + if len(step) <= 4: + raise ValueError("Step should be bigger than 1k") + step = step[:-4] + + args.output_dir = args.output_dir + f"-{step}k" + + # Check tokenizer path (If tokenizer_dir is not specified, get tokenizer path from tf checkpoint dir) + tokenizer_path = args.tokenizer_dir if args.tokenizer_dir else os.path.join(args.checkpoint_dir, "tokenizer") + + convert_tf_checkpoint_to_pytorch(args.checkpoint_dir, args.big_bird_config_file, args.output_dir, tokenizer_path) diff --git a/pretrain/create_pretraining_data.py b/pretrain/create_pretraining_data.py new file mode 100644 index 0000000..c5af85d --- /dev/null +++ b/pretrain/create_pretraining_data.py @@ -0,0 +1,698 @@ +# coding=utf-8 +# Copyright 2020 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Writes out text data as tfrecords""" + +import argparse +import collections +import itertools +import multiprocessing +import os +import random +import sys +import time +from pathlib import Path + +import ko_lm_dataformat as kldf +import numpy as np +import tensorflow as tf +from transformers import AutoTokenizer + + +def printable_text(text): + """Returns text encoded in a way suitable for print or `tf.logging`.""" + + # These functions want `str` for both Python2 and Python3, but in one case + # it's a Unicode string and in the other it's a byte string. + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + + +def create_int_feature(values): + feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) + return feature + + +def create_float_feature(values): + feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) + return feature + + +MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"]) + +# A _Gram is a [half-open) interval of token indices which form a word. +# E.g., +# words: ["The", "doghouse"] +# tokens: ["The", "dog", "##house"] +# grams: [(0,1), (1,3)] +_Gram = collections.namedtuple("_Gram", ["begin", "end"]) + + +def _window(iterable, size): + """Helper to create a sliding window iterator with a given size. + E.g., + input = [1, 2, 3, 4] + _window(input, 1) => [1], [2], [3], [4] + _window(input, 2) => [1, 2], [2, 3], [3, 4] + _window(input, 3) => [1, 2, 3], [2, 3, 4] + _window(input, 4) => [1, 2, 3, 4] + _window(input, 5) => None + Args: + iterable: elements to iterate over. + size: size of the window. + Yields: + Elements of `iterable` batched into a sliding window of length `size`. + """ + i = iter(iterable) + window = [] + try: + for e in range(0, size): + window.append(next(i)) + yield window + except StopIteration: + # handle the case where iterable's length is less than the window size. + return + for e in i: + window = window[1:] + [e] + yield window + + +def _contiguous(sorted_grams): + """Test whether a sequence of grams is contiguous. + Args: + sorted_grams: _Grams which are sorted in increasing order. + Returns: + True if `sorted_grams` are touching each other. + E.g., + _contiguous([(1, 4), (4, 5), (5, 10)]) == True + _contiguous([(1, 2), (4, 5)]) == False + """ + for a, b in _window(sorted_grams, 2): + if a.end != b.begin: + return False + return True + + +def _wordpieces_to_grams(tokens, special_tokens): + """Reconstitue grams (words) from `tokens`. + E.g., + tokens: ['[CLS]', 'That', 'lit', '##tle', 'blue', 'tru', '##ck', '[SEP]'] + grams: [ [1,2), [2, 4), [4,5) , [5, 6)] + Args: + tokens: list of wordpieces + Returns: + List of _Grams representing spans of whole words + (without "[CLS]" and "[SEP]"). + """ + grams = [] + gram_start_pos = None + for i, token in enumerate(tokens): + if gram_start_pos is not None and token.startswith("##"): + continue + if gram_start_pos is not None: + grams.append(_Gram(gram_start_pos, i)) + if token not in special_tokens: + gram_start_pos = i + else: + gram_start_pos = None + if gram_start_pos is not None: + grams.append(_Gram(gram_start_pos, len(tokens))) + return grams + + +def _masking_ngrams(grams, max_ngram_size, max_masked_tokens): + """Create a list of masking {1, ..., n}-grams from a list of one-grams. + This is an extention of 'whole word masking' to mask multiple, contiguous + words such as (e.g., "the red boat"). + Each input gram represents the token indices of a single word, + words: ["the", "red", "boat"] + tokens: ["the", "red", "boa", "##t"] + grams: [(0,1), (1,2), (2,4)] + For a `max_ngram_size` of three, possible outputs masks include: + 1-grams: (0,1), (1,2), (2,4) + 2-grams: (0,2), (1,4) + 3-grams; (0,4) + Output masks will not overlap and contain less than `max_masked_tokens` total + tokens. E.g., for the example above with `max_masked_tokens` as three, + valid outputs are, + [(0,1), (1,2)] # "the", "red" covering two tokens + [(1,2), (2,4)] # "red", "boa", "##t" covering three tokens + The length of the selected n-gram follows a zipf weighting to + favor shorter n-gram sizes (weight(1)=1, weight(2)=1/2, weight(3)=1/3, ...). + Args: + grams: List of one-grams. + max_ngram_size: Maximum number of contiguous one-grams combined to create + an n-gram. + max_masked_tokens: Maximum total number of tokens to be masked. + Returns: + A list of n-grams to be used as masks. + """ + if not grams: + return None + + grams = sorted(grams) + num_tokens = grams[-1].end + + # Ensure our grams are valid (i.e., they don't overlap). + for a, b in _window(grams, 2): + if a.end > b.begin: + raise ValueError("overlapping grams: {}".format(grams)) + + # Build map from n-gram length to list of n-grams. + ngrams = {i: [] for i in range(1, max_ngram_size + 1)} + for gram_size in range(1, max_ngram_size + 1): + for g in _window(grams, gram_size): + if _contiguous(g): + # Add an n-gram which spans these one-grams. + ngrams[gram_size].append(_Gram(g[0].begin, g[-1].end)) + + # Shuffle each list of n-grams. + for v in ngrams.values(): + random.shuffle(v) + + # Create the weighting for n-gram length selection. + # Stored cummulatively for `random.choices` below. + cummulative_weights = list(itertools.accumulate([1.0 / n for n in range(1, max_ngram_size + 1)])) + + output_ngrams = [] + # Keep a bitmask of which tokens have been masked. + masked_tokens = [False] * num_tokens + # Loop until we have enough masked tokens or there are no more candidate + # n-grams of any length. + # Each code path should ensure one or more elements from `ngrams` are removed + # to guarentee this loop terminates. + while sum(masked_tokens) < max_masked_tokens and sum(len(s) for s in ngrams.values()): + # Pick an n-gram size based on our weights. + sz = random.choices(range(1, max_ngram_size + 1), cum_weights=cummulative_weights)[0] + + # Ensure this size doesn't result in too many masked tokens. + # E.g., a two-gram contains _at least_ two tokens. + if sum(masked_tokens) + sz > max_masked_tokens: + # All n-grams of this length are too long and can be removed from + # consideration. + ngrams[sz].clear() + continue + + # All of the n-grams of this size have been used. + if not ngrams[sz]: + continue + + # Choose a random n-gram of the given size. + gram = ngrams[sz].pop() + num_gram_tokens = gram.end - gram.begin + + # Check if this would add too many tokens. + if num_gram_tokens + sum(masked_tokens) > max_masked_tokens: + continue + + # Check if any of the tokens in this gram have already been masked. + if sum(masked_tokens[gram.begin : gram.end]): + continue + + # Found a usable n-gram! Mark its tokens as masked and add it to return. + masked_tokens[gram.begin : gram.end] = [True] * (gram.end - gram.begin) + output_ngrams.append(gram) + return output_ngrams + + +def create_masked_lm_predictions( + tokens, + masked_lm_prob, + max_predictions_per_seq, + vocab_words, + do_whole_word_mask, + tokenizer, + max_ngram_size=None, +): + """Creates the predictions for the masked LM objective.""" + if do_whole_word_mask: + grams = _wordpieces_to_grams(tokens=tokens, special_tokens=tokenizer.special_tokens_map.values()) + else: + # Here we consider each token to be a word to allow for sub-word masking. + if max_ngram_size: + raise ValueError("cannot use ngram masking without whole word masking") + grams = [ + _Gram(i, i + 1) for i in range(0, len(tokens)) if tokens[i] not in tokenizer.special_tokens_map.values() + ] + + num_to_predict = min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob)))) + # Generate masks. If `max_ngram_size` in [0, None] it means we're doing + # whole word masking or token level masking. Both of these can be treated + # as the `max_ngram_size=1` case. + masked_grams = _masking_ngrams(grams, max_ngram_size or 1, num_to_predict) + masked_lms = [] + output_tokens = list(tokens) + for gram in masked_grams: + # 80% of the time, replace all n-gram tokens with [MASK] + if random.random() < 0.8: + replacement_action = lambda idx: tokenizer.mask_token + else: + # 10% of the time, keep all the original n-gram tokens. + if random.random() < 0.5: + replacement_action = lambda idx: tokens[idx] + # 10% of the time, replace each n-gram token with a random word. + else: + replacement_action = lambda idx: random.choice(vocab_words) + + for idx in range(gram.begin, gram.end): + output_tokens[idx] = replacement_action(idx) + masked_lms.append(MaskedLmInstance(index=idx, label=tokens[idx])) + + assert len(masked_lms) <= num_to_predict + masked_lms = sorted(masked_lms, key=lambda x: x.index) + + masked_lm_positions = [] + masked_lm_labels = [] + for p in masked_lms: + masked_lm_positions.append(p.index) + masked_lm_labels.append(p.label) + + return (output_tokens, masked_lm_positions, masked_lm_labels) + + +class ExampleBuilder(object): + """Given a stream of input text, creates pretraining examples.""" + + def __init__(self, args, tokenizer): + self.args = args + self._tokenizer = tokenizer + self._current_sentences = [] + self._current_length = 0 + self._max_length = args.max_seq_length + self._long_seq_length_limit = int(self._max_length * args.long_seq_threshold) + self._target_length = self._max_length + self.n_build = 0 + self.vocab_for_random_replacement = [] + + # NOTE This will except special tokens (e.g. [CLS], [unused0], ) + for token in self._tokenizer.vocab.keys(): + if not (token.startswith("[unused") and token.endswith("]")): + self.vocab_for_random_replacement.append(token) + + for special_token in self._tokenizer.special_tokens_map.values(): + if special_token not in self.vocab_for_random_replacement: + self.vocab_for_random_replacement.append(special_token) + + def add_line(self, line): + """Adds a line of text to the current example being built.""" + line = line.strip().replace("\n", " ") # NOTE BertTokenizer will cover whitespace cleaning + if not line: # NOTE If it is empty string + return None + bert_tokens = self._tokenizer.tokenize(line) + self._current_sentences.append(bert_tokens) + self._current_length += len(bert_tokens) + if self._current_length >= self._target_length: + return self._create_example() + return None + + def _create_example(self): + """Creates a pre-training example from the current list of sentences.""" + # small chance to have two segment + if random.random() < self.args.sentence_pair_prob: + # -3 due to not yet having [CLS]/[SEP] tokens in the input text + first_segment_target_length = (self._target_length - 3) // 2 + else: + # NOTE It will be only one segment for BigBird + first_segment_target_length = 100000 + + first_segment = [] + second_segment = [] + for sentence in self._current_sentences: + # the sentence goes to the first segment if (1) the first segment is + # empty, (2) the sentence doesn't put the first segment over length or + # (3) 50% of the time when it does put the first segment over length + if ( + len(first_segment) == 0 + or len(first_segment) + len(sentence) < first_segment_target_length + or ( + len(second_segment) == 0 + and len(first_segment) < first_segment_target_length + and random.random() < 0.5 + ) + ): + first_segment += sentence + else: + second_segment += sentence + + example_lst = [] + all_segment_len = len(first_segment) + len(second_segment) + + # NOTE + # If `first_seg + second_seg` is too long, we will make them to multiple chunk of single sentence + if all_segment_len >= self._long_seq_length_limit: + all_segment = first_segment + second_segment + + seq_len_for_example = self._max_length - 2 # NOTE -2 for [CLS]/[SEP] + for i in range(0, all_segment_len, seq_len_for_example): + example_lst.append( + self._make_tf_example( + first_segment=all_segment[i : i + seq_len_for_example], + second_segment=None, + ) + ) + else: + # trim to max_length while accounting for not-yet-added [CLS]/[SEP] tokens + first_segment = first_segment[: self._max_length - 2] + second_segment = second_segment[: max(0, self._max_length - len(first_segment) - 3)] + # NOTE Put it in List as batch size 1 + example_lst.append(self._make_tf_example(first_segment, second_segment)) + + # prepare to start building the next example + self._current_sentences = [] + self._current_length = 0 + # small chance for random-length instead of max_length-length example + if random.random() < self.args.short_seq_prob: + self._target_length = random.randint(5, self._max_length) + else: + self._target_length = self._max_length + + return example_lst + + def _make_tf_example(self, first_segment, second_segment): + """Converts two "segments" of text into a tf.train.Example.""" + tokens = [self._tokenizer.cls_token] + first_segment + [self._tokenizer.sep_token] + segment_ids = [0] * len(tokens) + if second_segment: + tokens += second_segment + [self._tokenizer.sep_token] + segment_ids += [1] * (len(second_segment) + 1) + + # Masking + (tokens, masked_lm_positions, masked_lm_labels) = create_masked_lm_predictions( + tokens=tokens, + masked_lm_prob=self.args.masked_lm_prob, + max_predictions_per_seq=self.args.max_predictions_per_seq, + vocab_words=self.vocab_for_random_replacement, + do_whole_word_mask=self.args.do_whole_word_mask, + tokenizer=self._tokenizer, + max_ngram_size=self.args.max_ngram_size, + ) + # tokens -> input_ids + input_ids = self._tokenizer.convert_tokens_to_ids(tokens) + + # Padding + input_ids += [self._tokenizer.pad_token_id] * (self._max_length - len(tokens)) + segment_ids += [0] * (self._max_length - len(segment_ids)) + + masked_lm_positions = list(masked_lm_positions) + masked_lm_ids = self._tokenizer.convert_tokens_to_ids(masked_lm_labels) + masked_lm_weights = [1.0] * len(masked_lm_ids) + + while len(masked_lm_positions) < self.args.max_predictions_per_seq: + masked_lm_positions.append(0) + masked_lm_ids.append(self._tokenizer.pad_token_id) + masked_lm_weights.append(0.0) + + assert len(input_ids) == self._max_length + assert len(segment_ids) == self._max_length + assert len(masked_lm_positions) == self.args.max_predictions_per_seq + assert len(masked_lm_ids) == self.args.max_predictions_per_seq + assert len(masked_lm_weights) == self.args.max_predictions_per_seq + + features = collections.OrderedDict() + features["input_ids"] = create_int_feature(input_ids) + features["segment_ids"] = create_int_feature(segment_ids) + features["masked_lm_positions"] = create_int_feature(masked_lm_positions) + features["masked_lm_ids"] = create_int_feature(masked_lm_ids) + features["masked_lm_weights"] = create_float_feature(masked_lm_weights) + features["next_sentence_labels"] = create_int_feature([0]) # NOTE Dummy value + + self.n_build += 1 + + if self.args.debug and self.n_build < 3: + tf.compat.v1.logging.info("*** Example ***") + tf.compat.v1.logging.info("tokens: %s", " ".join([printable_text(x) for x in tokens])) + + for feature_name in features.keys(): + feature = features[feature_name] + values = [] + if feature.int64_list.value: + values = feature.int64_list.value + elif feature.float_list.value: + values = feature.float_list.value + tf.compat.v1.logging.info("%s: %s", feature_name, " ".join([str(x) for x in values])) + + tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + return tf_example + + +class ExampleWriter(object): + """Writes pre-training examples to disk.""" + + def __init__( + self, + args, + job_id, + num_out_files=1000, + ): + self.tokenizer_dir = args.tokenizer_dir + self.output_dir = args.output_dir + self.max_seq_length = args.max_seq_length + self.num_jobs = args.num_processes + + tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_dir, use_fast=True) + self._example_builder = ExampleBuilder(args, tokenizer) + self._writers = [] + for i in range(num_out_files): + if i % self.num_jobs == job_id: + output_fname = os.path.join( + self.output_dir, + "pretrain_data.tfrecord-{:}-of-{:}".format(i, num_out_files), + ) + self._writers.append(tf.io.TFRecordWriter(output_fname)) + self.n_written = 0 + + def write_examples(self, input_file): + """Writes out examples from the provided input file.""" + if input_file.endswith(".txt"): + self.write_examples_from_txt(input_file) + elif input_file.endswith(".jsonl.zst"): + self.write_examples_from_kldf(input_file) + else: + print(f"{input_file} is not supported. Only supports `jsonl.zst` or `txt` file") + + # NOTE Flush + if self._example_builder._current_length != 0: + example_lst = self._example_builder._create_example() + if example_lst: + for example in example_lst: + self._writers[self.n_written % len(self._writers)].write(example.SerializeToString()) + self.n_written += 1 + + def write_examples_from_txt(self, input_file): + with open(input_file, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + example_lst = self._example_builder.add_line(line) + if example_lst: + for example in example_lst: + self._writers[self.n_written % len(self._writers)].write(example.SerializeToString()) + self.n_written += 1 + + def write_examples_from_kldf(self, input_file): + rdr = kldf.Reader(input_file) + for doc in rdr.stream_data(get_meta=False, threaded=False): + if type(doc) == str: + doc = [doc] + + for line in doc: + line = line.strip() + if line: + example_lst = self._example_builder.add_line(line) + if example_lst: + for example in example_lst: + self._writers[self.n_written % len(self._writers)].write(example.SerializeToString()) + self.n_written += 1 + + def finish(self): + for writer in self._writers: + writer.close() + + +def write_examples(fnames, job_id, args): + """A single process creating and writing out pre-processed examples.""" + + def log(*args): + msg = " ".join(map(str, args)) + print("Job {}:".format(job_id), msg) + + log("Creating example writer") + example_writer = ExampleWriter(args=args, job_id=job_id) + log("Writing tf examples") + fnames = [f for (i, f) in enumerate(fnames) if i % args.num_processes == job_id] + + # https://pytorch.org/docs/stable/data.html#data-loading-randomness + seed_worker(job_id, args) + + random.shuffle(fnames) + start_time = time.time() + # Add dupe factor + file_processed = 0 + for dupe_idx in range(args.dupe_factor): + for file_no, fname in enumerate(fnames): + if file_processed > 0: + elapsed = time.time() - start_time + log( + "Processed dupe {:}, {:}/{:} files ({:.1f}%), " + "{:} examples written, (ELAPSED: {:}s, ETA: {:}s)".format( + dupe_idx, + file_no, + len(fnames), + 100.0 * file_no / len(fnames), + example_writer.n_written, + int(elapsed), + int((len(fnames) * args.dupe_factor - file_processed) / (file_processed / elapsed)), + ) + ) + example_writer.write_examples(fname) + file_processed += 1 + example_writer.finish() + log("Done!") + + +def log_config(config): + def log(*args): + msg = ": ".join(map(str, args)) + sys.stdout.write(msg + "\n") + sys.stdout.flush() + + for key, value in sorted(config.__dict__.items()): + log(key, value) + log() + + +def seed_worker(job_id, args): + worker_seed = (args.seed + job_id) % 2 ** 32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +def get_files(input_dir): + """ + Get all files from input directory. + ONLY support `jsonl.zst` and `txt` (kldf format or plain text) + """ + filetypes = ["jsonl.zst", "txt"] + files = [list(Path(input_dir).rglob(f"*.{ft}")) for ft in filetypes] + # flatten list of list -> list and stringify Paths + flattened_list = [str(item) for sublist in files for item in sublist] + if not flattened_list: + raise Exception( + f"""did not find any files at this path {input_dir}, please also ensure your files are in format {filetypes}""" + ) + flattened_list = sorted(flattened_list) + return flattened_list + + +def main(): + tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO) + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input_dir", required=True, help="Location of text or ko_lm_dataformat files.") + parser.add_argument("--tokenizer_dir", required=True, help="Location of tokenizer directory.") + parser.add_argument("--output_dir", required=True, help="Where to write out the tfrecords.") + parser.add_argument("--max_seq_length", default=512, type=int, help="Number of tokens per example.") + parser.add_argument( + "--max_predictions_per_seq", + default=76, + type=int, + help="Maximum number of masked LM predictions per sequence.", + ) + parser.add_argument( + "--num_processes", + default=0, + type=int, + help="Parallelize across multiple processes. 0 will set the number of detected logical CPUs.", + ) + parser.add_argument( + "--sentence_pair_prob", + default=0.05, + type=float, + help="Probability for make input with sentence pair ([CLS] text_a [SEP] text_b [SEP])", + ) + parser.add_argument( + "--short_seq_prob", + default=0.01, + type=float, + help="Probability of creating sequences which are shorter than the maximum length.", + ) + parser.add_argument( + "--do_whole_word_mask", + action="store_true", + help="Whether to use whole word masking rather than per-WordPiece masking.", + ) + parser.add_argument( + "--max_ngram_size", + type=int, + default=None, + help="Mask contiguous whole words (n-grams) of up to `max_ngram_size` using a weighting scheme to favor shorter n-grams. " + "Note: `--do_whole_word_mask=True` must also be set when n-gram masking.", + ) + parser.add_argument( + "--dupe_factor", + type=int, + default=2, + help="Number of times to duplicate the input data (with different masks).", + ) + parser.add_argument("--masked_lm_prob", type=float, default=0.15, help="Masked LM probability.") + parser.add_argument( + "--debug", + action="store_true", + help="Debug the result of tokenization, masking etc.", + ) + parser.add_argument("--seed", default=12345, type=int, help="Random seed for data generation.") + parser.add_argument( + "--long_seq_threshold", + default=1.8, + type=float, + help="Threshold for extremely long sequence. " + "If sequence >= int(max_seq_len * threshold), split the sequence into multiple chunk", + ) + + args = parser.parse_args() + + assert args.long_seq_threshold > 1.0 + + log_config(args) + + if tf.io.gfile.exists(args.output_dir): + tf.io.gfile.rmtree(args.output_dir) + if not tf.io.gfile.exists(args.output_dir): + tf.io.gfile.makedirs(args.output_dir) + + fnames = get_files(args.input_dir) + print(f"Total number of files: {len(fnames)}") + + if args.num_processes == 1: + write_examples(fnames, 0, args) + else: + if args.num_processes == 0: + args.num_processes = multiprocessing.cpu_count() + + jobs = [] + for i in range(args.num_processes): + job = multiprocessing.Process(target=write_examples, args=(fnames, i, args)) + jobs.append(job) + job.start() + for job in jobs: + job.join() + + +if __name__ == "__main__": + main() diff --git a/pretrain/kobigbird/__init__.py b/pretrain/kobigbird/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pretrain/kobigbird/attention.py b/pretrain/kobigbird/attention.py new file mode 100644 index 0000000..e0ec0eb --- /dev/null +++ b/pretrain/kobigbird/attention.py @@ -0,0 +1,1062 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""BigBird Attention Layers.""" + +import numpy as np +import tensorflow.compat.v2 as tf +from absl import logging +from kobigbird import recompute_grad, utils + + +def get_single_block_row_attention( + block_id, + to_start_block_id, + to_end_block_id, + num_rand_blocks, + window_block_left=1, + window_block_right=1, + global_block_left=1, + global_block_right=1, +): + """For a single row block get random row attention. + + Args: + block_id: int. block id of row. + to_start_block_id: int. random attention coloum start id. + to_end_block_id: int. random attention coloum end id. + num_rand_blocks: int. number of random blocks to be selected. + window_block_left: int. number of blocks of window to left of a block. + window_block_right: int. number of blocks of window to right of a block. + global_block_left: int. Number of blocks globally used to the left. + global_block_right: int. Number of blocks globally used to the right. + + Returns: + row containing the random attention vector of size num_rand_blocks. + """ + + # list of to_blocks from which to choose random attention + to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32) + # permute the blocks + perm_block = np.random.permutation(to_block_list) + # print(perm_block) + + # illegal blocks for the current block id, using window + illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1)) + + # Add blocks at the start and at the end + illegal_blocks.extend(list(range(global_block_left))) + illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id))) + + # The second from_block cannot choose random attention on second last to_block + if block_id == 1: + illegal_blocks.append(to_end_block_id - 2) + + # The second last from_block cannot choose random attention on second to_block + if block_id == to_end_block_id - 2: + illegal_blocks.append(1) + + selected_random_blokcs = [] + + for i in range(to_end_block_id - to_start_block_id): + if perm_block[i] not in illegal_blocks: + selected_random_blokcs.append(perm_block[i]) + if len(selected_random_blokcs) == num_rand_blocks: + break + return np.array(selected_random_blokcs, dtype=np.int32) + + +def bigbird_block_rand_mask_with_head( + seq_length, + block_size, + num_heads, + plan_from_length, + plan_num_rand_blocks, + window_block_left=1, + window_block_right=1, + global_block_top=1, + global_block_bottom=1, + global_block_left=1, + global_block_right=1, +): + """Create adjacency list of random attention. + + Args: + seq_length: int. length of sequence. + block_size: int. size of block in sequence. + num_heads: int. total number of heads. + plan_from_length: list. plan from lenght where num_rand are choosen from. + plan_num_rand_blocks: list. number of rand blocks within the plan. + window_block_left: int. number of blocks of window to left of a block. + window_block_right: int. number of blocks of window to right of a block. + global_block_top: int. number of blocks at the top. + global_block_bottom: int. number of blocks at the bottom. + global_block_left: int. Number of blocks globally used to the left. + global_block_right: int. Number of blocks globally used to the right. + + Returns: + adjacency list of size num_head where each element is of size + from_seq_length//from_block_size-2 by num_rand_blocks + """ + # Total number of blocks in the mmask + num_blocks = seq_length // block_size + # Number of blocks per plan + plan_block_length = np.array(plan_from_length) // block_size + # till when to follow plan + max_plan_idx = plan_from_length.index(seq_length) + # Random Attention adjajency list + rand_attn = [ + np.zeros( + (num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), + dtype=np.int32, + ) + for i in range(num_heads) + ] + + # We will go iteratively over the plan blocks and pick random number of + # Attention blocks from the legally allowed blocks + for plan_idx in range(max_plan_idx + 1): + rnd_r_cnt = 0 + if plan_idx > 0: + # set the row for all from_blocks starting from 0 to + # plan_block_length[plan_idx-1] + # column indx start fromm plan_block_length[plan_idx-1] and ends at + # plan_block_length[plan_idx] + if plan_num_rand_blocks[plan_idx] > 0: + rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx])) + curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1])) + for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]): + for h in range(num_heads): + # print("head", h, "blk_rw_idx", blk_rw_idx) + rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = get_single_block_row_attention( + block_id=blk_rw_idx, + to_start_block_id=plan_block_length[plan_idx - 1], + to_end_block_id=plan_block_length[plan_idx], + num_rand_blocks=plan_num_rand_blocks[plan_idx], + window_block_left=window_block_left, + window_block_right=window_block_right, + global_block_left=global_block_left, + global_block_right=global_block_right, + ) + + for pl_id in range(plan_idx): + if plan_num_rand_blocks[pl_id] == 0: + continue + for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]): + rnd_r_cnt = 0 + to_start_block_id = 0 + if pl_id > 0: + rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id])) + to_start_block_id = plan_block_length[pl_id - 1] + curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1])) + for h in range(num_heads): + # print("head", h, "blk_rw_idx", blk_rw_idx) + rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = get_single_block_row_attention( + block_id=blk_rw_idx, + to_start_block_id=to_start_block_id, + to_end_block_id=plan_block_length[pl_id], + num_rand_blocks=plan_num_rand_blocks[pl_id], + window_block_left=window_block_left, + window_block_right=window_block_right, + global_block_left=global_block_left, + global_block_right=global_block_right, + ) + + if plan_num_rand_blocks[plan_idx] == 0: + continue + # print("Start from here") + curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1])) + from_start_block_id = global_block_top + to_start_block_id = 0 + if plan_idx > 0: + rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx])) + from_start_block_id = plan_block_length[plan_idx - 1] + to_start_block_id = plan_block_length[plan_idx - 1] + + for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]): + for h in range(num_heads): + # print("head", h, "blk_rw_idx", blk_rw_idx) + rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = get_single_block_row_attention( + block_id=blk_rw_idx, + to_start_block_id=to_start_block_id, + to_end_block_id=plan_block_length[plan_idx], + num_rand_blocks=plan_num_rand_blocks[plan_idx], + window_block_left=window_block_left, + window_block_right=window_block_right, + global_block_left=global_block_left, + global_block_right=global_block_right, + ) + + for nh in range(num_heads): + rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :] + return rand_attn + + +def get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks): + """Gives the plan of where to put random attention. + + Args: + from_seq_length: int. length of from sequence. + from_block_size: int. size of block in from sequence. + num_rand_blocks: int. Number of random chunks per row. + + Returns: + plan_from_length: ending location of from block + plan_num_rand_blocks: number of random ending location for each block + """ + # general plan + plan_from_length = [] + plan_num_rand_blocks = [] + if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size): + plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size)) + plan_num_rand_blocks.append(num_rand_blocks) + plan_from_length.append(from_seq_length) + plan_num_rand_blocks.append(0) + elif (num_rand_blocks + 5) < (from_seq_length // from_block_size): + plan_from_length.append(int((num_rand_blocks + 5) * from_block_size)) + plan_num_rand_blocks.append(num_rand_blocks // 2) + plan_from_length.append(from_seq_length) + plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2)) + else: + plan_from_length.append(from_seq_length) + plan_num_rand_blocks.append(num_rand_blocks) + + return plan_from_length, plan_num_rand_blocks + + +def bigbird_block_rand_mask( + from_seq_length, + to_seq_length, + from_block_size, + to_block_size, + num_rand_blocks, + last_idx=-1, +): + """Create adjacency list of random attention. + + Args: + from_seq_length: int. length of from sequence. + to_seq_length: int. length of to sequence. + from_block_size: int. size of block in from sequence. + to_block_size: int. size of block in to sequence. + num_rand_blocks: int. Number of random chunks per row. + last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence, + if positive then num_rand_blocks blocks choosen only upto last_idx. + + Returns: + adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks + """ + rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32) + middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32) + last = to_seq_length // to_block_size - 1 + if last_idx > (2 * to_block_size): + last = (last_idx // to_block_size) - 1 + + r = num_rand_blocks # shorthand + for i in range(1, from_seq_length // from_block_size - 1): + start = i - 2 + end = i + if i == 1: + rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r] + elif i == 2: + rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r] + elif i == from_seq_length // from_block_size - 3: + rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r] + # Missing -3: should have been sliced till last-3 + elif i == from_seq_length // from_block_size - 2: + rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r] + # Missing -4: should have been sliced till last-4 + else: + if start > last: + start = last + rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r] + elif (end + 1) == last: + rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r] + else: + rand_attn[i - 1, :] = np.random.permutation( + np.concatenate((middle_seq[:start], middle_seq[end + 1 : last])) + )[:r] + return rand_attn + + +def full_bigbird_mask(max_seq_length, from_seq_length, to_seq_length, from_block_size, to_block_size, rand_attn): + """Calculate BigBird attention pattern as a full dense matrix. + + Args: + max_seq_length: int. max sequence length of model + from_seq_length: int. length of from sequence. + to_seq_length: int. length of to sequence. + from_block_size: int. size of block in from sequence. + to_block_size: int. size of block in to sequence. + rand_attn: adjajency matrix for random attention. + + Returns: + attention mask matrix of shape [from_seq_length, to_seq_length] + """ + + attn_mask = np.zeros((max_seq_length, max_seq_length), dtype=np.int32) + for i in range(1, (max_seq_length // from_block_size) - 1): + attn_mask[ + (i) * from_block_size : (i + 1) * from_block_size, + (i - 1) * to_block_size : (i + 2) * to_block_size, + ] = 1 + for j in rand_attn[i - 1, :]: + attn_mask[ + i * from_block_size : (i + 1) * from_block_size, + j * to_block_size : (j + 1) * to_block_size, + ] = 1 + + attn_mask[:from_block_size, :] = 1 + attn_mask[:, :to_block_size] = 1 + attn_mask[:, -to_block_size:] = 1 + attn_mask[-from_block_size:, :] = 1 + clipped_attn_mask = attn_mask[:from_seq_length, :to_seq_length] + return np.array(clipped_attn_mask, dtype=bool) + + +def create_rand_mask_from_inputs( + from_blocked_mask, + to_blocked_mask, + rand_attn, + num_attention_heads, + num_rand_blocks, + from_seq_length, + from_block_size, +): + """Create 4D attention mask from a 3D tensor mask. + + Args: + from_blocked_mask: 2D Tensor of shape [batch_size, + from_seq_length//from_block_size, from_block_size]. + to_blocked_mask: int32 Tensor of shape [batch_size, + to_seq_length//to_block_size, to_block_size]. + rand_attn: [batch_size, num_attention_heads, + from_seq_length//from_block_size-2, num_rand_blocks] + num_attention_heads: int. Number of attention heads. + num_rand_blocks: int. Number of random chunks per row. + from_seq_length: int. length of from sequence. + from_block_size: int. size of block in from sequence. + + Returns: + float Tensor of shape [batch_size, num_attention_heads, + from_seq_length//from_block_size-2, + from_block_size, num_rand_blocks*to_block_size]. + """ + num_windows = from_seq_length // from_block_size - 2 + rand_mask = tf.reshape( + tf.gather(to_blocked_mask, rand_attn, batch_dims=1), + [-1, num_attention_heads, num_windows, num_rand_blocks * from_block_size], + ) + rand_mask = tf.einsum("BLQ,BHLK->BHLQK", from_blocked_mask[:, 1:-1], rand_mask) + return rand_mask + + +def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask): + """Create 4D attention mask from a 3D blocked tensor mask. + + Args: + from_blocked_mask: 3D Tensor of shape [batch_size, + from_seq_length//from_block_size, from_block_size]. + to_blocked_mask: 3D Tensor of shape [batch_size, + to_seq_length//to_block_size, to_block_size]. + + Returns: + float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, + from_block_size, 3*to_block_size]. + """ + exp_blocked_to_pad = tf.concat( + [to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], + 2, + ) + band_mask = tf.einsum("BLQ,BLK->BLQK", from_blocked_mask[:, 2:-2], exp_blocked_to_pad) + band_mask = tf.expand_dims(band_mask, 1) + return band_mask + + +def create_attention_mask_from_input_mask(from_mask, to_mask): + """Create attention mask from a 2D tensor mask. + + Args: + from_mask: float32 Tensor of shape [batch_size, from_seq_length]. + to_mask: float32 Tensor of shape [batch_size, to_seq_length]. + + Returns: + float32 Tensor of shape [batch_size, 1, from_seq_length, to_seq_length]. + """ + mask = tf.einsum("BF,BT->BFT", from_mask, to_mask) + + # expand to create a slot for heads. + mask = tf.expand_dims(mask, 1) + + return mask + + +def bigbird_block_sparse_attention( + query_layer, + key_layer, + value_layer, + band_mask, + from_mask, + to_mask, + from_blocked_mask, + to_blocked_mask, + rand_attn, + num_attention_heads, + size_per_head, + num_rand_blocks, + from_seq_length, + to_seq_length, + from_block_size, + to_block_size, +): + """BigBird attention sparse calculation using blocks in linear time. + + Assumes from_seq_length//from_block_size == to_seq_length//to_block_size. + A pure function with a long argument list to allow easy use outside our + framework. + + Args: + query_layer: float Tensor of shape [batch_size, num_attention_heads, + from_seq_length, size_per_head] + key_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + value_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + band_mask: float32 Tensor of shape [batch_size, 1, + from_seq_length//from_block_size-4, from_block_size, 3*to_block_size]. + The values should be 1 or 0. The attention scores will effectively be + set to -infinity for any positions in the mask that are 0, and will be + unchanged for positions that are 1. + from_mask: float32 Tensor of shape [batch_size, 1, from_seq_length, 1]. + The values should be 1 or 0. The attention scores will effectively be set + to -infinity for any positions in the mask that are 0, and will be + unchanged for positions that are 1. + to_mask: float32 Tensor of shape [batch_size, 1, 1, to_seq_length]. + The values should be 1 or 0. The attention scores will effectively be set + to -infinity for any positions in the mask that are 0, and will be + unchanged for positions that are 1. + from_blocked_mask: float32 Tensor of shape [batch_size, + from_seq_length//from_block_size, from_block_size]. + Same as from_mask, just reshaped. + to_blocked_mask: float32 Tensor of shape [batch_size, + to_seq_length//to_block_size, to_block_size]. + Same as to_mask, just reshaped. + rand_attn: int32 Tensor of shape [num_attention_heads, + from_seq_length//from_block_size-2, num_rand_blocks] specifying which + blocks to attend to for each from sequence block (except 2 global ones). + num_attention_heads: int. Number of attention heads. + size_per_head: int. Size of each attention head. + num_rand_blocks: int. Number of random chunks per row. + from_seq_length: int. length of from sequence. + to_seq_length: int. length of to sequence. + from_block_size: int. size of block in from sequence. + to_block_size: int. size of block in to sequence. + + Returns: + float Tensor of shape [batch_size, from_seq_length, num_attention_heads, + size_per_head]. + """ + assert from_seq_length // from_block_size == to_seq_length // to_block_size + + # repeat for batch size + batch_size = utils.get_shape_list(query_layer)[0] + rand_attn = tf.expand_dims(rand_attn, 0) + rand_attn = tf.repeat(rand_attn, batch_size, 0) + + rand_mask = create_rand_mask_from_inputs( + from_blocked_mask, + to_blocked_mask, + rand_attn, + num_attention_heads, + num_rand_blocks, + from_seq_length, + from_block_size, + ) + + # Define shorthands + # b = batch_size + h = num_attention_heads + r = num_rand_blocks + d = size_per_head + m = from_seq_length + n = to_seq_length + wm = from_block_size + wn = to_block_size + + blocked_query_matrix = tf.reshape(query_layer, (-1, h, m // wm, wm, d)) + blocked_key_matrix = tf.reshape(key_layer, (-1, h, n // wn, wn, d)) + blocked_value_matrix = tf.reshape(value_layer, (-1, h, n // wn, wn, d)) + gathered_key = tf.reshape( + tf.gather(blocked_key_matrix, rand_attn, batch_dims=2, name="gather_key"), + (-1, h, m // wm - 2, r * wn, d), + ) # [b, h, n//wn-2, r, wn, -1] + gathered_value = tf.reshape( + tf.gather(blocked_value_matrix, rand_attn, batch_dims=2, name="gather_value"), + (-1, h, m // wm - 2, r * wn, d), + ) # [b, h, n//wn-2, r, wn, -1] + + first_product = tf.einsum( + "BHQD,BHKD->BHQK", blocked_query_matrix[:, :, 0], key_layer + ) # [b, h, wm, -1] x [b, h, n, -1] ==> [b, h, wm, n] + first_product = tf.multiply(first_product, 1.0 / np.sqrt(d)) + first_product += (1.0 - to_mask) * -10000.0 + first_attn_weights = tf.nn.softmax(first_product) # [b, h, wm, n] + first_context_layer = tf.einsum( + "BHQK,BHKD->BHQD", first_attn_weights, value_layer + ) # [b, h, wm, n] x [b, h, n, -1] ==> [b, h, wm, -1] + first_context_layer = tf.expand_dims(first_context_layer, 2) + + second_key_mat = tf.concat( + [ + blocked_key_matrix[:, :, 0], + blocked_key_matrix[:, :, 1], + blocked_key_matrix[:, :, 2], + blocked_key_matrix[:, :, -1], + gathered_key[:, :, 0], + ], + 2, + ) # [b, h, (4+r)*wn, -1] + second_value_mat = tf.concat( + [ + blocked_value_matrix[:, :, 0], + blocked_value_matrix[:, :, 1], + blocked_value_matrix[:, :, 2], + blocked_value_matrix[:, :, -1], + gathered_value[:, :, 0], + ], + 2, + ) # [b, h, (4+r)*wn, -1] + second_product = tf.einsum( + "BHQD,BHKD->BHQK", blocked_query_matrix[:, :, 1], second_key_mat + ) # [b, h, wm, -1] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, (4+r)*wn] + second_seq_pad = tf.concat( + [ + to_mask[:, :, :, : 3 * wn], + to_mask[:, :, :, -wn:], + tf.ones_like(rand_mask[:, :1, 0, :1]), + ], + 3, + ) + second_rand_pad = tf.concat([tf.ones_like(second_product[:, :, :, : 4 * wn]), rand_mask[:, :, 0]], 3) + second_product = tf.multiply(second_product, 1.0 / np.sqrt(d)) + second_product += (1.0 - tf.minimum(second_seq_pad, second_rand_pad)) * -10000.0 + second_attn_weights = tf.nn.softmax(second_product) # [b , h, wm, (4+r)*wn] + second_context_layer = tf.einsum( + "BHQK,BHKD->BHQD", second_attn_weights, second_value_mat + ) # [b, h, wm, (4+r)*wn] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, -1] + second_context_layer = tf.expand_dims(second_context_layer, 2) + + exp_blocked_key_matrix = tf.concat( + [ + blocked_key_matrix[:, :, 1:-3], + blocked_key_matrix[:, :, 2:-2], + blocked_key_matrix[:, :, 3:-1], + ], + 3, + ) # [b, h, m//wm-4, 3*wn, -1] + exp_blocked_value_matrix = tf.concat( + [ + blocked_value_matrix[:, :, 1:-3], + blocked_value_matrix[:, :, 2:-2], + blocked_value_matrix[:, :, 3:-1], + ], + 3, + ) # [b, h, m//wm-4, 3*wn, -1] + middle_query_matrix = blocked_query_matrix[:, :, 2:-2] + inner_band_product = tf.einsum( + "BHLQD,BHLKD->BHLQK", middle_query_matrix, exp_blocked_key_matrix + ) # [b, h, m//wm-4, wm, -1] x [b, h, m//wm-4, 3*wn, -1] + # ==> [b, h, m//wm-4, wm, 3*wn] + inner_band_product = tf.multiply(inner_band_product, 1.0 / np.sqrt(d)) + rand_band_product = tf.einsum( + "BHLQD,BHLKD->BHLQK", middle_query_matrix, gathered_key[:, :, 1:-1] + ) # [b, h, m//wm-4, wm, -1] x [b, h, m//wm-4, r*wn, -1] + # ==> [b, h, m//wm-4, wm, r*wn] + rand_band_product = tf.multiply(rand_band_product, 1.0 / np.sqrt(d)) + first_band_product = tf.einsum( + "BHLQD,BHKD->BHLQK", middle_query_matrix, blocked_key_matrix[:, :, 0] + ) # [b, h, m//wm-4, wm, -1] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, wn] + first_band_product = tf.multiply(first_band_product, 1.0 / np.sqrt(d)) + last_band_product = tf.einsum( + "BHLQD,BHKD->BHLQK", middle_query_matrix, blocked_key_matrix[:, :, -1] + ) # [b, h, m//wm-4, wm, -1] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, wn] + last_band_product = tf.multiply(last_band_product, 1.0 / np.sqrt(d)) + inner_band_product += (1.0 - band_mask) * -10000.0 + first_band_product += (1.0 - tf.expand_dims(to_mask[:, :, :, :wn], 3)) * -10000.0 + last_band_product += (1.0 - tf.expand_dims(to_mask[:, :, :, -wn:], 3)) * -10000.0 + rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * -10000.0 + band_product = tf.concat( + [first_band_product, inner_band_product, rand_band_product, last_band_product], + -1, + ) # [b, h, m//wm-4, wm, (5+r)*wn] + attn_weights = tf.nn.softmax(band_product) # [b, h, m//wm-4, wm, (5+r)*wn] + context_layer = tf.einsum( + "BHLQK,BHLKD->BHLQD", + attn_weights[:, :, :, :, wn : 4 * wn], + exp_blocked_value_matrix, + ) # [b, h, m//wm-4, wm, 3*wn] x [b, h, m//wm-4, 3*wn, -1] + # ==> [b, h, m//wm-4, wm, -1] + context_layer += tf.einsum( + "BHLQK,BHLKD->BHLQD", + attn_weights[:, :, :, :, 4 * wn : -wn], + gathered_value[:, :, 1:-1], + ) # [b, h, m//wm-4, wm, r*wn] x [b, h, m//wm-4, r*wn, -1] + # ==> [b, h, m//wm-4, wm, -1] + context_layer += tf.einsum( + "BHLQK,BHKD->BHLQD", + attn_weights[:, :, :, :, :wn], + blocked_value_matrix[:, :, 0], + ) # [b, h, m//wm-4, wm, wn] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, -1] + context_layer += tf.einsum( + "BHLQK,BHKD->BHLQD", + attn_weights[:, :, :, :, -wn:], + blocked_value_matrix[:, :, -1], + ) # [b, h, m//wm-4, wm, wn] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, -1] + + second_last_key_mat = tf.concat( + [ + blocked_key_matrix[:, :, 0], + blocked_key_matrix[:, :, -3], + blocked_key_matrix[:, :, -2], + blocked_key_matrix[:, :, -1], + gathered_key[:, :, -1], + ], + 2, + ) # [b, h, (4+r)*wn, -1] + second_last_value_mat = tf.concat( + [ + blocked_value_matrix[:, :, 0], + blocked_value_matrix[:, :, -3], + blocked_value_matrix[:, :, -2], + blocked_value_matrix[:, :, -1], + gathered_value[:, :, -1], + ], + 2, + ) # [b, h, (4+r)*wn, -1] + second_last_product = tf.einsum( + "BHQD,BHKD->BHQK", blocked_query_matrix[:, :, -2], second_last_key_mat + ) # [b, h, wm, -1] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, (4+r)*wn] + second_last_seq_pad = tf.concat( + [ + to_mask[:, :, :, :wn], + to_mask[:, :, :, -3 * wn :], + tf.ones_like(rand_mask[:, :1, 0, :1]), + ], + 3, + ) + second_last_rand_pad = tf.concat([tf.ones_like(second_last_product[:, :, :, : 4 * wn]), rand_mask[:, :, -1]], 3) + second_last_product = tf.multiply(second_last_product, 1.0 / np.sqrt(d)) + second_last_product += (1.0 - tf.minimum(second_last_seq_pad, second_last_rand_pad)) * -10000.0 + second_last_attn_weights = tf.nn.softmax(second_last_product) # [b, h, wm, (4+r)*wn] + second_last_context_layer = tf.einsum( + "BHQK,BHKD->BHQD", second_last_attn_weights, second_last_value_mat + ) # [b, h, wm, (4+r)*wn] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, -1] + second_last_context_layer = tf.expand_dims(second_last_context_layer, 2) + + last_product = tf.einsum( + "BHQD,BHKD->BHQK", blocked_query_matrix[:, :, -1], key_layer + ) # [b, h, wm, -1] x [b, h, n, -1] ==> [b, h, wm, n] + last_product = tf.multiply(last_product, 1.0 / np.sqrt(d)) + last_product += (1.0 - to_mask) * -10000.0 + last_attn_weights = tf.nn.softmax(last_product) # [b, h, wm, n] + last_context_layer = tf.einsum( + "BHQK,BHKD->BHQD", last_attn_weights, value_layer + ) # [b, h, wm, n] x [b, h, n, -1] ==> [b, h, wm, -1] + last_context_layer = tf.expand_dims(last_context_layer, 2) + + context_layer = tf.concat( + [ + first_context_layer, + second_context_layer, + context_layer, + second_last_context_layer, + last_context_layer, + ], + 2, + ) + context_layer = tf.reshape(context_layer, (-1, h, m, d)) * from_mask + context_layer = tf.transpose(context_layer, (0, 2, 1, 3)) + return context_layer + + +class MultiHeadedAttentionLayer(tf.keras.layers.Layer): + """A multi-headed attention layer. + + It implements following types of multi-headed attention: + - original_full attention from "Attention is all you Need". + - simulated_sparse attention from BigBird with full quadratic implemention. + - block_sparse attention from BigBird with memory efficient linear impl. + """ + + def __init__( + self, + attention_type, + max_seq_length=4096, + num_attention_heads=1, + size_per_head=512, + num_rand_blocks=3, + from_seq_length=1024, + to_seq_length=1024, + from_block_size=64, + to_block_size=64, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + use_bias=True, + seed=None, + query_act=None, + key_act=None, + value_act=None, + name=None, + ): + """Constructor for a multi-headed attention layer. + + Args: + attention_type: Type of attention, needs to be one of ['original_full', + 'simulated_sparse', 'block_sparse']. + max_seq_length: (Optional) Maximum Sequence Length of model + num_attention_heads: (optional) int. Number of attention heads. + size_per_head: (optional) int. Size of each attention head. + num_rand_blocks: (optional) int. Number of random chunks per row. + from_seq_length: int. (optional) length of from sequence. + to_seq_length: int. (optional) length of to sequence. + from_block_size: (optional) int. size of block in from sequence. + to_block_size: (optional) int. size of block in to sequence. + attention_probs_dropout_prob: (optional) float. Dropout probability of the + attention probabilities. + initializer_range: (optional) float. Range of the weight initializer. + use_bias: Whether the layer uses a bias vector. + seed: (Optional) int. Reandom seed for generating random mask. + query_act: (optional) Activation function for the query transform. + key_act: (optional) Activation function for the key transform. + value_act: (optional) Activation function for the value transform. + name: The name scope of this layer. + """ + super(MultiHeadedAttentionLayer, self).__init__(name=name) + self.num_attention_heads = num_attention_heads + self.max_seq_length = max_seq_length + self.size_per_head = size_per_head + self.num_rand_blocks = num_rand_blocks + self.from_seq_length = from_seq_length + self.to_seq_length = to_seq_length + self.from_block_size = from_block_size + self.to_block_size = to_block_size + self.seed = seed + + with tf.compat.v1.variable_scope(name): + self.query_layer = utils.Dense3dLayer( + num_attention_heads, + size_per_head, + utils.create_initializer(initializer_range), + query_act, + "query", + head_first=True, + use_bias=use_bias, + ) + + self.key_layer = utils.Dense3dLayer( + num_attention_heads, + size_per_head, + utils.create_initializer(initializer_range), + key_act, + "key", + head_first=True, + use_bias=use_bias, + ) + + self.value_layer = utils.Dense3dLayer( + num_attention_heads, + size_per_head, + utils.create_initializer(initializer_range), + value_act, + "value", + head_first=True, + use_bias=use_bias, + ) + + if attention_type == "original_full": + logging.info("**** Using original full attention ****") + self.attention_dropout = recompute_grad.RecomputingDropout(attention_probs_dropout_prob) + self.attn_impl = self.original_full_attention + elif attention_type == "simulated_sparse": + logging.info("**** Using simulated sparse attention ****") + self.attention_dropout = lambda x, training=None: x + self.rand_attn = self.generate_rand_attn_list() + self.rand_block_mask = self.convert_attn_list_to_mask(self.rand_attn) + self.attn_impl = self.bigbird_simulated_attention + elif attention_type == "block_sparse": + logging.info("**** Using block sparse attention ****") + assert ( + from_seq_length // from_block_size == to_seq_length // to_block_size + ), "Error the number of blocks needs to be same!" + self.attention_dropout = None + self.rand_attn = self.generate_rand_attn_list() + self.attn_impl = self.bigbird_block_sparse_attention + else: + raise NotImplementedError("Attention type {} is not implemented".format(attention_type)) + + def generate_rand_attn_list(self): + # generate random attention and corresponding masks + if self.seed is not None: + np.random.seed(self.seed) + # old plans used in paper + if self.from_seq_length in [1024, 2048, 3072, 4096]: + rand_attn = [ + bigbird_block_rand_mask( # pylint: disable=g-complex-comprehension + self.max_seq_length, + self.max_seq_length, + self.from_block_size, + self.to_block_size, + self.num_rand_blocks, + last_idx=1024, + )[: (self.from_seq_length // self.from_block_size - 2)] + for _ in range(self.num_attention_heads) + ] + else: + plan_from_length, plan_num_rand_blocks = get_rand_attn_plan( + self.from_seq_length, self.from_block_size, self.num_rand_blocks + ) + rand_attn = bigbird_block_rand_mask_with_head( + seq_length=self.from_seq_length, + block_size=self.from_block_size, + num_heads=self.num_attention_heads, + plan_from_length=plan_from_length, + plan_num_rand_blocks=plan_num_rand_blocks, + ) + rand_attn = np.stack(rand_attn, axis=0) + return tf.constant(rand_attn, dtype=tf.int32) + + def convert_attn_list_to_mask(self, rand_attn): + temp_mask = [ + full_bigbird_mask( # pylint: disable=g-complex-comprehension + self.max_seq_length, + self.from_seq_length, + self.to_seq_length, + self.from_block_size, + self.to_block_size, + rand_attn=rand_attn[i], + ) + for i in range(self.num_attention_heads) + ] + temp_mask = np.stack(temp_mask, axis=0) + temp_mask = np.array(temp_mask, dtype=bool) + rand_block_mask = tf.constant(temp_mask, dtype=tf.bool) # [N, F, T] + return tf.cast(rand_block_mask, tf.float32) + + def original_full_attention(self, query_layer, key_layer, value_layer, masks, training=None): + """Full quadratic attention calculation. + + Args: + query_layer: float Tensor of shape [batch_size, num_attention_heads, + from_seq_length, size_per_head] + key_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + value_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + masks: a list containing float32 Tensor representing attention_mask + of shape [batch_size, from_seq_length, to_seq_length]. + The values should be 1 or 0. The attention scores will effectively be + set to -infinity for any positions in the mask that are 0, and + will be unchanged for positions that are 1. + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, from_seq_length, num_attention_heads, + size_per_head]. + """ + attention_mask = masks[0] + + # Directly take n^2 dot product between "query" and "key". + attention_scores = tf.einsum("BNFH,BNTH->BNFT", query_layer, key_layer) + attention_scores = tf.multiply(attention_scores, 1.0 / np.sqrt(float(self.size_per_head))) + + if attention_mask is not None: + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + adder = (1.0 - attention_mask) * -10000.0 + + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_scores += adder + + # Normalize the attention scores to probabilities. + # `attention_probs` = [B, N, F, T] + attention_probs = tf.nn.softmax(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs, training=training) + + # `context_layer` = [B, F, N, H] + context_layer = tf.einsum("BNFT,BNTH->BFNH", attention_probs, value_layer) + return context_layer + + def bigbird_simulated_attention(self, query_layer, key_layer, value_layer, masks, training=None): + """BigBird attention calculation using masks in quadratic time. + + Args: + query_layer: float Tensor of shape [batch_size, num_attention_heads, + from_seq_length, size_per_head] + key_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + value_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + masks: a list containing float32 Tensor representing attention_mask + of shape [batch_size, from_seq_length, to_seq_length]. + The values should be 1 or 0. The attention scores will effectively be + set to -infinity for any positions in the mask that are 0, and + will be unchanged for positions that are 1. + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, from_seq_length, num_attention_heads, + size_per_head]. + """ + attention_mask = masks[0] + rand_block_mask = tf.expand_dims(self.rand_block_mask, 0) # [1, N, F, T] + if attention_mask is not None: + attention_mask = tf.minimum(attention_mask, rand_block_mask) + else: + attention_mask = rand_block_mask + return self.original_full_attention(query_layer, key_layer, value_layer, [attention_mask], training=training) + + def bigbird_block_sparse_attention(self, query_layer, key_layer, value_layer, masks, training=None): + """BigBird attention sparse calculation using blocks in linear time. + + Args: + query_layer: float Tensor of shape [batch_size, num_attention_heads, + from_seq_length, size_per_head] + key_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + value_layer: float Tensor of shape [batch_size, num_attention_heads, + to_seq_length, size_per_head] + masks: A list of 5 masks used in BigBird attention at position 1 to 5. + Position 0 (first element) is not used can be left as none. In the mask, + the values should be 1 or 0. The attention scores will effectively + be set to -infinity for any positions in the mask that are 0, + and will be unchanged for positions that are 1. + "None": Not needed. + "band_mask": (optional) float32 Tensor of shape + [batch_size, 1, from_seq_length//from_block_size-4, + from_block_size, 3*to_block_size]. + "from_mask": (optional) float32 Tensor of shape + [batch_size, 1, from_seq_length, 1]. + "to_mask": (optional) float32 Tensor of shape + [batch_size, 1, 1, to_seq_length]. + "from_blocked_mask": (optional) float32 Tensor of shape + [batch_size, from_seq_length//from_block_size, from_block_size]. + Same as from_mask, just reshaped. + "to_blocked_mask": (optional) float32 Tensor of shape + [batch_size, to_seq_length//to_block_size, to_block_size]. + Same as to_mask, just reshaped.} + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, from_seq_length, num_attention_heads, + size_per_head]. + """ + + (_, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask) = masks + + return bigbird_block_sparse_attention( + query_layer, + key_layer, + value_layer, + band_mask, + from_mask, + to_mask, + from_blocked_mask, + to_blocked_mask, + self.rand_attn, + self.num_attention_heads, + self.size_per_head, + self.num_rand_blocks, + self.from_seq_length, + self.to_seq_length, + self.from_block_size, + self.to_block_size, + ) + + def call(self, from_tensor, to_tensor, masks, cache=None, decode_i=None, training=None): + """Implements a multi-headed attention layer from from_tensor to to_tensor. + + Args: + from_tensor: float Tensor of shape [batch_size, from_seq_length, + from_width] + to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width]. + masks: A list of masks used in different attention. Only relevant masks + need to be supplied and at other positions place None. In the mask, + the values should be 1 or 0. The attention scores will effectively + be set to -infinity for any positions in the mask that are 0, + and will be unchanged for positions that are 1. + "attention_mask": (optional) float32 Tensor of shape + [batch_size, from_seq_length, to_seq_length]. + "band_mask": (optional) float32 Tensor of shape + [batch_size, 1, from_seq_length//from_block_size-4, + from_block_size, 3*to_block_size]. + "from_mask": (optional) float32 Tensor of shape + [batch_size, 1, from_seq_length, 1]. + "to_mask": (optional) float32 Tensor of shape + [batch_size, 1, 1, to_seq_length]. + "from_blocked_mask": (optional) float32 Tensor of shape + [batch_size, from_seq_length//from_block_size, from_block_size]. + Same as from_mask, just reshaped. + "to_blocked_mask": (optional) float32 Tensor of shape + [batch_size, to_seq_length//to_block_size, to_block_size]. + Same as to_mask, just reshaped.} + cache: (Used during prediction) A dictionary with tensors containing + results of previous attentions. The dictionary must have the items: + {"k": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head], + "v": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head]} + decode_i: (Used during prediction) current location of decoding + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, from_seq_length, num_attention_heads, + size_per_head]. + + Raises: + ValueError: Any of the arguments or tensor shapes are invalid. + NotImplementedError: For unknown attention type. + """ + + # Scalar dimensions referenced here: + # b = batch size (number of sequences) + # m = `from_tensor` sequence length + # n = `to_tensor` sequence length + # h = `num_attention_heads` + # d = `size_per_head` + + # `query` = [b, h, m, d] + query = self.query_layer(from_tensor) + + # `key` = [b, h, n, d] + key = self.key_layer(to_tensor) + + # `value_layer` = [b, h, n, d] + value = self.value_layer(to_tensor) + + if cache is not None and decode_i is not None: + max_len = utils.get_shape_list(cache["k"])[2] + indices_select = tf.reshape(tf.one_hot(decode_i, max_len, dtype=to_tensor.dtype), [1, 1, max_len, 1]) + key = cache["k"] + key * indices_select + value = cache["v"] + value * indices_select + cache["k"] = key + cache["v"] = value + + contextual_output = self.attn_impl(query, key, value, masks, training=training) + + return contextual_output diff --git a/pretrain/kobigbird/beam_search.py b/pretrain/kobigbird/beam_search.py new file mode 100644 index 0000000..3b6a2ca --- /dev/null +++ b/pretrain/kobigbird/beam_search.py @@ -0,0 +1,242 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Beam search branched from Pegasus. + +Original source: +https://github.com/google-research/pegasus/blob/master/pegasus/layers/beam_search.py + +This beam search implementation is designed for TPU usage only and prefers +flexibility over efficiency. Transformer attention caching is not enabled yet. + +Mostly follows implementation in T2T. Several difference to pure beamsearch: +1. has finished and alive seqs, use 2 * beam_size to grow alive seqs, + which makes beam_size=1 doesn't equal greedy. +2. prefers finished seq over alive seqs. +3. prefers lower indices when equal probability (though unlikely). +4. with custom length normalization and constraint. + +Notations: + B: batch_size, M: beam_size, T: max_decode_len, V: vocab_size, U: undefined +""" +# pylint: disable=invalid-name + +import tensorflow.compat.v2 as tf + + +def length_normalization(start, alpha, min_len, max_len, out_of_range_penalty): + r"""Create length normalization function. + + Combines length penalty from https://arxiv.org/abs/1609.08144, + and length constraint from https://www.aclweb.org/anthology/W18-2706.pdf. + + scores = \sum_j log(P_j) / ((start + lengths)/(1 + start))**alpha + + out_of_range_penalty * (length > max_len or length < min_len) + + Args: + start: int, length normalization start offset. + alpha: float, [0, 1.0], length normalization power. + min_len: int, minimum decode length. + max_len: int, maximum decode lengths. + out_of_range_penalty: float, penalty for lengths outside min len and max + len. Use a negative number that penalize out of range decodes, does hard + constraint if set to -inf. + + Returns: + fn(log_probs_BxM, length)->scores_BxM: a function to normalize sum log + probabilities of sequence with current decoding lengths. + """ + + def length_norm_fn(log_probs_BxM, length_int): + """Normalize sum log probabilities given a sequence length.""" + dtype = log_probs_BxM.dtype + norm_flt = tf.pow(((start + tf.cast(length_int, dtype)) / (1.0 + start)), alpha) + log_probs_BxM /= norm_flt + too_short_bool = tf.less(length_int, min_len) + too_long_bool = tf.logical_and(tf.greater(length_int, max_len), max_len > 0) + out_of_range_bool = tf.logical_or(too_long_bool, too_short_bool) + log_probs_BxM += out_of_range_penalty * tf.cast(out_of_range_bool, dtype) + return log_probs_BxM + + return length_norm_fn + + +def beam_search( + symbols_to_logits_fn, + init_seq_BxT, + initial_cache_BxU, + vocab_size, + beam_size, + length_norm_fn, + eos_id=1, +): + """Beam search. + + Args: + symbols_to_logits_fn: fn(seq_BxT, cache_BxU, i) -> (logits_BxV, cache_BxU) + init_seq_BxT: initial sequence ids. + initial_cache_BxU: dictionary of tensors with shape BxU. + vocab_size: vocabulary size. + beam_size: beam size. + length_norm_fn: length normalization function. + eos_id: end of sequence. + + Returns: + Tuple of (beams_BxMxT, scores_BxM). Beam searched sequences and scores. + """ + B, T = init_seq_BxT.shape + M, V = beam_size, vocab_size + dtype = tf.float32 + int_dtype = init_seq_BxT.dtype + + def _loop_body( + i, + alive_seq_BxMxT, + alive_log_probs_BxM, + alive_cache_BxMxU, + finished_seq_BxMxT, + finished_scores_BxM, + ): + """Beam search loop body.""" + # Decode one step with beam + logits_BMxV, cache_BMxU = symbols_to_logits_fn( + _flatten_beam_dim(alive_seq_BxMxT), + tf.nest.map_structure(_flatten_beam_dim, alive_cache_BxMxU), + i, + ) + logits_BxMxV = _unflatten_beam_dim(logits_BMxV, M) + new_cache_BxMxU = tf.nest.map_structure(lambda t: _unflatten_beam_dim(t, M), cache_BMxU) + + # select top 2 * beam_size and fill alive and finished. + log_probs_BxMxV = logits_BxMxV - tf.reduce_logsumexp(logits_BxMxV, axis=2, keepdims=True) + log_probs_BxMxV += tf.expand_dims(alive_log_probs_BxM, axis=2) + log_probs_BxMV = tf.reshape(log_probs_BxMxV, [B, -1]) + new_log_probs_Bx2M, topk_indices_Bx2M = tf.nn.top_k(log_probs_BxMV, k=2 * M) + topk_beam_Bx2M = topk_indices_Bx2M // V + topk_seq_Bx2MxT, new_cache_Bx2MxU = _gather_nested([alive_seq_BxMxT, new_cache_BxMxU], topk_beam_Bx2M) + topk_ids_Bx2M = topk_indices_Bx2M % V + new_seq_Bx2MxT = _update_i(topk_seq_Bx2MxT, topk_ids_Bx2M, i) + new_finished_flags_Bx2M = tf.cast(tf.reduce_any(tf.equal(new_seq_Bx2MxT, eos_id), axis=-1), dtype) + + # get new alive + _, topk_alive_indices_BxM = tf.nn.top_k(new_log_probs_Bx2M + new_finished_flags_Bx2M * dtype.min, k=M) + (alive_seq_BxMxT, alive_log_probs_BxM, alive_cache_BxMxU) = _gather_nested( + [new_seq_Bx2MxT, new_log_probs_Bx2M, new_cache_Bx2MxU], + topk_alive_indices_BxM, + ) + + # get new finished + new_scores_Bx2M = length_norm_fn(new_log_probs_Bx2M, i + 1) + new_scores_Bx2M += (1 - new_finished_flags_Bx2M) * dtype.min + finished_seq_Bx3MxT = tf.concat([finished_seq_BxMxT, new_seq_Bx2MxT], axis=1) + finished_scores_Bx3M = tf.concat([finished_scores_BxM, new_scores_Bx2M], axis=1) + _, topk_finished_indices_BxM = tf.nn.top_k(finished_scores_Bx3M, k=M) + (finished_seq_BxMxT, finished_scores_BxM) = _gather_nested( + [finished_seq_Bx3MxT, finished_scores_Bx3M], topk_finished_indices_BxM + ) + + return [ + i + 1, + alive_seq_BxMxT, + alive_log_probs_BxM, + alive_cache_BxMxU, + finished_seq_BxMxT, + finished_scores_BxM, + ] + + # initialize. + init_i = tf.constant(0, dtype=int_dtype) + init_alive_seq_BxMxT = _expand_to_beam_size(init_seq_BxT, M) + log_probs_1xM = tf.constant([[0.0] + [dtype.min] * (M - 1)], dtype=dtype) + init_alive_log_probs_BxM = tf.tile(log_probs_1xM, [B, 1]) + init_alive_cache_BxMxU = tf.nest.map_structure(lambda t: _expand_to_beam_size(t, M), initial_cache_BxU) + init_finished_seq_BxMxT = tf.zeros(tf.shape(init_alive_seq_BxMxT), int_dtype) + init_finished_scores_BxM = tf.zeros([B, M], dtype=dtype) + dtype.min + + # run loop. + ( + _, + final_alive_seq_BxMxT, + final_alive_scores_BxM, + _, + final_finished_seq_BxMxT, + final_finished_scores_BxM, + ) = tf.while_loop( + lambda *args: True, # Always do T iterations + _loop_body, + loop_vars=[ + init_i, + init_alive_seq_BxMxT, + init_alive_log_probs_BxM, + init_alive_cache_BxMxU, + init_finished_seq_BxMxT, + init_finished_scores_BxM, + ], + parallel_iterations=1, + back_prop=False, + maximum_iterations=T, + ) + + # process finished. + final_finished_flag_BxMx1 = tf.reduce_any(tf.equal(final_finished_seq_BxMxT, eos_id), axis=-1, keepdims=True) + final_seq_BxMxT = tf.where( + tf.tile(final_finished_flag_BxMx1, [1, 1, T]), + final_finished_seq_BxMxT, + final_alive_seq_BxMxT, + ) + final_scores_BxM = tf.where( + tf.squeeze(final_finished_flag_BxMx1, axis=-1), + final_finished_scores_BxM, + final_alive_scores_BxM, + ) + return final_seq_BxMxT, final_scores_BxM + + +def _update_i(tensor_BxNxT, updates_BxN, i): + B, N, T = tensor_BxNxT.shape + tensor_BNxT = tf.reshape(tensor_BxNxT, [-1, T]) + updates_BN = tf.reshape(updates_BxN, [-1]) + batch_BN = tf.range(B * N, dtype=tf.int32) + i_BN = tf.fill([B * N], i) + ind_BNx2 = tf.stack([batch_BN, i_BN], axis=-1) + tensor_BNxT = tf.tensor_scatter_nd_update(tensor_BNxT, ind_BNx2, updates_BN) + return tf.reshape(tensor_BNxT, [B, N, T]) + + +def _expand_to_beam_size(tensor_BxU, beam_size): + tensor_Bx1xU = tf.expand_dims(tensor_BxU, axis=1) + tile_dims = [1] * tensor_Bx1xU.shape.ndims + tile_dims[1] = beam_size + tensor_BxMxU = tf.tile(tensor_Bx1xU, tile_dims) + return tensor_BxMxU + + +def _flatten_beam_dim(tensor_BxMxU): + shape = tensor_BxMxU.shape.as_list() + tensor_BMxU = tf.reshape(tensor_BxMxU, [shape[0] * shape[1]] + shape[2:]) + return tensor_BMxU + + +def _unflatten_beam_dim(tensor_BMxU, M): + shape = tensor_BMxU.shape.as_list() + tensor_BxMxU = tf.reshape(tensor_BMxU, [shape[0] // M, M] + shape[1:]) + return tensor_BxMxU + + +def _gather_nested(nested_BxMxU, indices_BxN): + def _gather_beam(tensor_BxMxU): + tensor_BxNxU = tf.gather(tensor_BxMxU, indices_BxN, batch_dims=1, axis=1) + return tensor_BxNxU + + return tf.nest.map_structure(_gather_beam, nested_BxMxU) diff --git a/pretrain/kobigbird/decoder.py b/pretrain/kobigbird/decoder.py new file mode 100644 index 0000000..e3608f8 --- /dev/null +++ b/pretrain/kobigbird/decoder.py @@ -0,0 +1,671 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""BigBird Decoder Layers.""" + +import tensorflow.compat.v2 as tf +from kobigbird import attention, beam_search, recompute_grad, utils + + +class PrenormDecoderLayer(tf.keras.layers.Layer): + """Decoder layer of a transformer in Pegasus style. + + The layer_norm is taken before self-attention. + """ + + def __init__( + self, + max_seq_length=4096, + hidden_size=768, + intermediate_size=3072, + intermediate_act_fn=utils.gelu, + attention_probs_dropout_prob=0.0, + hidden_dropout_prob=0.1, + initializer_range=0.02, + num_attention_heads=12, + use_bias=True, + name=None, + ): + """Constructor of a decoder layer of a transformer in Pegasus style. + + Args: + hidden_size: (optional) int. Size of hidden dimension. + intermediate_size: (optional) int. Size of intermediate dimension. + intermediate_act_fn: optional) Activation function for intermediate layer. + attention_probs_dropout_prob: (optional) float. Dropout probability of the + attention probabilities. + hidden_dropout_prob: (optional) float. Dropout probability of the + attention. + initializer_range: (optional) float. Range of the weight initializer. + num_attention_heads: (optional) int. Number of attention heads. + use_bias: (optional) bool. Whether key/query/value uses a bias vector. + name: The name scope of this layer. + """ + super(PrenormDecoderLayer, self).__init__(name=name) + + with tf.compat.v1.variable_scope(name): + + attention_head_size = hidden_size // num_attention_heads + with tf.compat.v1.variable_scope("attention"): + # Pre-Normalization layer + with tf.compat.v1.variable_scope("self"): + self.first_layer_norm = utils.NormLayer(hidden_size) + # Self-Attention layer + self.self_attn_layer = attention.MultiHeadedAttentionLayer( + "original_full", + max_seq_length=max_seq_length, + use_bias=use_bias, + name="self", + num_attention_heads=num_attention_heads, + size_per_head=attention_head_size, + initializer_range=initializer_range, + attention_probs_dropout_prob=attention_probs_dropout_prob, + ) + # Feedforward layer + with tf.compat.v1.variable_scope("output"): + self.self_proj_layer = utils.Dense3dProjLayer( + num_attention_heads, + attention_head_size, + utils.create_initializer(initializer_range), + None, + "dense", + use_bias, + ) + # Dropout + self.self_attn_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + # Pre-Normalization layer + with tf.compat.v1.variable_scope("encdec"): + self.second_layer_norm = utils.NormLayer(hidden_size) + # Cross-Attention layer + self.cross_attn_layer = attention.MultiHeadedAttentionLayer( + "original_full", + max_seq_length=max_seq_length, + use_bias=use_bias, + name="encdec", + num_attention_heads=num_attention_heads, + size_per_head=attention_head_size, + initializer_range=initializer_range, + attention_probs_dropout_prob=attention_probs_dropout_prob, + ) + # Feedforward layer + with tf.compat.v1.variable_scope("encdec_output"): + self.cross_proj_layer = utils.Dense3dProjLayer( + num_attention_heads, + attention_head_size, + utils.create_initializer(initializer_range), + None, + "dense", + use_bias, + ) + # Dropout + self.cross_attn_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + with tf.compat.v1.variable_scope("intermediate"): + # Normalization layer + self.third_layer_norm = utils.NormLayer(hidden_size) + # Feedforward layer + self.expand_layer = utils.Dense2dLayer( + hidden_size, + intermediate_size, + utils.create_initializer(initializer_range), + intermediate_act_fn, + "dense", + ) + + with tf.compat.v1.variable_scope("output"): + # Feedforward layer + self.contract_layer = utils.Dense2dLayer( + intermediate_size, + hidden_size, + utils.create_initializer(initializer_range), + None, + "dense", + ) + # Dropout + self.output_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + def call( + self, + layer_input, + encoder_outputs, + self_attention_mask, + attention_mask, + cache=None, + decode_i=None, + training=None, + ): + """Implements a decoder layer of a transformer in Pegasus style. + + The layer_norm is taken after self-attention. + + Args: + layer_input: float Tensor of shape [batch_size, seq_length, hidden_size]. + encoder_outputs: tensors with shape [batch_size, input_length, + num_hidden_layers, hidden_size] + self_attention_mask: bias for decoder self-attention layer. [1, 1, + target_length, target_length] + attention_mask: bias for encoder-decoder attention layer. [batch_size, 1, + 1, input_length] + cache: (Used during prediction) A dictionary with tensors containing + results of previous attentions. The dictionary must have the items: + {"k": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head], + "v": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head]} + decode_i: (Used during prediction) current location of decoding + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, seq_length, hidden_size]. + + Raises: + ValueError: Any of the arguments or tensor shapes are invalid. + NotImplementedError: For unknown attention type. + """ + # self-attention + normalized_layer_input = self.first_layer_norm(layer_input) + self_attention_output = self.self_attn_layer( + normalized_layer_input, + normalized_layer_input, + [self_attention_mask], + cache=cache, + decode_i=decode_i, + training=training, + ) + + # Run a linear projection of `hidden_size` then add a residual + # with `layer_input`. + self_attention_output = self.self_proj_layer(self_attention_output) + self_attention_output = self.self_attn_dropout(self_attention_output, training=training) + self_attention_output = self_attention_output + layer_input + + # Cross-attention + normalized_self_attention_output = self.second_layer_norm(self_attention_output) + attention_output = self.cross_attn_layer( + normalized_self_attention_output, + encoder_outputs, + [attention_mask], + training=training, + ) + + # Run a linear projection of `hidden_size` then add a residual + # with `layer_input`. + attention_output = self.cross_proj_layer(attention_output) + attention_output = self.cross_attn_dropout(attention_output, training=training) + attention_output = attention_output + self_attention_output + + # The activation is only applied to the "intermediate" hidden layer. + normalized_attention_output = self.third_layer_norm(attention_output) + intermediate_output = self.expand_layer(normalized_attention_output) + + # Down-project back to `hidden_size` then add the residual. + layer_output = self.contract_layer(intermediate_output) + layer_output = self.output_dropout(layer_output, training=training) + layer_output = layer_output + attention_output + return layer_output + + +class PostnormDecoderLayer(tf.keras.layers.Layer): + """Decoder layer of a transformer in BERT style. + + The layer_norm is taken before self-attention. + """ + + def __init__( + self, + max_seq_length=4096, + hidden_size=768, + intermediate_size=3072, + intermediate_act_fn=utils.gelu, + attention_probs_dropout_prob=0.0, + hidden_dropout_prob=0.1, + initializer_range=0.02, + num_attention_heads=12, + use_bias=True, + name=None, + ): + """Constructor of a decoder layer of a transformer in BERT style. + + Args: + hidden_size: (optional) int. Size of hidden dimension. + intermediate_size: (optional) int. Size of intermediate dimension. + intermediate_act_fn: optional) Activation function for intermediate layer. + attention_probs_dropout_prob: (optional) float. Dropout probability of the + attention probabilities. + hidden_dropout_prob: (optional) float. Dropout probability of the + attention. + initializer_range: (optional) float. Range of the weight initializer. + num_attention_heads: (optional) int. Number of attention heads. + use_bias: (optional) bool. Whether key/query/value uses a bias vector. + name: The name scope of this layer. + """ + super(PostnormDecoderLayer, self).__init__(name=name) + + with tf.compat.v1.variable_scope(name): + + attention_head_size = hidden_size // num_attention_heads + with tf.compat.v1.variable_scope("attention"): + # Self-Attention layers + self.self_attn_layer = attention.MultiHeadedAttentionLayer( + "original_full", + max_seq_length=max_seq_length, + use_bias=use_bias, + name="self", + num_attention_heads=num_attention_heads, + size_per_head=attention_head_size, + initializer_range=initializer_range, + attention_probs_dropout_prob=attention_probs_dropout_prob, + ) + + with tf.compat.v1.variable_scope("output"): + # Feedforward layer + self.self_proj_layer = utils.Dense3dProjLayer( + num_attention_heads, + attention_head_size, + utils.create_initializer(initializer_range), + None, + "dense", + use_bias, + ) + # Post-Normalization layer + self.first_layer_norm = utils.NormLayer(hidden_size) + # Dropout + self.self_attn_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + # Cross-Attention layers + self.cross_attn_layer = attention.MultiHeadedAttentionLayer( + "original_full", + max_seq_length=max_seq_length, + use_bias=use_bias, + name="encdec", + num_attention_heads=num_attention_heads, + size_per_head=attention_head_size, + initializer_range=initializer_range, + attention_probs_dropout_prob=attention_probs_dropout_prob, + ) + + with tf.compat.v1.variable_scope("encdec_output"): + # Feedforward layer + self.cross_proj_layer = utils.Dense3dProjLayer( + num_attention_heads, + attention_head_size, + utils.create_initializer(initializer_range), + None, + "dense", + use_bias, + ) + # Post-Normalization layer + self.second_layer_norm = utils.NormLayer(hidden_size) + # Dropout + self.cross_attn_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + with tf.compat.v1.variable_scope("intermediate"): + # Feedforward layer + self.expand_layer = utils.Dense2dLayer( + hidden_size, + intermediate_size, + utils.create_initializer(initializer_range), + intermediate_act_fn, + "dense", + ) + + with tf.compat.v1.variable_scope("output"): + # Feedforward layer + self.contract_layer = utils.Dense2dLayer( + intermediate_size, + hidden_size, + utils.create_initializer(initializer_range), + None, + "dense", + ) + # Normalization layer + self.third_layer_norm = utils.NormLayer(hidden_size) + # Dropout + self.output_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + def call( + self, + layer_input, + encoder_outputs, + self_attention_mask, + attention_mask, + cache=None, + decode_i=None, + training=None, + ): + """Implements a decoder layer of a transformer in BERT style. + + The layer_norm is taken after self-attention. + + Args: + layer_input: float Tensor of shape [batch_size, seq_length, hidden_size]. + encoder_outputs: tensors with shape [batch_size, input_length, + num_hidden_layers, hidden_size] + self_attention_mask: bias for decoder self-attention layer. [1, 1, + target_length, target_length] + attention_mask: bias for encoder-decoder attention layer. [batch_size, 1, + 1, input_length] + cache: (Used during prediction) A dictionary with tensors containing + results of previous attentions. The dictionary must have the items: + {"k": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head], + "v": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head]} + decode_i: (Used during prediction) current location of decoding + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, seq_length, hidden_size]. + + Raises: + ValueError: Any of the arguments or tensor shapes are invalid. + NotImplementedError: For unknown attention type. + """ + # self-attention + self_attention_output = self.self_attn_layer( + layer_input, + layer_input, + [self_attention_mask], + cache=cache, + decode_i=decode_i, + training=training, + ) + + # Run a linear projection of `hidden_size` then add a residual + # with `layer_input`. + self_attention_output = self.self_proj_layer(self_attention_output) + self_attention_output = self.self_attn_dropout(self_attention_output, training=training) + self_attention_output = self.first_layer_norm(self_attention_output + layer_input) + + # cross-attention + attention_output = self.cross_attn_layer( + self_attention_output, encoder_outputs, [attention_mask], training=training + ) + + # Run a linear projection of `hidden_size` then add a residual + # with `layer_input`. + attention_output = self.cross_proj_layer(attention_output) + attention_output = self.cross_attn_dropout(attention_output, training=training) + attention_output = self.second_layer_norm(attention_output + self_attention_output) + + # The activation is only applied to the "intermediate" hidden layer. + intermediate_output = self.expand_layer(attention_output) + + # Down-project back to `hidden_size` then add the residual. + layer_output = self.contract_layer(intermediate_output) + layer_output = self.output_dropout(layer_output, training=training) + layer_output = self.third_layer_norm(layer_output + attention_output) + return layer_output + + +def add_gradient_recomputation(original_class): + """Creats a subclass which enables gradient checkpointing.""" + + class RecomputeLayer(original_class): + """Transformer layer that recomputes the forward pass during backprop.""" + + def call( + self, + layer_input, + encoder_outputs, + self_attention_mask, + attention_mask, + cache=None, + decode_i=None, + training=None, + ): + def f(layer_input, encoder_outputs): + x = super(RecomputeLayer, self).call( + layer_input, + encoder_outputs, + self_attention_mask, + attention_mask, + cache, + decode_i, + training=training, + ) + return x + + f = recompute_grad.recompute_grad(f) + + return f(layer_input, encoder_outputs) + + return RecomputeLayer + + +class DecoderStack(tf.keras.layers.Layer): + """Transformer decoder stack.""" + + def __init__(self, params): + if params["couple_encoder_decoder"]: + name = "encoder" + super(DecoderStack, self).__init__(name=name) + else: + name = "decoder" + super(DecoderStack, self).__init__(name=name) + + self.params = params + + if params["norm_type"] == "prenorm": + decoder_class = PrenormDecoderLayer + elif params["norm_type"] == "postnorm": + decoder_class = PostnormDecoderLayer + else: + raise NotImplementedError("Norm type {} is not implemented".format(params["norm_type"])) + + if params["use_gradient_checkpointing"]: + decoder_class = add_gradient_recomputation(decoder_class) + + if self.params.get("num_decoder_layers", None) is not None: + num_hidden_layers = self.params["num_decoder_layers"] + else: + num_hidden_layers = self.params["num_hidden_layers"] + + with tf.compat.v1.variable_scope(name): + # Decoder layers + self.decoder_layers = [ + decoder_class( # pylint: disable=g-complex-comprehension + self.params["max_position_embeddings"], + self.params["hidden_size"], + self.params["intermediate_size"], + utils.get_activation(self.params["hidden_act"]), + self.params["attention_probs_dropout_prob"], + self.params["hidden_dropout_prob"], + self.params["initializer_range"], + self.params["num_attention_heads"], + self.params["use_bias"], + name="layer_%d" % layer_idx, + ) + for layer_idx in range(num_hidden_layers) + ] + + # Normalization layer + self.layer_norm = utils.NormLayer(self.params["hidden_size"]) + + def call( + self, + decoder_inputs, + self_attention_mask, + encoder_outputs, + encoder_mask, + cache=None, + decode_i=None, + training=None, + ): + """Return the output of the decoder layer stacks. + + Args: + decoder_inputs: tensor with shape + [batch_size, target_length, hidden_size] + self_attention_mask: bias for decoder self-attention layer. [1, 1, + target_length, target_length] + encoder_outputs: tensors with shape [batch_size, input_length, + hidden_size] + encoder_mask: bias for encoder-decoder attention layer. [batch_size, + input_length] + cache: (Used during prediction) A dictionary with tensors containing + results of previous attentions. The dictionary must have the items: + {"k": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head], + "v": tensor with shape + [batch_size, max_len, num_attention_heads, size_per_head]} + decode_i: (Used during prediction) current location of decoding. + training: Boolean indicating whether the call is training or inference. + + Returns: + Output of decoder layer stack. A float32 tensor with shape [batch_size, + target_length, hidden_size] + """ + # Expand encoder mask to broadcast over num heads and from_seq axis + attention_mask = tf.expand_dims(tf.expand_dims(encoder_mask, 1), 1) + attention_mask = tf.cast(attention_mask, tf.float32) + + if self.params["norm_type"] == "postnorm": + decoder_inputs = self.layer_norm(decoder_inputs) + + layer_output = decoder_inputs + for layer in self.decoder_layers: + layer_cache = cache[layer.name] if cache is not None else None + layer_output = layer( + layer_output, + encoder_outputs, + self_attention_mask, + attention_mask, + layer_cache, + decode_i, + training=training, + ) + + if self.params["norm_type"] == "prenorm": + layer_output = self.layer_norm(layer_output) + + return layer_output + + +def create_self_attention_mask(length): + with tf.name_scope("decoder_self_attention_mask"): + valid_locs = tf.linalg.band_part(tf.ones([length, length]), -1, 0) + valid_locs = tf.reshape(valid_locs, [1, 1, length, length]) + return valid_locs + + +def inplace_update_i(inp_tensor, updates, i): + """Inplace update a tensor. B: batch_size, L: tensor length.""" + batch_size = inp_tensor.shape[0] + indices = tf.stack( + [ + tf.range(batch_size, dtype=tf.int32), + tf.fill([batch_size], tf.cast(i, tf.int32)), + ], + axis=-1, + ) + return tf.tensor_scatter_nd_update(inp_tensor, indices, updates) + + +# pylint: disable=invalid-name +def left2right_decode( + symbols_to_logits_fn, + start_symbols, + context_BxU_dict, + batch_size, + max_decode_len, + vocab_size, + beam_size=1, + beam_start=5, + beam_alpha=0.6, + beam_min=0, + beam_max=-1, + eos_id=1, +): + """left to right decode. + + Notations: + B: batch_size, V: vocab_size, T: decode_len, U: undefined dimensions + + Args: + symbols_to_logits_fn: logits = fn(decodes, context, i). Shoud take + [batch_size, decoded_ids] and return [batch_size, vocab_size]. + start_symbols: starting ids [batch_size] + context_BxU_dict: dict of Tensors. + batch_size: int, decode batch size. + max_decode_len: int, maximum number of steps to decode. + vocab_size: int, output vocab size. + beam_size: Number of beams to decode. + beam_start: start length for scaling, default to 5. + beam_alpha: Length penalty for decoding. Should be between 0 (shorter) and 1 + (longer), default to 0.6. + beam_min: Minimum beam search lengths. + beam_max: Maximum beam search lengths. Set -1 to use unlimited. + eos_id: end of token id, default to 1. + + Returns: + decodes: Tensor[batch, decode_len] + """ + dtype = tf.int32 + start_symbols = tf.expand_dims(start_symbols, 1) + # When beam_size=1, beam_search does not behave exactly like greedy. + # This is due to using 2 * beam_size in grow_topk, and keep the top beam_size + # ones that haven't reached EOS into alive. + # In this case, alpha value for length penalty will take effect. + if beam_size == 1: + + def decode_loop(i, decodes_BxT, cache_BxU_dict): + logits_BxV = symbols_to_logits_fn(decodes_BxT, cache_BxU_dict, i) + decodes_BxT = inplace_update_i(decodes_BxT, tf.argmax(logits_BxV, -1, output_type=tf.int32), i) + return i + 1, decodes_BxT, cache_BxU_dict + + def loop_cond(i, decodes_BxT, unused_cache_BxU_dict): + finished_B = tf.reduce_any(tf.equal(decodes_BxT, eos_id), axis=1) + return tf.logical_and(i < max_decode_len, tf.logical_not(tf.reduce_all(finished_B))) + + init_dec_BxT = tf.concat( + [ + tf.cast(start_symbols, dtype=dtype), + tf.zeros([batch_size, max_decode_len - 1], dtype=dtype), + ], + axis=1, + ) + _, decodes, _ = tf.while_loop( + loop_cond, + decode_loop, + [tf.constant(0, dtype=dtype), init_dec_BxT, context_BxU_dict], + ) + return decodes + + else: + + def symbols_to_logits_fn_with_sampling(decodes_BxT, states_BxU_dict, i): + logits_BxV = symbols_to_logits_fn(decodes_BxT, states_BxU_dict, i) + return logits_BxV, states_BxU_dict + + length_norm_fn = beam_search.length_normalization(beam_start, beam_alpha, beam_min, beam_max, -1e3) + + init_dec_BxT = tf.concat( + [ + tf.cast(start_symbols, dtype=tf.int32), + tf.zeros([batch_size, max_decode_len - 1], dtype=tf.int32), + ], + axis=1, + ) + + beams, _ = beam_search.beam_search( + symbols_to_logits_fn_with_sampling, + init_dec_BxT, + context_BxU_dict, + vocab_size, + beam_size, + length_norm_fn, + eos_id, + ) + return beams[:, 0, :] diff --git a/pretrain/kobigbird/encoder.py b/pretrain/kobigbird/encoder.py new file mode 100644 index 0000000..fc25e68 --- /dev/null +++ b/pretrain/kobigbird/encoder.py @@ -0,0 +1,540 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""BigBird Encoder Layers.""" + +import tensorflow.compat.v2 as tf +from kobigbird import attention, recompute_grad, utils + + +class PrenormEncoderLayer(tf.keras.layers.Layer): + """Encoder layer of a transformer in Pegasus style. + + The layer_norm is taken before self-attention. + """ + + def __init__( + self, + attention_type, + max_seq_length=4096, + hidden_size=768, + intermediate_size=3072, + intermediate_act_fn=utils.gelu, + attention_probs_dropout_prob=0.0, + hidden_dropout_prob=0.1, + initializer_range=0.02, + num_attention_heads=12, + num_rand_blocks=3, + seq_length=1024, + block_size=64, + use_bias=True, + seed=None, + name=None, + ): + """Constructor of an encoder layer of a transformer in Pegasus style. + + Args: + attention_type: Type of attention, needs to be one of ['original_full', + 'simulated_sparse', 'block_sparse']. + max_seq_length: (Optional) Maximum Sequence Length of model + hidden_size: (optional) int. Size of hidden dimension. + intermediate_size: (optional) int. Size of intermediate dimension. + intermediate_act_fn: optional) Activation function for intermediate layer. + attention_probs_dropout_prob: (optional) float. Dropout probability of the + attention probabilities. + hidden_dropout_prob: (optional) float. Dropout probability of the + attention. + initializer_range: (optional) float. Range of the weight initializer. + num_attention_heads: (optional) int. Number of attention heads. + num_rand_blocks: (optional) int. Number of random chunks per row. + seq_length: (optional) int. length of sequence. + block_size: (optional) int. size of block in sequence. + use_bias: (optional) bool. Whether key/query/value uses a bias vector. + seed: (Optional) int. Reandom seed for generating random mask. + name: The name scope of this layer. + """ + super(PrenormEncoderLayer, self).__init__(name=name) + + with tf.compat.v1.variable_scope(name): + + attention_head_size = hidden_size // num_attention_heads + with tf.compat.v1.variable_scope("attention"): + # Pre-Normalization layer + with tf.compat.v1.variable_scope("self"): + self.first_layer_norm = utils.NormLayer(hidden_size) + # Self-Attention layer + self.attn_layer = attention.MultiHeadedAttentionLayer( + attention_type, + max_seq_length, + num_attention_heads, + attention_head_size, + num_rand_blocks, + seq_length, + seq_length, + block_size, + block_size, + attention_probs_dropout_prob, + initializer_range, + use_bias, + seed, + name="self", + ) + # Feedforward layer + with tf.compat.v1.variable_scope("output"): + self.projection_layer = utils.Dense3dProjLayer( + num_attention_heads, + attention_head_size, + utils.create_initializer(initializer_range), + None, + "dense", + use_bias, + ) + # Dropout + self.attention_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + with tf.compat.v1.variable_scope("intermediate"): + # Normalization layer + self.second_layer_norm = utils.NormLayer(hidden_size) + # Feedforward layer + self.expand_layer = utils.Dense2dLayer( + hidden_size, + intermediate_size, + utils.create_initializer(initializer_range), + intermediate_act_fn, + "dense", + ) + with tf.compat.v1.variable_scope("output"): + # Feedforward layer + self.contract_layer = utils.Dense2dLayer( + intermediate_size, + hidden_size, + utils.create_initializer(initializer_range), + None, + "dense", + ) + # Dropout + self.output_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + def call( + self, + layer_input, + attention_mask=None, + band_mask=None, + from_mask=None, + to_mask=None, + input_blocked_mask=None, + training=None, + ): + """Implements a encoder layer of a transformer in Pegasus style. + + Args: + layer_input: float Tensor of shape [batch_size, seq_length, hidden_size]. + attention_mask: (optional) float32 Tensor of shape [batch_size, + seq_length, seq_length]. The values should be 1 or 0. The + attention scores will effectively be set to -infinity for any positions + in the mask that are 0, and will be unchanged for positions that are 1. + band_mask: (optional) float32 Tensor of shape [batch_size, 1, + seq_length//block_size-4, block_size, 3*block_size]. + The values should be 1 or 0. The attention scores will effectively be + set to -infinity for any positions in the mask that are 0, and will be + unchanged for positions that are 1. + from_mask: (optional) float32 Tensor of shape [batch_size, 1, + seq_length, 1]. The values should be 1 or 0. The + attention scores will effectively be set to -infinity for any positions + in the mask that are 0, and will be unchanged for positions that are 1. + to_mask: (optional) float32 Tensor of shape [batch_size, 1, 1, + seq_length]. The values should be 1 or 0. The + attention scores will effectively be set to -infinity for any positions + in the mask that are 0, and will be unchanged for positions that are 1. + input_blocked_mask: (optional) float32 Tensor of shape [batch_size, + seq_length//block_size, block_size]. Same as from/to_mask, just + reshaped. + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, seq_length, hidden_size]. + + Raises: + ValueError: Any of the arguments or tensor shapes are invalid. + NotImplementedError: For unknown attention type. + """ + # self-attention + normalized_layer_input = self.first_layer_norm(layer_input) + attention_output = self.attn_layer( + normalized_layer_input, + normalized_layer_input, + [ + attention_mask, + band_mask, + from_mask, + to_mask, + input_blocked_mask, + input_blocked_mask, + ], + training=training, + ) + + # Run a linear projection of `hidden_size` then add a residual + # with `layer_input`. + attention_output = self.projection_layer(attention_output) + attention_output = self.attention_dropout(attention_output, training=training) + attention_output = attention_output + layer_input + + # The activation is only applied to the "intermediate" hidden layer. + normalized_attention_output = self.second_layer_norm(attention_output) + intermediate_output = self.expand_layer(normalized_attention_output) + + # Down-project back to `hidden_size` then add the residual. + layer_output = self.contract_layer(intermediate_output) + layer_output = self.output_dropout(layer_output, training=training) + layer_output = layer_output + attention_output + return layer_output + + +class PostnormEncoderLayer(tf.keras.layers.Layer): + """Encoder layer of a transformer in BERT style. + + The layer_norm is taken after self-attention. + """ + + def __init__( + self, + attention_type, + max_seq_length=4096, + hidden_size=768, + intermediate_size=3072, + intermediate_act_fn=utils.gelu, + attention_probs_dropout_prob=0.0, + hidden_dropout_prob=0.1, + initializer_range=0.02, + num_attention_heads=12, + num_rand_blocks=3, + seq_length=1024, + block_size=64, + use_bias=True, + seed=None, + name=None, + ): + """Constructor of an encoder layer of a transformer in BERT style. + + Args: + attention_type: Type of attention, needs to be one of ['original_full', + 'simulated_sparse', 'block_sparse']. + max_seq_length: (Optional) Maximum Sequence Length of model + hidden_size: (optional) int. Size of hidden dimension. + intermediate_size: (optional) int. Size of intermediate dimension. + intermediate_act_fn: optional) Activation function for intermediate layer. + attention_probs_dropout_prob: (optional) float. Dropout probability of the + attention probabilities. + hidden_dropout_prob: (optional) float. Dropout probability of the + attention. + initializer_range: (optional) float. Range of the weight initializer. + num_attention_heads: (optional) int. Number of attention heads. + num_rand_blocks: (optional) int. Number of random chunks per row. + seq_length: (optional) int. length of sequence. + block_size: (optional) int. size of block in sequence. + use_bias: (optional) bool. Whether key/query/value uses a bias vector. + seed: (Optional) int. Reandom seed for generating random mask. + name: The name scope of this layer. + """ + super(PostnormEncoderLayer, self).