diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..89ca792 --- /dev/null +++ b/.flake8 @@ -0,0 +1,9 @@ +[flake8] +max-line-length = 100 +extend-ignore = E203 +per-file-ignores = **/__init__.py:F401 +exclude = + .git, + __pycache__, + build, + dist, diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f9a6234 --- /dev/null +++ b/.gitignore @@ -0,0 +1,150 @@ +### Custom .gitignore ### +# Data stuff +*/*/data + +# IDE stuff +.idea/ +.vscode/ + +### GitHub's .gitignore Python template ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +datasets/*/data +datasets/**/resources +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/ + +#logs + +/**/**/outputs +/**/**/wandb +/**/logs/** +/**/multirun + +#custom extension +*.bak +**/e3c_llm/data/* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..1fbcaf5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,68 @@ +# This file defines all the hooks run by pre-commit. + +exclude: "poetry.lock" + +repos: + # Base pre-commit hook repository, for simple checks & fixes + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-added-large-files # Prevent giant files from being committed + - id: check-ast # Check whether the files parse as valid Python + - id: check-json # Check JSON files for parseable syntax + - id: check-merge-conflict # Check for files that contain merge conflict strings + - id: check-toml # Checks TOML files for parseable syntax + - id: check-yaml # Check YAML files for parseable syntax + - id: debug-statements # Check for debugger imports and `breakpoint()` calls in python + - id: detect-private-key # Detect the presence of private keys + - id: end-of-file-fixer # Ensures that files end with a newline + - id: name-tests-test # Verify that test files are named correctly + exclude: tests/utils/ + + # Black is used to format the code in Python + - repo: https://github.com/psf/black + rev: 22.12.0 + hooks: + - id: black + + # Isort is used to re-organize our import statements in Python + - repo: https://github.com/PyCQA/isort + rev: 5.12.0 + hooks: + - id: isort + args: ["--resolve-all-configs"] + + # nbstripout is used to remove Jupyter notebooks' cell outputs + - repo: https://github.com/kynan/nbstripout + rev: 0.6.1 + hooks: + - id: nbstripout + + # Prettier is a formatting tool for many non-Python files + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v3.0.0-alpha.4 + hooks: + - id: prettier + + # TOML-sort is used to re-organize alphabetically TOML files (such as pyproject.toml) + - repo: https://github.com/pappasam/toml-sort + rev: v0.22.1 + hooks: + - id: toml-sort + args: ["--all", "--in-place"] + + # Flake8 is used to perform various code sanity checks in Python + - repo: https://github.com/pycqa/flake8 + rev: 6.0.0 + hooks: + - id: flake8 + exclude: notebooks/ + + # Mypy is used to check the typing hints in Python; it is quite a restrictive tool, so we don't + # use it in tests + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v0.991 + hooks: + - id: mypy + exclude: apps/|notebooks/|tests/ + additional_dependencies: ["types-requests"] diff --git a/.project-root b/.project-root new file mode 100644 index 0000000..63eab77 --- /dev/null +++ b/.project-root @@ -0,0 +1,2 @@ +# this file is required for inferring the project root directory +# do not delete diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..85fe515 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,48 @@ +# Build: sudo docker build -t . +# Run: sudo docker run -v $(pwd):/workspace/project --gpus all -it --rm + + +FROM nvidia/cuda:12.0.0-devel-ubuntu20.04 + + +ENV CONDA_ENV_NAME=myenv +ENV PYTHON_VERSION=3.9 + + +# Basic setup +RUN apt update +RUN apt install -y zsh \ + build-essential \ + git \ + curl \ + ca-certificates \ + wget \ + && rm -rf /var/lib/apt/lists +RUN wget https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -O - | zsh || true + +# Set working directory +WORKDIR /workspace/project + + +# Install Miniconda and create main env +ADD https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh miniconda3.sh +RUN bash miniconda3.sh -b -p /conda \ + && echo export PATH=/conda/bin:$PATH >> .zshrc \ + && rm miniconda3.sh +ENV PATH="/conda/bin:${PATH}" +RUN conda create -n ${CONDA_ENV_NAME} python=${PYTHON_VERSION} + + +# Switch to zsh shell +SHELL ["/bin/zsh", "-c"] + + +# Install requirements +COPY pyproject.toml ./ +RUN source activate ${CONDA_ENV_NAME} \ + && pip install poetry \ + && poetry install + + +# Set ${CONDA_ENV_NAME} to default virutal environment +RUN echo "source activate ${CONDA_ENV_NAME}" >> ~/.zshrc 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..750df25 --- /dev/null +++ b/Makefile @@ -0,0 +1,56 @@ +help: ## Show help + @grep -E '^[.a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' + +clean: ## Clean autogenerated files + rm -rf dist + find . -type f -name "*.DS_Store" -ls -delete + find . | grep -E "(__pycache__|\.pyc|\.pyo)" | xargs rm -rf + find . | grep -E ".pytest_cache" | xargs rm -rf + find . | grep -E ".ipynb_checkpoints" | xargs rm -rf + rm -f .coverage + +clean-logs: ## Clean logs + rm -rf logs/** + +format: ## Run pre-commit hooks + pre-commit run -a + +sync: ## Merge changes from main branch to your current branch + git pull + git pull origin main + +test: ## Run not slow tests + pytest -k "not slow" + +test-full: ## Run all tests + pytest + +run_experiment_debug: ## Run experiments + python weak_supervision/train.py -m trainer.accelerator=gpu experiment=layer2_blended_comparison hparams_search=grid debug=fdr n_jobs=5 + + +# βš—οΈ Experiments +# πŸ” layer 2 comparison +# πŸ”— wandb link: https://wandb.ai/clinical-dream-team/weak-supervision-instructgpt-e3c/groups/layer_2_comparison +layer_2_comparison: + python weak_supervision/train.py -m trainer.accelerator=gpu experiment=layer2_comparison hparams_search=grid n_jobs=4 + +# πŸ” layer 2 validation comparison +# πŸ”— wandb link: https://wandb.ai/clinical-dream-team/weak-supervision-instructgpt-e3c/groups/layer_2_validation_comparison +layer_2_validation_comparison: + python weak_supervision/train.py -m trainer.accelerator=gpu experiment=layer2_validation_comparison hparams_search=grid n_jobs=4 + +# πŸ” layer 2 blended comparison +# πŸ”— wandb link: https://wandb.ai/clinical-dream-team/weak-supervision-instructgpt-e3c/groups/layer_2_blended_comparison +layer_2_blended_comparison: + python weak_supervision/train.py -m trainer.accelerator=gpu experiment=layer2_blended_comparison hparams_search=grid n_jobs=4 + +# πŸ” layer 2 blended methods +# πŸ”— wandb link: https://wandb.ai/clinical-dream-team/weak-supervision-instructgpt-e3c/groups/layer_2_blended_methods +layer_2_blended_methods: + python weak_supervision/train.py -m trainer.accelerator=gpu experiment=layer2_blended_methods hparams_search=grid n_jobs=4 + +# πŸ” layer 2 xlm +# πŸ”— wandb link: https://wandb.ai/clinical-dream-team/weak-supervision-instructgpt-e3c/groups/layer_2_xlm +layer_2_xlm: + python weak_supervision/train.py -m trainer.accelerator=gpu experiment=layer2_xlm n_jobs=4 diff --git a/README.md b/README.md new file mode 100644 index 0000000..3a468e3 --- /dev/null +++ b/README.md @@ -0,0 +1,54 @@ +# 🐊 Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction + +PyTorch +Lightning +Config: Hydra +Template
+[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539) +[![Conference](https://img.shields.io/badge/BioNLP-2023-blue)](https://aclweb.org/aclwiki/BioNLP_Workshop) + + + +# πŸ‘οΈ Description + +This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary. + +Considering the E3C dataset, we have compared the models trained with both annotations on the whole language in monolingual and multilingual contexts. + +# πŸš€ Quick start + +```bash +poetry install +``` + +Train model with default configuration + +Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/) + +```bash +python weak_supervision/train.py experiment={experiment_name} +``` + +You can override any parameter from command line like this: + +```bash +python weak_supervision/train.py trainer.max_epochs=20 data.batch_size=64 +``` + +To deploy the project run: + +```bash +docker build -t weak_supervision . +docker run -v $(pwd):/workspace/project -e WANDB_API_KEY=$WANDB_API_KEY --gpus all -it --rm weak_supervision zsh +``` + +# βš—οΈ Experiments + +here is a description for each experiment consigned in the Makefile. You see the configuration inside +hydra folder `configs/experiment`: + +- **layer_2_comparison**: Performance comparison between two encoder models trained with weak supervision dictionary and InstructGPT-3 annotations on layer 2. +- **layer 2 validation comparison**: Same but comparison between manual and InstructGPT-3 annotations on layer 2 subset. +- **layer 2 blended comparison**: Same experience as **layer_2_comparison** but for each dataset we add a slight quantity of manual annotation. +- **layer 2 blended methods**: we experiment different ratio to blend the dictionary and InstrucGPT-3 annotations. +- **layer 2 xlm**: we trained model with all the data available (all the languages are used) for layer 2. We compare with weak supervision dictionary and InstructGPT-3 annotations. diff --git a/configs/callbacks/default.yaml b/configs/callbacks/default.yaml new file mode 100644 index 0000000..5771685 --- /dev/null +++ b/configs/callbacks/default.yaml @@ -0,0 +1,22 @@ +defaults: + - model_checkpoint.yaml + - early_stopping.yaml + - model_summary.yaml + - rich_progress_bar.yaml + - _self_ + +model_checkpoint: + dirpath: ${paths.output_dir}/checkpoints + filename: "epoch_{epoch:03d}" + monitor: "val/phrases/BinaryF1Score" + mode: "max" + save_last: False + auto_insert_metric_name: False + +early_stopping: + monitor: "val/phrases/BinaryF1Score" + patience: 100 + mode: "max" + +model_summary: + max_depth: -1 diff --git a/configs/callbacks/early_stopping.yaml b/configs/callbacks/early_stopping.yaml new file mode 100644 index 0000000..20ed267 --- /dev/null +++ b/configs/callbacks/early_stopping.yaml @@ -0,0 +1,17 @@ +# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html + +# Monitor a metric and stop training when it stops improving. +# Look at the above link for more detailed information. +early_stopping: + _target_: pytorch_lightning.callbacks.EarlyStopping + monitor: ??? # quantity to be monitored, must be specified !!! + min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement + patience: 3 # number of checks with no improvement after which training will be stopped + verbose: False # verbosity mode + mode: "min" # "max" means higher metric value is better, can be also "min" + strict: True # whether to crash the training if monitor is not found in the validation metrics + check_finite: True # when set True, stops training when the monitor becomes NaN or infinite + stopping_threshold: null # stop training immediately once the monitored quantity reaches this threshold + divergence_threshold: null # stop training as soon as the monitored quantity becomes worse than this threshold + check_on_train_epoch_end: null # whether to run early stopping at the end of the training epoch + # log_rank_zero_only: False # this keyword argument isn't available in stable version diff --git a/configs/callbacks/model_checkpoint.yaml b/configs/callbacks/model_checkpoint.yaml new file mode 100644 index 0000000..8c9f273 --- /dev/null +++ b/configs/callbacks/model_checkpoint.yaml @@ -0,0 +1,19 @@ +# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.ModelCheckpoint.html + +# Save the model periodically by monitoring a quantity. +# Look at the above link for more detailed information. +model_checkpoint: + _target_: pytorch_lightning.callbacks.ModelCheckpoint + dirpath: null # directory to save the model file + filename: null # checkpoint filename + monitor: null # name of the logged metric which determines when model is improving + verbose: False # verbosity mode + save_last: null # additionally always save an exact copy of the last checkpoint to a file last.ckpt + save_top_k: 1 # save k best models (determined by above metric) + mode: "min" # "max" means higher metric value is better, can be also "min" + auto_insert_metric_name: True # when True, the checkpoints filenames will contain the metric name + save_weights_only: True # if True, then only the model’s weights will be saved + every_n_train_steps: null # number of training steps between