diff --git a/.editorconfig b/.editorconfig
deleted file mode 100644
index 814d7b2..0000000
--- a/.editorconfig
+++ /dev/null
@@ -1,24 +0,0 @@
-# http://editorconfig.org
-
-root = true
-
-[*]
-indent_style = space
-indent_size = 4
-trim_trailing_whitespace = true
-insert_final_newline = true
-charset = utf-8
-end_of_line = lf
-
-[*.bat]
-indent_style = tab
-end_of_line = crlf
-
-[LICENSE]
-insert_final_newline = false
-
-[Makefile]
-indent_style = tab
-
-[*.{yml, yaml}]
-indent_size = 2
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
deleted file mode 100644
index 0a59609..0000000
--- a/.pre-commit-config.yaml
+++ /dev/null
@@ -1,39 +0,0 @@
-repos:
- - repo: https://github.com/Lucas-C/pre-commit-hooks
- rev: v1.1.9
- hooks:
- - id: forbid-crlf
- - id: remove-crlf
- - repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v3.4.0
- hooks:
- - id: trailing-whitespace
- - id: end-of-file-fixer
- - id: check-merge-conflict
- - id: check-yaml
- args: [ --unsafe ]
- - repo: https://github.com/pre-commit/mirrors-isort
- rev: v5.8.0
- hooks:
- - id: isort
- args: [ "--filter-files" ]
- - repo: https://github.com/ambv/black
- rev: 21.5b1
- hooks:
- - id: black
- language_version: python3.8
- - repo: https://github.com/pycqa/flake8
- rev: 3.9.2
- hooks:
- - id: flake8
- additional_dependencies: [ flake8-typing-imports==1.10.0 ]
- - repo: https://github.com/pre-commit/mirrors-mypy
- rev: v0.901
- hooks:
- - id: mypy
- exclude: tests/
- - repo: https://github.com/nbQA-dev/nbQA
- rev: 1.2.2
- hooks:
- - id: nbqa-black
- - id: nbqa-isort
diff --git a/CHANGELOG.md b/CHANGELOG.md
deleted file mode 100644
index 28666c7..0000000
--- a/CHANGELOG.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Changelog
-
-## 0.1.0 (2021-12-17)
-
-* First release on PyPI.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
deleted file mode 100644
index 546f2a0..0000000
--- a/CONTRIBUTING.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# Contributing
-
-Contributions are welcome, and they are greatly appreciated! Every little bit
-helps, and credit will always be given.
-
-You can contribute in many ways:
-
-## Types of Contributions
-
-### Report Bugs
-
-Report bugs at [https://github.com/pietrolesci/energizer/issues](https://github.com/pietrolesci/energizer/issues).
-
-If you are reporting a bug, please include:
-
-* Your operating system name and version.
-* Any details about your local setup that might be helpful in troubleshooting.
-* Detailed steps to reproduce the bug.
-
-### Fix Bugs
-
-Look through the GitHub issues for bugs. Anything tagged with "bug" and "help
-wanted" is open to whoever wants to implement it.
-
-### Implement Features
-
-Look through the GitHub issues for features. Anything tagged with "enhancement"
-and "help wanted" is open to whoever wants to implement it.
-
-### Write Documentation
-
-Pytorch-Energizer could always use more documentation, whether as part of the
-official Pytorch-Energizer docs, in docstrings, or even on the web in blog posts,
-articles, and such.
-
-### Submit Feedback
-
-The best way to send feedback is to file an issue at https://github.com/pietrolesci/energizer/issues.
-
-If you are proposing a feature:
-
-* Explain in detail how it would work.
-* Keep the scope as narrow as possible, to make it easier to implement.
-* Remember that this is a volunteer-driven project, and that contributions
- are welcome :)
-
-## Set up local development environment!
-
-Ready to contribute? Here's how to set up `energizer` for local development.
-
-1. Fork the `energizer` repo on GitHub.
-
-1. Clone your fork locally
-
- ```bash
- git clone git@github.com:your_name_here/energizer.git
- ```
-
-1. Ensure [conda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links) is installed, otherwise install it
-
- ```bash
- LINK_TO_CONDA_INSTALLER= #(1)
- wget $LINK_TO_CONDA_INSTALLER
- #(2)
- ```
-
- 1. Check [here](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links) for a suitable version of the conda installer, copy the link, and paste it here to download the file.
-
- 2. Run the installer.
-
-1. Create a new conda environment
- ```bash
- CONDA_ENV_NAME= #(1)
- conda create -n $CONDA_ENV_NAME python=3.9 -y
- conda activate $CONDA_ENV_NAME
- ```
-
- 1. Put here the name of the conda environment.
-
-1. Ensure [poetry](https://python-poetry.org/docs/) is installed, otherwise install it
-
- ```bash
- curl -sSL https://install.python-poetry.org | python3 -
- ```
-
-1. Install dependencies and start your virtualenv:
-
- ```bash
- poetry install --all-extras --sync
- ```
-
-1. Create a branch for local development:
-
- ```bash
- git checkout -b name-of-your-bugfix-or-feature
- ```
-
- Now you can make your changes locally.
-
-1. When you're done making changes, check that your changes pass the
- tests, including testing other Python versions, with tox:
-
- ```bash
- poetry run tox
- ```
-
-1. Commit your changes and push your branch to GitHub:
-
- ```bash
- git add .
- git commit -m "Your detailed description of your changes."
- git push origin name-of-your-bugfix-or-feature
- ```
-
-1. Submit a pull request through the GitHub website.
-
-## Pull Request Guidelines
-
-Before you submit a pull request, check that it meets these guidelines:
-
-1. The pull request should include tests.
-2. If the pull request adds functionality, the docs should be updated. Put
- your new functionality into a function with a docstring, and add the
- feature to the list in README.md.
-3. The pull request should work for Python 3.7, 3.8, 3.9, 3.10. Check
- https://github.com/pietrolesci/energizer/actions
- and make sure that the tests pass for all supported Python versions.
-
-## Tips
-
-```bash
-poetry run pytest tests/some_test_file.py
-```
-
-To run a subset of tests.
-
-
-## Deploying
-
-A reminder for the maintainers on how to deploy.
-Make sure all your changes are committed (including an entry in CHANGELOG.md).
-Then run:
-
-```bash
-poetry run bump2version patch #(1)
-git push
-git push --tags
-```
-
-1. Possible values: `major` / `minor` / `patch`.
-
-GitHub Actions will then deploy to PyPI if tests pass.
diff --git a/docs/api/acquisition_functions.md b/docs/api/acquisition_functions.md
deleted file mode 100644
index cb5d6a8..0000000
--- a/docs/api/acquisition_functions.md
+++ /dev/null
@@ -1 +0,0 @@
-::: energizer.acquisition_functions
diff --git a/docs/api/query_strategies.md b/docs/api/query_strategies.md
deleted file mode 100644
index a2384bd..0000000
--- a/docs/api/query_strategies.md
+++ /dev/null
@@ -1,33 +0,0 @@
-## Random strategies
-
-::: energizer.query_strategies.RandomStrategy
- options:
- show_root_heading: true
-
-
-## Uncertainty-based query strategies
-
-Uncertainty-based query strategies select instance with high aleatoric uncertainty
-or epistemic uncertainty. Aleatoric uncertainty refers to the uncertainty
-in data due the data generation processes (sometimes called irreducible uncertainty).
-Epistemic uncertainty comes from the modeling/learning process and is caused by a
-lack of knowledge.
-
-::: energizer.query_strategies.LeastConfidenceStrategy
- options:
- show_root_heading: true
-
-::: energizer.query_strategies.MarginStrategy
- options:
- show_root_heading: true
-
-::: energizer.query_strategies.EntropyStrategy
- options:
- show_root_heading: true
-
-
-
----
-
-## Base classes
-::: energizer.query_strategies.base
diff --git a/docs/api/trainer.md b/docs/api/trainer.md
deleted file mode 100644
index f152302..0000000
--- a/docs/api/trainer.md
+++ /dev/null
@@ -1 +0,0 @@
-::: energizer.trainer
diff --git a/docs/changelog.md b/docs/changelog.md
deleted file mode 100644
index 67259da..0000000
--- a/docs/changelog.md
+++ /dev/null
@@ -1,3 +0,0 @@
-{%
- include-markdown "../CHANGELOG.md"
-%}
diff --git a/docs/contributing.md b/docs/contributing.md
deleted file mode 100644
index a485597..0000000
--- a/docs/contributing.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-hide:
- - navigation
----
-
-{%
- include-markdown "../CONTRIBUTING.md"
-%}
diff --git a/docs/examples/bert-agnews.ipynb b/docs/examples/bert-agnews.ipynb
deleted file mode 100644
index aab2969..0000000
--- a/docs/examples/bert-agnews.ipynb
+++ /dev/null
@@ -1,417 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# %load_ext autoreload\n",
- "# %autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "MODEL_NAME = \"google/bert_uncased_L-2_H-128_A-2\"\n",
- "BATCH_SIZE = 32\n",
- "EVAL_BATCH_SIZE = 512\n",
- "LEARNING_RATE = 0.0001\n",
- "\n",
- "trainer_kwargs = {\n",
- " \"query_size\": 1,\n",
- " \"max_epochs\": 3,\n",
- " \"max_labelling_epochs\": 5,\n",
- " \"test_after_labelling\": True,\n",
- " \"accelerator\": \"gpu\",\n",
- " \"limit_val_batches\": 1,\n",
- " # total_budget=5,\n",
- " # for testing purposes\n",
- " # limit_train_batches=10,\n",
- " # limit_test_batches=10,\n",
- " # limit_pool_batches=10,\n",
- " # log_every_n_steps=1,\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Pre"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "import os\n",
- "from copy import deepcopy\n",
- "from typing import Any, Dict, Tuple\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import torch\n",
- "import torch.nn.functional as F\n",
- "from datasets import load_dataset\n",
- "from pytorch_lightning import LightningModule\n",
- "from pytorch_lightning import Trainer as PLTrainer\n",
- "from pytorch_lightning import seed_everything\n",
- "from torch import Tensor, nn\n",
- "from torch.utils.data import DataLoader\n",
- "from torchmetrics import Accuracy, F1Score, MetricCollection, Precision, Recall\n",
- "from transformers import (\n",
- " AdamW,\n",
- " AutoModelForSequenceClassification,\n",
- " AutoTokenizer,\n",
- " DataCollatorWithPadding,\n",
- " get_constant_schedule_with_warmup,\n",
- ")\n",
- "\n",
- "from energizer import Trainer\n",
- "from energizer.acquisition_functions import entropy, expected_entropy\n",
- "from energizer.query_strategies import RandomStrategy\n",
- "from energizer.query_strategies.base import AccumulatorStrategy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
- "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
- "\n",
- "# renames \"label\" to \"labels\"\n",
- "collator = DataCollatorWithPadding(\n",
- " tokenizer=tokenizer, padding=True, return_tensors=\"pt\"\n",
- ")\n",
- "\n",
- "# load dataset\n",
- "dataset = load_dataset(\"pietrolesci/ag_news\", \"concat\")\n",
- "\n",
- "# tokenize\n",
- "dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True)\n",
- "columns_to_keep = [\"label\", \"input_ids\", \"token_type_ids\", \"attention_mask\"]\n",
- "\n",
- "# train-val split and record datasets\n",
- "train_set, test_set = dataset[\"train\"], dataset[\"test\"]\n",
- "_split = train_set.train_test_split(0.3)\n",
- "_, val_set = _split[\"train\"], _split[\"test\"]\n",
- "\n",
- "labels = train_set.features[\"label\"].names\n",
- "num_classes = len(labels)\n",
- "\n",
- "# create dataloaders\n",
- "batch_size = BATCH_SIZE\n",
- "eval_batch_size = EVAL_BATCH_SIZE # this is use when evaluating on the pool too\n",
- "train_dl = DataLoader(\n",
- " train_set.with_format(columns=columns_to_keep),\n",
- " batch_size=batch_size,\n",
- " collate_fn=collator,\n",
- " num_workers=2,\n",
- ")\n",
- "val_dl = DataLoader(\n",
- " val_set.with_format(columns=columns_to_keep),\n",
- " batch_size=eval_batch_size,\n",
- " collate_fn=collator,\n",
- " num_workers=2,\n",
- ")\n",
- "test_dl = DataLoader(\n",
- " test_set.with_format(columns=columns_to_keep),\n",
- " batch_size=eval_batch_size,\n",
- " collate_fn=collator,\n",
- " num_workers=2,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "class TransformerModel(LightningModule):\n",
- " def __init__(\n",
- " self,\n",
- " model_name: str,\n",
- " num_classes: int,\n",
- " learning_rate: float = 0.00001,\n",
- " num_warmup_steps: int = 50,\n",
- " ) -> None:\n",
- " super().__init__()\n",
- " self.model_name = model_name\n",
- " self.num_classes = num_classes\n",
- " self.model = AutoModelForSequenceClassification.from_pretrained(\n",
- " self.model_name,\n",
- " num_labels=self.num_classes,\n",
- " )\n",
- " self.learning_rate = learning_rate\n",
- " self.num_warmup_steps = num_warmup_steps\n",
- " for stage in (\"train\", \"val\", \"test\"):\n",
- " metrics = MetricCollection(\n",
- " {\n",
- " \"accuracy\": Accuracy(),\n",
- " \"precision_macro\": Precision(\n",
- " num_classes=num_classes, average=\"macro\"\n",
- " ),\n",
- " \"recall_macro\": Recall(num_classes=num_classes, average=\"macro\"),\n",
- " \"f1_macro\": F1Score(num_classes=num_classes, average=\"macro\"),\n",
- " \"f1_micro\": F1Score(num_classes=num_classes, average=\"micro\"),\n",
- " }\n",
- " )\n",
- " setattr(self, f\"{stage}_metrics\", metrics)\n",
- "\n",
- " def common_step(self, batch: Any, stage: str):\n",
- " \"\"\"Outputs loss and logits, logs loss and metrics.\"\"\"\n",
- " out = self(batch)\n",
- " logits, loss = out.logits, out.loss\n",
- " self.log(f\"{stage}/loss\", loss)\n",
- "\n",
- " metrics = getattr(self, f\"{stage}_metrics\")(logits, batch[\"labels\"])\n",
- " self.log_dict(metrics)\n",
- "\n",
- " return loss\n",
- "\n",
- " def forward(self, batch) -> torch.Tensor:\n",
- " return self.model(**batch)\n",
- "\n",
- " def training_step(\n",
- " self, batch: Any, batch_idx: int = 0, optimizer_idx: int = 0\n",
- " ) -> Dict[str, Any]:\n",
- " return self.common_step(batch, \"train\")\n",
- "\n",
- " def validation_step(self, batch: Any, batch_idx: int = 0) -> Dict[str, Any]:\n",
- " return self.common_step(batch, \"val\")\n",
- "\n",
- " def test_step(self, batch: Any, batch_idx: int = 0) -> Dict[str, Any]:\n",
- " return self.common_step(batch, \"test\")\n",
- "\n",
- " def configure_optimizers(self) -> Dict[str, Any]:\n",
- " optimizer = AdamW(\n",
- " filter(lambda p: p.requires_grad, self.parameters()),\n",
- " lr=self.learning_rate,\n",
- " )\n",
- " return {\n",
- " \"optimizer\": optimizer,\n",
- " \"lr_scheduler\": {\n",
- " \"scheduler\": get_constant_schedule_with_warmup(\n",
- " optimizer=optimizer, num_warmup_steps=self.num_warmup_steps\n",
- " ),\n",
- " \"monitor\": \"val/loss\",\n",
- " \"frequency\": 1,\n",
- " \"interval\": \"step\",\n",
- " },\n",
- " }"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Active fit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = TransformerModel(\n",
- " model_name=MODEL_NAME, num_classes=num_classes, learning_rate=LEARNING_RATE\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Random strategy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "random_strategy = RandomStrategy(deepcopy(model))\n",
- "\n",
- "seed_everything(1994)\n",
- "trainer = Trainer(**trainer_kwargs)\n",
- "results = trainer.active_fit(\n",
- " model=random_strategy,\n",
- " train_dataloaders=train_dl,\n",
- " val_dataloaders=val_dl,\n",
- " test_dataloaders=test_dl,\n",
- ")\n",
- "random_df = results.to_pandas()\n",
- "random_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### AccumulatorStrategy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "class EntropyStrategy(AccumulatorStrategy):\n",
- " \"\"\"A implememntation of the `Entropy` active learning strategy.\"\"\"\n",
- "\n",
- " def get_inputs_from_batch(self, batch: Dict[str, Tensor]) -> Dict[str, Tensor]:\n",
- " batch.pop(\"labels\")\n",
- " return batch\n",
- "\n",
- " def pool_step(self, batch: Dict[str, Tensor], batch_idx: int) -> Tensor:\n",
- " logits = self(batch).logits\n",
- " return entropy(logits)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "entropy_strategy = EntropyStrategy(deepcopy(model))\n",
- "\n",
- "seed_everything(1994)\n",
- "trainer = Trainer(**trainer_kwargs)\n",
- "results = trainer.active_fit(\n",
- " model=entropy_strategy,\n",
- " train_dataloaders=train_dl,\n",
- " val_dataloaders=val_dl,\n",
- " test_dataloaders=test_dl,\n",
- ")\n",
- "entropy_df = results.to_pandas()\n",
- "entropy_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### AnchorPointsStrategy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# class MyRandomArchorPointsStrategy(RandomArchorPointsStrategy):\n",
- "# def get_search_query_from_batch(self, batch: Any) -> Tensor:\n",
- "# return batch[\"input_ids\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# random_anchor_points_strategy = RandomArchorPointsStrategy(deepcopy(model), 10)\n",
- "\n",
- "# datastore = ActiveDataModuleWithIndex(\n",
- "# train_dataloader=train_dl,\n",
- "# val_dataloaders=val_dl,\n",
- "# test_dataloaders=test_dl,\n",
- "# faiss_index_path=\"all-mpnet-base-v2_ag-news_train.faiss\",\n",
- "# )\n",
- "\n",
- "# seed_everything(1994)\n",
- "# trainer = Trainer(**trainer_kwargs)\n",
- "# results = trainer.active_fit(\n",
- "# model=random_anchor_points_strategy,\n",
- "# datastore=datastore,\n",
- "# )\n",
- "# rap_df = results.to_pandas()\n",
- "# rap_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.plot(random_df[\"train_size\"], random_df[\"accuracy\"], label=\"random\")\n",
- "plt.plot(entropy_df[\"train_size\"], entropy_df[\"accuracy\"], label=\"entropy\")\n",
- "plt.legend()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "random_df[\"strategy\"] = \"random\"\n",
- "entropy_df[\"strategy\"] = \"entropy\"\n",
- "results = pd.concat([random_df, entropy_df], ignore_index=False, axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# results.to_parquet(\"results_al.parquet\", index=False)\n",
- "# with open(\"results_al_metadata.json\", \"w\") as fl:\n",
- "# json.dump(trainer_kwargs, fl)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.9.13 ('energizer-dev')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.13"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "944ffd4f8dbdaa69d919c942d599fc997735694f55f2ea363be8b6b69a40ba5a"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/cnn-mnist.ipynb b/docs/examples/cnn-mnist.ipynb
deleted file mode 100644
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--- a/docs/examples/cnn-mnist.ipynb
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@@ -1,1388 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "from typing import Tuple, Union\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "import torch\n",
- "import torch.nn.functional as F\n",
- "from pytorch_lightning import LightningModule, seed_everything\n",
- "from torch import Tensor, nn\n",
- "from torch.utils.data import DataLoader, random_split\n",
- "from torchmetrics import Accuracy, F1Score, MetricCollection, Precision, Recall\n",
- "from torchvision import transforms\n",
- "from torchvision.datasets import MNIST\n",
- "\n",
- "from energizer import Trainer\n",
- "from energizer.query_strategies import (\n",
- " BALDStrategy,\n",
- " EntropyStrategy,\n",
- " LeastConfidenceStrategy,\n",
- " MarginStrategy,\n",
- " RandomStrategy,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data preparation\n",
- "\n",
- "Load and preprocess data, and prepare dataloaders"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_dir = \"./data\"\n",
- "\n",
- "preprocessing_pipe = transforms.Compose(\n",
- " [\n",
- " transforms.ToTensor(),\n",
- " transforms.Normalize((0.1307,), (0.3081,)),\n",
- " ]\n",
- ")\n",
- "\n",
- "# create train and test sets\n",
- "train_set = MNIST(data_dir, train=True, download=True, transform=preprocessing_pipe)\n",
- "test_set = MNIST(data_dir, train=False, download=True, transform=preprocessing_pipe)\n",
- "\n",
- "# train-val split to create a validation set\n",
- "train_set, val_set = random_split(train_set, [55000, 5000])\n",
- "\n",
- "# create dataloaders\n",
- "batch_size = 32\n",
- "eval_batch_size = 128 # this is use when evaluating on the pool too\n",
- "num_workers = os.cpu_count()\n",
- "\n",
- "# dataloaders\n",
- "train_dl = DataLoader(train_set, batch_size=batch_size, num_workers=num_workers)\n",
- "val_dl = DataLoader(val_set, batch_size=eval_batch_size, num_workers=num_workers)\n",
- "test_dl = DataLoader(test_set, batch_size=eval_batch_size, num_workers=num_workers)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Model definition\n",
- "Define the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "class MNISTCNNModel(LightningModule):\n",
- " def __init__(self) -> None:\n",
- " \"\"\"Here we define the model architecture and the metrics.\"\"\"\n",
- " super().__init__()\n",
- "\n",
- " # create the CNN\n",
- " self.model = nn.Sequential(\n",
- " nn.Conv2d(1, 32, kernel_size=5),\n",
- " nn.ReLU(),\n",
- " nn.MaxPool2d(kernel_size=2),\n",
- " nn.Dropout2d(),\n",
- " nn.Conv2d(32, 64, kernel_size=5),\n",
- " nn.ReLU(),\n",
- " nn.MaxPool2d(kernel_size=2),\n",
- " nn.Dropout2d(),\n",
- " nn.Flatten(),\n",
- " nn.Linear(1024, 128),\n",
- " nn.Dropout(),\n",
- " nn.Linear(128, self.num_classes),\n",
- " )\n",
- "\n",
- " # implement metrics\n",
- " for stage in (\"train\", \"val\", \"test\"):\n",
- " metrics = MetricCollection(\n",
- " {\n",
- " \"accuracy\": Accuracy(),\n",
- " \"precision_macro\": Precision(\n",
- " num_classes=self.num_classes, average=\"macro\"\n",
- " ),\n",
- " \"recall_macro\": Recall(\n",
- " num_classes=self.num_classes, average=\"macro\"\n",
- " ),\n",
- " \"f1_macro\": F1Score(\n",
- " num_classes=self.num_classes, average=\"macro\"\n",
- " ),\n",
- " \"f1_micro\": F1Score(\n",
- " num_classes=self.num_classes, average=\"micro\"\n",
- " ),\n",
- " }\n",
- " )\n",
- " setattr(self, f\"{stage}_metrics\", metrics)\n",
- "\n",
- " @property\n",
- " def num_classes(self) -> int:\n",
- " \"\"\"This is very tailored to MNIST, so hard-code this propoerty.\"\"\"\n",
- " return 10\n",
- "\n",
- " def get_inputs_from_batch(self, batch: Tuple[Tensor, Tensor]) -> Tensor:\n",
- " return batch[0]\n",
- "\n",
- " def forward(self, x: Tensor) -> Tensor:\n",
- " return self.model(x)\n",
- "\n",
- " def common_step(self, batch: Tuple[Tensor, Tensor], stage: str) -> Tensor:\n",
- " \"\"\"For convenience define a common step.\"\"\"\n",
- "\n",
- " # forward pass\n",
- " x, y = batch\n",
- " logits = self(x)\n",
- "\n",
- " # metrics\n",
- " loss = F.cross_entropy(logits, y)\n",
- " metrics = getattr(self, f\"{stage}_metrics\")(logits, y)\n",
- "\n",
- " # log\n",
- " self.log(\"loss\", loss, on_step=False, on_epoch=True, prog_bar=False)\n",
- " self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=False)\n",
- "\n",
- " return loss\n",
- "\n",
- " def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:\n",
- " return self.common_step(batch, \"train\")\n",
- "\n",
- " def validation_step(\n",
- " self, batch: Tuple[Tensor, Tensor], batch_idx: int\n",
- " ) -> Tensor:\n",
- " return self.common_step(batch, \"val\")\n",
- "\n",
- " def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:\n",
- " return self.common_step(batch, \"test\")\n",
- "\n",
- " def configure_optimizers(self) -> None:\n",
- " return torch.optim.SGD(self.parameters(), lr=0.001)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Active fit"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = MNISTCNNModel()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "!!! info \"Batch unpacking\"\n",
- " When passing a model to build a strategy, internally a `deepcopy` will be created. This is done to avoid modifying the model state and passing it around when trying other strategies. It avoids messing up states."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "strategies = {\n",
- " \"random\": RandomStrategy(model),\n",
- " \"leastconfidence\": LeastConfidenceStrategy(model),\n",
- " \"margin\": MarginStrategy(model),\n",
- " \"entropy\": EntropyStrategy(model),\n",
- " \"bald\": BALDStrategy(model),\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For clarity let's pack the trainer kwargs in a dictionary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "trainer_kwargs = {\n",
- " \"query_size\": 10, # new instances will be queried at each iteration\n",
- " \"max_epochs\": 3, # the underlying model will be fit for 3 epochs\n",
- " \"max_labelling_epochs\": 2, # how many times to run the active learning loop\n",
- " \"accelerator\": \"cpu\", # use the gpu\n",
- " \"test_after_labelling\": True, # since we have a test set, we test after each labelling iteration\n",
- " \"limit_val_batches\": 0, # do not validate\n",
- " \"log_every_n_steps\": 1, # we will have a few batches while training, so log on each\n",
- "}\n",
- "\n",
- "results_dict = {}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Run experiments"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Global seed set to 42\n",
- "GPU available: True (mps), used: False\n",
- "TPU available: False, using: 0 TPU cores\n",
- "IPU available: False, using: 0 IPUs\n",
- "HPU available: False, using: 0 HPUs\n",
- "/Users/lescipi/miniconda3/envs/energizer-dev/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
- " rank_zero_warn(\n",
- "\u001b[1;32m[2022-09-26 17:46:39] energizer/INFO\u001b[0m ~ \u001b[1;33mtrainer:269\u001b[0m$ \u001b[37mTrainer: trainer active_fit stage\u001b[0m\n",
- "\n",
- " | Name | Type | Params\n",
- "---------------------------------------------------\n",
- "0 | model | Sequential | 184 K \n",
- "1 | train_metrics | MetricCollection | 0 \n",
- "2 | val_metrics | MetricCollection | 0 \n",
- "3 | test_metrics | MetricCollection | 0 \n",
- "---------------------------------------------------\n",
- "184 K Trainable params\n",
- "0 Non-trainable params\n",
- "184 K Total params\n",
- "0.738 Total estimated model params size (MB)\n",
- "\u001b[1;36m[2022-09-26 17:46:39] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `RandomStrategy`\u001b[0m\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-------------------------Labelling Iteration 0--------------------------\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[1;36m[2022-09-26 17:46:39] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n"
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- "\u001b[1;32m[2022-09-26 17:46:41] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
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- "`Trainer.fit` stopped: `max_epochs=3` reached.\n",
- "\u001b[1;36m[2022-09-26 17:46:46] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n"
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- "\u001b[1;32m[2022-09-26 17:46:49] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
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- "Global seed set to 42\n",
- "GPU available: True (mps), used: False\n",
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- "IPU available: False, using: 0 IPUs\n",
- "HPU available: False, using: 0 HPUs\n",
- "/Users/lescipi/miniconda3/envs/energizer-dev/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
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- " | Name | Type | Params\n",
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- "184 K Trainable params\n",
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- "\u001b[1;32m[2022-09-26 17:47:28] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
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- "\u001b[1;36m[2022-09-26 17:47:37] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `LeastConfidenceStrategy`\u001b[0m\n",
- "Global seed set to 42\n",
- "GPU available: True (mps), used: False\n",
- "TPU available: False, using: 0 TPU cores\n",
- "IPU available: False, using: 0 IPUs\n",
- "HPU available: False, using: 0 HPUs\n",
- "/Users/lescipi/miniconda3/envs/energizer-dev/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
- " rank_zero_warn(\n",
- "\u001b[1;32m[2022-09-26 17:47:37] energizer/INFO\u001b[0m ~ \u001b[1;33mtrainer:269\u001b[0m$ \u001b[37mTrainer: trainer active_fit stage\u001b[0m\n",
- "\n",
- " | Name | Type | Params\n",
- "---------------------------------------------------\n",
- "0 | model | Sequential | 184 K \n",
- "1 | train_metrics | MetricCollection | 0 \n",
- "2 | val_metrics | MetricCollection | 0 \n",
- "3 | test_metrics | MetricCollection | 0 \n",
- "---------------------------------------------------\n",
- "184 K Trainable params\n",
- "0 Non-trainable params\n",
- "184 K Total params\n",
- "0.738 Total estimated model params size (MB)\n",
- "\u001b[1;36m[2022-09-26 17:47:37] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `MarginStrategy`\u001b[0m\n"
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- "\u001b[1;36m[2022-09-26 17:47:54] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n"
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- "\u001b[1;32m[2022-09-26 17:48:07] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
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- "/Users/lescipi/miniconda3/envs/energizer-dev/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
- " rank_zero_warn(\n",
- "\u001b[1;32m[2022-09-26 17:48:16] energizer/INFO\u001b[0m ~ \u001b[1;33mtrainer:269\u001b[0m$ \u001b[37mTrainer: trainer active_fit stage\u001b[0m\n",
- "\n",
- " | Name | Type | Params\n",
- "---------------------------------------------------\n",
- "0 | model | Sequential | 184 K \n",
- "1 | train_metrics | MetricCollection | 0 \n",
- "2 | val_metrics | MetricCollection | 0 \n",
- "3 | test_metrics | MetricCollection | 0 \n",
- "---------------------------------------------------\n",
- "184 K Trainable params\n",
- "0 Non-trainable params\n",
- "184 K Total params\n",
- "0.738 Total estimated model params size (MB)\n",
- "\u001b[1;36m[2022-09-26 17:48:16] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `EntropyStrategy`\u001b[0m\n"
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- "\u001b[1;32m[2022-09-26 17:48:28] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
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- "\u001b[1;36m[2022-09-26 17:48:28] energizer/DEBUG\u001b[0m ~ \u001b[1;33mactive_learning_loop:250\u001b[0m$ \u001b[37mMNISTCNNModel state dict has been re-initialized\u001b[0m\n",
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- "\u001b[1;36m[2022-09-26 17:48:34] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n"
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- "\u001b[1;32m[2022-09-26 17:48:46] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
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- "Global seed set to 42\n",
- "GPU available: True (mps), used: False\n",
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- "IPU available: False, using: 0 IPUs\n",
- "HPU available: False, using: 0 HPUs\n",
- "/Users/lescipi/miniconda3/envs/energizer-dev/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
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- "\n",
- " | Name | Type | Params\n",
- "---------------------------------------------------\n",
- "0 | model | Sequential | 184 K \n",
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- "3 | test_metrics | MetricCollection | 0 \n",
- "---------------------------------------------------\n",
- "184 K Trainable params\n",
- "0 Non-trainable params\n",
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- "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
- "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
- "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
- "│\u001b[36m \u001b[0m\u001b[36m accuracy \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.09989999979734421 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m f1_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.07487398386001587 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m f1_micro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.09989999979734421 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2.383131742477417 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m precision_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.09914936870336533 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m recall_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10110820829868317 \u001b[0m\u001b[35m \u001b[0m│\n",
- "└───────────────────────────┴───────────────────────────┘\n"
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[1;36m[2022-09-26 17:48:57] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `BALDStrategy`\u001b[0m\n",
- "\u001b[1;32m[2022-09-26 17:49:59] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
- "\u001b[1;32m[2022-09-26 17:49:59] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:281\u001b[0m$ \u001b[37mAnnotated 10 instances\u001b[0m\n",
- "\u001b[1;32m[2022-09-26 17:49:59] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:282\u001b[0m$ \u001b[37mNew data statistics\n",
- "num_pool_batches: 430\n",
- "num_train_batches: 1\n",
- "pool_size: 54990\n",
- "total_data_size: 55000\n",
- "train_size: 10\n",
- "\u001b[0m\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-------------------------Labelling Iteration 1--------------------------\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[1;36m[2022-09-26 17:49:59] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n",
- "\u001b[1;36m[2022-09-26 17:49:59] energizer/DEBUG\u001b[0m ~ \u001b[1;33mactive_learning_loop:250\u001b[0m$ \u001b[37mMNISTCNNModel state dict has been re-initialized\u001b[0m\n",
- "`Trainer.fit` stopped: `max_epochs=3` reached.\n",
- "\u001b[1;36m[2022-09-26 17:50:04] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n"
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- {
- "data": {
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- "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
- "┃ Test metric ┃ DataLoader 0 ┃\n",
- "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
- "│ accuracy │ 0.10409999638795853 │\n",
- "│ f1_macro │ 0.07243089377880096 │\n",
- "│ f1_micro │ 0.10409999638795853 │\n",
- "│ loss │ 2.390538215637207 │\n",
- "│ precision_macro │ 0.10765033960342407 │\n",
- "│ recall_macro │ 0.1064007356762886 │\n",
- "└───────────────────────────┴───────────────────────────┘\n",
- "
\n"
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- "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
- "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
- "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
- "│\u001b[36m \u001b[0m\u001b[36m accuracy \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10409999638795853 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m f1_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.07243089377880096 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m f1_micro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10409999638795853 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2.390538215637207 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m precision_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10765033960342407 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m recall_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.1064007356762886 \u001b[0m\u001b[35m \u001b[0m│\n",
- "└───────────────────────────┴───────────────────────────┘\n"
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- "text": [
- "\u001b[1;36m[2022-09-26 17:50:08] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `BALDStrategy`\u001b[0m\n",
- "\u001b[1;32m[2022-09-26 17:51:22] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:193\u001b[0m$ \u001b[37mQueried 10 instance\u001b[0m\n",
- "\u001b[1;32m[2022-09-26 17:51:22] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:281\u001b[0m$ \u001b[37mAnnotated 10 instances\u001b[0m\n",
- "\u001b[1;32m[2022-09-26 17:51:22] energizer/INFO\u001b[0m ~ \u001b[1;33mactive_learning_loop:282\u001b[0m$ \u001b[37mNew data statistics\n",
- "num_pool_batches: 430\n",
- "num_train_batches: 1\n",
- "pool_size: 54980\n",
- "total_data_size: 55000\n",
- "train_size: 20\n",
- "\u001b[0m\n",
- "\u001b[1;36m[2022-09-26 17:51:22] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n",
- "\u001b[1;36m[2022-09-26 17:51:22] energizer/DEBUG\u001b[0m ~ \u001b[1;33mactive_learning_loop:250\u001b[0m$ \u001b[37mMNISTCNNModel state dict has been re-initialized\u001b[0m\n"
- ]
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-----------------------------Last fit_loop------------------------------\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "`Trainer.fit` stopped: `max_epochs=3` reached.\n",
- "\u001b[1;36m[2022-09-26 17:51:27] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:470\u001b[0m$ \u001b[37mUsing underlying `MNISTCNNModel`\u001b[0m\n"
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- {
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- "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
- "┃ Test metric ┃ DataLoader 0 ┃\n",
- "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
- "│ accuracy │ 0.10339999943971634 │\n",
- "│ f1_macro │ 0.07382594794034958 │\n",
- "│ f1_micro │ 0.10339999943971634 │\n",
- "│ loss │ 2.378535270690918 │\n",
- "│ precision_macro │ 0.10162188857793808 │\n",
- "│ recall_macro │ 0.10477112978696823 │\n",
- "└───────────────────────────┴───────────────────────────┘\n",
- "
\n"
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- "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
- "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
- "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
- "│\u001b[36m \u001b[0m\u001b[36m accuracy \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10339999943971634 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m f1_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.07382594794034958 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m f1_micro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10339999943971634 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2.378535270690918 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m precision_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10162188857793808 \u001b[0m\u001b[35m \u001b[0m│\n",
- "│\u001b[36m \u001b[0m\u001b[36m recall_macro \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.10477112978696823 \u001b[0m\u001b[35m \u001b[0m│\n",
- "└───────────────────────────┴───────────────────────────┘\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[1;36m[2022-09-26 17:51:30] energizer/DEBUG\u001b[0m ~ \u001b[1;33mtrainer:466\u001b[0m$ \u001b[37mUsing `BALDStrategy`\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "for name, strategy in strategies.items():\n",
- " seed_everything(42) # for reproducibility (e.g., dropout)\n",
- "\n",
- " trainer = Trainer(**trainer_kwargs, enable_progress_bar=False)\n",
- "\n",
- " results = trainer.active_fit(\n",
- " model=strategy,\n",
- " train_dataloaders=train_dl,\n",
- " val_dataloaders=val_dl,\n",
- " test_dataloaders=test_dl,\n",
- " )\n",
- "\n",
- " results_dict[name] = results.to_pandas()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Results\n",
- "Now let's look at the results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [],
- "source": [
- "results_df = pd.concat([v.assign(query_strategy=k) for k, v in results_dict.items()])\n",
- "results_df = results_df.melt(\n",
- " id_vars=[\"train_size\", \"query_strategy\"], var_name=\"metrics\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import warnings\n",
- "\n",
- "with warnings.catch_warnings():\n",
- " warnings.simplefilter(\"ignore\")\n",
- "\n",
- " sns.set_style(\"darkgrid\")\n",
- " g = sns.FacetGrid(\n",
- " data=results_df,\n",
- " col=\"metrics\",\n",
- " sharex=True,\n",
- " sharey=False,\n",
- " height=4,\n",
- " hue=\"query_strategy\",\n",
- " legend_out=False,\n",
- " )\n",
- " p = g.map(sns.lineplot, \"train_size\", \"value\")\n",
- " p = p.add_legend()\n",
- " sns.move_legend(\n",
- " p,\n",
- " \"lower center\",\n",
- " bbox_to_anchor=(0.5, 1),\n",
- " ncol=5,\n",
- " title=None,\n",
- " frameon=False,\n",
- " )\n",
- " p.set(ylabel=None, xlabel=None)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.9.13 ('energizer-dev')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.13"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "944ffd4f8dbdaa69d919c942d599fc997735694f55f2ea363be8b6b69a40ba5a"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/index.md b/docs/examples/index.md
deleted file mode 100644
index fdf702f..0000000
--- a/docs/examples/index.md
+++ /dev/null
@@ -1 +0,0 @@
-Coming soon...