__init__(name=name) + + with tf.compat.v1.variable_scope(name): + + attention_head_size = hidden_size // num_attention_heads + with tf.compat.v1.variable_scope("attention"): + # Self-Attention layer + self.attn_layer = attention.MultiHeadedAttentionLayer( + attention_type, + max_seq_length, + num_attention_heads, + attention_head_size, + num_rand_blocks, + seq_length, + seq_length, + block_size, + block_size, + attention_probs_dropout_prob, + initializer_range, + use_bias, + seed, + name="self", + ) + + with tf.compat.v1.variable_scope("output"): + # Feedforward layer + self.projection_layer = utils.Dense3dProjLayer( + num_attention_heads, + attention_head_size, + utils.create_initializer(initializer_range), + None, + "dense", + use_bias, + ) + # Post-Normalization layer + self.first_layer_norm = utils.NormLayer(hidden_size) + # Dropout + self.attention_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + with tf.compat.v1.variable_scope("intermediate"): + # Feedforward layer + self.expand_layer = utils.Dense2dLayer( + hidden_size, + intermediate_size, + utils.create_initializer(initializer_range), + intermediate_act_fn, + "dense", + ) + + with tf.compat.v1.variable_scope("output"): + # Feedforward layer + self.contract_layer = utils.Dense2dLayer( + intermediate_size, + hidden_size, + utils.create_initializer(initializer_range), + None, + "dense", + ) + # Normalization layer + self.second_layer_norm = utils.NormLayer(hidden_size) + # Dropout + self.output_dropout = recompute_grad.RecomputingDropout(hidden_dropout_prob) + + def call( + self, + layer_input, + attention_mask=None, + band_mask=None, + from_mask=None, + to_mask=None, + input_blocked_mask=None, + training=None, + ): + """Implements a encoder layer of a transformer in BERT style. + + Args: + layer_input: float Tensor of shape [batch_size, seq_length, hidden_size]. + attention_mask: (optional) float32 Tensor of shape [batch_size, + seq_length, seq_length]. The values should be 1 or 0. The + attention scores will effectively be set to -infinity for any positions + in the mask that are 0, and will be unchanged for positions that are 1. + band_mask: (optional) float32 Tensor of shape [batch_size, 1, + seq_length//block_size-4, block_size, 3*block_size]. + The values should be 1 or 0. The attention scores will effectively be + set to -infinity for any positions in the mask that are 0, and will be + unchanged for positions that are 1. + from_mask: (optional) float32 Tensor of shape [batch_size, 1, + seq_length, 1]. The values should be 1 or 0. The + attention scores will effectively be set to -infinity for any positions + in the mask that are 0, and will be unchanged for positions that are 1. + to_mask: (optional) float32 Tensor of shape [batch_size, 1, 1, + seq_length]. The values should be 1 or 0. The + attention scores will effectively be set to -infinity for any positions + in the mask that are 0, and will be unchanged for positions that are 1. + input_blocked_mask: (optional) float32 Tensor of shape [batch_size, + seq_length//block_size, block_size]. Same as from/to_mask, just + reshaped. + training: Boolean indicating whether the call is training or inference. + + Returns: + float Tensor of shape [batch_size, seq_length, hidden_size]. + + Raises: + ValueError: Any of the arguments or tensor shapes are invalid. + NotImplementedError: For unknown attention type. + """ + # self-attention + attention_output = self.attn_layer( + layer_input, + layer_input, + [ + attention_mask, + band_mask, + from_mask, + to_mask, + input_blocked_mask, + input_blocked_mask, + ], + training=training, + ) + + # Run a linear projection of `hidden_size` then add a residual + # with `layer_input`. + attention_output = self.projection_layer(attention_output) + attention_output = self.attention_dropout(attention_output, training=training) + attention_output = self.first_layer_norm(attention_output + layer_input) + + # The activation is only applied to the "intermediate" hidden layer. + intermediate_output = self.expand_layer(attention_output) + + # Down-project back to `hidden_size` then add the residual. + layer_output = self.contract_layer(intermediate_output) + layer_output = self.output_dropout(layer_output, training=training) + layer_output = self.second_layer_norm(layer_output + attention_output) + return layer_output + + +def add_gradient_recomputation(original_class): + """Creats a subclass which enables gradient checkpointing.""" + + class RecomputeLayer(original_class): + """Transformer layer that recomputes the forward pass during backprop.""" + + def call( + self, + layer_input, + attention_mask=None, + band_mask=None, + from_mask=None, + to_mask=None, + input_blocked_mask=None, + training=None, + ): + def f( + layer_input, + attention_mask, + band_mask, + from_mask, + to_mask, + input_blocked_mask, + ): + x = super(RecomputeLayer, self).call( + layer_input, + attention_mask, + band_mask, + from_mask, + to_mask, + input_blocked_mask, + training=training, + ) + return x + + f = recompute_grad.recompute_grad(f) + + return f( + layer_input, + attention_mask, + band_mask, + from_mask, + to_mask, + input_blocked_mask, + ) + + return RecomputeLayer + + +class EncoderStack(tf.keras.layers.Layer): + """Transformer encoder stack.""" + + def __init__(self, params): + name = "encoder" + super(EncoderStack, self).__init__(name=name) + self.params = params + + if params["norm_type"] == "prenorm": + encoder_class = PrenormEncoderLayer + elif params["norm_type"] == "postnorm": + encoder_class = PostnormEncoderLayer + else: + raise NotImplementedError("Norm type {} is not implemented".format(params["norm_type"])) + + if params["use_gradient_checkpointing"]: + encoder_class = add_gradient_recomputation(encoder_class) + + with tf.compat.v1.variable_scope(name): + # Encoder layers + self.encoder_layers = [ + encoder_class( # pylint: disable=g-complex-comprehension + self.params["attention_type"], + self.params["max_position_embeddings"], + self.params["hidden_size"], + self.params["intermediate_size"], + utils.get_activation(self.params["hidden_act"]), + self.params["attention_probs_dropout_prob"], + self.params["hidden_dropout_prob"], + self.params["initializer_range"], + self.params["num_attention_heads"], + self.params["num_rand_blocks"], + self.params["max_encoder_length"], + self.params["block_size"], + self.params["use_bias"], + seed=layer_idx, + name="layer_%d" % layer_idx, + ) + for layer_idx in range(self.params["num_hidden_layers"]) + ] + + # Normalization layer + self.layer_norm = utils.NormLayer(self.params["hidden_size"]) + + def call(self, encoder_inputs, encoder_inputs_mask, training=None): + """Return the output of the decoder layer stacks. + + Args: + encoder_inputs: tensor with shape + [batch_size, input_length, hidden_size] + encoder_inputs_mask: Mask for enccoder input. [batch_size, input_length] + training: Boolean indicating whether the call is training or inference. + + Returns: + Finaly layer encoder output. float tensor with shape + [batch_size, input_length, hidden_size] + """ + if self.params["attention_type"] == "block_sparse": + # reshape and cast for blocking + encoder_length = self.params["max_encoder_length"] + encoder_block_size = self.params["block_size"] + encoder_inputs_mask = tf.cast(encoder_inputs_mask, tf.float32) + blocked_encoder_mask = tf.reshape( + encoder_inputs_mask, + (-1, encoder_length // encoder_block_size, encoder_block_size), + ) + encoder_from_mask = tf.reshape(encoder_inputs_mask, (-1, 1, encoder_length, 1)) + encoder_to_mask = tf.reshape(encoder_inputs_mask, (-1, 1, 1, encoder_length)) + + # create band padding + band_mask = attention.create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask) + + # For unused masks 0 instead of None for compatilibity with recompute_grad + attention_mask = 0.0 + + else: + # For unused masks 0 instead of None for compatilibity with recompute_grad + blocked_encoder_mask = 0.0 + encoder_to_mask = 0.0 + encoder_from_mask = 0.0 + band_mask = 0.0 + + encoder_inputs_mask = tf.cast(encoder_inputs_mask, tf.float32) + attention_mask = attention.create_attention_mask_from_input_mask(encoder_inputs_mask, encoder_inputs_mask) + + if self.params["norm_type"] == "postnorm": + encoder_inputs = self.layer_norm(encoder_inputs) + + layer_output = encoder_inputs + for layer in self.encoder_layers: + layer_output = layer( + layer_output, + attention_mask, + band_mask, + encoder_from_mask, + encoder_to_mask, + blocked_encoder_mask, + training=training, + ) + + if self.params["norm_type"] == "prenorm": + layer_output = self.layer_norm(layer_output) + + return layer_output diff --git a/pretrain/kobigbird/flags.py b/pretrain/kobigbird/flags.py new file mode 100644 index 0000000..5592cf4 --- /dev/null +++ b/pretrain/kobigbird/flags.py @@ -0,0 +1,310 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Common flag definitions.""" + +import json + +import tensorflow.compat.v2 as tf +from absl import flags, logging +from transformers import AutoTokenizer + +############################### FLAGS UTILS #################################### + +FLAGS = flags.FLAGS +DEFINE_bool = flags.DEFINE_bool +DEFINE_enum = flags.DEFINE_enum +DEFINE_float = flags.DEFINE_float +DEFINE_integer = flags.DEFINE_integer +DEFINE_string = flags.DEFINE_string + + +# Flag names are globally defined! So in general, we need to be +# careful to pick names that are unlikely to be used by other libraries. +# If there is a conflict, we'll get an error at import time. + +# Basic model config flags + +flags.DEFINE_float( + "attention_probs_dropout_prob", + 0.1, + "The dropout probability for attention coefficients when using original.", +) +flags.DEFINE_string( + "hidden_act", + "gelu", + "The non-linear activation function (function or string) in the encoder " "and pooler.", +) +flags.DEFINE_float( + "hidden_dropout_prob", + 0.1, + "The dropout probability for all fully connected layers in the embeddings, " "encoder, decoder, and pooler.", +) +flags.DEFINE_integer("hidden_size", 768, "Size of the transformer layers and the pooler layer.") +flags.DEFINE_float( + "initializer_range", + 0.02, + "The stdev of the truncated_normal_initializer for initializing all " "weight matrices.", +) +flags.DEFINE_integer( + "intermediate_size", + 3072, + "The size of intermediate (i.e. feed-forward) layer in the Transformer.", +) +flags.DEFINE_integer( + "max_position_embeddings", + 4096, + "The size position embeddings of matrix, which dictates the maximum" "length for which the model can be run.", +) +flags.DEFINE_integer( + "num_attention_heads", + 12, + "Number of attention heads for each attention layer in the Transformer.", +) +flags.DEFINE_integer( + "num_hidden_layers", + 12, + "Number of hidden layers in the model (same for encoder and decoder).", +) +flags.DEFINE_bool("use_token_type", True, "Whether to use token type embedding (True for BERT, False for RoBERTa") +flags.DEFINE_integer("type_vocab_size", 2, "The vocabulary size of the `token_type_ids`.") +flags.DEFINE_bool("use_bias", True, "Whether to use bias for key/query/value.") +flags.DEFINE_bool( + "rescale_embedding", + False, + "Whether to rescale word embedding by hidden dimensions.", +) +flags.DEFINE_bool( + "use_gradient_checkpointing", + False, + "Whether to recompute encoder fwd pass during back prop for saving memory.", +) +flags.DEFINE_string("scope", "bert", "Variable scope name.") + +# NOTE Changed for BERT Wordpiece Tokenizer +flags.DEFINE_string( + "tokenizer_dir", + "tokenizer", + "Tokenizer directory. We will use BERT Wordpiece Tokenizer which is compatible with transformers library", +) + +# Simulated and Block attention settings + +flags.DEFINE_enum( + "attention_type", + "block_sparse", + ["original_full", "simulated_sparse", "block_sparse"], + "Selecting attention implementation. " + "'original_full': full attention from original bert. " + "'simulated_sparse': simulated sparse attention. " + "'block_sparse': blocked implementation of sparse attention.", +) +flags.DEFINE_enum( + "norm_type", + "postnorm", + ["prenorm", "postnorm"], + "Selecting when to apply layer-norm. " + "'prenorm': Before attention layer, e.g. Pegasus. " + "'postnorm': After attention layer, e.g. Bert.", +) +flags.DEFINE_integer("block_size", 16, "The block size for the attention mask.") +flags.DEFINE_integer("num_rand_blocks", 3, "Number of random blocks per row.") + +# Adaptive optimizer configs + +flags.DEFINE_float("weight_decay_rate", 0.01, "L2 penalty as weight decay to be used.") + +flags.DEFINE_float("optimizer_beta1", 0.9, "The exponential decay rate for the 1st moment estimates.") + +flags.DEFINE_float("optimizer_beta2", 0.999, "The exponential decay rate for the 2nd moment estimates.") + +flags.DEFINE_float("optimizer_epsilon", 1e-6, "Adaptivty trade-off parameter.") + +# TPU settings + +flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") + +flags.DEFINE_string( + "tpu_name", + None, + "The Cloud TPU to use for training. This should be either the name " + "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " + "url.", +) + +flags.DEFINE_string( + "tpu_zone", + None, + "[Optional] GCE zone where the Cloud TPU is located in. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.", +) + +flags.DEFINE_string("tpu_job_name", None, "Name of TPU worker, if anything other than 'tpu_worker'") + +flags.DEFINE_string( + "gcp_project", + None, + "[Optional] Project name for the Cloud TPU-enabled project. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.", +) + +flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") + +flags.DEFINE_integer( + "num_tpu_cores", + 8, + "Only used if `use_tpu` is True. Total number of TPU cores to use.", +) + +flags.DEFINE_integer("iterations_per_loop", 200, "How many steps to make in each estimator call.") + + +def as_dictionary(): + """Get current config from flag.""" + + config = { + # transformer basic configs + "attention_probs_dropout_prob": FLAGS.attention_probs_dropout_prob, + "hidden_act": FLAGS.hidden_act, + "hidden_dropout_prob": FLAGS.hidden_dropout_prob, + "hidden_size": FLAGS.hidden_size, + "initializer_range": FLAGS.initializer_range, + "intermediate_size": FLAGS.intermediate_size, + "max_position_embeddings": FLAGS.max_position_embeddings, + "num_attention_heads": FLAGS.num_attention_heads, + "num_hidden_layers": FLAGS.num_hidden_layers, + "type_vocab_size": FLAGS.type_vocab_size, + "scope": FLAGS.scope, + "use_bias": FLAGS.use_bias, + "rescale_embedding": FLAGS.rescale_embedding, + "use_gradient_checkpointing": FLAGS.use_gradient_checkpointing, + "tokenizer_dir": FLAGS.tokenizer_dir, + "use_token_type": FLAGS.use_token_type, + "random_pos_emb": FLAGS.random_pos_emb, + # sparse mask configs + "attention_type": FLAGS.attention_type, + "norm_type": FLAGS.norm_type, + "block_size": FLAGS.block_size, + "num_rand_blocks": FLAGS.num_rand_blocks, + # common bert configs + "data_dir": FLAGS.data_dir, + "output_dir": FLAGS.output_dir, + "init_checkpoint": FLAGS.init_checkpoint, + "max_encoder_length": FLAGS.max_encoder_length, + "do_train": FLAGS.do_train, + "do_eval": FLAGS.do_eval, + "train_batch_size": FLAGS.train_batch_size, + "eval_batch_size": FLAGS.eval_batch_size, + "optimizer": FLAGS.optimizer, + "learning_rate": FLAGS.learning_rate, + "num_train_steps": FLAGS.num_train_steps, + "num_warmup_steps": FLAGS.num_warmup_steps, + "save_checkpoints_steps": FLAGS.save_checkpoints_steps, + "weight_decay_rate": FLAGS.weight_decay_rate, + "optimizer_beta1": FLAGS.optimizer_beta1, + "optimizer_beta2": FLAGS.optimizer_beta2, + "optimizer_epsilon": FLAGS.optimizer_epsilon, + # TPU settings + "use_tpu": FLAGS.use_tpu, + "tpu_name": FLAGS.tpu_name, + "tpu_zone": FLAGS.tpu_zone, + "tpu_job_name": FLAGS.tpu_job_name, + "gcp_project": FLAGS.gcp_project, + "master": FLAGS.master, + "num_tpu_cores": FLAGS.num_tpu_cores, + "iterations_per_loop": FLAGS.iterations_per_loop, + } + + # pretraining dedicated flags + if hasattr(FLAGS, "max_predictions_per_seq"): + config["max_predictions_per_seq"] = FLAGS.max_predictions_per_seq + if hasattr(FLAGS, "masked_lm_prob"): + config["masked_lm_prob"] = FLAGS.masked_lm_prob + if hasattr(FLAGS, "max_eval_steps"): + config["max_eval_steps"] = FLAGS.max_eval_steps + if hasattr(FLAGS, "use_nsp"): + config["use_nsp"] = FLAGS.use_nsp + if hasattr(FLAGS, "keep_checkpoint_max"): + config["keep_checkpoint_max"] = FLAGS.keep_checkpoint_max + if hasattr(FLAGS, "seed"): + config["seed"] = FLAGS.seed + + # classifier dedicated flags + if hasattr(FLAGS, "num_labels"): + config["num_labels"] = FLAGS.num_labels + + # summarization dedicated flags + if hasattr(FLAGS, "max_decoder_length"): + config["max_decoder_length"] = FLAGS.max_decoder_length + if hasattr(FLAGS, "trainable_bias"): + config["trainable_bias"] = FLAGS.trainable_bias + if hasattr(FLAGS, "couple_encoder_decoder"): + config["couple_encoder_decoder"] = FLAGS.couple_encoder_decoder + if hasattr(FLAGS, "beam_size"): + config["beam_size"] = FLAGS.beam_size + if hasattr(FLAGS, "alpha"): + config["alpha"] = FLAGS.alpha + if hasattr(FLAGS, "label_smoothing"): + config["label_smoothing"] = FLAGS.label_smoothing + if hasattr(FLAGS, "do_mlm"): + config["do_mlm"] = FLAGS.do_mlm + + # Vocab info + tokenizer = AutoTokenizer.from_pretrained(FLAGS.tokenizer_dir) + config["vocab_size"] = tokenizer.vocab_size + config["pad_token_id"] = tokenizer.pad_token_id + config["unk_token_id"] = tokenizer.unk_token_id + config["cls_token_id"] = tokenizer.cls_token_id + config["sep_token_id"] = tokenizer.sep_token_id + config["mask_token_id"] = tokenizer.mask_token_id + config["bos_token_id"] = tokenizer.bos_token_id + config["eos_token_id"] = tokenizer.eos_token_id + + return config + + +def save(path): + """Save current flag config.""" + config = as_dictionary() + with tf.io.gfile.GFile(path, "w") as f: + json.dump(config, f, indent=4, sort_keys=True) + + # log flags + max_len = max([len(ii) for ii in config.keys()]) + fmt_string = "\t%" + str(max_len) + "s : %s" + logging.info("Arguments:") + for key, value in sorted(config.items()): + logging.info(fmt_string, key, value) + + return config + + +def load(path): + """Set flag from saved config.""" + + with tf.io.gfile.GFile(path) as f: + config = json.load(f) + + # log and set flags + max_len = max([len(ii) for ii in config.keys()]) + fmt_string = "\t%" + str(max_len) + "s : %s" + logging.info("Arguments:") + for key, value in config.items(): + if hasattr(FLAGS, key): + logging.info(fmt_string, key, value) + setattr(FLAGS, key, value) + + return config diff --git a/pretrain/kobigbird/modeling.py b/pretrain/kobigbird/modeling.py new file mode 100644 index 0000000..ef206b6 --- /dev/null +++ b/pretrain/kobigbird/modeling.py @@ -0,0 +1,470 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""The main BigBird model and related functions.""" + +import copy + +import tensorflow.compat.v2 as tf +from absl import logging +from kobigbird import decoder, encoder, utils + + +class BertModel(tf.keras.layers.Layer): + """BERT model ("Bidirectional Encoder Representations from Transformers"). + + Example usage: + + ```python + # Already been converted into SentencePiece token ids + input_ids = tf.constant([[31, 51, 99], [15, 5, 0]]) + token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]]) + + params = utils.BigBirdConfig(vocab_size=32000, hidden_size=512, + num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + + model = modeling.BertModel(params, train=True) + + _, pooled_output = model(input_ids=input_ids, token_type_ids=token_type_ids) + + label_embeddings = tf.get_variable(...) + logits = tf.matmul(pooled_output, label_embeddings) + ... + ``` + """ + + def __init__(self, params): + """Constructor for BertModel. + + Args: + params: `BigBirdConfig` dictionary. + """ + self.params = copy.deepcopy(params) + self.scope = params["scope"] + super(BertModel, self).__init__(name=self.scope) + + # validate params + self.pad = lambda x: x + if params["max_encoder_length"] <= 512: + logging.info("Switching to full attention for short sequences") + self.params["attention_type"] = "original_full" + if self.params["attention_type"] == "simulated_sparse" or self.params["attention_type"] == "block_sparse": + if params["max_encoder_length"] % params["block_size"]: + logging.info("Expand max_encoder_length to next multiple of block_size") + self.params["max_encoder_length"] = (params["max_encoder_length"] // params["block_size"] + 1) * params[ + "block_size" + ] + pad_size = self.params["max_encoder_length"] - params["max_encoder_length"] + paddings = [[0, 0], [0, pad_size]] + self.pad = lambda x: tf.pad(x, paddings) + + with tf.compat.v1.variable_scope(self.scope, reuse=tf.compat.v1.AUTO_REUSE): + self.embeder = utils.EmbeddingLayer( + vocab_size=self.params["vocab_size"], + emb_dim=self.params["hidden_size"], + initializer=utils.create_initializer(self.params["initializer_range"]), + scale_emb=self.params["rescale_embedding"], + use_token_type=self.params["use_token_type"], + num_token_types=self.params["type_vocab_size"], + use_position_embeddings=True, + max_position_embeddings=self.params["max_position_embeddings"], + dropout_prob=self.params["hidden_dropout_prob"], + ) + self.encoder = encoder.EncoderStack(self.params) + self.pooler = utils.SimpleDenseLayer( + input_size=self.params["hidden_size"], + output_size=self.params["hidden_size"], + initializer=utils.create_initializer(self.params["initializer_range"]), + activation=tf.tanh, + name="pooler/dense", + ) + + def call(self, input_ids, token_type_ids=None, training=None): + """Constructor for BertModel. + + Args: + input_ids: int32 Tensor of shape [batch_size, seq_length]. + token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. + training: Boolean indicating whether the call is training or inference. + + Returns: + sequence_output: Tensor of shape [batch_size, seq_length, hidden_size] + pooled_output: Tensor of shape [batch_size, hidden_size] + + Raises: + ValueError: The config is invalid or one of the input tensor shapes + is invalid. + """ + # pad if needed + input_ids = self.pad(input_ids) + + if token_type_ids is None: + token_type_ids = tf.zeros_like(input_ids, dtype=tf.int32) + else: + token_type_ids = self.pad(token_type_ids) + + # Perform embedding lookup on the word ids. + embedding_output = self.embeder( + input_ids, + self.params["max_encoder_length"], + token_type_ids=token_type_ids, + training=training, + ) + + # Generate mask. + input_mask = tf.where( + input_ids != self.params["pad_token_id"], tf.ones_like(input_ids), tf.zeros_like(input_ids) + ) + + # Run the stacked transformer. + sequence_output = self.encoder(embedding_output, input_mask, training) + + # The "pooler" converts the encoded sequence tensor of shape + # [batch_size, seq_length, hidden_size] to a tensor of shape + # [batch_size, hidden_size]. This is necessary for segment-level + # (or segment-pair-level) classification tasks where we need a fixed + # dimensional representation of the segment. + first_token_tensor = sequence_output[:, 0, :] + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. We assume that this has been pre-trained + pooled_output = self.pooler(first_token_tensor) + + return sequence_output, pooled_output + + +class TransformerModel(tf.keras.layers.Layer): + """Encoder-Decoder transformer model. + + Example usage: + + ```python + # Already been converted into SentencePiece token ids + input_ids = tf.constant([[31, 51, 99], [15, 5, 0]]) + target_ids = tf.constant([[43, 76, 38], [56, 8, 0]]) + + params = utils.BigBirdConfig(vocab_size=32000, hidden_size=512, + num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + + model = modeling.TransformerModel(params, train=True) + + predictions, _ = model(input_ids=input_ids, target_ids=target_ids) + + log_probs, logits, pred_ids = predictions + ... + ``` + """ + + def __init__(self, params): + """Constructor for TransformerModel. + + Args: + params: `BigBirdConfig` dictionary. + """ + self.params = copy.deepcopy(params) + self.scope = params["scope"] + super(TransformerModel, self).__init__(name=self.scope) + + # validate params + self.pad = lambda x: x + if params["max_encoder_length"] <= 512: + logging.info("Switching to full attention for short sequences") + self.params["attention_type"] = "original_full" + if self.params["attention_type"] == "simulated_sparse" or self.params["attention_type"] == "block_sparse": + if params["max_encoder_length"] % params["block_size"]: + logging.info("Expand max_encoder_length to next multiple of block_size") + self.params["max_encoder_length"] = (params["max_encoder_length"] // params["block_size"] + 1) * params[ + "block_size" + ] + pad_size = self.params["max_encoder_length"] - params["max_encoder_length"] + paddings = [[0, 0], [0, pad_size]] + self.pad = lambda x: tf.pad(x, paddings) + + with tf.compat.v1.variable_scope(self.scope, reuse=tf.compat.v1.AUTO_REUSE): + self.embeder = utils.EmbeddingLayer( + vocab_size=self.params["vocab_size"], + emb_dim=self.params["hidden_size"], + initializer=utils.create_initializer(self.params["initializer_range"]), + scale_emb=self.params["rescale_embedding"], + use_token_type=self.params["use_token_type"], + num_token_types=self.params["type_vocab_size"], + use_position_embeddings=True, + max_position_embeddings=self.params["max_position_embeddings"], + dropout_prob=self.params["hidden_dropout_prob"], + ) + self.encoder = encoder.EncoderStack(self.params) + self.decoder = decoder.DecoderStack(self.params) + + def _encode(self, input_ids, training=None): + """Generate continuous representation for ids. + + Args: + input_ids: Int tensor with shape [batch_size, input_length]. + training: Boolean indicating whether the call is training or inference. + + Returns: + A float tensors of shape + [batch_size, input_length, hidden_size]. + """ + # pad if needed + input_ids = self.pad(input_ids) + + # Perform embedding lookup on the word ids. + input_embs = self.embeder(input_ids, self.params["max_encoder_length"], training=training) + + # Generate mask. + input_mask = tf.where( + input_ids > self.params["pad_token_id"], tf.ones_like(input_ids), tf.zeros_like(input_ids) + ) + + # Run the stacked transformer. + encoder_output = self.encoder(input_embs, input_mask, training=training) + + return encoder_output, input_mask + + def _get_start_token_ids(self, tensor_for_shape): + batch_size = utils.get_shape_list(tensor_for_shape)[0] + return tf.ones([batch_size], dtype=tf.int32) * self.params["bos_token_id"] + + def get_inputs_from_targets(self, targets, start_token_ids): + """Converts target ids to input ids, i.e. adds and removes last.""" + length = tf.math.count_nonzero(targets, axis=1, dtype=tf.int32) + # Add start token ids. + inputs = tf.concat([tf.expand_dims(start_token_ids, axis=1), targets], 1) + # Remove from the input. + mask = tf.sequence_mask(length, self.params["max_decoder_length"] + 1, dtype=tf.int32) + inputs = (mask * inputs)[:, :-1] + return inputs + + def _decode( + self, + target_ids, + target_mask, + start_token_ids, + encoder_output, + encoder_mask, + training=None, + ): + """Compute likelihood of target tokens under the model. + + Args: + target_ids: tensor with shape [batch_size, target_length, hidden_size] + target_mask: self-attention bias for decoder attention layer. [batch_size, + input_length] + start_token_ids: int32 tensor of shape [batch_size] for first decoder + input. + encoder_output: Continuous representation of input sequence. Float tensor + with shape [batch_size, input_length, hidden_size]. + encoder_mask: Float tensor with shape [batch_size, input_length]. + training: Boolean indicating whether the call is training or inference. + + Returns: + A dict containing the output ids, the output log-probs, the output logits. + """ + + # Prepare inputs to decoder layers by shifting targets, embedding ids, + # adding positional encoding and applying dropout. + input_ids = self.get_inputs_from_targets(target_ids, start_token_ids) + + input_embs = self.embeder(input_ids, self.params["max_decoder_length"], training=training) + + outputs = self.decoder(input_embs, target_mask, encoder_output, encoder_mask, training=training) + + logits = self.embeder.linear(outputs) + output_ids = tf.cast(tf.argmax(logits, axis=-1), tf.int32) + + log_probs = -tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target_ids, logits=logits) + log_probs = tf.where(target_ids > self.params["pad_token_id"], log_probs, tf.zeros_like(log_probs, tf.float32)) + + return ( + tf.identity(log_probs, name="log_probs"), + tf.identity(logits, name="logits"), + tf.cast(output_ids, tf.int32, name="pred_ids"), + ) + + def _init_cache(self, batch_size): + """Initialize cache for decoding.""" + + max_decode_len = self.params["max_decoder_length"] + num_heads = self.params["num_attention_heads"] + head_size = int(self.params["hidden_size"] / num_heads) + + cache = {} + for layer in range(self.params["num_hidden_layers"]): + cache["layer_%d" % layer] = { + "k": tf.zeros([batch_size, num_heads, max_decode_len, head_size]), + "v": tf.zeros([batch_size, num_heads, max_decode_len, head_size]), + } + return cache + + def _get_symbols_to_logits_fn(self, decoder_self_attention_mask): + """Returns a decoding function that calculates logits of the next tokens.""" + + max_decode_len = self.params["max_decoder_length"] + + def _symbols_to_logits_fn(target_ids, cache, i): + """Generate logits for next candidate IDs. + + Args: + target_ids: Current decoded sequences. int tensor with shape + [batch_size, i + 1] + cache: dictionary of values storing the encoder output, encoder-decoder + attention bias, and previous decoder attention values. + i: Loop index + + Returns: + Tuple of + (logits with shape [batch_size * beam_size, vocab_size], + updated cache values) + """ + decoder_input = tf.slice( + target_ids, + [0, tf.maximum(tf.cast(0, i.dtype), i - 1)], + [target_ids.shape[0], 1], + ) + self_attention_mask = tf.slice(decoder_self_attention_mask, [0, 0, i, 0], [1, 1, 1, max_decode_len]) + + # Preprocess decoder input by getting embeddings and adding timing signal. + decoder_input = self.embeder(decoder_input, 1, start_pos=i, training=False) + + decoder_output = self.decoder( + decoder_input, + self_attention_mask, + cache.get("encoder_output"), + cache.get("encoder_mask"), + cache=cache, + decode_i=i, + training=False, + ) + + logits = self.embeder.linear(decoder_output) + logits = tf.squeeze(logits, axis=[1]) + + return logits + + return _symbols_to_logits_fn + + def _predict(self, target_ids, target_mask, start_token_ids, encoder_output, encoder_mask): + """Beam decode output tokens and probabilities. + + Args: + target_ids: tensor with shape [batch_size, target_length, hidden_size] + target_mask: self-attention bias for decoder attention layer. [batch_size, + input_length] + start_token_ids: int32 tensor of shape [batch_size] for first decoder + input. + encoder_output: Continuous representation of input sequence. Float + tensor with shape [batch_size, target_length, num_hidden_layers, + hidden_size] + encoder_mask: bias for encoder-decoder attention layer. [batch_size, + input_length] + + Returns: + A tuple of: + `log_probs`: Log-probs of output tokens. + `logits`: Logits of output tokens. + `pred_ids`: Predicted output sequence. + """ + batch_size = utils.get_shape_list(start_token_ids)[0] + end_token_id = self.params["eos_token_id"] + + # One step logit function. + symbols_to_logits_fn = self._get_symbols_to_logits_fn(target_mask) + + # Create cache storing decoder attention values for each layer. + cache = self._init_cache(batch_size) + + if encoder_output is not None: + # Add encoder output and attention bias to the cache. + cache["encoder_output"] = encoder_output + cache["encoder_mask"] = encoder_mask + + decoded_ids = decoder.left2right_decode( + symbols_to_logits_fn, + start_token_ids, + cache, + batch_size, + self.params["max_decoder_length"], + vocab_size=self.params["vocab_size"], + beam_size=self.params["beam_size"], + beam_start=5, + beam_alpha=self.params["alpha"], + beam_min=0, + beam_max=-1, + eos_id=end_token_id, + ) + + # Get the top sequence for each batch element + output_ids = tf.cast(decoded_ids, tf.int32, name="pred_ids") + + # Calculate log probs for given sequence if available. + calc_ids = output_ids if target_ids is None else target_ids + output_log_probs, output_logits, _ = self._decode( + calc_ids, + target_mask, + start_token_ids, + encoder_output, + encoder_mask, + training=False, + ) + + return (output_log_probs, output_logits, output_ids) + + def _decode_and_predict(self, target_ids, encoder_output, encoder_mask, training=None): + """Decodes a sequence given the input and the encoder. + + Args: + target_ids: tensor with shape [batch_size, target_length, hidden_size] + encoder_output: Continuous representation of input sequence. Float + tensor with shape [batch_size, target_length, num_hidden_layers, + hidden_size] + encoder_mask: bias for encoder-decoder attention layer. [batch_size, + input_length] + training: Boolean indicating whether the call is training or inference. + + Returns: + A tuple of: + `log_probs`: Log-probs of output tokens. + `logits`: Logits of output tokens. + `pred_ids`: Predicted output sequence. + """ + # Create initial set of IDs that will be passed into symbols_to_logits_fn. + start_token_ids = self._get_start_token_ids(encoder_output) + + # Create causal self-attention mask for decoder. + target_mask = decoder.create_self_attention_mask(self.params["max_decoder_length"]) + + predictions = {} + if training: + predictions = self._decode( + target_ids, + target_mask, + start_token_ids, + encoder_output, + encoder_mask, + training=True, + ) + else: + predictions = self._predict(target_ids, target_mask, start_token_ids, encoder_output, encoder_mask) + + return predictions + + def call(self, input_ids, target_ids=None, training=None): + # Run the inputs through the encoder layer to map the symbol + # representations to continuous representations. + encoder_output, encoder_mask = self._encode(input_ids, training=training) + + # Decode. + predictions = self._decode_and_predict(target_ids, encoder_output, encoder_mask, training=training) + + return predictions, encoder_output diff --git a/pretrain/kobigbird/optimization.py b/pretrain/kobigbird/optimization.py new file mode 100644 index 0000000..0eb8fa7 --- /dev/null +++ b/pretrain/kobigbird/optimization.py @@ -0,0 +1,273 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Functions and classes related to optimization (weight updates).""" + +import re + +import tensorflow.compat.v2 as tf +from absl import logging + +# pylint: disable=g-direct-tensorflow-import +from tensorflow.python.ops import resource_variable_ops + + +def get_linear_warmup_linear_decay_lr(init_lr, num_train_steps, num_warmup_steps): + """Calculate learning rate with linear warmup and linear decay.""" + global_step = tf.compat.v1.train.get_or_create_global_step() + + learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32) + + # Implements linear decay of the learning rate. + learning_rate = tf.compat.v1.train.polynomial_decay( + learning_rate, + global_step, + num_train_steps, + end_learning_rate=0.0, + power=1.0, + cycle=False, + ) + + # Implements linear warmup. I.e., if global_step < num_warmup_steps, the + # learning rate will be `global_step/num_warmup_steps * init_lr`. + if num_warmup_steps: + global_steps_int = tf.cast(global_step, tf.int32) + warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32) + + global_steps_float = tf.cast(global_step, tf.float32) + warmup_steps_float = tf.cast(num_warmup_steps, tf.float32) + + warmup_percent_done = global_steps_float / warmup_steps_float + warmup_learning_rate = init_lr * warmup_percent_done + + is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32) + learning_rate = (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate + + return learning_rate + + +def get_linear_warmup_rsqrt_decay_lr(init_lr, hidden_size, num_warmup_steps): + """Calculate learning rate with linear warmup and rsqrt decay.""" + num_warmup_steps = tf.cast(num_warmup_steps, tf.float32) + global_step = tf.compat.v1.train.get_or_create_global_step() + global_step = tf.cast(global_step, tf.float32) + + learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32) + learning_rate *= tf.math.rsqrt(tf.cast(hidden_size, tf.float32)) + # Apply linear warmup + learning_rate *= tf.minimum(1.0, global_step / num_warmup_steps) + # Apply rsqrt decay + learning_rate *= tf.math.rsqrt(tf.maximum(global_step, num_warmup_steps)) + + return learning_rate + + +def get_optimizer(params, learning_rate): + """Gets the optimzer based on the hparams and current mode (TPU vs. CPU/GPU). + + Args: + params: A dictionary containing training hyperparameters. + learning_rate: A float32 scalar. + + Returns: + A string or an optimizer instance. + """ + optimizer = None + + if params["optimizer"] == "Adafactor": + try: + from tensor2tensor.utils import adafactor # pylint: disable=g-import-not-at-top + + optimizer = adafactor.AdafactorOptimizer(learning_rate=learning_rate) + except ImportError: + logging.error("tensor2tensor not installed. Cannot use Adafactor." "Defaulting to Adam.") + params["optimizer"] = "Adam" + + if params["optimizer"] == "Adam": + optimizer = tf.compat.v1.train.AdamOptimizer( + learning_rate, + beta1=params["optimizer_beta1"], + beta2=params["optimizer_beta2"], + epsilon=params["optimizer_epsilon"], + ) + + if params["optimizer"] == "AdamWeightDecay": + optimizer = AdamWeightDecayOptimizer( + learning_rate, + weight_decay_rate=params["weight_decay_rate"], + beta_1=params["optimizer_beta1"], + beta_2=params["optimizer_beta2"], + epsilon=params["optimizer_epsilon"], + exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"], + ) + + if params["optimizer"] == "SGD": + optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate) + + if optimizer is None: + raise ValueError("Unknown optimizer: {}.".format(params["optimizer"])) + + if params["use_tpu"]: + # Average the gradients across TPU cores. + optimizer = tf.compat.v1.tpu.CrossShardOptimizer(optimizer) + + return optimizer + + +class AdamWeightDecayOptimizer(tf.compat.v1.train.Optimizer): + """A basic Adam optimizer that includes "correct" L2 weight decay.""" + + def __init__( + self, + learning_rate, + weight_decay_rate=0.0, + beta_1=0.9, + beta_2=0.999, + epsilon=1e-6, + exclude_from_weight_decay=None, + name="AdamWeightDecayOptimizer", + ): + """Constructs a AdamWeightDecayOptimizer.""" + super(AdamWeightDecayOptimizer, self).__init__(False, name) + + self.learning_rate = learning_rate + self.weight_decay_rate = weight_decay_rate + self.beta_1 = beta_1 + self.beta_2 = beta_2 + self.epsilon = epsilon + self.exclude_from_weight_decay = exclude_from_weight_decay + + def _create_slots(self, var_list): + # Create slots for the first and second moments. + for v in var_list: + self._zeros_slot(v, "m", self._name) + self._zeros_slot(v, "v", self._name) + + def _apply_dense(self, grad, var): + param_name = self._get_variable_name(var.name) + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + + # Standard Adam update. + next_m = tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad) + next_v = tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2, tf.square(grad)) + + update = next_m / (tf.sqrt(next_v) + self.epsilon) + + # Just adding the square of the weights to the loss function is *not* + # the correct way of using L2 regularization/weight decay with Adam, + # since that will interact with the m and v parameters in strange ways. + # + # Instead we want ot decay the weights in a manner that doesn't interact + # with the m/v parameters. This is equivalent to adding the square + # of the weights to the loss with plain (non-momentum) SGD. + if self._do_use_weight_decay(param_name): + update += self.weight_decay_rate * var + + update_with_lr = self.learning_rate * update + + next_param = var - update_with_lr + + return tf.group([var.assign(next_param), m.assign(next_m), v.assign(next_v)]) + + def _resource_apply_dense(self, grad, var): + """See `tf.train.Optimizer._resource_apply_dense()`.""" + return self._apply_dense(grad, var) + + def _apply_sparse(self, grad, var): + """See `tf.train.Optimizer._apply_sparse()`.""" + + def scatter_update_fn(x, i, v): + return tf.compat.v1.scatter_update(x, i, v, use_locking=self._use_locking) + + return self._apply_sparse_shared(grad.values, grad.indices, var, scatter_update_fn) + + def _resource_apply_sparse(self, grad, var, indices): + """See `tf.train.Optimizer._resource_apply_spase()`.""" + + def scatter_update_fn(x, i, v): + with tf.control_dependencies([resource_variable_ops.resource_scatter_update(x.handle, i, v)]): + return x.value() + + return self._apply_sparse_shared(grad, indices, var, scatter_update_fn) + + def _apply_sparse_shared(self, grad, indices, var, scatter_update_fn): + """Applies sparse gradients to a variable. + + Args: + grad: A tensor for the `values` of `tf.IndexedSlices`. + indices: A tensor for the `indices` of `tf.IndexedSlices`. + var: A `tf.Variable` object. + scatter_update_fn: A function which performs scattered update to + a `tf.Variable` object. It takes tuple of (x, i, v) where: + * x: A `tf.Variable` object which is updated by `i` and `v`, + * i: A tensor for the `indices` of `tf.IndexedSlices`, + * v: A tensor for the `values` of `tf.IndexedSlices`, + and returns a tensor after updating `x`. + + Returns: + An op which updates `var` with `grad` and `indices`. + """ + param_name = self._get_variable_name(var.name) + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + + # m_t = beta1 * m + (1 - beta1) * g_t + m_scaled_g_values = tf.multiply(1.0 - self.beta_1, grad) + m_t = m.assign(m * self.beta_1) + with tf.control_dependencies([m_t]): + m_slice = tf.gather(m, indices) + m_scaled_g_values + m_t = scatter_update_fn(m, indices, m_slice) + + # v_t = beta2 * v + (1 - beta2) * g_t^2 + v_scaled_g_values = tf.multiply(1.0 - self.beta_2, tf.square(grad)) + v_t = v.assign(v * self.beta_2) + with tf.control_dependencies([v_t]): + v_slice = tf.gather(v, indices) + v_scaled_g_values + v_t = scatter_update_fn(v, indices, v_slice) + + update = m_t / (tf.sqrt(v_t) + self.epsilon) + + # Just adding the square of the weights to the loss function is *not* + # the correct way of using L2 regularization/weight decay with Adam, + # since that will interact with the m and v parameters in strange ways. + # + # Instead we want ot decay the weights in a manner that doesn't interact + # with the m/v parameters. This is equivalent to adding the square + # of the weights to the loss with plain (non-momentum) SGD. + if self._do_use_weight_decay(param_name): + update += self.weight_decay_rate * var + + update_with_lr = self.learning_rate * update + + next_param = var - update_with_lr + + return tf.group([var.assign(next_param), m_t, v_t]) + + def _do_use_weight_decay(self, param_name): + """Whether to use L2 weight decay for `param_name`.""" + if not self.weight_decay_rate: + return False + if self.exclude_from_weight_decay: + for r in self.exclude_from_weight_decay: + if re.search(r, param_name) is not None: + return False + return True + + def _get_variable_name(self, param_name): + """Get the variable name from the tensor name.""" + m = re.match("^(.*):\\d+$", param_name) + if m is not None: + param_name = m.group(1) + return param_name diff --git a/pretrain/kobigbird/recompute_grad.py b/pretrain/kobigbird/recompute_grad.py new file mode 100644 index 0000000..6d0f6d8 --- /dev/null +++ b/pretrain/kobigbird/recompute_grad.py @@ -0,0 +1,526 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Library for rematerialization. + +Incubates a version of tf.recompute_grad that is XLA compatible. +""" +from typing import Deque, List, NamedTuple, Optional, Sequence, Text, Union + +import collections +import numbers +import os +import threading + +import numpy as np +import tensorflow.compat.v2 as tf +from absl import logging + +# pylint: disable=g-direct-tensorflow-import +from tensorflow.python.framework import ops +from tensorflow.python.ops import custom_gradient + + +# Remove when https://github.com/tensorflow/tensorflow/pull/45298 +# gets merged +def get_variable_by_name(var_name): + """Retrieves tf.Variable from name in MirroredStrategy (multi-gpu).""" + + # Get all variables, but it will have copies from different replicas + all_global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + + def _replica_filter(var): + """Filter out variables from different context.""" + try: + return var_name == var.op.name + except AttributeError: + return False + + candidate_vars = list(filter(_replica_filter, all_global_vars)) + + if len(candidate_vars) >= 1: + # Filter out non-trainable variables. + candidate_vars = [v for v in candidate_vars if v.trainable] + else: + raise ValueError("Unsuccessful at finding variable {}.".format(var_name)) + + if len(candidate_vars) == 1: + return candidate_vars[0] + elif len(candidate_vars) > 1: + raise ValueError( + "Unsuccessful at finding trainable variable {}. " + "Number of candidates: {}. " + "Candidates: {}".format(var_name, len(candidate_vars), candidate_vars) + ) + else: + # The variable is not trainable. + return None + + +custom_gradient.get_variable_by_name = get_variable_by_name + + +class RecomputeContext( + NamedTuple( + "RecomputeContext", + [ + ("is_recomputing", bool), + ("seed", tf.Tensor), + ("children", Deque["RecomputeContext"]), + ], + ) +): + """Context for recomputation. + + Attributes: + is_recomputing: Whether we are in a recomputation phase. + seed: Scalar integer tensor that should be used with stateless random ops + for deterministic behavior and correct computation of the gradient. + children: Nested `RecomputeContext` instances. Used internally by + `recompute_grad` to track nested instances of `RecomputeContext`. + """ + + def __enter__(self): + return _context_stack.push(self) + + def __exit__(self, exc_type, exc_value, traceback): + _context_stack.pop(self) + + +# Simplified version of `_DefaultStack` in +# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/ops.py. +class _ContextStack(threading.local): + """A thread-local stack for providing implicit recompute contexts.""" + + def __init__(self): + super(_ContextStack, self).__init__() + self._stack = [] + + def top(self) -> Optional[RecomputeContext]: + return self._stack[-1] if self._stack else None + + def push(self, context: RecomputeContext): + self._stack.append(context) + return context + + def pop(self, context: RecomputeContext): + if self._stack[-1] is not context: + raise AssertionError("Nesting violated for RecomputeContext.") + self._stack.pop() + + +_context_stack = _ContextStack() + + +def get_recompute_context() -> Optional[RecomputeContext]: + """Returns the current recomputing context if it exists.""" + return _context_stack.top() + + +# Adapted from +# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/control_flow_util.py. +def _get_containing_xla_context(graph: tf.Graph) -> Optional[object]: + """Returns the first ancestor `XLAControlFlowContext` in the `graph`.""" + ctxt = graph._get_control_flow_context() # pylint: disable=protected-access + while ctxt: + if ctxt.IsXLAContext(): + return ctxt + ctxt = ctxt.outer_context + return None + + +def _in_xla_context(graph: Optional[tf.Graph] = None) -> bool: + """Detects whether we are in an XLA context.""" + if "--tf_xla_auto_jit=2" in os.environ.get("TF_XLA_FLAGS", ""): + return True + graph = tf.compat.v1.get_default_graph() if graph is None else graph + while True: + if _get_containing_xla_context(graph) is not None: + return True + try: + graph = graph.outer_graph + except AttributeError: + return False + + +def _force_data_dependency(first_compute: Sequence[tf.Tensor], then_compute: Sequence[tf.Tensor]) -> List[tf.Tensor]: + """Force all of `then_compute` to depend on all of `first_compute`. + + Uses a dummy data dependency, which is useful when running on TPUs because + XLA ignores control dependencies. Only supports float arguments. + + Args: + first_compute: Sequence of `Tensor`s to be executed before `then_compute`. + then_compute: Sequence of `Tensor`s to executed after `first_compute`. + + Returns: + Sequence of `Tensor`s with same length of `then_compute`. + + Raises: + ValueError: if ranks are unknown or types are not floating. + """ + + def _first_element(x): + if x.shape.ndims is None: + raise ValueError("Rank of Tensor %s must be known" % x) + ndims = x.shape.ndims + begin = tf.zeros(ndims, dtype=tf.int32) + size = tf.ones(ndims, dtype=tf.int32) + return tf.reshape(tf.slice(x, begin, size), []) + + first_compute_sum = tf.add_n([_first_element(x) for x in first_compute if x is not None]) + dtype = first_compute_sum.dtype + if not dtype.is_floating: + raise ValueError("_force_data_dependency only supports floating dtypes.") + zero = np.finfo(dtype.as_numpy_dtype).tiny * first_compute_sum + return [x + tf.cast(zero, x.dtype) if x is not None else None for x in then_compute] + + +def _make_seed_if_none(seed: Optional[tf.Tensor]) -> tf.Tensor: + """Uses the global generator to make a seed if necessary.""" + if seed is not None: + return seed + generator = tf.random.experimental.get_global_generator() + # The two seeds for stateless random ops don't have individual semantics and + # are scrambled together, so providing one seed is fine. This makes it easier + # for users to provide a local seed without worrying about integer overflow. + # See `make_seeds` in + # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/stateful_random_ops.py. + try: + return generator.uniform_full_int([], tf.int32, name="recompute_grad_seed") + except (RuntimeError, TypeError, ValueError, tf.errors.NotFoundError) as e: + # For a number of reasons, the above operation can fail like using multiple + # graphs or toggling between eager and graph modes. Reset the generator. + logging.warn("Resetting the generator. %s: %s", type(e), e) + tf.random.experimental.set_global_generator(None) + generator = tf.random.experimental.get_global_generator() + return generator.uniform_full_int([], tf.int32, name="recompute_grad_seed") + + +def recompute_grad(f, seed=None): + """An eager-compatible version of recompute_grad. + + For f(*args, **kwargs), this supports gradients with respect to args, or to + gradients with respect to any variables residing in the kwarg 'variables'. + Note that for keras layer and model objects, this is handled automatically. + + Warning: If `f` was originally a tf.keras Model or Layer object, `g` will not + be able to access the member variables of that object, because `g` returns + through the wrapper function `inner`. When recomputing gradients through + objects that inherit from keras, we suggest keeping a reference to the + underlying object around for the purpose of accessing these variables. + + Args: + f: function `f(*x)` that returns a `Tensor` or sequence of `Tensor` outputs. + seed: Optional seed for random ops. `seed` should an integer scalar + `Tensor`. When compiling to XLA, `seed` must have dtype `tf.int32`. If + `seed` is not provided one will be generated. + + Returns: + A function `g` that wraps `f`, but which recomputes `f` on the backwards + pass of a gradient call. + """ + + @tf.custom_gradient + def inner(*args, **kwargs): + """Inner function closure for calculating gradients.""" + # Detect when we're nested and in the backwards pass, so we don't generate + # an additional seed. + parent_context = get_recompute_context() + if parent_context is not None and parent_context.is_recomputing: + # Use the cached context in the recomputation phase. + with parent_context.children.popleft()._replace(is_recomputing=True) as context: + result = f(*args, **kwargs) + else: + with RecomputeContext( + is_recomputing=False, + seed=_make_seed_if_none(seed), + children=collections.deque(), + ) as context: + result = f(*args, **kwargs) + # In the forward pass, build up a tree of recomputation contexts. + if parent_context is not None and not parent_context.is_recomputing: + parent_context.children.append(context) + + def grad(*dresult, **grad_kwargs): + """Gradient function calculation for inner function.""" + variables = grad_kwargs.pop("variables", None) + if grad_kwargs: + raise ValueError("Found unexpected kwargs for `grad`: ", list(grad_kwargs.keys())) + inputs, seed = list(args), context.seed + if _in_xla_context(): + inputs = _force_data_dependency(tf.nest.flatten(dresult), inputs + [seed]) + seed = inputs.pop() + # tf.keras.backend.set_learning_phase(1) + with tf.GradientTape() as tape: + tape.watch(inputs) + if variables is not None: + tape.watch(variables) + with tf.control_dependencies(dresult): + with context._replace(is_recomputing=True, seed=seed): + result = f(*inputs, **kwargs) + kw_vars = [] + if variables is not None: + kw_vars = list(variables) + grads = tape.gradient(result, list(inputs) + kw_vars, output_gradients=dresult) + return grads[: len(inputs)], grads[len(inputs) :] + + return result, grad + + return inner + + +######################## STATELESS DROPOUT LAYERS ############################## + + +def _as_shape(shape: Union[Sequence[int], tf.TensorShape]) -> tf.TensorShape: + """Converts the given object to a TensorShape.""" + return shape if isinstance(shape, tf.TensorShape) else tf.TensorShape(shape) + + +def _get_noise_shape( + x: tf.Tensor, noise_shape: Union[Sequence[int], tf.TensorShape] +) -> Union[tf.Tensor, tf.TensorShape, Sequence[int]]: + """Computes the shape of the binary mask for dropout.""" + # If noise_shape is none return immediately. + if noise_shape is None: + return tf.shape(x) + + try: + # Best effort to figure out the intended shape. + # If not possible, let the op to handle it. + # In eager mode exception will show up. + noise_shape_ = _as_shape(noise_shape) + except (TypeError, ValueError): + return noise_shape + + if x.shape.dims is not None and len(x.shape.dims) == len(noise_shape_.dims): + new_dims = [] + for i, dim in enumerate(x.shape.dims): + if noise_shape_.dims[i].value is None and dim.value is not None: + new_dims.append(dim.value) + else: + new_dims.append(noise_shape_.dims[i].value) + return tf.TensorShape(new_dims) + + return noise_shape + + +def stateless_dropout( + x: tf.Tensor, + rate: float, + seed: tf.Tensor, + noise_shape: Optional[Union[Sequence[int], tf.TensorShape]] = None, + name: Optional[Text] = None, +) -> tf.Tensor: + """Computes dropout: randomly sets elements to zero to prevent overfitting. + + See https://www.tensorflow.org/api_docs/python/tf/nn/dropout. + This version differs in that the seed is required if the rate is nonzero. + + Args: + x: A floating point tensor. + rate: A scalar `Tensor` with the same type as x. The probability that each + element is dropped. For example, setting rate=0.1 would drop 10% of input + elements. + seed: A shape [2] integer Tensor of seeds to the random number generator. + Must have dtype `tf.int32` when compiling to XLA. + noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for + randomly generated keep/drop flags. + name: A name for this operation (optional). + + Returns: + A `Tensor` of the same shape of `x`. + + Raises: + ValueError: If `rate` is not in `[0, 1)` or if `x` is not a floating point + tensor. `rate=1` is disallowed, because the output would be all zeros, + which is likely not what was intended. + """ + with tf.name_scope(name or "stateless_dropout") as name: + x = tf.convert_to_tensor(x, name="x") + if not x.dtype.is_floating: + raise ValueError( + "x has to be a floating point tensor since it's going " + " to be scaled. Got a %s tensor instead." % x.dtype + ) + if isinstance(rate, numbers.Real): + if not (rate >= 0 and rate < 1): + raise ValueError("rate must be a scalar tensor or a float in the " "range [0, 1), got %g" % rate) + if rate > 0.5: + logging.log_first_n( + logging.WARN, + "Large dropout rate: %g (>0.5). In TensorFlow " + ".x, dropout() uses dropout rate instead of keep_prob. " + "Please ensure that this is intended.", + 5, + rate, + ) + + # Early return if nothing needs to be dropped. + if tf.get_static_value(rate) == 0: + return x + + rate = tf.convert_to_tensor(rate, dtype=x.dtype, name="rate") + rate.shape.assert_has_rank(0) + noise_shape = _get_noise_shape(x, noise_shape) + # Sample a uniform distribution on [0.0, 1.0) and select values larger than + # rate. + # + # NOTE: Random uniform actually can only generate 2^23 floats on [1.0, 2.0) + # and subtract 1.0. + random_tensor = tf.random.stateless_uniform(noise_shape, seed=seed, dtype=x.dtype) + keep_prob = 1 - rate + scale = 1 / keep_prob + # NOTE: if (1.0 + rate) - 1 is equal to rate, then we want to consider that + # float to be selected, hence we use a >= comparison. + keep_mask = random_tensor >= rate + ret = x * scale * tf.cast(keep_mask, x.dtype) + if not tf.executing_eagerly(): + ret.set_shape(x.get_shape()) + return ret + + +# Reimplements internal function +# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/smart_cond.py. +def smart_cond(pred, true_fn=None, false_fn=None, name=None): + """Return either `true_fn()` if predicate `pred` is true else `false_fn()`. + + If `pred` is a bool or has a constant value, we return either `true_fn()` + or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both. + + Arguments: + pred: A scalar determining whether to return the result of `true_fn` or + `false_fn`. + true_fn: The callable to be performed if pred is true. + false_fn: The callable to be performed if pred is false. + name: Optional name prefix when using `tf.cond`. + + Returns: + Tensors returned by the call to either `true_fn` or `false_fn`. + + Raises: + TypeError: If `true_fn` or `false_fn` is not callable. + """ + if not callable(true_fn): + raise TypeError("`true_fn` must be callable.") + if not callable(false_fn): + raise TypeError("`false_fn` must be callable.") + pred_value = tf.get_static_value(pred) + if isinstance(pred, tf.Variable) or pred_value is None: + return tf.cond(pred, true_fn=true_fn, false_fn=false_fn, name=name) + if pred_value: + return true_fn() + else: + return false_fn() + + +# See https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout. +class RecomputingDropout(tf.keras.layers.Layer): + """`tf.keras.layers.Dropout` that supports `recompute_grad`.""" + + def __init__(self, rate, noise_shape=None, seed=None, force_recomputation=False, **kwargs): + """Initializes `RecomputingDropout`. + + Args: + rate: Float between 0 and 1. Fraction of the input units to drop. + noise_shape: 1D integer tensor representing the shape of the binary + dropout mask that will be multiplied with the input. For instance, if + inputs have shape `(batch_size, timesteps, features)` and you want the + dropout mask to be the same for all timesteps, you can use + `noise_shape=(batch_size, 1, features)`. + seed: A Python integer to use as random seed. + force_recomputation: If `True`, then raises an error if called outside a + recompute context. + **kwargs: Keyword arguments for `tf.keras.layers.Layer`. + """ + + super(RecomputingDropout, self).__init__(**kwargs) + self.rate = rate + self.noise_shape = noise_shape + self.seed = seed + self.force_recomputation = force_recomputation + self.supports_masking = True + # Create a layer-specific seed to combine with the global recompute seed. + self._recompute_seed = np.random.randint(-(2 ** 31), 2 ** 31, dtype=np.int32) if seed is None else seed + + def _get_noise_shape(self, inputs): + # Subclasses of `Dropout` may implement `_get_noise_shape(self, inputs)`, + # which will override `self.noise_shape`, and allows for custom noise + # shapes with dynamically sized inputs. + if self.noise_shape is None: + return None + + concrete_inputs_shape = tf.shape(inputs) + noise_shape = [] + for i, value in enumerate(self.noise_shape): + noise_shape.append(concrete_inputs_shape[i] if value is None else value) + return tf.convert_to_tensor(noise_shape) + + def call(self, inputs, training=None): + """Builds computation graph. + + Args: + inputs: Input tensor (of any rank). + training: Python boolean indicating whether the layer should behave in + training mode (adding dropout) or in inference mode (doing nothing). + + Returns: + `inputs` masked according to layer configuration. + + Raises: + ValueError: If `force_recomputation` is `True` and called outside a + a recompute context. + """ + if self.rate == 0: + return inputs + + if training is None: + training = tf.keras.backend.learning_phase() + + def dropped_inputs(): + """Randomly drops elements of `inputs` when `training=True`.""" + recompute_context = get_recompute_context() + if recompute_context is None: + if self.force_recomputation: + raise ValueError("RecomputeContext is required when force_recomputation=True.") + return tf.nn.dropout( + inputs, + noise_shape=self._get_noise_shape(inputs), + seed=self.seed, + rate=self.rate, + ) + seed = tf.stack([recompute_context.seed, self._recompute_seed]) + return stateless_dropout( + inputs, + rate=self.rate, + seed=seed, + noise_shape=self._get_noise_shape(inputs), + ) + + output = smart_cond(training, dropped_inputs, lambda: tf.identity(inputs)) + return output + + def compute_output_shape(self, input_shape): + return input_shape + + def get_config(self): + config = { + "rate": self.rate, + "noise_shape": self.noise_shape, + "seed": self.seed, + "force_recomputation": self.force_recomputation, + } + base_config = super(RecomputingDropout, self).get_config() + return dict(list(base_config.items()) + list(config.items())) diff --git a/pretrain/kobigbird/training_utils.py b/pretrain/kobigbird/training_utils.py new file mode 100644 index 0000000..964969a --- /dev/null +++ b/pretrain/kobigbird/training_utils.py @@ -0,0 +1,90 @@ +# coding=utf-8 +# Copyright 2020 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utilities for training the models.""" + +from __future__ import absolute_import, division, print_function + +import datetime +import re +import sys +import time + +import tensorflow.compat.v1 as tf + + +def sys_log(*args): + msg = " ".join(map(str, args)) + sys.stdout.write(msg + "\n") + sys.stdout.flush() + + +class ETAHook(tf.estimator.SessionRunHook): + """Print out the time remaining during training/evaluation.""" + + def __init__(self, to_log, n_steps, iterations_per_loop, on_tpu, log_every=1, is_training=True): + self._to_log = to_log + self._n_steps = n_steps + self._iterations_per_loop = iterations_per_loop + self._on_tpu = on_tpu + self._log_every = log_every + self._is_training = is_training + self._steps_run_so_far = 0 + self._global_step = None + self._global_step_tensor = None + self._start_step = None + self._start_time = None + + def begin(self): + self._global_step_tensor = tf.train.get_or_create_global_step() + + def before_run(self, run_context): + if self._start_time is None: + self._start_time = time.time() + return tf.estimator.SessionRunArgs(self._to_log) + + def after_run(self, run_context, run_values): + self._global_step = run_context.session.run(self._global_step_tensor) + self._steps_run_so_far += self._iterations_per_loop if self._on_tpu else 1 + if self._start_step is None: + self._start_step = self._global_step - (self._iterations_per_loop if self._on_tpu else 1) + self.log(run_values) + + def end(self, session): + self._global_step = session.run(self._global_step_tensor) + self.log() + + def log(self, run_values=None): + step = self._global_step if self._is_training else self._steps_run_so_far + if step % self._log_every != 0: + return + msg = "{:}/{:} = {:.1f}%".format(step, self._n_steps, 100.0 * step / self._n_steps) + time_elapsed = time.time() - self._start_time + time_per_step = time_elapsed / ((step - self._start_step) if self._is_training else step) + msg += ", SPS: {:.1f}".format(1 / time_per_step) + msg += ", ELAP: " + secs_to_str(time_elapsed) + msg += ", ETA: " + secs_to_str((self._n_steps - step) * time_per_step) + if run_values is not None: + for tag, value in run_values.results.items(): + msg += " - " + str(tag) + (": {:.4f}".format(value)) + sys_log(msg) + + +def secs_to_str(secs): + s = str(datetime.timedelta(seconds=int(round(secs)))) + s = re.sub("^0:", "", s) + s = re.sub("^0", "", s) + s = re.sub("^0:", "", s) + s = re.sub("^0", "", s) + return s diff --git a/pretrain/kobigbird/utils.py b/pretrain/kobigbird/utils.py new file mode 100644 index 0000000..9a9f58a --- /dev/null +++ b/pretrain/kobigbird/utils.py @@ -0,0 +1,786 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Helper and utility functions.""" + +import re + +import numpy as np +import tensorflow.compat.v2 as tf +from absl import logging + +############################### SHAPE UTILS #################################### + + +def get_shape_list(tensor, expected_rank=None, name=None): + """Returns a list of the shape of tensor, preferring static dimensions. + + Args: + tensor: A tf.Tensor object to find the shape of. + expected_rank: (optional) int. The expected rank of `tensor`. If this is + specified and the `tensor` has a different rank, and exception will be + thrown. + name: Optional name of the tensor for the error message. + + Returns: + A list of dimensions of the shape of tensor. All static dimensions will + be returned as python integers, and dynamic dimensions will be returned + as tf.Tensor scalars. + """ + if not tf.executing_eagerly() and name is None: + name = tensor.name + + if expected_rank is not None: + assert_rank(tensor, expected_rank, name) + + shape = tensor.shape.as_list() + + non_static_indexes = [] + for (index, dim) in enumerate(shape): + if dim is None: + non_static_indexes.append(index) + + if not non_static_indexes: + return shape + + # assert False, "Static shape not available for {}".format(tensor) + + dyn_shape = tf.shape(tensor) + for index in non_static_indexes: + shape[index] = dyn_shape[index] + return shape + + +def reshape_to_matrix(input_tensor): + """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix).""" + ndims = input_tensor.shape.ndims + if ndims < 2: + raise ValueError("Input tensor must have at least rank 2. Shape = %s" % (input_tensor.shape)) + if ndims == 2: + return input_tensor + + width = input_tensor.shape[-1] + output_tensor = tf.reshape(input_tensor, [-1, width]) + return output_tensor + + +def reshape_from_matrix(output_tensor, orig_shape_list): + """Reshapes a rank 2 tensor back to its original rank >= 2 tensor.""" + if len(orig_shape_list) == 2: + return output_tensor + + output_shape = get_shape_list(output_tensor) + + orig_dims = orig_shape_list[0:-1] + width = output_shape[-1] + + return tf.reshape(output_tensor, orig_dims + [width]) + + +def assert_rank(tensor, expected_rank, name=None): + """Raises an exception if the tensor rank is not of the expected rank. + + Args: + tensor: A tf.Tensor to check the rank of. + expected_rank: Python integer or list of integers, expected rank. + name: Optional name of the tensor for the error message. + + Raises: + ValueError: If the expected shape doesn't match the actual shape. + """ + if not tf.executing_eagerly() and name is None: + name = tensor.name + + expected_rank_dict = {} + if isinstance(expected_rank, int): + expected_rank_dict[expected_rank] = True + else: + for x in expected_rank: + expected_rank_dict[x] = True + + actual_rank = tensor.shape.ndims + if actual_rank not in expected_rank_dict: + scope_name = tf.compat.v1.get_variable_scope().name + raise ValueError( + "For the tensor `{}` in scope `{}`, the actual rank " + "`{}` (shape = {}) is not equal to the expected rank `{}`".format( + name, scope_name, actual_rank, str(tensor.shape), str(expected_rank) + ) + ) + + +############################### DENSE LAYERS ################################### + + +def create_initializer(initializer_range=0.02): + """Creates a `truncated_normal_initializer` with the given range.""" + return tf.compat.v1.truncated_normal_initializer(stddev=initializer_range) + + +class Dense3dLayer(tf.keras.layers.Layer): + """A dense layer with 3D kernel.""" + + def __init__( + self, + num_attention_heads, + size_per_head, + initializer, + activation, + name=None, + head_first=False, + use_bias=True, + ): + """Constructor for dense layer with 3D kernel. + + Args: + num_attention_heads: The size of output dimension. + size_per_head: The size per attention head. + initializer: Kernel initializer. + activation: Actication function. + name: The name scope of this layer. + head_first: Whether to output head dimension before or after sequence dim. + use_bias: Whether the layer uses a bias vector. + """ + super(Dense3dLayer, self).