checkpoints + train_time_interval: null # checkpoints are monitored at the specified time interval + every_n_epochs: null # number of epochs between checkpoints + save_on_train_epoch_end: null # whether to run checkpointing at the end of the training epoch or the end of validation diff --git a/configs/callbacks/model_summary.yaml b/configs/callbacks/model_summary.yaml new file mode 100644 index 0000000..04da98d --- /dev/null +++ b/configs/callbacks/model_summary.yaml @@ -0,0 +1,7 @@ +# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.RichModelSummary.html + +# Generates a summary of all layers in a LightningModule with rich text formatting. +# Look at the above link for more detailed information. +model_summary: + _target_: pytorch_lightning.callbacks.RichModelSummary + max_depth: 1 # the maximum depth of layer nesting that the summary will include diff --git a/configs/callbacks/none.yaml b/configs/callbacks/none.yaml new file mode 100644 index 0000000..e69de29 diff --git a/configs/callbacks/rich_progress_bar.yaml b/configs/callbacks/rich_progress_bar.yaml new file mode 100644 index 0000000..b6be5b4 --- /dev/null +++ b/configs/callbacks/rich_progress_bar.yaml @@ -0,0 +1,6 @@ +# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.RichProgressBar.html + +# Create a progress bar with rich text formatting. +# Look at the above link for more detailed information. +rich_progress_bar: + _target_: pytorch_lightning.callbacks.RichProgressBar diff --git a/configs/data/e3c.yaml b/configs/data/e3c.yaml new file mode 100644 index 0000000..a8e931b --- /dev/null +++ b/configs/data/e3c.yaml @@ -0,0 +1,8 @@ +_target_: weak_supervision.data.e3c_datamodule.E3CDataModule +batch_size: 24 +num_workers: 0 +pin_memory: False +language: ${model.lang} +tokenizer: ${model.model} +instructgpt_ws: false +fold: 0 diff --git a/configs/data/e3c_blended.yaml b/configs/data/e3c_blended.yaml new file mode 100644 index 0000000..12ffe47 --- /dev/null +++ b/configs/data/e3c_blended.yaml @@ -0,0 +1,8 @@ +_target_: weak_supervision.data.e3c_blended_datamodule.E3CBlendedDataModule +batch_size: 24 +num_workers: 0 +pin_memory: False +language: ${model.lang} +tokenizer: ${model.model} +instructgpt_ws: false +fold: 0 diff --git a/configs/data/e3c_blended_methods.yaml b/configs/data/e3c_blended_methods.yaml new file mode 100644 index 0000000..8c418f2 --- /dev/null +++ b/configs/data/e3c_blended_methods.yaml @@ -0,0 +1,8 @@ +_target_: weak_supervision.data.e3c_blended_methods_datamodule.E3CBlendedMethodsDataModule +batch_size: 24 +num_workers: 0 +pin_memory: False +language: ${model.lang} +tokenizer: ${model.model} +ratio: 0.5 +fold: 0 diff --git a/configs/data/e3c_validation.yaml b/configs/data/e3c_validation.yaml new file mode 100644 index 0000000..458a7ae --- /dev/null +++ b/configs/data/e3c_validation.yaml @@ -0,0 +1,8 @@ +_target_: weak_supervision.data.e3c_val_datamodule.E3CValidationDataModule +batch_size: 24 +num_workers: 0 +pin_memory: False +language: ${model.lang} +tokenizer: ${model.model} +instructgpt_ws: false +fold: 0 diff --git a/configs/data/e3c_xlm.yaml b/configs/data/e3c_xlm.yaml new file mode 100644 index 0000000..a081133 --- /dev/null +++ b/configs/data/e3c_xlm.yaml @@ -0,0 +1,8 @@ +_target_: weak_supervision.data.e3c_xlm_datamodule.E3CXlmDataModule +batch_size: 24 +num_workers: 0 +pin_memory: False +language: ${model.lang} +tokenizer: ${model.model} +ratio: 0.5 +fold: 0 diff --git a/configs/debug/default.yaml b/configs/debug/default.yaml new file mode 100644 index 0000000..1886902 --- /dev/null +++ b/configs/debug/default.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +# default debugging setup, runs 1 full epoch +# other debugging configs can inherit from this one + +# overwrite task name so debugging logs are stored in separate folder +task_name: "debug" + +# disable callbacks and loggers during debugging +callbacks: null +logger: null + +extras: + ignore_warnings: False + enforce_tags: False + +# sets level of all command line loggers to 'DEBUG' +# https://hydra.cc/docs/tutorials/basic/running_your_app/logging/ +hydra: + job_logging: + root: + level: DEBUG + + # use this to also set hydra loggers to 'DEBUG' + # verbose: True + +trainer: + max_epochs: 1 + accelerator: cpu # debuggers don't like gpus + devices: 1 # debuggers don't like multiprocessing + detect_anomaly: true # raise exception if NaN or +/-inf is detected in any tensor + +data: + num_workers: 0 # debuggers don't like multiprocessing + pin_memory: False # disable gpu memory pin diff --git a/configs/debug/fdr.yaml b/configs/debug/fdr.yaml new file mode 100644 index 0000000..98eba22 --- /dev/null +++ b/configs/debug/fdr.yaml @@ -0,0 +1,9 @@ +# @package _global_ + +# runs 1 train, 1 validation and 1 test step + +defaults: + - default.yaml + +trainer: + fast_dev_run: true diff --git a/configs/debug/limit.yaml b/configs/debug/limit.yaml new file mode 100644 index 0000000..cc28852 --- /dev/null +++ b/configs/debug/limit.yaml @@ -0,0 +1,12 @@ +# @package _global_ + +# uses only 1% of the training data and 5% of validation/test data + +defaults: + - default.yaml + +trainer: + max_epochs: 3 + limit_train_batches: 0.01 + limit_val_batches: 0.05 + limit_test_batches: 0.05 diff --git a/configs/debug/overfit.yaml b/configs/debug/overfit.yaml new file mode 100644 index 0000000..d1f63e8 --- /dev/null +++ b/configs/debug/overfit.yaml @@ -0,0 +1,13 @@ +# @package _global_ + +# overfits to 3 batches + +defaults: + - default.yaml + +trainer: + max_epochs: 20 + overfit_batches: 3 + +# model ckpt and early stopping need to be disabled during overfitting +callbacks: null diff --git a/configs/debug/profiler.yaml b/configs/debug/profiler.yaml new file mode 100644 index 0000000..e18df1c --- /dev/null +++ b/configs/debug/profiler.yaml @@ -0,0 +1,12 @@ +# @package _global_ + +# runs with execution time profiling + +defaults: + - default.yaml + +trainer: + max_epochs: 1 + profiler: "simple" + # profiler: "advanced" + # profiler: "pytorch" diff --git a/configs/eval.yaml b/configs/eval.yaml new file mode 100644 index 0000000..1935a48 --- /dev/null +++ b/configs/eval.yaml @@ -0,0 +1,18 @@ +# @package _global_ + +defaults: + - _self_ + - data: e3c.yaml # choose datamodule with `test_dataloader()` for evaluation + - model: e3c.yaml + - logger: null + - trainer: default.yaml + - paths: default.yaml + - extras: default.yaml + - hydra: default.yaml + +task_name: "eval" + +tags: ["dev"] + +# passing checkpoint path is necessary for evaluation +ckpt_path: ??? diff --git a/configs/experiment/layer2_blended_comparison.yaml b/configs/experiment/layer2_blended_comparison.yaml new file mode 100644 index 0000000..f2cbc7a --- /dev/null +++ b/configs/experiment/layer2_blended_comparison.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +defaults: + - override /data: e3c_blended.yaml + - override /model: e3c.yaml + - override /callbacks: default.yaml + - override /trainer: default.yaml + - override /logger: wandb.yaml + +tags: ["blended comparison"] + +seed: 42 + +trainer: + min_epochs: 1 + max_epochs: 5 + +model: + optimizer: + lr: 2e-5 + +data: + batch_size: 16 + +logger: + wandb: + tags: ${tags} + group: "layer_2_blended_comparison" + +hydra: + sweeper: + params: + data.instructgpt_ws: choice(true,false) diff --git a/configs/experiment/layer2_blended_methods.yaml b/configs/experiment/layer2_blended_methods.yaml new file mode 100644 index 0000000..8f43dcc --- /dev/null +++ b/configs/experiment/layer2_blended_methods.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +defaults: + - override /data: e3c_blended_methods.yaml + - override /model: e3c.yaml + - override /callbacks: default.yaml + - override /trainer: default.yaml + - override /logger: wandb.yaml + +tags: ["blended methods"] + +seed: 42 + +trainer: + min_epochs: 1 + max_epochs: 5 + +model: + optimizer: + lr: 2e-5 + +data: + batch_size: 16 + +logger: + wandb: + tags: ${tags} + group: "layer_2_blended_methods" + +hydra: + sweeper: + params: + data.ratio: choice(0, 0.2, 0.4, 0.5, 0.6, 0.8, 1.0) diff --git a/configs/experiment/layer2_comparison.yaml b/configs/experiment/layer2_comparison.yaml new file mode 100644 index 0000000..23653f6 --- /dev/null +++ b/configs/experiment/layer2_comparison.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +defaults: + - override /data: e3c.yaml + - override /model: e3c.yaml + - override /callbacks: default.yaml + - override /trainer: default.yaml + - override /logger: wandb.yaml + +tags: ["comparison"] + +seed: 42 + +trainer: + min_epochs: 1 + max_epochs: 5 + +model: + optimizer: + lr: 2e-5 + +data: + batch_size: 16 + +logger: + wandb: + tags: ${tags} + group: "layer_2_comparison" + +hydra: + sweeper: + params: + data.instructgpt_ws: choice(true,false) diff --git a/configs/experiment/layer2_validation_comparison.yaml b/configs/experiment/layer2_validation_comparison.yaml new file mode 100644 index 0000000..5e8bba5 --- /dev/null +++ b/configs/experiment/layer2_validation_comparison.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +defaults: + - override /data: e3c_validation.yaml + - override /model: e3c.yaml + - override /callbacks: default.yaml + - override /trainer: default.yaml + - override /logger: wandb.yaml + +tags: ["validation"] + +seed: 42 + +trainer: + min_epochs: 1 + max_epochs: 5 + +model: + optimizer: + lr: 1e-5 + +data: + batch_size: 4 + +logger: + wandb: + tags: ${tags} + group: "layer_2_validation_comparison" + +hydra: + sweeper: + params: + data.instructgpt_ws: choice(true,false) diff --git a/configs/experiment/layer2_xlm.yaml b/configs/experiment/layer2_xlm.yaml new file mode 100644 index 0000000..f671eab --- /dev/null +++ b/configs/experiment/layer2_xlm.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +defaults: + - override /data: e3c_xlm.yaml + - override /model: e3c.yaml + - override /callbacks: default.yaml + - override /trainer: default.yaml + - override /logger: wandb.yaml + - override /hydra/sweeper: basic + +tags: ["xlm"] + +seed: 42 + +trainer: + min_epochs: 1 + max_epochs: 7 + +model: + optimizer: + lr: 2e-5 + +data: + batch_size: 16 + +logger: + wandb: + tags: ${tags} + group: "layer2_xlm" + +hydra: + sweeper: + params: + data.ratio: choice(0, 0.2, 0.4, 0.5, 0.6, 0.8, 1.0) + data.fold: choice(0, 1, 2, 3, 4) + model: choice("en/xlm-roberta", "es/xlm-roberta", "eu/xlm-roberta", "fr/xlm-roberta", "it/xlm-roberta") diff --git a/configs/extras/default.yaml b/configs/extras/default.yaml new file mode 100644 index 0000000..b9c6b62 --- /dev/null +++ b/configs/extras/default.yaml @@ -0,0 +1,8 @@ +# disable python warnings if they annoy you +ignore_warnings: False + +# ask user for tags if none are provided in the config +enforce_tags: True + +# pretty print config tree at the start of the run using Rich library +print_config: True diff --git a/configs/hparams_search/grid.yaml b/configs/hparams_search/grid.yaml new file mode 100644 index 0000000..76447b6 --- /dev/null +++ b/configs/hparams_search/grid.