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
deleted file mode 100644
index c111bed..0000000
--- a/docs/index.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-hide:
-- navigation
----
-
-# Getting started with `Energizer`
-
-{% include-markdown "../README.md" %}
\ No newline at end of file
diff --git a/docs/javascript/mathjax.js b/docs/javascript/mathjax.js
deleted file mode 100644
index 8552839..0000000
--- a/docs/javascript/mathjax.js
+++ /dev/null
@@ -1,16 +0,0 @@
-window.MathJax = {
- tex: {
- inlineMath: [["\\(", "\\)"]],
- displayMath: [["\\[", "\\]"]],
- processEscapes: true,
- processEnvironments: true
- },
- options: {
- ignoreHtmlClass: ".*|",
- processHtmlClass: "arithmatex"
- }
- };
-
- document$.subscribe(() => {
- MathJax.typesetPromise()
- })
diff --git a/docs/overrides/main.html b/docs/overrides/main.html
deleted file mode 100644
index 2ace027..0000000
--- a/docs/overrides/main.html
+++ /dev/null
@@ -1,21 +0,0 @@
-{% extends "base.html" %}
-
-{% block announce %}
- For updates follow
-
- @pietro_lesci
-
- on
-
- Twitter
-
-{% endblock %}
diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css
deleted file mode 100644
index 67e43d5..0000000
--- a/docs/stylesheets/extra.css
+++ /dev/null
@@ -1,15 +0,0 @@
-/* make code font-size smaller: JetBrains Mono is bigger than others */
-
-/* .md-typeset .code {
- font-size: 0.7rem;
-}
-
-.highlighttable .linenos {
- font-size: 0.7rem;
-} */
-
-.highlighttable .linenos a {
-
- /* make link in linenumber of the same color as background */
- background-color: var(--md-code-bg-color);
-}
\ No newline at end of file
diff --git a/energizer/active_learning/callbacks.py b/energizer/active_learning/callbacks.py
index f825933..49044d7 100644
--- a/energizer/active_learning/callbacks.py
+++ b/energizer/active_learning/callbacks.py
@@ -1,5 +1,5 @@
import time
-from typing import Any, List
+from typing import Any
from lightning.fabric.wrappers import _FabricModule
@@ -50,7 +50,7 @@ def on_query_start(self, estimator: ActiveEstimator, model: _FabricModule, datas
...
def on_query_end(
- self, estimator: ActiveEstimator, model: _FabricModule, datastore: Datastore, indices: List[int]
+ self, estimator: ActiveEstimator, model: _FabricModule, datastore: Datastore, indices: list[int]
) -> None:
...
diff --git a/energizer/active_learning/clustering_utilities.py b/energizer/active_learning/clustering_utilities.py
index a379908..9eb11c9 100644
--- a/energizer/active_learning/clustering_utilities.py
+++ b/energizer/active_learning/clustering_utilities.py
@@ -1,5 +1,3 @@
-from typing import List
-
import numpy as np
from numpy.random import RandomState
@@ -16,11 +14,11 @@ def _similarity(centers: np.ndarray, X: np.ndarray, normalized: bool) -> np.ndar
return sim
-def _get_nearest_to_centers_batch(centers: np.ndarray, X: np.ndarray, normalized: bool) -> List[int]:
+def _get_nearest_to_centers_batch(centers: np.ndarray, X: np.ndarray, normalized: bool) -> list[int]:
return _similarity(centers, X, normalized).argmax(axis=1).tolist()
-def _get_nearest_to_centers_iterative(centers: np.ndarray, X: np.ndarray, normalized: bool) -> List[int]:
+def _get_nearest_to_centers_iterative(centers: np.ndarray, X: np.ndarray, normalized: bool) -> list[int]:
indices = np.empty(centers.shape[0], dtype=int)
for i in range(centers.shape[0]):
sim = _similarity(centers[None, i], X, normalized)
@@ -30,7 +28,7 @@ def _get_nearest_to_centers_iterative(centers: np.ndarray, X: np.ndarray, normal
return indices.tolist()
-def _get_nearest_to_centers(centers: np.ndarray, X: np.ndarray, normalized: bool, num_clusters: int) -> List[int]:
+def _get_nearest_to_centers(centers: np.ndarray, X: np.ndarray, normalized: bool, num_clusters: int) -> list[int]:
indices = _get_nearest_to_centers_batch(centers, X, normalized)
# fall back to an iterative version if one or more vectors are most similar
@@ -59,7 +57,7 @@ def _silhouette_k_select(X: np.ndarray, max_k: int, rng: RandomState) -> int:
def _kmeans(
X: np.ndarray, num_clusters: int, rng: RandomState, use_silhouette: bool = False, normalize: bool = True
-) -> List[int]:
+) -> list[int]:
if normalize:
X = StandardScaler().fit_transform(X)
@@ -74,15 +72,15 @@ def _kmeans(
return _get_nearest_to_centers(centers, X, normalize, num_clusters)
-def kmeans_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, normalize: bool = True) -> List[int]:
+def kmeans_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, normalize: bool = True) -> list[int]:
return _kmeans(X, num_clusters, rng, False, normalize)
-def kmeans_silhouette_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, normalize: bool = True) -> List[int]:
+def kmeans_silhouette_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, normalize: bool = True) -> list[int]:
return _kmeans(X, num_clusters, rng, True, normalize)
-def kmeans_pp_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, *args, **kwargs) -> List[int]:
+def kmeans_pp_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, *args, **kwargs) -> list[int]:
_, indices = kmeans_plusplus(X, num_clusters, random_state=rng)
unique_ids = list(set(indices.tolist()))
diff --git a/energizer/active_learning/datastores/base.py b/energizer/active_learning/datastores/base.py
index d3c37e9..4eade8e 100644
--- a/energizer/active_learning/datastores/base.py
+++ b/energizer/active_learning/datastores/base.py
@@ -1,7 +1,7 @@
from abc import ABC, abstractmethod
from math import floor
from pathlib import Path
-from typing import List, Literal, Optional, Union
+from typing import Literal, Optional, Union
import numpy as np
import pandas as pd
@@ -9,7 +9,7 @@
from numpy.random import RandomState
from torch.utils.data import DataLoader
-from energizer.datastores.base import Datastore, IndexMixin, PandasDataStore
+from energizer.datastores.base import Datastore, IndexMixin, PandasDatastore
from energizer.enums import InputKeys, RunningStage, SpecialKeys
from energizer.utilities import sample
@@ -34,7 +34,7 @@ def total_rounds(self) -> int:
@abstractmethod
def label(
self,
- indices: List[int],
+ indices: list[int],
round: int,
validation_perc: Optional[float] = None,
validation_sampling: Optional[Literal["uniform", "stratified"]] = "uniform",
@@ -47,9 +47,9 @@ def sample_from_pool(
size: int,
round: Optional[int] = None,
random_state: Optional[RandomState] = None,
- with_indices: Optional[List[int]] = None,
+ with_indices: Optional[list[int]] = None,
**kwargs,
- ) -> List[int]:
+ ) -> list[int]:
...
@abstractmethod
@@ -64,23 +64,23 @@ def reset(self) -> None:
...
@abstractmethod
- def get_train_ids(self, round: Optional[int] = None) -> List[int]:
+ def get_train_ids(self, round: Optional[int] = None) -> list[int]:
...
@abstractmethod
- def get_validation_ids(self, round: Optional[int] = None) -> List[int]:
+ def get_validation_ids(self, round: Optional[int] = None) -> list[int]:
...
@abstractmethod
- def get_pool_ids(self, round: Optional[int] = None) -> List[int]:
+ def get_pool_ids(self, round: Optional[int] = None) -> list[int]:
...
-class ActiveDataStore(ActiveLearningMixin, Datastore):
+class ActiveDatastore(ActiveLearningMixin, Datastore):
...
-class ActivePandasDataStore(ActiveLearningMixin, PandasDataStore):
+class ActivePandasDatastore(ActiveLearningMixin, PandasDatastore):
_train_data: pd.DataFrame
_test_data: Optional[Dataset]
@@ -108,7 +108,7 @@ def total_rounds(self) -> int:
return self._train_data[SpecialKeys.LABELLING_ROUND].max()
def train_dataset(
- self, round: Optional[int] = None, passive: Optional[bool] = False, with_indices: Optional[List[int]] = None
+ self, round: Optional[int] = None, passive: Optional[bool] = False, with_indices: Optional[list[int]] = None
) -> Optional[Dataset]:
if passive:
return super().train_dataset()
@@ -127,7 +127,7 @@ def validation_dataset(self, round: Optional[int] = None) -> Optional[Dataset]:
if mask.sum() > 0:
return Dataset.from_pandas(self._train_data.loc[mask], preserve_index=False)
- def pool_dataset(self, round: Optional[int] = None, with_indices: Optional[List[int]] = None) -> Optional[Dataset]:
+ def pool_dataset(self, round: Optional[int] = None, with_indices: Optional[list[int]] = None) -> Optional[Dataset]:
mask = self._pool_mask(round)
if with_indices is not None:
mask = mask & self._train_data[SpecialKeys.ID].isin(with_indices)
@@ -137,7 +137,7 @@ def pool_dataset(self, round: Optional[int] = None, with_indices: Optional[List[
def label(
self,
- indices: List[int],
+ indices: list[int],
round: int,
validation_perc: Optional[float] = None,
validation_sampling: Literal["uniform", "stratified"] = "uniform",
@@ -172,9 +172,9 @@ def sample_from_pool(
size: int,
round: Optional[int] = None,
random_state: Optional[RandomState] = None,
- with_indices: Optional[List[int]] = None,
+ with_indices: Optional[list[int]] = None,
**kwargs,
- ) -> List[int]:
+ ) -> list[int]:
"""Performs `uniform` or `stratified` sampling from the pool."""
mask = self._pool_mask(round)
@@ -217,21 +217,21 @@ def _pool_mask(self, round: Optional[int] = None) -> pd.Series:
mask = mask | (self._train_data[SpecialKeys.LABELLING_ROUND] > round)
return mask
- def get_train_ids(self, round: Optional[int] = None) -> List[int]:
+ def get_train_ids(self, round: Optional[int] = None) -> list[int]:
return self._train_data.loc[self._train_mask(round), SpecialKeys.ID].tolist()
- def get_validation_ids(self, round: Optional[int] = None) -> List[int]:
+ def get_validation_ids(self, round: Optional[int] = None) -> list[int]:
return self._train_data.loc[self._validation_mask(round), SpecialKeys.ID].tolist()
- def get_pool_ids(self, round: Optional[int] = None) -> List[int]:
+ def get_pool_ids(self, round: Optional[int] = None) -> list[int]:
return self._train_data.loc[self._pool_mask(round), SpecialKeys.ID].tolist()
class ActiveIndexMixin(IndexMixin):
- def get_pool_embeddings(self, ids: List[int]) -> np.ndarray:
+ def get_pool_embeddings(self, ids: list[int]) -> np.ndarray:
return np.stack(self.index.get_items(ids))
- def get_train_embeddings(self, ids: List[int]) -> np.ndarray:
+ def get_train_embeddings(self, ids: list[int]) -> np.ndarray:
# check all the ids are training ids
train_ids = self.get_train_ids() # type: ignore
assert all(i in train_ids for i in ids), set(train_ids).difference(set(ids))
@@ -245,18 +245,18 @@ def get_train_embeddings(self, ids: List[int]) -> np.ndarray:
return emb
- def get_embeddings(self, ids: List[int]) -> None:
+ def get_embeddings(self, ids: list[int]) -> None:
raise ValueError("Use `get_{train/pool}_embeddings` methods instead.")
-class ActiveDataStoreWithIndex(ActiveIndexMixin, ActiveDataStore):
+class ActiveDatastoreWithIndex(ActiveIndexMixin, ActiveDatastore):
...
-class ActivePandasDataStoreWithIndex(ActiveIndexMixin, ActivePandasDataStore):
+class ActivePandasDatastoreWithIndex(ActiveIndexMixin, ActivePandasDatastore):
def label(
self,
- indices: List[int],
+ indices: list[int],
round: int,
validation_perc: Optional[float] = None,
validation_sampling: Literal["uniform", "stratified"] = "uniform",
diff --git a/energizer/active_learning/datastores/classification.py b/energizer/active_learning/datastores/classification.py
index cec50f9..c179862 100644
--- a/energizer/active_learning/datastores/classification.py
+++ b/energizer/active_learning/datastores/classification.py
@@ -1,21 +1,21 @@
-from typing import List, Optional
+from typing import Optional
from datasets import Dataset
from transformers import PreTrainedTokenizerBase
from typing_extensions import Self
-from energizer.active_learning.datastores.base import ActivePandasDataStoreWithIndex
+from energizer.active_learning.datastores.base import ActivePandasDatastoreWithIndex
from energizer.datastores.classification import SequenceClassificationMixin, _from_datasets
from energizer.enums import SpecialKeys
-class ActivePandasDataStoreForSequenceClassification(SequenceClassificationMixin, ActivePandasDataStoreWithIndex):
+class ActivePandasDatastoreForSequenceClassification(SequenceClassificationMixin, ActivePandasDatastoreWithIndex):
@classmethod
def from_datasets(
cls,
tokenizer: PreTrainedTokenizerBase,
uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
+ on_cpu: Optional[list[str]] = None,
seed: Optional[int] = 42,
train_dataset: Optional[Dataset] = None,
validation_dataset: Optional[Dataset] = None,
diff --git a/energizer/active_learning/strategies/base.py b/energizer/active_learning/strategies/base.py
index b18b998..ab35c76 100644
--- a/energizer/active_learning/strategies/base.py
+++ b/energizer/active_learning/strategies/base.py
@@ -1,13 +1,14 @@
from abc import ABC, abstractmethod
+from collections.abc import Callable
from pathlib import Path
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Literal, Optional, Union
import numpy as np
import torch
from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer
from torch.optim.lr_scheduler import _LRScheduler
-from energizer.active_learning.datastores.base import ActiveDataStore
+from energizer.active_learning.datastores.base import ActiveDatastore
from energizer.active_learning.trackers import ActiveProgressTracker
from energizer.enums import InputKeys, OutputKeys, RunningStage, SpecialKeys
from energizer.estimator import Estimator, OptimizationArgs, SchedulerArgs
@@ -31,7 +32,7 @@ def tracker(self) -> ActiveProgressTracker:
def active_fit(
self,
- datastore: ActiveDataStore,
+ datastore: ActiveDatastore,
query_size: int,
max_rounds: Optional[int] = None,
max_budget: Optional[int] = None,
@@ -47,9 +48,9 @@ def active_fit(
gradient_accumulation_steps: Optional[int] = None,
learning_rate: Optional[float] = None,
optimizer: Optional[str] = None,
- optimizer_kwargs: Optional[Union[Dict, OptimizationArgs]] = None,
+ optimizer_kwargs: Optional[Union[dict, OptimizationArgs]] = None,
scheduler: Optional[str] = None,
- scheduler_kwargs: Optional[Union[Dict, SchedulerArgs]] = None,
+ scheduler_kwargs: Optional[Union[dict, SchedulerArgs]] = None,
log_interval: int = 1,
enable_progress_bar: bool = True,
limit_train_batches: Optional[int] = None,
@@ -102,11 +103,7 @@ def active_fit(
)
def run_active_fit(
- self,
- datastore: ActiveDataStore,
- reinit_model: bool,
- model_cache_dir: Union[str, Path],
- **kwargs,
+ self, datastore: ActiveDatastore, reinit_model: bool, model_cache_dir: Union[str, Path], **kwargs
) -> Any:
if reinit_model:
self.save_state_dict(model_cache_dir)
@@ -150,21 +147,17 @@ def run_active_fit(
def run_round(
self,
- datastore: ActiveDataStore,
+ datastore: ActiveDatastore,
query_size: int,
replay: bool,
- fit_loop_kwargs: Dict,
- fit_opt_kwargs: Dict,
- test_kwargs: Dict,
- query_kwargs: Dict,
- label_kwargs: Dict,
+ fit_loop_kwargs: dict,
+ fit_opt_kwargs: dict,
+ test_kwargs: dict,
+ query_kwargs: dict,
+ label_kwargs: dict,
) -> ROUND_OUTPUT:
model, optimizer, scheduler, train_loader, validation_loader, test_loader = self._setup_round(
- datastore,
- replay,
- fit_loop_kwargs,
- fit_opt_kwargs,
- test_kwargs,
+ datastore, replay, fit_loop_kwargs, fit_opt_kwargs, test_kwargs
)
output = {}
@@ -194,14 +187,8 @@ def run_round(
return output
def run_annotation(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- query_kwargs: Dict,
- label_kwargs: Dict,
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, query_kwargs: dict, label_kwargs: dict
) -> int:
-
# === QUERY === #
self.callback("on_query_start", model=model, datastore=datastore)
@@ -235,29 +222,18 @@ def run_annotation(
return n_labelled
- def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> List[int]:
+ def run_query(self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs) -> list[int]:
raise NotImplementedError
- def active_fit_end(self, datastore: ActiveDataStore, output: List[ROUND_OUTPUT]) -> Any:
+ def active_fit_end(self, datastore: ActiveDatastore, output: list[ROUND_OUTPUT]) -> Any:
return output
- def round_end(self, datastore: ActiveDataStore, output: ROUND_OUTPUT) -> ROUND_OUTPUT:
+ def round_end(self, datastore: ActiveDatastore, output: ROUND_OUTPUT) -> ROUND_OUTPUT:
return output
def _setup_round(
- self,
- datastore: ActiveDataStore,
- replay: bool,
- fit_loop_kwargs: Dict,
- fit_opt_kwargs: Dict,
- test_kwargs: Dict,
- ) -> Tuple[
+ self, datastore: ActiveDatastore, replay: bool, fit_loop_kwargs: dict, fit_opt_kwargs: dict, test_kwargs: dict
+ ) -> tuple[
_FabricModule,
_FabricOptimizer,
Optional[_LRScheduler],
@@ -278,9 +254,7 @@ def _setup_round(
**fit_loop_kwargs,
)
model, optimizer, scheduler, train_loader, validation_loader = self._setup_fit(
- train_loader,
- validation_loader,
- **fit_opt_kwargs,
+ train_loader, validation_loader, **fit_opt_kwargs
)
# configuration test
@@ -297,8 +271,8 @@ class PoolBasedMixin(ABC):
POOL_OUTPUT_KEY: OutputKeys
- def run_pool_evaluation(self, model: _FabricModule, loader: _FabricDataLoader) -> Dict[str, np.ndarray]:
- out: List[Dict] = self.run_evaluation(model, loader, RunningStage.POOL) # type: ignore
+ def run_pool_evaluation(self, model: _FabricModule, loader: _FabricDataLoader) -> dict[str, np.ndarray]:
+ out: list[dict] = self.run_evaluation(model, loader, RunningStage.POOL) # type: ignore
_out = ld_to_dl(out)
return {k: np.concatenate(v) for k, v in _out.items()}
@@ -310,7 +284,7 @@ def evaluation_step(
loss_fn: Optional[Union[torch.nn.Module, Callable]],
metrics: Optional[METRIC],
stage: Union[str, RunningStage],
- ) -> Union[Dict, BATCH_OUTPUT]:
+ ) -> Union[dict, BATCH_OUTPUT]:
if stage != RunningStage.POOL:
return super().evaluation_step(model, batch, batch_idx, loss_fn, metrics, stage) # type: ignore
@@ -334,10 +308,10 @@ def pool_step(
) -> torch.Tensor:
...
- def pool_epoch_end(self, output: List[Dict], metrics: Optional[METRIC]) -> List[Dict]:
+ def pool_epoch_end(self, output: list[dict], metrics: Optional[METRIC]) -> list[dict]:
return output
- def get_pool_loader(self, datastore: ActiveDataStore, **kwargs) -> Optional[_FabricDataLoader]:
+ def get_pool_loader(self, datastore: ActiveDatastore, **kwargs) -> Optional[_FabricDataLoader]:
subpool_ids = kwargs.get("subpool_ids", None)
loader = datastore.pool_loader(with_indices=subpool_ids) if subpool_ids is not None else datastore.pool_loader()
@@ -350,13 +324,13 @@ def get_pool_loader(self, datastore: ActiveDataStore, **kwargs) -> Optional[_Fab
)
return pool_loader
- def get_train_loader(self, datastore: ActiveDataStore, **kwargs) -> Optional[_FabricDataLoader]:
-
+ def get_train_loader(self, datastore: ActiveDatastore, **kwargs) -> Optional[_FabricDataLoader]:
# NOTE: hack -- load train dataloader with the evaluation batch size
- batch_size = datastore.loading_params["batch_size"]
- datastore._loading_params["batch_size"] = datastore.loading_params["eval_batch_size"]
+ assert datastore._loading_params is not None
+ batch_size = datastore.loading_params.batch_size
+ datastore._loading_params.batch_size = datastore.loading_params.eval_batch_size
loader = datastore.train_loader(**kwargs)
- datastore._loading_params["batch_size"] = batch_size
+ datastore._loading_params.batch_size = batch_size
if loader is not None:
train_loader = self.configure_dataloader(loader) # type: ignore
diff --git a/energizer/active_learning/strategies/diversity.py b/energizer/active_learning/strategies/diversity.py
index 32381f5..310ea03 100644
--- a/energizer/active_learning/strategies/diversity.py
+++ b/energizer/active_learning/strategies/diversity.py
@@ -1,5 +1,6 @@
from abc import ABC, abstractmethod
-from typing import Any, Callable, List, Literal, Optional, Tuple, Union
+from collections.abc import Callable
+from typing import Any, Literal, Optional, Union
import numpy as np
import torch
@@ -7,7 +8,7 @@
from numpy.random import RandomState
from sklearn.utils.validation import check_random_state
-from energizer.active_learning.datastores.base import ActiveDataStore
+from energizer.active_learning.datastores.base import ActiveDatastore
from energizer.active_learning.registries import CLUSTERING_FUNCTIONS
from energizer.active_learning.strategies.base import ActiveEstimator, PoolBasedMixin
from energizer.enums import OutputKeys, SpecialKeys
@@ -27,13 +28,7 @@ def __init__(self, *args, seed: int = 42, **kwargs) -> None:
self.seed = seed
self.rng = check_random_state(seed) # reproducibility
- def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> List[int]:
+ def run_query(self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs) -> list[int]:
embeddings_and_ids = self.get_embeddings_and_ids(model, datastore, query_size, **kwargs)
if embeddings_and_ids is None:
return []
@@ -43,12 +38,8 @@ def run_query(
@abstractmethod
def get_embeddings_and_ids(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> Optional[Tuple[np.ndarray, np.ndarray]]:
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs
+ ) -> Optional[tuple[np.ndarray, np.ndarray]]:
# NOTE: Always need the ids because you might not return the entire pool
...
@@ -56,12 +47,12 @@ def get_embeddings_and_ids(
def select_from_embeddings(
self,
model: _FabricModule,
- datastore: ActiveDataStore,
+ datastore: ActiveDatastore,
query_size: int,
embeddings: np.ndarray,
ids: np.ndarray,
**kwargs,
- ) -> List[int]:
+ ) -> list[int]:
...