__init__(name=name) + self.num_attention_heads = num_attention_heads + self.size_per_head = size_per_head + self.initializer = initializer + self.activation = activation + self.head_first = head_first + self.use_bias = use_bias + + with tf.compat.v1.variable_scope(name): + hidden_size = self.num_attention_heads * self.size_per_head + self.w = tf.compat.v1.get_variable( + name="kernel", + shape=[hidden_size, hidden_size], + initializer=self.initializer, + ) + + if self.use_bias: + self.b = tf.compat.v1.get_variable(name="bias", shape=[hidden_size], initializer=tf.zeros_initializer()) + else: + self.b = None + + def call(self, input_tensor): + """Constructor for dense layer with 3D kernel. + + Args: + input_tensor: float Tensor of shape [batch, seq_length, hidden_size]. + + Returns: + float logits Tensor. + """ + hidden_size = self.num_attention_heads * self.size_per_head + reshape_w = tf.reshape(self.w, [hidden_size, self.num_attention_heads, self.size_per_head]) + if self.head_first: + ret = tf.einsum("abc,cde->adbe", input_tensor, reshape_w) + else: + ret = tf.einsum("abc,cde->abde", input_tensor, reshape_w) + + if self.use_bias: + if self.head_first: + reshape_b = tf.reshape(self.b, [1, self.num_attention_heads, 1, self.size_per_head]) + else: + reshape_b = tf.reshape(self.b, [self.num_attention_heads, self.size_per_head]) + ret += reshape_b + + if self.activation is not None: + return self.activation(ret) + else: + return ret + + +class Dense3dProjLayer(tf.keras.layers.Layer): + """A dense layer with 3D kernel for projection.""" + + def __init__( + self, + num_attention_heads, + size_per_head, + initializer, + activation, + name=None, + use_bias=True, + ): + """Constructor for dense layer with 3D kernel for projection. + + Args: + num_attention_heads: The size of output dimension. + size_per_head: The size per attention head. + initializer: Kernel initializer. + activation: Actication function. + name: The name scope of this layer. + use_bias: Whether the layer uses a bias vector. + """ + super(Dense3dProjLayer, self).__init__(name=name) + self.num_attention_heads = num_attention_heads + self.size_per_head = size_per_head + self.initializer = initializer + self.activation = activation + self.use_bias = use_bias + + with tf.compat.v1.variable_scope(name): + hidden_size = self.num_attention_heads * self.size_per_head + self.w = tf.compat.v1.get_variable( + name="kernel", + shape=[hidden_size, hidden_size], + initializer=self.initializer, + ) + + if self.use_bias: + self.b = tf.compat.v1.get_variable(name="bias", shape=[hidden_size], initializer=tf.zeros_initializer()) + else: + self.b = None + + def call(self, input_tensor): + """Constructor for dense layer with 3D kernel for projection. + + Args: + input_tensor: float Tensor of shape [batch,from_seq_length, + num_attention_heads, size_per_head]. + + Returns: + float logits Tensor. + """ + hidden_size = self.num_attention_heads * self.size_per_head + reshape_w = tf.reshape(self.w, [self.num_attention_heads, self.size_per_head, hidden_size]) + ret = tf.einsum("BFNH,NHD->BFD", input_tensor, reshape_w) + + if self.use_bias: + ret += self.b + + if self.activation is not None: + return self.activation(ret) + else: + return ret + + +class Dense2dLayer(tf.keras.layers.Layer): + """A dense layer with 2D kernel.""" + + def __init__(self, input_size, output_size, initializer, activation, name=None, use_bias=True): + """Constructor for dense layer with 2D kernel. + + Args: + input_size: The size of input dimension. + output_size: The size of output dimension. + initializer: Kernel initializer. + activation: Actication function. + name: The name scope of this layer. + use_bias: Whether the layer uses a bias vector. + """ + super(Dense2dLayer, self).__init__(name=name) + self.input_size = input_size + self.output_size = output_size + self.initializer = initializer + self.activation = activation + self.use_bias = use_bias + + with tf.compat.v1.variable_scope(name): + self.w = tf.compat.v1.get_variable( + name="kernel", + shape=[self.input_size, self.output_size], + initializer=self.initializer, + ) + + if self.use_bias: + self.b = tf.compat.v1.get_variable( + name="bias", + shape=[self.output_size], + initializer=tf.zeros_initializer(), + ) + else: + self.b = None + + def call(self, input_tensor): + """Forward pass for dense layer with 2D kernel. + + Args: + input_tensor: Float tensor with rank 3. + + Returns: + float logits Tensor. + """ + ret = tf.einsum("abc,cd->abd", input_tensor, self.w) + + if self.use_bias: + ret += self.b + + if self.activation is not None: + return self.activation(ret) + else: + return ret + + +class SimpleDenseLayer(tf.keras.layers.Layer): + """A simple dense layer with 2D kernel.""" + + def __init__(self, input_size, output_size, initializer, activation, name=None, use_bias=True): + """Constructor for dense layer with 2D kernel. + + Args: + input_size: The size of input dimension. + output_size: The size of output dimension. + initializer: Kernel initializer. + activation: Actication function. + name: The name scope of this layer. + use_bias: Whether the layer uses a bias vector. + """ + super(SimpleDenseLayer, self).__init__(name=name) + self.input_size = input_size + self.output_size = output_size + self.initializer = initializer + self.activation = activation + self.use_bias = use_bias + + with tf.compat.v1.variable_scope(name): + self.w = tf.compat.v1.get_variable( + name="kernel", + shape=[self.input_size, self.output_size], + initializer=self.initializer, + ) + + if self.use_bias: + self.b = tf.compat.v1.get_variable( + name="bias", + shape=[self.output_size], + initializer=tf.zeros_initializer(), + ) + else: + self.b = None + + def call(self, input_tensor): + """Forward pass for dense layer with 2D kernel. + + Args: + input_tensor: Float tensor with rank 2. + + Returns: + float logits Tensor. + """ + ret = tf.einsum("ab,bc->ac", input_tensor, self.w) + + if self.use_bias: + ret += self.b + + if self.activation is not None: + return self.activation(ret) + else: + return ret + + +def gelu(x): + """Gaussian Error Linear Unit. + + This is a smoother version of the RELU. + Original paper: https://arxiv.org/abs/1606.08415 + Args: + x: float Tensor to perform activation. + + Returns: + `x` with the GELU activation applied. + """ + cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))) + return x * cdf + + +def get_activation(activation_string): + """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`. + + Args: + activation_string: String name of the activation function. + + Returns: + A Python function corresponding to the activation function. If + `activation_string` is None, empty, or "linear", this will return None. + If `activation_string` is not a string, it will return `activation_string`. + + Raises: + ValueError: The `activation_string` does not correspond to a known + activation. + """ + + # We assume that anything that"s not a string is already an activation + # function, so we just return it. + if not isinstance(activation_string, str): + return activation_string + + if not activation_string: + return None + + act = activation_string.lower() + if act == "linear": + return None + elif act == "relu": + return tf.nn.relu + elif act == "gelu": + return gelu + elif act == "tanh": + return tf.tanh + else: + raise ValueError("Unsupported activation: %s" % act) + + +############################## NORM LAYERS ##################################### + + +class NormLayer(tf.keras.layers.Layer): + """Replacement for contrib_layers.layer_norm.""" + + def __init__(self, hdim, dtype=tf.float32, name="LayerNorm"): + super(NormLayer, self).__init__(name=name) + self._dtype = dtype + + with tf.compat.v1.variable_scope(name): + self.beta = tf.compat.v1.get_variable("beta", [hdim], dtype=dtype, initializer=tf.zeros_initializer()) + self.gamma = tf.compat.v1.get_variable("gamma", [hdim], dtype=dtype, initializer=tf.ones_initializer()) + + def call(self, inputs): + inputs_shape = inputs.shape + + # Compute norm along last axis + mean, variance = tf.nn.moments(inputs, [-1], keepdims=True) + # Compute layer normalization using the batch_normalization function. + # Note that epsilon must be increased for float16 due to the limited + # representable range. + variance_epsilon = 1e-12 if self._dtype != tf.float16 else 1e-3 + outputs = tf.nn.batch_normalization( + inputs, + mean, + variance, + offset=self.beta, + scale=self.gamma, + variance_epsilon=variance_epsilon, + ) + outputs.set_shape(inputs_shape) + return outputs + + +############################# EMBEDDING LAYER ################################## + + +class EmbeddingLayer(tf.keras.layers.Layer): + """An embedding layer.""" + + def __init__( + self, + vocab_size, + emb_dim, + initializer, + scale_emb=False, + use_token_type=True, + num_token_types=2, + use_position_embeddings=True, + max_position_embeddings=4096, + dropout_prob=0.0, + name="embeddings", + ): + super(EmbeddingLayer, self).__init__(name=name) + self.vocab_size = vocab_size + self.emb_dim = emb_dim + self.scale_emb = scale_emb + self.num_token_types = num_token_types + self.max_position_embeddings = max_position_embeddings + self.dropout_prob = dropout_prob + + with tf.compat.v1.variable_scope(name): + self.word_embeddings = tf.compat.v1.get_variable( + "word_embeddings", + [vocab_size, emb_dim], + dtype=tf.float32, + initializer=initializer, + ) + + if use_token_type: + self.token_type_table = tf.compat.v1.get_variable( + "token_type_embeddings", + [num_token_types, emb_dim], + dtype=tf.float32, + initializer=initializer, + ) + else: + self.token_type_table = None + + if use_position_embeddings: + self.position_embeddings = tf.compat.v1.get_variable( + "position_embeddings", + [max_position_embeddings, emb_dim], + dtype=tf.float32, + initializer=initializer, + ) + else: + self.position_embeddings = None + + def call(self, input_ids, seq_length, start_pos=0, token_type_ids=None, training=None): + if input_ids is None: + return None + + # subtoken embedding + output = tf.nn.embedding_lookup(params=self.word_embeddings, ids=input_ids) + + if self.scale_emb: + output = output * self.emb_dim ** 0.5 + + if self.token_type_table is not None: + # This vocab will be small so we always do one-hot here, since it is + # always faster for a small vocabulary. + one_hot_ids = tf.one_hot(token_type_ids, depth=self.num_token_types) + token_type_embeddings = tf.tensordot(one_hot_ids, self.token_type_table, 1) + output += token_type_embeddings + + if self.position_embeddings is not None: + # assert_op = tf.compat.v1.assert_less_equal( + # start_pos + seq_length, self.max_position_embeddings) + # with tf.control_dependencies([assert_op]): + # So `position_embeddings` is effectively an embedding table for + # position [0, 1, 2, ..., max_position_embeddings-1], and the current + # sequence has positions [0, 1, 2, ... seq_length-1], so we can just + # perform a slice. + position_embeddings = tf.slice(self.position_embeddings, [start_pos, 0], [seq_length, self.emb_dim]) + output += tf.expand_dims(position_embeddings, axis=0) + + if training and self.dropout_prob > 0: + output = tf.nn.dropout(output, self.dropout_prob) + + return output + + def linear(self, x): + """Computes logits by running x through a linear layer. + + Args: + x: A float32 tensor with shape [..., hidden_size] + Returns: + float32 tensor with shape [..., vocab_size]. + """ + with tf.compat.v1.name_scope("presoftmax_linear"): + logits = tf.tensordot(x, self.word_embeddings, [[-1], [1]]) + return logits + + +########################## TPU/CHECKPOINT UTILS ################################ + + +def get_estimator(config, model_fn): + """Create TPUEstimator object for given config and model_fn.""" + tpu_cluster_resolver = None + if config["use_tpu"] and config["tpu_name"]: + tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver( + config["tpu_name"], zone=config["tpu_zone"], project=config["gcp_project"] + ) + + # Batch size book-keeping + # Estimators handle batch sizes differently among GPUs and TPUs + # GPU: Estimator needs per core batch size + # TPU: Estimator needs total batch size, i.e. num_cores * per core batch size + config_train_batch_size = config["train_batch_size"] # For estimator + config_eval_batch_size = config["eval_batch_size"] # For estimator + effective_train_batch_size = config["train_batch_size"] # For human + effective_eval_batch_size = config["eval_batch_size"] # For human + session_config = None + if config["use_tpu"]: + sliced_eval_mode = tf.compat.v1.estimator.tpu.InputPipelineConfig.SLICED + distribute_strategy = None + config_train_batch_size *= config["num_tpu_cores"] + config_eval_batch_size *= config["num_tpu_cores"] + effective_train_batch_size = config_train_batch_size + effective_eval_batch_size = config_eval_batch_size + else: + session_config = tf.compat.v1.ConfigProto( + allow_soft_placement=True, + gpu_options=tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=1.2), + ) + cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver() + with tf.compat.v1.Session(cluster_resolver.master(), config=session_config) as sess: + logging.info(sess.list_devices()) + sliced_eval_mode = tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V1 + distribute_strategy = tf.distribute.MirroredStrategy(devices=None) + effective_train_batch_size *= distribute_strategy.num_replicas_in_sync + # effective_eval_batch_size *= distribute_strategy.num_replicas_in_sync + + is_per_host = tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V2 + run_config = tf.compat.v1.estimator.tpu.RunConfig( + cluster=tpu_cluster_resolver, + master=config["master"], + model_dir=config["output_dir"], + save_checkpoints_steps=config["save_checkpoints_steps"], + keep_checkpoint_max=config["keep_checkpoint_max"], + train_distribute=distribute_strategy, + session_config=session_config, + tpu_config=tf.compat.v1.estimator.tpu.TPUConfig( + tpu_job_name=config["tpu_job_name"], + iterations_per_loop=config["iterations_per_loop"], + num_shards=config["num_tpu_cores"], + per_host_input_for_training=is_per_host, + eval_training_input_configuration=sliced_eval_mode, + ), + ) + + # NOTE fixed by @monologg + if config["init_checkpoint"]: + ckpt_var_list = tf.compat.v1.train.list_variables(config["init_checkpoint"]) + ckpt_var_list = { + name: shape + for name, shape in ckpt_var_list + if not re.findall( + "(Adam|Adafactor|global_step|adam_m|adam_v|AdamWeightDecayOptimizer|AdamWeightDecayOptimizer_1)", name + ) + } + ckpt_var_list["bert/encoder/LayerNorm/beta"] = ckpt_var_list["bert/embeddings/LayerNorm/beta"] + ckpt_var_list["bert/encoder/LayerNorm/gamma"] = ckpt_var_list["bert/embeddings/LayerNorm/gamma"] + + # Because orignal bert's pos emb is 512, we random init pos emb which is bigger than 512 (e.g. 4096) + if config["random_pos_emb"]: + logging.info("*" * 40) + logging.info("Random initialize the positional embedding!!") + logging.info("*" * 40) + filtered_ckpt_var_list = [] + for ckpt_var in ckpt_var_list.keys(): + if "embeddings/position_embeddings" not in ckpt_var: + filtered_ckpt_var_list.append(ckpt_var) + vars_to_warm_start = "({})".format("|".join(filtered_ckpt_var_list)) + else: + vars_to_warm_start = "({})".format("|".join(ckpt_var_list.keys())) + warm_start_settings = tf.estimator.WarmStartSettings( + ckpt_to_initialize_from=config["init_checkpoint"], + vars_to_warm_start=vars_to_warm_start, + var_name_to_prev_var_name={ + "bert/encoder/LayerNorm/beta": "bert/embeddings/LayerNorm/beta", + "bert/encoder/LayerNorm/gamma": "bert/embeddings/LayerNorm/gamma", + }, + ) + else: + ckpt_var_list = {} + warm_start_settings = None + config["ckpt_var_list"] = ckpt_var_list + + # If no TPU, this will fall back to normal Estimator on CPU or GPU. + estimator = tf.compat.v1.estimator.tpu.TPUEstimator( + use_tpu=config["use_tpu"], + model_fn=model_fn, + config=run_config, + train_batch_size=config_train_batch_size, + eval_batch_size=config_eval_batch_size, + warm_start_from=warm_start_settings, + ) + + # assign batch sizes + estimator.train_batch_size = effective_train_batch_size + estimator.eval_batch_size = effective_eval_batch_size + + return estimator + + +def log_variables(variables, ckpt_var_list): + """Log trainable variables.""" + logging.info("**** Trainable Variables ****") + + model_var_list = {var.name: var.get_shape().as_list() for var in variables} + num_params = sum(np.prod(shape) for shape in model_var_list.values()) + length = max(len(name) for name in model_var_list) + 2 + line = "{{:<{}}}{{:<13}}{{}}".format(length) + + logging.info("The model has {} trainable variables " "({:,} parameters):\n".format(len(model_var_list), num_params)) + logging.info(line.format("Name", "Initialized", "Shape")) + logging.info(line.format("----", "-----------", "-----")) + + ckpt_var_list = ckpt_var_list.copy() + for name, shape in model_var_list.items(): + name = name.split(":")[0] + if name in ckpt_var_list: + warm_started = "from ckpt" + del ckpt_var_list[name] + else: + warm_started = "random" + logging.info(line.format(name, warm_started, shape)) + + if ckpt_var_list: + logging.warning( + "The warm start checkpoint contained %d variables that were not used " "for the model:\n", + len(ckpt_var_list), + ) + for name, shape in ckpt_var_list.items(): + logging.warning(line.format(name, "not used", shape)) + + +def add_scalars_to_summary(summary_dir, scalar_tensors_dict): + """Creates a host_call function that writes summaries on TPU.""" + + # All tensors outfed from TPU should preserve batch size dimension. + scalar_tensors_dict = {k: tf.reshape(v, [1]) for k, v in scalar_tensors_dict.items()} + + def host_call_fn(**kwargs): + writer = tf.summary.create_file_writer(summary_dir, max_queue=1000) + always_record = tf.summary.record_if(True) + with writer.as_default(), always_record: + for name, scalar in kwargs.items(): + tf.summary.scalar( + name, + tf.reduce_mean(scalar), + tf.compat.v1.train.get_or_create_global_step(), + ) + return tf.compat.v1.summary.all_v2_summary_ops() + + return host_call_fn, scalar_tensors_dict + + +########################## DEFAULT CONFIG UTILS ################################ + + +def get_default_config(): + """Default values for BigBird.""" + + default_config = { + # transformer basic configs + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "max_position_embeddings": 4096, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "use_bias": True, + "rescale_embedding": False, + "scope": "bert", + # sparse mask configs + "attention_type": "block_sparse", + "norm_type": "postnorm", + "block_size": 16, + "num_rand_blocks": 3, + # common bert configs + "max_encoder_length": 1024, + "max_decoder_length": 64, + "couple_encoder_decoder": False, + "beam_size": 5, + "alpha": 0.7, + "label_smoothing": 0.1, + "weight_decay_rate": 0.01, + "optimizer_beta1": 0.9, + "optimizer_beta2": 0.999, + "optimizer_epsilon": 1e-6, + # TPU settings + "use_tpu": True, + "tpu_name": None, + "tpu_zone": None, + "tpu_job_name": None, + "gcp_project": None, + "master": None, + "num_tpu_cores": 8, + "iterations_per_loop": "1000", + } + + return default_config diff --git a/pretrain/requirements.txt b/pretrain/requirements.txt new file mode 100644 index 0000000..109b189 --- /dev/null +++ b/pretrain/requirements.txt @@ -0,0 +1,4 @@ +absl-py +tensorflow==2.3.1 +transformers==4.11.3 +ko_lm_dataformat>=0.1.0 diff --git a/pretrain/run_pretraining.py b/pretrain/run_pretraining.py new file mode 100644 index 0000000..eb5d231 --- /dev/null +++ b/pretrain/run_pretraining.py @@ -0,0 +1,529 @@ +# Copyright 2021 The BigBird Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Run masked LM/next sentence pre-training for BigBird.""" + +import os +import time + +import tensorflow.compat.v2 as tf +from absl import app, logging +from kobigbird import flags, modeling, optimization, training_utils, utils + +FLAGS = flags.FLAGS + +## Required parameters + +flags.DEFINE_string("data_dir", "pretrain_tfrecords", "The input data dir. Should contain the TFRecord files.") + +flags.DEFINE_string( + "output_dir", + "/tmp/bigb", + "The output directory where the model checkpoints will be written.", +) + +## Other parameters +flags.DEFINE_string( + "init_checkpoint", + None, + "Initial checkpoint (usually from a pre-trained BERT model).", +) + +flags.DEFINE_integer( + "max_encoder_length", + 4096, + "The maximum total input sequence length after BERT Wordpiece tokenization. " + "Sequences longer than this will be truncated, and sequences shorter " + "than this will be padded.", +) + +flags.DEFINE_integer( + "max_predictions_per_seq", + 640, + "Maximum number of masked LM predictions per sequence.", +) + +flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.") + +flags.DEFINE_bool("do_train", True, "Whether to run training.") + +flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") + +flags.DEFINE_integer( + "train_batch_size", + 4, + "Local batch size for training. " "Total batch size will be multiplied by number gpu/tpu cores available.", +) + +flags.DEFINE_integer( + "eval_batch_size", + 4, + "Local batch size for eval. " "Total batch size will be multiplied by number gpu/tpu cores available.", +) + +flags.DEFINE_string( + "optimizer", + "AdamWeightDecay", + "Optimizer to use. Can be Adafactor, Adam, and AdamWeightDecay.", +) + +flags.DEFINE_float("learning_rate", 1e-4, "The initial learning rate for Adam.") + +flags.DEFINE_integer("num_train_steps", 100000, "Total number of training steps to perform.") + +flags.DEFINE_integer("num_warmup_steps", 10000, "Number of steps to perform linear warmup.") + +flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") + +flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") + +flags.DEFINE_bool("use_nsp", False, "Whether to use next sentence prediction loss.") + +flags.DEFINE_integer("keep_checkpoint_max", 5, "How many checkpoints to keep.") + +flags.DEFINE_bool( + "random_pos_emb", True, "Random initialize for positional embedding (original 512 doesn't matched to 4096)" +) + +flags.DEFINE_integer("seed", 42, "Seed for Pretraining") + + +def input_fn_builder( + data_dir, + max_encoder_length, + max_predictions_per_seq, + is_training, +): + """Creates an `input_fn` closure to be passed to TPUEstimator.""" + + feature_shapes = { + "input_ids": [max_encoder_length], + "segment_ids": [max_encoder_length], + "masked_lm_positions": [max_predictions_per_seq], + "masked_lm_ids": [max_predictions_per_seq], + "masked_lm_weights": [max_predictions_per_seq], + "next_sentence_labels": [1], + } + + def _decode_record(record): + """Decodes a record to a TensorFlow example.""" + name_to_features = { + "input_ids": tf.io.FixedLenFeature([max_encoder_length], tf.int64), + "segment_ids": tf.io.FixedLenFeature([max_encoder_length], tf.int64), + "masked_lm_positions": tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), + "masked_lm_ids": tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), + "masked_lm_weights": tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32), + "next_sentence_labels": tf.io.FixedLenFeature([1], tf.int64), # NOTE Not using NSP task + } + example = tf.io.parse_single_example(record, name_to_features) + + # tf.Example only supports tf.int64, but the TPU only supports tf.int32. + # So cast all int64 to int32. + for name in list(example.keys()): + t = example[name] + if t.dtype == tf.int64: + t = tf.cast(t, tf.int32) + example[name] = t + + return example + + def input_fn(params): + """The actual input function.""" + batch_size = params["batch_size"] + + # Load dataset and handle tfds separately + if "tfds://" == data_dir[:7]: + raise ValueError("We don't support tfds in kobigbird pretraining code") + else: + # NOTE Directly read TFRecord + input_files = tf.io.gfile.glob(os.path.join(data_dir, "pretrain_data.tfrecord*")) + + # For training, we want a lot of parallel reading and shuffling. + # For eval, we want no shuffling and parallel reading doesn't matter. + if is_training: + d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) + d = d.shuffle(buffer_size=len(input_files)) + + # Non deterministic mode means that the interleaving is not exact. + # This adds even more randomness to the training pipeline. + d = d.interleave( + tf.data.TFRecordDataset, + deterministic=False, + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + else: + d = tf.data.TFRecordDataset(input_files) + + # NOTE Only accept preprocessed tfrecord + d = d.map(_decode_record, num_parallel_calls=tf.data.experimental.AUTOTUNE) + + if is_training: + d = d.shuffle(buffer_size=10000, reshuffle_each_iteration=True) + d = d.repeat() + + d = d.padded_batch(batch_size, feature_shapes, drop_remainder=True) # For static shape + return d + + return input_fn + + +def model_fn_builder(bert_config): + """Returns `model_fn` closure for TPUEstimator.""" + + def model_fn(features, labels, mode, params): # pylint: disable=unused-argument + """The `model_fn` for TPUEstimator.""" + + is_training = mode == tf.estimator.ModeKeys.TRAIN + + model = modeling.BertModel(bert_config) + masked_lm = MaskedLMLayer( + bert_config["hidden_size"], + bert_config["vocab_size"], + model.embeder, + initializer=utils.create_initializer(bert_config["initializer_range"]), + activation_fn=utils.get_activation(bert_config["hidden_act"]), + ) + next_sentence = NSPLayer( + bert_config["hidden_size"], + initializer=utils.create_initializer(bert_config["initializer_range"]), + ) + + sequence_output, pooled_output = model( + features["input_ids"], + training=is_training, + token_type_ids=features.get("segment_ids"), + ) + + masked_lm_loss, masked_lm_log_probs = masked_lm( + sequence_output, + label_ids=features.get("masked_lm_ids"), + label_weights=features.get("masked_lm_weights"), + masked_lm_positions=features.get("masked_lm_positions"), + ) + + if bert_config["use_nsp"]: + next_sentence_loss, next_sentence_log_probs = next_sentence( + pooled_output, features.get("next_sentence_labels") + ) + total_loss = masked_lm_loss + next_sentence_loss + else: + total_loss = masked_lm_loss + + tvars = tf.compat.v1.trainable_variables() + utils.log_variables(tvars, bert_config["ckpt_var_list"]) + + output_spec = None + if mode == tf.estimator.ModeKeys.TRAIN: + + learning_rate = optimization.get_linear_warmup_linear_decay_lr( + init_lr=bert_config["learning_rate"], + num_train_steps=bert_config["num_train_steps"], + num_warmup_steps=bert_config["num_warmup_steps"], + ) + + optimizer = optimization.get_optimizer(bert_config, learning_rate) + + global_step = tf.compat.v1.train.get_global_step() + + gradients = optimizer.compute_gradients(total_loss, tvars) + train_op = optimizer.apply_gradients(gradients, global_step=global_step) + + output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec( + mode=mode, + loss=total_loss, + train_op=train_op, + host_call=utils.add_scalars_to_summary(bert_config["output_dir"], {"learning_rate": learning_rate}), + training_hooks=[ + training_utils.ETAHook( + {} if bert_config["use_tpu"] else dict(loss=total_loss), + bert_config["num_train_steps"], + bert_config["iterations_per_loop"], + bert_config["use_tpu"], + ) + ], + ) + + elif mode == tf.estimator.ModeKeys.EVAL: + + def metric_fn( + masked_lm_loss_value, + masked_lm_log_probs, + masked_lm_ids, + masked_lm_weights, + next_sentence_loss_value, + next_sentence_log_probs, + next_sentence_labels, + ): + """Computes the loss and accuracy of the model.""" + masked_lm_predictions = tf.argmax(masked_lm_log_probs, axis=-1, output_type=tf.int32) + masked_lm_accuracy = tf.compat.v1.metrics.accuracy( + labels=masked_lm_ids, + predictions=masked_lm_predictions, + weights=masked_lm_weights, + ) + masked_lm_mean_loss = tf.compat.v1.metrics.mean(values=masked_lm_loss_value) + + next_sentence_predictions = tf.argmax(next_sentence_log_probs, axis=-1, output_type=tf.int32) + next_sentence_accuracy = tf.compat.v1.metrics.accuracy( + labels=next_sentence_labels, predictions=next_sentence_predictions + ) + next_sentence_mean_loss = tf.compat.v1.metrics.mean(values=next_sentence_loss_value) + + return { + "masked_lm_accuracy": masked_lm_accuracy, + "masked_lm_loss": masked_lm_mean_loss, + "next_sentence_accuracy": next_sentence_accuracy, + "next_sentence_loss": next_sentence_mean_loss, + } + + eval_metrics = ( + metric_fn, + [ + masked_lm_loss, + masked_lm_log_probs, + features["masked_lm_ids"], + features["masked_lm_weights"], + next_sentence_loss, + next_sentence_log_probs, + features["next_sentence_labels"], + ], + ) + output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec( + mode=mode, + loss=total_loss, + eval_metrics=eval_metrics, + training_hooks=[ + training_utils.ETAHook( + {} if bert_config["use_tpu"] else dict(loss=total_loss), + bert_config["max_eval_steps"], + bert_config["iterations_per_loop"], + bert_config["use_tpu"], + is_training=False, + ) + ], + ) + else: + + output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec( + mode=mode, + predictions={ + "log-probabilities": masked_lm_log_probs, + "seq-embeddings": sequence_output, + }, + ) + + return output_spec + + return model_fn + + +class MaskedLMLayer(tf.keras.layers.Layer): + """Get loss and log probs for the masked LM.""" + + def __init__( + self, + hidden_size, + vocab_size, + embeder, + initializer=None, + activation_fn=None, + name="cls/predictions", + ): + super(MaskedLMLayer, self).__init__(name=name) + self.hidden_size = hidden_size + self.vocab_size = vocab_size + self.embeder = embeder + + # NOTE fixed by @monologg + # change the name of scope for BERT init checkpoint + with tf.compat.v1.variable_scope(name): + # We apply one more non-linear transformation before the output layer. + # This matrix is not used after pre-training. + self.extra_layer = utils.Dense2dLayer( + hidden_size, hidden_size, initializer, activation_fn, name="transform/dense" + ) + self.norm_layer = utils.NormLayer(hidden_size, name="transform/LayerNorm") + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.output_bias = tf.compat.v1.get_variable( + "output_bias", shape=[vocab_size], initializer=tf.zeros_initializer() + ) + + @property + def trainable_weights(self): + self._trainable_weights = ( + self.extra_layer.trainable_weights + self.norm_layer.trainable_weights + [self.output_bias] + ) + return self._trainable_weights + + def call(self, input_tensor, label_ids=None, label_weights=None, masked_lm_positions=None): + if masked_lm_positions is not None: + input_tensor = tf.gather(input_tensor, masked_lm_positions, batch_dims=1) + + # We apply one more non-linear transformation before the output layer. + # This matrix is not used after pre-training. + input_tensor = self.extra_layer(input_tensor) + input_tensor = self.norm_layer(input_tensor) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + logits = self.embeder.linear(input_tensor) + logits = tf.nn.bias_add(logits, self.output_bias) + log_probs = tf.nn.log_softmax(logits, axis=-1) + + if label_ids is not None: + one_hot_labels = tf.one_hot(label_ids, depth=self.vocab_size, dtype=tf.float32) + + # The `positions` tensor might be zero-padded (if the sequence is too + # short to have the maximum number of predictions). The `label_weights` + # tensor has a value of 1.0 for every real prediction and 0.0 for the + # padding predictions. + per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=-1) + numerator = tf.reduce_sum(label_weights * per_example_loss) + denominator = tf.reduce_sum(label_weights) + 1e-5 + loss = numerator / denominator + else: + loss = tf.constant(0.0) + + return loss, log_probs + + +class NSPLayer(tf.keras.layers.Layer): + """Get loss and log probs for the next sentence prediction.""" + + def __init__(self, hidden_size, initializer=None, name="cls/seq_relationship"): + super(NSPLayer, self).