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +# example hyperparameter optimization of some experiment with Optuna: +# python train.py -m hparams_search=e3c_optuna experiment=example + +defaults: + - override /hydra/sweeper: basic + - override /hydra/launcher: joblib + +hydra: + sweeper: + params: + data.fold: choice(0, 1, 2, 3, 4) + model: choice("en/bio-clinicalbert", + "en/roberta-base", + "en/xlm-roberta", + "es/bert-base-spanish-wwm-cased", + "es/roberta-base-biomedical-es", + "es/xlm-roberta", + "eu/berteus-base-cased", + "eu/xlm-roberta", + "fr/camembert", + "fr/dr-bert", + "fr/xlm-roberta", + "it/bert-base-italian-cased", + "it/xlm-roberta") diff --git a/configs/hydra/default.yaml b/configs/hydra/default.yaml new file mode 100644 index 0000000..5f996c6 --- /dev/null +++ b/configs/hydra/default.yaml @@ -0,0 +1,28 @@ +# https://hydra.cc/docs/configure_hydra/intro/ + +# enable color logging +defaults: + - override hydra_logging: colorlog + - override job_logging: colorlog + - override launcher: joblib + +# output directory, generated dynamically on each run +run: + dir: ${paths.log_dir}/${task_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S} +sweep: + dir: ${paths.log_dir}/${task_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S} + subdir: ${hydra.job.num} + +launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: ${n_jobs} + backend: null + prefer: processes + require: null + verbose: 0 + timeout: null + pre_dispatch: 2*n_jobs + batch_size: auto + temp_folder: null + max_nbytes: null + mmap_mode: r diff --git a/configs/local/.gitkeep b/configs/local/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/configs/logger/csv.yaml b/configs/logger/csv.yaml new file mode 100644 index 0000000..844ec67 --- /dev/null +++ b/configs/logger/csv.yaml @@ -0,0 +1,7 @@ +# csv logger built in lightning + +csv: + _target_: pytorch_lightning.loggers.csv_logs.CSVLogger + save_dir: "${paths.output_dir}" + name: "csv/" + prefix: "" diff --git a/configs/logger/many_loggers.yaml b/configs/logger/many_loggers.yaml new file mode 100644 index 0000000..801444d --- /dev/null +++ b/configs/logger/many_loggers.yaml @@ -0,0 +1,9 @@ +# train with many loggers at once + +defaults: + # - comet.yaml + - csv.yaml + # - mlflow.yaml + # - neptune.yaml + - tensorboard.yaml + - wandb.yaml diff --git a/configs/logger/tensorboard.yaml b/configs/logger/tensorboard.yaml new file mode 100644 index 0000000..29067c9 --- /dev/null +++ b/configs/logger/tensorboard.yaml @@ -0,0 +1,10 @@ +# https://www.tensorflow.org/tensorboard/ + +tensorboard: + _target_: pytorch_lightning.loggers.tensorboard.TensorBoardLogger + save_dir: "${paths.output_dir}/tensorboard/" + name: null + log_graph: False + default_hp_metric: True + prefix: "" + # version: "" diff --git a/configs/logger/wandb.yaml b/configs/logger/wandb.yaml new file mode 100644 index 0000000..fea90f8 --- /dev/null +++ b/configs/logger/wandb.yaml @@ -0,0 +1,16 @@ +# https://wandb.ai + +wandb: + _target_: pytorch_lightning.loggers.wandb.WandbLogger + # name: "" # name of the run (normally generated by wandb) + save_dir: "${paths.output_dir}" + offline: False + id: null # pass correct id to resume experiment! + anonymous: null # enable anonymous logging + project: "weak-supervision-instructgpt-e3c" + log_model: False # upload lightning ckpts + prefix: "" # a string to put at the beginning of metric keys + # entity: "" # set to name of your wandb team + group: "" + tags: [] + job_type: "" diff --git a/configs/model/e3c.yaml b/configs/model/e3c.yaml new file mode 100644 index 0000000..d474534 --- /dev/null +++ b/configs/model/e3c.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "camembert-base" +lang: "fr" diff --git a/configs/model/en/bio-clinicalbert.yaml b/configs/model/en/bio-clinicalbert.yaml new file mode 100644 index 0000000..1445671 --- /dev/null +++ b/configs/model/en/bio-clinicalbert.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "emilyalsentzer/Bio_ClinicalBERT" +lang: "en" diff --git a/configs/model/en/roberta-base.yaml b/configs/model/en/roberta-base.yaml new file mode 100644 index 0000000..c8ddb4d --- /dev/null +++ b/configs/model/en/roberta-base.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "roberta-base" +lang: "en" diff --git a/configs/model/en/xlm-roberta.yaml b/configs/model/en/xlm-roberta.yaml new file mode 100644 index 0000000..53776dd --- /dev/null +++ b/configs/model/en/xlm-roberta.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "xlm-roberta-base" +lang: "en" diff --git a/configs/model/es/bert-base-spanish-wwm-cased.yaml b/configs/model/es/bert-base-spanish-wwm-cased.yaml new file mode 100644 index 0000000..2eaa407 --- /dev/null +++ b/configs/model/es/bert-base-spanish-wwm-cased.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "dccuchile/bert-base-spanish-wwm-cased" +lang: "es" diff --git a/configs/model/es/roberta-base-biomedical-es.yaml b/configs/model/es/roberta-base-biomedical-es.yaml new file mode 100644 index 0000000..62b619d --- /dev/null +++ b/configs/model/es/roberta-base-biomedical-es.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "BSC-LT/roberta-base-biomedical-es" +lang: "es" diff --git a/configs/model/es/xlm-roberta.yaml b/configs/model/es/xlm-roberta.yaml new file mode 100644 index 0000000..f5f27bd --- /dev/null +++ b/configs/model/es/xlm-roberta.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "xlm-roberta-base" +lang: "es" diff --git a/configs/model/eu/berteus-base-cased.yaml b/configs/model/eu/berteus-base-cased.yaml new file mode 100644 index 0000000..576654b --- /dev/null +++ b/configs/model/eu/berteus-base-cased.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "ixa-ehu/berteus-base-cased" +lang: "eu" diff --git a/configs/model/eu/xlm-roberta.yaml b/configs/model/eu/xlm-roberta.yaml new file mode 100644 index 0000000..ea34702 --- /dev/null +++ b/configs/model/eu/xlm-roberta.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "xlm-roberta-base" +lang: "eu" diff --git a/configs/model/fr/camembert-bio-v2.yaml b/configs/model/fr/camembert-bio-v2.yaml new file mode 100644 index 0000000..be24e6f --- /dev/null +++ b/configs/model/fr/camembert-bio-v2.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "rntc/camembert-bio-finetune-biomed-fr-v2" +lang: "fr" diff --git a/configs/model/fr/camembert-bio.yaml b/configs/model/fr/camembert-bio.yaml new file mode 100644 index 0000000..75c9ece --- /dev/null +++ b/configs/model/fr/camembert-bio.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "rntc/camembert-bio-base" +lang: "fr" diff --git a/configs/model/fr/camembert.yaml b/configs/model/fr/camembert.yaml new file mode 100644 index 0000000..d474534 --- /dev/null +++ b/configs/model/fr/camembert.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "camembert-base" +lang: "fr" diff --git a/configs/model/fr/dr-bert.yaml b/configs/model/fr/dr-bert.yaml new file mode 100644 index 0000000..773b096 --- /dev/null +++ b/configs/model/fr/dr-bert.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "Dr-BERT/DrBERT-7GB" +lang: "fr" diff --git a/configs/model/fr/xlm-roberta.yaml b/configs/model/fr/xlm-roberta.yaml new file mode 100644 index 0000000..a104dfe --- /dev/null +++ b/configs/model/fr/xlm-roberta.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "xlm-roberta-base" +lang: "fr" diff --git a/configs/model/it/bert-base-italian-cased.yaml b/configs/model/it/bert-base-italian-cased.yaml new file mode 100644 index 0000000..aea9cd1 --- /dev/null +++ b/configs/model/it/bert-base-italian-cased.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "dbmdz/bert-base-italian-cased" +lang: "it" diff --git a/configs/model/it/xlm-roberta.yaml b/configs/model/it/xlm-roberta.yaml new file mode 100644 index 0000000..c25f397 --- /dev/null +++ b/configs/model/it/xlm-roberta.yaml @@ -0,0 +1,11 @@ +_target_: weak_supervision.models.e3c_module.E3CTokenClassificationModule + +optimizer: + _target_: torch.optim.Adam + _partial_: true + lr: 2e-5 + weight_decay: 0.0 + +scheduler: null +model: "xlm-roberta-base" +lang: "it" diff --git a/configs/paths/default.yaml b/configs/paths/default.yaml new file mode 100644 index 0000000..ec81db2 --- /dev/null +++ b/configs/paths/default.yaml @@ -0,0 +1,18 @@ +# path to root directory +# this requires PROJECT_ROOT environment variable to exist +# you can replace it with "." if you want the root to be the current working directory +root_dir: ${oc.env:PROJECT_ROOT} + +# path to data directory +data_dir: ${paths.root_dir}/data/ + +# path to logging directory +log_dir: ${paths.root_dir}/logs/ + +# path to output directory, created dynamically by hydra +# path generation pattern is specified in `configs/hydra/default.yaml` +# use it to store all files generated during the run, like ckpts and metrics +output_dir: ${hydra:runtime.output_dir} + +# path to working directory +work_dir: ${hydra:runtime.cwd} diff --git a/configs/train.yaml b/configs/train.yaml new file mode 100644 index 0000000..f2c6592 --- /dev/null +++ b/configs/train.yaml @@ -0,0 +1,54 @@ +# @package _global_ + +# specify here default configuration +# order of defaults determines the order in which configs override each other +defaults: + - _self_ + - data: e3c.yaml + - model: e3c.yaml + - callbacks: default.yaml + - logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`) + - trainer: default.yaml + - paths: default.yaml + - extras: default.yaml + - hydra: default.yaml + + # experiment configs allow for version control of specific hyperparameters + # e.g. best hyperparameters for given model and datamodule + - experiment: null + + # config for hyperparameter optimization + - hparams_search: null + + # optional local config for machine/user specific settings + # it's optional since it doesn't need to exist and is excluded from version control + - optional local: default.yaml + + # debugging config (enable through command line, e.g. `python train.py debug=default) + - debug: null + +# task name, determines output directory path +task_name: "train" + +# tags to help you identify your experiments +# you can overwrite this in experiment configs +# overwrite from command line with `python train.py tags="[first_tag, second_tag]"` +# appending lists from command line is currently not supported :( +# https://github.com/facebookresearch/hydra/issues/1547 +tags: ["dev"] + +# set False to skip model training +train: True + +# evaluate on test set, using best model weights achieved during training +# lightning chooses best weights based on the metric specified in checkpoint callback +test: True + +# simply provide checkpoint path to resume training +ckpt_path: null + +# seed for random number generators in pytorch, numpy and python.random +seed: 42 + +# n_jobs for parallelization +n_jobs: 1 diff --git a/configs/trainer/cpu.yaml b/configs/trainer/cpu.yaml new file mode 100644 index 0000000..640f71d --- /dev/null +++ b/configs/trainer/cpu.yaml @@ -0,0 +1,5 @@ +defaults: + - default.yaml + +accelerator: cpu +devices: 1 diff --git a/configs/trainer/ddp.yaml b/configs/trainer/ddp.yaml new file mode 100644 index 0000000..4e5238e --- /dev/null +++ b/configs/trainer/ddp.yaml @@ -0,0 +1,13 @@ +defaults: + - default.yaml + +# use "ddp_spawn" instead of "ddp", +# it's slower but normal "ddp" currently doesn't work ideally with hydra +# https://github.com/facebookresearch/hydra/issues/2070 +# https://pytorch-lightning.readthedocs.io/en/latest/accelerators/gpu_intermediate.html#distributed-data-parallel-spawn +strategy: ddp_spawn + +accelerator: gpu +devices: 4 +num_nodes: 1 +sync_batchnorm: True diff --git a/configs/trainer/ddp_sim.yaml b/configs/trainer/ddp_sim.yaml new file mode 100644 index 0000000..42626be --- /dev/null +++ b/configs/trainer/ddp_sim.yaml @@ -0,0 +1,7 @@ +defaults: + - default.yaml + +# simulate DDP on CPU, useful for debugging +accelerator: cpu +devices: 2 +strategy: ddp_spawn diff --git a/configs/trainer/default.yaml b/configs/trainer/default.yaml new file mode 100644 index 0000000..1a336e8 --- /dev/null +++ b/configs/trainer/default.yaml @@ -0,0 +1,19 @@ +_target_: pytorch_lightning.Trainer + +default_root_dir: ${paths.output_dir} + +min_epochs: 1 # prevents early stopping +max_epochs: 10 + +accelerator: cpu +devices: 1 + +# mixed precision for extra speed-up +# precision: 16 + +# perform a validation loop every N training epochs +check_val_every_n_epoch: 1 + +# set True to to ensure deterministic results +# makes training slower but gives more reproducibility than just setting seeds +deterministic: False diff --git a/configs/trainer/gpu.yaml b/configs/trainer/gpu.yaml new file mode 100644 index 0000000..d5e5773 --- /dev/null +++ b/configs/trainer/gpu.yaml @@ -0,0 +1,5 @@ +defaults: + - default.yaml + +accelerator: gpu +devices: 1 diff --git a/configs/trainer/mps.yaml b/configs/trainer/mps.yaml new file mode 100644 index 0000000..73d2cdd --- 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"pragma: no cover", + "raise NotImplementedError", + "raise TypeError", + "raise ValueError" +] + +[tool.isort] +known_local_folder = [ + 'tests', + 'weak-supervision' +] +line_length = 100 +profile = 'black' + +[tool.poetry] +authors = ["Arkhn's AI Team "] +description = "A project to train and evaluate models with weak supervision techniques" +license = "Apache-2.0" +name = "weak-supervision" +readme = "README.md" +repository = "https://github.com/arkhn/ai-lembic/tree/main/experiements/weak-supervision" +version = "0.1.0" + +[tool.poetry.dependencies] +bs4 = "^0.0.1" +datasets = "^2.8.0" +hydra-colorlog = "^1.2.0" +hydra-core = "^1.3.1" +hydra-joblib-launcher = "^1.2.0" +hydra-optuna-sweeper = "^1.2.0" +lxml = "^4.9.2" +protobuf = "3.20.1" +pyrootutils = "^1.0.4" +python = "~3.9" +pytorch-lightning = "^1.8.3" +rich = "^13.2.0" +scikit-learn = "^1.2.1" +syntok = "^1.4.4" +torch = "^1.13.1" +torchmetrics = "^0.11.0" +transformers = "^4.11.0" +wandb = "^0.14.2" + +[tool.poetry.group.test.dependencies] +pytest = "^7.1.1" +pytest-cov = "^3.0.0" + +[tool.pytest.ini_options] +addopts = "--cov-report term-missing:skip-covered" +markers = [ + "serial", + "slow: marks tests as slow (deselect with '-m \"not slow\"')" +] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/configs_test.py b/tests/configs_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/datamodules_test.py b/tests/datamodules_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/eval_test.py b/tests/eval_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/helpers/package_available_test.py b/tests/helpers/package_available_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/helpers/run_if_test.py b/tests/helpers/run_if_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/helpers/run_sh_command_test.py b/tests/helpers/run_sh_command_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/sweeps_test.py b/tests/sweeps_test.