@@ -69,12 +60,8 @@ class DiversityBasedStrategyWithPool(PoolBasedMixin, DiversityBasedStrategy):
POOL_OUTPUT_KEY: OutputKeys = OutputKeys.EMBEDDINGS
def get_embeddings_and_ids(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> Optional[Tuple[np.ndarray, np.ndarray]]:
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs
+ ) -> Optional[tuple[np.ndarray, np.ndarray]]:
pool_loader = self.get_pool_loader(datastore, **kwargs)
if pool_loader is not None and len(pool_loader.dataset or []) > query_size: # type: ignore
# enough instances
@@ -95,12 +82,12 @@ def __init__(
def select_from_embeddings(
self,
model: _FabricModule,
- datastore: ActiveDataStore,
+ datastore: ActiveDatastore,
query_size: int,
embeddings: np.ndarray,
ids: np.ndarray,
**kwargs,
- ) -> List[int]:
+ ) -> list[int]:
center_ids = self.clustering_fn(embeddings, query_size, rng=self.rng) # type: ignore
return ids[center_ids].tolist()
diff --git a/energizer/active_learning/strategies/hybrid.py b/energizer/active_learning/strategies/hybrid.py
index 9c46697..9630d13 100644
--- a/energizer/active_learning/strategies/hybrid.py
+++ b/energizer/active_learning/strategies/hybrid.py
@@ -1,4 +1,5 @@
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from collections.abc import Callable
+from typing import Any, Optional, Union
import numpy as np
import torch
@@ -8,7 +9,7 @@
from torch.nn.functional import one_hot
from energizer.active_learning.clustering_utilities import kmeans_pp_sampling
-from energizer.active_learning.datastores.base import ActiveDataStore, ActiveDataStoreWithIndex
+from energizer.active_learning.datastores.base import ActiveDatastore, ActiveDatastoreWithIndex
from energizer.active_learning.registries import CLUSTERING_FUNCTIONS
from energizer.active_learning.strategies.diversity import DiversityBasedStrategy, DiversityBasedStrategyWithPool
from energizer.active_learning.strategies.uncertainty import UncertaintyBasedStrategy
@@ -42,7 +43,7 @@ def __init__(
seed: int = 42,
r_factor: int,
clustering_algorithm: str = "kmeans",
- clustering_kwargs: Optional[Dict] = None,
+ clustering_kwargs: Optional[dict] = None,
**kwargs,
) -> None:
super().__init__(*args, score_fn=score_fn, seed=seed, **kwargs)
@@ -60,13 +61,8 @@ def clustering_fn(self) -> Callable:
return self._clustering_fn
def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStoreWithIndex,
- query_size: int,
- **kwargs,
- ) -> List[int]:
-
+ self, model: _FabricModule, datastore: ActiveDatastoreWithIndex, query_size: int, **kwargs
+ ) -> list[int]:
# === DIVERSITY === #
embeddings_and_ids = self.get_embeddings_and_ids(model, datastore, query_size, **kwargs)
if embeddings_and_ids is None:
@@ -84,25 +80,20 @@ def run_query(
return self.compute_most_uncertain(model, pool_loader, query_size)
def get_embeddings_and_ids(
- self,
- model: _FabricModule,
- datastore: ActiveDataStoreWithIndex,
- query_size: int,
- **kwargs,
- ) -> Optional[Tuple[np.ndarray, np.ndarray]]:
+ self, model: _FabricModule, datastore: ActiveDatastoreWithIndex, query_size: int, **kwargs
+ ) -> Optional[tuple[np.ndarray, np.ndarray]]:
pool_ids = kwargs.get("subpool_ids", None) or datastore.get_pool_ids()
return datastore.get_pool_embeddings(pool_ids), np.array(pool_ids)
def select_from_embeddings(
self,
model: _FabricModule,
- datastore: ActiveDataStoreWithIndex,
+ datastore: ActiveDatastoreWithIndex,
query_size: int,
embeddings: np.ndarray,
ids: np.ndarray,
**kwargs,
- ) -> List[int]:
-
+ ) -> list[int]:
num_clusters = query_size * self.r_factor
centers_ids = self.clustering_fn(embeddings, num_clusters, rng=self.clustering_rng, **self.clustering_kwargs)
@@ -114,12 +105,12 @@ class BADGE(DiversityBasedStrategyWithPool):
def select_from_embeddings(
self,
model: _FabricModule,
- datastore: ActiveDataStore,
+ datastore: ActiveDatastore,
query_size: int,
embeddings: np.ndarray,
ids: np.ndarray,
**kwargs,
- ) -> List[int]:
+ ) -> list[int]:
# k-means++ sampling
center_ids = kmeans_pp_sampling(embeddings, query_size, rng=self.rng)
return ids[center_ids].tolist()
diff --git a/energizer/active_learning/strategies/random.py b/energizer/active_learning/strategies/random.py
index a04ae74..8c3fc73 100644
--- a/energizer/active_learning/strategies/random.py
+++ b/energizer/active_learning/strategies/random.py
@@ -1,12 +1,10 @@
-from typing import List
-
from lightning.fabric.wrappers import _FabricModule
from numpy.random import RandomState
# https://scikit-learn.org/stable/developers/develop.html#random-numbers
from sklearn.utils.validation import check_random_state
-from energizer.active_learning.datastores.base import ActiveDataStore
+from energizer.active_learning.datastores.base import ActiveDatastore
from energizer.active_learning.strategies.base import ActiveEstimator
@@ -18,11 +16,5 @@ def __init__(self, *args, seed: int = 42, **kwargs) -> None:
self.seed = seed
self.rng = check_random_state(seed) # reproducibility
- def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> List[int]:
+ def run_query(self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs) -> list[int]:
return datastore.sample_from_pool(size=query_size, random_state=self.rng)
diff --git a/energizer/active_learning/strategies/two_stage.py b/energizer/active_learning/strategies/two_stage.py
index ee7bbd9..e7e3a18 100644
--- a/energizer/active_learning/strategies/two_stage.py
+++ b/energizer/active_learning/strategies/two_stage.py
@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
-from typing import Any, List, Optional
+from typing import Any, Optional
import numpy as np
import pandas as pd
@@ -7,7 +7,7 @@
from numpy.random import RandomState
from sklearn.utils import check_random_state
-from energizer.active_learning.datastores.base import ActiveDataStore, ActiveDataStoreWithIndex
+from energizer.active_learning.datastores.base import ActiveDatastore, ActiveDatastoreWithIndex
from energizer.active_learning.strategies.base import ActiveEstimator
from energizer.enums import SpecialKeys
@@ -42,13 +42,7 @@ def subpool_size(self) -> int:
def base_strategy(self) -> ActiveEstimator:
return self._base_strategy
- def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> List[int]:
+ def run_query(self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs) -> list[int]:
if datastore.pool_size() > self.subpool_size:
subpool_ids = self.select_pool_subset(model, datastore, query_size, **kwargs)
kwargs["subpool_ids"] = subpool_ids
@@ -57,8 +51,8 @@ def run_query(
@abstractmethod
def select_pool_subset(
- self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs
- ) -> List[int]:
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs
+ ) -> list[int]:
...
def __getattr__(self, attr: str) -> Any:
@@ -69,8 +63,8 @@ def __getattr__(self, attr: str) -> Any:
class RandomSubsetStrategy(BaseSubsetStrategy):
def select_pool_subset(
- self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs
- ) -> List[int]:
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs
+ ) -> list[int]:
subpool_size = min(datastore.pool_size(), self.subpool_size)
return datastore.sample_from_pool(size=subpool_size, random_state=self.rng)
@@ -82,9 +76,8 @@ def __init__(self, *args, num_neighbours: int, max_search_size: Optional[int] =
self.max_search_size = max_search_size
def select_pool_subset(
- self, model: _FabricModule, datastore: ActiveDataStoreWithIndex, query_size: int, **kwargs
- ) -> List[int]:
-
+ self, model: _FabricModule, datastore: ActiveDatastoreWithIndex, query_size: int, **kwargs
+ ) -> list[int]:
# SELECT QUERIES
search_query_ids = self.select_search_query(model, datastore, query_size, **kwargs)
@@ -101,7 +94,7 @@ def select_pool_subset(
return self.get_subpool_ids_from_search_results(candidate_df, datastore)
def search_pool(
- self, datastore: ActiveDataStoreWithIndex, search_query_embeddings: np.ndarray, search_query_ids: List[int]
+ self, datastore: ActiveDatastoreWithIndex, search_query_embeddings: np.ndarray, search_query_ids: list[int]
) -> pd.DataFrame:
num_neighbours = self.num_neighbours
if self.max_search_size is not None:
@@ -123,34 +116,28 @@ def search_pool(
@abstractmethod
def select_search_query(
- self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs
- ) -> List[int]:
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs
+ ) -> list[int]:
...
@abstractmethod
- def get_query_embeddings(self, datastore: ActiveDataStoreWithIndex, search_query_ids: List[int]) -> np.ndarray:
+ def get_query_embeddings(self, datastore: ActiveDatastoreWithIndex, search_query_ids: list[int]) -> np.ndarray:
...
@abstractmethod
def get_subpool_ids_from_search_results(
- self, candidate_df: pd.DataFrame, datastore: ActiveDataStoreWithIndex
- ) -> List[int]:
+ self, candidate_df: pd.DataFrame, datastore: ActiveDatastoreWithIndex
+ ) -> list[int]:
...
class SEALSStrategy(BaseSubsetWithSearchStrategy):
"""Colemann et al. (2020) `Similarity Search for Efficient Active Learning and Search of Rare Concepts`."""
- to_search: List[int] = []
- subpool_ids: List[int] = []
+ to_search: list[int] = []
+ subpool_ids: list[int] = []
- def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> List[int]:
+ def run_query(self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs) -> list[int]:
annotated_ids = super().run_query(model, datastore, query_size, **kwargs)
# in the next round we only need to search the newly labelled data
@@ -161,20 +148,20 @@ def run_query(
return annotated_ids
def select_search_query(
- self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs
- ) -> List[int]:
+ self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs
+ ) -> list[int]:
if len(self.to_search) < 1:
return datastore.get_train_ids()
# we only search the newly labelled data at the previous round
return self.to_search
- def get_query_embeddings(self, datastore: ActiveDataStoreWithIndex, search_query_ids: List[int]) -> np.ndarray:
+ def get_query_embeddings(self, datastore: ActiveDatastoreWithIndex, search_query_ids: list[int]) -> np.ndarray:
# queries are always from the training set
return datastore.get_train_embeddings(search_query_ids)
def get_subpool_ids_from_search_results(
- self, candidate_df: pd.DataFrame, datastore: ActiveDataStoreWithIndex
- ) -> List[int]:
+ self, candidate_df: pd.DataFrame, datastore: ActiveDatastoreWithIndex
+ ) -> list[int]:
# we return all unique instances from the pool that are neighbours of the training set
selected_ids = candidate_df[SpecialKeys.ID].unique().tolist()
diff --git a/energizer/active_learning/strategies/uncertainty.py b/energizer/active_learning/strategies/uncertainty.py
index 33d76dd..8849b3b 100644
--- a/energizer/active_learning/strategies/uncertainty.py
+++ b/energizer/active_learning/strategies/uncertainty.py
@@ -1,10 +1,11 @@
-from typing import Callable, List, Union
+from collections.abc import Callable
+from typing import Union
from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule
from numpy.random import RandomState
from sklearn.utils.validation import check_random_state
-from energizer.active_learning.datastores.base import ActiveDataStore
+from energizer.active_learning.datastores.base import ActiveDatastore
from energizer.active_learning.registries import SCORING_FUNCTIONS
from energizer.active_learning.strategies.base import ActiveEstimator, PoolBasedMixin
from energizer.enums import OutputKeys, SpecialKeys
@@ -26,21 +27,14 @@ def __init__(self, *args, score_fn: Union[str, Callable], seed: int = 42, **kwar
def score_fn(self) -> Callable:
return self._score_fn
- def run_query(
- self,
- model: _FabricModule,
- datastore: ActiveDataStore,
- query_size: int,
- **kwargs,
- ) -> List[int]:
+ def run_query(self, model: _FabricModule, datastore: ActiveDatastore, query_size: int, **kwargs) -> list[int]:
pool_loader = self.get_pool_loader(datastore, **kwargs)
if pool_loader is None or len(pool_loader.dataset or []) <= query_size: # type: ignore
# not enough instances
return []
return self.compute_most_uncertain(model, pool_loader, query_size)
- def compute_most_uncertain(self, model: _FabricModule, loader: _FabricDataLoader, query_size: int) -> List[int]:
-
+ def compute_most_uncertain(self, model: _FabricModule, loader: _FabricDataLoader, query_size: int) -> list[int]:
# run evaluation
out = self.run_pool_evaluation(model, loader)
scores = out[self.POOL_OUTPUT_KEY]
diff --git a/energizer/active_learning/trackers.py b/energizer/active_learning/trackers.py
index 71e522d..459aa9d 100644
--- a/energizer/active_learning/trackers.py
+++ b/energizer/active_learning/trackers.py
@@ -4,7 +4,7 @@
import numpy as np
from tqdm.auto import tqdm
-from energizer.active_learning.datastores.base import ActiveDataStore
+from energizer.active_learning.datastores.base import ActiveDatastore
from energizer.enums import RunningStage
from energizer.trackers import ProgressTracker, StageTracker, Tracker
@@ -17,11 +17,7 @@ def reset(self) -> None:
def make_progress_bar(self) -> None:
self.progress_bar = tqdm(
- total=self.max,
- desc="Completed rounds",
- dynamic_ncols=True,
- leave=True,
- colour="#32a852",
+ total=self.max, desc="Completed rounds", dynamic_ncols=True, leave=True, colour="#32a852"
)
def increment(self) -> None:
@@ -40,13 +36,7 @@ def reset(self) -> None:
return super().reset()
def make_progress_bar(self) -> None:
- self.progress_bar = tqdm(
- total=self.max,
- desc="Labelled",
- dynamic_ncols=True,
- leave=True,
- colour="#32a852",
- )
+ self.progress_bar = tqdm(total=self.max, desc="Labelled", dynamic_ncols=True, leave=True, colour="#32a852")
if self.current > 0:
self.progress_bar.update(self.current)
@@ -80,7 +70,7 @@ def setup_active(
max_rounds: Optional[int],
max_budget: Optional[int],
query_size: int,
- datastore: ActiveDataStore,
+ datastore: ActiveDatastore,
validation_perc: Optional[float],
log_interval: int,
enable_progress_bar: bool,
@@ -101,9 +91,7 @@ def setup_active(
), "At least one of `max_rounds` or `max_budget` must be not None."
assert max_budget > initial_budget, ValueError(f"`{max_budget=}` must be bigger than `{initial_budget=}`.")
- max_rounds = min(
- int(np.ceil((max_budget - initial_budget) / query_size)), max_rounds or float("Inf")
- ) # type: ignore
+ max_rounds = min(int(np.ceil((max_budget - initial_budget) / query_size)), max_rounds or float("Inf")) # type: ignore
max_budget = (query_size * max_rounds) + initial_budget # type: ignore
self.has_test = datastore.test_size() is not None and datastore.test_size() > 0 # type: ignore
diff --git a/energizer/callbacks/__init__.py b/energizer/callbacks/__init__.py
index bbaab84..98ff789 100644
--- a/energizer/callbacks/__init__.py
+++ b/energizer/callbacks/__init__.py
@@ -1,6 +1,6 @@
from energizer.callbacks.base import Callback
from energizer.callbacks.early_stopping import EarlyStopping
-from energizer.callbacks.grad_norm import GradNorm
+from energizer.callbacks.grad_stats import GradNorm
from energizer.callbacks.model_checkpoint import ModelCheckpoint
from energizer.callbacks.pytorch_profiler import PytorchTensorboardProfiler
from energizer.callbacks.timer import Timer
diff --git a/energizer/callbacks/base.py b/energizer/callbacks/base.py
index 46e6fda..68e3449 100644
--- a/energizer/callbacks/base.py
+++ b/energizer/callbacks/base.py
@@ -1,8 +1,10 @@
-from typing import Any, Callable, Dict, List, Union
+from collections.abc import Callable
+from typing import Any, Optional, Union
import numpy as np
from lightning.fabric.wrappers import _FabricModule, _FabricOptimizer
from torch.optim import Optimizer
+from torch.optim.lr_scheduler import _LRScheduler
from energizer.enums import RunningStage
from energizer.estimator import Estimator
@@ -24,7 +26,7 @@ class Callback:
def on_fit_start(self, estimator: Estimator, model: _FabricModule) -> None:
...
- def on_fit_end(self, estimator: Estimator, model: _FabricModule, output: List[FIT_OUTPUT]) -> None:
+ def on_fit_end(self, estimator: Estimator, model: _FabricModule, output: list[FIT_OUTPUT]) -> None:
...
"""
@@ -86,12 +88,7 @@ def on_batch_start(
...
def on_train_batch_start(
- self,
- estimator: Estimator,
- model: _FabricModule,
- batch: Any,
- batch_idx: int,
- optimizer: Optimizer,
+ self, estimator: Estimator, model: _FabricModule, batch: Any, batch_idx: int, optimizer: Optimizer
) -> None:
return self.on_batch_start(RunningStage.TRAIN, estimator, model, batch, batch_idx, optimizer=optimizer)
@@ -133,6 +130,16 @@ def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimi
def on_after_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
...
+ def on_before_scheduler(
+ self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer, scheduler: _LRScheduler
+ ) -> None:
+ ...
+
+ def on_after_scheduler(
+ self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer, scheduler: _LRScheduler
+ ) -> None:
+ ...
+
class CallbackWithMonitor(Callback):
mode: str
@@ -153,8 +160,8 @@ def optim_op(self) -> Callable:
def reverse_optim_op(self) -> Callable:
return self.reverse_optim_dict[self.mode]
- def _get_monitor(self, output: Union[BATCH_OUTPUT, EPOCH_OUTPUT]) -> float:
- if not isinstance(output, Dict):
+ def _get_monitor(self, output: Optional[Union[BATCH_OUTPUT, EPOCH_OUTPUT]]) -> float:
+ if not isinstance(output, dict) or output is None:
raise RuntimeError(
"From `*_step` and `*_epoch_end` method you need to return dict to use ",
"monitoring Callbacks like EarlyStopping and ModelCheckpoint.",
diff --git a/energizer/callbacks/early_stopping.py b/energizer/callbacks/early_stopping.py
index 4557941..052b796 100644
--- a/energizer/callbacks/early_stopping.py
+++ b/energizer/callbacks/early_stopping.py
@@ -1,5 +1,5 @@
from pathlib import Path
-from typing import Any, Optional, Tuple, Union
+from typing import Any, Optional, Union
import numpy as np
import srsly
@@ -41,7 +41,7 @@ def __init__(
def _check_stopping_criteria(
self, output: Union[BATCH_OUTPUT, EPOCH_OUTPUT], step: int
- ) -> Tuple[bool, Union[str, None]]:
+ ) -> tuple[bool, Union[str, None]]:
current = self._get_monitor(output)
should_stop = False
diff --git a/energizer/callbacks/grad_norm.py b/energizer/callbacks/grad_norm.py
deleted file mode 100644
index 6508b0a..0000000
--- a/energizer/callbacks/grad_norm.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from typing import Union
-
-from lightning.fabric.wrappers import _FabricModule, _FabricOptimizer
-from lightning.pytorch.utilities.grads import grad_norm
-
-from energizer.callbacks import Callback
-from energizer.estimator import Estimator
-
-
-class GradNorm(Callback):
- def __init__(self, norm_type: Union[float, int, str], group_separator: str = "/") -> None:
- """Compute each parameter's gradient's norm and their overall norm.
-
- The overall norm is computed over all gradients together, as if they
- were concatenated into a single vector.
-
- Args:
- norm_type: The type of the used p-norm, cast to float if necessary.
- Can be ``'inf'`` for infinity norm.
- group_separator: The separator string used by the logger to group
- the gradients norms in their own subfolder instead of the logs one.
-
- """
- self.group_separator = group_separator
- self.norm_type = float(norm_type)
- if self.norm_type <= 0:
- raise ValueError(f"`norm_type` must be a positive number or 'inf' (infinity norm). Got {self.norm_type}")
-
- def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
- norms = grad_norm(model, norm_type=self.norm_type, group_separator=self.group_separator)
- estimator.log_dict(norms, step=estimator.tracker.global_step)
diff --git a/energizer/callbacks/grad_stats.py b/energizer/callbacks/grad_stats.py
new file mode 100644
index 0000000..eb2cc8f
--- /dev/null
+++ b/energizer/callbacks/grad_stats.py
@@ -0,0 +1,218 @@
+from collections.abc import Iterable
+from typing import Union
+
+import torch
+from lightning.fabric.wrappers import _FabricModule, _FabricOptimizer
+
+from energizer.callbacks import Callback
+from energizer.estimator import Estimator
+
+
+def grad_norm(module: torch.nn.Module, norm_types: list[float], group_separator: str = "/") -> dict[str, float]:
+ # compute on device
+ return {
+ f"grad_{norm_type}_norm{group_separator}{n}": torch.linalg.vector_norm(
+ p.grad.detach().data.flatten(), ord=norm_type
+ ).item()
+ for norm_type in norm_types
+ for n, p in module.named_parameters()
+ if p.grad is not None and p.requires_grad
+ }
+
+
+def empirical_fim_norm(
+ module: torch.nn.Module, norm_types: list[float], group_separator: str = "/"
+) -> dict[str, float]:
+ # compute on device
+ return {
+ f"efim_{norm_type}_norm{group_separator}{n}": torch.linalg.vector_norm(
+ p.grad.detach().data.flatten() ** 2, ord=norm_type
+ ).item()
+ for norm_type in norm_types
+ for n, p in module.named_parameters()
+ if p.grad is not None and p.requires_grad
+ }
+
+
+def empirical_fim_trace(module: torch.nn.Module, group_separator: str = "/") -> dict[str, float]:
+ # compute on device
+ return {
+ f"efim_trace{group_separator}{n}": torch.sum(p.grad.detach().data.flatten() ** 2).item()
+ for n, p in module.named_parameters()
+ if p.grad is not None and p.requires_grad
+ }
+
+
+def update_size_norm(
+ differences: list[tuple[str, torch.Tensor]], norm_types: list[float], group_separator: str = "/"
+) -> dict[str, float]:
+ """Compute each parameter's gradient update (difference between after and before update) norm.
+
+ Args:
+ current_params: the new parameters after the update
+ previous_params: the paramenters before the update
+ norm_type: The type of the used p-norm, cast to float if necessary.
+ Can be ``'inf'`` for infinity norm.
+ group_separator: The separator string used by the logger to group
+ the gradients norms in their own subfolder instead of the logs one.
+
+ Return:
+ norms: The dictionary of p-norms of each parameter's gradient and
+ a special entry for the total p-norm of the gradients viewed
+ as a single vector.
+
+ """
+
+ # compute on device
+ return {
+ f"update_{norm_type}_norm{group_separator}{n}": torch.linalg.vector_norm(diff.flatten(), ord=norm_type).item()
+ for norm_type in norm_types
+ for n, diff in differences
+ }
+
+
+def relative_stdlog10_update_size(
+ differences: list[tuple[str, torch.Tensor]], previous_params: list[torch.Tensor], group_separator: str = "/"
+) -> dict[str, float]:
+ # compute on device
+ return {
+ f"relative_stdlog10_update{group_separator}{n}": (diff.std() / p_before.std()).log10().item()
+ for (n, diff), p_before in zip(differences, previous_params)
+ }
+
+
+def relative_norm_update_size(
+ differences: list[tuple[str, torch.Tensor]],
+ previous_params: list[torch.Tensor],
+ norm_types: list[float],
+ group_separator: str = "/",
+) -> dict[str, float]:
+ # compute on device
+ return {
+ f"relative_update_{norm_type}_norm{group_separator}{n}": torch.linalg.vector_norm(
+ diff.flatten(), ord=norm_type
+ ).item()
+ / torch.linalg.vector_norm(p_before.flatten(), ord=norm_type).item()
+ for norm_type in norm_types
+ for (n, diff), p_before in zip(differences, previous_params)
+ }
+
+
+class GradNorm(Callback):
+ def __init__(
+ self, norm_types: Union[float, int, str, list[Union[float, int, str]]], group_separator: str = "/"
+ ) -> None:
+ """Compute each parameter's gradient's norm and their overall norm.
+
+ The overall norm is computed over all gradients together, as if they
+ were concatenated into a single vector.
+
+ Args:
+ norm_type: The type of the used p-norm, cast to float if necessary.
+ Can be ``'inf'`` for infinity norm.
+ group_separator: The separator string used by the logger to group
+ the gradients norms in their own subfolder instead of the logs one.
+
+ """
+ self.group_separator = group_separator
+
+ if not isinstance(norm_types, Iterable) or isinstance(norm_types, str):
+ norm_types = [norm_types]
+
+ self.norm_types = [float(i) for i in norm_types]
+
+ assert all(i > 0 for i in self.norm_types), ValueError(
+ f"`norm_type` must be a positive number or 'inf' (infinity norm). Got {self.norm_types}"
+ )
+
+ def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
+ norms = grad_norm(model, norm_types=self.norm_types, group_separator=self.group_separator)
+ estimator.log_dict(norms, step=estimator.tracker.global_step)
+
+
+class EmpiricalFIMNorm(GradNorm):
+ def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
+ norms = empirical_fim_norm(model, norm_types=self.norm_types, group_separator=self.group_separator)
+ estimator.log_dict(norms, step=estimator.tracker.global_step)
+
+
+class EmpiricalFIMTrace(Callback):
+ def __init__(self, group_separator: str = "/") -> None:
+ self.group_separator = group_separator
+
+ def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
+ norms = empirical_fim_trace(model, group_separator=self.group_separator)
+ estimator.log_dict(norms, step=estimator.tracker.global_step)
+
+
+class ParameterUpdateStats(GradNorm):
+ _previous_params: list[torch.Tensor] = []
+
+ def __init__(
+ self,
+ norm_types: Union[float, int, str, list[Union[float, int, str]]],
+ group_separator: str = "/",
+ return_update_size_norm: bool = True,
+ return_relative_std_update: bool = True,
+ return_relative_norm_update: bool = True,
+ ) -> None:
+ """Compute each parameter's gradient's norm and their overall norm.
+
+ The overall norm is computed over all gradients together, as if they
+ were concatenated into a single vector.
+
+ Args:
+ norm_type: The type of the used p-norm, cast to float if necessary.
+ Can be ``'inf'`` for infinity norm.
+ group_separator: The separator string used by the logger to group
+ the gradients norms in their own subfolder instead of the logs one.
+
+ """
+ super().__init__(norm_types, group_separator)
+ self.return_update_size_norm = return_update_size_norm
+ self.return_relative_std_update = return_relative_std_update
+ self.return_relative_norm_update = return_relative_norm_update
+
+ def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
+ self._previous_params = [
+ p.data.clone().detach() for _, p in model.named_parameters() if p.grad is not None and p.requires_grad
+ ]
+
+ def on_after_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
+ # assert len(current_params) == len(previous_params), ValueError(
+ # f"Current and previous parameter lists are not the same: {len(current_params)=} and {len(previous_params)=}"
+ # )
+
+ current_params = (
+ (n, p.data.clone().detach()) for n, p in model.named_parameters() if p.grad is not None and p.requires_grad
+ )
+
+ diffs = [(n, p_after - p_before) for (n, p_after), p_before in zip(current_params, self._previous_params)]
+
+ logs = {}
+ if self.return_update_size_norm:
+ update_size_norms = update_size_norm(
+ differences=diffs, norm_types=self.norm_types, group_separator=self.group_separator
+ )
+ logs.update(update_size_norms)
+
+ if self.return_relative_std_update:
+ relative_std = relative_stdlog10_update_size(
+ differences=diffs, previous_params=self._previous_params, group_separator=self.group_separator
+ )
+ logs.update(relative_std)
+
+ if self.return_relative_norm_update:
+ relative_norms = relative_norm_update_size(
+ differences=diffs,
+ previous_params=self._previous_params,
+ norm_types=self.norm_types,
+ group_separator=self.group_separator,
+ )
+ logs.update(relative_norms)
+
+ if len(logs) > 0:
+ estimator.log_dict(logs, step=estimator.tracker.global_step)
+
+ # free memory
+ self._previous_params = []
diff --git a/energizer/callbacks/lr_monitor.py b/energizer/callbacks/lr_monitor.py
new file mode 100644
index 0000000..d7d745d
--- /dev/null
+++ b/energizer/callbacks/lr_monitor.py
@@ -0,0 +1,19 @@
+from lightning.fabric.wrappers import _FabricModule, _FabricOptimizer
+
+from energizer.callbacks import Callback
+from energizer.estimator import Estimator
+
+
+class LearningRateMonitor(Callback):
+ def __init__(self, prefix: str = "lr_monitor/") -> None:
+ super().__init__()
+ self.prefix = prefix
+
+ def on_before_optimizer(self, estimator: Estimator, model: _FabricModule, optimizer: _FabricOptimizer) -> None:
+ last_lrs = [group["lr"] for group in optimizer.param_groups]
+
+ if len(last_lrs) > 1:
+ lrs = {f"{self.prefix}lr_param_group{idx}": lr for idx, lr in enumerate(last_lrs)}
+ estimator.log_dict(lrs, step=estimator.tracker.global_step)
+ else:
+ estimator.log(f"{self.prefix}lr", next(iter(last_lrs)), step=estimator.tracker.global_step)
diff --git a/energizer/callbacks/model_checkpoint.py b/energizer/callbacks/model_checkpoint.py
index e20d240..62dc97b 100644
--- a/energizer/callbacks/model_checkpoint.py
+++ b/energizer/callbacks/model_checkpoint.py
@@ -1,60 +1,67 @@
import os
+import shutil
from pathlib import Path
-from typing import Dict, Optional, Union
+from typing import Any, Optional, Union
import srsly
from lightning.fabric.wrappers import _FabricModule
+from lightning_utilities.core.rank_zero import rank_zero_info
from energizer.callbacks.base import CallbackWithMonitor
-from energizer.enums import RunningStage
+from energizer.enums import Interval, RunningStage
from energizer.estimator import Estimator
-from energizer.types import EPOCH_OUTPUT, METRIC
+from energizer.types import BATCH_OUTPUT, EPOCH_OUTPUT, METRIC
from energizer.utilities import make_dict_json_serializable
class ModelCheckpoint(CallbackWithMonitor):
- _best_k_models: Dict[str, float] = {}
+ _best_k_models: dict[str, float] = {}
+ monitor: Optional[str] = None
+ mode: Optional[str] = "min"
def __init__(
self,
dirpath: Union[Path, str],
- monitor: str,
stage: Union[str, RunningStage],
- mode: str = "min",
- save_last: Optional[bool] = None,
- save_top_k: int = 1,
+ frequency: str = "1:epoch",
+ monitor: Optional[str] = None,
+ mode: Optional[str] = "min",
+ save_last: Optional[bool] = False,
+ save_top_k: Optional[int] = 1,
verbose: bool = True,
) -> None:
super().__init__()
self.dirpath = Path(dirpath)
- self.monitor = monitor
self.stage = stage
+
+ # monitor
+ self.monitor = monitor
self.mode = mode
self.save_last = save_last
self.save_top_k = save_top_k
self.verbose = verbose
+ # frequency
+ self.frequency = frequency
+ every_n, interval = self.frequency.split(":")
+ every_n = int(every_n)
+ assert every_n > 0
+ assert interval in list(Interval)
+
+ rank_zero_info(f"Running ModelCheckpoint callback every {every_n} {interval}")
+ self.every_n = every_n
+ self.interval = interval
+
@property
def best_model_path(self) -> str:
return self.optim_op(self._best_k_models, key=self._best_k_models.get)
- def on_train_epoch_end(
- self, estimator: Estimator, model: _FabricModule, output: EPOCH_OUTPUT, metrics: METRIC
- ) -> None:
- self.epoch_end(estimator, output, RunningStage.TRAIN)
-
- def on_validation_epoch_end(
- self, estimator: Estimator, model: _FabricModule, output: EPOCH_OUTPUT, metrics: METRIC
- ) -> None:
- self.epoch_end(estimator, output, RunningStage.VALIDATION)
-
def on_fit_start(self, *args, **kwargs) -> None:
# prepare directory
if self.dirpath.exists():
# during active learning we do not want to keep checkpoints from previous iterations
for ckpt_path in self.dirpath.glob("*.pt"):
ckpt_path.unlink()
- # shutil.rmtree(str(i))
self.dirpath.mkdir(parents=True, exist_ok=True)
self._best_k_models = {}
@@ -64,12 +71,9 @@ def on_fit_end(self, estimator: Estimator, *args, **kwargs) -> None:
estimator.load_state_dict(self.dirpath, self.best_model_path)
if self.verbose:
- logs = {
- "selected": self.best_model_path,
- "step": estimator.tracker.safe_global_epoch,
- }
+ logs = {"selected": self.best_model_path, "step": estimator.tracker.safe_global_epoch}
if hasattr(estimator.tracker, "global_round"):
- logs["round"] = getattr(estimator.tracker, "global_round")
+ logs["round"] = estimator.tracker.global_round
srsly.write_jsonl(
self.dirpath / "checkpoint_logs.jsonl",
@@ -82,32 +86,59 @@ def on_fit_end(self, estimator: Estimator, *args, **kwargs) -> None:
Helpers
"""
- def epoch_end(self, estimator: Estimator, output: EPOCH_OUTPUT, stage: Union[str, RunningStage]) -> None:
- if stage != self.stage:
- return
+ def should_checkpoint(self, stage: Union[str, RunningStage], interval: Interval, step_or_epoch: int) -> bool:
+ # when we get to batch_end or epoch_end the step tracker has already been incremented!