__init__(name=name) + self.hidden_size = hidden_size + + # Simple binary classification. Note that 0 is "next sentence" and 1 is + # "random sentence". This weight matrix is not used after pre-training. + with tf.compat.v1.variable_scope(name): + self.output_weights = tf.compat.v1.get_variable( + "output_weights", shape=[2, hidden_size], initializer=initializer + ) + self._trainable_weights.append(self.output_weights) + self.output_bias = tf.compat.v1.get_variable("output_bias", shape=[2], initializer=tf.zeros_initializer()) + self._trainable_weights.append(self.output_bias) + + def call(self, input_tensor, next_sentence_labels=None): + logits = tf.matmul(input_tensor, self.output_weights, transpose_b=True) + logits = tf.nn.bias_add(logits, self.output_bias) + log_probs = tf.nn.log_softmax(logits, axis=-1) + + if next_sentence_labels is not None: + labels = tf.reshape(next_sentence_labels, [-1]) + one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32) + per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) + loss = tf.reduce_mean(per_example_loss) + else: + loss = tf.constant(0.0) + return loss, log_probs + + +def main(_): + tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) + tf.compat.v1.random.set_random_seed(FLAGS.seed) + + if not FLAGS.do_train and not FLAGS.do_eval: + raise ValueError("At least one of `do_train`, `do_eval` must be True.") + + bert_config = flags.as_dictionary() + + if FLAGS.max_encoder_length > bert_config["max_position_embeddings"]: + raise ValueError( + "Cannot use sequence length %d because the BERT model " + "was only trained up to sequence length %d" + % (FLAGS.max_encoder_length, bert_config["max_position_embeddings"]) + ) + + tf.io.gfile.makedirs(FLAGS.output_dir) + if FLAGS.do_train: + # Save pretrain_config.json + flags.save(os.path.join(FLAGS.output_dir, "pretrain_config.json")) + training_utils.sys_log(f"Save pretrain_config.json at `{FLAGS.output_dir}`") + + # Save tokenizer to output dir + tokenizer_output_dir = os.path.join(FLAGS.output_dir, "tokenizer") + if not tf.io.gfile.exists(tokenizer_output_dir): + tf.io.gfile.makedirs(tokenizer_output_dir) + + for filename in os.listdir(FLAGS.tokenizer_dir): + tf.io.gfile.copy( + os.path.join(FLAGS.tokenizer_dir, filename), + os.path.join(tokenizer_output_dir, filename), + ) + else: + training_utils.sys_log(f"Tokenizer is already saved at `{tokenizer_output_dir}`") + + model_fn = model_fn_builder(bert_config) + estimator = utils.get_estimator(bert_config, model_fn) + + if FLAGS.do_train: + logging.info("***** Running training *****") + logging.info(" Batch size = %d", estimator.train_batch_size) + logging.info(" Num steps = %d", FLAGS.num_train_steps) + train_input_fn = input_fn_builder( + data_dir=FLAGS.data_dir, + max_encoder_length=FLAGS.max_encoder_length, + max_predictions_per_seq=FLAGS.max_predictions_per_seq, + is_training=True, + ) + estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps) + + if FLAGS.do_eval: + logging.info("***** Running evaluation *****") + logging.info(" Batch size = %d", estimator.eval_batch_size) + + eval_input_fn = input_fn_builder( + data_dir=FLAGS.data_dir, + max_encoder_length=FLAGS.max_encoder_length, + max_predictions_per_seq=FLAGS.max_predictions_per_seq, + is_training=False, + ) + + # Run continuous evaluation for latest checkpoint as training progresses. + last_evaluated = None + while True: + latest = tf.train.latest_checkpoint(FLAGS.output_dir) + if latest == last_evaluated: + if not latest: + logging.info("No checkpoints found yet.") + else: + logging.info("Latest checkpoint %s already evaluated.", latest) + time.sleep(300) + continue + else: + logging.info("Evaluating check point %s", latest) + last_evaluated = latest + + current_step = int(os.path.basename(latest).split("-")[1]) + output_eval_file = os.path.join(FLAGS.output_dir, "eval_results_{}.txt".format(current_step)) + result = estimator.evaluate( + input_fn=eval_input_fn, + steps=FLAGS.max_eval_steps, + checkpoint_path=latest, + ) + + with tf.io.gfile.GFile(output_eval_file, "w") as writer: + logging.info("***** Eval results *****") + for key in sorted(result.keys()): + logging.info(" %s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + + +if __name__ == "__main__": + tf.compat.v1.disable_v2_behavior() + tf.compat.v1.enable_resource_variables() + app.run(main) diff --git a/pretrain/scripts/base_size_tpu.sh b/pretrain/scripts/base_size_tpu.sh new file mode 100644 index 0000000..8a7f5a0 --- /dev/null +++ b/pretrain/scripts/base_size_tpu.sh @@ -0,0 +1,60 @@ +#!/bin/bash + +### GCP Settings (SHOULD change for your own settings) ### +BUCKET_NAME="kobigbird" +MODEL_NAME="kobigbird-bert-base" +TPU_NAME="kobigbird-bert-base" +TPU_ZONE="europe-west4-a" +NUM_TPU_CORES=8 +########################################### + +ATTENTION_TYPE="block_sparse" +MAX_ENCODER_LENGTH=4096 +BLOCK_SIZE=64 + +NUM_TRAIN_STEPS=2000000 +SAVE_CHECKPOINTS_STEPS=100000 +NUM_WARMUP_STEPS=20000 +ITERATIONS_PER_LOOP=200 +LEARNING_RATE=1e-4 +KEEP_CHECKPOINT_MAX=20 +MAX_PREDICTIONS_PER_SEQ=640 +TRAIN_BATCH_SIZE_PER_DEVICE=4 +EVAL_BATCH_SIZE_PER_DEVICE=8 + +USE_GRADIENT_CHECKPOINTING=False +USE_NSP=False +RANDOM_POS_EMB=True + +SEED=42 + +python3 run_pretraining.py \ + --data_dir="gs://$BUCKET_NAME/pretrain_tfrecords" \ + --output_dir="gs://$BUCKET_NAME/models/$MODEL_NAME" \ + --attention_type=$ATTENTION_TYPE \ + --max_encoder_length=$MAX_ENCODER_LENGTH \ + --max_position_embeddings=$MAX_ENCODER_LENGTH \ + --block_size=$BLOCK_SIZE \ + --num_attention_heads=12 \ + --num_hidden_layers=12 \ + --hidden_size=768 \ + --intermediate_size=3072 \ + --do_train=True \ + --do_eval=False \ + --use_tpu=True \ + --tpu_name=$TPU_NAME \ + --tpu_zone=$TPU_ZONE \ + --num_tpu_cores=$NUM_TPU_CORES \ + --train_batch_size=$TRAIN_BATCH_SIZE_PER_DEVICE \ + --eval_batch_size=$EVAL_BATCH_SIZE_PER_DEVICE \ + --num_train_steps=$NUM_TRAIN_STEPS \ + --num_warmup_steps=$NUM_WARMUP_STEPS \ + --save_checkpoints_steps=$SAVE_CHECKPOINTS_STEPS \ + --learning_rate=$LEARNING_RATE \ + --keep_checkpoint_max=$KEEP_CHECKPOINT_MAX \ + --use_gradient_checkpointing=$USE_GRADIENT_CHECKPOINTING \ + --use_nsp=$USE_NSP \ + --max_predictions_per_seq=$MAX_PREDICTIONS_PER_SEQ \ + --random_pos_emb=$RANDOM_POS_EMB \ + --iterations_per_loop=$ITERATIONS_PER_LOOP \ + --seed=$SEED diff --git a/pretrain/scripts/build_tfrecord.sh b/pretrain/scripts/build_tfrecord.sh new file mode 100644 index 0000000..2b79196 --- /dev/null +++ b/pretrain/scripts/build_tfrecord.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +### SHOULD change for your own settings ### +INPUT_DIR=data +OUTPUT_DIR=pretrain_tfrecords +TOKENIZER_DIR=tokenizer +########################################### + +NUM_PROCESSES=0 +DUPE_FACTOR=3 + +MAX_SEQ_LENGTH=4096 +MASKED_LM_PROB=0.15 +MAX_PREDICTIONS_PER_SEQ=640 + +SENTENCE_PAIR_PROB=0.0 +SHORT_SEQ_PROB=0.0 +MAX_NGRAM_SIZE=2 + +LONG_SEQ_THRESHOLD=1.8 + +python3 create_pretraining_data.py \ + --input_dir $INPUT_DIR \ + --tokenizer_dir $TOKENIZER_DIR \ + --output_dir $OUTPUT_DIR \ + --max_seq_length $MAX_SEQ_LENGTH \ + --max_predictions_per_seq $MAX_PREDICTIONS_PER_SEQ \ + --num_processes $NUM_PROCESSES \ + --masked_lm_prob $MASKED_LM_PROB \ + --sentence_pair_prob $SENTENCE_PAIR_PROB \ + --short_seq_prob $SHORT_SEQ_PROB \ + --do_whole_word_mask \ + --max_ngram_size $MAX_NGRAM_SIZE \ + --dupe_factor $DUPE_FACTOR \ + --long_seq_threshold $LONG_SEQ_THRESHOLD \ + --debug diff --git a/pretrain/tokenizer/special_tokens_map.json b/pretrain/tokenizer/special_tokens_map.json new file mode 100644 index 0000000..f40edd4 --- /dev/null +++ b/pretrain/tokenizer/special_tokens_map.json @@ -0,0 +1,9 @@ +{ + "bos_token": "", + "eos_token": "", + "unk_token": "[UNK]", + "sep_token": "[SEP]", + "pad_token": "[PAD]", + "cls_token": "[CLS]", + "mask_token": "[MASK]" +} diff --git a/pretrain/tokenizer/tokenizer_config.json b/pretrain/tokenizer/tokenizer_config.json new file mode 100644 index 0000000..1ee2897 --- /dev/null +++ b/pretrain/tokenizer/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "do_lower_case": false, + "do_basic_tokenize": true, + "never_split": null, + "unk_token": "[UNK]", + "sep_token": "[SEP]", + "pad_token": "[PAD]", + "cls_token": "[CLS]", + "mask_token": "[MASK]", + "bos_token": "", + "eos_token": "", + "tokenize_chinese_chars": true, + "strip_accents": null, + "model_max_length": 4096, + "tokenizer_class": "BertTokenizer" +} diff --git a/pretrain/tokenizer/vocab.txt b/pretrain/tokenizer/vocab.txt new file mode 100644 index 0000000..fd409a4 --- /dev/null +++ b/pretrain/tokenizer/vocab.txt @@ -0,0 +1,32500 @@ +[PAD] +[UNK] +[CLS] +[SEP] +[MASK] + + +[unused2] +[unused3] +[unused4] +[unused5] +[unused6] +[unused7] +[unused8] +[unused9] +[unused10] +[unused11] +[unused12] +[unused13] +[unused14] +[unused15] +[unused16] +[unused17] +[unused18] +[unused19] +[unused20] +[unused21] +[unused22] +[unused23] +[unused24] +[unused25] +[unused26] +[unused27] +[unused28] +[unused29] +[unused30] +[unused31] +[unused32] +[unused33] +[unused34] +[unused35] 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+์„œ๋ช… +๋•Œ๋กœ +๋ฏผ์ค‘ +์žฅ๋ฅด +์žฌ์ƒ +๊ณผ๋„ +์—ฌ์˜ +์ข…์ข… +๋ฉˆ์ถ” +์†Œ์Šค +ํ•œ์ฐธ +์ƒ๋ฌด +์˜์›… +์•„๋ฉ” +##๋‹ค๋ฆฌ +๊ธ‰๊ฒฉ +ํŠธ์œ„ +๊ทธ๋Œ€ +์†Œํ˜• +๋ฐ”์œ„ +๊น€ํ•ด +๋“ค์–ด์™” +๊ณ ์–‘์ด +์•„์‹œ +##๊ฑฐ๋ ธ +๋‚˜ํƒ€๋‚œ +์—๊ฒ +##๋ž˜๋„ +ํ•œ์ง€ +ํ์‡„ +์ผ๋ฐฉ +์•„๋งˆ๋„ +์‹ฑ๊ธ€ +์ž…์‹œ +์›์ • +ํ•œ๋ฒˆ +GS +76 +##์ƒํ™œ +์ „์ฃผ +์žฅํ•™ +๊ธฐ๊ธˆ +์กฐ์ง€ +๊ทน๋‹จ +ํ•™ํšŒ +์‹œ๊ณ„ +์ด๋ฏผ +์‚ฌํšŒ์ฃผ์˜ +์ง€๋ƒˆ +ํ…Œ์ด๋ธ” +๋†๊ตฌ +๋‚จ๊ฒจ +ํœด์‹ +๋น ์ง„ +๋ชจ์ƒ‰ +##์˜จ๋‹ค +์•ผ๊ถŒ +ํ•ฉ๋ฅ˜ +๊น€๋Œ€ +##์‹œ์ฝ” +์นญ์ฐฌ +๊ตญ๋ฐฉ๋ถ€ +๊ฑฐ์ง“๋ง +๋กœ์Šค +##ใ…‹ใ…‹ใ…‹ใ…‹ +์—ฌ์„ฏ +์ตœ์šฐ +์˜์ž +์ฒญ๋ฌธ +์•„๋‹๊นŒ +๋ถ•๊ดด +##์‹œ๋‹ค +##์ •๋ถ€ +๋น„ํ–‰๊ธฐ +69 +๊ฑฐ์ ˆ +๋ฒ•์น™ +์•„์ € +์ž…๊ตฌ +์šฉ์—ญ +์กด์ค‘ +์Œ๋ฃŒ +๋ฆฌ๋ผ +์ฒœ์ฒœ +์ฝ”๋“œ +๊ฐ€๋ฐฉ +์ง„์˜ +๋”๊ตฐ +์ฃผ๊ธฐ +##iv +์–ด์šธ๋ฆฌ +88 +์ค‘๋ฐ˜ +์นจ๋Œ€ +์ •์„ฑ +์ง„๋ฆฌ +๊น€ํƒœ +๋‹ด๊ธด +์—ฐ๋ด‰ +๋งˆ์‚ฌ +๋Œ€๋ฅ™ +##ํ•ญ๊ณต +์ฒœ์ฒœํžˆ +์ด์šฉ์ž +์ข‹์•„ํ•˜ +##ion +์„ธ์šด +์ž„์ง์› +์ˆ˜์ง€ +๋ณต์ˆ˜ +๋ฏฟ์Œ +๋ถ™์—ฌ +์ง„์ฃผ +##๋ธŒ๋ฆฌ +ํ’ˆ๋ชฉ +์•„์ €์”จ +์…‹์งธ 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+##์•„๋ฆฌ +๋ฐ”์ด๋Ÿฌ์Šค +ํŠน์œ  +์˜ˆ์™ธ +๋น„๋กœ์†Œ +๋Œ€๋Ÿ‰ +๋ฌด์–ธ +ํ˜ธ์ˆ˜ +์„ฑ์ทจ +์ผ๋ณธ์ธ +์œก์ฒด +๋˜๊ฐ€ +์ฆ‰๊ฐ +##ak +์™œ๊ณก +๊ด€ํ•  +๋งค๋‹ˆ +์‹œ๊ธ‰ +์˜คํ”ผ์Šค +##ํฌ๋ฅด +##ํ†จ๋ฆญ +์ปจ์„คํŒ… +์œผ๋ฆฌ +๋ด๋„ +์™„๊ณต +๋„์•ฝ +์ด์ข… +๋งˆ๋ผ +87 +๋Œ€์ฒด๋กœ +์ค‘ํ™” +ํ•ด์ œ +๊ทœ๋ช… +์—ญ์ž„ +##๋ฆฌ์ฆ˜ +ํƒ‘์žฌ +๊ธฐ์žฌ +FI +ํ„ฐ๋ฏธ +๋ชจ๋‹ˆํ„ฐ +์ฒ ์ €ํžˆ +ํ—ˆ์œ„ +์ด์  +1988 +์–ธ์   +##uc +๋น„ํƒ€๋ฏผ +์••๋„ +##๋ฐ”์ด +202 +์›Œ์‹ฑํ„ด +์‹ฌํ™” +๋ง‰๋Œ€ +์ƒ๋‹น์ˆ˜ +##๋ฆฝ๋‹ˆ๋‹ค +๋นŒ๋”ฉ +##eg +์š•์‹ฌ +๋‘๋ ค์›€ +๋””์Šคํ”Œ๋ ˆ์ด +์‹ ์„ธ๊ณ„ +##all +์ฒ˜์ง€ +๊ฐ์ƒ +๋ฌด๊ด€ +๋ณธ์„  +๊ฐ„์ฃผ +96 +์Šค์œ„์Šค +์šด์†ก +ํŒ์ • +ํƒ„๋ ฅ +์˜ฎ๊ธฐ +๊ณต์‹œ +์กด๊ฒฝ +##์ ธ์•ผ +๊น€๋™ +๊ตฌ๋ฉ +##๋กœ์ง€ +๋…ธ์˜ˆ +๋‹นํ™ฉ +##์–ด์งˆ +##ip +##ram +์‹ ๋ฐœ +์ด›๋ถˆ +๋‹น์ผ +๋ด…๋‹ˆ๋‹ค +ํ•ด์ง„๋‹ค +๋ฐฉํ•™ +์ž์‹ ๊ฐ +๊ฐ€ํ•ด +๋…์žฌ +๊ฟˆ๊พธ +์บ ํผ์Šค +์„ ๊ฑฐ๊ตฌ +์„ฑํญ +##๋„ค์‹œ์•„ +์ถฉ์ „ +๋‚ด๋ฉด +์˜ค๊ฐ€ +##ํ•˜์šฐ +์›์ž๋ ฅ +๋ฒ„์ „ +๊ณ ๋ฐฑ +co +๋‹น๋‹น +ํŽ˜๋ฅด +๊ผฌ๋ฆฌ +์‹œ์ƒ +๋ถ€ํ•˜ +๊ด‘์ฃผ์‹œ +์ฒญ๋ฌธํšŒ +ํด๋ผ +์ง€์ง€์œจ 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+๋ชจ๋ฅผ +๊ฐ€์ „ +์‹ ์ž„ +์—ฐ์ธ +๋‹น์› +์•„๋ผ +๋‹ค๋ฅผ +ํ˜•ํŽธ +๋ถ€์ž‘์šฉ +๋‹ฌ์•„ +๋ฉด์„ธ +์ฃผ๋ฅ˜ +์ƒ์ž +์ง€์ธ +๊ณ ์ง€ +๊ณ ์‹œ +##์ ธ์„œ +๋ฌด๋ ฅ +๊ฑฐ์นœ +๋ด์š” +์†Œํ™˜ +์‚ฐ์—…๋‹จ์ง€ +94 +์•ž์„ธ +์ผ๊ฐ +์ฃผ์—ฐ +์ •๋ฉด +Pr +์ƒ๊ธˆ +๋ฐ์ผ๋ฆฌ +ํ‘œ๊ธฐ +์ฐจ๊ธฐ +ํŠน๊ฒ€ +๋Œ์–ด์˜ฌ +๊ณต๊ณ  +์ธ๋„๋„ค์‹œ์•„ +๋ถ™์ด +ํŠนํ™” +##์•„์ด +๊ธฐ์˜จ +์Šค์Šน +๋ชจ์ˆœ +์• ์ • +๋…ธํ•˜์šฐ +##์†Œ๋“ +์˜ˆ๋Šฅ +์‹œ๋‚˜ +์˜คํŽ˜๋ผ +์š”์› +๊ฑด๊ฐ€ +๋ถ€๋“œ๋Ÿฝ +์Šค์›จ๋ด +##ud +์ž…์‚ฌ +250 +๋– ๋‚œ +๊ทธ๊ฐ„ +๋ถ€ํŒจ +##๋“ ์ง€ +์‹œ์ง‘ +๋ถ€๋ฆฌ +##์ผ๋žœ๋“œ +##TE +ํšŒ์ˆ˜ +๋งˆ์‚ฐ +ํ–‰์ •๋ถ€ +ํ‰ํƒ +์˜ฌ๋ฐ”๋ฅธ +๋†์‚ฐ๋ฌผ +์‚ฌ๋ผ์กŒ +๊ฐ€ํ†จ๋ฆญ +๊น€์žฌ +๊ฒฉ๋ ค +๋ถ€๋ฅธ๋‹ค +์ƒ์‹ +์ธ๋”” +๋จผ์ง€ +##๋ฐ”์ง€ +๊ฐ์˜ค +๊ฐ€๋งน +์šด์ „์ž +์•ˆํƒ€๊นŒ +๋‚œ๋‹ค +๋ฆฌ์–ผ +์‚ฌ์šฐ +๊ธ‰์‹ +๊ฐˆ๋ผ +##๋ฐ›์นจ +๋ฆฌ์กฐํŠธ +์ŠคํŽ˜ +๋”๊ตฌ๋‚˜ +์šฉ์‚ฐ +ํ•ญ๊ณต๊ธฐ +๋ฐฐํ„ฐ๋ฆฌ +์‹œ์ค‘ +์ฐธ์œผ๋กœ +##์ด์ฆˆ +ํ–‰์ง„ +๊ถŒ๊ณ  +๊ฒŒ์‹œ +๊ตญ๋‹น +๋ถ„์‚ฐ +๋ถ๋ฏธ +์ˆ˜๋ฉด +์ง€๋‚ธ +์•ˆ๋™ +##๋Š๋‹˜ +๋ผ์„œ +##๋‘˜๋Ÿฌ +์ฆ์–ธ +ํŠน์ด +์•„์ดํฐ +๋„๋• +๋ฏธ์น  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+##์ €์šฐ +##๋‘๋ฆฌ +๋ฐ”๋ž๋‹ˆ๋‹ค +##๊ตญ์ˆ˜ +119 +์‚ฌ๋ชจ +๋†๋„ +๋น„์ถ” +๋‹จ์ ˆ +๋ฐฐํ›„ +๋งˆ์ธ +ํ•จ์ˆ˜ +์ˆ˜์ƒ‰ +ํ•˜ํ•˜ +์†Œ๋‚˜๋ฌด +##๋ฆฌ์ผ€์ด์…˜ +ํ˜‘์—… +ํฌ๋ฅดํˆฌ๊ฐˆ +๋ณด์„ +์ด์› +๋‚˜ํƒ€๋‚  +๋“œ๋ ˆ์Šค +๊ณผ์ž +##๋กœ์šฐ +๋ชจ์˜ +์–ด๋ ธ +๋“ฑ๋“ฑ +์ค€๋‹ค๋Š” +##์„ธ์ด +์ฒ˜๋…€ +##๋ชจํ†  +ํ„ฐ์น˜ +์‚ฌ๋ผ์ง„ +๊ฐ•๋™ +##๋ชฌ๋“œ +์ €์†Œ๋“์ธต +๊ณต์กด +๋–จ์–ด์ง„๋‹ค +๋ฐฐ๊ตฌ +๊ด‘์‚ฐ +๋ฐฐ์‹  +ํ•œ์ „ +์•„ํ•˜ +๋‘๋ ค์›Œ +์ขŒํŒŒ +170 +์ธ์ž +##ER +์ผ์ˆ˜๋ก +์ƒ์ • +ํฌ๋ฆฌ +์œ ์„ฑ +์ˆ˜์งˆ +๊ธˆ์š”์ผ +##ld +์ •ํ•ด์ง„ +##๋„๋“œ +์ „๋ฐฉ +##๋„ค์Šค +๊ตญ์„ธ์ฒญ +์ง€์˜ฅ +๋‚ด๊ฑธ +์†ํ•œ +์ƒ์šฉ +๋ฆฌ๋“ฌ +์—”์ง€๋‹ˆ์–ด +ํ•œ๋ฐฉ +๊ฒฐ์Šน์ „ +##์‚ฌ์—… +์Šค๋ฆฌ +์—ฐ์ด +๋“ํ‘œ +์•„์คŒ +๋Œ€์ฃผ์ฃผ +์—ด์•… +Pro +์—ฐ์•ˆ +๊ฐ„์ง +๊ป์งˆ +์„ ๋น„ +์ผ์œผํ‚จ +ํƒ„์•• +์ฒœ์‚ฌ +๊ตฌํ•ด +์ž…์  +##ook +์ˆ˜์žฅ +##ํ–‰์œ„ +๋ถˆ๋ฆฐ๋‹ค +์˜ค์ด +๋‚ด๋‹ค +๊ฒ€์ง„ +๊ณ ์น˜ +์š”๋ฒ• +์ฒœ์žฅ +##PGA +ํ™์ค€ +ํ–‰์šด +๋ฏผ์† +๊ณ ์„ฑ +์œˆ๋„ +๋‚ด๋ ค๋†“ +1973 +์ŠคํŽ™ +์ž์˜์—… +์•„์คŒ๋งˆ +๊ตฌ์ ˆ +ํ‹ฐ๋น„ +์นจ๋ชฐ +##์นด๋ฝ +##oun +##ํ‹ฐ์•„ +๋จธ๋ฆฌ์นด๋ฝ +์• ์ธ +๊ฐœ์ฒด +์ œ์ž‘์ง„ +์‹ ์ƒ +##์„œ๋ฆฌ +์Šค์นด์ด +๋จน๊ฑฐ๋ฆฌ +๋‹ค๋ฉด์„œ +๋กœ์ผ“ +์–ด์Œ +ํ†ต์‹ ์‚ฌ +๋ฐœ์˜ +Sh +##๋ ˆ์ดํฌ +์ด๋ฉ”์ผ +๋–จ์–ด์งˆ +์ฒ™์ถ” +ํ•ฉ์ฐฝ +์˜ค๋ฝ +๋Œ€์„ธ +์œ ๊ฐ +๋“œ๋Ÿฌ๋‚œ +##ํฌ์Šค +##์“ฐ๊ธฐ +์ฐจ๋ ค +๋ง์„ค +์œ ์Šน +๋‚˜๋…ธ +##age +ํƒˆ๋‹น +๊ณ ๊ณ  +๋ณด์ฆ๊ธˆ +๊ฐ„์„ญ +๊ฐํžˆ +๋‚˜๋ˆŒ +์š”์‹œ +1978 +๊ฑฐ์‹ค +๋น„์ž +์ƒ์ƒ๋ ฅ +ํŒŒ์žฅ +๋ง๋ ˆ์ด์‹œ์•„ +##์ง€๋ถ€ +๋งค๊ฐœ +๋†์–ด +์ŠคํŽ˜์…œ +์‚ฌ์–‘ +์ธ๊ณผ +๋Œ๋ณด +์“ฐ์—ฌ +๋ถ€์œ  +์ผ์ผ์ด +ํฌ์ง€์…˜ +๊ตํ†ต๋ถ€ +๋‹น์„ ์ธ +LC +๋ฉ”๋ชจ๋ฆฌ +##๋‚ด๋ฆฌ +์ผ์ œํžˆ +์—ฐ๊ณ  +์‹œ์–ด +ํก์—ฐ +์‚ฌ์†Œ +##๋”์Šค +๊ทธ๋ ˆ์ด +์žฌํ•ด +๋“ค๋ ธ +๋Œ€์‚ฌ๊ด€ +๋„‰๋„‰ +์•ˆ์‚ฐ +ํœด์–‘ +์œ„ํ•ด์„  +๋งค์šด +์•Œ์ฝ” +๊ฑธ๊นŒ +์ถœ๋™ +์ด์–ด์„œ +์ตํžˆ +์›”์„ธ +๊ทน์‹ฌ +์ดˆ๋“ฑํ•™์ƒ +๋„์ถœ +๋‹จ์‹ +๋„์šฐ +์‹ค์€ +๊ฐ„ํ˜ธ์‚ฌ +๋ฒŒ์—ฌ +๋ช…์†Œ +์ค‘์‚ฐ +์ž์œ ์ฃผ์˜ +๊ทธ๋ฃจ +์Œ“์—ฌ +๋˜๋Œ์•„ +์œ ๋ฃŒ +ํ–ฅ์ˆ˜ +๋ธ”๋ผ +##๋ฉ˜ํŠธ +๊ฑฐ์น  +์˜คํ”„๋ผ์ธ +Sp +ํ™์ค€ํ‘œ +๋ถ€์ž„ +๊ฐ€๋ น +ํšŒํ™” +๋ฐฑ์‹  +์‚ฐ์ • +๊ทธ๋žœ๋“œ +์ œ๋ฒ• +์ถ”์œ„ 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+์ƒ๊ด€์—†์ด +ํ™”๋‘ +์‹ ํ•œ์€ํ–‰ +๊ต๋ถ€ +ํ–‡๋น› +##์น˜๊ธฐ +ํƒˆํ‡ด +##์‹œ์„ค +์‹ ๋„ +์–ด์„  +๊ตฐ๋‹จ +ํŽผ์ณ์ง€ +์—‰๋ฉ์ด +##๋ฅด๋ฅด +๋ฉฐ๋Š๋ฆฌ +์‚ฌ์ง +์นด๋ผ +๋‘”ํ™” +๋ฐฐ์ˆ˜ +๋ ˆ์˜ค +์—ํ”ผ์†Œ๋“œ +ํ’์š” +ํŒŒ์ƒ +์„ฑํญ๋ ฅ +์•„์‚ฐ +๋‚จ๋™ +์‹œ์•ผ +์ €๋ ‡๊ฒŒ +##๊ธˆ๋ฆฌ +##์‚ฐ๋ถ€ +์ดํ™” +๋จธ๋ฌผ๋ € +์™ธ์‹ +์˜ค๋งŒ +ํ”„๋ž€ +๋”๋ผ๋ฉด +๋‹ฅํ„ฐ +์ฒœ์ง€ +์˜นํ˜ธ +์ ธ์•ผ +๋ฌธ๋ฒ• +์™•์œ„ +ํ•œ๊ฒจ๋ ˆ +DNA +๋ชจ์”จ +ํ…์‚ฌ์Šค +์ง€์šฐ +์กฐ๋ฅ˜ +##ํ”„๋ฆฌ +์นœ์ • +##์ฝ”๋“œ +์ž…์ฒด +์ž…๊ตญ +๊น€์—ฐ์•„ +๋‹ฌ๋ ค๊ฐ€ +์œ„์ž„ +์ด์ˆœ์‹  +์ง‘ํ•„ +๊น€์„  +๊ฐ€์„ค +ํ•€๋ž€๋“œ +๋ฐœ๊ฑธ์Œ +์ „๋ณต +ํ˜์˜ค +Cl +์ž„๋Œ€๋ฃŒ +์œ ์˜ +์šฐํšŒ +์ถœ๊ตญ +๋‹ฌ๋ผ์กŒ +์ข…์‚ฌ์ž +์ด๋…ธ +ํฅ๊ตญ +๊ธฐ์ƒ +์ƒ์˜ +์™•์‹ค +๋ฐ•์ข… +์„์‚ฌ +์‹ค์Šต +OEC +์•ก์ˆ˜ +๊ฐ•์ • +1977 +ํŒŒ์šด๋“œ +์‹ํƒ +๋ง๋ฆฌ +์ˆจ๊ธฐ +๋’ค๋”ฐ +์›์ˆ˜ +์„ผํŠธ +์ถ”๊ฒฝ +์ด์ˆ˜ +๋ณด๋ฆ„ +์œ ๋จธ +๋ด์„œ +##์—ฐ๊ตฌ +์พŒ์  +์บ์Šค +์ˆ™์ œ +OECD +๋งž๋ฌผ +์œ„๋ฒ• +์„ฑ์ฃผ +์‹ฌ์ƒ +์นœ์ฒ™ +HD +ํœฉ์‹ธ +์—ญ๋™ +##๋จผ์Šค +๋ถ€ํ’€ +##์ง‘๋‹ˆ๋‹ค +##๊ฑธ๋ฆฌ +์ฒœ์ฃผ๊ต +์žฌํ‚ท 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+์ˆ˜ํ‰ +์ž”๋”” +์ „์›” +์„ค๊ต +์ƒ๋ฅ˜ +ํ•ญ์Ÿ +##๋งค๋งค +##ํ‹€๋žœ๋“œ +์ตœ์šฐ์ˆ˜ +๋ถˆ์Œ +์žฅ๊ฑฐ๋ฆฌ +ํ–‰ํ•ด +์„ธ๋กœ +๋“œ๋ฌธ +ํ•™๊ธ‰ +๊ณ„ํ†ต +๋ฅด๋„ค +๊ฒฝ์ธ +์šฐ๋“œ +์—ํ”„ +ํƒœ๋ธ”๋ฆฟ +๋งˆ์„ธ์š” +์›ฌ๋งŒ +DJ +๋งˆ์ˆ  +๋งˆ๋“œ๋ฆฌ +ํˆฌ๊ตฌ +ํ•™์‚ฌ +๋ฐ”์น˜ +๋‹ค๋‹Œ +์˜ˆ๋ฏผ +์‹ ์กฐ +์ฐฝ๋‹น +ํ•œ์ž” +๊ณ ๋งˆ์›Œ +์ฃฝ์—ฌ +ํŒŒ์Šคํƒ€ +์˜จ์‹ค๊ฐ€์Šค +์†Œ์•ก +์•ฝ์„ธ +๋ถ€ํ‰ +๋‚œ๋ฆฌ +๋ ˆ์ด์ € +๋ˆ„๋ฆฌ๊พผ +์–ต์ง€๋กœ +์—ฌ์šฐ +ํ•ต๋ฌด๊ธฐ +STX +์„ ๋ณ„ +1965 +UEFA +์„ ์ฐฉ +์Šคํ”„๋ง +์ƒ๊ฒจ๋‚˜ +๊ณ ์˜ +vs +ํ›ˆ์žฅ +์œ ๋ณด +๊พธ๋ ค +์ •๋…„ +๋…ธํŠธ๋ถ +๋งˆ๋“œ๋ฆฌ๋“œ +์‹ญ์ž๊ฐ€ +๋Œ€๋ช… +๊ทผ์ ‘ +๋ฉ์–ด๋ฆฌ +๊ฒฝ์Ÿ์ž +์‚ฌ๋ฆฌ +์ ํ”„ +๊ต๋ฆฌ +์„ค๋“๋ ฅ +##์ง€๋Œ€ +๋ช…๋ช… +์Šคํƒ€์ผ๋ง +๋ผ์น˜ +๊ผฌ์ง‘ +1962 +๊ด‘๊ฒฝ +์œ ๋Œ€์ธ +๋ฒ•๊ด€ +์–ด๋ฆฐ์•„์ด +์–ด๋”˜๊ฐ€ +์šฉ๋ณ‘ +์ „๋™ +๋ฐœ๋ณ‘ +##๋ฒ ํŠธ +์ง€๊ฐ‘ +์Ÿ์•„์ ธ +๋“œ๋ž˜๊ณค +์ฆํ›„๊ตฐ +ํšŒ์ƒ +๋Œ€๊ต +##๋ˆ„์Šค +##์ปค์Šค +์„๋ฐฉ +##์ค„๊ธฐ +์กฐ๋ณ„ +##ark +๊ณ ๋“ฑํ•™์ƒ +์žฌ๋นจ๋ฆฌ +์ค€์ˆ˜ +๊ตญ๋„ +daily +๊ธ‰ํ•˜ +๋„๋•์˜€ +์–‘์ฃผ +๋ฐฉ์šธ +์•„๋ฒ„๋‹˜ +TF +๊ฒฝ์—ฐ +ํ–‰์žฅ +๋‹ค๋“ฌ 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+์น ๋ ˆ +์˜ํ™”๋ฐฐ์šฐ +์ ˆ๋ฒฝ +ํ† ๋„ˆ +๋„๋งค +ํ• ๊นŒ์š” +์•„๋น„ +์–ด๋จธ๋‹˜ +๋ง‰ํ˜€ +##์Šˆํƒ€์ธ +์œ ์–ธ +์ฐพ์•„์™€ +๋ฒ—์–ด๋‚  +์„ฑ๋งค๋งค +๋‘๋ ค์›Œํ•˜ +์œ ๊ด€ +๋งŒ๋‘ +์ ๊ธˆ +์Šค์ฝ”ํ‹€๋žœ๋“œ +Pl +๊ณ ์‹ฌ +Ed +์†ก์ด +๊ท€ํ•œ +์ƒ์ž„์œ„ +์Šน์ž +๋กœ๋งจ์Šค +๋ฌด์•ˆ +ํ•ธ๋“œํฐ +์กฐ๋ง +์ผ์œผํ‚ฌ +##๋ฒŒ์ด +์ œ์ž๋ฆฌ +##๋ฌด๋ผ +ํ•ฉ๊ฒฉ์ž +์‹ ์šฉ์นด๋“œ +๊ถŒ์žฅ +##์†Œ์‹œ์—„ +ํƒœ์ข… +๊ธธ๊ฑฐ๋ฆฌ +EBS +์ž ์ˆ˜ํ•จ +ES +์ปจ์†Œ์‹œ์—„ +๋ฏธํŒ… +์—ฐํ•ฉ๊ตฐ +์ด์‚ฐํ™” +ํƒ€์„ +ํƒ€๊ฒฐ +๊ฐ„ํ–‰ +ํฌ๋ฏธ +์‹ธ์›Œ +์œ ๋Ÿฝ์—ฐํ•ฉ +๊ท€์ค‘ +##ํฌ๋ฆฌํŠธ +๊ถ์ „ +๋งŒ์ฐฌ +์ฆ๊ฐ€์„ธ +##์—‘์Šค +์ •์‚ฐ +์™œ๋ƒ๋ฉด +๊ฐ•๊ตญ +๋ฐ์Šค +๊ต๋„์†Œ +๊ฑฐ์•ก +##๋ผ๋…ธ +์„œ์šธํŠน๋ณ„์‹œ +์ƒ๊ณ  +์„น์‹œ +##ํ™”๋ฌผ +๊ผฌ๋งˆ +๋ธŒ๋ ˆ์ดํฌ +KCC +์‹œ๊ณต์‚ฌ +๋ณธ๋ฌธ +์˜์™• +์ฃผ์ƒ +ํ’๊ธฐ +์Œ“์ด +์„ธ์† +##๋งˆ๋ฅด +##๋ ˆ์ดํ„ฐ +์—ญ์„ธ +์ง€ํœ˜์ž +๊ตฌ์‹ค +๋–จ๋ฆฌ +์‹œํ•œ +ํ•˜์ฒญ +๋ถ€์ง€๋Ÿฐ +๋ช…์‹ฌ +์‹œ๊ทธ +๋Š‘๋Œ€ +ํ’๋ ฅ +๋ฃจ์ˆ˜ +์Šค๋ฉฐ +103 +์ €์ฃผ +๋™๋ช… +go +๋ฃจ์Šค +111 +๋ชจ๋ฐœ +##ํ•˜์ด +๋น„๋น” +๋ถˆํ‰ +์˜๊ฑฐ +๋Œ์–ด์˜ฌ๋ฆฌ +์นœ๊ทผ +##๋ฐ์ดํŠธ +์•„๋งˆ์กด +๋‚˜์นด +ํ™์‚ผ +์ง€์ค‘ +๋Œ์—ฐ +๊ฐ•๋Œ€ +๋‚ด์…”๋„ +๊ทธ๋ฆด +๊น€์ธ +์ž„ํ•˜ +##ํ•ฉ๋‹ˆ๋‹ค +Br +์œ ๋ น +๋ฌด์‹ฌ +ํ‘œ์  +##๋‚˜์™€ +๋ฐค์ƒˆ +์ผํ™” +์œ ์ € +ํ”ผ์Šค +์‚ผ์ดŒ +๊ฐœ์› +์ผ์–ด์„œ +๋ฆฌ์ฒ˜๋“œ +๊ธฐ์šฉ +์˜ค์ง•์–ด +์งˆํˆฌ +ํ—ˆ๊ณต +์˜์„ +๋‚˜์ฃผ +์—ฐ์‡„ +ํญํฌ +๊น€ํฌ +๋ฐ”์ง +์›จ๋”ฉ +๊ฑฐ์ฐฝ +##ํ‹ฐ์Šค +๊ณจ์ž +์œค์„ +##ํ”„๋ ˆ +##ํ”„๋ž€ +์ธ๋งฅ +##GA +๊ณ ๋ถ€ +์—…๋ฐ์ดํŠธ +##ํ…Œ๋ฉด +ํž™ํ•ฉ +์ด์ฐฝ +ํŠน๋ก€ +๋‚จ๋ถํ•œ +##ual +์ฃผ์‹œ +๋ฐ”์šธ +ํ• ๋‹น +์˜ค๋ฅธ๋‹ค +๊ฑฐ์Šฌ๋Ÿฌ +์ƒ๋žต +158 +์„ฑ๋ณ„ +๋™๋“ฑ +์•„๋‹ด +##ํŒก์ด +์„ธ๋ก€ +๊ฐ„ํ˜น +ํšŒ์˜์†Œ +๋งˆ์“ฐ +์ฝ˜ํฌ๋ฆฌํŠธ +##๊ตฌ์žฅ +๋ฐœ์กฑ +๋ธ”๋กœ +ํ•ด๋‹ต +์„ฑ์› +์œ ๊ธฐ๋† +์ด์œฝ +์ž ์ž๋ฆฌ +๋Œ€๋ฆฌ์  +๋ฏธ์ฃผ +์ •๋ฐ˜ +๋งŒ๋ฃŒ +๋ถ๋ฐฉ +์Šต์ง€ +##์ ค๋ ˆ์Šค +##๋งˆ๋ฆฌ +๋ฏธ์–€ +๋ฒš๊ฝƒ +์ŠˆํŒ… +๊ทธ๋ผ์šด๋“œ +ํผ์‹œํ”ฝ +##์Šคํ‹ฑ +ํ’์ž +๊น€๋ช… +๊ฐ€ํ˜น +์—ฌ๋Š +ํŒํƒ€์ง€ +##๋„ค์Šค์ฝ” +๋‹ค์ด์•„๋ชฌ๋“œ +##yst +##์˜ฌ๋ž +๊ธฐ๊บผ์ด +ํฌ๋ถ€ +##ํ”Œ๋ ‰์Šค +๊ณ ๋ถ„ +๋ณธ๋ณด +##๋Œ€๊ต +##์ „๋ ฅ +154 +์ŠคํŒŒ์ด +์‹œ๋ฉ˜ํŠธ +๋ฏธ์•ผ +์•Œ๋ฐ” +๋“ญ๋‹ˆ๋‹ค +##๊ด€๊ด‘ +์‚ฐํ–‰ +##๋ฐ์š” +์ œ๊ตญ์ฃผ์˜ +๋ฒ…์Šค +์ฃผํŒŒ์ˆ˜ +์‹œ๋‹น +๋Š๊ปด์ง„๋‹ค +ํŠน์ƒ‰ +์ด์œฝ๊ณ  +์นจ์ฐฉ +์‹ค์‚ฌ +ํ”„๋žญ +๋ฐ”๋‹ท๊ฐ€ +์˜๋™ +##๋ถ์ž +์•„๋ฆฌ๋ž‘ +์›๋™๋ ฅ +์•ฝํ’ˆ +๊ฐ•์ „ +์š”๋ ‡๊ฒŒ +์นด์ง€๋…ธ +๋ด‰์‡„ +์ ์ ˆํžˆ +๋‚ฎ์ถฐ +์ฃผ์ง€ +##โ”€โ”€โ”€โ”€ +๋‘˜๋ ˆ +์–ด์•ผ์ง€ +์ง๊ฒฐ +##BA +๊ทน๋„ +์—๋ฆญ +ํญ๊ฒฉ +๋“ฑ์žฌ +์šฐ์ฒด +ํ”ผ์ง€ +1900 +์ƒ‰์ฑ„ +์ฆํญ +115 +์ˆ˜์˜์žฅ +์ค‘์œ„ +##๋ผ๋ฆฌ +##๋””์Šค +๊ธ‰๊ฐ +ed +์ด์šฉ๊ฐ +์€ํ +์—ฐ์ฃผ์ž +์ˆ˜๋Ÿ‰ +##์–ด๋จน +์žฅ์ž +๊นƒ๋ฐœ +ํ•˜๋‚จ +177 +์ด๋ฐฉ +ํ„ฐ๋œจ๋ ธ +ํŠน๊ธ‰ +์ค‘์ถ” +์ด๋ฅผํ…Œ๋ฉด +์–‘ํ‰ +์šฐ๋ผ +์„œ์—ด +์ž์ทจ +๊ธฐ์Šต +์ƒํ™œ๋น„ +ํฌํฌ +๋ฏธ์“ฐ +ํ…ƒ๋ฐญ +##ใ…œใ…œ +ํ›„๊ณ„์ž +๊ฐ€ํ‰ +์–ดํ•™ +๋น„๋ฒ• +๋ฉ‹์ง€ +์ฒ˜ํ•ด +๋“์—ฌ +๋งจ๋‚  +๊ฐ€์ ธ์˜ฌ +##๋‚˜ํƒ€ +##๋™์› +์ „์ž‘ +์นด๋ฅผ +์Šน๋ฌด์› +์Šคํƒ€๋””์›€ +๋งˆํ‹ด +ํ€ด์ฆˆ +ํ˜ธ์ „ +์šฐ๋Ÿ‰ +๋…ธ์ˆ™ +๋™์กฐ +ํ›„์œ ์ฆ +๊ตญ์ฑ… +๋ฌด์‚ฌํžˆ +์‹œ์•„ +ํ‘œ๊ฒฐ +##๋กœ๋ฆฌ +##์ง€๊ธฐ +์กฐ์žฅ +์‹ ์› +์œ ๋„ค์Šค์ฝ” +์š•์„ค +ํŠธ๋ ˆ์ด๋‹ +##ology +์ต๋ช… +ํฌ์ผ“ +ํ›„๋ฐ˜๊ธฐ +##ign 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+์„œ๊ฑฐ +๊ฑฐ์ธ +๊น€๊ด€ +๊ฐ•ํŒ +๋ฐฉํƒ„ +๋ž„๊นŒ +##์ปคํŠธ +134 +์กฐ์†ํžˆ +์‚ผ์ฒ™ +์™ธ๊ณผ +๋ถˆ๊ณ ๊ธฐ +330 +๊น€ํšจ +๋Œ€์ง€์ง„ +์Šน์†Œ +ํ†ต์งธ +##๋‚˜ํ•ญ๊ณต +๋นจ๊ฐ„์ƒ‰ +๋ฒ ํ†  +๊ธˆ๊ธฐ +๋…๋ฆฝ์„ฑ +##๋ฏธ๋ฅด +2022 +์ฆ๊ฐ• +์ง‘์•ฝ +ํ•˜์†Œ์—ฐ +๋Œ€๋‚ด +์‚ฌ๋ผ์ง„๋‹ค +๋‹น๋‹นํžˆ +๋‚™์„  +์˜ˆ์ˆ ์ธ +3500 +ํ•ด์  +VIP +๋ฌด์ด +์•„์‹œ์•„๋‚˜ํ•ญ๊ณต +๋ณธ์ง€ +๊ณ ๋ฅธ +##๋ฆฌ๋‚˜ +์€๊ทผํžˆ +##๋ฌด์ง„ +ํ•ด์ธ +์‡ผํฌ +##์ƒ์ˆ˜ +์งฌ๋ฝ• +์ปค์ง„๋‹ค +์œ ๋ผ +์ด๋ฐ”์ง€ +JTBC +๋ˆ„๋น„ +ใ†๋ฏธ +๋‹ต์‚ฌ +์•„๋‚  +##์Šต๊ธฐ +๋ฌด๋„ˆ์กŒ +ํ˜•๋ฒŒ +๋ถˆํ—ˆ +์น˜๋ถ€ +์˜ค์…˜ +๊นŒ๋‹ค๋กœ์šด +๋นผ๋Œ +๋Œ€๊ตฐ +๊ฐœ์—… +์น˜๋Ÿฌ์ง„ +์Œ๋ž€ +์ณ๋‹ค +IOC +์ˆ˜์šฉ์†Œ +๋‹ด๋‹ด +##ox +UAE +์ฝœ๋กฌ๋น„์•„ +์‹œ๋งˆ +๋ฏธ๋‚˜ +๋ฅด๋…ธ์‚ผ์„ฑ +ํŽ˜๋„ +1932 +ํ•จ๊ฒฝ +๊ต์œก๋น„ +๊ธฐ์œ +์ „๋ณด +##๋ถˆ๋ฆฌ +์†์žก์ด +์šดํ•˜ +๋ถ€๋Ÿฝ +์ดํ•œ +์ง„๊ณต +์ฃผ์ž„ +์ฒœ๋„ +๋ณ‘์‹ค +ํŠธ์œˆ +๋‹น๋„ +์†์ถœ +์ฃฝ์ผ +์•ฝ์‹ +์‚ฌ๋‚˜ +์นด์šดํ‹ฐ +์ „์ง‘ +ํŽธ์ง‘์ž +๊น€๋ฏธ +##yn +๊ฒฝ์ œํ•™์ž +๋งŒ์žฅ +์ˆ˜๊ฑด +ํœ˜๋‘˜ +##์ผ€์ด +์ Š์Œ +์‹ ๋ฏผ +์–ด์šฉ +์กฐ๊ด‘ +๋Š”๋ฐ๋„ +DMZ 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+๋Œ€๋ช…์‚ฌ +ํˆฌ์Šค +๊ทน๋™ +##๋œจ๋ฆด +์†Œ์‹œ +์ด์†Œ +ํ›„๋ฌธ +pl +์ผ๊ฐ +ํ˜ธ๋‘ +์ทจํ•ญ +๊ฒฝํ•ฉ +๊ตฌ์ง์ž +๋ฝ๋‚ด +์Šฌ์ฉ +์ผ๋ ‰ +์Šคํ…Œ์ดํŠธ +##BM +ํŒ€์› +์ž ๊ฒจ +์ƒ์—ฌ +1943 +๋ฌผ๋Ÿฌ์„œ +์„ฑํฌ๋กฑ +์ดˆ์ƒํ™” +ํ’ํ†  +๋ด‰ํ™” +์ฒด๋ฉด +์™ธํ™˜์€ํ–‰ +์—ฐํ–‰ +๋‚™ํ•˜์‚ฐ +๊ฑด๋„ˆํŽธ +๊ธฐ๋…์ผ +์ฆ์‹ +173 +๋งˆ๋‚˜ +๋…์ผ์–ด +์•„์˜ค +๊ด€์ƒ +๋‚ด์„ฑ +๋ฐ”๋ž +๊ธฐ๋‹ค๋ฆด +๊ตญ๋ณด +์—”ํŠธ๋ฆฌ +##๋„๋ฆฌ +๊ฐ€๊ฒฐ +์ผ€๋„ค๋”” +๊ฐ์„œ +ํฌ์ง„ +๊ฑด๋งŒ +๊น€๋ถ€ +์ „์† +Korea +ํ–„๋ฒ„๊ฑฐ +์„œ๋ฆฌ +์Œ์ฃผ์šด์ „ +๊ด€๋ฆฌ๋ฒ• +์ˆœ์ข… +๊ฐ€์†”๋ฆฐ +์ง„ํ–‰์ž +ํŠธ๋กœํ”ผ +##์ œ์ด +๋ถˆ๊ฑฐ์ง„ +์œ ์‚ฐ๊ท  +์ƒ๊ฒจ๋‚œ +ํƒ€์ผ +์›”๋“ฑ +ํฌํ•ญ์‹œ +์‹ฌ๋ฌธ +๋Œ€ํƒ€ +ํˆฌ์‹  +๋งˆํฌ๊ตฌ +๋ฐ•์„ธ +์ฒจ๋ถ€ +์นธํŠธ +ํšŒ๊ท€ +๋ง์ปจ +๊ผผ์ˆ˜ +๋ฐค์ค‘ +์‹ฌ์‹  +์ด๋…ธ๋ฒ ์ด์…˜ +International +์ ˆ๋‹จ +๋กœ๋ผ +์ฒญ์ž +์ปคํŠผ +์–ด๋•  +์•„์šฐ๋”” +์น˜๊ฐ€ +ํˆฌ์„ฑ์ด +๊ฑฐ์ฒ˜ +##๋ง‰์ด +๋‹ˆ์ฆˆ +๋ ˆ์ผ +์žฅํƒ€ +๋ฌด์ž‘์ • +๋ถ€๋”ช์น˜ +๋Šฅ๊ฐ€ +์†ก์˜ +ํ™•์ง„ +##tit +์ž์—ฐํ™˜๊ฒฝ +๊ทธ์นœ +๋งก๊ฒผ +FM +์ด์‚ฐ๊ฐ€์กฑ +๋กœ๋ Œ +ํ›„๋ฐฉ +์ž์˜ +๋žœ๋“œ๋งˆํฌ +๋‚˜๋ฌผ +๋ถ€๋…€ +๋“ค๋ ค์˜ค +์•„ํ† ํ”ผ +๋ ๊นŒ์š” +##ere +1935 +๋Œ€์ „์‹œ +๋‹ฌ๋Ÿฌํ™” +์„œํ•œ +๋‚ด๋น„์ณค +์ด๋นจ +์˜คํฌ +๋ฏธ์ง€์ˆ˜ +๊ตฌ๋… +ํ…Œํฌ๋†€ +##rans +ํ‡ด์ง๊ธˆ +๊ต๋ฌ˜ +์–‘ํ˜„ +๋ฉ˜ํ† ๋ง +##๋„ˆ๋ฌด +์˜๋ฃŒ๋น„ +์ธํ•ด์„œ +์Šฌ๋ฆผ +์œ ๊ณ  +๋‚œ์ด๋„ +๋ณด๋„ˆ์Šค +##๊ฒฐ์Šน +๊ตํฌ +์ถœ๊ณ  +##์ฒœ๋™ +ํ‰๊ธฐ +์ œ์ธ +์ฐจ๋“ฑ +๋”๊ตฐ๋‹ค๋‚˜ +ํ•˜๋ถ€ +##ina +์—ฐํฌ +ํ•˜์—ฌํŠผ +์ฒ ์ƒˆ +์‹ ์ƒ์•„ +031 +ํ–‰๋ฐฉ +ํผ์กŒ +##์•„๋ฅด +์ •๋Œ€ +์™€์ดํŒŒ์ด +๊ตฌ๋ฅด +๋ฐฐ์ž„ +๋™๊ทธ +๋ถˆ์‘ฅ +##์ด๋‚˜ +320 +๊ทธ์น  +์ข…๋ž˜ +๋ณตํŒ +ํ•œํŒŒ +ํ—ค๋ผ +##ํ…์Šค +๋’ค์„ž +๊ฐ€์นญ +##๊ณต์› +๊ฐ€์œ„ +##์Šฌ๋ผ +ํ”Œ๋ผ์ด +๋ผํ‹ด์–ด +๋ฐฉ์ˆ˜ +ํ† ์š” +๋ฏธ๋ผ +ํƒ๊ตฌ +๊ธฐ์ €๊ท€ +๊นจ์–ด๋‚˜ +๋Šฅ์„  +์‹ฑ์–ด +๋…ผ์ฆ +์•Œ๋ ‰์‚ฐ๋” +์ฐฉํ•˜ +๋‘ํ„ฐ +์—ฐ๊ตฌ์‹ค +##์‚ฐ๋™ +๊ณ ๋ฃจ +์ •ํ•ด์ ธ +๋ฉ”์šฐ +์•„๊น +๊น€์„ +๊ธ‰๊ธ‰ +์บ์‹œ +์ง„๋‚˜๋ผ +ํŒ”๋กœ์šฐ +์žฌ๊ฒ€ +๋งํ•˜ +ํ™”๋ฒ• +๋Œ€์กธ +๊ฒŒ์œผ +์‚ด๋ฆด +ํ•ด๋‚ด +๋ฐ€๋ ˆ +๊ตญํ™” +์†Œ๊ฐ +##ํœด์ผ +์•Œ์•„๋ณผ +##๋ฃจ์—”์ž +ํ™˜๋ถˆ +์ง€ํ +์‚ฌ์ถ˜๊ธฐ +๋ชฉ๊ฑธ์ด +๋™๋… +GT +##๋ฐฑํ™”์  +์ ๊ฑฐ +์กฐ์„ ์—… +๊ธฐ๋ฅ˜ +๊ณ„์ง‘ +์ค‘์•™์ผ๋ณด +์ดˆ์› +๊ธ‰์„ฑ์žฅ +131 +๋ชธํ†ต +์˜ฌ๋ผ์„ฐ +๋‹ฌํ•  +์ž„๋Œ€์ฐจ +ใ†์ • +์ง‘์‚ฌ +๋ถ€๋‹ด๊ฐ +์ข…์–‘ +##๋กœ์ด +All +์‹ ์ • +๋ชจํ„ฐ์‡ผ +๋‹ฌ๋ผ๋ถ™ +๊ฒฉํŒŒ +๋ฒ ๊ฐœ +์–ด์šฐ๋Ÿฌ์ ธ +์• ์ฐฉ +๋ฌด์ƒ‰ +์ˆ™๋ จ +##๋ฆฌ์‹œ +##์‚ฌ๋ž‘ +##์•„ํ‹ฐ์•„ +##์‹ญ์‹œ์˜ค +๊ฐ•๊ธฐ +๋‹ด์•„ +๊ธˆ์„ฑ +๊ตฌํ–ˆ +ํฌํ™” +ํšŒ์ฐจ +ํ˜ผ์žก +์ •์ค€ +์–ด๋ฏผ +##๋งˆํ‚น +ํ™ฉ๊ต +๊ฐ€์ „์ œํ’ˆ +ํ•ญ์•„๋ฆฌ +๊ฒฌํ•™ +๋ฒ ํƒ€ +##๋ž˜ํ”„ํŠธ +๊ฒฝ์ œ์„ฑ์žฅ +๋‹ฌ๋ ค๊ฐ” +์„ ์ฒœ +๋‹ฅ์น˜ +ํ™”์งˆ +์ด๊ณต +260 +๋ฆด๋ ˆ์ด +์ทจ๋“์„ธ +##์„œ์Šค +๊ฒฝ๋Ÿ‰ +๊ฐ„์ ˆํžˆ +๋งˆํฌ +๋ฒค์น˜๋งˆํ‚น +์ธํ”Œ๋ฃจ์—”์ž +์ตœ๊ฒฝํ™˜ +์ปจ์„คํ„ดํŠธ +ํฌ๋กœ์•„ํ‹ฐ์•„ +ํŠน์‚ฐ๋ฌผ +๋“œ๋ ˆ +๋”๋Ÿฌ์šด +์†Œ๋น„์—ํŠธ +ํƒ€์ด์™„ +๋†“์ด +์žก์Šค +์ดˆ์•ˆ +๊ณฑ์ฐฝ +๋‚˜์ธ +์ค€์šฐ +์ฃผ์„  +##iel +์œ„์Šคํ‚ค +๊ฐ•์„œ๊ตฌ +์ด์˜ค +๋ฐ˜๊ฐ€์šด +๋‹น๊ถŒ +์ง€๋งŒ์€ +๋ถˆ๋ณต +๋ฐ˜๊ณต +์ดํฌ +๊ณต๊ด€ +์œ ํฅ +๊ฒฝ์ „์ฒ  +์ขํ˜€ +์ธ์–‘ +##ution +๋ฐœ๋ฆฌ +์˜์–‘์†Œ +์ง„ํ•˜ +๋‚จ๋„ +์ž…๊ฐ€ +์กฐํ•ฉ์žฅ +์šฉ์‚ฌ +์–ฝํžŒ +์ ˆ๋„ +๊ฑฐ๋ž˜์ผ +๋ฏผ๊ฐ„์ธ +์„œ๋… +์žํ–‰ 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+๋‚ด์‹ค +๊ณตํœด์ผ +ํ˜ผ๋™ +๋ถ์  +์ ์‹ญ์ž +๋ด…์‹œ๋‹ค +๋ธŒ๋ผ์šฐ +๋น„์‹œ +๊ฐ€๋ฉด +๊ตฌ์šด +๋ฒฝ๋ฉด +ํํŠธ +ํ‰๊ฐ€์ „ +๋‹ฌ์ธ +BB +##์—˜๋ผ +์„ฑ๋ฆฌํ•™ +ํƒˆํ™˜ +๊ปด์•ˆ +๋ฒˆ๊ฑฐ +์†Œ์น˜ +1924 +๋กฏ๋ฐ์›”๋“œ +๊ฐ€์•ก +ํ™์ต +์ถœ์„ธ +๊ฐˆ๋ฆด +์œ„๋ฐฐ +์ˆ˜๋— +at +๋‹จ์˜ +์•„์‰ฌ์›  +๋‹จ๋ จ +์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค +์ฃผ์–ด์กŒ +ํ•ด๋ฌผ +๋„ˆ๋ฌด๋„ˆ๋ฌด +๊ณผํ•™๋ถ€ +๋ชธ์ง‘ +์šฐ์ฆˆ๋ฒ  +๋ฒŒ์–ด๋“ค +์ด๋“œ +๋ฉ”์ด๋“œ +๋ฐ›์นจ +์•Œ์„  +##ํ•„๋“œ +๋ฐฉ์ฝ• +๋ง‰๋ง‰ +๋ฐธ๋Ÿฐ์Šค +๋ณด๋ น +ใ†์ด +๊ฒฝ์ž‘ +์•Œ๋ฆด +์œ ์˜ +์ „ํ†ต๋ฌธํ™” +๋กœ์ด +์นจ๋ฒ” +1934 +์˜์„ฑ +๋ฐ˜๋Œ€ํŽธ +ํ’๋ฏธ +ํ–‰์ •๊ด€ +ํ•™์ฐฝ +์ „๊ด‘ +์ค‘ํ™”๋ฏผ๊ตญ +ํŽŒํ”„ +์–ด์ด์—† +์•ฝํ˜ผ +My +๋”๋Ÿฝ +๋งค๋“ญ +๋ณดํ–‰์ž +ID +my +๋ถ€์ œ +๋‹จ์ฒด์ „ +##์• ๋‚˜ +๋ƒ„๋น„ +ํ™๋ช… +##๋ผ๊ธฐ +ํ…Œ๋ผ์Šค +์‹คํ—˜์‹ค +๋‹น์ฒจ์ž +์†Œ๋ฐฉ๊ด€ +๋‹ค์› +๋™์Œ +์Šค์œ„์น˜ +ํŠธ๋ฃจ +์˜์กด๋„ +๊ณ ๋งˆ์šด +##๋“œ๋ผ๋งˆ +ํœด์—… +๋ฐฉ์‚ฌ์„ฑ +ํƒค๋ŸฐํŠธ +๊ณ ์ถฉ +์œ ์ ์ง€ +์žฌ์ƒ +๋ฏธ์šฉ์‹ค +ํƒ์ง€ +๋ชจ๋ฅธ๋‹ค๊ณ  +์†Œ์†ก๋ฒ• +ํ† ๋จธ์Šค +๊ตณ๊ฑด +MM +๊ธฐ๋‹ค๋ ค์•ผ +ํ™”๋ฐฑ +๊ฒฐํ•ต +๊ฐ„์†Œ +1928 +์•„์ธ์Šˆํƒ€์ธ +##์•„๋ณด +๊ฟฐ๋šซ +๋ˆˆ๋ถ€์‹  +์“ธ๋ฐ์—† +##๋‚ด๋ ค +๋ฌด์Šน๋ถ€ +๊น€๋ฌธ์ˆ˜ +์ž๋ฐฑ +๋†’์ธ +ํ‰์˜จ +##ublic +์ฒดํ•˜ +ํ•œ์•ฝ +์•ผ๋‹จ +like +์•„๋“œ +์• ๋„๋ฆฌ์ŠคํŠธ +์›…์žฅ +ํญ์šฐ +์žฌํŒ์žฅ +์ •์ธ +๋ถ€๋„๋Ÿฌ์šด +๋ฐ€์–ด๋‚ด +##ํ–‰์‚ฌ +์ซ“๊ธฐ +์š”๋ฆฌ์‚ฌ +์—ฐ์ฃผํšŒ +๋…๊ฐ +๋น„์œ„ +##์•„๋จน +๋žจํ”„ +๊ท€์† +์†๋‹ด +์ŠนํŒจ +ํ™ฉ๊ต์•ˆ +๋…ธ๋ž˜๋ฐฉ +์‚ฌ๋ญ‡ +์ผ์ฃผ +์•ผ์ฟ  +์˜ˆ์ •์ž +์•„๋‚ ๋กœ๊ทธ +Mon +๋ฐœ์ „๊ธฐ +์‚ฌ๋ฅด +์žŠ์–ด๋ฒ„๋ฆฌ +1931 +ํ—ˆ๋ฆฌ์ผ€์ธ +์œ„๋ก€ +์–ดํ•„ +์—๋„ +์‚ฐ๋ฌธ +์ฃ„์ฑ… +์„ ๋กœ +On +์ฐฝ๋…• +๋ถ€์ƒ์ž +์นญํ˜ธ +๋ชจ์€๋‹ค +๋ถˆ์ถœ๋งˆ +๋‚˜์˜ต๋‹ˆ๋‹ค +์กฑ๋ฐœ +๋ฐ˜๊ธฐ๋ฌธ +์‹์š• +๋ฏผ๊ด€ +##๊ผฌ๋ฆฌ +ํ•„์ง€ +์ฒœ๋ง‰ +์ผํƒˆ +1918 +๊ทธ๋Ÿฌ๋ฉด์€ +๋“œ๋‚˜๋“ค +๋ถˆ๋ฒ•ํ–‰์œ„ +๊ตฌ๋งˆ +##์ฒœ๋ฆฌ +๋ฌด๋„ˆ์ง„ +๋„ค์˜ค +##co +##evel +๋“œ๋ฃจ +๋ฐ˜์ฃฝ +๊ฑธ์น˜ +๋ฒผ๋ž‘ +๋ชจ์—ฌ๋“ค +๋‹ค๊ฐ€์˜ฌ +๋ธŒ๋กœ์ปค +์žฌํ˜ผ +ํŒŒ์ด๋‚ธ +ํ˜„์ € +๋Œ์ถœ +๋ด‰์ง€ +##์†๊ธฐ +ํ˜๋Ÿฌ๋‚˜์˜ค +##์„ธ์šฐ +##iam +๋ฐ๋ชจ +๋…์ผ๊ตฐ +์•ผ๋งŒ +๋ฌด๋ผ +et +์˜ค์ฐฌ +์ค‘์•™๋‹น +๋™๋Œ€ +ํ˜œ์„ฑ +##CO +๋‹ค์‚ฐ +##ale +์„ธ์กฐ 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+์ž…์žฅ๋ฃŒ +์ƒˆ๊ฒจ์ ธ +์œ ๊ฒฉ์ˆ˜ +์šฐ์ฆˆ๋ฒ ํ‚ค์Šคํƒ„ +LIG +๊ฐœ์  +๋ฒˆ๋ฒˆ +##ven +์ถœ์‚ฌ +์žฅ๋™ +##์˜ˆํ”„ +##์ƒ๊ฐ€ +์•„๋ฏธ๋…ธ +NLL +์Šˆํผ์Šคํƒ€ +์ž๊ฐˆ +์ฝ˜ํผ๋Ÿฐ์Šค +๋ˆ„๊ตฐ์ง€ +๋Œ€๋ฏธ +ใ…‡ใ…‡ +์น ๊ณก +ํŒŒํƒ„ +๋ฌดํ•œ๋„์ „ +์ถœ๋ฃจ +์ง€์ผœ๋ณธ +ํŽผ์ณ์กŒ +์‡ผํŠธํŠธ๋ž™ +๋™๋ฌด +์‚ฌ์šฉ๋ฃŒ +์„ฑ์ˆ˜ +##ํ”Œ๋Ÿฌ +๋””๋ฐ” +์กฐ์ œ +ํ•จ์ถ• +์ดํƒœ์› +์ธํŒŒ +##๋‰ด์Šค +๋ฐ”๊นฅ์ชฝ +๋•๋ชฉ +๋ฌผ๋“ค +๊น€๋„ +์ƒ์ˆ˜ +์„ ์˜ +๋Š๋‹ท +๊ณ ๋‹จ +๋กœ์šธ +์ •๊ธ€ +์„ ์–ธ๋ฌธ +ํšŒ์‚ฌ์› +์œค์ข… +๋ˆŒ๋ € +๋ฐํ˜€์ง„ +ํ”ผ๋”” +Go +ํƒœํ™” +๊ธฐ์Šน +๋ฌผ๊ธฐ +ํ›”์น˜ +์•„๋กœ +๊ด€๊ด‘๊ณต์‚ฌ +์ค€์šฐ์Šน +์›ƒ๋Œ +ํ›„์›์ž +##ํƒœํ‰ +์‚ดํˆ +##์‹ญ๋‹ˆ๋‹ค +์ฑ„์ฐ +์ณ์„œ +1917 +์ „๋ฆฝ +๋ถ„ํ–ฅ +##inc +##๊ด€๋ฆฌ์œ„์›ํšŒ +๋ฏธ๋ฅต +์—ฐ์œ  +ํ”ผ์นด +##CI +##์›ƒ์Œ +##๊ตํšŒ +์žฅ์„ธ +OK +์ง€๋‚ผ +์–ฝํ˜€ +๋Š์Šจ +์—ฌ์ข… +๋ญ˜๊นŒ +TK +ํž˜๊ฒน +650 +๊ฐœ๋™ +##๋‹ˆ์ง€ +๋’ค์ฒ˜ +์••๋ฅ˜ +๋‹ค๋ฌธํ™”๊ฐ€์ • +๊ฒฝ๋ณต๊ถ +๋ง๊ณ  +๊ธฐ๊ณ  +์นดํ”„ +๋‹ˆ์‹œ +์ฃ„์ˆ˜ +ํ˜„์ €ํžˆ +๊ต์™ธ +๊ณ ์†Œ๋“ +์ฒจ์˜ˆ +์œ„ํฌ +๋ฃจ๋งˆ๋‹ˆ์•„ +์ด๋ž˜์„œ +##ps +๋งˆ์Šค +ํ”ผ์›Œ 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+์˜ค๋”” +์ฒญ์ •๊ธฐ +๋‹ด๊ทธ +์ฑ„ํŒ… +๊ทผ๋ฆฐ +์˜์•” +๊ฐ€์žฅ์ž๋ฆฌ +๋‚ด๋ ค๋‹ค +##๋ถ€์กฑ +๋‹น์ • +์‚ฌ์ƒ๊ฐ€ +์ตœํ•˜ +์นœ๋‹ค +์ง€์œผ +##ํŒŒ์—… +์šธ์ฃผ๊ตฐ +##ics +์ฑ„์›  +org +๊ฟˆํ‹€ +๋ฌด๋ฆ… +๋ถ€๋ฅด์ฃผ์•„ +์˜ค๋งˆ์ด +ํ์ˆ˜ +1908 +##๋ชจ๋ฆฌ +๋„๊ฐ€ +์ œํŠธ +๊ฒŒ์„ +ํ™ฉ์†Œ +ํ•œ๋ฐ”ํƒ• +ํ‰ํ„ฐ +๊ฐํ•˜ +๊ฐ€์ถœ +์ธ์นœ +ํšกํฌ +ํ™ฉํ +์‚ฌ์‹œ +cat +์ž์•„๋ƒˆ +๋‹ค๋„ค +์ˆ˜์–ต +##ํ•ด์กŒ +๋ฐ€ํฌ +์‚ฌ์ด์–ธ์Šค +๊ฒฝ์˜ +๋ฐฐํ‹€ +์‹ ๋‹ค๋ฉด +์ €๋ณ€ +๋ฐ•๋ณ‘ํ˜ธ +์™„์ˆ˜ +์ ˆ๊ฐœ +ํƒ„์‹ +ํž˜์จ +์ €๋„๋ฆฌ์ฆ˜ +ํ‹€์–ด +Med +๊ต์ „ +๊ฐœ๋ช… +๊ฑด๋„จ +๋ฌด๊ณ  +์ผํ„ฐ +์ถœ๊ฐ€ +์„œ์Šด +ํƒˆ์ทจ +And +##ondon +์š”๋Ÿฐ +๋‹ค์ณค +ํ™•์—ฐํžˆ +ํฌ์ผ“๋ชฌ +์„œ๋Š˜ +ํฌ์‹ +ํž˜๊ป +๊ณ ์„ +ํ๋ง‰ +์‚ฌ์ƒ‰ +๋ฌธํ•™์‚ฌ +์‚ฐ๊ณจ +์ด๋ฐฉ์ธ +๋ฐ€์–ด๋ถ™์ด +ํŠน๊ฐ€ +๋‹ค๊ฐ€๊ฐ” +์ ํผ +์ดํŒŒ์—… +##๋ถ€๋ฆฌ +๋ชฝ๊ณ  +##์ต์Šคํ”ผ์–ด +CT +ํ‰์ค€ +์‹ซ์–ดํ•˜ +๋‚™ํƒœ +์นดํ†ก +๋ธ”๋ž™๋ฆฌ์ŠคํŠธ +๋‚ด๊ณผ +##ization +##oon +๋‚˜๋“ค์ด +์…ฐ์ต์Šคํ”ผ์–ด +์†Œ์ด +ํœด๋จผ +##์ œ๋‹น +๊ตฌํƒœ +๊ณ ํ’ˆ์งˆ +์‹ ํ•™์ž +๋‹ค์„ฏ์งธ +์œ ๋ชฉ +net +๊ธฐ์ž์žฌ +์‚ฌ์  +๋ฑƒ์† +ํŒŒ์—ด +์ˆ˜์ธ +์ฐฝํ”ผ +๊ฐ€๋”” +์—ด๊ฑฐ +์„ค๊ณ„์‚ฌ +๊ท€์—ฌ์›Œ +์ ˆ์นœ +##๋งŒ๊ธˆ +์˜†๊ตฌ๋ฆฌ +์„ญ์™ธ +์ดํ† ๋ก +๋‚ญ๋… +๊ฐœํ‘œ +์ „๋ฌธ์ง +์œ ๋žŒ +์ง€์ผœ๋ด์•ผ +๋ณ€ํ•  +์–ผํ• +๋งˆ๋Š” +๋Œ๋ฆด +๋น„์—”๋‚ ๋ ˆ +๋ฆฌํŠฌ +๋ช…์žฅ +์บ์Šฌ +๋ถ€์—ฌ๋ฐ› +์ข‹์•„ํ•œ๋‹ค +๋ถ€ํ”ผ +ํ์•” +ํšŒ์˜์žฅ +์ง€์˜ค +๋–จ์ณ +##conom +##๋Ÿฐ์น˜ +ํ‡ด๊ณ„ +๊ฐœ๋„ +se +๊ณต์ƒ +์ถ”์›Œ +ํžˆ๋กœ์‹œ๋งˆ +๊ฐ€์Šต๊ธฐ +๊ฒ€์ƒ‰์–ด +ํ”ผ๋ผ๋ฏธ๋“œ +ํ—ค์ณ +์ทจ์ž„์‹ +๊ธ‰์†๋„ +์ค„์ธ +๋ถ€์‚ฐํ•ญ +๋‚ด์‹œ๊ฒฝ +๋ณด์˜จ +์บก์Š +##tics +ํ˜ธํˆฌ +๋œจ๊ฑฐ์›Œ +๊ณ„๋ฅ˜ +๋ฌธ์ • +๋‚˜์‚ฌ +ํœด์ง€ +์œ ํ‚ค +์„œ์˜ +ํ˜„๋Œ€์‚ฌ +Ag +์ œ์ผ์ œ๋‹น +ํ”„๋žญํฌ +๊ตฌ์น˜ +๊ฐˆ์ฆ +ํ†ตํ‹€์–ด +ํ•ฉํ•˜ +๋ฏธ๋• +ํ…Œ๋‘๋ฆฌ +##์ˆ˜์—˜๋ผ +๋“ค์ฉ +์–ด๋ฒ• +##๊ฐˆ์ด +์—๋ฏธ +ํƒœ์–ด๋‚  +์ „๊ณผ +##๊ฒŒ์ด์…˜ +์œ ์ž +ํ˜„์ฃผ +๊ฑฐํ–‰ +์•„๋‹Œ๊ฐ€์š” +ํƒ„์ˆ˜ +๋ฐ”์ด์—๋ฅธ +๋ชจ์•„์„œ +๋›ฐ์ณ +์•„๋ฆฌ์•„ +์œ ์‹œ +๊น€ํ˜„์ˆ˜ +์•„์ง€ +CGV +๋„๋ฆฌ์–ด +##๋งˆ๋Š” +๋Š๊ปด์งˆ +์กฐ๊ธ‰ +๋‹ค์กŒ +์ˆ˜๋ ฅ +์นด์žํ์Šคํƒ„ +์ฐจ์šฉ +๋Œ€์ถœ๊ธˆ +๋‚ด๋น„๊ฒŒ์ด์…˜ +์Šค์น˜ +์‹œ๊ฐ„๋Œ€ +๋ฌด๊ถํ™” +์ €์ˆ˜์ง€ +ํ† ์Šค +IPTV +์žฅ์ • +๊ณ ๊ณต +ํ›„์ƒ +๋งž์ถœ +##own +๊ธฐ์šธ์—ฌ์•ผ +๊น€์†Œ +์—…์ฃผ +์ž๊ฐ€ +Rev +์„ธ์›Œ์•ผ +๋น ์ ธ๋“ค +์ค‘์šฉ +๊ฑธ๋Ÿฌ +์„œํƒœ +๊ณ ๋“ฑ์–ด +ํ•ฉ๋ณ‘์ฆ +์•…์˜ +๊ฐ€์Šด์‚ด +##๋ ˆ์˜ค +PK +์›์ƒ +op +๋…ธํšŒ +๊ณ ์ทจ +๊ธฐ๊ณต +๊ฐ•์†Œ +์›์‚ฌ +์กฐ๋ ฅ +๋ฐฐ์„ค +์ˆ˜๊ต +์ธํ—ˆ๊ฐ€ +์ผ๊ณผ +๊ฑฐ๋ž˜์ฒ˜ +๋‘ฌ์•ผ +ํ˜„๋Œ€๊ทธ๋ฃน +์ง„์œ„ +์ตœ์„ฑ +๊ธฐ์–ต๋ ฅ +๋‹ญ๊ณ ๊ธฐ +์‹œํ”ผ +๊ธธ๊ฐ€ +์˜ˆ์–ธ์ž +๋„ฅํƒ€์ด +๋ฐ•์ž +๋””๋ ‰ +๋ถˆ๋กœ +Jour +์Šค์นด์šฐํŠธ +๋“ค๋ € +์ปค๋ฆฌ์–ด +ํฌ์Šค์ฝ”๊ฑด์„ค +IR +์—ด์„ธ +๊ตฐ๋ฆผ +๊ฐœ๋…„ +์ฃผ์› +์ •์„ธ๊ท  +๋ง๋ฆฐ +๊ฒฝ์งˆ +์บํ”ผ +##์ง€์šฐ +##๋ฌด์‚ฌ +##tical +์ •์—ด +๋ช…์‹ค์ƒ๋ถ€ +๋…ธํƒœ์šฐ +์‹ ํ•ญ +๊พธ๋ฏผ +๋…๊ฑฐ +์™„๋งŒ +์ž‡๋‹จ +##์ƒ๊ธด +ํŽ˜๋ฏธ๋‹ˆ์ฆ˜ +์˜ค์ฃฝ +ํญ๋™ +๋‹ฌ์„ฑ๊ตฐ +๋ฐ”์ณ +์–ด์„œ๋‹ค +ํƒ€ํŒŒ +ํ™ˆ์ฆˆ +๋‚ด๋ฟœ +London +์‚ฐ๋ž€ +์Œ๋ฃŒ์ˆ˜ +๊ณ„๋Ÿ‰ +๋‚˜์ด๋‹ค +๋œป๋Œ€๋กœ +์ €ํƒ„ +ํŽผ์ณ์ ธ +์ธ๊ถŒ์œ„ +์œผํ  +์ฆ๋น„ +๋ฒ ๋„ค์ˆ˜์—˜๋ผ +๊ฒฝ๊ฑด +์ƒ์ฒด +๊ธฐํ˜• +๋ณ€์ œ +##๋ˆ„๋ฅด +ํ•ด๋ณธ๋‹ค +์—ฐํ•„ +ํํ—ˆ +์„ ๊ฑฐ์ธ +๋ฉˆ์ถ˜ +์„ธ๋ฒ• +๊ฒฝ๋ฉธ +##๋งค๊ธฐ +์›”๋‚จ +์ถฉ์› +##๋ณด์‚ด +๊ฑฐ์ทจ +์ฐพ์•„๋‹ค๋‹ˆ +์•Œ์•„๋“ค +ํ”„๋ฆฐํŠธ +์–ด๋“œ +๊ธ‰์ˆ˜ +์†์ง“ +๊ท€๋” +ํ™ฉํ™€ +๊ทธ๋ฆฌ์Šค๋„์ธ +๋ฐ•์Šน +##๋Œ€๊ตฐ +๋”๋ฏธ +๊ฐ„์—ผ +๋ชจํƒœ +ํ๋ญ‡ +๋•…๊ฐ’ +ํ† ํŠธ +##๋‹ค๋“œ +Rep +์ƒ์กด์ž +ํผ์Šค +๋ฐฉ๋ฒ•๋ก  +์• ๋ฆฌ +๋ฌธ๊ณผ +๋„ํ‘œ +๊ฑฐํŠธ +์Šคํ„ฐ๋”” +๋ธŒ๋ ˆ์ธ +๋‚˜ํƒ€๋‚ผ +์‚ฌ๊ณ„์ ˆ +๊ธฐ๊ฐ• +๋ฒ—์–ด๋‚ฌ +ํผ์ฆ +์Šฌ๋ผ +๋‘๊ฐ +๊ฐˆ๋Œ€ +์ฐธ๋‹ด +๋„์„ฑ +์ˆ™์—ฌ +์‹คํ™” +๋„˜๋ฒ„ +์ €๋Ÿฌ +์ œ๋„ค์‹œ์Šค +์ค‘์‹ฌ๋ถ€ +์•„๋‹ˆ์•ผ +๋“ํ‘œ์œจ +๊ถŒ์˜ค +๋ผ๋ฏธ +๋ฉดํšŒ +๋ฆฝ์Šคํ‹ฑ +##๋„๋ก +์ƒˆ๋งŒ๊ธˆ +##์‚ฌ๊ด€ +์•„๋‚Œ +์™ธ์ง€ +๊ฐ•์ˆ˜๋Ÿ‰ +๊ตฌ๋ฏผ +๋ชธ์‚ด +KL +์ˆœ์ฒœ์‹œ +์ค‘์•™์„  +์—ฐ๋ฆฝ +๋‚œํ•ญ +๋…น์—ฌ +##์ผ์น˜ +๋งŒ๋“ ๋‹ค +๊ฐˆ๊นŒ +๊ถŒ๋ ฅ์ž +์•ˆ์žฅ +from +์ง€๋ง +๋Š๊ธ‹ +๋ผ์นœ +##๊ฐ€์‹œ +๋…ธ๋ฒจ์ƒ +ใ†๋„ +1912 +๋“œ๋Ÿฌ๋‚ธ๋‹ค +์ฑ…๋ฌด +##ํŠธ๋ฆญ +PF +๋•…์ฝฉ +๋ผํŒŒ +๋ฐ•๋ช… +์•„์ฐ” +๋†์ž‘๋ฌผ +์กฐํ˜•๋ฌผ +ํ•™์ž๊ธˆ +์š”๊ธฐ +์ธํ„ฐํŽ˜์ด์Šค +๋Œ€์ถ” +ํ‡ด์น˜ +ํฌ๋ฆฌ์Šคํ†  +๋Œ์•„์™€์„œ +๊ด‘ํ•™ +์„ ํ›„ +๋…ธ์  +๋ฉ”๋ฐ€ +์งˆ์†Œ +##ํญ๋ ฅ +๋ง๋ฌธ +##๊บผํ’€ +๋ฐฐ์ฒ™ +๋นŒ๋ฆฐ +##yp +๊ทธ๋Ÿฌ๋‹ˆ +##์„๊นŒ +์ฐฝ๊ฑด +์•ˆ์ฐฉ +CM +์™”์—ˆ +BN +๋ฏธ๋น„ +๋ถ€๋ฆผ +##์˜์žฅ +๋‹จ์ธต +๋ฒ•๋Œ€ +์“ฐ๋ฆฌ +์žก์•„๋‹น +##์ง„๊ตฌ +##์˜ˆ๊ธˆ +##๋ฐธ๋ฆฌ +์ธ์ˆ˜์œ„ +๋ณ€์ธ +๊ฒฝ์ฃผ์‹œ +##oo +์‚ฐ์ฒญ +##์†ก์ด +๋Šฅ๋ฅ  +๋ง‰์Šค +๋งด๋Œ +์˜ค์คŒ +์ƒ๋™ +ํ˜„ํ–‰๋ฒ• +์•„๋“ +์ˆ˜์ˆœ +๊ธˆ๋…„ +๋ณด์ž‰ +๋ฒ•์น˜ +204 +์š”๋ฅด +์‚ฌ์ •๊ด€ +๋ฏธ์•„ +์ž๋ง‰ +์™„๋„ +์ง€๋ฏธ +##ings +๋ชจ์Šค +๋ณต์‹ฑ +ํ”๋“ค๋ฆผ +์‚ฐ์ „ +๊ฐ์„ +์„œ์šธ์—ญ +์‚ฐ๋œป +๋ฉํ•˜๋‹ˆ +์„œ๋ž +ํฌ์†Œ +์ฃผ๊ฑฐ์ง€ +ํ˜ˆํ†ต +##์‚ฌํšŒ +ํ• ๋ถ€ +๋„์šฐ +์นดํ…Œ๊ณ ๋ฆฌ +์ „์—… +๋ฐฐ๋ฐ˜ +๊ฐ์ดˆ +๋ฐ”ํ‹ฐ +์ด๋™๊ตญ +๋ถ€๋Ÿฌ +๋ผ๋‹ˆ๊นŒ +##๋…๋ถ€ +์„ฑ์žฅ๊ธฐ +์†Œ๋Ÿ‰ +๋…ธ๋ž‘ +์ œ์†Œ +์ด์•Œ +๋ ˆ๋ฏธ +ํ™˜ํ•œ +๋งˆ์ผ๋ฆฌ +##์ด์–ดํ‹ฐ +์œ ํ•™์ƒ +##ma +๊ฑฐ์ฃผ์ง€ +ํ›„์ผ +์™•์กฑ +##sch +์•„์ฟ ์•„ +๋Œ๋ฆฐ +์†Œ์ž +์ „๋ฅ˜ +๊ตญ๊ณผ +์ค‘์†Œ๊ธฐ์—…์ฒญ +##uct +๋ณ€์ด +์ œ๋ช… +์„ธ์„ธ +NP +##์•„์•„ +##ery +๊นœ๋นก +์Šค๋งˆ +##์œ„์„ฑ +์ฒœ์•ˆ์‹œ +##rat +์•ˆํƒ€๊นŒ์›€ +๊น€๊ด‘ํ˜„ +๋ถ„์žฅ +์ ์ค‘ +๊ณ ๊ท€ +๊ฑธ์–ด๊ฐ” +๊ฐ•์„œ +๊บผ์ง€ +##app +##๋ ˆํƒ€ +ํ—ท๊ฐˆ +์ฒœ์—ฐ๊ฐ€์Šค +##ํ•˜์šฐ์Šค +์Šค์ผ€์น˜ +์˜๊ฒฐ๊ถŒ +๋ฌด์ˆ  +๊น€์ข…์ธ +๋ฌธํ™”์˜ˆ์ˆ  +##ble +ํ—Œ์ • +##gram +##๋“์ด +380 +##๋ธŒ์Šค +ํžˆ์–ด๋กœ์ฆˆ +LPG +##์ง€์› +๋Œ๋ฐœ +ํŽด๋‚ธ +์—ญ์‚ฌํ•™ +##๋“ค๋ฆฌ +์ฃผ์  +##๋…ธ์†Œ +์ฆ์—ฌ์„ธ +ํ—ฌ๋กœ +ํ’์Šต +##echn +์ œ๋„ค๋ฐ” +๋ž˜ํผ +์นด์ŠคํŠธ +๋ชธ๋‹ด +์—๋กœ +##๋ชจ์Šค +์นด๋„ค +๋ฐœ์ž‘ +์—์ด์ฆˆ +์ด๊ธ€ +๋‹ค์œˆ +๊ณ„ํš์•ˆ +๋งˆ์นด์˜ค +๊ฐœํ•ญ +๋ฐ‘๋ฐ”๋‹ฅ +##aus +๊ณผ๋Œ€ +๋‚ฎ์ท„ +ํ™ฉํ†  +๋ ˆ์ „ +๊ฒฝ๋น„์› +๋™์‹œ๋Œ€ +๋ฐฐ๋‹น๊ธˆ +์ดˆ์ŒํŒŒ +System +๋’ค์ง‘์–ด +์†Œ์šฉ๋Œ์ด +๋…๋ฆฝ์šด๋™๊ฐ€ +๋˜๋ ท +์‚ผํ‚ค +๋ณด๊ณ ํšŒ +๊ฒ€์‚ฌ์žฅ +Ne +๋ฝ‘ํ˜” +์—Ž๋“œ๋ ค +๋ณด์œ ์ž +์Šคํ…Œ์ธ +๋…์žฌ์ž +๋“€์˜ค +์ œ๊ฒฉ +ํƒ„์ˆ˜ํ™”๋ฌผ +์ œ์ฒœ +๊ถŒํ–ˆ +##์ ˆ๊ธฐ +Am +๊ฐ•์•• +์ถœ์ž…๋ฌธ +ํ™˜์ „ +๊ท€๊ฒฐ +๊ฑด๋“œ +๊ธฐ๋…ํ’ˆ +NO +์œผ๋ฉด์€ +๋งž์„ฐ +์Šต๋„ +๋™์งˆ +์ขŒ์ธก +It +์ฝฉ์ฟ ๋ฅด +์ฒญ๊ณ„์ฒœ +๋ณผ๊ฒŒ์š” +๊ดด๋กœ์›€ +์บํ”ผํƒˆ +์ด˜์ด˜ +์ฐธ์—ฌ์—ฐ๋Œ€ +๋‹ค์นœ +์นด๋”” +๋“ฑ๊ต +๋งˆ์•ผ +๋ณ€์ฆ๋ฒ• +##๋”์Šจ +๋‹ฌ์•„๋‚ฌ +๋’ค์ง„ +์‚ฐ๊ธธ +##red +๋ฐ›์ณ +๊ณ ์‚ฐ +๊ตํ†ต์•ˆ์ „ +sc +๊ณต์–ธ +ํœ˜ํ•˜ +๋Œ€์›… +##์—๋ฏธ +๋‚ด๊ตฌ +์‹ ๋ณด +##์˜ค๋ฅธ +์„ฑ๊ณผ๊ธ‰ +์ง‘๋ฌด์‹ค +๊ธˆ์ • +##ํ˜ธํก +##์žฌ๋ฃŒ +์ด์—ฌ +์žํŒ +CA +์–ด์ฒ˜ +์ •์€ +์ผ€๋ƒ +์ž๊ธ‰ +๋„˜๊ธธ +์ธ๋Œ€ +์ˆ˜์‹ญ์–ต +์ˆ ์ž๋ฆฌ +๊ณ ์ธต +๋ธŒ๋žœ +์ „๋Ÿ‰ +๋‚ด๋ฌด +๋งน์ž +ํฌ์œ  +์„ค๋‚  +๋…ธ์กฐ์› +์‚ฌ๋Œ€๋ถ€ +๋…์†Œ +์ง„๋ณด์‹  +##์ˆ˜๋‹จ +์ง€์ง€๋ถ€์ง„ +##์ด์ƒ +์œก์‹  +๋ชจ์„ฑ +๋…ธ๋ น +์ ˆ๋ฌ˜ +๋ด„๋‚  +์•ผ์‹ +๋ฆฌ์–ผ๋ฆฌ์ฆ˜ +๋Œ์•„๊ฐ„๋‹ค +##ream +์นด์šดํ„ฐ +์—ฐํ˜ธ +##ํฌ๋ผ์ด๋‚˜ +๊ณ„์•ฝ์ž +๊ธฐ๋…์‚ฌ์—… +๋‹ด์•„๋‚ด +๋น„์ฃผ๋ฅ˜ +์†ก๊ตฌ +์†Œ์ž‘ +์•Œ๋ฆผ +๋‚จ์ธ +๋ณ‘๊ธฐ +##์†Œ์ˆ˜ +##๋ฉ”๋ฅด +๋ƒ‰์†Œ +๊ธฐํƒ +๋งค์นญ +##๋“œ๋ฆฌํžˆ +์—ฌ์ฃผ์ธ๊ณต +์ฃผ์ฐฝ +์ปค๋จธ์Šค +Fl +์–ด์‹œ์ŠคํŠธ +์ •์˜ˆ +##ium +๋ˆˆ๊ฐ€ +๊ผฌ์น˜ +ํ™œ์ฃผ๋กœ +ํ›„๋ฉด +๊ฐ‘์˜ท +๊ฒฝ์•… +์˜ฌ๋ ค๋‹ค๋ณด +๋…ธ๋ฆฐ +์‹์ˆ˜ +๋งŒ์กฑ๊ฐ +๋ ค๋‹ค๊ฐ€ +๊ฒธ์ž„ +ํ—ˆ์ • +##que +๋„์  +์ค„๋ฆฌ +##์นด๋ฅด +ํ”ผ์น˜ +์บ˜๋ฆฌํฌ๋‹ˆ์•„์ฃผ +์–ด์ฒ˜๊ตฌ๋‹ˆ +์—ด์ „ +4500 +์„œ๋‘ +SE +โ €โ €โ € +ํ†ต์น˜์ž +ใ… ใ… ใ… ใ…  +์šฐํฌ๋ผ์ด๋‚˜ +๋‹นํŒŒ +์กฐ์ง์› +๋ชปํ•ด์„œ +์ƒ‰๊ฐ +์Šฌ๋ฆฌ +ํ•œํƒ„ +๋งˆ๋‹ˆ +ํ’์ˆ˜ +##22 +SDI +##๋ฐฐ๊ธฐ +ํ™ฉ์šฐ +๋‹ด๋‹น๊ด€ +ํ• ์•  +์ „์ง€ํ›ˆ๋ จ +๊ฒ€์žฅ +๋ถˆ๋ฆฐ +๋‘๋ฅด +๋ฒ„๋ ค์ง„ +์šฐ์Šต +์ž ์‹ +ํ”ผ์‹ฑ +๋งž๋‹ฟ +ํ•œํŒ +##๋ธํ”ผ์•„ +๋ฌธ๊ณ  +๊ฐ€ํ–ˆ +ํ•™์žฅ +๋‚ด๊ณต +ํ•œํ…Œ์„œ +๋‚จ๋…€๋…ธ์†Œ +์—ฌ๊ฐ์ฒ ๋„ +์•„์ด์œ  +์ถ•๊ตฌ๋‹จ +์†๋ณด +๋งŒ๋Šฅ +์Šต๊ธฐ +ํ™”์•ฝ +์ €์„ฑ์žฅ +์พŒ๊ฐ +์—ญ๋ ฅ +์™ธ๋กœ์šด +์š”์ฝ”ํ•˜๋งˆ +์•„์น˜ +์ด์šฐ +๋™์›” +๋ฒ„ํ• +ํŽธ๊ณก +์ถฉ์ฒญ๋„ +ํ›„์˜ˆ +๋‘˜๋Ÿฌ์‹ธ์—ฌ +์ „์ž… +he +๋ชจ๊ตญ +์–ธํ–‰ +์ •ํ•  +๋ ˆ์ „๋“œ +ABC +๋‹จ๋ง› +๋’ค๋Œ +##ons +์œผ๋ƒ +์ดํ„ฐ +๊ฒ€์ฐฐ์ฒญ +๊ฐ๊ด€์ ์œผ๋กœ +ํ•„๋ผ๋ธํ”ผ์•„ +ํฌ๋ฆฌ์Šคํ‹ฐ +์ ์‹œํƒ€ +์ž๊ธ +ํ™”๊ธฐ +๋ฌผ์˜ +๋ฌด๋„ +์‚ฌ์—…์ฒด +์ŠคํŠธ๋กœ +๋‹ค์ง„ +##FF +๊ฐ€์ ธ์™€ +ํ”„๋กœํ•„ +๊ธ‰๋ถ€์ƒ +1906 +์ œ๋„ˆ +์ŠคํŠธ๋ ˆ์นญ +์ˆ˜์ถœ์•ก +๋ฐ•์ธ๋น„ +๊ณผ๋ถ€ +๋น„๋ฌธ +์ฐธ๋œ +##์ฐฉ์˜ค +##ward +์—„๊ฒฉํžˆ +๊ฐœ์—ฐ +๋ฐ˜์ชฝ +์ฒฉ๋ณด +##๋ผ์ง€ +์ถ•๋ฌผ +##ํด๋ฆฌ์Šค +๊ฒฝ๊ณ„์„  +๋ฆฌ์ธ  +๋ช…๋ฃŒ +๋ณ€๋น„ +##๋‹ˆ๋‹น +๊ณ„์–‘ +ํŒ๋‹ค +ํŠน๋ชฉ +Ab +์Šฌ๊ธฐ +๋ชจํ…” +๋„ค์ผ +์ˆ˜์ฒœ๋งŒ +์ €ํƒ„์†Œ +์‹œํ–‰์ฐฉ์˜ค +์†Œ์ • +์˜€์œผ๋‹ˆ +ํฌ๋ผ์ด +๋‹จ๊ตญ +๋น„์„ +์ฐธํŒจ +์ด์„ฑ๊ณ„ +Is +##๊ฐ€์š” +๋ธŒ๋ฆฌ์ง€ +์ ‘์  +##ํ•˜ํ‚ค +์„์ƒ +ํ† ํ•ด +์ด์กฐ +๋ช…๋ถ€ +์บํ”ผํ„ธ +ํฌ๋งท +์Šคํ…” +์šฉ์ˆ˜ +์ž‘๋ณ„ +##๋ฌธ๊ณ  +์‹ ์„ธ๊ณ„๋ฐฑํ™”์  +์•จ๋ฆฌ์Šค +๋งก๊ธธ +##rav +##๋ด๋„ +Mc +๊ตต์ง +ํ†ต์ฐฐ๋ ฅ +์ˆ˜์ค +ํ—ค์—„ +๋ฌธ์„ฑ +๊ฑธ๊นŒ์š” +์ถ”์ง„๋ ฅ +์ž„์ฐฝ +์ฒดํฌ์นด๋“œ +##์ผ€๋ฏธ +๋‹ค๋ฃฌ๋‹ค +์šฉ์  +๋‚จ๋ฐฉ +LP +Pa +ํƒ€๋ฅด +๋ชฉ์š•ํƒ• +์ƒ์ถ” +##๋ถ€์ธ๊ณผ +##๋ณด์ฆ +๋ˆ„์Šค +์—˜์ง€ +๊ณผ๊ฑฐ์‚ฌ +์—ญ๋ถ€์กฑ +๋†“์˜€ +๋‚œ๋ฌด +์š”์ฆˆ์Œ +์šฐ์™• +์•ˆ๋งˆ +๊ธˆ์ƒ +์ •์„ +ํ๋ ด +์‹ ๋ณ‘ +์ €๋ ฅ +##ํ…Œ๋ฆฌ์•„ +๋Œ€ํ•œ์ œ๊ตญ +๋ถ€๋ฆ„ +๊ธฐ๊ป +๊นŒ๋‹ค๋กญ +์‚ฐ๋ถ€์ธ๊ณผ +๋›ฐ์–ด๋“  +๋ธ”๋ž™ํ™€ +##๋ชจ์–ด +์ผ€๋นˆ +ํ†ตํ†ต +๋‹น๊ฒจ +๋Œ€๋‚ด์™ธ +๋’ค์ ธ +๋‚ด์ค€ +๊ตฝํžˆ +๊ณต์‚ฐ์ฃผ์˜์ž +##๋ ˆ์ŠคํŠธ +##๋–จ์–ด์ง„ +์ˆ˜๋‡Œ +์›๋„ +์ˆ˜๋กœ +์• ์ผ +์ฃผ์กฐ +์•ˆ์ƒ์ˆ˜ +๊ป๋ฐ๊ธฐ +์žฅ๋‚ด +์›”ํ™” +๋– ์˜ค๋ฅธ๋‹ค +๋‚˜๋ญ‡์žŽ +์ง€๋ฆ„๊ธธ +##rid +ํ™œ์ž +์ฆ๊ฑฐ์›Œ +์›จ์ผ +๋‹ค๊ตฌ +##์–ด๋ง +๋งก๊ธด +์ „์ž„์ž +๋งˆํ•˜ +##ํ“ฐ๋ฆฌ์ฆ˜ +์กฐ์„ ์ด +ํ๋ฆฌ +ํ‚ค๋ณด๋“œ +##์ด์Šคํ„ฐ +์ถ˜์ฒœ์‹œ +ํฌํ“ฐ๋ฆฌ์ฆ˜ +์ด๋žœ๋“œ +๊ฒฝํ™” +๊ถ๋ฆฌ +##๊ฒŒ์ด +์—ฐ๋ฏผ +๊ณ ์กธ +์ “๊ฐ€๋ฝ +์ปค๋ธŒ +ํ–‰์•ˆ +๊ธฐ์–ต๋‚˜ +๋ญก๋‹ˆ๊นŒ +์ง€๋ฐ” +๋ถˆ๊ฐ€๋ฆฌ์•„ +์ง„๋‹ค๊ณ  +##ํžˆ๋กœ +ํŠœ๋ธŒ +์ด์ฒญ์šฉ +์‹œ๊ฐ€์ง€ +##ํผํŠธ +์„ผํ‹ฐ๋ฏธํ„ฐ +๊ตฐ๋ณต +์žฌ์—ฐ +์ „๋งค +์ž๊ธ์‹ฌ +๊ณต๊ธ‰์ž +์กฐ์„ ์ด๋…๋ถ€ +์ •๋ น +##ํ„ฐ์นด +์กฐ์„ ์กฑ +ํŒŒ์ถœ์†Œ +๋†“์•„ +๋ฐ•ํ˜„ +์™”์œผ๋‚˜ +์†Œ๋น„๋Ÿ‰ +์•„๋ ˆ๋‚˜ +์„ ์ฃผ +์ฒ˜์ ˆ +##ope +ํŒŒ์ฃผ์‹œ +์‹ ๋ถ„์ฆ +ํœด๋ฌด +ํ”Œ๋ผ์›Œ +์™•๋ž˜ +๋ณ„์ž๋ฆฌ +์‹œ์•  +ํŒŒ๋ฉธ +##๋ฆฌ์ผ€ +๋ Œํ„ฐ์นด +##์ง€์—ญ +ํ•ดํ˜‘ +์—ญ์‚ฌ๊ฐ€ +๋ฐ”๋ž„ +์ ๊ตฐ +์„œ์œ ๋Ÿฝ +์œ ๋‹› +๊ตด๋š +ํŠน์•ฝ +๋ฌด์˜ +์ •๋ชฝ์ค€ +๋…ผ์„ค +##ํŠธ๋Ÿผ +##์žํ‚ค +์†กํ™˜ +๋ชฐ์•„๋‚ด +##๋‚ ๋“œ +์ „์ธ +์ •ํ˜ธ +์ €์ž‘๋ฌผ +##์ง•๊ณ„ +๊นŒ์น˜ +๋‚จ๋™์ƒ +์ง€ํฌ +๋ฌผ๋™ +์•ผ์ˆ˜ +##๋šœ๊ธฐ +##ํ˜œ์ง„ +๊นŒ๋งˆ๊ท€ +๋ณต๋ฉด +์ˆ˜ํ•™์ž +##๋ฆฌ์šฐ์Šค +๋งŒ๋ฅ˜ +์‹ฌ๋ถ€๋ฆ„ +##๋ฑ…์ด +##์ œํ‘œ +์œŒ๋ฆฌ์—„์Šค +๋จธ์ง€์•Š +์žฌํ˜‘ +๋“ฑ์šฉ +์ž„์‹œํšŒ +๋Œ€๋‹ˆ +๋‹ฌ์„œ๊ตฌ +์„œํฌํ„ฐ +์‹œ๋„๋Ÿฝ +๋ฌ˜์—ญ +์†Œ์‹ค +ํŒŒ๋ฌป +์›€์ง์ธ๋‹ค +๋‚ด์‚ฌ +์—ฐ๊ณจ +์ž‘์ž +##amb +ํ•˜๋ฒ„๋“œ๋Œ€ +๋†€๋ผ์›€ +์“ฐ๋Ÿฌ์ง„ +์šฐ๋ฃจ๊ณผ์ด +์—ฐํ‰๋„ +์ผ์ง€๋ผ๋„ +์Œ์–‘ +์ด์œ +๊ฐ๋Ÿ‰ +๋ฌด๋ฆ…์“ฐ +๋ฐ•๋ฏผ +๋‚ ๋กœ +ํ† ๋ฅด +์ˆœ์‘ +##ํญํ’ +๊ตฐํ•จ +๋ฒˆ๋ณต +๊ธฐํ–‰ +๋ฐฉ๋ง์ด +๋•Œ๋ ธ +๊ฐœํšŒ +๊ณต์  +๋ฐœ๊ฐ€๋ฝ +๋ฐฉ์‚ฐ +์ˆ™์› +์œผ๋ผ๋Š” +์ˆ˜์œ  +๋ฒ™์ปค +์œค์„๋ฏผ +์›์žฌ๋ฃŒ +๋Œ€์ œ +์œ ๋ถ€ +์ด‰๋งค +๋ณด๋”” +Journal +SD +##๋ ˆ์ธ์ง€ +์• ๊ฒฌ +ํœด๊ฐ€์ฒ  +์žฌ๋ฌด์ œํ‘œ +์„ธ์ฐจ +๊ฐˆ๋ฆฌ +๊ฐ•๋‚จ์—ญ +##๋ผ๋งˆ +๊ตฌ๋ช… +๋– ์•ˆ +##๊ตฌ๋ฆ„ +ํ•„๋‘ +์ง๊ฒฉํƒ„ +๋ช…์ธ +์ŠคํŽ™ํŠธ๋Ÿผ +๋ชจ๋‚˜ +##ํ‘ธ๋ฅดํŠธ +์‹œํ™ฉ +๋ถ€์ธ  +ํ†ตํ•œ๋‹ค +๋งˆ์ผ๋ฆฌ์ง€ +์„ฑํ˜•์™ธ๊ณผ +์ •์‹ ์—†์ด +๋น„์ถ• +ํ”Œ๋ ˆ์ด์Šค +์‹ฌ๊ธฐ +๋ฏธ์‹ +๊ต์œก์žฅ +์ดํƒ€ +์žฌ๊ธฐ +##์กฐ๋ฆผ +NBA +๋‹ค์ด๋น™ +์œ„์—„ +ํ˜„์ถฉ +์Šน๋‚™ +์œก๋ฅ˜ +์ง€ํŒก์ด +๊ตฌ๋ณธ +์ ๊ฒฉ +##์ฆˆํ‚ค +๋ฒˆ๋ฒˆ์ด +์œ ์ฐฝ +๊ฒฐ๋ ฌ +ํŒŒํŠธ๋„ˆ์‹ญ +๋ถ€์ง€์‚ฌ +์ œ๊ฐ๊ฐ +##au +์‚ฌ์•… +##iness +๊พธ์ง– +##์ด์„ผ +๋Œ€์ž‘ +์“ฐ์ผ +##์†Œ์†ก +์ธ์ฒœํ•ญ +์ฒœ์ž +##์ง„๋‹ค๋Š” +์‚ฌ์นด +๋ฐœ์ง +๊ณ ์ฐฝ +์ง€์ ์žฅ +ํ—ˆ๋‹ˆ +์–ด๋ ค์šฐ +๋Š”๋”” +##PA +์ด์—ฐ +์ ‘๊ฒฝ +ํ•™๊ต์žฅ +์—ฐํ•œ +ํ”„๋กœ์ด์„ผ +์ „ํ•  +ํƒ€๊ฐœ +##๋”๋‹ˆ +์‹œ๋‹ฌ๋ ธ +๋ฌ˜์†Œ +์นญ์†ก +๋”ฐ๋ผ์•ผ +๊ธฐ๋ณด +์›ฌ์ผ +๋†“์ณค +์ œ์ˆ˜ +##ํƒœํ‰์–‘ +๋ฒจ๋ผ +์ƒํ›„ +ํ‹ˆํ‹ˆ +๋ผ๋ฒจ +๋“ฑ๋‹จ +##ough +ํ—Œํ˜ˆ +##์—๋ฏธ๋ฆฌํŠธ +๊ตญ์ œ์„  +ํ”„๋ฆฌ๋“œ๋ฆฌํžˆ +์ œ์•ฝ์‚ฌ +์ด๊ฐ +์ง€๋ฐฐ์ž +##์˜คํ”ผ์•„ +๋Œ€์šฉ๋Ÿ‰ +๋‚˜ํŠธ๋ฅจ +์ปจํผ๋Ÿฐ์Šค +์›ํšจ +๊ทธ๋ฆฌ์Šค์–ด +์•„๋ž์—๋ฏธ๋ฆฌํŠธ +##rap +์ž‰ํฌ +ใ†๊น€ +๋ผ์šด์ง€ +๊ทธ๋ฆฌ์›Œ +ํŒŒ์šฐ๋” +์žก์Œ +์–ด์ˆ˜ +##we +๋งˆ๋ฅดํฌ์Šค์ฃผ์˜ +๋†€๋ฆฌ +์‹ญ์ƒ +##ute +AF +์†ก์ • +์–ด์„ค +ํ˜ธ์„ธ +๋‘ํˆผ +์—ํ‹ฐ์˜คํ”ผ์•„ +์ง„์†” +๋ƒ ๋ƒ  +์กฐ๊ทธ๋งˆ +์•ผ๊ฒฝ +##afe +์ง€์ฃผ์‚ฌ +์ง€์–‘ +##๋ฐฐ์ถ” +not +๋‚ด๋ ค์กŒ +๊ทธ์ง€์—† +๋‹จ๊ฑฐ๋ฆฌ +๊ฑฐ๋ž˜๋ฒ• +์„๊ถŒ +๊ฑฐ๋ฌผ +๊น€์˜ฅ +์ธ์Šน +๊ฐ€๋ฒผ์›Œ +๋ถ์œ ๋Ÿฝ +์„ ์ข… +ํ•จ์•ˆ +์™„๊ฒฐ +์ชฝ์ง€ +์ฝ”์ŠคํŠธ +์˜จํ™” +์ž์นด +๊ธฐ๋…๋น„ +ํด๋ผ์ด +๋Ÿฌ์Šค +์ด๋ณด +๊ณ ์ด +์‹ฌ๋ฆฌํ•™์ž +์ฃผ์ด +์ฒด๊ฒฉ +ํ”ผ๋ Œ +##์ฒด์ธ +๋ฐฉ์กฐ +์„ ์ง€ +๋Œ์–ด๋‹น +์ฒญ์‚ฌ์ง„ +์ƒ๊น€ +๊ด€ํ˜„ +์กฐ์ง๋ ฅ +์ŠคํŠธ๋ผ์ดํฌ +ํŽธ์ค‘ +๋งž์ถฐ์„œ +์œผ์…” +ํ…๋ฐ์š” +์ •์•ฝ +์—ญ๊ฒฝ +์—ด์—ฐ +๋‘๋ ค์šด +๋‚˜๋Œ +์›€์ธ  +์ฃผ์˜๋ณด +๋ชฉ์กฐ +์ „๋ฆฝ์„  +์™ธ๊ตํ†ต +##๋Œํ”„ +##IE +๊ฑฐ์นจ์—†์ด +์›Œ๋“œ +์ง„์ฒ™ +##any +์ข…์˜ +##์šฐ๋ณ‘ +##ose +์Šคํƒ ๋“œ +๋‹ค์Šน +์‹ ๋ด‰ +์ž๋ณธ๊ฐ€ +๋งŒ์ธ +ํ…Œ๋ ˆ +์ง€๋กœ +์ „์ž์ฑ… +์ž์•„๋‚ด +์˜ˆ๊ฒฐ +๋‹นํ•œ๋‹ค +ํ๋Š +๊ณ„๋ฃก +๋‚ฎ์ถ˜ +๋น„์นœ +์ฒ™๊ฒฐ +์†Œ์ด๋‹ค +๊ด‘๊ธฐ +๋Šฆ์ถฐ +๊น€ํ˜ธ +##๋ธ”๋žœ๋“œ +์ˆ˜๋ฐฐ +ํ•œ์Šค +์ •์ƒ๊ธ‰ +์ค‘๋žต +์‹๊ธฐ +##ott +์ด์™€ +NHK +์•„์ด์Šคํ•˜ํ‚ค +##๋‹ค์˜ค +์ƒ๊ฒจ์„œ +๊ณก์„ฑ +์ด๋ณ€ +ํ“จ์ „ +์กฐ๋ฏธ +์–ด๋•Œ์š” +์ฒ ๊ทผ +##ํƒฑํฌ +์‹œ์• ํ‹€ +๋ฏธ๋Ÿฌ +์—ฐ๋ฃŒ์ „์ง€ +์œ„์•” +ํŒŒ์ด๋‚ธ์…œ +##๊ทธ๋จธ๋‹ˆ +##์Šค์ผ€ +ํŒŒ์ด์–ด +ํžˆ๋ง๋ผ +๊ด‘๊ณ ์ฃผ +๋ถ€์ฒœ์‹œ +##์„ธ๋ฐ์Šค +๋ฐฑ์ƒ‰ +๋ฆฌ๋ฉ” +์กฐ๋™ +์œค์˜ +๋ ˆ์ด๋”” +์†์•„ +##GB +์›์–ด +ํ•ด๊ตฐ๊ธฐ์ง€ +๋Œ€๋„ +ํ•ดํƒœ +๋ ˆ์ด์‹ฑ +ํ•™์ˆ ์ง€ +์•Œ์ œ๋ฆฌ +๋ฉ”๋ฅด์„ธ๋ฐ์Šค +๊ณฐํŒก์ด +๋‚ด๋”› +par +๊ณต์ „ +๊ณจ๋ฌธ +##๋“ค๋ ˆ +๋ถ€๋Ÿฌ์›Œ +์›์ดˆ +์ •๊ฑฐ +ํด๋ฆฌ๋ธ”๋žœ๋“œ +1904 +๊ตฐ๋ถ€๋Œ€ +์ „์œ„ +์Šค์ผ€์ดํŠธ +El +๋ถˆ๋ถ„ +์œผ๋ฆฌ๋ผ๊ณ  +์ฐฐ๋ฆฌ +์—ฐ๊ธฐ๋ ฅ +ํ•ญ์šฐ +๊ณ ์„  +##๋ฐ”ํ‚ค์•„ +์ฒ˜๋ฆฌ์žฅ +##๋ฐ๋ฅด +๋ฐœํ™” +๋‚˜๊ฐ„๋‹ค๋Š” +๊ณ ๋ฅผ +ํ˜•์šฉ +์ฐฝ๋ฐฑ +##๊ณต์„œ +##ener +๋™์ฒœ +์ œ๊ณผ +์˜ฌ์ธ +ํ”ผํ•ด์•ผ +ํ•ญ์‚ฐ +ํŠธ๋ผ์ด +๋ณดํƒฌ +๋ฒ„๊ธˆ +ํŠธ๋กœ์ด +ํ–ˆ์ž–์•„์š” +์ง€๊ทธ +##๊ธฐ๋Šฅ +์‹คํ˜• +์ •ํŒŒ +๊ฐ€์ ธ๊ฐ” +๋ฃจ๋น„ +์‚ฌ๋“ค์ด +์Šฌ์Šฌ +์‚ฌ๋ฌด๊ด€ +๋ณ„์„ธ +์™•๊ถ +ํ‰๋…„ +๋ณ€๋™์„ฑ +๋‰ดํ„ด +๋ฐ”๊ตฌ๋‹ˆ +๋…ธ์‚ฌ์ • +ํƒœ์–ด๋‚ฌ์œผ๋ฉฐ +์˜ค์„ธํ›ˆ +##ange +์‹œ๋“ค +์Šค์ปคํŠธ +์‹ญ์ž๊ตฐ +๋ณ‘์šฐ +ํ‚คํŠธ +ํ‰ํƒ์‹œ +์ „๋ž˜ +์ •๋ณ‘ +๋ฆฝ์‹ +##ision +๊ต๋ž€ +์ƒˆ๊ธฐ +##ํ‹€๋ฆฌ +์ˆ˜ํ˜‘ +ํ‰๋ฏผ +์‚ฌ์€ +์ฐพ์•„๋ƒˆ +๊ฐ€๋„ +##ํ๋ฌผ +๋ฌผ๋Ÿฌ๋‚œ +์ž๋ผ๋‚˜ +์ฝ”์•ž +ํ‡ด์‚ฌ +WBC +๋‚ด๋ถ„ +ํžˆ๋ง๋ผ์•ผ +##์ผ€๋ฏธ์นผ +์šฉ์‚ฐ๊ตฌ +์˜ค๋น„ +์น˜๋ฃŒ๋น„ +##ames +๋”ฑ์ง€ +์ฃผํƒ๊ฐ€ +์˜ค๋ธ +์–ด๋ฌต +๋„์‹ +ํŒจ์†Œ +๋ฌด์˜์‹ +๋น„์”จ +๋ฐฐ์—ญ +์‹ฌ์žฌ +๊ฐ€๋œฉ +์•ˆ์ค‘๊ทผ +๋ฐ›์•„๋“ค์ธ +##๋…ธ๋™ +์นด๋ฉ” +์ฆ‰ํฅ +Dav +๋–จ์น˜ +ํ•˜๋ฆฌ +์–‘ํ‚ค์Šค +ํ˜ธํ™˜ +์™ธ์ฃผ +Ins +๊ฐ‡ํžŒ +๊ฐ„๊ฐ„ +CIA +์•„๋‡จ +์ด๋™์› +์žฅ๋…„์ธต +๊ทผ๊ต +##์ค€ํ˜ธ +์—ผ๊ธฐ +๋‹ค๋ถ„ +์ˆ˜์„  +๋“ค๋ฆด +##๋ฉ”์ธ +์ตœ๋ฏผ +๋ฒกํ„ฐ +๋ถˆ๋ถ„๋ช… +SA +๋™๊ธ‰ +์•Œ๋ฆฐ +์˜ํ–ฅ +1909 +๊ฑฐ๋จธ์ฅ +๋ชฌํ…Œ +๋ฐ˜๋™ +##๋ ๋ผ +๊ฐ€๋ฆฌ์ผœ +์žฌํ˜‘์ƒ +ํŒŒ์‹œ์ฆ˜ +๊ฐ€๊นŒ์›  +ํ›„๋“œ +๋ฏธ์›Œํ•˜ +##๋จน์ด +์ฒดํ˜• +##erg +์™ธํˆฌ +๋‚˜๊ธฐ +์‹œํฌ๋ฆฟ +์Šค๋ชจ +์‹ฌ๊ทผ +๋ถˆ์ˆœ +์œ„์ธ +๋ผ๊ตฌ์š” +์ขŒํ‘œ +์ธ์‹ฌ +ํ’ˆ์•ˆ +์ •๋ฏผ +๋…ธํŒŒ +ํ›„ํญํ’ +์œผ๋ฆฌ๋ผ๋Š” +๋šฑ๋šฑ +##๋…ธ๋ฏธ +EX +๋งˆ๋ฆฌ์˜ค +์•„๋‹ˆํ•œ +๊ฝ๊ฝ +ํ•˜๋…ธ์ด +๋ ˆ๋ฐ”๋…ผ +int +์‚ฌ์ƒ์ž +๊ณ„์‹ค +##ํฌ๋™ +๊ตด๋ฆฌ +์งˆ์ˆ˜๋ก +์•„๋ฏธ๋…ธ์‚ฐ +์• ๋ฆฌ์กฐ +๋ฒ•๋ฌด๋ฒ•์ธ +##VE +๋งˆ์ด๋„ˆ +์šด์ „๊ธฐ์‚ฌ +์—ฐ์ถœ๊ฐ€ +ํ™ฉ๊ธ‰ +๋‚จ๊ถ +์†Œํ™”๊ธฐ +๋ฒ ๋ž€๋‹ค +์˜ˆ์‹ +##์˜ˆ์‚ฐ +๊ทธ๋ฆผ์ฑ… +##ke +์˜น๊ธฐ +๊ฐ€๋œฉ์ด๋‚˜ +์žํ˜• +์ˆ˜์‹์–ด +๊ตฌ๋ ค +๊ฒŒ์ด์ธ  +๋“ค์˜€ +ํ€„๋ฆฌํ‹ฐ +ํŒŒ๋‚˜ +์™•๋”ฐ +์ง€์„  +์—ฐ์„ธ +์ฒญ์ฃผ์‹œ +์šธ๋ถ€ +##์ง€๋จผํŠธ +๋งค๋‹ˆ์ง€๋จผํŠธ +ํ†ตํ’ +##์„ธ์ง€ +์ดํ˜œ +๋Œ€๋ฆผ์‚ฐ์—… +ํžˆ์Šค +์‚ผ๊ตญ์ง€ +๊ฐ‘์ƒ์„  +์˜ค๋ฆฌ์—” +๊ฐ•๊ฐ€ +ํ•ญ๋ณ€ +๊ฐ„์„  +๋ฐ˜์—ด +์ŠˆํŠธ +๊ฒธํ•˜ +๋ฐคํ•˜๋Š˜ +##ํ”„๋กœ +์Šคํ…Œ์ด +์˜๊ณผ +์†์—ฌ +American +์™“์Šจ +์กฐ์„œ +๊ฒŒ๋ฆด๋ผ +๋Œ์•„์˜จ๋‹ค +๋„ค๋ชจ +์•„์ด์˜ค +์ƒํƒœ๊ณ„ +์ปฌ๋ง +๋กค๋Ÿฌ +์–ธ์–ดํ•™ +๋ถ€์กฐ +ํŒŒํŒŒ +Love +๊ฒฝ์‹œ +##RS +์„ค๊ฑฐ์ง€ +ํฌ๊ตฌ +res +ํ„ฐํŠธ +##earch +์‚ฌ๊ต์œก๋น„ +์–‘๋ถ„ +##๋ธŒ๋ฅด +์ดˆ์„ +ํ‘œ์ง€ํŒ +์ง๊ฑฐ๋ž˜ +๋ฐฑ์šด +์‚ฐ์—…์ฒด +์•„์•ผ์ง€ +๊ณ ์ฐฉ +๋“œ์…” +ํ•˜์˜ +์‹ค๋ฆฌ์ฝ˜๋ฐธ๋ฆฌ +๊ฐ„์ง€ +์ผ๊นจ์›Œ +##๊ณ„์•ฝ +##US +์„ธ๊ณ„์‚ฌ +ํ‚ฌ๋Ÿฌ +##der +๋„๋‚œ +์ˆ˜์—ญ +์ •์Šน +Mark +์šธ๋ถ€์ง– +ํ™•๊ณ ํžˆ +Vol +๋น„์„œ์‹ค +๋Œ€๋“ฑ +๋…ธ๋ จ +๊ฒฝ๋กœ๋‹น +ํŠน๋ชฉ๊ณ  +ํ”„๋ž‘ํฌํ‘ธ๋ฅดํŠธ +##๋”ฐ๋ฆฌ +๋‚˜์•„์ง€ +##ene +##RT +์ƒ์—ฌ๊ธˆ +๊ด€์‚ฌ +ํ”ผ๋ Œ์ฒด +์˜์—…์‹œ๊ฐ„ +๋‹ฌ๋ฆด +ํ”„๋กœํ†  +ํ•œ๋ฐ์š” +๋„๋ด‰ +์‹ ๋ณ€ +ํŒ”์ž +๊ธธ๋“œ +๋ฐ€์–ด +ํ˜‘์ฐฌ +์˜ค๋ฉ”๊ฐ€ +la +์ด๊ฒฉ +์„ ์ œ๊ณจ +##๋ฉฐ์‹œ +##๋งˆ์ฐจ +์„ฑ์”จ +์ˆ˜๊ฐœ +1911 +์žฌ๊ฐ€ +ํƒ€์› +๋‚ญ๋งŒ์ฃผ์˜ +์Šคํ•€ +##unic +๋ฏธ์›€ +๋ƒ‰๋ฐฉ +์ˆญ๊ณ  +๋™ํ•ด์•ˆ +ํ†ตํ–‰๋ฃŒ +์†Œ์ค‘ํžˆ +์•ˆ์ „์„ฑ +๋ฆผํ”„ +๋งˆ์š” +##์ฝ”๋‹ˆ +์‹์ƒํ™œ +##orean +๊ถ์ • +์ดˆ๋ก์ƒ‰ +๋ฌด์ œํ•œ +์‹œ์ฐจ +๋ณ‘์ž +ํผ๋ ˆ์ด๋“œ +๋ณด๋”ฐ๋ฆฌ +##์šฉํ’ˆ +์Šค์œ„ํŠธ +์˜์ • +ํŽ˜๋‹ฌ +์™•๊ฐ€ +์‹œ์ดˆ +์ธ๋ฑ์Šค +ํ‹ฐ์Šค +๊ตํ†ต๋ง +์ €์šธ +์—ฌ๊ธธ +์ˆ˜๋ณ€ +์˜ค๋ฅด๋‚ด๋ฆฌ +์‚ฌ์กฐ +์ผ์ง„ +์•ฝ์ดˆ +์‚ฌ๋กœ์žกํ˜€ +๋ฌด์ด์ž +๋…ธ์„ +๋„๋„ +์ž…ํ˜€ +์ „ํ™˜์  +์ •๊ฑฐ์žฅ +๋ฃจ๋จธ +์„ ํ•œ +๊ฒฝ์ง„ +๊น€ํƒœ๊ท  +๋ ์ˆ˜๋ก +ํŠน์ „ +##sy +๊ฐ•์ถ” +์—„์ˆ™ +์ตœ์Šน +๋ถ•์–ด +์นดํ„ฐ +ํ—ˆ์  +๋‚จํ•ด์•ˆ +๊ฐ€์‚ฐ์  +ํ•œ๊ธฐ +๋’ค๋ฐ” +๊ธฐํ‹€ +๊นƒํ„ธ +๋ถˆ์šฐ +์ฑ…์žฅ +์—ฐ์Šต์žฅ +์„ ๊ตฌ์ž +##๋ ˆ์ดํŠธ +ํŒฌํ‹ฐ +##oup +๊น€๊ฒฝ์ˆ˜ +์š”๋ฅด๋‹จ +๋ฒ ์Šค +##๋ฐด๋“œ +์•„ํ”„๊ฐ„ +์นดํƒˆ +๋ณ‘์žฅ +ํƒ€์ด๊ฑฐ์ฆˆ +์ด๋ถ +๋งค๋ฃŒ +์ผ์‚ฌ +##cess +๊ฐ€์ฆˆ +๋Œ์–ด์˜ฌ๋ ธ +๋ฐœํ•˜ +์žฅ์ค‘ +๊ฒฐ์  +๋‘˜๋Ÿฌ์‹ธ์ธ +์‰ฝ์‚ฌ๋ฆฌ +AK +๊ทน๋ณธ +์ด์–ดํฐ +##att +๊ณฐ๊ณฐ +YTN +ํ˜„์ข… +One +UHD +๋ฌผ๋ฆฌํ•™์ž +์ž ๋“  +##๋ž‘์นด +๋งˆ์ค‘ +๊ณคํ˜น +์‚ฌ์—…์ฃผ +๊ด€์Œ +ํฌ๋Œ€ +ํฌ๋ฐ• +##IM +์ผ€์ดํ‹ฐ +ํฌํš +์ดˆ๋ฏธ +๊ฐ€์ ธ๋‹ค์ฃผ +์ด๋‹ˆ +์—˜ํ”„ +##now +ํ…Œ์Šค +์ง€์ €๋ถ„ +๊ฐ–์ถœ +์ˆ˜์กฑ +๋ณ€ํ•œ๋‹ค +##์ผ์ฒด +๋‚ด๋ž€ +ํ™๋ฌธ +๊ฑธ์ž‘ +##ors +๊ฒ ๋‹ค +์œ„์„  +๋น ์ง์—†์ด +์ฒœ๋งˆ +Eng +์ˆ˜์กฐ +##์ถง๊ฐ€๋ฃจ +๊ณ ์˜จ +##obal +์‚ฌํšŒ์ƒํ™œ +๊นจ๋ฌผ +์ฐจ์ธ +์ƒค๋ฅผ +์ƒค์˜ค +์ˆ˜๊ธ +๋ฐ”๋ผ๋ณธ +์ƒคํ”„ +340 +์ž‡๋ชธ +์•ก์ž +ํ–‰ํ•œ +##๋ฆฌ์Šคํƒ€ +ํ–ฅํ•ด์„œ +์œผ๋กœ์„  +์—ฐ๋ฝ์ฒ˜ +##๋ถ€ํŒจ +๋™์ข… +ํ•˜๊ธ‰ +##RO +์‚ฌ์ฒด +๊ทœ์•ฝ +๋‚˜์™€์š” +ํ˜„๊ธฐ +์„ฑ๊ณตํšŒ +์‚ฌ๋ณธ +ํ† ๊ธฐ 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+##net +๋งˆ์ดํฌ๋กœ +ํ”ผ์กฐ +์ŠคํŒŒํฌ +์‹ ๋™๋นˆ +๊ฐˆ๋ฆผ +๊ฑฐ๋ฅด +๋ชป๋‚œ +์–‘๋ฆฝ +๋ฐœ๋“ฑ +๋ฆฌ๋”ฉ +์ธ์ƒ‰ +์•„๋ธŒ๋ผํ•จ +๋ฌปํžˆ +์–ดํ—ˆ +##๋‹ฌ๋ฆฌ +๊ฒฐ์ •์ง“ +๊ด€์šฐ +์—ฐ์ฒœ +์—์ŠคํŒŒ๋ƒ +Cal +์ข…ํŒŒ +mod +๋“œ๋ ค์š” +๊ผญ์ง€ +๋Œ€๋‚ฎ +๋‹ค๋… +์„œ์ฒœ +์žฅ์Šน +๋„์ž +์†Œ๊ทน์žฅ +์ด์ฑ„ +๋‹ด์š” +##์žฅ์ˆ˜ +๋Œ€์ฃผ๊ต +๋‹ค๊ทธ +์†ํ–ˆ +์—ฝ์„œ +์ฒœํ˜œ +##๋”๋“œ +id +์„œ๋ณ‘ +์˜ค๊ฐ” +์ง„๋‹ฌ๋ž˜ +๋นจ๋ ค +##์‚ฌ์žฅ +##์—์ดํ„ฐ +##์ฐฉ๋ฅ™ +์†ก๋ผ์ดํ„ฐ +์บ์„œ +์•ˆ์žฌ +์„œ์„ฑ +์ ˆํ•˜ +์ฒด๋ฒŒ +ํ‚ค์›Œ์•ผ +SN +๋‘๋ชฉ +๋ฒ ๋ฅดํฌ +๋“œ๋Ÿฌ๋‚ผ +๋Œ€ํ–‰์‚ฌ +์ดˆ๊ณ ์ธต +๋ณ‘๋“ค +์˜์ „ +๋ฏธ์นผ +์นด๋ฐ” +๋งŒ๋ฆฌ +๋จธ์Šค +๋ฐ์นด๋ฅดํŠธ +๋Ÿฌ์„œ +์—ฐ๋งˆ +##์ง„์ง„ +##์ฉŒ๋‘ฅ +์ฒœ์ผ +์ฑ…์ž„์ ธ์•ผ +ํŒฉํ† ๋ฆฌ +##๊ฒŒ์ดํŠธ +##ely +๋ถ€๋Ÿฌ์ง€ +ํ•œ๊ตญ๊ตฐ +์™„๋ฒฝํžˆ +๋นˆ๊ณค์ธต +์ œ์Šค์ฒ˜ +ํŒ”์ฐŒ +ํ•„์Šน +๋จธ๋ฌธ +ํ•ดํ”„ +##์†Œํƒ€ +๋ถˆํ†ต +์ด์˜ +๋ฐฉ์  +##์ฝฅํ„ฐ +๋ฐํžŒ๋‹ค +์„ธ์ผ์ฆˆ๋งจ +NASA +๋ถ€์›์žฅ +์Šค๋ชฐ +์˜ค์†ก +ํ–ฅ๊ต +๋งˆ์˜ค์ฉŒ๋‘ฅ +๋กœ๋ฆฌ +๋ณ„๊ด€ +๋ง๋ถ™์ด +๊ฐœ์ฐฐ +๋– ๋„˜๊ธฐ +์ƒ์—ญ +ํ•˜์ดํผ +์ž˜์ƒ๊ธด +##21 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+๊ฑฐ์‚ฌ +๋“ค์ด๋Œ€ +##23 +๋ˆ์งˆ๊ธฐ +ใ…กใ…ก +๊น€๋งŒ +##24 +๊ฒธ์ง +์ตœํ˜„ +๋ฉ”๋“œ +์ €ํŽธ +ํ•˜๋ฌผ +๊ณ ๊ด€ +๋ฎ๊ฐœ +์†ก์ถœ +UC +์˜ˆ๋ฌธ +##๋ น๊ด€ +๋””๋ ‰ํ„ฐ +Rec +##๋ ŒํŠธ +##ํ‹ธ๋ฆฌํ‹ฐ +์ง‘์  +##28 +๋ฐ”๊ฒ +์›จ์ดํŠธ +ํ”ผ์›  +Ver +์‹๋„ +์นญ๊ธฐ +ํ•œ๋“ค +์ดํ™ฉ +์ง•์šฉ +์˜ท์žฅ +ํ”ผ์‚ด +๋ชจ์ข… +๋ฒ ์–ด์Šค +์ „์„ฑ +์Šน๋ถ€์ฐจ๊ธฐ +๋ฏธ๋„ค์†Œํƒ€ +์žฅ์ œ +##๋ฅด๋…ธ +์—๋ฐ€๋ฆฌ +์—ฌ๋ผ +์žฌํšŒ +ํ”Œ๋ž˜๊ทธ +gr +##์Šค๋Ÿฌ์šด +์—ฌ๋Ÿฌ๋ชจ๋กœ +๋˜์ƒˆ๊ธฐ +์š”์ถฉ +##์€ํฌ +์ฐพ์•„์˜ฌ +๊ธฐ์ œ +๋ฐœ๋ ˆ๋‹จ +์กฐ์–‘ +๋ฐ”ํ‹ฐ์นธ +์šฉ์•” +๊ด€๋ง +์‹ ์ฒœ +##ํ‹ฐ์‹œ +##๋ฆผ๋™ +๋…ธ๋™๊ณ„ +์™€์šฐ +์ „๋ฒ” +ํ๊ต +๊ธ€๋ฃจ +์œ ํ‹ธ๋ฆฌํ‹ฐ +์ œ์ฒญ +ํžˆํžˆ +rel +์ฒ ํŒ +๋‘๋“œ๋ ค +##์žฃ์ง‘ +์„œ๋„ +๊ทธ๋žœ์ € +ํŽธํŒŒ +๋นผ๊ณก +naver +Ma +ํžˆ๋ฐ์š” +๋ช…๋ฌผ +##์‹ค๋ฒ ์ด +๋„ค๊ฑฐ +๋Œ€ํ†ต๋ น๋ น +##์‹ค๋ฒ ์ด๋‹ˆ์•„ +๋ณต์•ˆ +์‚ด์‚ด +์–‘์น˜ +๋ฒ•์›์žฅ +Int +์•…๋ช… +๋ถ€์„œ์ง€ +์ด๋„ˆ +๊ณตํ•™๋ถ€ +๋ฐœ์‹  +์ด๊ณผ +ํฅ๋ฏธ์ง„์ง„ +๋ฐ‘๋ฐ˜์ฐฌ +ํƒ„์› +##๋ฐ”์ด๋ฒŒ +ํ”„๋ž€์น˜์Šค์ฝ” +์ง„์‹œ +๋งค์„œ +๋ฝ‘ํžŒ +๋‹น์€ +ํ•œ๊ฒฝ +##olution +ํŠ€์–ด๋‚˜์˜จ +##์ž–์•„์š” +๋ฏธ๋„๋Ÿฌ์ง€ +ํ”Œ๋Ÿฌ๊ทธ +ํ—Œ๋ณ‘ +์„ฑ์š• +ํŽœ์‹ค๋ฒ ์ด๋‹ˆ์•„ +##ther +๊น€์ค‘ +์ตœํ•˜์œ„ +์ค‘๋ž‘ +์ฒด์Šค +๋ผ๋ฉ˜ +ํ—ค์น˜ +ํ•ดํ”„๋‹ +๊ฑฐ๋‹ +๋‚จ์„œ์ชฝ +##๊ฐ๋… +RPG +##์ •์ฑ… +##์ณ์•ผ +๊ด‘์ฑ„ +##ency +์ œ๋„ˆ๋Ÿด +ํ™”์›จ์ด +##fl +์˜์•„ +์ ‘์ฐฉ +ํ˜•์ฒด +๋ง‰์•„๋‚ด +์Šคํƒ ๋”๋“œ +ํ”„๋ ˆ๋“œ +๋กœ์—ดํ‹ฐ +์ดˆ๊ฐ€ +##์น˜์‚ฐ +ํ›‘์–ด๋ณด +๊ณ ํ•ด +foodp +์Šน๋ถ€์กฐ์ž‘ +๋‚œํญ +๊ธฐ์ƒ์ถฉ +ํ•ด์ด +๋น ๋œจ๋ฆฌ +##ํ”Œ๋ฆญ์Šค +ํ”„๋žญํด๋ฆฐ +##์„ํฌ +์šฐ๋ฆฌํˆฌ์ž์ฆ๊ถŒ +ํ•™๋ฒŒ +215 +ํ”์น˜ +##formation +##16 +์„€๋„ +๋ฐฐ์šด๋‹ค +๋„ทํ”Œ๋ฆญ์Šค +๋ถˆ์‹ +##ye +##์ฒจ๋‹จ +์–ด๋กœ +์ธํ™” +ํ†ต๊ด€ +ํฉ์–ด์ง€ +์ˆœ์ˆœํžˆ +๋ช…์ง€ +๋Œ€์žฅ์ • +ํฌ๋ผ์šด +์ฝ˜์Šคํƒ„ํ‹ฐ๋…ธ +์ˆ˜์น™ +์‚ฌ๋“ค์˜€ +๋ฐ˜๋‹ฌ +์˜์ƒ๋ฌผ +IA +์ธ๋จผ +์˜ค๋‹ˆ +##์ˆ˜๋ฌด +์‹œ๊ธˆ์น˜ +์•ˆ๋‚˜ +ํ–ฅ๋…„ +๋‡Œ๋ฆฌ +##๋ธŒ๋ฆฌ์ง€ +์„ญ๋ฆฌ +์žฌ์‚ฐ์„ธ +๋งˆ์ž +ํ”ผ์–ด๋‚˜ +๊ณจ์ˆ˜ +๋ฐฉ์ค‘ +์‚ดํŽด๋ณธ๋‹ค +์‚ฟํฌ๋กœ +์•„์ž +๋กœ๋ฒ„ +๋Ÿฌ์…€ +๊ทธ๋ ค์ง€ +๋ธ”๋ผ์šฐ์Šค +๋ฌธ์ง‘ +๊ณ ๊ตฐ +์ผ์„ +์ง€์–ด +ํ•œ๋Œ€ +ํ—ค๋น„ +๋‹ฌํŒฝ์ด +์–ด์ฉœ +์•„๋ฆ„๋‹ค์›  +์›ํ•  +ํ”„๋ก  +๊ฐ•์›๋žœ๋“œ +๋”๋ƒ +๊ท ์ผ +์•„๋ชฌ๋“œ +๋‚˜ํŒ” +##ck +ํ•˜ํšŒ +##lish +๋˜˜๋˜˜ +์ •๋ชฝ๊ตฌ +๋‚ด๋ฆผ +์ •์‹ ๋ณ‘ +์•„ํŠธํ™€ +์‚ฌํƒ„ +๋ชจ๋”๋‹ˆ์ฆ˜ +SL +๊ฐ€๊ณก +์žฌ์น˜ +์ด๊ธ€์Šค +๋ฌดํŒจ +์ „์ฃผ์‹œ +์ง€๋‚˜๊ฐˆ +์‚ฌ๋ณ€ +##๋ฏธํŠธ +๊ตด์ ˆ +๋„์ฒ˜ +##85 +ํ•™์ƒํšŒ +์•Œ๋กœ +๋‚˜์ž +์กฐ๋ถ€ +๋Œ€ํ•™๊ฐ€ +์†๋ณด +์ง€ํ”„ +๋‹ˆ์ฝ” +๋ชจ๋ชจ +๊ณ ์น  +๋‚ ์•„๊ฐ” +์„์กฐ +์ •๊ฐ• +์žฅ๋‹จ์  +๊ฐ•๊ด€ +ํŒฌํƒ +์ž๊ฒฐ +ํ•˜์ธต +์ž๊ฐ€์šฉ +๋ฆฌํ‹ฐ +๋ฌธ์ „ +๋งˆํ‹ฐ +ํŠธ๋ ˆ์ด๋„ˆ +๋ชปํ•จ +์ง‘์‹œ +์–‘ํƒœ +๋’ค๋”ฐ๋ฅด +๋ค๋ฒผ +๊ฒฝ์‹ค +๋ถ๋Œ€ +ํ˜„์šฐ +๋ฆฌ๋น™ +์œค๋™ +ํˆฌํ•ญ +##๋น„ํžˆ +์‹ฌ๋ฏธ +๋ณ€ํ•จ์—† +์˜ˆ์˜ˆ์˜ˆ +์น˜์›  +ํ—ˆ์ „ +pol +##str +์ธํฌ +##35 +๊ธˆ์ฃผ +์‡ ์•ฝ +AB +##ravel +์Šคํƒ€ํฌ๋ž˜ํ”„ํŠธ +๋””๋”ค +์—ฌ๋Œ€์ƒ +๋ฐฉ๋ถˆ +๋„“ํ˜” +##๋จธ๋‹ˆ์ฆ˜ +##๋ฒ„ํ„ฐ +##ํ•˜๋ฅดํŠธ +๊ณ ๋“  +๊ธฐ๋งˆ +๋ฒŒ๋ ค +ํผ์ง +์ƒ๊ณ„๋น„ +๋ถ„ํ†ต +์—ฐํ•ด +๋ฏธ๋“ค +์ฒด๋… +๊บผ๋‚ธ +์ ‘ํ–ˆ +##uss +ํ”„๋ Œ์ฆˆ +ํšŒ๊ฐœ +์Šˆํผ๋งจ +๋‚œ์ œ +์™€๋ผ +MLB +๊ฒธํ—ˆ +๋ณ‘๋งˆ +์šธ์ฐฝ +๊ธˆ์ˆ˜ +์„ฑ์‹ฌ +์ฐฝ์—…์ฃผ +ํ—ฌ๋ฆฌ์ฝฅํ„ฐ +๋น„ํ˜ธ +๊ฐ€์˜ +์–‘์ฃผ์‹œ +์ž์นญ +์ด์ •ํฌ +๋ฝ‘์•„ +ํ—ˆ๋ง +##์ƒค์˜ค +์ฒœ๋ฌธํ•™์ž +์ผ์›” +๊ฐ€์••๋ฅ˜ +์ „๋ฒ• +์—ญ์™ธ +##๋ˆ„์ด +##๋ฉ์–ด๋ฆฌ +๋‚ด๋ณด๋ƒˆ +ํ‘œ์ œ +๊ฐ€๋žŒ +๋ฐ€์ˆ˜ +์šฐ๋จผ +์›์„ธ +ํ•˜๋‚˜๊ฐ™์ด +๋‚ ๋ฆฐ +์€์„œ +##๋ ˆ๋ผ +์•Œ๋ชธ +์–ด๋”จ +๊ฐ๊ทค +์ฆ๊ธด๋‹ค +๋‘๋“ค๊ฒจ +๋…ธํšŒ์ฐฌ +๋ฒŒ์–ด์ ธ +##ํŽ ๋Ÿฌ +SDS +386 +ใ†๋ณด +์„ ํ•˜ +ํ›„์ถ” +๋„ค๊ฑฐํ‹ฐ๋ธŒ +์ง€์€์ด +์ด์œ ๋กœ +์‚ฌ์˜ +๊ธฐ๋ก๋ถ€ +๋…ธ๋Ÿ‰ +์ฝœ๋Ÿผ +์ด๋ฆฌํ•˜์—ฌ +๋ˆ„์›  +##ํƒ€์˜ค +๊ฑด์„ฑ +๋Š๋ ค +์•Œ์งœ +์œค๋ฆฌํ•™ +์กฐ์šฐ +๋ฐฉ์ง€๋ฒ• +๊ธฐํƒ€๋ฆฌ์ŠคํŠธ +์‹์ค‘๋… +##sych +์ถ”์ˆ˜ +์—ฐ์„ํšŒ์˜ +์‹œ๋Œ +์งˆ์‹ +์–ด์ง„ +##ator +๋ฐ˜์  +์‹œ๋ฆ„ +๊ธ‰์„  +๋ธŒ๋ผ์šฐ์ € +๋ฒ ์–ด +๋…๋ฆฝ์˜ํ™” +ํ•˜์›Œ๋“œ +##๋งํ„ด +235 +์ •์‹ ๋ ฅ +๋ฌด๋„ˆ์งˆ +์˜์˜์ • +Thom +๋ถˆ๊ฒฝ +๊ถŒํ˜ +ํŽ˜์Šค +ํ”„๋ผํ•˜ +๋‚™์„œ +์ •ํ•œ๋‹ค +ํ”ผ์‹ +Global +๋ฒˆ์ ธ +์œ„๊ธฐ๊ด€๋ฆฌ +##MCA +ํ”ผ์นด์†Œ +cod +ํ˜„์•… +๋‚™์  +์‹œํŽธ +ICBM +์ „์ƒ +์ ˆํ˜ธ +๋ถˆ๋ช…์˜ˆ +๋งˆ๋‹ด +PA +ํˆฌ๋ฅดํฌ +์ €์  +๋ณ„๋ฐ˜ +๋‚˜๊ทธ๋„ค +๋ชจ๋กœ์ฝ” +์•ผํ›„ +##๊ต์‚ฌ +ํ™”์ดํŒ… +##ude +##์—ฐ๋ฃŒ +๋˜์ง„๋‹ค +๋“ฑ๋ถˆ +์˜ฅ์‹œ +์“ฐ๋ ˆ๊ธฐํ†ต 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+๋™๊ฐ• +์•„ํ‚ฌ +๋ณ€๋• +##ein +๋ผ๊ฐ€ +ํˆฌํ‘œ์†Œ +##์“ธ์ด +์˜ํ™”์‚ฌ +ํ™˜๊ฒฝ์šด๋™์—ฐํ•ฉ +๋‚˜์˜จ๋‹ค๋Š” +์‚ฌ๋ฌด์ฒ˜ +##์‚ฐ์—…๊ฐœ๋ฐœ +Off +ํ˜•์šฉ์‚ฌ +๋ ˆ์ด์–ด +ํ•™๋ฒˆ +๋ฌด์šฉ๋‹จ +##irst +์—ฐ๋‘ +##ody +์ด์š” +๊ฐˆ๋ฆผ๊ธธ +Che +ํ›คํžˆ +๋ชฉํŒ +ํ—ฌ๋ฉง +๋ถํ•œ์‚ฐ +๋ผ์˜จ +์˜ค์ผ€์ด +๋‚˜ํ†  +์กธ๋ฆฌ +์ง๊ฒฝ +๋ฐœํ–‰์ธ +๋งˆ์ฃผ์ณค +์ฒญ์ƒ‰ +ํŒŒ๋ฒŒ +ํ—ค์–ด์กŒ +๊ธฐ๋ปํ–ˆ +##๋ฐ˜๊ตฌ +๋ชจํ‹ฐ +##๋ฒ„์Šคํ„ฐ +##ection +๋…ธ๋ฉด +ํฌ์„ญ +๋ฐ€๋ž€ +์‹น์“ธ์ด +YMCA +๋ˆ„์šด +์ˆœํ•œ +๋ฌธ์ง„ +๋ถ€์ด์žฅ +๋นฝ๋นฝ +์™ธ์•• +ํŽ˜๋” +๊ฑฐ๋ž€ +ํ•ต์—ฐ๋ฃŒ +์šฐํ‘œ +Good +์ฃฝ์ž„ +์ฐŒ๊ฐœ +์•„์ด๋น„ +์™€ํ•ด +์š”์‹œ๋‹ค +##18 +##์ƒ์ • +์‚ฌ์ƒ +ํŒจ์ŠคํŠธํ‘ธ๋“œ +์‹œํ–ฅ +##ict +์ž์ฒญ +##๋ฏธ์Šค +ํ•˜์ด๋“œ +1901 +๋””ํŽœ +ํŽ˜๋‹ˆ +๊ด‘๋‘ฅ +์ˆ˜๋†“ +๋…ธ๋ธ” +๋Œ๋ฉฉ์ด +ํˆฌ์•ฝ +๊ฑด๋„ +์˜ค๋งˆ +##bs +##๋ถ„ํˆฌ +1903 +๋ฐฑ๋‚จ +์†๋ ฅ +์ž์Œ +์„œ๋Œ€๋ฌธ๊ตฌ +James +๋ถ€์นจ +##ield +##๊ฑฐํ’ˆ +ํ•„๋กœ +์–ด๋ ดํ’‹ +๊ฐ’๋น„ +ํ•ด๋ฐ€ +๋„๋น„ +๋‚˜์•„๊ฐ” +๋ฐ•์ œ +์œค์น˜ +์ˆ˜์•„ +##arm +์„ธ๋„ค +์‚ด๋ผ +์„œ๋‹น +col +##์‚ฌ์ด๋“œ +๋๋‚ผ +1902 +##์ค‘๊ธฐ +๋‹ค๊ฐ€๊ฐˆ +๋ณด์Œˆ +๋‚ดํ‚ค +ํšŒ๊ฐ€ +๋ฌธ์ฐฝ +##์Šคํ†ค +๋ฐํ˜€์ ธ +Fed +LO +๊ต์˜ +์ตœํƒœ์› +๊ฐ๋ง‰ +ํ˜•ํŽธ์—† +๋น ์ ธ์„œ +ํ”„๋กœ๊ทธ๋ž˜๋จธ +์ง€๋‚ ์ˆ˜๋ก +์ด๋งŒํผ +๊ธˆํ˜• +๊น€์„ฑ์ˆ˜ +์‚ดํ•€ +์žฌ๊ฒฐ +๋ฌธ๋ž€ +์—†์•ค +์˜๋ น +##๊ฐ„์ ‘ +##์‚ฌํ•ญ +์ฐพ์•„๋‚ผ +๊ด€์ฐฐ์‚ฌ +ํ’€๋ฆด +์ด์™„๊ตฌ +์ŠคํŠธ๋ผ์ด์ปค +์งœ์žฅ๋ฉด +๋…ผ์–ด +์‹œ๋Š‰ +๊ฒฝ์ฐจ +๋ณ„๋ฏธ +์ง„๊ฐ€ +๋‚ฑ๋‚ฑ +##aby +์ฐธ๋ชจ์ด์žฅ +์ƒ๋„ +๋งˆ์ด์–ด +ํ ์ง‘ +์ด์šฉ๋ฃŒ +##uth +๊ต์Šต +๋ถ€๋“œ๋Ÿฌ์›Œ +๊นœ๊นœ +๋ฏธ๋ฐฑ +์š”๊ฒฉ +##๊ฐ์ƒ‰ +##inal +ํœ˜๋‘˜๋ € +์•Œ๋ฒ ๋ฅด +ํ์‚ฌ +๋น…ํ† ๋ฅด +๋ ค๋‚˜ +๋ฏผ๋ฌผ +์šฐ์Šค๊ฝ +Ev +๊ณค๋‘์„ธ์šฐ +๊ณต๋ณต +ํ™”๊ต +๊ทธ๋ฆฐ๋ฒจํŠธ +์ „๊ธฐ๋ฃŒ +๋ชฉ์ฒญ +ํƒ€์นด +ํŠนํŒŒ +ํŒŒ๋‚˜๋งˆ +์œก์‹ +##ํŒ”ํŠธ +๋‹‰์Šค +์•…์—ญ +##์—ฌ์ • +์Šฌ๋Ÿผํ”„ +์•„๋‚Œ์—†์ด +##๊ฐœ๊ณจ +๋ถ„๋‹ด๊ธˆ +์‹œ์ข…์ผ๊ด€ +์ˆ™๋ถ€ +##๊ฐˆ์ƒ‰ +ํ’€์–ด๋†“ +๋…ธ์›๊ตฌ +์ž”์žฌ +๋ถ์•„๋ฉ”๋ฆฌ์นด +์˜ค๋‹ค +๋‹ดํšŒ +๋ผ์ด๋” +๊ณ ํƒ +๋‘์œ  +์‚ฌ์ € +์—ญํ’ +์กฐ์„ ์‚ฌ +๋“œ์„ธ์š” +์„ฑํ˜„ +๊ฐ์—ผ๋ณ‘ +์ฐจ๋ฆฐ +##๋…„๊ธฐ +##acy +๋ณ‘๋“  +์†Œ๋ง +์กฐ์‹ฌ์Šค๋ ˆ +##ํ”„๋ผ๋…ธ +๋ ‰์Šค +๋ฏธ๊ฐ„ +๊ฐ€๋ฅด์นœ +##๋‚˜๋ฅด๋„ +์ž„ํƒœ +์†Œ๋‚˜๊ธฐ +๋นจ์•„๋“ค +##eau +๋ฌผ๋Ÿฌ๊ฐ€ +์ž์˜ค +##์ข…์ผ +ํฌ๋ฆฌ์Šคํ† ํผ +##์ƒ์Šน +๊ฐ€๋ธŒ๋ฆฌ +์„ ํ™” +ใ†์ค‘ใ† +์•„์ŠคํŒ”ํŠธ +๋– ๋ฐ€ +์•ค๋”์Šจ +##ont +๊ธฐ๋ผ +์‚ฌ์ด๋‹ค +๋‹จํ˜ธ๋ฐ• +๊ทธ๋ ค์กŒ +ํ‘œ๊ณ  +FBI +์„ฑํ˜ธ +์ˆ˜์™„ +##ral +๋ฏธํ•˜ +์ซ“์•„๊ฐ€ +##์Šน์ง€ +##์ง„์šฐ +ํ•ญ๊ณต์šฐ์ฃผ +##ํ—Œ์˜ +TS +๊ฐ๋ช… +ํฌ์ƒ์–‘ +ํ”Œ๋ž˜๋‹› +##์ž์™€ +GTX +์ดํšจ๋ฆฌ +๋„˜์–ด๊ฐ„ +##ํ…Œ๋ฅด๋ถ€๋ฅดํฌ +๊ฐ€๊ฐ€ +๋ฝ๋ƒˆ +์ถฉ์นญ +๊ฒฝ์ข… +๋ฐœ๋Œ€์‹ +ํ—Œ๊ธˆ +##๋ถ€์‚ฌ +##eng +๋“ค์ด๋‹ฅ +์ž์‚ฐ๊ฐ€ +๋Œ์ดํ‚ฌ +๊ทธ๋ฆฌ์šด +๋…ธ๋ฆฐ๋‹ค +์Œ์ƒ‰ +์ฃ„๋‹ค +์ฑ„์ƒ‰ +๋ณด์ˆ˜์ฃผ์˜ +๋ฐ˜๋…„ +##ior +1897 +์ž๊ธˆ๋‚œ +๊ทธ๋ฆฌ์Šค์ธ +์ด๋‚™ +์ „์žํŒŒ +๊ฒฝ๊ตฌ +์‹œ์ถ” +์ •์ +##๋งค์žฅ +๋ฐ”์ž +๋”ฐ์˜จ +์…”ํ„ฐ +ํ”ผํ”Œ +์งˆ๋ ค +๋ฐ˜๋Œ€์ชฝ +์ธ์นญ +๋ฒŒ๊ฑฐ +์Šค๋งˆ์ผ +๋ฐ”๋ž˜ +์‚ฐ๋งŒ +๊ฐ€์ง€๋Ÿฐ +๋””๋ฐ”์ด์Šค +๋ง๋ง‰ +์ผ€์ดํŠธ +ํ˜ˆ์ค‘ +ํ˜ผ์žฃ +๊ธฐ๊ถŒ +์ชผ๊ธˆ +๋ฏธ๋‚จ +์ฐฌ์†ก +์Œ์ ˆ +์ถ”๋ฏธ์•  +์ผ€์ดํ”„ +์–‘์ฒœ๊ตฌ +cont +๋Œ€๋‹น +๊ฒจ์ž +์ ๋ง‰ +์œ ์›์ง€ +ํ๋‹จ +๋š๋”ฑ +์ฆ๋น™ +##๋ถ€๋Œ€ +๋ฉ”์•„๋ฆฌ +์ฃผํšจ +##์‹œ์› +214 +##๋“ญ๋‹ˆ๋‹ค +##๋งฅ์Šค +์‹ ํ˜ธ๋“ฑ +์–ด๋ฒค +์ฐฌ์ฐฌ +๋ผ์ง€ +์ดํ†ต +์˜ˆ๋น„๊ตฐ +์†”์„  +์ปค๋…• +ํ—ˆ๋ฆ„ +##์น˜๋งˆ +์™ธ์–‘ +์ฝ”์ธ +ํ‰๋…ธ +๊ด‘์„  +๋…ธ์ด +๋ฐ•๋Œ€ +์‹ ๋“œ +์‚ผ๋ฆผ +์•ž๋‹น๊ธฐ +์ˆฏ๋ถˆ +๊ฐ•๊ฒฝํŒŒ +์•„์ด๊ตฌ +๊ทธ์„ +๋„ฌ์Šจ +ํ—ˆ๋‘ฅ +##berg +๋ชจ๊ธฐ์ง€ +๊ฐ๋…๊ด€ +ํˆฌ์ž์ฒ˜ +app +##์‚ฐ๋ฉด +##์˜์ง€ +์ž์—ฐ์žฌํ•ด +ํ”ผ๋ณดํ—˜ +์ด๋ป +์ •์œก +์™„์ œํ’ˆ +์˜ฌ๋ ค์•ผ +##ํ† ๋ฏธ +๋ฏธ์ƒ +ํ›„๋ ค +๊ฐ€์—พ +##ํšจ์ง„ +์ž”ํ•ด +offic +์—ฐ๋Œ€๊ธฐ +์ˆ˜์˜๋ณต +##๋ฉ”๋‹ˆ์•„ +##์„๊ธฐ +##ํ”Œ๋ฆฟ +ํ™ฉ์šฐ์—ฌ +๊ณผ์˜ค +ํ™”๋ชฉ +๊ฑฐ๋“ญ๋‚  +๋ˆ๋… +##๋œ๋ฏธ +##์‚ฐ๋ฆฌ +##bert +๋‹ซํ˜€ +์—๋ฐ€ +ํ†ตํ–ˆ +๋ถ„๋ถ„ +์Œ€๊ตญ์ˆ˜ +์ƒ๋ฆฌ๋Œ€ +trans +๋ฒ„ํŒ€ +##๋กœํ”„ +๊ณต๋ถ€๋ฐฉ +460 +์ฒญ๋™๊ธฐ +๋ฌถ์ธ +๋ฐ€ํ +์žกํ™” +๊ฒฝ์‹ค๋ จ +๋น„๊ฒ +์›Œ์‹œ +ํ•ด๋…€ +##๋ฒ„๋ฆด +์ด์Šนํ›ˆ +##์ง€๊ตฌ +##95 +kW +๊ธธ๋“ค์—ฌ +๋ฐ˜๋ถ€ +๋งŒ๋ฃจ +๋ถˆ๋Ÿฌ์˜ค +##์—ฌ๋ฆ„ +##DC +์ž ๊ธด +๊ณ ๋“ฑ๊ต์œก +๋Ÿฐ๋‹ +ํŽ˜๋ฅด๋‚œ +##๋‹ˆ์‹œ +์„ธ์šด๋‹ค +๋งˆํƒœ +๋ถ€๋“ค +์ˆ˜๋นˆ +์—ฐ๋‹จ +ํ•ฉ์„ฑ์–ด +๋“ฑ์‹ฌ +๋ถˆ๋ฅœ +๊ธฐํŒ +๋‚จ์กฐ์„  +์ด๋Œ€ +๋ฌด์ž๋น„ +๋™์‹๋ฌผ +์ทจ๋ก +##์ง€๋ฌผ +๋ถˆ๋งค +ํ•œ์†” +ํšจ์ข… +๋“ค๋ ค์ค€๋‹ค +์ขŒ์ง€์šฐ +์ขŒ์ง€์šฐ์ง€ +๋ฌด๋”์šด +๋ฐœ๋งž์ถฐ +๊ฐœ์ง„ +๋ ˆํŠธ +๋ฏผ์›์ธ +ํ™•์—ฐ +๋ฒ„ํด๋ฆฌ +์†Œ๋‚˜ํƒ€ +##์ค‘๊ฐ„ +Chris +gl +์ถ”ํ–‰ +##์ œ๊ฐ• +๋Œ€ํ•  +๋ฟŒ๋ฆฌ์น˜ +##์ˆœ์œ„ +ํ˜„๋Œ€์‚ฐ์—…๊ฐœ๋ฐœ +๋งค์šด๋ง› +๋ถˆ๋ฉด์ฆ +Don +์‹ ํ˜‘ +์ง„๋‹ค๋ฉด +๊ฒฝ์ˆ˜ +##๊ฑฐ์Šจ +์ž„์˜ +๋ธ”๋ก๋ฒ„์Šคํ„ฐ +216 +WSJ +๋‹‰์Šจ +์ผ์‹  +KBL +๋Œ€ํ•ฉ +๋Ÿฌ๋ธ” +์‚ฌํ–‰ +์œ„์ž๋ฃŒ +์นด๋‚˜ +ํ”ผ์‚ฌ +์ง€๊ตฌ๋Œ€ +๋ชธ๋šฑ +์—ฐ์ˆ˜๊ตฌ +๊ณ ์กฐ์„  +๋กœํŽ˜ +๋ฆฌํ—ˆ +๋นŒํ—ฌ +์•„๋ฅด๋ฉ”๋‹ˆ์•„ +##์‹ ํ˜ธ +์“ด๋‹ค๋Š” +๊ตํ•™ +๋œ๋‹ค๋ฉฐ +์ง€์ •ํ•™ +##๋ฏธ๋ฆฌ +๋…์„  +##ific +์ค‘๊ฑฐ๋ฆฌ +์žฌ์กฐ๋ช… +KOTRA +๋ฒˆ์ฐฝ +์ฃผ์–ด +์Šค์นดํ”„ +๋ฌธ์‚ฌ +##ํ†ก์Šค +๋งˆํ”ผ์•„ +ํ–‰์—ฌ +์—๋„ˆ์ง€์› +์‚ฌ๋“ค์ธ +์œจ๊ณก +์Šค๊ฐ€ +##ally +์•Œ๋ž˜์Šค์นด +##์‹œ์šธ +Bro +์—ฌ์˜์น˜ +๊ธˆ์˜ค +์‹ฌ์žฅ๋ณ‘ +๊ตญ๋ฌธํ•™ +๋ˆˆ์‹œ์šธ +์„œ์ปค์Šค +๊ฐ€ํ‰๊ตฐ +์‚ฐ์‚ฐ +๋’ท์ „ +์ ๋ฆผ +##ํ˜ˆ์•• +๋ชจ์‚ฌ +Net +๋…๊ณผ +๋นจ๊ฐ› +๊ตฌ๊ธ‰์ฐจ +์ฐฝ์‚ฌ +##๊ฒฐ์—ฐ +๋ถ€๋ก +์„ธ๋ฌด์„œ +๋„์ +ํ˜„๋ž€ +์„ ๋• +##๊ทน์žฅ +๋Œ€์˜ +Do 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+Research +๋ช…์ฃผ +์•„์—ญ +์›์ƒ‰ +ํƒœ์˜ +##์„ฑ๋Œ€ +##๋‚˜๊ฐ„ +can +๊ด„๋ชฉ +๊ธฐ์›ƒ +๋ปฃ๋ปฃ +ํ”๋“ค๋ฆด +ํŒ๊ฐ€ +๋ฐฑ์„ค +์—ฌ์„œ๋Š” +1860 +์ง„์—์–ด +๋ณ€๋ณ€ +์˜ค์˜ +์ง•ํฌ์Šค +๋‹ฌ๋ž‘ +๋ฐฑ๋ฒ” +๋ชจ์•„์•ผ +ํ™˜ํ˜ธ์„ฑ +์น ๊ณก๊ตฐ +์๋‹ˆ๋‹ค +์‚ฌ๋ฏธ +์ด์ œ๊ป +๊ตฌ๊ธฐ +##SI +์ธ์†” +์ฐจ์ƒ +์ž์œ ํ˜• +ํ™์˜ +์•Œ์•„๋ณธ๋‹ค +์™ธ์ œ +๊ฐ์ฒญ +๋‹จ์ดˆ +##ํšŒ๊ด€ +๊ธฐ๋‹ค๋ฆฐ +work +์†ก์Šน +์ถ•์ถ• +๊ธฐํ‘œ +๋ง‰๋ฌด +์•„๋ฏธํƒ€ +๊ณ ๊ตฐ๋ถ„ํˆฌ +๋‚˜์ง +##๋ธ”๋Ÿฌ +์ด์ƒ๋ฏผ +์ œ์–ธ +##์‹œ์•ˆ +๋ฒ•์ „ +๋Š˜์–ด์„  +์•ฝ์ œ +์ง•๋ณ‘ +๋‹ค์‹œ๋งˆ +๋ง‰๋ฌด๊ฐ€๋‚ด +๋ง๋‹จ +๋ณผํ„ด +์ ธ์š” +์›์„ธํ›ˆ +๋ฌดํ‘œ์ • +์•™์ฝ”๋ฅด +##๋“œ๋ž˜ +##๋ณดํ˜ธ +๊ตฌ์ „ +์œ ํƒ€ +์€๋‘” +์ฃผ์„ธ +ํด๋ผ๋ผ +์ซ€๋“ +์ •์„ค +CMA +๋ถ€์‚ฐ๋ฌผ +์Šคํ”„๋ ˆ์ด +๋™์ฐฝํšŒ +##๋ Œ์Šค +246 +๋ฐœ์ทŒ +์—๋ฆฌ +##๋ฒ„๋ฆฌ์ง€ +๋ฌด์šฉ์ง€๋ฌผ +์ž๋ฌผ์‡  +Win +๋‘๋“œ๋ ธ +๋ชฉ์ˆ˜ +๋ฐฉ๋ž‘ +ํฌ๋ณ‘ +๋ธ”๋Ÿฌ +์‹ฌ์ฒญ +##96 +์ตœ๊ณ ์  +1870 +๋ฏธ์ผ +์‚ผ๊ฑฐ๋ฆฌ +์šฉ์„  +์ด์ถ˜ +sub +์˜ค์ฆˆ +๊ฒฉํ•˜ +##๋†์‚ฌ +์•‰ํžˆ +von +์น˜์ˆ˜ +ํ˜ธ์šฐ +ํ™˜๊ฐ +ํŽ„ํŽ„ +##rip +ํ”„๋กœํ† ์ฝœ +๋ฏธํŠธ +##els +์œ„์Šค +๋‚˜์ผ +๋ง˜๊ป +##๋ผ์น˜ +์ธ์‚ฌ๋™ +FDA +๋ง๋ ธ +๋“์ธ +ํ—›๋˜ +์ฒด์ง€๋ฐฉ +ํ’๋…„ +ํ—ˆ์šฐ +์˜ค๋‘๋ง‰ +๋ˆ๋ฒŒ์ด +์ด๋ช… +##๋›ฐ๊ธฐ +์ผ๋ฅ  +์œค์ง€ +##๋ชจ๋‹ +์ง€ํ•˜์‹ค +์ดˆ๋ฏธ์„ธ +์ž์žฅ +์น˜์ด +##๊ฑฐ๋ž˜์†Œ +Cap +ํ–‰์ง„๊ณก +๋™์ง„ +๋ ˆํฌ์ธ  +์ง„๋‘ +##์ค€ํ˜ +๋ฐฑ๊ณผ์‚ฌ์ „ +ํƒ•์ˆ˜์œก +๋ถ์ƒ +์„ ์•… +์žํ +๋งž์ถฐ์•ผ +ํ†ฑ์Šคํƒ€ +์ œ๊ฐ๊ธฐ +๋– ๋งก +๋‚ ์•„๋‹ค๋‹ˆ +์นธ๋ง‰์ด +์ด์„ ๊ฑฐ +์•„์•… +์ง€๋‚œ๋‚  +์šฐ๋„ +์ฒ˜๋‚จ +๋Œ์•„๋‹ค๋…” +์œˆ๋“œ +์ž๋ฉฐ +๋Œ๋ณ€ +ํ†ต์พŒ +ํ•˜์—ผ +์˜ค๋ฏธ์ž +##ara +rep +์€ํ‰๊ตฌ +๋‚œ๋‹ค๊ณ  +๋ฌดํ˜ +์œ ๊ธ‰ +##์น ์„ฑ +255 +์ถ”์ง•๊ธˆ +ํŠ•๊ฒจ +##๋คํ•‘ +๋น„ํ‚ค๋‹ˆ +๊ตฌ๊ฒจ +๋ฌธ๋‹ต +์ง€ํผ +ํ›„๋ฐœ +๋ชจ๋ฆ„ +์—ฌ๋… +์ฐฝ๋…€ +๋ฌด๊ถ๋ฌด์ง„ +์ฃผ๋ˆ… +ํ”ผ์ฒ˜ +๋ฏธ๋‚˜๋ฆฌ +๋ฐฑ๋งˆ +์ž ๊ทธ +๊ทธ๋งŒ๋‘” +##ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ +์˜ˆ์ฐฌ +์˜ตํ‹ฐ +ํ‚คํ”„ +##๋„คํŠธ +๊ฒจ๋“œ +์•ˆ๋Œ€ +์ •์œ ๋ผ +์–Œ์ „ +ํ์œ„ +๋งˆ๋ฅด์ฝ” +์œ ๋ฆผ +ํ˜ˆ์ „ +์ฒ ๋ถ„ +##๋ดํ‹ฐํ‹ฐ +์”จ์”จ +##์ €๊ฐ€ +๋…ธ๋ฃจ +๋“ค๋œฌ +์•„์ด๋ดํ‹ฐํ‹ฐ +๋”๋ผ๋Š” +์™ธ์žฅ +์˜ค๋ฆญ์Šค +ํ•ฉ์ณ์„œ +##ํ‹ฐ์›€ +์ฐจํ›„ +##๊ตญ์‚ฌ 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+ํœดํ•™ +์•„๋งŒ +๋‚จ์นœ +๋ฐ์ด๋ธŒ +๋ฝ€๋ฝ€ +๊ทผ์† +๋ฌธ์ค‘ +์—Š๊ทธ +ํ˜ธ์กฑ +##์šฐ์˜Œ +๊ด€์ฐฐ์ž +์ฒœ์ˆ˜ +๊ฐ„๋Œ€ +๊ฐ•๋ฏผํ˜ธ +์šฐ์น˜ +์ˆ˜ํ™” +๊ท€์–‘ +์ „์—ด +ํ•ด์ง +##๋ฌธํ™”ํšŒ๊ด€ +์ฒญ๋Ÿ‰๋ฆฌ +ํˆฌ๊ฒŒ +์–‘๋…์žฅ +์ค‘๋ฌธ +๊ด€๋ฆฌ์‚ฌ +##๋ชจ์ž +๊น€๊ทผ +์„์ขŒ +RF +Red +๋ ˆํ„ฐ +์žฌ๋ ฅ +ISA +์ œํšŒ +ํ”„๋ฆฌํ‚ฅ +์ผ๊ธฐ์žฅ +##ํƒ€๋‹ˆ +์ฝœ๋Ÿผ๋ฒ„์Šค +์šฐ๋ฒ„ +ํ˜„๊ธฐ์ฆ +๊ฑฐ๋งŒ +์…จ์ž–์•„์š” +์นผ๋นˆ +๊ฐ•ํƒœ +ํ•ด์ค˜ +์›๋น„ +์บ๋”” +๋ชฝ๋‘ฅ์ด +์น˜๋Ÿฌ์กŒ +๋ณผํŽœ +์—ฐ์•” +์ „์–ธ +๊ตฐ๊ด€ +##๊ผญ์ง€ +์„ธ์ข…๋ฌธํ™”ํšŒ๊ด€ +PEF +ํŒ๋ณ„ +ํŒฌํด๋Ÿฝ +์œ ํ’ˆ +##๋ฐฉ์œ„ +์ค€๊ณต์‹ +์†Œ์ง€์ž +์ ˆ๊ธฐ +๊ฒฐ์ „ +๊ท€ํ‰์ด +##๋ฆฐํŠธ +์ง€๋ฐฐ์ธ +๋‹์šฐ +์• ํƒ€ +์—…๋กœ๋“œ +##์กฐ๋ถ€ +Bu +Up +##71 +ํ”ผ๋ถ€์—ผ +์–ด๋ฒค์ ธ +๋ถ์•„ํ”„๋ฆฌ์นด +์„ธํƒœ +์ฒ˜๋งˆ +์›ํฌ +ํˆฌ๊ฒŒ๋” +Black +์•”์ดˆ +๋„๋ผ์ง€ +์œ ๋‹ˆ์˜จ +์—์–ด๋ฐฑ +๊ธฐ์–ด์ด +ํ„ฐ๋œจ๋ฆฐ +๋นˆํ˜ˆ +์•„๋ณด์นด๋„ +์š”๊ตฌ๋ฅดํŠธ +NBC +์บ์ŠคํŠธ +์•„๋‹ˆํ•œ๋‹ค +##์ˆ˜๋ฒ” +์ž„์ฐฝ์šฉ +๋ณธ๋”” +##ํƒ€๋กœ +์˜๊ฒฌ์„œ +์˜ˆ์„ ์ „ +##๋‹ˆ์•ˆ +์Šค๋‹ˆ +์ •๋žต +๊ถŒ์œ„์ž +๊ฐ์ถ• +๋‚œ์ƒ‰ +Service 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+##์œ ์ง„ +ํ™ฉ์ง„ +์ง๊ฐ„์ ‘ +##EX +์–ด๋“œ๋ฒค์ฒ˜ +์‹œ๋„๋Ÿฌ์šด +์ •ํ‰ +๊ฒฐํƒ +๋ธ”๋ž‘ +์นœ์ˆ˜ +์ˆ˜์—…๋ฃŒ +์—ฐ์Šต์ƒ +ํœฉ์‹ธ์ธ +##ํด๋กœ +์„ฑ์•…๊ฐ€ +##49 +์•ผ์ž +์ฐป์ž” +AFP +๋…ธ๋ชจ +ํŽ˜๋ผ +##์–ด๋จธ๋‹ˆ +##36 +##์„ ์ง€ +๋Œ€ํ˜๋ช… +์ฝ”์Šค๋ชจ์Šค +์ด์ฃผ๋…ธ +##์…ˆ๋ถ€๋ฅดํฌ +์™€์ด๋ฒˆ์Šค +๊ตญํŒ +์–ด์˜ +์ค‘์˜์› +๋ณธ๋‹ค๊ณ  +์„ ์„  +ํ™ฉํฌ +์žฅํŒ +๋‹ค์ณ +๋‹นํ—Œ +ํ—ค๊ฒŒ +๋…ธ์—ญ +์ˆ˜์ž” +ํŒŒ๋…ธ +๋ฐœ๋ˆ +์†Œ๋ฆด +ํ„ฐํŠธ๋ ธ +IV +๋ฝ‘ํžˆ +์œ ํ˜ˆ +##orge +Str +##๋ƒ‰๋ฉด +Air +์„œํ•‘ +ํ•ด๋™ +๋ฐ”๋ฅผ +์œค๋™์ฃผ +##์†Œ์‹œํšจ +Thomas +๋ฌธ์ง€ +##์Šคํ† ์ด +Sim +์‹์ „ +๊น”๊น” +์ตœ์ˆ˜ +๊ณก์ธ +๋‚œ์‚ฌ +๋ณด๊ตญ +์›”ํ„ฐ +๋นจ์น˜์‚ฐ +๊ฐ์‹ +##๋ณด์ฆ๊ธฐ๊ธˆ +๊ต๊ถŒ +๋“œ๋ฆฝ +์„๊ตด +์นดํ†จ๋ฆญ +๋ฉ์ด +์–ต์ƒˆ +์„œ์–‘์ธ +๊ณต์†Œ์‹œํšจ +๋งจ๋ฐœ +##anian +##ological +ํ—Œ๋ฐ +์šฉ๋ฌธ +์ฐจ๋ถ„ํžˆ +์‹œ๊ทธ๋„ +๊ฐœํฌ +์œ„์ฆ +##๋‘๊ทผ +๋‹จ์‹œ๊ฐ„ +์ ˆ์ ˆ +์ข…์†Œ๋ฆฌ +##์ŠคํŠธ๋กœ +์ธํ’ˆ +๋ฒ„๋Ÿญ +ํšŒ๊ณ„์‚ฌ +##ucation +๋™์žฅ +๋‹ค์ด๋…ธ์Šค +์„ญ์ • +๋‚จํ•˜ +์›์ œ +์‹ ์ถ˜๋ฌธ์˜ˆ +ํํ +๊ดด๋‹ด +๊ตฌ๋ณด +##์ƒ์˜ +๊น€๊ตฌ๋ผ +ref +๋งˆ์˜ +ํŠ€๊ธฐ +ํ›„์›ํšŒ +ํ”„๋ผ์ด๋น— +์ปจํ…Œ์ด๋„ˆ์„  +๋ฃฉ์…ˆ๋ถ€๋ฅดํฌ +have +์ „๊ณก +๋Šํ‹ฐ๋‚˜๋ฌด +๋‘๊ณ  +๋Œ€์šด +๋ฌด๋ฆ‡ +WWE +๋ˆˆ๊ฐ +์ฃผ๊ฒฝ +Colle +์กฐ์„ +๋ฌดํ„ฑ๋Œ€ +๋งฅ์•„๋” +๋ณดํ˜ธ๊ด€ +ํ˜„์ƒํ•™ +๋ฌด์„œ์›Œ์„œ +๋ธ”๋Ÿญ +##way +##๋ž‘์Šค +222 +ํ˜ธ์‹ค +##37 +๋ฐ•์—ฐ์ฐจ +๊ทธ๋žŒ +๋‚˜๋กœํ˜ธ +์ธ๋„์ฃผ์˜ +์‚ผ์ผฐ +ํ•˜์—ญ +ro +๊ฐ€๊ตฐ +๋ด‰์‚ฐ +์ž ์ž… +this +ํ•˜์—ผ์—†์ด +๋‚™์–‘ +์˜ค๋” +์˜ท๊ฐ +๋Œ€์„œ์–‘ +๋ฌด์  +๋ฆฌ๋””์•„ +๋ฒ ๋ฅธ +ํŠน๊ณต๋Œ€ +Ibid +๋‹จ์ž +์‹ ์Šน +##ํŽœํ•˜๊ฒ +๋ฆฌ์šฐ์Šค +์ตœ๋ช… +##์ž‘์ „ +##๋„Œ์Šค +์†Œ๋Œ€ +๋กœ์ €์Šค +ํ˜ธ์ฐŒ +์šฉํ•ด +์ง„ํ”ผ +##์—ฐ์„ค +##๊ณ ์ƒ +##ux +##PR +๋‹ค๋ฆผ +##๋ถ€๋žด +##๋‹ค๋ž— +๊ณ„๋žต +์ฝ”ํŽœํ•˜๊ฒ +๋ผ์ณ +##๋ฐ”๋ผ์ง€ +๋„ค์ž„ +๋ ˆ๋ฒ„๋ฆฌ์ง€ +์›๋ฐ˜ +ํƒœ๋ฐ˜ +ํŠ€๋‹ˆ์ง€ +##์šฉ์ง€ +Park +์ฐจ์ฃผ +์ค‘ํ‡ด +๋„๋‚˜ +๋ฐ€๋ ค์˜ค +๋’ท๋ฌธ +์†Œ๋‹ค +ํŒจ๊ฑฐ๋ฆฌ +At +์Ÿํƒˆ +์˜ˆ๋น„์—ญ +๋ณด์‚ดํŽด +##eek +๋‘๊ณ ๋‘๊ณ  +๋งŒ์œ  +์‚ฌ๊ฒฉ์žฅ +๊ณต์˜ˆํ’ˆ +๋ถ์„œ๋ถ€ +์ž์˜์‹ +ํƒ€๋ฆฌ +๋ฒŒ์–ด๋“ค์ธ +๊ดœํ•œ +๋„์‚ฌ๋ฆฌ +Mo +๋Œ๋‹ด +##๊ตฌ์กฐ +๊ณ ํ’ˆ๊ฒฉ +๋””์ œ์ด +์„ค๊ณ„๋„ +๋…ธ๋Ÿ‰์ง„ +##87 +3600 +์•”๊ฐํ™” +๋„๊ธˆ +์ž…ํ•ญ +์†”์„ ์ˆ˜๋ฒ” +๋ชฉ๋œ๋ฏธ +๊ณ ์šฉ์ฃผ +๊ธฐ๋„ค์Šค +์ฐจ๊ฐ +ํ—ค๊ฒŒ๋ชจ๋‹ˆ +๋ณ„๋น› +์—ฐ๊ฐœ +236 +๋ฌดํ˜‘ +์ ๋ฆฐ๋‹ค +๋‚˜์˜ฌ์ง€ +์˜ํ™”์ธ +##iter +3300 +์ €์ž‘๊ถŒ +ํ’์กฑ +ํž์Šค +##์žฅ๋‚œ +ํƒ€์Šค +##IL +๋‹ค๋ผ +๋ฑƒ์‚ด +๋ฆฌ์‚ฌ +##bps +๋ฏธ์ˆ ๊ฐ€ +์žฅ๋ฐ‹๋น› +580 +ํŒ๋ณธ +์ƒดํŽ˜์ธ +##๋ž‘๋“œ +๋ฒ ์ด์ง +Ap +์„ฑ์ฐฌ +์ž ๊ธˆ +๊ทผ๊ฑฐ๋ฆฌ +๋ฉ˜ํƒˆ +์„ฑ๊ด€ +์˜ค๋šœ๊ธฐ +์–‘์‹์žฅ +๊ฐ•๋ฆผ +๊ธˆ์น˜ +์ด๋ถ€ +##์‚ฌ๊ด€ํ•™๊ต +ํ™ฉ์˜ +VOD +์ˆ˜ํ–‰์ž +๋ถ€๋žด๋ถ€๋žด +์ฑ„๊ด‘ +Cons +##๋ฉ”์ด์นด +์žํŒ๊ธฐ +๊ณผ์ˆ˜์› +๊ตญ์ธ +๊ดด๋กญํ˜” +ootd +์ƒ๋Œ€ํŽธ +์œผ๋ฃจ +๊ฑธ์Œ๊ฑธ์ด +์ƒˆ๋Œ +์‹ ๊ฒฝ์ฆ +์œ ๋ฏธ +๊ฐ€์†๋„ +์‹์ž์žฌ +์‚ฌ๋‹จ์žฅ +์‚ฐ์‚ผ +##ki +๊ฐ€์ฐจ +๊ฐ์–‘ +์–ด์„œ์š” +์ฒœ๋ฐ• +##์ €์ถ• +Bas +๋‹ค๋˜ +์•„๋ธŒ +์„ ๊ฑฐ์ผ +##์‹ ๊ธฐ +๊น€๊ด€์šฉ +๋งค๋„๋Ÿฌ์šด +ํ˜ธ๋Ÿฌ +์–ด์„คํ”ˆ +์žฅ์ƒ +์‚ดํŽด์•ผ +##iki +๊ทธ์œฝ +์ฃผ๋ง‰ +๊ณ ์‚ฌ๋ฆฌ +๊ทธ๋ฆฌ๊ตฌ +๋‚ด๊ฒ +๋จธ์Šด +car +ํ•™์˜ˆ +ํ—Œํ„ฐ +์šฐ์„ ์ฃผ +๋™๋Œ€๋ฌธ๊ตฌ +๊ฝƒ๊ฒŒ +์•ž๋จธ๋ฆฌ +##wo +##๋ฏน์Šค +๋„˜์–ด์˜ค +๊ผญ๊ผญ +๋ฒผ๋ฃฉ +๋น„์ข +๊ฑธ๋ ค์„œ +๋™๊ฐ‘๋‚ด๊ธฐ +์ตœ์ „๋ฐฉ +ํ…Œ๋„ˆ +โ”€โ”€ +๋–จ์–ด๋œจ๋ ธ +๊ด‘๊ตฌ +๋ถ€๊ฒฝ +์„œํ‰ +์— ๋งˆ +##91 +##๊ฒจ์›Œ +๋ถ€ํ’€๋ฆฌ +๋Š๊ปด์ ธ +##๋ณต์ง€ +์ฃผ์ฒด์‚ฌ์ƒ +๋†์ˆ˜์‚ฐ +ใ†์™ธ +๋‚˜์„ฑ +์˜€์—ˆ +##HA +์—ฐ๋ฐœ +##์–‘์ด +##์นด์ฟ  +๊ณ ๋”• +์ˆœ๊ต์ž +๋…ธ์ƒ +์กฐ๋ฐ˜ +์ง€ํ˜„ +์Œ๋ž€๋ฌผ +##76 +๊ณ ๋† +๊ฑฐ์Šค๋ฅด +์—ญ๋ฅ˜ +ํ˜„์•„ +Mic +๋ฒก์Šค์ฝ” +๋ถ€๋Ÿฌ์ง„ +์‹œ์›” +์—„์—ฐ +ํฌ์—ด +์ž„ํ•ด +ํ—›๋œ +์ค€๊ฑฐ +##์„ ๋ฏธ +๋งˆ๋ฅด์…€ +๋ฌดํ„ฑ๋Œ€๊ณ  +ใ†์ง€ +๋ชจ์ž์ดํฌ +์ด๋ฆ„๋‚œ +์„ฑ์šด +๋“ค์ด๋ฐ› +๋ฉด์ฃ„๋ถ€ +๋ฏผํ™” +๋ฐฑ๋ฐœ +๊ฐ‘์ž‘์Šค๋ ˆ +์ •๊ฐˆ +๋”ฐ๋Œ๋ฆผ +์ฃผ๋จน๋ฐฅ +๋ณด์ฒญ +๋น„๋…ธ +ํ•ด์•ˆ๊ฐ€ +๊ฐ„์‚ฌ์ธ +##hys +ํŠน์ˆ˜๊ฐ• +๋„๋Ÿ‰ +๋ฏธ๋…ธ +Society +๊ฐ‡ํžˆ +๊ณจ๋Œ€ +์กฐ๊ฐ• +๋ถ„์ˆ˜๋ น +์œ ๊ฒฝ +๊ฑฐ๋ฒ„๋„Œ์Šค +๋ ˆ์•„ +์š”์  +gre +๊ฐ“๋‚œ +๋นš์–ด์ง„ +์žฌ์˜ +๋ธŒ๋คผ์…€ +์‚ฐ๋‹ค๊ณ  +ํ™ฉ๋ณด +์ œ์„ค +ํ• ๊ฒŒ +##๋ถ€์ง€ +์šด๋™ํ™” +๋ฒ…์ฐฌ +์ด๋ฐ” +์žฅ์‹ค +๊ฐœํšŒ์‹ +์›Œ๋Ÿฐ +##์‹œ๊ณ  +๋ถ€๋”ช์ณ +์‹œ์‚ฌ์  +๋ฌด์ธ๊ธฐ +๋’ค์„ž์—ฌ +##84 +์ƒˆ๋กœ์›€ +์ƒํŠธ +์ฝ˜๋„ +์•„์†Œ +๋“ค์–ด์˜ฌ๋ฆฌ +๊ตญ๋นˆ +##๋ฐœ๋ ฅ +๋Œ€์ž +๋ณธ์ „ 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+์—ฐ์„ค๋ฌธ +๋‹ค๋ฐฉ๋ฉด +##๋ถ€์‚ฐ +๋ฏธ๋„๋Ÿฌ์ ธ +๊ทผํ™ฉ +##ํ”Œ๋กœ +๊ฑธ๋ ˆ +์ถœ๋‘ +##์ฒ˜์น˜ +๋…๋ฐฑ +ํ™€๋ชธ +์Šนํ•ฉ +reg +์ฃผ๋ชฝ +์‹ ์นธ์„ผ +๋ฐ•์ˆœ +์ž„๋งˆ +๋•์„ฑ +๋ฐ˜๋ฐ”์ง€ +๋ฐฐํšŒ +์‹ฌ๋ฆฌ์ „ +์ƒ‰์†Œํฐ +love +๋‹น๊ท€ +์ €๊ธˆ +ใ†์žฅ +๊ณ ๊ธฐ์•• +๋ฐ”๋น„ํ +๋ถ€๊ด€ +์–ด์ • +๊ธ‰์‚ฌ +๋ด‰์ œ +๋ถˆํ•ฉ๊ฒฉ +์œ„๋‹ˆ +์˜์ ‘ +๋‚ญ์†ก +์ฒด๋Œ€ +##์Šค๋ฆ„ +๊ณ ํ˜ˆ์•• +์Šค๋‚ต +##์ €๋ž˜ +๋ชจ๊ธฐ์—… +##๋ฐ”์•ผ +์˜์ƒ +##์—๋„ˆ์ง€ +ํ•ด์šด๋Œ€๊ตฌ +๋”์ƒต +ํ•œ๊ธธ +##67 +ํ™ฉ๋„ +์—ฐ๊ธˆ์ˆ  +์—ฌํฌ +์ด์•„ +ํžˆ์Šคํ…Œ +##๊ณ„์„  +๊ธฐ๋‹ˆ +์ฝ”์Šคํƒ€๋ฆฌ์นด +์• ์Š๋ฆฌ +๋ฐฐ๋ถˆ +์†ก์œ  +##ํŒŒํ‹ฐ +##์—์ด๊ณ  +๋’ค๋Œ์•„๋ณด +Christian +๋ฑƒ๊ธธ +##๋””ํ‘ธ์Šค +##ํŽ˜์ŠคํŠธ +๋’ท๋‚  +์ˆ™์‹ +๊ณ„๋ชฝ์ฃผ์˜ +๋’ค๋กœ +์ถ”๋Œ +ํ—ฌ์‹ฑ +ํŠน์งˆ +์‚๊ฑฑ +์†ก์ค‘๊ธฐ +##์ŠคํŠธ๋ฆฌ +๋ˆˆ์น˜์ฑ„ +์ƒŒ๋””์—์ด๊ณ  +์ „์‚ฌ์ž +์ถœ๋ชฐ +์ฝ”๋‚œ +์‹คํƒ„ +์˜ค์ด๋””ํ‘ธ์Šค +##์›Œํ”„ +๋กœ๋นˆ์Šจ +ํ™€๋Œ€ +์›€์ง์—ฌ์•ผ +๊ฒฉ์–ธ +์—์Šคํ‹ฐ +์ง•๊ฒ€๋‹ค๋ฆฌ +๋Œ€ํ•ฉ์‹ค +##63 +๋“ค์—ฌ์˜ค +๊ฑด๋‹ด +๋šซ๋ ค +๋ƒ๊ตฌ +๋„์‚ด +##gl +George +ํŒŒ์ปค +ํ™์„ฑํ” +instadaily 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+##ush +๊ท“๊ฐ€ +์‚ฌํฌ +๊ฐˆ๋ž +๋‹ค๊ตฌ์š” +์ž…ํšŒ +ํ•ธ๋”” +##ํ™€๋ฆ„ +์ตœ์ผ +๋…ํ•™ +์ถ”์‹ฌ +๊ฐ–์€ +๋‚จ๋™๋ถ€ +์˜๊ฒฝ +##ao +##ari +510 +์ฒœ์  +์†์•„๊ท€ +๋ชฉ๋ จ +๋น—๋ฐฉ์šธ +์–ด์งˆ +๊ผญ๋‘ +์ˆœ์ต +์ฐจ๋ ค์ž… +์ขŒ์žฅ +##๋ฌผ์‚ด +##์ฟผํ„ฐ์Šค +์–ด๊ธด +Sw +์˜ˆ๋ฌผ +๋ƒ๋ฉด์€ +๊ธ‰๋ฌผ์‚ด +์•„๊ตฌ +##์Šค์œ™ +##ulti +Psych +์™ธ๋™ +์‹ ๊ณ ์ž +์‹ฌํ•ด์กŒ +์ตœ์น˜ +ํ‘ธ์กฐ +์ถ”์ฒœ์„œ +ํ™ฉ์ƒˆ +##๋ธŒ๋กœ +๋‚ด๋น„์น˜ +์ ์žฅ +224 +ํ™”๊ฐ•์•” +##๋Š๋Ÿฌ๋ฏธ +##์ธ ํ•˜์ด๋จธ +๋ถˆ์‘ +์ƒค๋ธŒ +ํ™˜๋ฉธ +##์ง€๊ธ‹ +๊ฐ€๋ ค์›€ +์ฐŒํ‘ธ๋ ธ +๋…ธ๋ฅธ์ž +ํ•ฉ๊ธˆ +๋ ˆ์ธ๋ณด +##ํด๋ผ +##ental +๊ฒฝ์šด +์•ผ๊ฒ ๋‹ค +SG +์†Œ์˜ +์ฒด๋กœ +ํ€˜์ŠคํŠธ +ํฅ๋• +##์–ด๊ฐ€ +345 +๋ชจํ‹ฐํ”„ +์˜๋กœ +๊ธฐํŠน +๋ฆฌํžˆ +๋งŒ๋งŒ์ฐฎ +์ง€๊ธ‹์ง€๊ธ‹ +๋„์น˜ +์ถ”์•™ +๋ถˆ๋Ÿฌ์•ผ +##une +์—ญ๋ชจ +ํ•œ์ž์–ด +๋งˆ๊ณ  +๋ฐ˜์ผ +ํ†ต์˜์‹œ +๋…์„ค +์ค€์œ„ +ํƒ•ํ‰ +์•Œ์ธ ํ•˜์ด๋จธ +ํ’์‚ฐ +ใ†ํ˜„ +๊ฐ€ํ†  +๋ฏผ๋‚ฏ +๋น›๋‚ด +์ƒํฌ๋ฆผ +์ง€๋ฐฐ์ธต +์กฐ์ง€ํ”„ +์œ ๋น„์ฟผํ„ฐ์Šค +์—ญํšจ๊ณผ +์œ„๋ฌธ +๋งน์ฃผ +์ž˜๋ผ์„œ +์„ญ๋ ต +๋ถ€๋‹จํžˆ +๋ฏผํ˜ +ํฌ๋‚˜ํฐ +์†ก์ˆ˜ +์•„ํ 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+Technology +์ˆœ์ฐฝ +์•ˆ๋‚ด์„œ +๋‚จ์ƒ +ํ‘œํ•˜ +ํ•˜๊ฐ +๋‹ˆ์ฝœ๋ผ์Šค +##์Šคํ…Œ์ด +์ฒญ์ง„ +Mil +์ง€์ฐธ +ํฌ๋ฆฌ์Šคํ„ธ +์žฅ๋™๊ฑด +๊ฐ์‚ฌ +์‹ญ์ด +์•„์›ƒ๋ › +๋ฐ›์•„๋“ค์—ฌ์กŒ +์•„๋ฐ +๋ง‘์Šค +##๊ฒŒํŠธ +##ini +๊ฒŒ๋†ˆ +์ž์—ฐ์‚ฌ +์บ ๋ฒจ +##ํ˜€์„œ +์–ด๋ฃจ๋งŒ์ง€ +๊ถŒ์„ฑ +##ka +##์ฒœํ›„ +์†กํ˜œ +West +์˜ฌ์ง€ +์ดŒ๋ฝ +๋“œ๋ ˆ์‹ฑ +๋™๊ทธ๋ผ๋ฏธ +๋žœ๋ค +์•„๋ฆฐ +๋ฝ€๋กœ๋กœ +์‚ฌ๋ง๋ฅ  +๊ณค์š• +๋‹ค๋น„๋“œ +์Šฌ๋ฉฐ์‹œ +๊ถŒ๋Šฅ +##๊ณ„๋ฆฌ +๊ตฌ์™€ +์ง€๋ ์ด +๊ทน์žฅํŒ +์‚ฌ์Šค +์ˆจ๊ฒผ +์ง„๊ตฌ +ํ™˜๊ฐ‘ +์„ธ๋ผ +์น˜์šฐ์น˜ +##๋‹น์„  +##๋ฒ„๋ฅธ +stud +๋ง์„ค์˜€ +์‹œํผ +์ ๋ ค +ํฌ์›Œ๋“œ +์™ธ๊ฐ€ +ํ•ดํ•™ +๊ฐœ๊ฐ„ +์—๋ด +์ข…๊ตญ +ํ™ฉ์„  +๋“œ๋ฆฌ๋ธ” +๋ฐ•์„๋ฏผ +์„œ๋ ค +##ason +์•ผ์‹œ์žฅ +๋ชฉ๋„ +์ด์ฐฝํ˜ธ +๋ช…์–ธ +์œ„๋ น +##๊ฐ๊ฐ +์ธ์žฅ +์ฒ ๊ธฐ +์กฐ๋ฆฌ๋ฒ• +์ „๊ตฐ +##๋กœ์›  +์€์ƒ +Bank +##๋ถ€์ ˆ +์‚ฌ๊ณต +์•”๋ชจ +๊ณ ์†ํ™” +๋ฐฉ๋ฐฐ +์–ด์ข… +์—ฐ๋งค +๊ณต๋ฐฉ์ „ +##๊ณ„์‚ฐ +์ค‘๋Œ€์žฅ +๊ด€ํ˜„์•…๋‹จ +์‹œ๋ณด +ํ†ต์† +TEST +๋ณด๋ฐฐ +์ฐํžˆ +cons +ํŠน์ข… +229 +239 +์†Œ๊ตญ +ํ’€๋ ค๋‚ฌ +๊ฐ„ํ–‰๋ฌผ +๋‹ฌ๋ผ์ด +๋ธŒ๋žŒ +์ตœ์ƒ๊ธ‰ +1868 +๊ทน์ง„ +ํœ˜๋‚ ๋ฆฌ +๊ณต๊ณต์—ฐํžˆ +760 +##์—ฐ์ˆ˜ +์ธ์‚ฌ์ด๋“œ +##ํ•˜์ด์˜ค +๊ตฌ์Šคํƒ€ +์ €๋‹น๊ถŒ +์ŠคํŒŸ +๋‹ค๋นˆ์น˜ +์™ผํŽธ +##OM +์˜ฌ๋ฆฌ๋ฒ„ +๋ ˆ์ธ์ง€ +##๋‚˜๋„ +##iver +ํ™”๊ธฐ์• ์•  +๋–จ์ณค +ํ•ตํƒ„๋‘ +##์‹œ์ค‘ +##์€๋•€ +๊ฐ์ •ํ‰๊ฐ€ +๋Œ€์ฒฉ +์„ธ๋‚œ +1592 +๋ฏธ์™„์„ฑ +๋™์ˆ˜ +์„ธ๋ ˆ +ํ˜„์ง„ +##OA +๋ผ๋Š”๋ฐ +์ •๋ถ€์•ˆ +๋Œ€๋Œ€์žฅ +๋ฒ ์ด๋น„๋ถ +life +๊ฑฐ๋ ค +์˜ˆ์กฐ +์œ ๋…„๊ธฐ +๋…ธ์•ฝ์ž +ํ˜ธ๋“ค๊ฐ‘ +##๋Œ€์›๊ตฐ +ํฌ๋ฆฌ์Šคํ‹ด +Great +์‹์€๋•€ +##๋ฐ”์‹œ +์—ฌ์ œ +ํ†ต๋‹ญ +๊ธˆ๋‚จ +##์ง€๊ฒ +๋งˆ๋„ค +๋ถ„๋ž€ +##by +ํ…Œ๋Ÿฌ๋ฆฌ์ฆ˜ +๋ณผ์…ฐ๋น„ํ‚ค +์ƒ๋… +ํŒŒ๋“œ +์ถœ์—ฐ์ž +์ด๊ฒจ๋‚ผ +๋งค์šดํƒ• +์นด๋ฉ”๋ฃฌ +๊ฑด์žฅ +์ด๊ฐ๋… +##ano +๋ฌด์•ˆํƒ€ +๋ฐฐ์„œ +๋ฐ”์ดํ‚น +490 +๋‚ด๋ฆฌ๋ง‰๊ธธ +##๋ฉ”๋ž„ +ํ—ˆ๊ฒ์ง€๊ฒ +๋‚จ๊ฐ• +์ฐจ๋Œ +๋ ˆ์ธ์ €์Šค +ํ•˜ํ† ์•ผ๋งˆ +๊นŒ๋ฅด +๋ฒŒ์ง‘ +์•„์ฐจ +๋„ˆ๋ผ +##์ ค๋กœ +์˜ค๋ž˜์˜ค๋ž˜ +์—ฌ์ง€์—†์ด +์ƒ์ˆ  +์ค‘ํ„ฑ +์›ƒ๊ธด +๊ทธ๋Ÿด์ˆ˜๋ก +๋ฆฌ์ฐจ๋“œ +๋ผ์‹œ +๋ผ๋น„ +์›จ์Šฌ๋ฆฌ +##์ƒˆ๋ผ +๋…ธ๋™๋ฒ• +##ํ•˜์ž„ +๋ชฉํ‘œ๋ฌผ +์˜ด๋‹ˆ +์ •ํ›ˆ +๋˜๋„๋ก +๊ฒฉํ•œ +์˜ฅ์ค‘ +ํ•˜์Šค +๋‚จ๋ฌธ +๋ณ‘๋ณ€ +SAT +๋Œ€์œ  +##๋Ÿฌ์šฐ +์™ธ์‹์—… +์žฌ๋ž˜์‹ +Bri +๋งŒ๋ฌด +์„ค์š• +##์ด๋‹ค +##๊ณ ์–‘์ด +์ž„๋Œ€์ธ +๋งคํšŒ +์˜คํ•˜์ด์˜ค +243 +๋จธ์Šคํฌ +์ค„์ธ๋‹ค +๋Š”๋‹ค๋ฉฐ +Big +ํ•œ๊ฐ€์œ„ +๋ณด์˜จ์„ฑ +๋ถ„์› +249 +ํ™”๋‚ด +๋Œ€๊ฐ€๋ฆฌ +##๋ฏผ์ •์Œ +์ด์„ฑ +๋ฏธ๋ค˜ +์• ์“ด +์—ญ๋ช… +์—์Šคํ”„๋ ˆ์†Œ +๊ตฐ๋ฌด +์›ฐ์Šค +##์ ˆ๋ถ€์ ˆ +242 +ํ—ˆ์ •๋ฌด +ํฝ๋‹ˆ๋‹ค +๋ฏธ๋™ +์‹œํ‹ฐ์ฆŒ +Ref +์•ˆ์ ˆ๋ถ€์ ˆ +์• ๋กœ +์™„๊ณ  +๋Œ์–ด๋‹น๊ธฐ +๋Œ๋ ค์„œ +๊น€ํƒœํฌ +๋ผ์šฐ +##์—”์ง€๋‹ˆ์–ด +๋ฏธํ‹ฐ +##์žฅ๊ณ  +๋†์‚ฌ์ผ +์šฐ์ƒํ˜ธ +์•„์ด์–ธ๋งจ +๋งˆ์นจํ‘œ +๋ฐœ๋ Œํƒ€์ธ +ํ•ด๋ฆฌ์Šค +##์—‰์ด +์กฐ๊น… +์—ฌํƒœ๊ป +๊ฐ€๋ง +ํ–‰์ž๋ถ€ +์•ŒํŽœ์‹œ์•„ +๋‘˜๋Ÿฌ์•‰ +๊ฐ•์ง€ +ํŒจํ‹ฐ +์žฅํ•™์‚ฌ +BL +๋‚™ํญ +๋ง๋ž‘ +๊ดด์ƒ +##๋ผ์ด๋” +๋‚จ๊ธด๋‹ค +์ƒŒ๋”์Šค +ํ‰๋ช… +์ดํฌ +์นด๋กค +##ability +์—ผํ™” +์ขŒ์ต์ˆ˜ +๊ฐ€๊ฐ +๊ฐ์ •๊ฐ€ +์–ต์–‘ +##ues +๋ณด์˜€์œผ๋‚˜ +##ha +ํŽ„๋Ÿญ +์ž„์‹ ๋ถ€ +์†Œ๊ฐœํŒ… +str +๋Š”๊ฑธ +๋น„์Šคํƒ€ +์—ฌ๋ฅ˜ +##๊ฒŒ์ด๋จธ +์†์‚ฌ +์ƒ๋‹ด์†Œ +๋ฐฉ์„ +์ง€๊ฒŒ +๋ฏธ๋„ค๋ฅด๋ฐ” +๋ณ€๋ณ„๋ ฅ +ํ”„๋กœ๊ฒŒ์ด๋จธ diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ce114ab --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,30 @@ +[tool.black] +line-length = 120 +target-version = ['py36', 'py37', 'py38'] +include = '\.py$' +exclude = ''' +/( + \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist + | env + | venv +)/ +''' + +[tool.isort] +multi_line_output = 3 +include_trailing_comma = true +force_grid_wrap = 0 +use_parentheses = true +ensure_newline_before_comments = true +line_length = 120 +known_typing = ["typing", "types", "typing_extensions", "mypy", "mypy_extensions"] +sections = ["FUTURE", "TYPING", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"] +profile = "black" diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..e6de360 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,3 @@ +[flake8] +extend-ignore = E203, W503, E501, E231, E402, E731, E266 +max-line-length = 120