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/train_test.py b/tests/train_test.py new file mode 100644 index 0000000..e69de29 diff --git a/weak_supervision/__init__.py b/weak_supervision/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/weak_supervision/analytics.py b/weak_supervision/analytics.py new file mode 100644 index 0000000..6d1c0a7 --- /dev/null +++ b/weak_supervision/analytics.py @@ -0,0 +1,376 @@ +import datasets +import spacy +import torch +import wandb +from spacy.tokens import Doc +from torchmetrics import MetricCollection +from torchmetrics.classification import MulticlassF1Score, MulticlassPrecision, MulticlassRecall +from transformers import AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("camembert-base") +nlp = spacy.blank("fr") + +""" +The following scripts are used to analyze the E3C dataset and the E3C-LLM dataset specifically +for the French layer. We analyze the number of sentences, tokens and BI tags in the dataset, +and we render the NER tags to compare the InstructGPT predictions with the "ground truth". +Also we compute the confusion matrix to see the annotation differences between the two datasets. +""" + + +def main(): + # set up wandb + run = wandb.init( + project="weak-supervision-instructgpt-e3c", group="analytics_french", job_type="analytics" + ) + # load datasets + e3c_dataset = ( + datasets.load_dataset("bio-datasets/e3c") + .map(tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + e3c_llm_dataset = ( + datasets.load_dataset("bio-datasets/e3c-llm") + .map(map_offset_to_text, batched=True) + .map(tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + # get e3c-llm labels and convert it as a tensor + e3c_llm_labels = e3c_llm_dataset["fr.layer1"]["labels"] + e3c_llm_labels = torch.tensor([entity for examples in e3c_llm_labels for entity in examples]) + e3c_llm_labels[e3c_llm_labels == -100] = 0 + + # instructgpt_performance(e3c_dataset, e3c_llm_labels, run) + # render_quantities_table(e3c_dataset, e3c_llm_dataset, run) + render_ner_clustering( + e3c_dataset, + e3c_llm_dataset, + run, + [ + [0, 2], # O -> I + [0, 1], # O -> B + [2, 1], # I -> B + [1, 2], # B -> I + ], + [ + "ner_clustering/O_to_I", + "ner_clustering/O_to_B", + "ner_clustering/I_to_B", + "ner_clustering/B_to_I", + ], + ) + # render_ner_html_tags(e3c_dataset, e3c_llm_dataset, run) + # render_confusion_matrix(e3c_dataset, e3c_llm_dataset) + + +def render_ner_clustering(e3c_dataset, e3c_llm_dataset, run, clusters, table_names): + for layer in ["fr.layer2", "fr.layer2.validation"]: + for cluster, table_name in zip(clusters, table_names): + texts = e3c_dataset[layer].filter( + lambda x, x_idx: any( + (torch.tensor(x["labels"]) == cluster[0]) + & (torch.tensor(e3c_llm_dataset[layer][x_idx]["labels"]) == cluster[1]) + ), + with_indices=True, + )["text"] + render_ner_text( + e3c_dataset, + e3c_llm_dataset, + layer, + run, + texts, + table_name, + merge=False, + ) + + +def render_confusion_matrix(e3c_dataset, e3c_llm_dataset): + # log confusion matrix + for layer in ["fr.layer2", "fr.layer2.validation"]: + ground_truth = torch.LongTensor( + [tag for sentence in e3c_dataset[layer]["labels"] for tag in sentence] + ) + predictions = torch.LongTensor( + [tag for sentence in e3c_llm_dataset[layer]["labels"] for tag in sentence] + ) + predictions = predictions.tolist() + predictions = [prediction for prediction in predictions if prediction != -100] + ground_truth = ground_truth.tolist() + ground_truth = [tag for tag in ground_truth if tag != -100] + class_names = e3c_dataset[layer].features["clinical_entity_tags"].feature.names + wandb.log( + { + f"{layer}/confusion_mat": wandb.plot.confusion_matrix( + probs=None, + y_true=ground_truth, + preds=predictions, + class_names=class_names, + ) + } + ) + + +def render_ner(e3c_dataset, e3c_llm_dataset, run): + for layer in ["fr.layer2", "fr.layer2.validation"]: + render_ner_text( + e3c_dataset, + e3c_llm_dataset, + layer=layer, + run=run, + texts=e3c_dataset[layer]["text"], + table_name=f"{layer}/ner", + ) + + +def render_ner_text( + e3c_dataset, e3c_llm_dataset, layer, run, texts, table_name, merge: bool = True +): + data_tags = [] + for text in texts: + data_tags.append( + [ + render_html( + e3c_dataset, layer, e3c_dataset[layer]["text"].index(text), merge=merge + ), + render_html( + e3c_llm_dataset, layer, e3c_llm_dataset[layer]["text"].index(text), merge=merge + ), + ] + ) + run.log({f"{layer}/{table_name}": wandb.Table(data=data_tags, columns=["e3c", "e3c-llm"])}) + + +def render_quantities_table(e3c_dataset, e3c_llm_dataset, run): + # get the number of sentences, tokens and BI tags + data = [] + for dataset in [e3c_dataset, e3c_llm_dataset]: + for layer in ["fr.layer2", "fr.layer1", "fr.layer2.validation"]: + if layer in dataset.column_names.keys(): + data.append( + [ + f"{dataset[layer].builder_name}/{layer}", + dataset[layer].num_rows, + get_tokens_number(dataset[layer]), + get_bi_tags_number(dataset[layer]), + get_tag_number(dataset[layer], 1), + get_tag_number(dataset[layer], 2), + ] + ) + table = wandb.Table( + columns=[ + "name", + "sentences_number", + "tokens_number", + "BI_tags_number", + "B_tag_number", + "I_tag_number", + ], + data=data, + ) + run.log({"e3c": table}) + + +def instructgpt_performance(e3c_dataset, e3c_llm_labels, run): + # get e3c labels and convert it as a tensor + e3c_labels = e3c_dataset["fr.layer1"]["labels"] + e3c_labels = torch.tensor([entity for examples in e3c_labels for entity in examples]) + # use torchmetrics to get the f1 score, the recall and the precision no aggregated + no_agg_scores = MetricCollection( + MulticlassF1Score(average=None, num_classes=3, ignore_index=-100), + MulticlassPrecision(average=None, num_classes=3, ignore_index=-100), + MulticlassRecall(average=None, num_classes=3, ignore_index=-100), + ) + no_agg_scores.update(e3c_llm_labels, e3c_labels) + no_aggregated_metrics = no_agg_scores.compute() + # log the metrics + dict_metrics = { + f"test/{log_key}/{label}": log_value[idx_label] + for idx_label, label in enumerate( + e3c_dataset["fr.layer1"].features["clinical_entity_tags"].feature.names + ) + for log_key, log_value in no_aggregated_metrics.items() + } + run.log(dict_metrics) + # use torchmetrics to get the f1 score, the recall and the precision agragated in + # a macro average + f1_macro = MulticlassF1Score(num_classes=3, average="macro", ignore_index=-100) + f1_macro.update(e3c_llm_labels, e3c_labels) + f1_score_macro = f1_macro.compute() + recall_macro = MulticlassRecall(num_classes=3, average="macro", ignore_index=-100) + recall_macro.update(e3c_llm_labels, e3c_labels) + recall_score_macro = recall_macro.compute() + precision_macro = MulticlassPrecision(num_classes=3, average="macro", ignore_index=-100) + precision_macro.update(e3c_llm_labels, e3c_labels) + precision_score_macro = precision_macro.compute() + # log the metrics + run.log( + { + "test/tokens/MulticlassF1Score": f1_score_macro, + "test/tokens/MulticlassPrecision": precision_score_macro, + "test/tokens/MulticlassRecall": recall_score_macro, + } + ) + + +def render_html( + dataset: datasets.DatasetDict, layer: str, text_idx: int, merge: bool = False +) -> wandb.Html: + """Render the NER tags of a given text. + + Args: + dataset: A given e3c dataset. + layer: A given e3c layer. + text_idx: The index of the text in the layer. + + Returns: + A wandb.Html object. Contains the rendered NER tags using spacy. + """ + offset = dataset[layer]["tokens_offsets"][text_idx] + spaces = [True] * len(offset) + last_end = -1 + for o_idx, o in enumerate(offset): + if last_end == o[0]: + spaces[o_idx - 1] = False + last_end = o[1] + + if merge: + doc = Doc( + nlp.vocab, + words=dataset[layer]["tokens"][text_idx], + spaces=spaces, + ents=[ + f'{dataset[layer].features["clinical_entity_tags"].feature.int2str(entity)}' + for entity in dataset[layer]["clinical_entity_tags"][text_idx] + ], + ) + html = spacy.displacy.render(doc, style="ent", page=True, minify=True) + else: + doc = Doc( + nlp.vocab, + words=dataset[layer]["tokens"][text_idx], + spaces=spaces, + ents=[ + f'B-{dataset[layer].features["clinical_entity_tags"].feature.int2str(entity)}' + for entity in dataset[layer]["clinical_entity_tags"][text_idx] + ], + ) + html = spacy.displacy.render( + doc, + style="ent", + page=True, + minify=True, + options={ + "colors": { + "B-CLINENTITY": "#28eba8", + "I-CLINENTITY": "#cb3feb", + }, + }, + ) + return wandb.Html(html) + + +def get_bi_tags_number(layer: dict) -> int: + """Get the number of BI tags in a layer. + + Args: + layer: A given e3c layer. + + Returns: + The number of BI tags in the layer. + """ + + tokens = [ + [text[offset[0] : offset[1]] for offset, tag in zip(token_offset, tags) if tag != 0] + for text, token_offset, tags in zip( + layer["text"], layer["tokens_offsets"], layer["clinical_entity_tags"] + ) + ] + tokens = [token for token_list in tokens for token in token_list] + return len(tokens) + + +def get_tag_number(layer: datasets.Dataset, tag_id: int) -> int: + """Get the number of a specific tag in a layer. + + Args: + layer: A given e3c layer. + tag_id: A given tag id. + + Returns: + The number of BI tags in the layer. + """ + + tokens = [ + [text[offset[0] : offset[1]] for offset, tag in zip(token_offset, tags) if tag == tag_id] + for text, token_offset, tags in zip( + layer["text"], layer["tokens_offsets"], layer["clinical_entity_tags"] + ) + ] + tokens = [token for token_list in tokens for token in token_list] + return len(tokens) + + +def get_tokens_number(layer: datasets.DatasetDict) -> int: + """Get the number of tokens in a layer. + + Args: + layer: A given e3c layer. + + Returns: + The number of tokens in the layer. + """ + return torch.LongTensor( + [token for sentence in layer["clinical_entity_tags"] for token in sentence] + ).shape[0] + + +def tokenize_and_align_labels(examples: dict) -> dict: + """Tokenize the text and align the labels with the sub-tokens. + + Args: + examples: A dictionary containing the text and the labels. + + Returns: + A dictionary containing the tokenized text and the labels. + """ + + tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True) + labels = [] + for i, label in enumerate(examples["clinical_entity_tags"]): + # Map tokens to their respective word. + word_ids = tokenized_inputs.word_ids(batch_index=i) + previous_word_idx = None + label_ids = [] + for word_idx in word_ids: # Set the special tokens to -100. + if word_idx is None: + label_ids.append(-100) + elif word_idx != previous_word_idx: # Only label the first token of a given word. + label_ids.append(label[word_idx]) + else: + label_ids.append(-100) + previous_word_idx = word_idx + labels.append(label_ids) + + tokenized_inputs["labels"] = labels + return tokenized_inputs + + +def map_offset_to_text(examples: dict) -> dict: + """Map the offsets to the text. To compute the tokens. + + Args: + examples: the examples to map. + + Returns: return the tokens of the text in a dict. + """ + return { + "tokens": [ + [text[offset[0] : offset[1]] for offset in offsets] + for text, offsets in zip(examples["text"], examples["tokens_offsets"]) + ] + } + + +if __name__ == "__main__": + main() diff --git a/weak_supervision/data/__init__.py b/weak_supervision/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/weak_supervision/data/e3c_blended_datamodule.py