+ step_or_epoch = step_or_epoch + 1 if interval == Interval.EPOCH else step_or_epoch
+ return stage == self.stage and interval == self.interval and step_or_epoch % self.every_n == 0
- current = self._get_monitor(output)
+ def on_epoch_end(
+ self,
+ stage: Union[str, RunningStage],
+ estimator: Estimator,
+ model: _FabricModule,
+ output: EPOCH_OUTPUT,
+ metrics: METRIC,
+ ) -> None:
+ if self.should_checkpoint(stage, Interval.EPOCH, estimator.tracker.global_epoch):
+ self.checkpoint(stage, estimator, output)
- if self._check_should_save(stage, current):
- # checkpoint
- name = self._get_name(estimator, current)
- estimator.save_state_dict(self.dirpath, name)
- self._update_best_models(name, current)
+ def on_batch_end(
+ self,
+ stage: Union[str, RunningStage],
+ estimator: Estimator,
+ model: _FabricModule,
+ output: BATCH_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ ) -> None:
+ if self.should_checkpoint(stage, Interval.STEP, estimator.tracker.global_step):
+ self.checkpoint(stage, estimator, output)
- # log
- if self.verbose:
- logs = {
- "step": estimator.tracker.global_step,
- **self._best_k_models,
- }
- if hasattr(estimator.tracker, "global_round"):
- logs["round"] = getattr(estimator.tracker, "global_round", None)
- srsly.write_jsonl(
- self.dirpath / "checkpoint_logs.jsonl",
- [make_dict_json_serializable(logs)],
- append=True,
- append_new_line=False,
- )
+ def checkpoint(
+ self, stage: Union[str, RunningStage], estimator: Estimator, output: Union[EPOCH_OUTPUT, BATCH_OUTPUT]
+ ) -> None:
+ if self.monitor is not None:
+ current = self._get_monitor(output)
+ if self._check_should_save(stage, current):
+ # checkpoint
+ name = self._get_name(estimator, current)
+ estimator.save_state_dict(self.dirpath, name)
+ self._update_best_models(name, current)
+
+ # log
+ if self.verbose:
+ logs = {"step": estimator.tracker.global_step, **self._best_k_models}
+ if hasattr(estimator.tracker, "global_round"):
+ logs["round"] = getattr(estimator.tracker, "global_round", None)
+ srsly.write_jsonl(
+ self.dirpath / "checkpoint_logs.jsonl",
+ [make_dict_json_serializable(logs)],
+ append=True,
+ append_new_line=False,
+ )
+ else:
+ name = f"{self.interval}_{getattr(estimator.tracker, f'global_{self.interval}')}"
+ estimator.save_state_dict(self.dirpath, name)
def _check_should_save(self, stage: Union[str, RunningStage], current: Optional[float]) -> bool:
should_save = False
@@ -142,5 +173,8 @@ def _update_best_models(self, name: str, current: Optional[float]) -> None:
if self.save_top_k is not None and len(self._best_k_models) >= self.save_top_k:
worst_ckpt = self.reverse_optim_op(self._best_k_models, key=self._best_k_models.get)
self._best_k_models.pop(worst_ckpt)
- os.remove(self.dirpath / worst_ckpt)
+ if (self.dirpath / worst_ckpt).is_dir():
+ shutil.rmtree(self.dirpath / worst_ckpt)
+ else:
+ os.remove(self.dirpath / worst_ckpt)
self._best_k_models[name] = current
diff --git a/energizer/callbacks/pytorch_profiler.py b/energizer/callbacks/pytorch_profiler.py
index 6d5c733..8569ca1 100644
--- a/energizer/callbacks/pytorch_profiler.py
+++ b/energizer/callbacks/pytorch_profiler.py
@@ -8,13 +8,7 @@
class PytorchTensorboardProfiler(Callback):
def __init__(
- self,
- dirpath: Union[str, Path],
- wait: int = 1,
- warmup: int = 1,
- active: int = 1,
- repeat: int = 2,
- **kwargs,
+ self, dirpath: Union[str, Path], wait: int = 1, warmup: int = 1, active: int = 1, repeat: int = 2, **kwargs
) -> None:
self.prof = torch.profiler.profile(
schedule=torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=repeat),
diff --git a/energizer/datastores/base.py b/energizer/datastores/base.py
index ed727e9..bf37fc1 100644
--- a/energizer/datastores/base.py
+++ b/energizer/datastores/base.py
@@ -3,25 +3,24 @@
"""
from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any, Optional, Union
-import hnswlib as hb
-import numpy as np
import pandas as pd
-import srsly
import torch
from datasets import Dataset
-from lightning_utilities.core.rank_zero import rank_zero_warn
from numpy.random import RandomState
from sklearn.utils import check_random_state # type: ignore
from torch.utils.data import DataLoader, RandomSampler, Sampler, SequentialSampler
+from energizer.datastores.mixins import IndexMixin
from energizer.enums import RunningStage, SpecialKeys
from energizer.types import DATA_SOURCE, DATASET
+from energizer.utilities import Args
-class BaseDataStore(ABC):
+class BaseDatastore(ABC):
"""General container for data."""
"""
@@ -103,11 +102,25 @@ def test_size(self, *args, **kwargs) -> Optional[int]:
...
-class Datastore(BaseDataStore):
+@dataclass
+class DataloaderArgs(Args):
+ batch_size: int
+ eval_batch_size: int
+ num_workers: int
+ pin_memory: bool
+ drop_last: bool
+ persistent_workers: bool
+ shuffle: bool
+ replacement: bool
+ data_seed: int
+ multiprocessing_context: Optional[str]
+
+
+class Datastore(BaseDatastore):
"""Defines dataloading for training and evaluation."""
_collate_fn: Optional[Callable]
- _loading_params: Dict[str, Any] = {}
+ _loading_params: Optional[DataloaderArgs] = None
_rng: RandomState
def __init__(self, seed: Optional[int] = 42) -> None:
@@ -124,10 +137,11 @@ def prepare_for_loading(
drop_last: bool = False,
persistent_workers: bool = False,
shuffle: bool = True,
- data_seed: int = 42,
replacement: bool = False,
+ data_seed: int = 42,
+ multiprocessing_context: Optional[str] = None,
) -> None:
- self._loading_params = dict(
+ self._loading_params = DataloaderArgs(
batch_size=batch_size,
eval_batch_size=eval_batch_size,
num_workers=num_workers,
@@ -137,13 +151,13 @@ def prepare_for_loading(
shuffle=shuffle,
replacement=replacement,
data_seed=data_seed,
+ multiprocessing_context=multiprocessing_context,
)
@property
- def loading_params(self) -> Dict[str, Any]:
- if len(self._loading_params) > 0:
- return self._loading_params
- raise ValueError("You need to `prepare_for_loading`")
+ def loading_params(self) -> DataloaderArgs:
+ assert self._loading_params is not None, ValueError("You need to `prepare_for_loading`")
+ return self._loading_params
def reset_rng(self, seed: Optional[int]) -> None:
self._rng = check_random_state(seed)
@@ -158,32 +172,35 @@ def show_batch(self, stage: Union[str, RunningStage] = RunningStage.TRAIN, *args
return next(iter(loader))
def get_loader(self, stage: str, *args, **kwargs) -> Optional[DataLoader]:
-
# get dataset
dataset = getattr(self, f"{stage}_dataset")(*args, **kwargs)
if dataset is None:
return
batch_size = (
- self.loading_params["batch_size"] if stage == RunningStage.TRAIN else self.loading_params["eval_batch_size"]
+ self.loading_params.batch_size if stage == RunningStage.TRAIN else self.loading_params.eval_batch_size
)
batch_size = min(batch_size, len(dataset))
# sampler
sampler = _get_sampler(
dataset,
- shuffle=self.loading_params["shuffle"] if stage == RunningStage.TRAIN else False,
- replacement=self.loading_params["replacement"],
- seed=self.loading_params["data_seed"],
+ shuffle=self.loading_params.shuffle if stage == RunningStage.TRAIN else False,
+ replacement=self.loading_params.replacement,
+ seed=self.loading_params.data_seed,
)
# put everything together
return DataLoader(
dataset=dataset,
- batch_size=self.loading_params["batch_size"],
+ batch_size=batch_size,
sampler=sampler,
collate_fn=self.get_collate_fn(stage),
- drop_last=self.loading_params["drop_last"],
+ drop_last=self.loading_params.drop_last,
+ num_workers=min(batch_size, self.loading_params.num_workers),
+ pin_memory=self.loading_params.pin_memory,
+ persistent_workers=self.loading_params.persistent_workers,
+ multiprocessing_context=self.loading_params.multiprocessing_context,
)
def _get_size(self, stage: RunningStage, *args, **kwargs) -> Optional[int]:
@@ -211,15 +228,9 @@ def validation_size(self, *args, **kwargs) -> Optional[int]:
def test_size(self, *args, **kwargs) -> Optional[int]:
return self._get_size(RunningStage.TEST, *args, **kwargs)
-
-class PandasDataStore(Datastore):
- _train_data: Optional[pd.DataFrame]
- _validation_data: Optional[Dataset]
- _test_data: Optional[Dataset]
-
def train_dataset(self) -> Optional[Dataset]:
if self._train_data is not None:
- return Dataset.from_pandas(self._train_data, preserve_index=False)
+ return self._train_data
def validation_dataset(self) -> Optional[Dataset]:
if self._validation_data is not None:
@@ -229,57 +240,22 @@ def test_dataset(self) -> Optional[Dataset]:
if self._test_data is not None:
return self._test_data
- def get_by_ids(self, ids: List[int]) -> pd.DataFrame:
+
+class PandasDatastore(Datastore):
+ _train_data: Optional[pd.DataFrame]
+ _validation_data: Optional[Dataset]
+ _test_data: Optional[Dataset]
+
+ def train_dataset(self) -> Optional[Dataset]:
+ if self._train_data is not None:
+ return Dataset.from_pandas(self._train_data, preserve_index=False)
+
+ def get_by_ids(self, ids: list[int]) -> pd.DataFrame:
assert self._train_data is not None, "To `get_by_ids` you need to specify the train_data." # type: ignore
return self._train_data.loc[self._train_data[SpecialKeys.ID].isin(ids)] # type: ignore
-class IndexMixin:
- index: hb.Index = None
- embedding_name: str
-
- def search(self, query: np.ndarray, query_size: int, query_in_set: bool = True) -> Tuple[np.ndarray, np.ndarray]:
- # retrieve one additional element if the query is in the set we are looking in
- # because the query itself is returned as the most similar element and we need to remove it
- query_size = query_size + 1 if query_in_set else query_size
- indices, distances = self.index.knn_query(data=query, k=query_size)
- if query_in_set:
- # remove the first element retrieved if the query is in the set since it's the element itself
- indices, distances = indices[:, 1:], distances[:, 1:]
- return indices, distances
-
- def load_index(self, index_path: Union[str, Path], metadata_path: Union[str, Path]) -> None:
- meta: Dict = srsly.read_json(metadata_path) # type: ignore
- index = hb.Index(space=meta["metric"], dim=meta["dim"])
- index.load_index(str(index_path))
- self.index = index
-
- # consistency check: data in index must be the same or more
- assert self._train_data is not None # type: ignore
- assert len(index.get_ids_list()) >= len(self._train_data[SpecialKeys.ID]), "Index is not compatible with data." # type: ignore
-
- # if dataset has been downsampled, mask the ids
- if len(index.get_ids_list()) > len(self._train_data[SpecialKeys.ID]): # type: ignore
- rank_zero_warn(
- "Index has more ids than dataset. Masking the missing ids from the index. "
- "If this is expected (e.g., you downsampled your dataset), everything is fine."
- )
- missing_ids = set(index.get_ids_list()).difference(set(self._train_data[SpecialKeys.ID])) # type: ignore
- self.mask_ids_from_index(list(missing_ids))
-
- def mask_ids_from_index(self, ids: List[int]) -> None:
- for i in ids:
- self.index.mark_deleted(i)
-
- def unmask_ids_from_index(self, ids: List[int]) -> None:
- for i in ids:
- self.index.unmark_deleted(i)
-
- def get_embeddings(self, ids: List[int]) -> np.ndarray:
- return np.stack(self.index.get_items(ids))
-
-
-class PandasDataStoreWithIndex(IndexMixin, PandasDataStore):
+class PandasDatastoreWithIndex(IndexMixin, PandasDatastore):
"""DataModule that defines dataloading and indexing logic."""
@@ -288,12 +264,7 @@ class PandasDataStoreWithIndex(IndexMixin, PandasDataStore):
"""
-def _get_sampler(
- dataset: DATASET,
- shuffle: bool,
- replacement: bool,
- seed: int,
-) -> Sampler:
+def _get_sampler(dataset: DATASET, shuffle: bool, replacement: bool, seed: int) -> Sampler:
"""Get a sampler optimizer to work with `datasets.Dataset`.
ref: https://huggingface.co/docs/datasets/use_with_pytorch
diff --git a/energizer/datastores/classification.py b/energizer/datastores/classification.py
index 5db06a5..f5138c4 100644
--- a/energizer/datastores/classification.py
+++ b/energizer/datastores/classification.py
@@ -1,29 +1,59 @@
-from abc import ABC
from collections import Counter
+from collections.abc import Callable
+from dataclasses import dataclass
from functools import partial
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Optional, Union
import torch
-from datasets import Dataset, DatasetDict # type: ignore
+from datasets import Dataset
from torch import Tensor
from transformers import PreTrainedTokenizerBase
-from typing_extensions import Self
-from energizer.datastores.base import PandasDataStoreWithIndex
-from energizer.enums import InputKeys, RunningStage, SpecialKeys
-from energizer.utilities import _pad, ld_to_dl, sequential_numbers
+from energizer.datastores.base import DataloaderArgs, Datastore, PandasDatastoreWithIndex
+from energizer.datastores.mixins import TextMixin
+from energizer.enums import InputKeys, RunningStage
+from energizer.utilities import _pad, ld_to_dl
-class SequenceClassificationMixin(ABC):
- MANDATORY_INPUT_NAMES: List[str] = [InputKeys.INPUT_IDS, InputKeys.ATT_MASK]
- OPTIONAL_INPUT_NAMES: List[str] = [InputKeys.TOKEN_TYPE_IDS]
- MANDATORY_TARGET_NAME: str = InputKeys.LABELS
+def collate_fn_for_sequence_classification(
+ batch: list[dict[str, Union[list[str], Tensor]]],
+ input_names: list[str],
+ on_cpu: list[str],
+ max_length: Optional[int],
+ pad_token_id: Optional[int],
+ pad_fn: Callable,
+) -> dict[str, Union[list[str], Tensor]]:
+ new_batch = ld_to_dl(batch)
+
+ # remove string columns that cannot be transfered on gpu
+ values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch}
+
+ labels = new_batch.pop(InputKeys.LABELS, None)
- _tokenizer: PreTrainedTokenizerBase
- _labels: List[str]
- _label_distribution: Dict[str, int]
- input_names: List[str]
- on_cpu: List[str]
+ # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad
+ new_batch = {k: pad_fn(inputs=new_batch[k], padding_value=pad_token_id, max_length=max_length) for k in input_names}
+
+ if labels is not None:
+ new_batch[InputKeys.LABELS] = torch.tensor(labels, dtype=torch.long)
+
+ # add things that need to remain on cpu
+ if len(values_on_cpu) > 0:
+ new_batch[InputKeys.ON_CPU] = values_on_cpu
+
+ return new_batch
+
+
+@dataclass
+class SequenceClassificationDataloaderArgs(DataloaderArgs):
+ max_length: int
+
+
+class SequenceClassificationMixin(TextMixin):
+ MANDATORY_TARGET_NAME: Optional[str] = InputKeys.LABELS
+ _loading_params: Optional[SequenceClassificationDataloaderArgs] = None
+
+ _labels: list[str]
+ _label_distribution: dict[str, dict[str, int]]
def prepare_for_loading(
self,
@@ -34,235 +64,84 @@ def prepare_for_loading(
drop_last: bool = False,
persistent_workers: bool = False,
shuffle: bool = True,
- seed: int = 42,
replacement: bool = False,
+ data_seed: int = 42,
+ multiprocessing_context: Optional[str] = None,
max_length: int = 512,
) -> None:
- super().prepare_for_loading( # type: ignore
- batch_size,
- eval_batch_size,
- num_workers,
- pin_memory,
- drop_last,
- persistent_workers,
- shuffle,
- seed,
- replacement,
+ self._loading_params = SequenceClassificationDataloaderArgs(
+ batch_size=batch_size,
+ eval_batch_size=eval_batch_size,
+ num_workers=num_workers,
+ pin_memory=pin_memory,
+ drop_last=drop_last,
+ persistent_workers=persistent_workers,
+ shuffle=shuffle,
+ replacement=replacement,
+ data_seed=data_seed,
+ multiprocessing_context=multiprocessing_context,
+ max_length=max_length,
)
- self._loading_params["max_length"] = max_length # type: ignore
-
- @property
- def tokenizer(self) -> PreTrainedTokenizerBase:
- return self._tokenizer
@property
- def labels(self) -> List[str]:
+ def labels(self) -> list[str]:
return self._labels
@property
- def id2label(self) -> Dict[int, str]:
+ def id2label(self) -> dict[int, str]:
return dict(enumerate(self.labels))
@property
- def label2id(self) -> Dict[str, int]:
+ def label2id(self) -> dict[str, int]:
return {v: k for k, v in self.id2label.items()}
- def label_distribution(self, normalized: bool = False) -> Dict[str, Union[float, int]]:
+ def label_distribution(self, normalized: bool = False) -> dict[str, dict]:
if normalized:
- total = sum(self._label_distribution.values())
- return {k: self._label_distribution[k] / total for k in self._label_distribution}
- return dict(self._label_distribution)
-
- @classmethod
- def from_datasets(
- cls,
- tokenizer: PreTrainedTokenizerBase,
- uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
- seed: Optional[int] = 42,
- train_dataset: Optional[Dataset] = None,
- validation_dataset: Optional[Dataset] = None,
- test_dataset: Optional[Dataset] = None,
- ) -> Self:
- obj = cls(seed) # type: ignore
- return _from_datasets(
- obj=obj,
- mandatory_input_names=cls.MANDATORY_INPUT_NAMES,
- optional_input_names=cls.OPTIONAL_INPUT_NAMES,
- mandatory_target_name=cls.MANDATORY_TARGET_NAME,
- tokenizer=tokenizer,
- uid_name=uid_name,
- on_cpu=on_cpu,
- train_dataset=train_dataset,
- validation_dataset=validation_dataset,
- test_dataset=test_dataset,
- )
-
- @classmethod
- def from_dataset_dict(
- cls,
- dataset_dict: DatasetDict,
- tokenizer: PreTrainedTokenizerBase,
- uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
- seed: Optional[int] = 42,
- ) -> Self:
- return cls.from_datasets(
- tokenizer=tokenizer,
- uid_name=uid_name,
- on_cpu=on_cpu,
- seed=seed,
- train_dataset=dataset_dict.get(RunningStage.TRAIN),
- validation_dataset=dataset_dict.get(RunningStage.VALIDATION),
- test_dataset=dataset_dict.get(RunningStage.TEST),
- )
+ norm_label_distribution = {}
+ for split, label_dist in self._label_distribution.items():
+ total = sum(label_dist.values())
+ norm_label_distribution[split] = {k: label_dist[k] / total for k in label_dist}
+ return norm_label_distribution
+
+ return self._label_distribution
+
+ def _set_attributes(self, dataset_dict: dict[RunningStage, Dataset], tokenizer: PreTrainedTokenizerBase) -> None:
+ super()._set_attributes(dataset_dict, tokenizer)
+
+ # === SET ATTRIBUTES === #
+ _label_distribution = {}
+ for k in dataset_dict:
+ _label_distribution[k] = dict(Counter(dataset_dict[k][self.MANDATORY_TARGET_NAME])) # type: ignore
+ self._label_distribution = _label_distribution
+ self._labels = next(iter(dataset_dict.values())).features[self.MANDATORY_TARGET_NAME].names
+
+ def _check_labels(self, dataset_dict: dict[RunningStage, Dataset], mandatory_target_name: str) -> None:
+ labels = []
+ for split, dataset in dataset_dict.items():
+ assert (
+ mandatory_target_name in dataset.features
+ ), f"Mandatory column {mandatory_target_name} not in dataset[{split}]."
+ labels.append(set(dataset.features[mandatory_target_name].names))
+
+ # check labels are consistent
+ assert all(s == labels[0] for s in labels), "Labels are inconsistent across splits"
def get_collate_fn(self, stage: Optional[RunningStage] = None, show_batch: bool = False) -> Optional[Callable]:
return partial(
- collate_fn,
+ collate_fn_for_sequence_classification,
input_names=self.input_names,
on_cpu=self.on_cpu,
- max_length=None if show_batch else self.loading_params["max_length"], # type: ignore
+ max_length=None if show_batch else self.loading_params.max_length, # type: ignore
pad_token_id=self.tokenizer.pad_token_id,
pad_fn=_pad,
)
-class PandasDataStoreForSequenceClassification(SequenceClassificationMixin, PandasDataStoreWithIndex):
+class DatastoreForSequenceClassification(SequenceClassificationMixin, Datastore):
...
-"""
-Utilities
-"""
-
-
-def collate_fn(
- batch: List[Dict[str, Union[List[str], Tensor]]],
- input_names: List[str],
- on_cpu: List[str],
- max_length: Optional[int],
- pad_token_id: Optional[int],
- pad_fn: Callable,
-) -> Dict[str, Union[List[str], Tensor]]:
- new_batch = ld_to_dl(batch)
-
- # remove string columns that cannot be transfered on gpu
- values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch}
-
- labels = new_batch.pop(InputKeys.LABELS, None)
-
- # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad
- new_batch = {
- k: pad_fn(
- inputs=new_batch[k],
- padding_value=pad_token_id,
- max_length=max_length,
- )
- for k in input_names
- }
-
- if labels is not None:
- new_batch[InputKeys.LABELS] = torch.tensor(labels, dtype=torch.long)
-
- # add things that need to remain on cpu
- if len(on_cpu) > 0:
- new_batch[InputKeys.ON_CPU] = values_on_cpu
-
- return new_batch
-
-
-def _from_datasets(
- obj,
- mandatory_input_names: List[str],
- optional_input_names: List[str],
- mandatory_target_name: str,
- tokenizer: PreTrainedTokenizerBase,
- uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
- train_dataset: Optional[Dataset] = None,
- validation_dataset: Optional[Dataset] = None,
- test_dataset: Optional[Dataset] = None,
-) -> Any:
-
- _datasets = {
- RunningStage.TRAIN: train_dataset,
- RunningStage.VALIDATION: validation_dataset,
- RunningStage.TEST: test_dataset,
- }
- datasets: Dict[RunningStage, Dataset] = {k: v for k, v in _datasets.items() if v is not None}
- if len(datasets) < 1:
- raise ValueError("You need to pass at least one dataset.")
-
- # === INPUT NAMES === #
- input_names = []
- for name in mandatory_input_names:
- for split, dataset in datasets.items():
- if name in dataset.features:
- input_names.append(name)
- else:
- raise ValueError(f"Mandatory column {name} not in dataset[{split}].")
-
- for name in optional_input_names:
- for dataset in datasets.values():
- if name in dataset.features:
- input_names.append(name)
-
- # === TARGET NAME === #
- labels = []
- for split, dataset in datasets.items():
- assert (
- mandatory_target_name in dataset.features
- ), f"Mandatory column {mandatory_target_name} not in dataset[{split}]."
- labels.append(set(dataset.features[mandatory_target_name].names))
-
- # check labels are consistent
- assert all(s == labels[0] for s in labels), "Labels are inconsistent across splits"
-
- # === ON_CPU === #
- if on_cpu is not None:
- for name in on_cpu:
- for split, dataset in datasets.items():
- assert name in dataset.features, f"{name=} not in dataset[{split}]={dataset.features.keys()}"
- else:
- on_cpu = []
-
- # === UID NAME === #
- uid_generator = sequential_numbers()
- new_datasets = {}
- for k, d in datasets.items():
- if uid_name is None:
- uids = [next(uid_generator) for _ in range(len(d))]
- new_dataset = d.add_column(SpecialKeys.ID, uids) # type: ignore
- print(f"UID column {SpecialKeys.ID} automatically created in dataset[{k}]")
- else:
- assert uid_name in d.features, f"{uid_name=} not in dataset[{k}]={d.features.keys()}"
- ids = d[uid_name]
- assert len(set(ids)) == len(ids), f"`uid_column` {uid_name} is not unique."
-
- new_dataset = d
- if uid_name != SpecialKeys.ID:
- new_dataset = new_dataset.rename_columns({uid_name: SpecialKeys.ID})
- print(f"UID column {uid_name} automatically renamed to {SpecialKeys.ID} in dataset[{k}]")
-
- new_datasets[k] = new_dataset
-
- on_cpu += [SpecialKeys.ID]
-
- # === FORMAT (KEEP ONLY USEFUL COLUMNS) === #
- columns = input_names + on_cpu + [mandatory_target_name]
- new_datasets = {k: v.with_format(columns=columns) for k, v in new_datasets.items()}
-
- # === SET ATTRIBUTES === #
- if RunningStage.TRAIN in new_datasets:
- obj._label_distribution = Counter(new_datasets[RunningStage.TRAIN][mandatory_target_name])
-
- obj._labels = next(iter(new_datasets.values())).features[mandatory_target_name].names
- obj.input_names = input_names
- obj.on_cpu = on_cpu
- obj._tokenizer = tokenizer
- obj._train_data = new_datasets[RunningStage.TRAIN].to_pandas() # type: ignore
- obj._validation_data = new_datasets.get(RunningStage.VALIDATION) # type: ignore
- obj._test_data = new_datasets.get(RunningStage.TEST) # type: ignore
-
- return obj
+class PandasDatastoreForSequenceClassification(SequenceClassificationMixin, PandasDatastoreWithIndex):
+ def _set_attributes(self, dataset_dict: dict[RunningStage, Dataset], tokenizer: PreTrainedTokenizerBase) -> None:
+ super()._set_attributes(dataset_dict, tokenizer)
+ self._train_data = self._train_data.to_pandas() # type: ignore
diff --git a/energizer/datastores/language_modelling.py b/energizer/datastores/language_modelling.py
index b2e926a..0b07b93 100644
--- a/energizer/datastores/language_modelling.py
+++ b/energizer/datastores/language_modelling.py
@@ -1,22 +1,88 @@
+from collections.abc import Callable # , Generator, Any
+from dataclasses import dataclass
from functools import partial
-from itertools import chain
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Optional, Union
-from datasets import Dataset, DatasetDict # type: ignore
+from datasets import Dataset
+from lightning_utilities.core.rank_zero import rank_zero_info
from torch import Tensor
from transformers import PreTrainedTokenizerBase
-from typing_extensions import Self
-from energizer.datastores.base import PandasDataStoreWithIndex
-from energizer.enums import InputKeys, RunningStage, SpecialKeys
-from energizer.utilities import _pad, ld_to_dl, sequential_numbers
+from energizer.datastores.base import DataloaderArgs, Datastore, PandasDatastoreWithIndex
+from energizer.datastores.mixins import TextMixin
+from energizer.enums import InputKeys, RunningStage
+from energizer.utilities import _pad, ld_to_dl
+# from itertools import islice
-class LanguageModellingMixin:
- _tokenizer: PreTrainedTokenizerBase
- input_names: List[str]
- on_cpu: List[str]
+def collate_fn_for_language_modelling(
+ batch: list[dict[str, Union[list[str], Tensor]]],
+ input_names: list[str],
+ on_cpu: list[str],
+ max_length: Optional[int],
+ pad_token_id: Optional[int],
+ pad_fn: Callable,
+ return_labels: bool,
+) -> dict[str, Union[list[str], Tensor]]:
+ new_batch = ld_to_dl(batch)
+
+ # remove string columns that cannot be transfered on gpu
+ values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch}
+
+ labels = new_batch.pop(InputKeys.LABELS, None)
+
+ # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad
+ new_batch = {k: pad_fn(inputs=new_batch[k], padding_value=pad_token_id, max_length=max_length) for k in input_names}
+
+ # labels substitute pad_token_id with -100
+ if return_labels:
+ labels = new_batch[InputKeys.INPUT_IDS].clone()
+ if pad_token_id is not None:
+ labels[labels == pad_token_id] = -100
+ new_batch[InputKeys.LABELS] = labels
+
+ # add things that need to remain on cpu
+ if len(values_on_cpu) > 0:
+ new_batch[InputKeys.ON_CPU] = values_on_cpu
+
+ return new_batch
+
+
+@dataclass
+class LanguageModellingDataloaderArgs(DataloaderArgs):
+ max_length: int
+
+
+class LanguageModellingMixin(TextMixin):
+ MANDATORY_INPUT_NAMES: list[str] = [InputKeys.INPUT_IDS]
+ MANDATORY_TARGET_NAME: Optional[str] = None
+ OPTIONAL_INPUT_NAMES: list[str] = []
+
+ _loading_params: Optional[LanguageModellingDataloaderArgs] = None
+ _return_labels: bool = True
+
+ def set_return_labels(self, mode: bool = True) -> None:
+ self._return_labels = mode
+ msg = f"Return labels is {self._return_labels}."