b/weak_supervision/data/e3c_blended_datamodule.py new file mode 100644 index 0000000..c761be1 --- /dev/null +++ b/weak_supervision/data/e3c_blended_datamodule.py @@ -0,0 +1,101 @@ +from typing import Optional + +import datasets +from datasets import concatenate_datasets +from sklearn.model_selection import KFold +from torch.utils.data import Subset +from weak_supervision.data.e3c_datamodule import E3CDataModule + + +class E3CBlendedDataModule(E3CDataModule): + """This dataset blend manual annotations (layer 2 validation) with weak supervision annotations. + + A DataModule implements 5 key methods: + + def prepare_data(self): + # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP) + # download data, pre-process, split, save to disk, etc... + def setup(self, stage): + # things to do on every process in DDP + # load data, set variables, etc... + def train_dataloader(self): + # return train dataloader + def val_dataloader(self): + # return validation dataloader + def test_dataloader(self): + # return test dataloader + def teardown(self): + # called on every process in DDP + # clean up after fit or test + + This allows you to share a full dataset without explaining how to download, + split, transform and process the data. + + Read the docs: + https://pytorch-lightning.readthedocs.io/en/latest/data/datamodule.html + """ + + def setup(self, stage: Optional[str] = None) -> None: + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be + careful not to execute things like random split twice! + This method setup the different huggingface datasets and the k-fold split. + Several mappings are applied to the datasets to tokenize and align the labels. + + Args: + stage: the stage to setup. Can be either 'fit' or 'test'. + """ + e3c_dataset = ( + datasets.load_dataset("bio-datasets/e3c") + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + if self.hparams.instructgpt_ws: + e3c_llm_dataset = ( + datasets.load_dataset("bio-datasets/e3c-llm") + .map(self.map_offset_to_text, batched=True) + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + train_set = e3c_llm_dataset[f"{self.hparams.language}_layer2"] + else: + train_set = e3c_dataset[f"{self.hparams.language}.layer2"] + + train_set = self.blend_dataset( + train_set, e3c_dataset[f"{self.hparams.language}.layer2.validation"] + ) + test_set = e3c_dataset[f"{self.hparams.language}.layer1"] + + k_fold = KFold(n_splits=5, shuffle=True) + self.labels = e3c_dataset[f"{self.hparams.language}.layer2"].features[ + "clinical_entity_tags" + ] + + data_train_ids, data_val_ids = list( + k_fold.split(train_set), + )[self.hparams.fold] + self.data_train = Subset(train_set, data_train_ids.tolist()) + self.data_val = Subset(train_set, data_val_ids.tolist()) + self.data_test = test_set + + @staticmethod + def blend_dataset( + train_set: datasets.Dataset, validation_layer: datasets.Dataset + ) -> datasets.Dataset: + """Blend the validation layer with the validation set where some common examples + has been corrected. + + Args: + train_set: the training set + validation_layer: the validation layer with corrected examples + + Returns: + the blended dataset + """ + + filtered_train_dataset = train_set.filter( + lambda x: x["text"] not in validation_layer["text"] + ) + return concatenate_datasets([filtered_train_dataset, validation_layer]) diff --git a/weak_supervision/data/e3c_blended_methods_datamodule.py b/weak_supervision/data/e3c_blended_methods_datamodule.py new file mode 100644 index 0000000..103ca64 --- /dev/null +++ b/weak_supervision/data/e3c_blended_methods_datamodule.py @@ -0,0 +1,134 @@ +from typing import Optional + +import datasets +import numpy as np +from datasets import Dataset +from sklearn.model_selection import KFold +from torch.utils.data import Subset +from transformers import AutoTokenizer +from weak_supervision.data.e3c_datamodule import E3CDataModule + + +class E3CBlendedMethodsDataModule(E3CDataModule): + """This dataset mix annotation from instructGPT and dictionary extraction given a ratio. + + A DataModule implements 5 key methods: + + def prepare_data(self): + # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP) + # download data, pre-process, split, save to disk, etc... + def setup(self, stage): + # things to do on every process in DDP + # load data, set variables, etc... + def train_dataloader(self): + # return train dataloader + def val_dataloader(self): + # return validation dataloader + def test_dataloader(self): + # return test dataloader + def teardown(self): + # called on every process in DDP + # clean up after fit or test + + This allows you to share a full dataset without explaining how to download, + split, transform and process the data. + + Read the docs: + https://pytorch-lightning.readthedocs.io/en/latest/data/datamodule.html + """ + + def __init__( + self, + batch_size: int = 16, + num_workers: int = 0, + pin_memory: bool = False, + tokenizer: str = "camembert-base", + layer: str = "fr.layer2", + language: str = "fr", + fold: int = 0, + ratio: float = 0.5, + ): + """Initialize a DataModule. + + Args: + batch_size: the batch size to use for the dataloaders. + num_workers: the number of workers to use for loading data. + pin_memory: whether to copy Tensors into CUDA pinned memory. + tokenizer: the tokenizer to use associated with the model. + layer: the layer to use for the E3C dataset. + In this format: "{language}.layer{layer number}". + language: the language to use for the E3C dataset. + fold: The current fold distribution . The dataset is split in k folds. + This fold hyperparameter is used to select the n-th fold as a validation set and + the other as a training set. + ratio: the ratio to set a proportion between the dataset annotated with InstructGPT and + the other annotated with dictionary extraction. A ratio equals to 1 means that the + dataset is composed of only InstructGPT annotations. + """ + + super().__init__() + + # this line allows to access init params with 'self.hparams' attribute + # also ensures init params will be stored in ckpt + self.save_hyperparameters(logger=False) + + self.data_train: Optional[Dataset] = None + self.data_val: Optional[Dataset] = None + self.data_test: Optional[Dataset] = None + self.labels: Optional[list] = None + self.tokenizer = AutoTokenizer.from_pretrained( + self.hparams.tokenizer, + add_prefix_space=True, + ) + + def setup(self, stage: Optional[str] = None) -> None: + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be + careful not to execute things like random split twice! + This method setup the different huggingface datasets and the k-fold split. + Several mappings are applied to the datasets to tokenize and align the labels. + + Args: + stage: the stage to setup. Can be either 'fit' or 'test' or None. + """ + e3c_dataset = ( + datasets.load_dataset("bio-datasets/e3c") + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + e3c_llm_dataset = ( + datasets.load_dataset("bio-datasets/e3c-llm") + .map(self.map_offset_to_text, batched=True) + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + train_set = e3c_llm_dataset[f"{self.hparams.language}_layer2"] + test_set = e3c_dataset[f"{self.hparams.language}.layer1"] + + k_fold = KFold(n_splits=5, shuffle=True) + self.labels = e3c_dataset[f"{self.hparams.language}.layer2"].features[ + "clinical_entity_tags" + ] + + data_train_ids, data_val_ids = list( + k_fold.split(train_set), + )[self.hparams.fold] + e3c_train_dataset = Dataset.from_dict( + e3c_dataset[f"{self.hparams.language}.layer2"][data_train_ids] + ) + e3c_llm_train_dataset = Dataset.from_dict( + e3c_llm_dataset[f"{self.hparams.language}_layer2"][data_train_ids] + ) + self.data_train = e3c_llm_train_dataset.map( + ( + lambda x, x_idx: x + if np.random.choice([0, 1], p=[self.hparams.ratio, 1.0 - self.hparams.ratio]) == 0 + else e3c_train_dataset[x_idx] + ), + with_indices=True, + ) + self.data_val = Subset(train_set, data_val_ids.tolist()) + self.data_test = test_set diff --git a/weak_supervision/data/e3c_datamodule.py b/weak_supervision/data/e3c_datamodule.py new file mode 100644 index 0000000..e588f13 --- /dev/null +++ b/weak_supervision/data/e3c_datamodule.py @@ -0,0 +1,223 @@ +from typing import Optional + +import datasets +from pytorch_lightning import LightningDataModule +from sklearn.model_selection import KFold +from torch.utils.data import DataLoader, Dataset, Subset +from transformers import AutoTokenizer, DataCollatorForTokenClassification + + +class E3CDataModule(LightningDataModule): + """Basic e3c dataset with only instructGPT annotations or dictionary extraction annotations. + + A DataModule implements 5 key methods: + + def prepare_data(self): + # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP) + # download data, pre-process, split, save to disk, etc... + def setup(self, stage): + # things to do on every process in DDP + # load data, set variables, etc... + def train_dataloader(self): + # return train dataloader + def val_dataloader(self): + # return validation dataloader + def test_dataloader(self): + # return test dataloader + def teardown(self): + # called on every process in DDP + # clean up after fit or test + + This allows you to share a full dataset without explaining how to download, + split, transform and process the data. + + Read the docs: + https://pytorch-lightning.readthedocs.io/en/latest/data/datamodule.html + """ + + def __init__( + self, + batch_size: int = 16, + num_workers: int = 0, + pin_memory: bool = False, + tokenizer: str = "camembert-base", + layer: str = "fr.layer2", + instructgpt_ws: bool = True, + language: str = "fr", + fold: int = 0, + ): + """Initialize a DataModule. + + Args: + batch_size: the batch size to use for the dataloaders. + num_workers: the number of workers to use for loading data. + pin_memory: whether to copy Tensors into CUDA pinned memory. + tokenizer: the tokenizer to use associated with the model. + layer: the layer to use for the E3C dataset. + In this format: "{language}.layer{layer number}". + instructgpt_ws: whether to use the instructgpt_ws dataset. + language: the language to use for the E3C dataset. + fold: The current fold distribution . The dataset is split in k folds. + This fold hyperparameter is used to select the n-th fold as a validation set and + the other as a training set. + """ + + super().__init__() + + # this line allows to access init params with 'self.hparams' attribute + # also ensures init params will be stored in ckpt + self.save_hyperparameters(logger=False) + + self.data_train: Optional[Dataset] = None + self.data_val: Optional[Dataset] = None + self.data_test: Optional[Dataset] = None + self.labels: Optional[list] = None + self.tokenizer = AutoTokenizer.from_pretrained( + self.hparams.tokenizer, add_prefix_space=True + ) + + def prepare_data(self) -> None: + """Download data if needed. + + Do not use it to assign state (self.x = y). + """ + pass + + def setup(self, stage: Optional[str] = None) -> None: + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be + careful not to execute things like random split twice! + This method setup the different huggingface datasets and the k-fold split. + Several mappings are applied to the datasets to tokenize and align the labels. + + Args: + stage: the stage to setup. Can be either 'fit' or 'test'. + """ + e3c_dataset = ( + datasets.load_dataset("bio-datasets/e3c") + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + if self.hparams.instructgpt_ws: + e3c_llm_dataset = ( + datasets.load_dataset("bio-datasets/e3c-llm") + .map(self.map_offset_to_text, batched=True) + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + train_set = e3c_llm_dataset[f"{self.hparams.language}_layer2"] + else: + train_set = e3c_dataset[f"{self.hparams.language}.layer2"] + test_set = e3c_dataset[f"{self.hparams.language}.layer1"] + + k_fold = KFold(n_splits=5, shuffle=True) + self.labels = e3c_dataset[f"{self.hparams.language}.layer2"].features[ + "clinical_entity_tags" + ] + + data_train_ids, data_val_ids = list( + k_fold.split(train_set), + )[self.hparams.fold] + self.data_train = Subset(train_set, data_train_ids.tolist()) + self.data_val = Subset(train_set, data_val_ids.tolist()) + self.data_test = test_set + + def train_dataloader(self) -> DataLoader: + """Return the train dataloader. + + Returns: the train dataloader. + """ + return