+ if not self._return_labels:
+ msg += " Remeber that you now need to manually shift the `input_ids` to obtain the labels."
+ rank_zero_info(msg)
+
+ @property
+ def return_labels(self) -> bool:
+ return self._return_labels
+
+ def get_collate_fn(self, stage: Optional[RunningStage] = None, show_batch: bool = False) -> Optional[Callable]:
+ return partial(
+ collate_fn_for_language_modelling,
+ input_names=self.input_names,
+ on_cpu=self.on_cpu,
+ max_length=None if show_batch else self.loading_params.max_length, # type: ignore
+ pad_token_id=self.tokenizer.pad_token_id,
+ pad_fn=_pad,
+ return_labels=self.return_labels,
+ )
def prepare_for_loading(
self,
@@ -27,207 +93,77 @@ def prepare_for_loading(
drop_last: bool = False,
persistent_workers: bool = False,
shuffle: bool = True,
- seed: int = 42,
replacement: bool = False,
+ data_seed: int = 42,
+ multiprocessing_context: Optional[str] = None,
max_length: int = 512,
) -> None:
- super().prepare_for_loading(
- batch_size,
- eval_batch_size,
- num_workers,
- pin_memory,
- drop_last,
- persistent_workers,
- shuffle,
- seed,
- replacement,
+ self._loading_params = LanguageModellingDataloaderArgs(
+ batch_size=batch_size,
+ eval_batch_size=eval_batch_size,
+ num_workers=num_workers,
+ pin_memory=pin_memory,
+ drop_last=drop_last,
+ persistent_workers=persistent_workers,
+ shuffle=shuffle,
+ data_seed=data_seed,
+ replacement=replacement,
+ multiprocessing_context=multiprocessing_context,
+ max_length=max_length,
)
- self._loading_params["max_length"] = max_length
- @property
- def tokenizer(self) -> PreTrainedTokenizerBase:
- return self._tokenizer
-
- @classmethod
- def from_datasets(
- cls,
- tokenizer: PreTrainedTokenizerBase,
- uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
- seed: Optional[int] = 42,
- train_dataset: Optional[Dataset] = None,
- validation_dataset: Optional[Dataset] = None,
- test_dataset: Optional[Dataset] = None,
- ) -> Self:
- obj = cls(seed)
- return _from_datasets(
- obj=obj,
- tokenizer=tokenizer,
- uid_name=uid_name,
- on_cpu=on_cpu,
- train_dataset=train_dataset,
- validation_dataset=validation_dataset,
- test_dataset=test_dataset,
- )
+ # def get_packed_dataset(self, dataset: Dataset, block_size: int) -> Dataset:
+ # iterable_dataset = dataset.to_iterable_dataset()
+ # input_ids_iterator = iter(token for ex in iterable_dataset for token in ex[InputKeys.INPUT_IDS])
+ # packed_dataset = Dataset.from_generator(batched(input_ids_iterator, block_size))
+ # return packed_dataset
- @classmethod
- def from_dataset_dict(
- cls,
- dataset_dict: DatasetDict,
- tokenizer: PreTrainedTokenizerBase,
- uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
- seed: Optional[int] = 42,
- ) -> Self:
- return cls.from_datasets(
- tokenizer=tokenizer,
- uid_name=uid_name,
- on_cpu=on_cpu,
- seed=seed,
- train_dataset=dataset_dict.get(RunningStage.TRAIN),
- validation_dataset=dataset_dict.get(RunningStage.VALIDATION),
- test_dataset=dataset_dict.get(RunningStage.TEST),
- )
+ # def train_dataset(self) -> Optional[Dataset]:
+ # if self._train_data is not None:
+ # return self._train_data
- def get_collate_fn(self, stage: Optional[RunningStage] = None, show_batch: bool = False) -> Optional[Callable]:
- return partial(
- collate_fn,
- input_names=self.input_names,
- on_cpu=self.on_cpu,
- max_length=None if show_batch else self.loading_params["max_length"],
- pad_token_id=self.tokenizer.pad_token_id,
- pad_fn=_pad,
- )
+ # def validation_dataset(self) -> Optional[Dataset]:
+ # if self._validation_data is not None:
+ # return self._validation_data
- def group_texts(self, block_size: int) -> None:
- dataset = self.train_dataset()
- dataset = dataset.with_format(columns=self.input_names)
- dataset = dataset.map(lambda ex: group_texts(ex, block_size=block_size), batched=True)
- self._train_data = dataset.to_pandas()
+ # def test_dataset(self) -> Optional[Dataset]:
+ # if self._test_data is not None:
+ # return self._test_data
-class PandasDataStoreForLanguageModelling(LanguageModellingMixin, PandasDataStoreWithIndex):
+class DatastoreForLanguageModelling(LanguageModellingMixin, Datastore):
...
-"""
-Utilities
-"""
+class PandasDatastoreForLanguageModelling(LanguageModellingMixin, PandasDatastoreWithIndex):
+ def _set_attributes(self, dataset_dict: dict[RunningStage, Dataset], tokenizer: PreTrainedTokenizerBase) -> None:
+ super()._set_attributes(dataset_dict, tokenizer)
+ self._train_data = self._train_data.to_pandas() # type: ignore
-def collate_fn(
- batch: List[Dict[str, Union[List[str], Tensor]]],
- input_names: str,
- on_cpu: List[str],
- max_length: Optional[int],
- pad_token_id: Optional[int],
- pad_fn: Callable,
-) -> Dict[str, Union[List[str], Tensor]]:
- new_batch = ld_to_dl(batch)
+# def batched(iterator, chunk_size) -> Generator[list[Any], Any, None]:
+# while chunk := list(islice(iterator, chunk_size)):
+# yield chunk
- # remove string columns that cannot be transfered on gpu
- values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch}
+# def group_texts(examples: dict, block_size: int) -> dict:
+# """Concatenate all texts from our dataset and generate chunks of block_size.
- labels = new_batch.pop(InputKeys.LABELS, None)
+# Refs: https://github.com/huggingface/transformers/blob/bfb1895e3346cb8a2bf2560c75d45e70edf46a47/examples/pytorch/language-modeling/run_clm_no_trainer.py#L456
+# """
- # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad
- new_batch = {
- k: pad_fn(
- inputs=new_batch[k],
- padding_value=pad_token_id,
- max_length=max_length,
- )
- for k in input_names
- }
+# # Concatenate all texts
+# concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
+# # concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
- # labels substitute pad_token_id with -100
- labels = new_batch[InputKeys.INPUT_IDS].clone()
- if pad_token_id is not None:
- labels[labels == pad_token_id] = -100
- new_batch[InputKeys.LABELS] = labels
+# total_length = len(concatenated_examples[next(iter(examples.keys()))])
- # add things that need to remain on cpu
- if len(on_cpu) > 0:
- new_batch[InputKeys.ON_CPU] = values_on_cpu
-
- return new_batch
+# # We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict
+# # We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
+# total_length = (total_length // block_size) * block_size
+# # Split by chunks of max_len
+# result = {
+# k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items()
+# }
-def _from_datasets(
- obj: Any,
- tokenizer: PreTrainedTokenizerBase,
- uid_name: Optional[str] = None,
- on_cpu: Optional[List[str]] = None,
- train_dataset: Optional[Dataset] = None,
- validation_dataset: Optional[Dataset] = None,
- test_dataset: Optional[Dataset] = None,
-) -> Any:
- obj._tokenizer = tokenizer
- obj.input_names = [InputKeys.INPUT_IDS, InputKeys.ATT_MASK]
- obj.on_cpu = on_cpu or []
-
- _datasets = {
- RunningStage.TRAIN: train_dataset,
- RunningStage.VALIDATION: validation_dataset,
- RunningStage.TEST: test_dataset,
- }
- datasets: Dict[RunningStage, Dataset] = {k: v for k, v in _datasets.items() if v is not None}
- if len(datasets) < 1:
- raise ValueError("You need to pass at least one dataset.")
-
- uid_generator = sequential_numbers()
- columns = obj.input_names + obj.on_cpu
- if uid_name is not None:
- columns.append(uid_name)
-
- new_datasets = {}
- for k, d in datasets.items():
- # check all columns are present in all datasets
- for col in columns:
- assert col in d.features.keys(), f"{col=} not in dataset={d.features.keys()}"
-
- if uid_name is None:
- uids = [next(uid_generator) for _ in range(len(d))]
- new_dataset = d.add_column(SpecialKeys.ID, uids)
- else:
- # check
- col = list(datasets[RunningStage.TRAIN][SpecialKeys.ID])
- assert len(set(col)) == len(col), f"`uid_column` {uid_name} is not unique."
- new_dataset = d.rename_columns({uid_name: SpecialKeys.ID})
-
- new_datasets[k] = new_dataset
-
- obj.on_cpu += [SpecialKeys.ID]
-
- # set data sources
- new_datasets = {k: v.with_format(columns=obj.input_names + obj.on_cpu) for k, v in new_datasets.items()}
-
- obj._train_data = new_datasets[RunningStage.TRAIN].to_pandas()
- obj._validation_data = new_datasets.get(RunningStage.VALIDATION)
- obj._test_data = new_datasets.get(RunningStage.TEST)
-
- return obj
-
-
-def group_texts(examples: Dict, block_size: int) -> Dict:
- """Concatenate all texts from our dataset and generate chunks of block_size.
-
- Refs: https://github.com/huggingface/transformers/blob/bfb1895e3346cb8a2bf2560c75d45e70edf46a47/examples/pytorch/language-modeling/run_clm_no_trainer.py#L456
- """
-
- # Concatenate all texts
- concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
- # concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
-
- total_length = len(concatenated_examples[list(examples.keys())[0]])
-
- # We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict
- # We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
- total_length = (total_length // block_size) * block_size
-
- # Split by chunks of max_len
- result = {
- k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items()
- }
-
- return result
+# return result
diff --git a/energizer/datastores/mixins.py b/energizer/datastores/mixins.py
new file mode 100644
index 0000000..ba06b96
--- /dev/null
+++ b/energizer/datastores/mixins.py
@@ -0,0 +1,239 @@
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from pathlib import Path
+from typing import Optional, Union
+
+import hnswlib as hb
+import numpy as np
+import srsly
+from datasets import Dataset, DatasetDict # type: ignore
+from lightning_utilities.core.rank_zero import rank_zero_warn
+from transformers import PreTrainedTokenizerBase
+from typing_extensions import Self
+
+from energizer.enums import InputKeys, RunningStage, SpecialKeys
+from energizer.utilities import sequential_numbers
+
+
+class TextMixin(ABC):
+ MANDATORY_INPUT_NAMES: list[str] = [InputKeys.INPUT_IDS, InputKeys.ATT_MASK]
+ OPTIONAL_INPUT_NAMES: list[str] = [InputKeys.TOKEN_TYPE_IDS]
+ MANDATORY_TARGET_NAME: Optional[str] = None
+
+ _tokenizer: PreTrainedTokenizerBase
+ input_names: list[str]
+ on_cpu: list[str]
+
+ @abstractmethod
+ def get_collate_fn(self, stage: Optional[RunningStage] = None, show_batch: bool = False) -> Optional[Callable]:
+ ...
+
+ @property
+ def tokenizer(self) -> PreTrainedTokenizerBase:
+ return self._tokenizer
+
+ @classmethod
+ def from_datasets(
+ cls,
+ tokenizer: PreTrainedTokenizerBase,
+ uid_name: Optional[str] = None,
+ on_cpu: Optional[list[str]] = None,
+ seed: Optional[int] = 42,
+ train_dataset: Optional[Dataset] = None,
+ validation_dataset: Optional[Dataset] = None,
+ test_dataset: Optional[Dataset] = None,
+ ) -> Self:
+ obj = cls(seed) # type: ignore
+ obj.load_datasets(
+ tokenizer=tokenizer,
+ uid_name=uid_name,
+ on_cpu=on_cpu,
+ train_dataset=train_dataset,
+ validation_dataset=validation_dataset,
+ test_dataset=test_dataset,
+ mandatory_input_names=obj.MANDATORY_INPUT_NAMES,
+ optional_input_names=obj.OPTIONAL_INPUT_NAMES,
+ mandatory_target_name=obj.MANDATORY_TARGET_NAME,
+ )
+ return obj
+
+ @classmethod
+ def from_dataset_dict(
+ cls,
+ dataset_dict: DatasetDict,
+ tokenizer: PreTrainedTokenizerBase,
+ uid_name: Optional[str] = None,
+ on_cpu: Optional[list[str]] = None,
+ seed: Optional[int] = 42,
+ ) -> Self:
+ obj = cls(seed) # type: ignore
+ obj.load_datasets(
+ tokenizer=tokenizer,
+ uid_name=uid_name,
+ on_cpu=on_cpu,
+ train_dataset=dataset_dict.get(RunningStage.TRAIN),
+ validation_dataset=dataset_dict.get(RunningStage.VALIDATION),
+ test_dataset=dataset_dict.get(RunningStage.TEST),
+ mandatory_input_names=obj.MANDATORY_INPUT_NAMES,
+ optional_input_names=obj.OPTIONAL_INPUT_NAMES,
+ mandatory_target_name=obj.MANDATORY_TARGET_NAME,
+ )
+ return obj
+
+ def load_datasets(
+ self,
+ mandatory_input_names: list[str],
+ optional_input_names: list[str],
+ mandatory_target_name: Optional[str],
+ tokenizer: PreTrainedTokenizerBase,
+ uid_name: Optional[str] = None,
+ on_cpu: Optional[list[str]] = None,
+ train_dataset: Optional[Dataset] = None,
+ validation_dataset: Optional[Dataset] = None,
+ test_dataset: Optional[Dataset] = None,
+ ) -> None:
+ _datasets = {
+ RunningStage.TRAIN: train_dataset,
+ RunningStage.VALIDATION: validation_dataset,
+ RunningStage.TEST: test_dataset,
+ }
+ datasets: dict[RunningStage, Dataset] = {k: v for k, v in _datasets.items() if v is not None}
+ if len(datasets) < 1:
+ raise ValueError("You need to pass at least one dataset.")
+
+ # === INPUT NAMES === #
+ self._check_input_names(datasets, mandatory_input_names, optional_input_names)
+
+ # === TARGET NAME === #
+ if mandatory_target_name is not None:
+ self._check_labels(datasets, mandatory_target_name)
+
+ # === ON_CPU === #
+ self._check_on_cpu(datasets, on_cpu)
+
+ # === UID NAME === #
+ datasets = self._check_uid(datasets, uid_name)
+ self.on_cpu += [SpecialKeys.ID]
+
+ # === FORMAT (KEEP ONLY USEFUL COLUMNS) === #
+ datasets = self._format_datasets(datasets)
+
+ # === SET ATTRIBUTES === #
+ self._set_attributes(datasets, tokenizer)
+
+ def _check_input_names(
+ self,
+ dataset_dict: dict[RunningStage, Dataset],
+ mandatory_input_names: list[str],
+ optional_input_names: list[str],
+ ) -> None:
+ input_names = []
+ for name in mandatory_input_names:
+ for split, dataset in dataset_dict.items():
+ if name in dataset.features:
+ input_names.append(name)
+ else:
+ raise ValueError(f"Mandatory column {name} not in dataset[{split}].")
+
+ for name in optional_input_names:
+ for dataset in dataset_dict.values():
+ if name in dataset.features:
+ input_names.append(name)
+
+ self.input_names = list(set(input_names))
+
+ def _check_on_cpu(self, dataset_dict: dict[RunningStage, Dataset], on_cpu: Optional[list[str]]) -> None:
+ _on_cpu = []
+ if on_cpu is not None:
+ for name in on_cpu:
+ for split, dataset in dataset_dict.items():
+ assert name in dataset.features, f"{name=} not in dataset[{split}]={dataset.features.keys()}"
+ _on_cpu.append(name)
+ self.on_cpu = list(set(_on_cpu))
+
+ def _check_uid(
+ self, dataset_dict: dict[RunningStage, Dataset], uid_name: Optional[str]
+ ) -> dict[RunningStage, Dataset]:
+ uid_generator = sequential_numbers()
+ new_datasets = {}
+ for k, d in dataset_dict.items():
+ if uid_name is None:
+ uids = [next(uid_generator) for _ in range(len(d))]
+ new_dataset = d.add_column(SpecialKeys.ID, uids) # type: ignore
+ print(f"UID column {SpecialKeys.ID} automatically created in dataset[{k}]")
+ else:
+ assert uid_name in d.features, f"{uid_name=} not in dataset[{k}]={d.features.keys()}"
+ ids = d[uid_name]
+ assert len(set(ids)) == len(ids), f"`uid_column` {uid_name} is not unique."
+
+ new_dataset = d
+ if uid_name != SpecialKeys.ID:
+ new_dataset = new_dataset.rename_columns({uid_name: SpecialKeys.ID})
+ print(f"UID column {uid_name} automatically renamed to {SpecialKeys.ID} in dataset[{k}]")
+
+ new_datasets[k] = new_dataset
+
+ return new_datasets
+
+ def _check_labels(self, dataset_dict: dict[RunningStage, Dataset], mandatory_target_name: str) -> None:
+ for split, dataset in dataset_dict.items():
+ assert (
+ mandatory_target_name in dataset.features
+ ), f"Mandatory column {mandatory_target_name} not in dataset[{split}]."
+
+ def _format_datasets(self, dataset_dict: dict[RunningStage, Dataset]) -> dict[RunningStage, Dataset]:
+ columns = self.input_names + self.on_cpu
+ if self.MANDATORY_TARGET_NAME is not None:
+ columns.append(self.MANDATORY_TARGET_NAME)
+ return {k: v.with_format(columns=columns) for k, v in dataset_dict.items()}
+
+ def _set_attributes(self, dataset_dict: dict[RunningStage, Dataset], tokenizer: PreTrainedTokenizerBase) -> None:
+ self._tokenizer = tokenizer
+ self._train_data = dataset_dict.get(RunningStage.TRAIN) # type: ignore
+ self._validation_data = dataset_dict.get(RunningStage.VALIDATION) # type: ignore
+ self._test_data = dataset_dict.get(RunningStage.TEST) # type: ignore
+
+
+class IndexMixin:
+ index: hb.Index = None
+ embedding_name: str
+
+ def search(self, query: np.ndarray, query_size: int, query_in_set: bool = True) -> tuple[np.ndarray, np.ndarray]:
+ # retrieve one additional element if the query is in the set we are looking in
+ # because the query itself is returned as the most similar element and we need to remove it
+ query_size = query_size + 1 if query_in_set else query_size
+ indices, distances = self.index.knn_query(data=query, k=query_size)
+ if query_in_set:
+ # remove the first element retrieved if the query is in the set since it's the element itself
+ indices, distances = indices[:, 1:], distances[:, 1:]
+ return indices, distances
+
+ def load_index(self, index_path: Union[str, Path], metadata_path: Union[str, Path]) -> None:
+ meta: dict = srsly.read_json(metadata_path) # type: ignore
+ index = hb.Index(space=meta["metric"], dim=meta["dim"])
+ index.load_index(str(index_path))
+ self.index = index
+
+ # consistency check: data in index must be the same or more
+ assert self._train_data is not None # type: ignore
+ assert len(index.get_ids_list()) >= len(self._train_data[SpecialKeys.ID]), "Index is not compatible with data." # type: ignore
+
+ # if dataset has been downsampled, mask the ids
+ if len(index.get_ids_list()) > len(self._train_data[SpecialKeys.ID]): # type: ignore
+ rank_zero_warn(
+ "Index has more ids than dataset. Masking the missing ids from the index. "
+ "If this is expected (e.g., you downsampled your dataset), everything is fine."
+ )
+ missing_ids = set(index.get_ids_list()).difference(set(self._train_data[SpecialKeys.ID])) # type: ignore
+ self.mask_ids_from_index(list(missing_ids))
+
+ def mask_ids_from_index(self, ids: list[int]) -> None:
+ for i in ids:
+ self.index.mark_deleted(i)
+
+ def unmask_ids_from_index(self, ids: list[int]) -> None:
+ for i in ids:
+ self.index.unmark_deleted(i)
+
+ def get_embeddings(self, ids: list[int]) -> np.ndarray:
+ return np.stack(self.index.get_items(ids))
diff --git a/energizer/datastores/seq2seq.py b/energizer/datastores/seq2seq.py
new file mode 100644
index 0000000..8a869ee
--- /dev/null
+++ b/energizer/datastores/seq2seq.py
@@ -0,0 +1,109 @@
+from collections.abc import Callable
+from dataclasses import dataclass
+from functools import partial
+from typing import Optional, Union
+
+from datasets import Dataset
+from torch import Tensor
+from transformers import PreTrainedTokenizerBase
+
+from energizer.datastores.base import DataloaderArgs, Datastore, PandasDatastoreWithIndex
+from energizer.datastores.mixins import TextMixin
+from energizer.enums import InputKeys, RunningStage
+from energizer.utilities import _pad, ld_to_dl
+
+
+def collate_fn_for_seq2seq(
+ batch: list[dict[str, Union[list[str], Tensor]]],
+ input_names: list[str],
+ on_cpu: list[str],
+ max_source_length: Optional[int],
+ max_target_length: Optional[int],
+ pad_token_id: Optional[int],
+ pad_fn: Callable,
+) -> dict[str, Union[list[str], Tensor]]:
+ new_batch = ld_to_dl(batch)
+
+ # remove string columns that cannot be transfered on gpu
+ values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch}
+
+ labels = new_batch.pop(InputKeys.LABELS, None)
+
+ # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad
+ new_batch = {
+ k: pad_fn(inputs=new_batch[k], padding_value=pad_token_id, max_length=max_source_length) for k in input_names
+ }
+
+ # labels substitute pad_token_id with -100
+ labels = pad_fn(inputs=labels, padding_value=-100, max_length=max_target_length)
+ new_batch[InputKeys.LABELS] = labels
+
+ # add things that need to remain on cpu
+ if len(values_on_cpu) > 0:
+ new_batch[InputKeys.ON_CPU] = values_on_cpu
+
+ return new_batch
+
+
+@dataclass
+class Seq2SeqDataloaderArgs(DataloaderArgs):
+ max_source_length: int
+ max_target_length: int
+
+
+class Seq2SeqMixin(TextMixin):
+ MANDATORY_TARGET_NAME: Optional[str] = InputKeys.LABELS
+ OPTIONAL_INPUT_NAMES: list[str] = []
+ BLOCK_SIZE: int = 1_000
+ _loading_params: Optional[Seq2SeqDataloaderArgs] = None
+
+ def prepare_for_loading(
+ self,
+ batch_size: int = 32,
+ eval_batch_size: int = 32,
+ num_workers: int = 0,
+ pin_memory: bool = True,
+ drop_last: bool = False,
+ persistent_workers: bool = False,
+ shuffle: bool = True,
+ replacement: bool = False,
+ data_seed: int = 42,
+ multiprocessing_context: Optional[str] = None,
+ max_source_length: int = 512,
+ max_target_length: int = 512,
+ ) -> None:
+ self._loading_params = Seq2SeqDataloaderArgs(
+ batch_size=batch_size,
+ eval_batch_size=eval_batch_size,
+ num_workers=num_workers,
+ pin_memory=pin_memory,
+ drop_last=drop_last,
+ persistent_workers=persistent_workers,
+ shuffle=shuffle,
+ replacement=replacement,
+ data_seed=data_seed,
+ multiprocessing_context=multiprocessing_context,
+ max_source_length=max_source_length,
+ max_target_length=max_target_length,
+ )
+
+ def get_collate_fn(self, stage: Optional[RunningStage] = None, show_batch: bool = False) -> Optional[Callable]:
+ return partial(
+ collate_fn_for_seq2seq,
+ input_names=self.input_names,
+ on_cpu=self.on_cpu,
+ max_source_length=None if show_batch else self.loading_params.max_source_length, # type: ignore
+ max_target_length=None if show_batch else self.loading_params.max_target_length, # type: ignore
+ pad_token_id=self.tokenizer.pad_token_id,
+ pad_fn=_pad,
+ )
+
+
+class DatastoreForSeq2Seq(Seq2SeqMixin, Datastore):
+ ...
+
+
+class PandasDatastoreForSeq2Seq(Seq2SeqMixin, PandasDatastoreWithIndex):
+ def _set_attributes(self, dataset_dict: dict[RunningStage, Dataset], tokenizer: PreTrainedTokenizerBase) -> None:
+ super()._set_attributes(dataset_dict, tokenizer)
+ self._train_data = self._train_data.to_pandas() # type: ignore
diff --git a/energizer/enums.py b/energizer/enums.py
index aad60e7..e485569 100644
--- a/energizer/enums.py
+++ b/energizer/enums.py
@@ -1,5 +1,6 @@
+from collections.abc import Generator
from enum import Enum, EnumMeta
-from typing import Any, Generator
+from typing import Any
class ValueOnlyEnumMeta(EnumMeta):
diff --git a/energizer/estimator.py b/energizer/estimator.py
index f1c01bd..5895320 100644
--- a/energizer/estimator.py
+++ b/energizer/estimator.py
@@ -1,15 +1,19 @@
-import copy
+from collections.abc import Callable, Mapping
from dataclasses import dataclass, field
from pathlib import Path
-from typing import Any, Callable, Dict, List, Literal, Mapping, Optional, Tuple, Union
+from typing import Any, Literal, Optional, Union
+import bitsandbytes as bnb
import numpy as np
import torch
from lightning.fabric import Fabric
from lightning.fabric.accelerators.accelerator import Accelerator
+from lightning.fabric.connector import _PLUGIN_INPUT
from lightning.fabric.loggers.logger import Logger
+from lightning.fabric.plugins.precision.bitsandbytes import BitsandbytesPrecision
from lightning.fabric.plugins.precision.precision import _PRECISION_INPUT
from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer
+from lightning_utilities.core.rank_zero import rank_zero_info
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
@@ -18,23 +22,16 @@
from energizer.registries import OPTIMIZER_REGISTRY, SCHEDULER_REGISTRY
from energizer.trackers import ProgressTracker
from energizer.types import BATCH_OUTPUT, EPOCH_OUTPUT, FIT_OUTPUT, METRIC
-from energizer.utilities import move_to_cpu, set_deterministic
+from energizer.utilities import Args, move_to_cpu, set_deterministic
from energizer.utilities.model_summary import summarize
-@dataclass
-class Args:
- def to_dict(self) -> Dict[str, Any]:
- out = copy.deepcopy(self.__dict__)
- return out
-
-
@dataclass
class SchedulerArgs(Args):
name: Optional[str] = None
num_warmup_steps: Optional[int] = None
num_training_steps: Optional[int] = None
- init_kwargs: Dict = field(default_factory=dict)
+ init_kwargs: dict = field(default_factory=dict)
@dataclass
@@ -42,17 +39,19 @@ class OptimizationArgs(Args):
name: Optional[str] = None
lr: Optional[float] = None
weight_decay: Optional[float] = None
- no_decay: Optional[List[str]] = None
+ no_decay: Optional[list[str]] = None
set_to_none: bool = False
clip_val: Optional[Union[float, int]] = None
max_norm: Optional[Union[float, int]] = None
norm_type: Union[float, int] = 2.0
- init_kwargs: Dict = field(default_factory=dict)
+ init_kwargs: dict = field(default_factory=dict)
scheduler_kwargs: SchedulerArgs = field(default_factory=lambda: SchedulerArgs())
+ backward_create_graph: bool = False
+ backward_retain_graph: Optional[bool] = None
class Estimator:
- _model: Union[torch.nn.Module, Callable]
+ _model: torch.nn.Module
_tracker: ProgressTracker
_optimization_args: OptimizationArgs
_is_compiled: bool = False
@@ -62,10 +61,11 @@ def __init__(
model: Any,
accelerator: Union[str, Accelerator] = "cpu",
precision: _PRECISION_INPUT = 32,
- callbacks: Optional[Union[List[Any], Any]] = None,
- loggers: Optional[Union[Logger, List[Logger]]] = None,
+ callbacks: Optional[Union[list[Any], Any]] = None,
+ loggers: Optional[Union[Logger, list[Logger]]] = None,
deterministic: Union[bool, Literal["warn_only"]] = "warn_only",
tf32_mode: Literal["highest", "high", "medium"] = "highest",
+ plugins: Optional[Union[_PLUGIN_INPUT, list[_PLUGIN_INPUT]]] = None,
**kwargs,
) -> None:
super().__init__()
@@ -76,16 +76,24 @@ def __init__(
loggers=loggers,
devices=1, # only works with single-GPU
num_nodes=1, # only works with single-node
+ plugins=plugins,
)
self.set_deterministic(deterministic)
self.set_torch_matmul_precision(tf32_mode)
- self.init_model(model, **kwargs)
self.init_tracker()
+ self.configure_model(model, **kwargs)
+
+ def init_tracker(self) -> None:
+ self._tracker = ProgressTracker()
+
+ """
+ Properties
+ """
@property
- def model(self) -> Union[torch.nn.Module, Callable]:
+ def model(self) -> torch.nn.Module:
return self._model
@property
@@ -98,10 +106,44 @@ def device(self) -> torch.device:
@property
def precision(self) -> str:
+ # NOTE: doing this because model is not cast at __init__ but only when `self.fabric.setup` is called
+ if self.is_quantized:
+ rank_zero_info(
+ "Model is loaded with the `BitsandbytesPrecision` plugin thus it currently is not cast to the correct dtype. "
+ f"It will only be cast during `fit` or `test`. Furthermore, the linear layers will be cast to {self.quantization_mode}"
+ )
+ dtype = self.fabric._precision.dtype # type: ignore
+ return "bf16-true" if dtype == torch.bfloat16 else "16-true"
return self.fabric._precision.precision
@property
- def loggers(self) -> List[Logger]:
+ def is_quantized(self) -> bool:
+ # NOTE: hacky -- this is very specific to the BitsandbytesPrecision plugin
+ return isinstance(self.fabric._precision, BitsandbytesPrecision)
+
+ @property
+ def quantization_mode(self) -> Optional[str]:
+ # NOTE: look at the BitsandbytesPrecision class
+ cls_to_mode = {
+ "_NF4Linear": "nf4",
+ "_NF4DQLinear": "nf4-dq",
+ "_FP4Linear": "fp4",
+ "_FP4DQLinear": "fp4-dq",
+ "_Linear8bitLt": "int8-training",
+ "_Int8LinearInference": "int8",
+ }
+ if self.is_quantized:
+ return cls_to_mode[self.fabric._precision._linear_cls.__name__] # type: ignore
+ rank_zero_info("Model is not quantized")
+
+ @property
+ def dtypes(self) -> set[str]:
+ # need to setup for changes to take effect
+ model = self.fabric.setup(self.model)
+ return {str(p.dtype) for p in model.parameters()}
+
+ @property
+ def loggers(self) -> list[Logger]:
"""Returns all loggers passed to Fabric."""
return self.fabric.loggers
@@ -110,14 +152,6 @@ def logger(self) -> Logger:
"""Returns the first logger in the list passed to Fabric, which is considered the main logger."""
return self.fabric.logger
- # @property
- # def prediction_dtype(self) -> torch.dtype:
- # dtype = self.precision
- # if "-" in dtype:
- # dtype = dtype.split("-")[0]
- # dtype = f"bfloat{dtype[2:]}" if dtype.startswith("b") else f"float{dtype}"
- # return getattr(torch, dtype)
-
@property
def model_summary(self) -> str:
return summarize(self)
@@ -130,32 +164,9 @@ def is_compiled(self) -> bool:
def optimization_args(self) -> OptimizationArgs:
return self._optimization_args
- def init_tracker(self) -> None:
- self._tracker = ProgressTracker()
-
- def init_model(self, model: Any, **kwargs) -> None:
- self._model = model
-
- def compile(self, **kwargs) -> None:
- # model becomes a Callable
- self._model = torch.compile(self._model, **kwargs)
- self._is_compiled = True
-
- def set_torch_matmul_precision(self, tf32_mode: Literal["highest", "high", "medium"] = "highest") -> None:
- # equivalent to `torch.backends.cudnn.allow_tf32 = True`
- # convolutions are not changed, to do that you need
- # `torch.backends.cudnn.allow_tf32 = True`
- torch.set_float32_matmul_precision(tf32_mode)
-
- def set_deterministic(self, deterministic: Union[bool, Literal["warn_only"]]) -> None:
- # sets deterministic convolutions too
- set_deterministic(deterministic)
-
- def set_eval_mode(self) -> None:
- self.model.eval()
-
- def set_train_mode(self, mode: bool = True) -> None:
- self.model.train(mode)
+ @property
+ def callbacks(self) -> list:
+ return self.fabric._callbacks
"""
Entry points
@@ -165,7 +176,7 @@ def fit(
self,
train_loader: DataLoader,
validation_loader: Optional[DataLoader] = None,
- max_epochs: Optional[int] = 3,
+ max_epochs: Optional[int] = None,
min_epochs: Optional[int] = None,
max_steps: Optional[int] = None,
min_steps: Optional[int] = None,
@@ -173,14 +184,14 @@ def fit(
gradient_accumulation_steps: Optional[int] = None,
learning_rate: Optional[float] = None,
optimizer: Optional[str] = None,
- optimizer_kwargs: Optional[Union[Dict, OptimizationArgs]] = None,
+ optimizer_kwargs: Optional[Union[dict, OptimizationArgs]] = None,
scheduler: Optional[str] = None,
- scheduler_kwargs: Optional[Union[Dict, SchedulerArgs]] = None,
+ scheduler_kwargs: Optional[Union[dict, SchedulerArgs]] = None,
log_interval: int = 1,
enable_progress_bar: bool = True,
limit_train_batches: Optional[int] = None,
limit_validation_batches: Optional[int] = None,
- ) -> List[FIT_OUTPUT]:
+ ) -> list[FIT_OUTPUT]:
"""Entry point for model training.