DataLoader( + dataset=self.data_train, + batch_size=self.hparams.batch_size, + num_workers=self.hparams.num_workers, + pin_memory=self.hparams.pin_memory, + collate_fn=DataCollatorForTokenClassification(tokenizer=self.tokenizer), + shuffle=False, + ) + + def val_dataloader(self) -> DataLoader: + """Return the validation dataloader. + + Returns: the validation dataloader. + """ + return DataLoader( + dataset=self.data_val, + batch_size=self.hparams.batch_size, + num_workers=self.hparams.num_workers, + pin_memory=self.hparams.pin_memory, + collate_fn=DataCollatorForTokenClassification(tokenizer=self.tokenizer), + shuffle=False, + ) + + def test_dataloader(self) -> DataLoader: + """Return the test dataloader. + + Returns: the test dataloader. + """ + return DataLoader( + dataset=self.data_test, + batch_size=self.hparams.batch_size, + num_workers=self.hparams.num_workers, + pin_memory=self.hparams.pin_memory, + collate_fn=DataCollatorForTokenClassification(tokenizer=self.tokenizer), + shuffle=False, + ) + + def teardown(self, stage: Optional[str] = None) -> None: + """Clean up after fit or test. + + Args: + stage: the stage to teardown. Can be either 'fit' or 'test'. + """ + pass + + def state_dict(self) -> dict: + """Extra things to save to checkpoint.""" + return {} + + def load_state_dict(self, state_dict: dict) -> None: + """Things to do when loading checkpoint.""" + pass + + def tokenize_and_align_labels(self, examples: dict) -> dict: + tokenized_inputs = self.tokenizer( + examples["tokens"], truncation=True, is_split_into_words=True + ) + + labels = [] + for i, label in enumerate(examples["clinical_entity_tags"]): + # Map tokens to their respective word. + word_ids = tokenized_inputs.word_ids(batch_index=i) + previous_word_idx = None + label_ids = [] + for word_idx in word_ids: # Set the special tokens to -100. + if word_idx is None: + label_ids.append(-100) + elif word_idx != previous_word_idx: # Only label the first token of a given word. + label_ids.append(label[word_idx]) + else: + label_ids.append(-100) + previous_word_idx = word_idx + labels.append(label_ids) + + tokenized_inputs["labels"] = labels + return tokenized_inputs + + @staticmethod + def map_offset_to_text(examples: dict) -> dict: + """Map the offsets to the text. To compute the tokens. + + Args: + examples: the examples to map. + + Returns: return the tokens of the text in a dict. + """ + return { + "tokens": [ + [text[offset[0] : offset[1]] for offset in offsets] + for text, offsets in zip(examples["text"], examples["tokens_offsets"]) + ] + } diff --git a/weak_supervision/data/e3c_val_datamodule.py b/weak_supervision/data/e3c_val_datamodule.py new file mode 100644 index 0000000..3526c13 --- /dev/null +++ b/weak_supervision/data/e3c_val_datamodule.py @@ -0,0 +1,69 @@ +from typing import Optional + +import datasets +from weak_supervision.data.e3c_datamodule import E3CDataModule + + +class E3CValidationDataModule(E3CDataModule): + """This dataset use the validation dataset either from instructGPT or using manual extraction. + + A DataModule implements 5 key methods: + + def prepare_data(self): + # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP) + # download data, pre-process, split, save to disk, etc... + def setup(self, stage): + # things to do on every process in DDP + # load data, set variables, etc... + def train_dataloader(self): + # return train dataloader + def val_dataloader(self): + # return validation dataloader + def test_dataloader(self): + # return test dataloader + def teardown(self): + # called on every process in DDP + # clean up after fit or test + + This allows you to share a full dataset without explaining how to download, + split, transform and process the data. + + Read the docs: + https://pytorch-lightning.readthedocs.io/en/latest/data/datamodule.html + """ + + def setup(self, stage: Optional[str] = None) -> None: + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be + careful not to execute things like random split twice! + This method setup the different huggingface datasets and the k-fold split. + Several mappings are applied to the datasets to tokenize and align the labels. + + Args: + stage: the stage to setup. Can be either 'fit' or 'test'. + """ + e3c_dataset = ( + datasets.load_dataset("bio-datasets/e3c") + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + if self.hparams.instructgpt_ws: + e3c_llm_dataset = ( + datasets.load_dataset("bio-datasets/e3c-llm") + .map(self.map_offset_to_text, batched=True) + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + train_set = e3c_llm_dataset[f"{self.hparams.language}_layer2_validation"] + else: + train_set = e3c_dataset[f"{self.hparams.language}.layer2.validation"] + test_set = e3c_dataset[f"{self.hparams.language}.layer1"] + + self.labels = e3c_dataset[f"{self.hparams.language}.layer2.validation"].features[ + "clinical_entity_tags" + ] + self.data_train = train_set + self.data_val = test_set + self.data_test = test_set diff --git a/weak_supervision/data/e3c_xlm_datamodule.py b/weak_supervision/data/e3c_xlm_datamodule.py new file mode 100644 index 0000000..9132bd0 --- /dev/null +++ b/weak_supervision/data/e3c_xlm_datamodule.py @@ -0,0 +1,144 @@ +from typing import Optional + +import datasets +import numpy as np +from datasets import Dataset +from sklearn.model_selection import KFold +from torch.utils.data import Subset +from transformers import AutoTokenizer +from weak_supervision.data.e3c_datamodule import E3CDataModule + + +class E3CXlmDataModule(E3CDataModule): + """This dataset is the whole languages from layer 2 as train set. The validation is monolingual. + + A DataModule implements 5 key methods: + + def prepare_data(self): + # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP) + # download data, pre-process, split, save to disk, etc... + def setup(self, stage): + # things to do on every process in DDP + # load data, set variables, etc... + def train_dataloader(self): + # return train dataloader + def val_dataloader(self): + # return validation dataloader + def test_dataloader(self): + # return test dataloader + def teardown(self): + # called on every process in DDP + # clean up after fit or test + + This allows you to share a full dataset without explaining how to download, + split, transform and process the data. + + Read the docs: + https://pytorch-lightning.readthedocs.io/en/latest/data/datamodule.html + """ + + def __init__( + self, + batch_size: int = 16, + num_workers: int = 0, + pin_memory: bool = False, + tokenizer: str = "camembert-base", + layer: str = "fr.layer2", + language: str = "fr", + fold: int = 0, + ratio: float = 0.5, + ): + """Initialize a DataModule. + + Args: + batch_size: the batch size to use for the dataloaders. + num_workers: the number of workers to use for loading data. + pin_memory: whether to copy Tensors into CUDA pinned memory. + tokenizer: the tokenizer to use associated with the model. + layer: the layer to use for the E3C dataset. + In this format: "{language}.layer{layer number}". + language: the language to use for the E3C dataset. + fold: The current fold distribution . The dataset is split in k folds. + This fold hyperparameter is used to select the n-th fold as a validation set and + the other as a training set. + ratio: the ratio to set a proportion between the dataset annotated with InstructGPT and + the other annotated with dictionary extraction. A ratio equals to 1 means that the + dataset is composed of only InstructGPT annotations. + """ + + super().__init__() + + # this line allows to access init params with 'self.hparams' attribute + # also ensures init params will be stored in ckpt + self.save_hyperparameters(logger=False) + + self.data_train: Optional[Dataset] = None + self.data_val: Optional[Dataset] = None + self.data_test: Optional[Dataset] = None + self.labels: Optional[list] = None + self.tokenizer = AutoTokenizer.from_pretrained(self.hparams.tokenizer) + + def setup(self, stage: Optional[str] = None) -> None: + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be + careful not to execute things like random split twice! + This method setup the different huggingface datasets and the k-fold split. + Several mappings are applied to the datasets to tokenize and align the labels. + + Args: + stage: the stage to setup. Can be either 'fit' or 'test' or None. + """ + e3c_dataset = ( + datasets.load_dataset("bio-datasets/e3c") + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + e3c_llm_dataset = ( + datasets.load_dataset("bio-datasets/e3c-llm") + .map(self.map_offset_to_text, batched=True) + .map(self.tokenize_and_align_labels, batched=True) + .with_format(columns=["input_ids", "attention_mask", "labels"]) + ) + + train_llm_set = datasets.concatenate_datasets( + [ + e3c_llm_dataset["en_layer2"], + e3c_llm_dataset["fr_layer2"], + e3c_llm_dataset["es_layer2"], + e3c_llm_dataset["eu_layer2"], + e3c_llm_dataset["it_layer2"], + ], + ) + train_set = datasets.concatenate_datasets( + [ + e3c_dataset["en.layer2"], + e3c_dataset["fr.layer2"], + e3c_dataset["es.layer2"], + e3c_dataset["eu.layer2"], + e3c_dataset["it.layer2"], + ], + ) + test_set = e3c_dataset[f"{self.hparams.language}.layer1"] + + k_fold = KFold(n_splits=5, shuffle=True) + self.labels = e3c_dataset[f"{self.hparams.language}.layer2"].features[ + "clinical_entity_tags" + ] + + data_train_ids, data_val_ids = list( + k_fold.split(train_set), + )[self.hparams.fold] + e3c_train_dataset = Dataset.from_dict(train_set[data_train_ids]) + e3c_llm_train_dataset = Dataset.from_dict(train_llm_set[data_train_ids]) + self.data_train = e3c_llm_train_dataset.map( + ( + lambda x, x_idx: x + if np.random.choice([0, 1], p=[self.hparams.ratio, 1.0 - self.hparams.ratio]) == 0 + else e3c_train_dataset[x_idx] + ), + with_indices=True, + ) + self.data_val = Subset(train_set, data_val_ids.tolist()) + self.data_test = test_set diff --git a/weak_supervision/eval.py b/weak_supervision/eval.py new file mode 100644 index 0000000..5f62b7a --- /dev/null +++ b/weak_supervision/eval.py @@ -0,0 +1,89 @@ +from typing import List, Tuple + +import hydra +import pyrootutils +from omegaconf import DictConfig +from pytorch_lightning import LightningDataModule, LightningModule, Trainer +from pytorch_lightning.loggers import Logger + +pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) +# ------------------------------------------------------------------------------------ # +# the setup_root above is equivalent to: +# - adding project root dir to PYTHONPATH +# (so you don't need to force user to install project as a package) +# (necessary before importing any local modules e.g. `from re_verbalization import utils`) +# - setting up PROJECT_ROOT environment variable +# (which is used as a base for paths in "configs/paths/default.yaml") +# (this way all filepaths are the same no matter where you run the code) +# - loading environment variables from ".env" in root dir +# +# you can remove it if you: +# 1. either install project as a package or move entry files to project root dir +# 2. set `root_dir` to "." in "configs/paths/default.yaml" +# +# more info: https://github.com/ashleve/pyrootutils +# ------------------------------------------------------------------------------------ # + +from weak_supervision import utils # noqa: E402 + +log = utils.get_pylogger(__name__) + + +@utils.task_wrapper +def evaluate(cfg: DictConfig) -> Tuple[dict, dict]: + """Evaluates given checkpoint on a datamodule testset. + + This method is wrapped in optional @task_wrapper decorator which applies extra utilities + before and after the call. + + Args: + cfg (DictConfig): Configuration composed by Hydra. + + Returns: + Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects. + """ + + assert cfg.ckpt_path # nosec + + log.info(f"Instantiating datamodule <{cfg.data._target_}>") + datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data) + + log.info(f"Instantiating model <{cfg.model._target_}>") + model: LightningModule = hydra.utils.instantiate(cfg.model) + + log.info("Instantiating loggers...") + logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger")) + + log.info(f"Instantiating trainer <{cfg.trainer._target_}>") + trainer: Trainer = hydra.utils.instantiate(cfg.trainer, logger=logger) + + object_dict = { + "cfg": cfg, + "datamodule": datamodule, + "model": model, + "logger": logger, + "trainer": trainer, + } + + if logger: + log.info("Logging hyperparameters!") + utils.log_hyperparameters(object_dict) + + log.info("Starting testing!") + trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path) + + # for predictions use trainer.predict(...) + # predictions = trainer.predict(model=model, dataloaders=dataloaders, ckpt_path=cfg.ckpt_path) + + metric_dict = trainer.callback_metrics + + return metric_dict, object_dict + + +@hydra.main(version_base="1.3", config_path="../configs", config_name="eval.yaml") +def main(cfg: DictConfig) -> None: + evaluate(cfg) + + +if __name__ == "__main__": + main() diff --git a/weak_supervision/models/__init__.py b/weak_supervision/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/weak_supervision/models/e3c_module.py b/weak_supervision/models/e3c_module.py new file mode 100644 index 0000000..8cd3d48 --- /dev/null +++ b/weak_supervision/models/e3c_module.py @@ -0,0 +1,246 @@ +import torch +from pytorch_lightning import LightningModule +from torch import Tensor +from torchmetrics import MaxMetric, MeanMetric, MetricCollection +from torchmetrics.classification import ( + BinaryF1Score, + BinaryPrecision, + BinaryRecall, + MulticlassF1Score, + MulticlassPrecision, + MulticlassRecall, +) +from torchmetrics.classification.accuracy import MulticlassAccuracy +from transformers import AutoModelForTokenClassification + + +class E3CTokenClassificationModule(LightningModule): + """Example of LightningModule for E3C classification. + + A LightningModule organizes your PyTorch code into 6 sections: + - Computations (init) + - Train loop (training_step) + - Validation loop (validation_step) + - Test loop (test_step) + - Prediction Loop (predict_step) + - Optimizers and LR Schedulers (configure_optimizers) + + Docs: + https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html + """ + + def __init__( + self, + model: str, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler, + lang: str, + ): + """Initialize model. + We use the init to define the model architecture and the different metrics we want to track. + + Args: + model: The model to use for the classification task. + optimizer: The optimizer to use for the classification task. + scheduler: The scheduler to use for the classification task. + lang: The language of the model. + """ + super().__init__() + + # this line allows to access init params with 'self.hparams' attribute + # also ensures init params will be stored in ckpt + self.save_hyperparameters(logger=False) + + self.transformers = AutoModelForTokenClassification.from_pretrained( + self.hparams.model, num_labels=3 + ) + + # loss function + self.criterion = torch.nn.CrossEntropyLoss() + + # metric objects for calculating and averaging accuracy across batches + self.tokens_scores = MetricCollection( + MulticlassAccuracy(average="macro", num_classes=3, ignore_index=-100), + MulticlassF1Score(average="macro", num_classes=3, ignore_index=-100), + MulticlassPrecision(average="macro", num_classes=3, ignore_index=-100), + MulticlassRecall(average="macro", num_classes=3, ignore_index=-100), + ) + self.phrases_scores = MetricCollection( + BinaryF1Score(ignore_index=3), + BinaryPrecision(ignore_index=3), + BinaryRecall(ignore_index=3), + ) + + self.no_agg_scores = MetricCollection( + MulticlassF1Score(average=None, num_classes=3, ignore_index=-100), + MulticlassPrecision(average=None, num_classes=3, ignore_index=-100), + MulticlassRecall(average=None, num_classes=3, ignore_index=-100), + ) + + self.train_tokens_metrics = self.tokens_scores.clone(prefix="train/tokens/") + self.val_tokens_metrics = self.tokens_scores.clone(prefix="val/tokens/") + self.test_tokens_metrics = self.tokens_scores.clone(prefix="test/tokens/") + + self.train_phrases_metrics = self.phrases_scores.clone(prefix="train/phrases/") + self.val_phrases_metrics = self.phrases_scores.clone(prefix="val/phrases/") + self.test_phrases_metrics = self.phrases_scores.clone(prefix="test/phrases/") + self.test_no_agg_metrics = self.no_agg_scores.clone() + + # for averaging loss across batches + self.train_loss = MeanMetric() + self.val_loss = MeanMetric() + self.test_loss = MeanMetric() + + # for tracking best so far validation accuracy + self.val_f1_best = MaxMetric() + + def on_train_start(self) -> None: + """Called when the train begins.""" + # by default lightning executes validation step sanity checks before training starts, + # so we need to make sure val_acc_best doesn't store accuracy from these checks + self.val_f1_best.reset() + + def model_step(self, batch: dict) -> tuple[Tensor, Tensor]: + """Perform a forward pass through the model. + + Args: + batch: The batch to perform the forward pass on. + + Returns: + The loss and the predictions probabilities. + """ + outputs = self.transformers(**batch) + return outputs.loss, outputs.logits + + def training_step(self, batch: dict, batch_idx: int) -> dict: + """Perform a training step. We monitor the loss, the accuracy and the F1 score. + + Args: + batch: The batch to perform the training step on. + batch_idx: The index of the batch. + + Returns: + The loss, the predictions probabilities and the gold labels. + """ + loss, predictions = self.model_step(batch) + labels = batch["labels"] + # update and log metrics + self.train_loss(loss) + self.train_tokens_metrics(predictions.view(-1, 3), labels.view(-1)) + self.log("train/loss", self.train_loss, on_step=False, on_epoch=True, prog_bar=True) + self.log_dict(self.train_tokens_metrics, on_step=False, on_epoch=True, prog_bar=True) + + # we can return here dict with any tensors + # and then read it in some callback or in `training_epoch_end()` below + # remember to always return loss from `training_step()` or backpropagation will fail! + return {"loss": loss, "predictions": predictions, "targets": batch["labels"]} + + def training_epoch_end(self, outputs: list) -> None: + """Called at the end of the training epoch.""" + # `outputs` is a list of dicts returned from `training_step()` + + # Warning: when overriding `training_epoch_end()`, lightning accumulates outputs + # from all batches of the epoch + # this may not be an issue when training on e3c + # but on larger datasets/models it's easy to run into out-of-memory errors + + # consider detaching tensors before returning them from `training_step()` + # or using `on_train_epoch_end()` instead which doesn't accumulate outputs + + pass + + def validation_step(self, batch: dict, batch_idx: int) -> dict: + """Perform a validation step. We monitor the loss, the accuracy and the F1 score. + + Args: + batch: The batch to perform the validation step on. + batch_idx: The index of the batch. + + Returns: + The loss, the predictions probabilities and the gold labels. + """ + + loss, predictions = self.model_step(batch) + labels = batch["labels"] + binary_predictions = predictions.clone().softmax(dim=-1).argmax(dim=-1) + binary_predictions[binary_predictions == 2] = 1 + binary_labels = labels.clone() + binary_labels[binary_labels == 2] = 1 + binary_labels[binary_labels == -100] = 3 + # update and log metrics + self.val_loss(loss) + self.val_tokens_metrics(predictions.view(-1, 3), labels.view(-1)) + self.val_phrases_metrics(binary_predictions.view(-1), binary_labels.view(-1)) + self.log("val/loss", self.val_loss, on_step=False, on_epoch=True, prog_bar=True) + self.log_dict(self.val_tokens_metrics, on_step=False, on_epoch=True, prog_bar=True) + self.log_dict(self.val_phrases_metrics, on_step=False, on_epoch=True, prog_bar=True) + return {"loss": loss, "predictions": predictions, "targets": batch["labels"]} + + def validation_epoch_end(self, outputs: list) -> None: + """Called at the end of the validation epoch. + We log the best so far validation accuracy and F1 score to select the best model. + + Args: + outputs: The outputs of the validation step. + """ + current_metrics = self.val_phrases_metrics.compute() + self.val_f1_best(current_metrics["val/phrases/BinaryF1Score"]) # update best so far val f1 + # log `val_acc_best` as a value through `.compute()` method, instead of as a metric object + # otherwise metric would be reset by lightning after each epoch + self.log("val/f1_best", self.val_f1_best.compute(), prog_bar=True) + + def test_step(self, batch: dict, batch_idx: int) -> dict: + """Perform a test step. We monitor the loss, the accuracy and the F1 score. + + Args: + batch: The batch to perform the test step on. + batch_idx: The index of the batch. + """ + + loss, predictions = self.model_step(batch) + labels = batch["labels"] + binary_predictions = predictions.clone().softmax(dim=-1).argmax(dim=-1) + binary_predictions[binary_predictions == 2] = 1 + binary_labels = labels.clone() + binary_labels[binary_labels == 2] = 1 + binary_labels[binary_labels == -100] = 3 + self.test_loss(loss) + self.test_tokens_metrics(predictions.view(-1, 3), labels.view(-1)) + self.test_phrases_metrics(binary_predictions.view(-1), binary_labels.view(-1)) + self.test_no_agg_metrics(predictions.view(-1, 3), labels.view(-1)) + self.log("test/loss", self.test_loss, on_step=False, on_epoch=True, prog_bar=True) + self.log_dict(self.test_tokens_metrics, on_step=False, on_epoch=True, prog_bar=True) + self.log_dict(self.test_phrases_metrics, on_step=False, on_epoch=True, prog_bar=True) + return {"loss": loss, "preds": predictions} + + def test_epoch_end(self, outputs: list) -> None: + """Perform the test epoch end.""" + no_aggregated_metrics = self.test_no_agg_metrics.compute() + dict_metrics = { + f"test/{log_key}/{label}": log_value[idx_label] + for idx_label, label in enumerate(self.trainer.datamodule.labels.feature.names) + for log_key, log_value in no_aggregated_metrics.items() + } + self.log_dict(dict_metrics, on_step=False, on_epoch=True, prog_bar=True) + + def configure_optimizers(self) -> dict: + """Choose what optimizers and learning-rate schedulers to use in your optimization. + Normally you'd need one. But in the case of GANs or similar you might have multiple. + + Examples: + https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html# + configure-optimizers + """ + optimizer = self.hparams.optimizer(params=self.parameters()) + if self.hparams.scheduler is not None: + scheduler = self.hparams.scheduler(optimizer=optimizer) + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": scheduler, + "monitor": "val/loss", + "interval": "epoch", + "frequency": 1, + }, + } + return {"optimizer": optimizer} diff --git a/weak_supervision/train.py b/weak_supervision/train.py new file mode 100644 index 0000000..c208175 --- /dev/null +++ b/weak_supervision/train.py @@ -0,0 +1,119 @@ +from typing import List, Optional, Tuple + +import hydra +import pyrootutils +import pytorch_lightning as pl +from omegaconf import DictConfig +from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer +from pytorch_lightning.loggers import Logger + +pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) +# ------------------------------------------------------------------------------------ # +# the setup_root above is equivalent to: +# - adding project root dir to PYTHONPATH +# (so you don't need to force user to install project as a package) +# (necessary before importing any local modules e.g. `from re_verbalization import utils`) +# - setting up PROJECT_ROOT environment variable +# (which is used as a base for paths in "configs/paths/default.yaml") +# (this way all filepaths are the same no matter where you run the code) +# - loading environment variables from ".env" in root dir +# +# you can remove it if you: +# 1. either install project as a package or move entry files to project root dir +# 2. set `root_dir` to "." in "configs/paths/default.yaml" +# +# more info: https://github.com/ashleve/pyrootutils +# ------------------------------------------------------------------------------------ # + +from weak_supervision import utils # noqa: E402 + +log = utils.get_pylogger(__name__) + + +@utils.task_wrapper +def train(cfg: DictConfig) -> Tuple[dict, dict]: + """Trains the model. Can additionally evaluate on a testset, using best weights obtained during + training. + + This method is wrapped in optional @task_wrapper decorator which applies extra utilities + before and after the call. + + Args: + cfg (DictConfig): Configuration composed by Hydra. + + Returns: + Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects. + """ + + # set seed for random number generators in