Calls `fit -> run_fit -> run_epoch -> run_training_step`.
@@ -207,13 +218,7 @@ def fit(
)
model, _optimizer, _scheduler, _train_loader, _validation_loader = self._setup_fit(
- train_loader,
- validation_loader,
- learning_rate,
- optimizer,
- optimizer_kwargs,
- scheduler,
- scheduler_kwargs,
+ train_loader, validation_loader, learning_rate, optimizer, optimizer_kwargs, scheduler, scheduler_kwargs
)
return self.run_fit(model, _optimizer, _scheduler, _train_loader, _validation_loader) # type: ignore
@@ -253,7 +258,7 @@ def run_fit(
scheduler: Optional[_LRScheduler],
train_loader: _FabricDataLoader,
validation_loader: Optional[_FabricDataLoader],
- ) -> List[FIT_OUTPUT]:
+ ) -> list[FIT_OUTPUT]:
self.tracker.start_fit()
self.callback("on_fit_start", model=model)
@@ -303,18 +308,16 @@ def run_epoch(
self.callback("on_train_batch_start", model=model, optimizer=optimizer, batch=batch, batch_idx=batch_idx)
- # print("=======")
-
# run model on batch
batch_out = self.run_training_step(model, optimizer, scheduler, batch, batch_idx, loss_fn, metrics)
self.callback("on_train_batch_end", model=model, output=batch_out, batch=batch, batch_idx=batch_idx)
# record output
- train_out.append(move_to_cpu(batch_out))
+ if batch_out is not None:
+ train_out.append(move_to_cpu(batch_out))
# validation loop
- # print("IN ->", self.tracker.should_validate, "Step:", self.tracker.global_step, "Batch:", self.tracker.global_batch)
if self.tracker.should_validate:
out = self.run_evaluation(model, validation_loader, RunningStage.VALIDATION) # type: ignore
if out is not None:
@@ -329,7 +332,6 @@ def run_epoch(
self.callback("on_train_epoch_end", model=model, output=train_out, metrics=metrics)
# validation loop
- # print("OUT ->", self.tracker.should_validate)
if self.tracker.should_validate:
out = self.run_evaluation(model, validation_loader, RunningStage.VALIDATION) # type: ignore
if out is not None:
@@ -337,7 +339,8 @@ def run_epoch(
self.tracker.end()
- return train_out, validation_out
+ # validation_out is already on cpu, but here we might need to move
+ return move_to_cpu(train_out), validation_out
def run_training_step(
self,
@@ -350,17 +353,19 @@ def run_training_step(
metrics: Optional[METRIC],
) -> BATCH_OUTPUT:
"""Runs over a single batch of data."""
- # print(self.tracker.is_accumulating, self.tracker.global_batch)
with self.fabric.no_backward_sync(model, enabled=self.tracker.is_accumulating):
# compute loss
output = self.train_step(model, batch, batch_idx, loss_fn, metrics)
loss = output if isinstance(output, torch.Tensor) else output[OutputKeys.LOSS]
- # compute gradients
- self.fabric.backward(loss / self.tracker.gradient_accumulation_steps) # instead of loss.backward()
+ # compute gradients (instead of loss.backward())
+ self.fabric.backward(
+ loss / self.tracker.gradient_accumulation_steps,
+ create_graph=self.optimization_args.backward_create_graph,
+ retain_graph=self.optimization_args.backward_retain_graph,
+ )
- # print("Accumulating?", self.tracker.is_accumulating)
if not self.tracker.is_accumulating:
# clip gradients
if self.optimization_args.clip_val or self.optimization_args.max_norm:
@@ -377,17 +382,18 @@ def run_training_step(
optimizer.step()
self.callback("on_after_optimizer", model=model, optimizer=optimizer)
+ # reset the gradients
optimizer.zero_grad(set_to_none=self.optimization_args.set_to_none) # type: ignore
# update scheduler
if scheduler is not None:
+ self.callback("on_before_scheduler", model=model, optimizer=optimizer, scheduler=scheduler)
scheduler.step()
+ self.callback("on_after_scheduler", model=model, optimizer=optimizer, scheduler=scheduler)
# update tracker
self.tracker.increment_step()
- # print("UPDATED")
-
return output
def run_evaluation(
@@ -442,7 +448,7 @@ def run_evaluation(
self.tracker.end()
- return output
+ return move_to_cpu(output)
def evaluation_step(
self,
@@ -499,30 +505,33 @@ def test_step(
return self.step(RunningStage.TEST, model, batch, batch_idx, loss_fn, metrics)
def epoch_end(
- self, stage: Union[str, RunningStage], output: List[BATCH_OUTPUT], metrics: Optional[METRIC]
+ self, stage: Union[str, RunningStage], output: list[BATCH_OUTPUT], metrics: Optional[METRIC]
) -> EPOCH_OUTPUT:
return output
- def train_epoch_end(self, output: List[BATCH_OUTPUT], metrics: Optional[METRIC]) -> EPOCH_OUTPUT:
+ def train_epoch_end(self, output: list[BATCH_OUTPUT], metrics: Optional[METRIC]) -> EPOCH_OUTPUT:
return self.epoch_end(RunningStage.TRAIN, output, metrics)
- def validation_epoch_end(self, output: List[BATCH_OUTPUT], metrics: Optional[METRIC]) -> EPOCH_OUTPUT:
+ def validation_epoch_end(self, output: list[BATCH_OUTPUT], metrics: Optional[METRIC]) -> EPOCH_OUTPUT:
return self.epoch_end(RunningStage.VALIDATION, output, metrics)
- def test_epoch_end(self, output: List[BATCH_OUTPUT], metrics: Optional[METRIC]) -> EPOCH_OUTPUT:
+ def test_epoch_end(self, output: list[BATCH_OUTPUT], metrics: Optional[METRIC]) -> EPOCH_OUTPUT:
return self.epoch_end(RunningStage.TEST, output, metrics)
"""
Configuration
"""
+ def configure_model(self, model: Any, **kwargs) -> None:
+ self._model = model
+
def configure_optimization_args(
self,
learning_rate: Optional[float] = None,
optimizer: Optional[str] = None,
- optimizer_kwargs: Optional[Union[Dict, OptimizationArgs]] = None,
+ optimizer_kwargs: Optional[Union[dict, OptimizationArgs]] = None,
scheduler: Optional[str] = None,
- scheduler_kwargs: Optional[Union[Dict, SchedulerArgs]] = None,
+ scheduler_kwargs: Optional[Union[dict, SchedulerArgs]] = None,
) -> None:
# parse optimizer args
opt_kwargs = optimizer_kwargs or {} # if None
@@ -542,6 +551,7 @@ def configure_optimization_args(
sch_kwargs = scheduler_kwargs or {} # if None
sch_kwargs = sch_kwargs.to_dict() if isinstance(sch_kwargs, SchedulerArgs) else sch_kwargs
+ # defaults to constant schedule
sch_kwargs["name"] = scheduler or sch_kwargs.get("name")
num_train_steps = self.tracker.step_tracker.max
@@ -622,19 +632,18 @@ def log(self, name: str, value: Any, step: int) -> None:
def log_dict(self, value_dict: Mapping[str, Any], step: int) -> None:
"""Automatically moves to cpu and then logs mapping of values."""
if self.tracker.should_log:
- self.fabric.log_dict(value_dict, step)
+ self.fabric.log_dict(move_to_cpu(value_dict), step)
- def save_state_dict(self, cache_dir: Union[str, Path], name: str = "state_dict.pt") -> None:
+ def save_state_dict(self, cache_dir: Union[str, Path], name: str = "state_dict.pt", **kwargs) -> None:
cache_dir = Path(cache_dir)
cache_dir.mkdir(parents=True, exist_ok=True)
self.fabric.save(state=self.model.state_dict(), path=cache_dir / name)
- def load_state_dict(self, cache_dir: Union[str, Path], name: str = "state_dict.pt") -> None:
+ def load_state_dict(self, cache_dir: Union[str, Path], name: str = "state_dict.pt", **kwargs) -> None:
cache_dir = Path(cache_dir)
self.model.load_state_dict(self.fabric.load(cache_dir / name))
def callback(self, hook: str, *args, **kwargs) -> Optional[Any]:
-
# if estimator has the method
method = getattr(self, hook, None)
if method is not None and callable(method):
@@ -643,16 +652,82 @@ def callback(self, hook: str, *args, **kwargs) -> Optional[Any]:
# passes self as first argument
return self.fabric.call(hook, self, *args, **kwargs)
+ def compile(self, **kwargs) -> None:
+ # model becomes a Callable
+ self._model = torch.compile(self._model, **kwargs) # type: ignore
+ self._is_compiled = True
+
+ def set_torch_matmul_precision(self, tf32_mode: Literal["highest", "high", "medium"] = "highest") -> None:
+ # equivalent to `torch.backends.cudnn.allow_tf32 = True`
+ # convolutions are not changed, to do that you need
+ # `torch.backends.cudnn.allow_tf32 = True`
+ torch.set_float32_matmul_precision(tf32_mode)
+
+ def set_deterministic(self, deterministic: Union[bool, Literal["warn_only"]]) -> None:
+ # sets deterministic convolutions too
+ set_deterministic(deterministic)
+
+ def set_eval_mode(self) -> None:
+ self.model.eval()
+
+ def set_train_mode(self, mode: bool = True) -> None:
+ self.model.train(mode)
+
+ def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
+ """Get number of (optionally, trainable or non-embeddings) parameters in the module.
+
+ Refs: https://github.com/huggingface/transformers/blob/ae093eef016533a3670561fa9e26addb42d446d1/src/transformers/modeling_utils.py#L976-L1021
+
+ Args:
+ only_trainable (`bool`, *optional*, defaults to `False`):
+ Whether or not to return only the number of trainable parameters
+
+ exclude_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether or not to return only the number of non-embeddings parameters
+
+ Returns:
+ `int`: The number of parameters.
+ """
+
+ if exclude_embeddings:
+ embedding_param_names = [
+ f"{name}.weight"
+ for name, module_type in self.model.named_modules()
+ if isinstance(module_type, torch.nn.Embedding)
+ ]
+ total_parameters = [
+ parameter for name, parameter in self.model.named_parameters() if name not in embedding_param_names
+ ]
+ else:
+ total_parameters = list(self.model.parameters())
+
+ total_numel = []
+ is_loaded_in_4bit = self.is_quantized and "4" in self.quantization_mode # type: ignore
+ for param in total_parameters:
+ if param.requires_grad or not only_trainable:
+ # For 4bit models, we need to multiply the number of parameters by 2 as half of the parameters are
+ # used for the 4bit quantization (uint8 tensors are stored)
+ if is_loaded_in_4bit and isinstance(param, bnb.nn.Params4bit):
+ total_numel.append(param.numel() * 2)
+ else:
+ total_numel.append(param.numel())
+
+ return sum(total_numel)
+
+ """
+ Private methods
+ """
+
def _setup_fit(
self,
train_loader: Optional[DataLoader],
validation_loader: Optional[DataLoader],
learning_rate: Optional[float],
optimizer: Optional[str],
- optimizer_kwargs: Optional[Union[Dict, OptimizationArgs]],
+ optimizer_kwargs: Optional[Union[dict, OptimizationArgs]],
scheduler: Optional[str],
- scheduler_kwargs: Optional[Union[Dict, SchedulerArgs]],
- ) -> Tuple[_FabricModule, _FabricOptimizer, Optional[_LRScheduler], _FabricDataLoader, Optional[_FabricDataLoader]]:
+ scheduler_kwargs: Optional[Union[dict, SchedulerArgs]],
+ ) -> tuple[_FabricModule, _FabricOptimizer, Optional[_LRScheduler], _FabricDataLoader, Optional[_FabricDataLoader]]:
# configuration
_train_loader = self.configure_dataloader(train_loader)
_validation_loader = self.configure_dataloader(validation_loader)
diff --git a/energizer/loggers/tensorboard.py b/energizer/loggers/tensorboard.py
index a7f4139..b2b6d9e 100644
--- a/energizer/loggers/tensorboard.py
+++ b/energizer/loggers/tensorboard.py
@@ -1,5 +1,5 @@
from pathlib import Path
-from typing import Union
+from typing import Optional, Union
from lightning.fabric.loggers.tensorboard import TensorBoardLogger as _TensorBoardLogger
from tbparse import SummaryReader
@@ -8,6 +8,14 @@
class TensorBoardLogger(_TensorBoardLogger):
LOGGER_NAME: str = "tensorboard"
+ def __init__(
+ self,
+ root_dir: Union[str, Path],
+ name: Optional[str] = "tensorboard_logs",
+ version: Optional[Union[int, str]] = None,
+ ) -> None:
+ super().__init__(root_dir, name, version)
+
@property
def logger_name(self) -> str:
return self.LOGGER_NAME
diff --git a/energizer/loggers/wandb.py b/energizer/loggers/wandb.py
index 0943642..4ed8a3e 100644
--- a/energizer/loggers/wandb.py
+++ b/energizer/loggers/wandb.py
@@ -1,7 +1,7 @@
import os
from argparse import Namespace
from pathlib import Path
-from typing import Any, Dict, Mapping, Optional, Union
+from typing import Any, Optional, Union
import pandas as pd
import torch.nn as nn
@@ -24,12 +24,13 @@ def __init__(
name: Optional[str] = None,
dir: _PATH = ".",
anonymous: Optional[bool] = None,
+ start_method: Optional[str] = None,
**kwargs: Any,
) -> None:
super().__init__()
# set wandb init arguments
- self._wandb_init: Dict[str, Any] = {
+ self._wandb_init: dict[str, Any] = {
"project": project or os.environ.get("WANDB_PROJECT", "energizer_logs"),
"dir": os.fspath(dir) if dir is not None else dir,
"name": name,
@@ -37,12 +38,14 @@ def __init__(
"anonymous": ("allow" if anonymous else None),
**kwargs,
}
+ if start_method is not None:
+ self._wandb_init["settings"] = wandb.Settings(start_method=start_method)
# start wandb run (to create an attach_id for distributed modes)
wandb.require("service") # type: ignore
_ = self.experiment
- def __getstate__(self) -> Dict[str, Any]:
+ def __getstate__(self) -> dict[str, Any]:
state = self.__dict__.copy()
# args needed to reload correct experiment
if self._experiment is not None:
@@ -124,17 +127,18 @@ def watch(self, model: nn.Module, log: str = "gradients", log_freq: int = 100, l
@rank_zero_only
def finalize(self, status: str) -> None:
self.experiment.finish()
+ wandb.finish()
@rank_zero_only
- def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+ def log_hyperparams(self, params: Union[dict[str, Any], Namespace]) -> None:
params = _convert_params(params)
params = _sanitize_callable_params(params)
self.experiment.config.update(params, allow_val_change=True)
@rank_zero_only
- def log_metrics(self, metrics: Mapping, step: int) -> None:
+ def log_metrics(self, metrics: dict, step: int) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
- self.experiment.log(dict(metrics, **{"step": step}))
+ self.experiment.log({**metrics, "step": step})
def save_to_parquet(self, path: Union[str, Path]) -> None:
run = wandb.Api().run(self.run_path)
diff --git a/energizer/registries.py b/energizer/registries.py
index 9301b74..ed3adc7 100644
--- a/energizer/registries.py
+++ b/energizer/registries.py
@@ -1,6 +1,7 @@
import inspect
+from collections.abc import Callable, Generator
from types import ModuleType
-from typing import Any, Callable, Generator, List, Optional, Tuple, Type
+from typing import Any, Optional
import torch
import torch_optimizer
@@ -18,11 +19,7 @@ def register_functions(self, module: ModuleType, filter_fn: Optional[Callable] =
self[k.lower()] = v
def register_classes(
- self,
- module: ModuleType,
- base_cls: Type,
- override: bool = False,
- to_snake_case: bool = False,
+ self, module: ModuleType, base_cls: type, override: bool = False, to_snake_case: bool = False
) -> None:
"""This function is an utility to register all classes from a module."""
for cls in self.get_members(module, base_cls):
@@ -31,7 +28,7 @@ def register_classes(
self[key.lower()] = cls
@staticmethod
- def get_members(module: ModuleType, base_cls: Type) -> Generator[Type, None, None]:
+ def get_members(module: ModuleType, base_cls: type) -> Generator[type, None, None]:
return (
cls
for _, cls in inspect.getmembers(module, predicate=inspect.isclass)
@@ -39,12 +36,12 @@ def get_members(module: ModuleType, base_cls: Type) -> Generator[Type, None, Non
)
@property
- def names(self) -> List[str]:
+ def names(self) -> list[str]:
"""Returns the registered names."""
return list(self.keys())
@property
- def classes(self) -> Tuple[Type, ...]:
+ def classes(self) -> tuple[type, ...]:
"""Returns the registered classes."""
return tuple(self.values())
diff --git a/energizer/trackers.py b/energizer/trackers.py
index 341b550..bb7e217 100644
--- a/energizer/trackers.py
+++ b/energizer/trackers.py
@@ -2,6 +2,7 @@
from typing import Optional, Union
import numpy as np
+from lightning_utilities.core.rank_zero import rank_zero_info
from tqdm.auto import tqdm
from energizer.enums import Interval, RunningStage
@@ -53,23 +54,13 @@ def close_progress_bar(self) -> None:
@dataclass
class EpochTracker(Tracker):
def make_progress_bar(self) -> Optional[tqdm]:
- self.progress_bar = tqdm(
- total=self.max,
- desc="Completed epochs",
- dynamic_ncols=True,
- leave=True,
- )
+ self.progress_bar = tqdm(total=self.max, desc="Completed epochs", dynamic_ncols=True, leave=True)
@dataclass
class StepTracker(Tracker):
def make_progress_bar(self) -> Optional[tqdm]:
- self.progress_bar = tqdm(
- total=self.max,
- desc="Optimisation steps",
- dynamic_ncols=True,
- leave=True,
- )
+ self.progress_bar = tqdm(total=self.max, desc="Optimisation steps", dynamic_ncols=True, leave=True)
@dataclass
@@ -78,12 +69,7 @@ class StageTracker(Tracker):
def make_progress_bar(self) -> Optional[tqdm]:
desc = f"Epoch {self.total}".strip() if self.stage == RunningStage.TRAIN else f"{self.stage.title()}"
- self.progress_bar = tqdm(
- total=self.max,
- desc=desc,
- dynamic_ncols=True,
- leave=True,
- )
+ self.progress_bar = tqdm(total=self.max, desc=desc, dynamic_ncols=True, leave=True)
@dataclass
@@ -101,9 +87,14 @@ def __post_init__(self) -> None:
self.log_interval: int = 1
self.enable_progress_bar: bool = True
self.current_stage: Optional[RunningStage] = None
+
+ # validation logic
self.has_validation: bool = False
self.validate_every_n: Optional[int] = None
self.validation_interval: Optional[str] = None
+ self.validate_on_epoch_end: bool = False
+ self._last_epoch_num_steps: int = 0
+ self._xepoch_set: bool = False
def setup(self, stage: Union[str, RunningStage], log_interval: int, enable_progress_bar: bool, **kwargs) -> None:
"""Do all the math here and create progress bars for every stage."""
@@ -172,19 +163,37 @@ def setup_fit(
# validation schedule
if validation_freq is not None and max_validation_batches > 0:
- every_n, interval = validation_freq.split(":")
- every_n = int(every_n)
- assert interval in list(Interval)
- assert every_n > 0
-
+ if validation_freq.endswith("+"):
+ self.validate_on_epoch_end = True
+ validation_freq = validation_freq.removesuffix("+")
+
+ if "xepoch" in validation_freq:
+ # automatically compute number of times per epoch
+ times_per_epoch = int(validation_freq.split("x")[0])
+ assert times_per_epoch > 0
+ every_n = int(np.floor(np.ceil(total_steps / total_epochs) / times_per_epoch))
+ # print(f"{total_steps=}\n{total_epochs=}\n{every_n=}")
+ interval = Interval.STEP
+ self._xepoch_set = True
+ msg = f"Validating {times_per_epoch} times per epoch"
+ else:
+ every_n, interval = validation_freq.split(":")
+ every_n = int(every_n)
+ assert every_n > 0
+ assert interval in list(Interval)
+ msg = f"Validating every {every_n} {interval}"
+
+ if self.validate_on_epoch_end and interval != Interval.EPOCH:
+ msg += ". You passed `+` so will always validate on epoch end"
+
+ rank_zero_info(msg)
self.has_validation = True
self.validate_every_n = every_n
self.validation_interval = interval
+ self.validation_tracker.max = max_validation_batches
else:
self.has_validation = False
- self.validation_tracker.max = max_validation_batches
-
def setup_eval(self, stage: Union[str, RunningStage], num_batches: int, limit_batches: Optional[int]) -> None:
getattr(self, f"{stage}_tracker").max = int(min(num_batches, limit_batches or float("Inf")))
@@ -220,9 +229,7 @@ def is_accumulating(self) -> bool:
def is_done(self) -> bool:
"""Whether a stage is done."""
return (
- self.get_stage_tracker().max_reached()
- or self.current_stage == RunningStage.TRAIN
- and self.stop_training
+ self.get_stage_tracker().max_reached() or self.current_stage == RunningStage.TRAIN and self.stop_training
# or (self.epoch_tracker.remaining() <= 1 and self.gradient_accumulation_steps > self.train_tracker.remaining())
)
@@ -243,6 +250,7 @@ def _check(iter: int) -> bool:
return (iter + 1) % self.validate_every_n == 0 # type: ignore
should_validate = False
+
if self.validation_interval == Interval.EPOCH:
should_validate = _check(self.global_epoch) and self.is_done
@@ -250,11 +258,22 @@ def _check(iter: int) -> bool:
should_validate = _check(self.global_batch) and not self.is_done # type: ignore
elif self.validation_interval == Interval.STEP:
- should_validate = _check(self.global_step) and not self.is_done and not self.is_accumulating
+ # this makes sure that when we pass xepoch we exactly validate the same number of times per epoch
+ # if we use `self.global_step` some epochs mights have more validations
+ step = self.global_step - self._last_epoch_num_steps if self._xepoch_set else self.global_step
+ should_validate = (
+ (_check(step) and not self.is_done and not self.is_accumulating)
+ or (self.validate_on_epoch_end and self.is_done)
+ # this check avoids validating on epoch end when the steps happens to be at the end of the epoch
+ # not _check(self.global_step)
+ )
else:
raise NotImplementedError
+ if should_validate:
+ print(f"{self.global_epoch=} {self.global_step=} {self.is_done=}")
+
return should_validate
"""Methods"""
@@ -262,6 +281,7 @@ def _check(iter: int) -> bool:
def start_fit(self) -> None:
self.epoch_tracker.reset()
self.step_tracker.reset()
+ self._last_epoch_num_steps = 0
def start(self, stage: Union[str, RunningStage]) -> None:
"""Make progress bars and reset the counters of stage trackers."""
@@ -287,12 +307,16 @@ def start(self, stage: Union[str, RunningStage]) -> None:
def end(self) -> None:
"""Close progress bars of stage tracker when testing or re-attach training when validating."""
+
+ # NOTE: when testing we directly close the progress bar when we are done
if not self.is_fitting:
return self.get_stage_tracker().close_progress_bar()
self.get_stage_tracker().terminate_progress_bar()
+
+ # NOTE: if this is the end of the validation stage we need to reattach the training tracker
if self.current_stage == RunningStage.VALIDATION:
- self.current_stage = RunningStage.TRAIN # reattach training
+ self.current_stage = RunningStage.TRAIN
if self.train_tracker.progress_bar is not None:
self.train_tracker.progress_bar.set_postfix_str("")
@@ -309,9 +333,12 @@ def increment(self) -> None:
def increment_epoch(self) -> None:
self.epoch_tracker.increment()
+ self._last_epoch_num_steps += self.global_step
def increment_step(self) -> None:
self.step_tracker.increment()
+ if self.step_tracker.max_reached():
+ self.stop_training = True
"""Helpers"""
diff --git a/energizer/types.py b/energizer/types.py
index 8be1c19..615ee27 100644
--- a/energizer/types.py
+++ b/energizer/types.py
@@ -1,15 +1,16 @@
-from typing import Any, Dict, List, Mapping, Tuple, Union
+from collections.abc import Mapping
+from typing import Any, Union
from torch import Tensor
from torchmetrics import Metric
METRIC = Union[Metric, Any]
-DATASET = List[Mapping]
+DATASET = list[Mapping]
DATA_SOURCE = Any
-BATCH_OUTPUT = Union[Tensor, Dict]
+BATCH_OUTPUT = Union[Tensor, dict]
-EPOCH_OUTPUT = Union[List[BATCH_OUTPUT], Any]
-FIT_OUTPUT = Tuple[EPOCH_OUTPUT, EPOCH_OUTPUT]
+EPOCH_OUTPUT = Union[list[BATCH_OUTPUT], Any]
+FIT_OUTPUT = tuple[EPOCH_OUTPUT, EPOCH_OUTPUT]
ROUND_OUTPUT = Union[Mapping[str, Any], Any]
diff --git a/energizer/utilities/__init__.py b/energizer/utilities/__init__.py
index dd76f9d..457a26b 100644
--- a/energizer/utilities/__init__.py
+++ b/energizer/utilities/__init__.py
@@ -1,9 +1,12 @@
# import inspect
import contextlib
+import copy
import os
import random
import re
-from typing import Any, Dict, Generator, List, Literal, Union
+from collections.abc import Generator, Iterator
+from dataclasses import dataclass
+from typing import Any, Literal, Union
import numpy as np
import torch
@@ -16,7 +19,25 @@
from torch.nn.utils.rnn import pad_sequence
-def parse_locals(vars) -> Dict:
+@dataclass
+class Args:
+ """Dataclass which is subscriptable like a dict"""
+
+ def to_dict(self) -> dict[str, Any]:
+ out = copy.deepcopy(self.__dict__)
+ return out
+
+ def __getitem__(self, k: str) -> Any:
+ return self.__dict__[k]
+
+ def __iter__(self) -> Iterator[str]:
+ return iter(self.__dict__)
+
+ def __len__(self) -> int:
+ return len(self.__dict__)
+
+
+def parse_locals(vars) -> dict:
return {k: v for k, v in vars.items() if k not in ("self", "__class__")}
@@ -35,7 +56,7 @@ def tensor_to_python(t: Tensor, *_) -> Union[ndarray, float, int]:
# return round(t.detach().cpu().item(), 6)
-def make_dict_json_serializable(d: Dict) -> Dict:
+def make_dict_json_serializable(d: dict) -> dict:
return {k: round(v.item(), 6) if isinstance(v, (ndarray, generic)) else v for k, v in d.items()}
@@ -44,10 +65,14 @@ def move_to_cpu(output: Any) -> Any:
return apply_to_collection(output, *args)
-def ld_to_dl(ld: List[Dict]) -> Dict[str, List]:
+def ld_to_dl(ld: list[dict]) -> dict[str, list]:
return {k: [dic[k] for dic in ld] for k in ld[0]}
+def dl_to_ld(dl: dict[str, list]) -> list[dict]:
+ return [dict(zip(dl, t)) for t in zip(*dl.values())]
+
+
@contextlib.contextmanager
def local_seed(seed: int) -> Generator[None, None, None]:
"""A context manager that allows to locally change the seed.
@@ -94,22 +119,18 @@ def set_deterministic(deterministic: Union[bool, Literal["warn_only"]]) -> None:
torch.backends.cudnn.benchmark = False # type: ignore
-def _pad(inputs: List[Tensor], padding_value: float, max_length: int) -> Tensor:
+def _pad(inputs: list[list[Union[int, float]]], padding_value: Union[int, float], max_length: int) -> Tensor:
# truncate -> convert to tensor -> pad
- return pad_sequence(
- [torch.tensor(t[:max_length]) for t in inputs],
- batch_first=True,
- padding_value=padding_value,
- )
+ return pad_sequence([torch.tensor(t[:max_length]) for t in inputs], batch_first=True, padding_value=padding_value)
def sample(
- indices: List[int],
+ indices: list[int],
size: int,
random_state: RandomState,
mode: Literal["uniform", "stratified"] = "uniform",
**kwargs,
-) -> List[int]:
+) -> list[int]:
"""Makes sure to seed everything consistently."""
if mode == "uniform":
@@ -118,11 +139,7 @@ def sample(
elif mode == "stratified":
assert "labels" in kwargs, ValueError("Must pass `labels` for stratified sampling.")
sample = resample(
- indices,
- replace=False,
- stratify=kwargs.get("labels"),
- n_samples=size,
- random_state=random_state,
+ indices, replace=False, stratify=kwargs.get("labels"), n_samples=size, random_state=random_state
)
else:
diff --git a/energizer/utilities/model_summary.py b/energizer/utilities/model_summary.py
index a2314bf..bd9068d 100644
--- a/energizer/utilities/model_summary.py
+++ b/energizer/utilities/model_summary.py
@@ -1,5 +1,5 @@
from collections import OrderedDict
-from typing import Dict, List, Tuple, cast
+from typing import cast
import numpy as np
import torch
@@ -43,8 +43,8 @@ def __init__(self, estimator, max_depth: int = 1) -> None:
self._precision_megabytes = (precision / 8.0) * 1e-6
@property
- def named_modules(self) -> List[Tuple[str, torch.nn.Module]]:
- mods: List[Tuple[str, torch.nn.Module]]
+ def named_modules(self) -> list[tuple[str, torch.nn.Module]]:
+ mods: list[tuple[str, torch.nn.Module]]
if self._max_depth == 0:
mods = []
elif self._max_depth == 1:
@@ -56,23 +56,23 @@ def named_modules(self) -> List[Tuple[str, torch.nn.Module]]:
return mods
@property
- def layer_names(self) -> List[str]:
+ def layer_names(self) -> list[str]:
return list(self._layer_summary.keys())
@property
- def layer_types(self) -> List[str]:
+ def layer_types(self) -> list[str]:
return [layer.layer_type for layer in self._layer_summary.values()]
@property
- def in_sizes(self) -> List:
+ def in_sizes(self) -> list:
return [layer.in_size for layer in self._layer_summary.values()] # type: ignore
@property
- def out_sizes(self) -> List:
+ def out_sizes(self) -> list:
return [layer.out_size for layer in self._layer_summary.values()] # type: ignore
@property
- def param_nums(self) -> List[int]:
+ def param_nums(self) -> list[int]:
return [layer.num_parameters for layer in self._layer_summary.values()]
@property
@@ -92,7 +92,7 @@ def model_size(self) -> float:
# todo: seems it does not work with quantized models - it returns 0.0
return self.total_parameters * self._precision_megabytes
- def summarize(self) -> Dict[str, LayerSummary]:
+ def summarize(self) -> dict[str, LayerSummary]:
summary = OrderedDict((name, LayerSummary(module)) for name, module in self.named_modules)
if self._max_depth >= 1:
@@ -102,7 +102,7 @@ def summarize(self) -> Dict[str, LayerSummary]:
return summary
- def _get_summary_data(self) -> List[Tuple[str, List[str]]]:
+ def _get_summary_data(self) -> list[tuple[str, list[str]]]:
"""Makes a summary listing with:
Layer Name, Layer Type, Number of Parameters, Input Sizes, Output Sizes, Model Size
diff --git a/examples/active_estimators.ipynb b/examples/active_estimators.ipynb
index 740045b..b927bfa 100644
--- a/examples/active_estimators.ipynb
+++ b/examples/active_estimators.ipynb
@@ -16,21 +16,20 @@
"metadata": {},
"outputs": [],
"source": [
- "from energizer.datastores import PandasDataStoreForSequenceClassification\n",
- "from energizer.estimator import Estimator\n",
- "from transformers import AutoModelForSequenceClassification\n",
- "from typing import Dict, List\n",
+ "import numpy as np\n",
"import torch\n",
+ "from lightning.fabric import seed_everything\n",
+ "from lightning.fabric.loggers import TensorBoardLogger\n",
"from torchmetrics import MetricCollection\n",
"from torchmetrics.classification import Accuracy, F1Score, Precision, Recall\n",
"from transformers import AutoModelForSequenceClassification\n",
+ "\n",
+ "from energizer.callbacks import EarlyStopping, GradNorm, ModelCheckpoint\n",
+ "from energizer.datastores import PandasDatastoreForSequenceClassification\n",
"from energizer.enums import InputKeys, OutputKeys, RunningStage\n",
- "import numpy as np\n",
- "from energizer.utilities import move_to_cpu\n",
- "from lightning.fabric.loggers import TensorBoardLogger\n",
- "from lightning.fabric import seed_everything\n",
- "from energizer.callbacks import GradNorm, PytorchTensorboardProfiler, EarlyStopping, ModelCheckpoint\n",
- "from energizer.strategies import RandomStrategy, UncertaintyBasedStrategy"
+ "from energizer.estimator import Estimator\n",
+ "from energizer.strategies import RandomStrategy, UncertaintyBasedStrategy\n",
+ "from energizer.utilities import move_to_cpu"
]
},
{
@@ -39,7 +38,7 @@
"metadata": {},
"outputs": [],
"source": [
- "ds = PandasDataStoreForSequenceClassification.load(\"./agnews_datastore/\")"
+ "ds = PandasDatastoreForSequenceClassification.load(\"./agnews_datastore/\")"
]
},
{
@@ -48,27 +47,26 @@
"metadata": {},
"outputs": [],
"source": [
- "class EstimatorForSequenceClassification(Estimator):\n",
- "\n",
- " def train_step(self, model, batch, batch_idx, loss_fn, metrics: MetricCollection) -> Dict:\n",
+ "class HFEstimatorForSequenceClassification(Estimator):\n",
+ " def train_step(self, model, batch, batch_idx, loss_fn, metrics: MetricCollection) -> dict:\n",
" return self.step(model, batch, metrics, RunningStage.TRAIN)\n",
"\n",
- " def validation_step(self, model, batch, batch_idx, loss_fn, metrics: MetricCollection) -> Dict:\n",
+ " def validation_step(self, model, batch, batch_idx, loss_fn, metrics: MetricCollection) -> dict:\n",
" return self.step(model, batch, metrics, RunningStage.VALIDATION)\n",
"\n",
- " def test_step(self, model, batch, batch_idx, loss_fn, metrics: MetricCollection) -> Dict:\n",
+ " def test_step(self, model, batch, batch_idx, loss_fn, metrics: MetricCollection) -> dict:\n",
" return self.step(model, batch, metrics, RunningStage.TEST)\n",
- " \n",
- " def train_epoch_end(self, output: List[np.ndarray], metrics: MetricCollection) -> Dict:\n",
+ "\n",
+ " def train_epoch_end(self, output: list[np.ndarray], metrics: MetricCollection) -> dict:\n",
" return self.epoch_end(output, metrics, RunningStage.TRAIN)\n",
"\n",
- " def validation_epoch_end(self, output: List[np.ndarray], metrics: MetricCollection) -> Dict:\n",
+ " def validation_epoch_end(self, output: list[np.ndarray], metrics: MetricCollection) -> dict:\n",
" return self.epoch_end(output, metrics, RunningStage.VALIDATION)\n",
"\n",
- " def test_epoch_end(self, output: List[np.ndarray], metrics: MetricCollection) -> Dict:\n",
+ " def test_epoch_end(self, output: list[np.ndarray], metrics: MetricCollection) -> dict:\n",
" return self.epoch_end(output, metrics, RunningStage.TEST)\n",
"\n",
- " def step(self, model, batch: Dict, metrics: MetricCollection, stage: RunningStage) -> torch.Tensor:\n",
+ " def step(self, model, batch: dict, metrics: MetricCollection, stage: RunningStage) -> torch.Tensor:\n",
" _ = batch.pop(InputKeys.ON_CPU, None)\n",
"\n",
" out = model(**batch)\n",
@@ -79,11 +77,11 @@
" self.log_dict({f\"{stage}/{k}\": v for k, v in logs.items()}, step=self.tracker.global_batch)\n",
"\n",
" return out.loss\n",
- " \n",
- " def epoch_end(self, output: List[np.ndarray], metrics: MetricCollection, stage: RunningStage) -> float:\n",
+ "\n",
+ " def epoch_end(self, output: list[np.ndarray], metrics: MetricCollection, stage: RunningStage) -> float:\n",
" aggregated_metrics = move_to_cpu(metrics.compute()) # NOTE: metrics are still on device\n",
" aggregated_loss = round(np.mean(output).item(), 6)\n",
- " \n",
+ "\n",
" logs = {OutputKeys.LOSS: aggregated_loss, **aggregated_metrics}\n",
" self.log_dict({f\"{stage}_end/{k}\": v for k, v in logs.items()}, step=self.tracker.safe_global_epoch)\n",
"\n",
@@ -120,24 +118,21 @@
"metadata": {},
"outputs": [],
"source": [
- "class RandomStrategyForSequenceClassification(EstimatorForSequenceClassification, RandomStrategy):\n",
+ "class RandomStrategyForSequenceClassification(HFEstimatorForSequenceClassification, RandomStrategy):\n",
" ...\n",
"\n",
"\n",
"seed_everything(42)\n",
"model = AutoModelForSequenceClassification.from_pretrained(\n",
- " ds.tokenizer.name_or_path,\n",
- " id2label=ds.id2label,\n",
- " label2id=ds.label2id,\n",
- " num_labels=len(ds.labels),\n",
+ " ds.tokenizer.name_or_path, id2label=ds.id2label, label2id=ds.label2id, num_labels=len(ds.labels)\n",
")\n",
"\n",
"estimator = RandomStrategyForSequenceClassification(\n",
- " model, \n",
- " accelerator=\"gpu\", \n",
+ " model,\n",
+ " accelerator=\"gpu\",\n",
" loggers=[TensorBoardLogger(\"./\", name=\"tb_logs\")],\n",
" callbacks=[\n",
- " GradNorm(2), \n",
+ " GradNorm(2),\n",
" ModelCheckpoint(\"./checkpoints\", monitor=\"f1_macro\", stage=\"train\", mode=\"max\"),\n",
" EarlyStopping(monitor=\"f1_macro\", stage=\"train\", interval=\"epoch\", mode=\"max\"),\n",
" ],\n",
@@ -153,12 +148,12 @@
"source": [
"ds.prepare_for_loading()\n",
"results = estimator.active_fit(\n",
- " datastore=ds, \n",
+ " datastore=ds,\n",
" query_size=50,\n",
- " max_rounds=20, \n",
+ " max_rounds=20,\n",
" min_steps=50,\n",
" reinit_model=False,\n",
- " # limit_pool_batches=10, \n",
+ " # limit_pool_batches=10,\n",
" # limit_test_batches=10,\n",
")"
]
@@ -177,27 +172,25 @@
"metadata": {},
"outputs": [],
"source": [
- "class UncertaintyBasedStrategyForSequenceClassification(EstimatorForSequenceClassification, UncertaintyBasedStrategy):\n",
- " def pool_step( self, model, batch: Dict, batch_idx: int, metrics: MetricCollection) -> Dict:\n",
+ "class UncertaintyBasedStrategyForSequenceClassification(HFEstimatorForSequenceClassification, UncertaintyBasedStrategy):\n",
+ " def pool_step(self, model, batch: dict, batch_idx: int, metrics: MetricCollection) -> dict:\n",
" _ = batch.pop(InputKeys.ON_CPU) # this is already handled in the `evaluation_step`\n",
" logits = model(**batch).logits\n",
" return self.score_fn(logits)\n",
"\n",
+ "\n",
"seed_everything(42)\n",
"model = AutoModelForSequenceClassification.from_pretrained(\n",
- " ds.tokenizer.name_or_path,\n",
- " id2label=ds.id2label,\n",
- " label2id=ds.label2id,\n",
- " num_labels=len(ds.labels),\n",
+ " ds.tokenizer.name_or_path, id2label=ds.id2label, label2id=ds.label2id, num_labels=len(ds.labels)\n",
")\n",
"\n",
"estimator = UncertaintyBasedStrategyForSequenceClassification(\n",
" score_fn=\"entropy\",\n",
- " model=model, \n",
- " accelerator=\"gpu\", \n",
+ " model=model,\n",
+ " accelerator=\"gpu\",\n",
" loggers=[TensorBoardLogger(\"./\", name=\"tb_logs\")],\n",
" callbacks=[\n",
- " GradNorm(2), \n",
+ " GradNorm(2),\n",
" ModelCheckpoint(\"./checkpoints\", monitor=\"f1_macro\", stage=\"train\", mode=\"max\"),\n",
" EarlyStopping(monitor=\"f1_macro\", stage=\"train\", interval=\"epoch\", mode=\"max\"),\n",
" ],\n",
@@ -211,12 +204,7 @@
"outputs": [],
"source": [
"ds.prepare_for_loading()\n",
- "results = estimator.active_fit(\n",
- " datastore=ds, \n",
- " query_size=50,\n",
- " max_rounds=20, \n",
- " min_steps=50,\n",
- ")"
+ "results = estimator.active_fit(datastore=ds, query_size=50, max_rounds=20, min_steps=50)"
]
},
{
diff --git a/examples/bert_badge.py b/examples/bert_badge.py
index ae43e8d..a01776a 100644
--- a/examples/bert_badge.py
+++ b/examples/bert_badge.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any
import numpy as np
import torch
@@ -9,7 +9,7 @@
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from energizer import seed_everything
-from energizer.active_learning.datastores.classification import ActivePandasDataStoreForSequenceClassification
+from energizer.active_learning.datastores.classification import ActivePandasDatastoreForSequenceClassification
from energizer.active_learning.strategies.diversity import BADGE
from energizer.enums import InputKeys, OutputKeys, RunningStage
from energizer.utilities import move_to_cpu
@@ -29,13 +29,7 @@ def get_logits_from_penultimate_layer_out(
return model.classifier(penultimate_layer_out)
def step(
- self,
- stage: RunningStage,
- model,
- batch: Dict,
- batch_idx: int,
- loss_fn,
- metrics: MetricCollection,
+ self, stage: RunningStage, model, batch: dict, batch_idx: int, loss_fn, metrics: MetricCollection
) -> torch.Tensor:
_ = batch.pop(InputKeys.ON_CPU, None)
out = model(**batch)
@@ -47,7 +41,7 @@ def step(
return out.loss
- def epoch_end(self, stage: RunningStage, output: List[np.ndarray], metrics: MetricCollection) -> float:
+ def epoch_end(self, stage: RunningStage, output: list[np.ndarray], metrics: MetricCollection) -> float:
# aggregate and log
aggregated_metrics = move_to_cpu(metrics.compute()) # NOTE: metrics are still on device
aggregated_loss = round(np.mean(output).item(), 6)
@@ -87,7 +81,7 @@ def configure_metrics(self, *_) -> MetricCollection:
dataset_dict = dataset_dict.map(lambda ex: tokenizer(ex["text"]), batched=True)
# create datastore
- datastore = ActivePandasDataStoreForSequenceClassification.from_dataset_dict(
+ datastore = ActivePandasDatastoreForSequenceClassification.from_dataset_dict(
dataset_dict=dataset_dict,
input_names=["input_ids", "attention_mask"],
target_name="labels",
@@ -100,10 +94,7 @@ def configure_metrics(self, *_) -> MetricCollection:
# model
model = AutoModelForSequenceClassification.from_pretrained(
- MODEL_NAME,
- id2label=datastore.id2label,
- label2id=datastore.label2id,
- num_labels=len(datastore.labels),
+ MODEL_NAME, id2label=datastore.id2label, label2id=datastore.label2id, num_labels=len(datastore.labels)
)
# active learning loop
diff --git a/examples/bert_entropy.py b/examples/bert_entropy.py
index 66529cc..a6b5f6d 100644
--- a/examples/bert_entropy.py
+++ b/examples/bert_entropy.py
@@ -1,4 +1,5 @@
-from typing import Any, Callable, Dict, List, Optional, Union
+from collections.abc import Callable
+from typing import Any, Optional, Union
import numpy as np
import torch
@@ -9,7 +10,7 @@
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from energizer import seed_everything
-from energizer.active_learning.datastores.classification import ActivePandasDataStoreForSequenceClassification
+from energizer.active_learning.datastores.classification import ActivePandasDatastoreForSequenceClassification
from energizer.active_learning.strategies.uncertainty import UncertaintyBasedStrategy
from energizer.enums import InputKeys, OutputKeys, RunningStage
from energizer.utilities import move_to_cpu
@@ -20,13 +21,7 @@
class UncertaintyStrategyForSequenceClassification(UncertaintyBasedStrategy):
def step(
- self,
- stage: RunningStage,
- model,
- batch: Dict,
- batch_idx: int,
- loss_fn,
- metrics: MetricCollection,
+ self, stage: RunningStage, model, batch: dict, batch_idx: int, loss_fn, metrics: MetricCollection
) -> torch.Tensor:
_ = batch.pop(InputKeys.ON_CPU, None)
out = model(**batch)
@@ -52,7 +47,7 @@ def pool_step(
# simply redirect to step
return self.step(RunningStage.POOL, model, batch, batch_idx, loss_fn, metrics)
- def epoch_end(self, stage: RunningStage, output: List[np.ndarray], metrics: MetricCollection) -> float:
+ def epoch_end(self, stage: RunningStage, output: list[np.ndarray], metrics: MetricCollection) -> float:
# aggregate and log
aggregated_metrics = move_to_cpu(metrics.compute()) # NOTE: metrics are still on device
aggregated_loss = round(np.mean(output).item(), 6)
@@ -92,7 +87,7 @@ def configure_metrics(self, *_) -> MetricCollection:
dataset_dict = dataset_dict.map(lambda ex: tokenizer(ex["text"]), batched=True)
# create datastore
- datastore = ActivePandasDataStoreForSequenceClassification.from_dataset_dict(
+ datastore = ActivePandasDatastoreForSequenceClassification.from_dataset_dict(
dataset_dict=dataset_dict,
input_names=["input_ids", "attention_mask"],
target_name="labels",
@@ -105,10 +100,7 @@ def configure_metrics(self, *_) -> MetricCollection:
# model
model = AutoModelForSequenceClassification.from_pretrained(
- MODEL_NAME,
- id2label=datastore.id2label,
- label2id=datastore.label2id,
- num_labels=len(datastore.labels),
+ MODEL_NAME, id2label=datastore.id2label, label2id=datastore.label2id, num_labels=len(datastore.labels)
)
# active learning loop
diff --git a/examples/bert_random.py b/examples/bert_random.py
index 9af74f7..05ccaa4 100755
--- a/examples/bert_random.py
+++ b/examples/bert_random.py
@@ -1,5 +1,3 @@
-from typing import Dict, List
-
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
@@ -8,7 +6,7 @@
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from energizer import seed_everything
-from energizer.active_learning.datastores.classification import ActivePandasDataStoreForSequenceClassification
+from energizer.active_learning.datastores.classification import ActivePandasDatastoreForSequenceClassification
from energizer.active_learning.strategies.random import RandomStrategy
from energizer.enums import InputKeys, OutputKeys, RunningStage
from energizer.utilities import move_to_cpu
@@ -19,13 +17,7 @@
class RandomStrategyForSequenceClassification(RandomStrategy):
def step(
- self,
- stage: RunningStage,
- model,
- batch: Dict,
- batch_idx: int,
- loss_fn,
- metrics: MetricCollection,
+ self, stage: RunningStage, model, batch: dict, batch_idx: int, loss_fn, metrics: MetricCollection
) -> torch.Tensor:
_ = batch.pop(InputKeys.ON_CPU, None)
out = model(**batch)
@@ -37,7 +29,7 @@ def step(
return out.loss
- def epoch_end(self, stage: RunningStage, output: List[np.ndarray], metrics: MetricCollection) -> float:
+ def epoch_end(self, stage: RunningStage, output: list[np.ndarray], metrics: MetricCollection) -> float:
# aggregate and log
aggregated_metrics = move_to_cpu(metrics.compute()) # NOTE: metrics are still on device
aggregated_loss = round(np.mean(output).item(), 6)
@@ -77,7 +69,7 @@ def configure_metrics(self, *_) -> MetricCollection:
dataset_dict = dataset_dict.map(lambda ex: tokenizer(ex["text"]), batched=True)
# create datastore
- datastore = ActivePandasDataStoreForSequenceClassification.from_dataset_dict(
+ datastore = ActivePandasDatastoreForSequenceClassification.from_dataset_dict(
dataset_dict=dataset_dict,
input_names=["input_ids", "attention_mask"],
target_name="labels",
@@ -90,10 +82,7 @@ def configure_metrics(self, *_) -> MetricCollection:
# model
model = AutoModelForSequenceClassification.from_pretrained(
- MODEL_NAME,
- id2label=datastore.id2label,
- label2id=datastore.label2id,
- num_labels=len(datastore.labels),
+ MODEL_NAME, id2label=datastore.id2label, label2id=datastore.label2id, num_labels=len(datastore.labels)
)
# active learning loop
diff --git a/examples/datastores.ipynb b/examples/datastores.ipynb
index 7012346..2ee6511 100644
--- a/examples/datastores.ipynb
+++ b/examples/datastores.ipynb
@@ -26,10 +26,10 @@
"outputs": [],
"source": [
"from datasets import load_dataset, load_from_disk\n",
- "from energizer.active_learning.datastores.classification import ActivePandasDataStoreForSequenceClassification\n",
- "\n",
+ "from sentence_transformers import SentenceTransformer\n",
"from transformers import AutoTokenizer\n",
- "from sentence_transformers import SentenceTransformer"
+ "\n",
+ "from energizer.active_learning.datastores.classification import ActivePandasDatastoreForSequenceClassification"
]
},
{
@@ -58,7 +58,7 @@
"metadata": {},
"outputs": [],
"source": [
- "datastore = ActivePandasDataStoreForSequenceClassification.from_dataset_dict(\n",
+ "datastore = ActivePandasDatastoreForSequenceClassification.from_dataset_dict(\n",
" dataset_dict=dataset_dict, # type:ignore\n",
" input_names=[\"input_ids\", \"attention_mask\"],\n",
" target_name=\"labels\",\n",
@@ -140,12 +140,8 @@
"dataset_dict = load_dataset(\"ag_news\").rename_columns({\"label\": \"labels\"})\n",
"\n",
"# embed training data\n",
- "dataset_dict[\"train\"] = (\n",
- " dataset_dict[\"train\"]\n",
- " .map(\n",
- " lambda ex: {\"embedding\": embedder.encode(ex[\"text\"], device=\"cuda\", batch_size=512)},\n",
- " batched=True,\n",
- " )\n",
+ "dataset_dict[\"train\"] = dataset_dict[\"train\"].map(\n",
+ " lambda ex: {\"embedding\": embedder.encode(ex[\"text\"], device=\"cuda\", batch_size=512)}, batched=True\n",
")\n",
"\n",
"dataset_dict.save_to_disk(\"agnews\")"
@@ -175,9 +171,9 @@
"metadata": {},
"outputs": [],
"source": [
- "datastore = PandasDataStoreForSequenceClassification()\n",
+ "datastore = PandasDatastoreForSequenceClassification()\n",
"datastore.from_dataset_dict(\n",
- " dataset_dict=dataset_dict, \n",
+ " dataset_dict=dataset_dict,\n",
" tokenizer=tokenizer,\n",
" # on_cpu=[\"embedding\", \"text\"],\n",
" input_names=[\"input_ids\", \"attention_mask\"],\n",
@@ -209,7 +205,7 @@
"metadata": {},
"outputs": [],
"source": [
- "ds = PandasDataStoreForSequenceClassification.load(\"./agnews_datastore\")"
+ "ds = PandasDatastoreForSequenceClassification.load(\"./agnews_datastore\")"
]
},
{
diff --git a/examples/docarray.ipynb b/examples/docarray.ipynb
index 243f039..1245aef 100644
--- a/examples/docarray.ipynb
+++ b/examples/docarray.ipynb
@@ -16,14 +16,13 @@
"metadata": {},
"outputs": [],
"source": [
+ "import numpy as np\n",
"from datasets import load_dataset\n",
- "from transformers import AutoTokenizer\n",
"from docarray import BaseDoc, DocList\n",
+ "from docarray.index import HnswDocumentIndex\n",
"from docarray.typing import NdArray\n",
"from sentence_transformers import SentenceTransformer\n",
- "from docarray.index import HnswDocumentIndex\n",
- "import numpy as np\n",
- "from typing import List"
+ "from transformers import AutoTokenizer"
]
},
{
@@ -82,7 +81,10 @@
"source": [
"ds_dict = (\n",
" load_dataset(\"pietrolesci/ag_news\", \"concat\")[\"test\"]\n",
- " .map(lambda ex: {**tokenizer(ex[\"text\"]), \"embedding\": embedder.encode(ex[\"text\"], device=\"cuda\", batch_size=256)}, batched=True)\n",
+ " .map(\n",
+ " lambda ex: {**tokenizer(ex[\"text\"]), \"embedding\": embedder.encode(ex[\"text\"], device=\"cuda\", batch_size=256)},\n",
+ " batched=True,\n",
+ " )\n",
" .rename_columns({\"label\": \"labels\"})\n",
")"
]
@@ -176,16 +178,16 @@
"db.configure(\n",
" default_column_config={\n",
" np.ndarray: {\n",
- " 'dim': -1,\n",
- " 'index': True,\n",
- " 'space': 'cosine',\n",
- " 'max_elements': 10^10,\n",
- " 'ef_construction': 200,\n",
- " 'ef': 15,\n",
- " 'M': 8,\n",
- " 'allow_replace_deleted': True,\n",
- " 'num_threads': 5,\n",
- " },\n",
+ " \"dim\": -1,\n",
+ " \"index\": True,\n",
+ " \"space\": \"cosine\",\n",
+ " \"max_elements\": 10 ^ 10,\n",
+ " \"ef_construction\": 200,\n",
+ " \"ef\": 15,\n",
+ " \"M\": 8,\n",
+ " \"allow_replace_deleted\": True,\n",
+ " \"num_threads\": 5,\n",
+ " }\n",
" # None: {},\n",
" }\n",
")"
@@ -243,11 +245,8 @@
"metadata": {},
"outputs": [],
"source": [
- "datastore = PandasDataStoreForSequenceClassification()\n",
- "datastore.from_dataset_dict(\n",
- " dataset_dict=ds_dict, \n",
- " tokenizer=tokenizer,\n",
- ")"
+ "datastore = PandasDatastoreForSequenceClassification()\n",
+ "datastore.from_dataset_dict(dataset_dict=ds_dict, tokenizer=tokenizer)"
]
},
{
diff --git a/examples/estimators.ipynb b/examples/estimators.ipynb
index f75d5d4..feec504 100644
--- a/examples/estimators.ipynb
+++ b/examples/estimators.ipynb
@@ -16,22 +16,21 @@
"metadata": {},
"outputs": [],
"source": [
- "from energizer.datastores.classification import PandasDataStoreForSequenceClassification\n",
- "from energizer.estimator import Estimator\n",
- "from energizer.utilities import move_to_cpu\n",
- "from energizer.enums import InputKeys, OutputKeys, RunningStage\n",
- "from energizer import seed_everything\n",
- "from energizer.callbacks import GradNorm\n",
- "from energizer.active_learning.datastores.classification import ActivePandasDataStoreForSequenceClassification\n",
- "from typing import List, Dict\n",
- "import torch\n",
"import numpy as np\n",
- "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "import torch\n",
+ "from datasets import load_dataset\n",
"from torchmetrics import MetricCollection\n",
"from torchmetrics.classification import Accuracy, F1Score, Precision, Recall\n",
- "from datasets import load_dataset\n",
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "\n",
+ "from energizer import seed_everything\n",
+ "from energizer.active_learning.datastores.classification import ActivePandasDatastoreForSequenceClassification\n",
"from energizer.active_learning.strategies.random import RandomStrategy\n",
- "from energizer.active_learning.strategies.uncertainty import UncertaintyBasedStrategy"
+ "from energizer.active_learning.strategies.uncertainty import UncertaintyBasedStrategy\n",
+ "from energizer.datastores.classification import PandasDatastoreForSequenceClassification\n",
+ "from energizer.enums import InputKeys, OutputKeys, RunningStage\n",
+ "from energizer.estimator import Estimator\n",
+ "from energizer.utilities import move_to_cpu"
]
},
{
@@ -90,11 +89,8 @@
}
],
"source": [
- "ds = PandasDataStoreForSequenceClassification.from_dataset_dict(\n",
- " dataset_dict=dataset_dict,\n",
- " input_names=[\"input_ids\", \"attention_mask\"],\n",
- " target_name=\"labels\",\n",
- " tokenizer=tokenizer,\n",
+ "ds = PandasDatastoreForSequenceClassification.from_dataset_dict(\n",
+ " dataset_dict=dataset_dict, input_names=[\"input_ids\", \"attention_mask\"], target_name=\"labels\", tokenizer=tokenizer\n",
")"
]
},
@@ -104,18 +100,10 @@
"metadata": {},
"outputs": [],
"source": [
- "class EstimatorForSequenceClassification(Estimator):\n",
- "\n",
+ "class HFEstimatorForSequenceClassification(Estimator):\n",
" def step(\n",
- " self,\n",
- " stage: RunningStage,\n",
- " model,\n",
- " batch: Dict,\n",
- " batch_idx: int,\n",
- " loss_fn,\n",
- " metrics: MetricCollection,\n",
+ " self, stage: RunningStage, model, batch: dict, batch_idx: int, loss_fn, metrics: MetricCollection\n",
" ) -> torch.Tensor:\n",
- " \n",
" _ = batch.pop(InputKeys.ON_CPU, None)\n",
"\n",
" out = model(**batch)\n",
@@ -129,11 +117,11 @@
" self.log_dict({f\"{stage}/{k}\": v for k, v in logs.items()}, step=self.tracker.global_batch)\n",
"\n",
" return out.loss\n",
- " \n",
- " def epoch_end(self, stage: RunningStage, output: List[np.ndarray], metrics: MetricCollection) -> float:\n",
+ "\n",
+ " def epoch_end(self, stage: RunningStage, output: list[np.ndarray], metrics: MetricCollection) -> float:\n",
" aggregated_metrics = move_to_cpu(metrics.compute()) # NOTE: metrics are still on device\n",
" aggregated_loss = round(np.mean(output).item(), 6)\n",
- " \n",
+ "\n",
" logs = {OutputKeys.LOSS: aggregated_loss, **aggregated_metrics}\n",
" self.log_dict({f\"{stage}_end/{k}\": v for k, v in logs.items()}, step=self.tracker.safe_global_epoch)\n",
"\n",
@@ -153,8 +141,7 @@
" \"precision_micro\": Precision(task, num_classes=num_classes, average=\"micro\"),\n",
" \"recall_micro\": Recall(task, num_classes=num_classes, average=\"micro\"),\n",
" }\n",
- " ).to(self.device)\n",
- " \n"
+ " ).to(self.device)"
]
},
{
@@ -175,11 +162,8 @@
"source": [
"seed_everything(42)\n",
"model = AutoModelForSequenceClassification.from_pretrained(\n",
- " ds.tokenizer.name_or_path,\n",
- " id2label=ds.id2label,\n",
- " label2id=ds.label2id,\n",
- " num_labels=len(ds.labels),\n",
- ")\n"
+ " ds.tokenizer.name_or_path, id2label=ds.id2label, label2id=ds.label2id, num_labels=len(ds.labels)\n",
+ ")"
]
},
{
@@ -269,8 +253,8 @@
"source": [
"ds.prepare_for_loading()\n",
"\n",
- "estimator = EstimatorForSequenceClassification(\n",
- " model, \n",
+ "estimator = HFEstimatorForSequenceClassification(\n",
+ " model,\n",
" accelerator=\"gpu\",\n",
" tf32_mode=\"high\",\n",
" # loggers=[TensorBoardLogger(\"./\")],\n",
@@ -288,7 +272,7 @@
" optimizer=\"adamw\",\n",
" gradient_accumulation_steps=2,\n",
" scheduler=\"cosine_schedule_with_warmup\",\n",
- " scheduler_kwargs={\"num_warmup_steps\": .1},\n",
+ " scheduler_kwargs={\"num_warmup_steps\": 0.1},\n",
")\n",
"\n",
"estimator.test(loader=ds.test_loader())"
@@ -300,18 +284,14 @@
"metadata": {},
"outputs": [],
"source": [
- "class RandomStrategyForSequenceClassification(EstimatorForSequenceClassification, RandomStrategy):\n",
+ "class RandomStrategyForSequenceClassification(HFEstimatorForSequenceClassification, RandomStrategy):\n",
" ...\n",
"\n",
- "random = RandomStrategyForSequenceClassification(\n",
- " model=model, accelerator=\"gpu\", tf32_mode=\"high\",\n",
- ")\n",
"\n",
- "ads = ActivePandasDataStoreForSequenceClassification.from_dataset_dict(\n",
- " dataset_dict=dataset_dict,\n",
- " input_names=[\"input_ids\", \"attention_mask\"],\n",
- " target_name=\"labels\",\n",
- " tokenizer=tokenizer,\n",
+ "random = RandomStrategyForSequenceClassification(model=model, accelerator=\"gpu\", tf32_mode=\"high\")\n",
+ "\n",
+ "ads = ActivePandasDatastoreForSequenceClassification.from_dataset_dict(\n",
+ " dataset_dict=dataset_dict, input_names=[\"input_ids\", \"attention_mask\"], target_name=\"labels\", tokenizer=tokenizer\n",
")\n",
"\n",
"ads.prepare_for_loading()\n",
@@ -327,10 +307,10 @@
" optimizer=\"adamw\",\n",
" gradient_accumulation_steps=2,\n",
" scheduler=\"cosine_schedule_with_warmup\",\n",
- " scheduler_kwargs={\"num_warmup_steps\": .1},\n",
+ " scheduler_kwargs={\"num_warmup_steps\": 0.1},\n",
" query_size=15,\n",
- " limit_test_batches=2\n",
- ")\n"
+ " limit_test_batches=2,\n",
+ ")"
]
},
{
@@ -339,20 +319,14 @@
"metadata": {},
"outputs": [],
"source": [
- "\n",
- "class UncertaintyStrategy(EstimatorForSequenceClassification, UncertaintyBasedStrategy):\n",
+ "class UncertaintyStrategy(HFEstimatorForSequenceClassification, UncertaintyBasedStrategy):\n",
" def pool_step(self, model, batch, batch_idx: int, metrics):\n",
" return super().step(RunningStage.POOL, model, batch, batch_idx, None, metrics)\n",
"\n",
"\n",
- "least_conf = UncertaintyStrategy(\n",
- " model=model, accelerator=\"gpu\", tf32_mode=\"high\", score_fn=\"least_confidence\",\n",
- ")\n",
- "ads = ActivePandasDataStoreForSequenceClassification.from_dataset_dict(\n",
- " dataset_dict=dataset_dict,\n",
- " input_names=[\"input_ids\", \"attention_mask\"],\n",
- " target_name=\"labels\",\n",
- " tokenizer=tokenizer,\n",
+ "least_conf = UncertaintyStrategy(model=model, accelerator=\"gpu\", tf32_mode=\"high\", score_fn=\"least_confidence\")\n",
+ "ads = ActivePandasDatastoreForSequenceClassification.from_dataset_dict(\n",
+ " dataset_dict=dataset_dict, input_names=[\"input_ids\", \"attention_mask\"], target_name=\"labels\", tokenizer=tokenizer\n",
")\n",
"\n",
"ads.prepare_for_loading()\n",
@@ -367,10 +341,10 @@
" optimizer=\"adamw\",\n",
" gradient_accumulation_steps=2,\n",
" scheduler=\"cosine_schedule_with_warmup\",\n",
- " scheduler_kwargs={\"num_warmup_steps\": .1},\n",
+ " scheduler_kwargs={\"num_warmup_steps\": 0.1},\n",
" query_size=15,\n",
- " limit_test_batches=2\n",
- ")\n"
+ " limit_test_batches=2,\n",
+ ")"
]
},
{
diff --git a/makefile b/makefile
index 012f79c..fcbd267 100644
--- a/makefile
+++ b/makefile
@@ -4,14 +4,11 @@ sources = energizer
test: format lint unittest
format:
- isort $(sources) tests examples
- black $(sources) tests examples
- nbqa isort docs/examples
- nbqa black docs/examples --line-length 85
+ ruff format $(sources)
lint:
- flake8 $(sources) tests
- # mypy $(sources) tests
+ ruff check $(sources) --fix
+ torchfix $(sources) --fix
unittest:
pytest
@@ -31,4 +28,7 @@ clean:
rm -rf site
serve_docs:
- mkdocs serve --watch .