pytorch, numpy and python.random + if cfg.get("seed"): + pl.seed_everything(cfg.seed, workers=True) + + log.info(f"Instantiating datamodule <{cfg.data._target_}>") + datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data) + + log.info(f"Instantiating model <{cfg.model._target_}>") + model: LightningModule = hydra.utils.instantiate(cfg.model) + + log.info("Instantiating callbacks...") + callbacks: List[Callback] = utils.instantiate_callbacks(cfg.get("callbacks")) + + log.info("Instantiating loggers...") + logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger")) + + log.info(f"Instantiating trainer <{cfg.trainer._target_}>") + trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger) + + object_dict = { + "cfg": cfg, + "datamodule": datamodule, + "model": model, + "callbacks": callbacks, + "logger": logger, + "trainer": trainer, + } + + if logger: + log.info("Logging hyperparameters!") + utils.log_hyperparameters(object_dict) + + if cfg.get("train"): + log.info("Starting training!") + trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path")) + + train_metrics = trainer.callback_metrics + + if cfg.get("test"): + log.info("Starting testing!") + ckpt_path = trainer.checkpoint_callback.best_model_path + if ckpt_path == "": + log.warning("Best ckpt not found! Using current weights for testing...") + ckpt_path = None + trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path) + log.info(f"Best ckpt path: {ckpt_path}") + + test_metrics = trainer.callback_metrics + + # merge train and test metrics + metric_dict = {**train_metrics, **test_metrics} + + return metric_dict, object_dict + + +@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml") +def main(cfg: DictConfig) -> Optional[float]: + + # train the model + metric_dict, _ = train(cfg) + + # safely retrieve metric value for hydra-based hyperparameter optimization + metric_value = utils.get_metric_value( + metric_dict=metric_dict, metric_name=cfg.get("optimized_metric") + ) + + # return optimized metric + return metric_value + + +if __name__ == "__main__": + main() diff --git a/weak_supervision/utils/__init__.py b/weak_supervision/utils/__init__.py new file mode 100644 index 0000000..d4be256 --- /dev/null +++ b/weak_supervision/utils/__init__.py @@ -0,0 +1,12 @@ +from weak_supervision.utils.pylogger import get_pylogger +from weak_supervision.utils.rich_utils import enforce_tags, print_config_tree +from weak_supervision.utils.utils import ( + close_loggers, + extras, + get_metric_value, + instantiate_callbacks, + instantiate_loggers, + log_hyperparameters, + save_file, + task_wrapper, +) diff --git a/weak_supervision/utils/pylogger.py b/weak_supervision/utils/pylogger.py new file mode 100644 index 0000000..92ffa71 --- /dev/null +++ b/weak_supervision/utils/pylogger.py @@ -0,0 +1,17 @@ +import logging + +from pytorch_lightning.utilities import rank_zero_only + + +def get_pylogger(name=__name__) -> logging.Logger: + """Initializes multi-GPU-friendly python command line logger.""" + + logger = logging.getLogger(name) + + # this ensures all logging levels get marked with the rank zero decorator + # otherwise logs would get multiplied for each GPU process in multi-GPU setup + logging_levels = ("debug", "info", "warning", "error", "exception", "fatal", "critical") + for level in logging_levels: + setattr(logger, level, rank_zero_only(getattr(logger, level))) + + return logger diff --git a/weak_supervision/utils/rich_utils.py b/weak_supervision/utils/rich_utils.py new file mode 100644 index 0000000..9bd87cf --- /dev/null +++ b/weak_supervision/utils/rich_utils.py @@ -0,0 +1,97 @@ +from pathlib import Path +from typing import Sequence + +import rich +import rich.syntax +import rich.tree +from hydra.core.hydra_config import HydraConfig +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning.utilities import rank_zero_only +from rich.prompt import Prompt +from weak_supervision.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def print_config_tree( + cfg: DictConfig, + print_order: Sequence[str] = ( + "data", + "model", + "callbacks", + "logger", + "trainer", + "paths", + "extras", + ), + resolve: bool = False, + save_to_file: bool = False, +) -> None: + """Prints content of DictConfig using Rich library and its tree structure. + + Args: + cfg (DictConfig): Configuration composed by Hydra. + print_order (Sequence[str], optional): Determines in what order config components + are printed. + resolve (bool, optional): Whether to resolve reference fields of DictConfig. + save_to_file (bool, optional): Whether to export config to the hydra output folder. + """ + + style = "dim" + tree = rich.tree.Tree("CONFIG", style=style, guide_style=style) + + queue = [] + + # add fields from `print_order` to queue + for field in print_order: + queue.append(field) if field in cfg else log.warning( + f"Field '{field}' not found in config. Skipping '{field}' config printing..." + ) + + # add all the other fields to queue (not specified in `print_order`) + for field in cfg: + if field not in queue: + queue.append(field) + + # generate config tree from queue + for field in queue: + branch = tree.add(field, style=style, guide_style=style) + + config_group = cfg[field] + if isinstance(config_group, DictConfig): + branch_content = OmegaConf.to_yaml(config_group, resolve=resolve) + else: + branch_content = str(config_group) + + branch.add(rich.syntax.Syntax(branch_content, "yaml")) + + # print config tree + rich.print(tree) + + # save config tree to file + if save_to_file: + with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file: + rich.print(tree, file=file) + + +@rank_zero_only +def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None: + """Prompts user to input tags from command line if no tags are provided in config.""" + + if not cfg.get("tags"): + if "id" in HydraConfig().cfg.hydra.job: + raise ValueError("Specify tags before launching a multirun!") + + log.warning("No tags provided in config. Prompting user to input tags...") + tags = Prompt.ask("Enter a list of comma separated tags", default="dev") + tags = [t.strip() for t in tags.split(",") if t != ""] + + with open_dict(cfg): + cfg.tags = tags + + log.info(f"Tags: {cfg.tags}") + + if save_to_file: + with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file: + rich.print(cfg.tags, file=file) diff --git a/weak_supervision/utils/utils.py b/weak_supervision/utils/utils.py new file mode 100644 index 0000000..cae5881 --- /dev/null +++ b/weak_supervision/utils/utils.py @@ -0,0 +1,211 @@ +import warnings +from importlib.util import find_spec +from typing import Callable, List + +import hydra +from omegaconf import DictConfig +from pytorch_lightning import Callback +from pytorch_lightning.loggers import Logger +from pytorch_lightning.utilities import rank_zero_only +from weak_supervision.utils import pylogger, rich_utils + +log = pylogger.get_pylogger(__name__) + + +def task_wrapper(task_func: Callable) -> Callable: + """Optional decorator that wraps the task function in extra utilities. + + Makes multirun more resistant to failure. + + Utilities: + - Calling the `utils.extras()` before the task is started + - Calling the `utils.close_loggers()` after the task is finished or failed + - Logging the exception if occurs + - Logging the output dir + """ + + def wrap(cfg: DictConfig): + + # execute the task + try: + + # apply extra utilities + extras(cfg) + + metric_dict, object_dict = task_func(cfg=cfg) + + # things to do if exception occurs + except Exception as ex: + + # save exception to `.log` file + log.exception("") + + # when using hydra plugins like Optuna, you might want to disable raising exception + # to avoid multirun failure + raise ex + + # things to always do after either success or exception + finally: + + # display output dir path in terminal + log.info(f"Output dir: {cfg.paths.output_dir}") + + # close loggers (even if exception occurs so multirun won't fail) + close_loggers() + + return metric_dict, object_dict + + return wrap + + +def extras(cfg: DictConfig) -> None: + """Applies optional utilities before the task is started. + + Utilities: + - Ignoring python warnings + - Setting tags from command line + - Rich config printing + """ + + # return if no `extras` config + if not cfg.get("extras"): + log.warning("Extras config not found! ") + return + + # disable python warnings + if cfg.extras.get("ignore_warnings"): + log.info("Disabling python warnings! ") + warnings.filterwarnings("ignore") + + # prompt user to input tags from command line if none are provided in the config + if cfg.extras.get("enforce_tags"): + log.info("Enforcing tags! ") + rich_utils.enforce_tags(cfg, save_to_file=True) + + # pretty print config tree using Rich library + if cfg.extras.get("print_config"): + log.info("Printing config tree with Rich! ") + rich_utils.print_config_tree(cfg, resolve=True, save_to_file=True) + + +def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]: + """Instantiates callbacks from config.""" + callbacks: List[Callback] = [] + + if not callbacks_cfg: + log.warning("No callback configs found! Skipping..") + return callbacks + + if not isinstance(callbacks_cfg, DictConfig): + raise TypeError("Callbacks config must be a DictConfig!") + + for _, cb_conf in callbacks_cfg.items(): + if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf: + log.info(f"Instantiating callback <{cb_conf._target_}>") + callbacks.append(hydra.utils.instantiate(cb_conf)) + + return callbacks + + +def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]: + """Instantiates loggers from config.""" + logger: List[Logger] = [] + + if not logger_cfg: + log.warning("No logger configs found! Skipping...") + return logger + + if not isinstance(logger_cfg, DictConfig): + raise TypeError("Logger config must be a DictConfig!") + + for _, lg_conf in logger_cfg.items(): + if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf: + log.info(f"Instantiating logger <{lg_conf._target_}>") + logger.append(hydra.utils.instantiate(lg_conf)) + + return logger + + +@rank_zero_only +def log_hyperparameters(object_dict: dict) -> None: + """Controls which config parts are saved by lightning loggers. + + Additionally saves: + - Number of model parameters + """ + + hparams = {} + + cfg = object_dict["cfg"] + model = object_dict["model"] + trainer = object_dict["trainer"] + + if not trainer.logger: + log.warning("Logger not found! Skipping hyperparameter logging...") + return + + hparams["model"] = cfg["model"] + + # save number of model parameters + hparams["model/params/total"] = sum(p.numel() for p in model.parameters()) + hparams["model/params/trainable"] = sum( + p.numel() for p in model.parameters() if p.requires_grad + ) + hparams["model/params/non_trainable"] = sum( + p.numel() for p in model.parameters() if not p.requires_grad + ) + + hparams["data"] = cfg["data"] + hparams["trainer"] = cfg["trainer"] + + hparams["callbacks"] = cfg.get("callbacks") + hparams["extras"] = cfg.get("extras") + + hparams["task_name"] = cfg.get("task_name") + hparams["tags"] = cfg.get("tags") + hparams["ckpt_path"] = cfg.get("ckpt_path") + hparams["seed"] = cfg.get("seed") + + # send hparams to all loggers + for logger in trainer.loggers: + logger.log_hyperparams(hparams) + + +def get_metric_value(metric_dict: dict, metric_name: str) -> float: + """Safely retrieves value of the metric logged in LightningModule.""" + + if not metric_name: + log.info("Metric name is None! Skipping metric value retrieval...") + return 0.0 + + if metric_name not in metric_dict: + raise Exception( + f"Metric value not found! \n" + "Make sure metric name logged in LightningModule is correct!\n" + "Make sure `optimized_metric` name in `hparams_search` config is correct!" + ) + + metric_value = metric_dict[metric_name].item() + log.info(f"Retrieved metric value! <{metric_name}={metric_value}>") + + return metric_value + + +def close_loggers() -> None: + """Makes sure all loggers closed properly (prevents logging failure during multirun).""" + + log.info("Closing loggers...") + + if find_spec("wandb"): # if wandb is installed + import wandb + + if wandb.run: + log.info("Closing wandb!") + wandb.finish() + + +@rank_zero_only +def save_file(path: str, content: str) -> None: + """Save file in rank zero mode (only on one process in multi-GPU setup).""" + with open(path, "w+") as file: + file.write(content)