\ No newline at end of file
+ mkdocs serve --watch .
+
+clean-poetry-cache:
+ rm -rf ~/.cache/pypoetry/virtualenvs/
diff --git a/mkdocs.yml b/mkdocs.yml
deleted file mode 100644
index 1413105..0000000
--- a/mkdocs.yml
+++ /dev/null
@@ -1,166 +0,0 @@
-# docs here: https://squidfunk.github.io/mkdocs-material/
-
-site_name: Energizer
-site_url: https://pietrolesci.github.io/energizer
-repo_url: https://github.com/pietrolesci/energizer
-repo_name: pietrolesci/energizer
-edit_uri: tree/main/docs
-
-nav:
- - Home: index.md
- - Examples:
- - examples/index.md
- # - Simple CNN on MNIST: examples/cnn-mnist.ipynb
- # - BERT on AG-News: examples/bert-agnews.ipynb
- - API Reference:
- - Trainer: api/trainer.md
- - Query strategies: api/query_strategies.md
- - Acquisition function: api/acquisition_functions.md
- - Contributing: contributing.md
- - Changelog: changelog.md
-
-theme:
- name: material
- custom_dir: docs/overrides
- language: en
- icon:
- logo: material/book-education-outline
- repo: fontawesome/brands/github
- font:
- text: Fira Sans Condensed
- code: JetBrains Mono
-
- #logo: assets/logo.png
- palette:
-
- # Palette toggle for automatic mode
- - media: "(prefers-color-scheme)"
- primary: teal
- accent: teal
- toggle:
- icon: material/brightness-auto
- name: Switch to light mode
-
- # Palette toggle for light mode
- - scheme: default
- primary: teal
- accent: teal
- toggle:
- icon: material/brightness-7
- name: Switch to dark mode
-
- # Palette toggle for dark mode
- - scheme: slate
- primary: teal
- accent: teal
- toggle:
- icon: material/brightness-4
- name: Switch to light mode
-
- features:
- - navigation.expand
- - navigation.sections
- - navigation.instant
- - navigation.tracking
- - navigation.tabs
- - navigation.tabs.sticky
- - navigation.indexes
- # - toc.integrate
- - navigation.top
- - search.suggest
- - search.highlight
- - search.share
- - header.autohide
- - announce.dismiss
- - content.tabs.link
- - content.code.annotate
-
-markdown_extensions:
- - admonition
- - pymdownx.details
- - pymdownx.critic
- - pymdownx.superfences
- - tables
- - def_list
- - footnotes
- - md_in_html
- - pymdownx.emoji:
- emoji_index: !!python/name:materialx.emoji.twemoji
- emoji_generator: !!python/name:materialx.emoji.to_svg
- - pymdownx.caret
- - pymdownx.mark
- - pymdownx.tilde
- - pymdownx.betterem
- - pymdownx.tabbed:
- alternate_style: true
- - pymdownx.tasklist:
- custom_checkbox: true
- - attr_list
- - pymdownx.arithmatex:
- generic: true
- - pymdownx.highlight:
- linenums: true
- use_pygments: true
- anchor_linenums: true
- - pymdownx.inlinehilite
- - pymdownx.keys
- - pymdownx.smartsymbols
- - pymdownx.snippets
- - toc:
- permalink: true
- slugify: !!python/name:pymdownx.slugs.uslugify
-
-plugins:
- - tags
- - include-markdown
- - mknotebooks
- # binder: true
- # binder_service_name: "gh"
- # binder_branch: "main"
- # binder_ui: "lab"
- - git-revision-date-localized:
- enable_creation_date: true
- - search:
- lang: en
- - mkdocstrings:
- default_handler: python
- handlers:
- python:
- options:
- show_source: true
- show_root_heading: false
- heading_level: 3
-
-extra:
- version:
- provider: mike
- default: stable
-
- social:
- - icon: fontawesome/brands/twitter
- link: https://twitter.com/pietro_lesci
- name: Twitter
- - icon: fontawesome/brands/linkedin
- link: https://www.linkedin.com/in/pietrolesci
- name: LinkedIn
- - icon: fontawesome/brands/github
- link: https://github.com/pietrolesci/
- name: Github
- - icon: material/email
- link: "mailto:pietrolesci@outlook.com"
-
-extra_javascript:
- - javascripts/mathjax.js
- - https://polyfill.io/v3/polyfill.min.js?features=es6
- - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
-
-extra_css:
- - stylesheets/extra.css
-
-copyright: Copyright © 2022 - Pietro Lesci
- # to enable disqus, uncomment the following and put your disqus id below
- # disqus: disqus_id
-# uncomment the following and put your google tracking id below to enable GA
-#google_analytics:
- #- UA-xxx
- #- auto
diff --git a/poetry.lock b/poetry.lock
index ac1580f..25e5d6f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,110 +1,110 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
[[package]]
name = "absl-py"
-version = "1.4.0"
+version = "2.0.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
files = [
- {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"},
- {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
+ {file = "absl-py-2.0.0.tar.gz", hash = "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"},
+ {file = "absl_py-2.0.0-py3-none-any.whl", hash = "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3"},
]
[[package]]
name = "aiohttp"
-version = "3.8.5"
+version = "3.8.6"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
files = [
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idna = ">=2.8"
sniffio = ">=1.1"
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name = "appdirs"
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description = "Better dates & times for Python"
optional = false
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+version = "2.4.1"
description = "Annotate AST trees with source code positions"
optional = false
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[[package]]
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+docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)", "watchdog"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic (<2.0)"]
-quality = ["black (==23.7)", "mypy (==1.5.1)", "ruff (>=0.0.241)"]
+inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
torch = ["torch"]
-typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
[[package]]
name = "idna"
@@ -1306,24 +1174,6 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker
perf = ["ipython"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
-[[package]]
-name = "importlib-resources"
-version = "6.0.1"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
[[package]]
name = "iniconfig"
version = "2.0.0"
@@ -1335,31 +1185,15 @@ files = [
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]
-[[package]]
-name = "inquirer"
-version = "3.1.3"
-description = "Collection of common interactive command line user interfaces, based on Inquirer.js"
-optional = false
-python-versions = ">=3.8"
-files = [
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- {file = "inquirer-3.1.3.tar.gz", hash = "sha256:aac309406f5b49d4b8ab7c6872117f43bf082a552dc256aa16bc95e16bb58bec"},
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-
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-blessed = ">=1.19.0"
-python-editor = ">=1.0.4"
-readchar = ">=3.0.6"
-
[[package]]
name = "ipykernel"
-version = "6.25.2"
+version = "6.26.0"
description = "IPython Kernel for Jupyter"
optional = false
python-versions = ">=3.8"
files = [
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- {file = "ipykernel-6.25.2.tar.gz", hash = "sha256:f468ddd1f17acb48c8ce67fcfa49ba6d46d4f9ac0438c1f441be7c3d1372230b"},
+ {file = "ipykernel-6.26.0-py3-none-any.whl", hash = "sha256:3ba3dc97424b87b31bb46586b5167b3161b32d7820b9201a9e698c71e271602c"},
+ {file = "ipykernel-6.26.0.tar.gz", hash = "sha256:553856658eb8430bbe9653ea041a41bff63e9606fc4628873fc92a6cf3abd404"},
]
[package.dependencies]
@@ -1386,24 +1220,23 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
[[package]]
name = "ipython"
-version = "8.12.2"
+version = "8.17.2"
description = "IPython: Productive Interactive Computing"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
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+ {file = "ipython-8.17.2.tar.gz", hash = "sha256:126bb57e1895594bb0d91ea3090bbd39384f6fe87c3d57fd558d0670f50339bb"},
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[package.dependencies]
appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
colorama = {version = "*", markers = "sys_platform == \"win32\""}
decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
jedi = ">=0.16"
matplotlib-inline = "*"
pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
pygments = ">=2.4.0"
stack-data = "*"
@@ -1411,17 +1244,17 @@ traitlets = ">=5"
typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
[package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
kernel = ["ipykernel"]
nbconvert = ["nbconvert"]
nbformat = ["nbformat"]
notebook = ["ipywidgets", "notebook"]
parallel = ["ipyparallel"]
qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
[[package]]
name = "ipython-genutils"
@@ -1470,43 +1303,15 @@ files = [
[package.dependencies]
arrow = ">=0.15.0"
-[[package]]
-name = "isort"
-version = "5.12.0"
-description = "A Python utility / library to sort Python imports."
-optional = false
-python-versions = ">=3.8.0"
-files = [
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- {file = "isort-5.12.0.tar.gz", hash = "sha256:8bef7dde241278824a6d83f44a544709b065191b95b6e50894bdc722fcba0504"},
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-
-[package.extras]
-colors = ["colorama (>=0.4.3)"]
-pipfile-deprecated-finder = ["pip-shims (>=0.5.2)", "pipreqs", "requirementslib"]
-plugins = ["setuptools"]
-requirements-deprecated-finder = ["pip-api", "pipreqs"]
-
-[[package]]
-name = "itsdangerous"
-version = "2.1.2"
-description = "Safely pass data to untrusted environments and back."
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
[[package]]
name = "jedi"
-version = "0.19.0"
+version = "0.19.1"
description = "An autocompletion tool for Python that can be used for text editors."
optional = false
python-versions = ">=3.6"
files = [
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+ {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
+ {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
]
[package.dependencies]
@@ -1515,7 +1320,7 @@ parso = ">=0.8.3,<0.9.0"
[package.extras]
docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
[[package]]
name = "jinja2"
@@ -1534,20 +1339,6 @@ MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
-[[package]]
-name = "jinxed"
-version = "1.2.0"
-description = "Jinxed Terminal Library"
-optional = false
-python-versions = "*"
-files = [
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-
-[package.dependencies]
-ansicon = {version = "*", markers = "platform_system == \"Windows\""}
-
[[package]]
name = "joblib"
version = "1.3.2"
@@ -1586,24 +1377,22 @@ files = [
[[package]]
name = "jsonschema"
-version = "4.19.0"
+version = "4.20.0"
description = "An implementation of JSON Schema validation for Python"
optional = false
python-versions = ">=3.8"
files = [
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+ {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"},
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fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
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isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
jsonschema-specifications = ">=2023.03.6"
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referencing = ">=0.28.4"
rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
@@ -1617,28 +1406,27 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
[[package]]
name = "jsonschema-specifications"
-version = "2023.7.1"
+version = "2023.11.1"
description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
optional = false
python-versions = ">=3.8"
files = [
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-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
-referencing = ">=0.28.0"
+referencing = ">=0.31.0"
[[package]]
name = "jupyter-client"
-version = "8.3.1"
+version = "8.6.0"
description = "Jupyter protocol implementation and client libraries"
optional = false
python-versions = ">=3.8"
files = [
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@@ -1655,13 +1443,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
[[package]]
name = "jupyter-core"
-version = "5.3.1"
+version = "5.5.0"
description = "Jupyter core package. A base package on which Jupyter projects rely."
optional = false
python-versions = ">=3.8"
files = [
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@@ -1670,18 +1458,18 @@ pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_
traitlets = ">=5.3"
[package.extras]
-docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
[[package]]
name = "jupyter-events"
-version = "0.7.0"
+version = "0.9.0"
description = "Jupyter Event System library"
optional = false
python-versions = ">=3.8"
files = [
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[[package]]
name = "jupyter-server"
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description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
optional = false
python-versions = ">=3.8"
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jupyter-server-terminals = "*"
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nbformat = ">=5.3.0"
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optional = false
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@@ -2086,25 +1855,25 @@ files = [
traitlets = "*"
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-description = "Markdown URL utilities"
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description = "A sane and fast Markdown parser with useful plugins and renderers"
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@@ -2248,13 +2017,13 @@ files = [
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name = "nbclient"
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optional = false
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[package.extras]
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
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[[package]]
name = "threadpoolctl"
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optional = false
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+nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.18.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
sympy = "*"
+triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
typing-extensions = "*"
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+dynamo = ["jinja2"]
opt-einsum = ["opt-einsum (>=3.3)"]
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@@ -4467,13 +4318,13 @@ torch = ">=1.5.0"
[[package]]
name = "torch-tb-profiler"
-version = "0.4.1"
+version = "0.4.3"
description = "PyTorch Profiler TensorBoard Plugin"
optional = false
python-versions = ">=3.6.2"
files = [
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@@ -4483,33 +4334,49 @@ tensorboard = ">=1.15,<2.1.0 || >2.1.0"
[package.extras]
blob = ["azure-storage-blob"]
gs = ["google-cloud-storage"]
+hdfs = ["fsspec", "pyarrow"]
s3 = ["boto3"]
+[[package]]
+name = "torchfix"
+version = "0.1.1"
+description = ""
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+flake8 = ">=3.8.2"
+libcst = ">=1.0.1,<1.1.0"
+PyYAML = "*"
+
[[package]]
name = "torchmetrics"
-version = "1.1.2"
+version = "1.2.0"
description = "PyTorch native Metrics"
optional = false
python-versions = ">=3.8"
files = [
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+ {file = "torchmetrics-1.2.0-py3-none-any.whl", hash = "sha256:da2cb18822b285786d082c40efb9e1d861aac425f58230234fe6ce233cf002f8"},
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lightning-utilities = ">=0.8.0"
numpy = ">1.20.0"
torch = ">=1.8.1"
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@@ -4556,56 +4423,55 @@ telegram = ["requests"]
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name = "traitlets"
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+version = "5.13.0"
description = "Traitlets Python configuration system"
optional = false
-python-versions = ">=3.7"
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files = [
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docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
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name = "transformers"
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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python-versions = ">=3.8.0"
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@@ -4625,30 +4491,81 @@ serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
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@@ -4676,48 +4593,29 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
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description = "A CLI and library for interacting with the Weights & Biases API."
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@@ -4727,7 +4625,6 @@ docker-pycreds = ">=0.4.0"
GitPython = ">=1.0.0,<3.1.29 || >3.1.29"
pathtools = "*"
protobuf = [
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@@ -4745,21 +4642,22 @@ aws = ["boto3"]
azure = ["azure-identity", "azure-storage-blob"]
gcp = ["google-cloud-storage"]
kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "optuna", "typing-extensions"]
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media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
models = ["cloudpickle"]
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perf = ["orjson"]
sweeps = ["sweeps (>=0.2.0)"]
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name = "wcwidth"
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test = ["websockets"]
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[[package]]
@@ -5132,20 +4962,20 @@ multidict = ">=4.0"
[[package]]
name = "zipp"
-version = "3.16.2"
+version = "3.17.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
- {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
- {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
+ {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+ {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
[metadata]
lock-version = "2.0"
-python-versions = "^3.8"
-content-hash = "d70c0dbbce4fab0587a87febf4438d7df0d7d46d4700d06a74012aed77e9307b"
+python-versions = ">=3.9,<3.13"
+content-hash = "2bf6f97721a25934da3330f640d1826bb556fe75eb10a95f9c0d948475dbbbaa"
diff --git a/pyproject.toml b/pyproject.toml
index 882734a..0134bbd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,7 @@
+[build-system]
+requires = [ "poetry-core" ]
+build-backend = "poetry.core.masonry.api"
+
[tool.poetry]
name = "energizer"
version = "0.1.0"
@@ -8,72 +12,98 @@ readme = "README.md"
packages = [ { include = "energizer" } ]
[tool.poetry.dependencies]
-python = "^3.8"
-lightning = "^2.0.6"
-transformers = "^4.31.0"
-datasets = "^2.14.1"
+python = ">=3.9,<3.13"
+lightning = "^2.1.0"
+transformers = "^4.34.0"
+datasets = "^2.14.5"
torch-optimizer = "^0.3.0"
-srsly = "^2.4.5"
+srsly = "^2.4.8"
hnswlib = { git = "https://github.com/nmslib/hnswlib.git" }
torch-tb-profiler = "^0.4.1"
-tensorboard = "^2.12.0"
+tensorboard = "^2.14.1"
tbparse = "^0.0.7"
wandb = "^0.15.4"
-pandas = "^2.0.3"
+pandas = "^2.1.1"
tabulate = "^0.9.0"
-openpyxl = "^3.1.1"
-tqdm = "^4.65.0"
-scikit-learn = "^1.3.0"
+openpyxl = "^3.1.2"
+tqdm = "^4.66.1"
+scikit-learn = "^1.3.1"
+bitsandbytes = "^0.41.1"
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
-black = "^22.12.0"
nbqa = "^1.6.1"
ipykernel = "^6.20.2"
ipywidgets = "7.7.1"
-isort = "^5.11.4"
pytest = "^7.2.1"
nvitop = "^1.0.0"
+ruff = "^0.1.3"
+torchfix = "^0.1.1"
+[tool.ruff]
+# Exclude a variety of commonly ignored directories.
+exclude = [
+".bzr",
+".direnv",
+".eggs",
+".git",
+".git-rewrite",
+".hg",
+".mypy_cache",
+".nox",
+".pants.d",
+".pytype",
+".ruff_cache",
+".svn",
+".tox",
+".venv",
+"__pypackages__",
+"_build",
+"buck-out",
+"build",
+"dist",
+"node_modules",
+"venv",
+]
+
+extend-include = [ "*.ipynb" ]
-[tool.black]
+# Same as Black.
line-length = 120
-skip-string-normalization = false
-target-version = [ 'py38' ]
-include = '\.pyi?$'
-exclude = '''
-/(
- \.eggs
- | \.git
- | \.hg
- | \.mypy_cache
- | \.tox
- | \.venv
- | _build
- | buck-out
- | build
- | dist
-)/
-'''
-[tool.ruff]
-line-length = 120
-src = [ "src", "test" ]
-target-version = "py38"
+# Assume Python 3.10
+target-version = "py310"
+[tool.ruff.mccabe]
+# Unlike Flake8, default to a complexity level of 10.
+max-complexity = 10
-[tool.isort]
-multi_line_output = 3
-include_trailing_comma = true
-force_grid_wrap = 0
-use_parentheses = true
-ensure_newline_before_comments = true
-line_length = 120
-skip_gitignore = true
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = true
-[build-system]
-requires = [ "poetry-core" ]
-build-backend = "poetry.core.masonry.api"
+
+[tool.ruff.isort]
+combine-as-imports = true
+split-on-trailing-comma = false
+
+
+[tool.ruff.lint]
+select = [
+# pycodestyle
+"E",
+# Pyflakes
+"F",
+# pyupgrade
+"UP",
+# flake8-bugbear
+"B",
+# flake8-simplify
+"SIM",
+# isort
+"I",
+]
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 211da2c..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,94 +0,0 @@
-[flake8]
-max-line-length = 120
-max-complexity = 18
-ignore = E203, E266, W503, D104, D100, D105, D107, D101, D102
-docstring-convention = google
-per-file-ignores = __init__.py:F401,D107
-exclude = .git,
- __pycache__,
- setup.py,
- build,
- dist,
- docs,
- releases,
- .venv,
- .tox,
- .mypy_cache,
- .pytest_cache,
- .vscode,
- .github,
- # By default test codes will be linted.
- tests
-
-[mypy]
-ignore_missing_imports = True
-
-[coverage:run]
-# uncomment the following to omit files during running
-#omit =
-[coverage:report]
-exclude_lines =
- pragma: no cover
- def __repr__
- if self.debug:
- if settings.DEBUG
- raise AssertionError
- raise NotImplementedError
- if 0:
- if __name__ == .__main__.:
- def main
-
-[tox:tox]
-isolated_build = true
-envlist = py38, py39, format, lint, build
-
-[gh-actions]
-python =
- 3.9: py39, format, lint, build
- 3.8: py38, format, lint, build
-
-[testenv]
-allowlist_externals = pytest
-extras =
- test
- text
- vision
-passenv = *
-setenv =
- PYTHONPATH = {toxinidir}
- PYTHONWARNINGS = ignore
-commands =
- pytest --cov=energizer --cov-branch --cov-report=xml --cov-report=term-missing tests
-
-[testenv:format]
-allowlist_externals =
- isort
- black
-extras =
- test
-commands =
- isort energizer tests
- black energizer tests
-
-[testenv:lint]
-allowlist_externals =
- flake8
- # mypy
-extras =
- test
-commands =
- flake8 energizer
- # mypy energizer
-
-[testenv:build]
-allowlist_externals =
- poetry
- mkdocs
- twine
-extras =
- doc
- dev
-commands =
- poetry build
- mkdocs build
- twine check dist/*
diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index 8709fe4..0000000
--- a/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Unit test package for energizer."""
diff --git a/tests/conftest.py b/tests/conftest.py
deleted file mode 100644
index c82d2d8..0000000
--- a/tests/conftest.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import datasets
-import pytest
-from torch import nn
-from torch.utils.data import DataLoader
-from transformers import default_data_collator
-
-from tests.utils import BoringModel, RandomSupervisedDataset
-
-
-@pytest.fixture
-def mock_dataset():
- return RandomSupervisedDataset()
-
-
-@pytest.fixture
-def mock_hf_dataset():
- dataset = RandomSupervisedDataset()
- return datasets.Dataset.from_dict({"inputs": dataset.x, "labels": dataset.y})
-
-
-@pytest.fixture
-def dataloader(request):
- return request.getfixturevalue(request.param)
-
-
-@pytest.fixture
-def dataloader(mock_dataset):
- return DataLoader(mock_dataset, batch_size=10)
-
-
-@pytest.fixture
-def mock_hf_dataloader(mock_hf_dataset):
- return DataLoader(mock_hf_dataset, batch_size=10, collate_fn=default_data_collator)
-
-
-@pytest.fixture
-def dataloader_arg(request):
- return request.getfixturevalue(request.param)
-
-
-@pytest.fixture
-def boring_model():
- return BoringModel
-
-
-@pytest.fixture
-def dropout_module():
- module = nn.Sequential(
- nn.Linear(1, 1),
- nn.Dropout(0.1),
- nn.Dropout2d(0.2),
- nn.Dropout3d(0.3),
- nn.AlphaDropout(0.4),
- nn.FeatureAlphaDropout(0.5),
- )
- return module
diff --git a/tests/unit/data/test_datamodule.py b/tests/unit/data/test_datamodule.py
deleted file mode 100644
index eb0dc79..0000000
--- a/tests/unit/data/test_datamodule.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import pytest
-
-from energizer.datastores.datamodule import ActiveDataModule
-
-
-def test_len(dataloader):
- """Test that measures of length are consistent."""
- # no instances
- dm = ActiveDataModule(train_dataloader=dataloader)
- assert len(dataloader.dataset) == dm.train_size + dm.pool_size
- assert len(dataloader.dataset) == dm.total_data_size
- pool_size = dm.pool_size
-
- dm.label(0)
- assert len(dataloader.dataset) == dm.train_size + dm.pool_size
- assert len(dataloader.dataset) == dm.total_data_size
- assert dm.train_size == 1
- assert dm.pool_size == pool_size - 1
-
-
-def test_indexing(dataloader):
- """Test that ActiveDataModule is not indexable directly."""
- dm = ActiveDataModule(train_dataloader=dataloader)
- with pytest.raises(TypeError):
- assert dm[0]
-
-
-def test_labelling(dataloader):
- """Test that labelling changes all the required states."""
- dm = ActiveDataModule(train_dataloader=dataloader)
- len_dataset = len(dataloader.dataset)
-
- with pytest.raises(AssertionError):
- dm.label("0")
-
- with pytest.raises(ValueError):
- dm.label(list(range(len_dataset + 100)))
-
- with pytest.raises(ValueError):
- dm.label(int(1e6))
-
- assert dm.last_labelling_step == 0
- assert dm.train_size == 0
- assert dm.pool_size == len_dataset
- assert dm.has_labelled_data is False
- assert dm.has_unlabelled_data is True
- assert dm.train_dataset.indices == []
-
- for i in range(1, len_dataset + 1):
- dm.label(0) # always label the first instance in the pool
-
- assert dm.last_labelling_step == i
- assert dm.train_size == i
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is True
- if i < len_dataset:
- assert dm.has_unlabelled_data is True
- else:
- assert dm.has_unlabelled_data is False
- assert dm.train_dataset.indices == list(range(i))
-
- assert dm.last_labelling_step == len_dataset
- assert dm.train_size == len_dataset
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is True
- assert dm.has_unlabelled_data is False
- assert dm.train_dataset.indices == list(range(len_dataset))
-
-
-def test_labelling_multiple_indices(dataloader):
- """Test labelling multiple instances at once."""
- dm = ActiveDataModule(train_dataloader=dataloader)
- pool_ids = [0, 8, 7] # they are the first to be labelled so correspond to ids in oracle
- dm.label(pool_ids)
-
- assert dm.train_dataset.indices == sorted(pool_ids)
-
-
-def test_labelling_duplicates(dataloader):
- """Test that labelling duplicate indices results in a single instance to be labelled."""
-
- # check behaviour when batch of indices contains
- dm = ActiveDataModule(train_dataloader=dataloader)
- pool_ids = [0, 0] # they are the first to be labelled so correspond to ids in oracle
- dm.label(pool_ids)
- assert dm.train_size == 1
-
-
-def test_reset_at_labelling_step(dataloader):
- """Test that resetting the labelling steps sets the correct states."""
- dm = ActiveDataModule(train_dataloader=dataloader)
- len_dataset = len(dataloader.dataset)
-
- dm.label(0) # label first
- assert dm.last_labelling_step == 1
- assert dm.train_size == 1
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is True
- assert dm.has_unlabelled_data is True
- assert dm.train_dataset.indices == [0]
-
- dm.label(list(range(len_dataset - 1))) # label the rest
- assert dm.train_size == len_dataset
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is True
- assert dm.has_unlabelled_data is False
- assert dm.train_dataset.indices == list(range(len_dataset))
-
- dm.reset_at_labelling_step(1) # go back to when there was one instance
- assert dm.train_size == 1
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is True
- assert dm.has_unlabelled_data is True
- assert dm.train_dataset.indices == [0]
-
- dm.reset_at_labelling_step(0) # go back to when there was nothing labelled
- assert dm.last_labelling_step == 2
- assert dm.train_size == 0
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is False
- assert dm.has_unlabelled_data is True
- assert dm.train_dataset.indices == []
-
- dm.reset_at_labelling_step(dm.last_labelling_step) # reset to the last step
- assert dm.train_size == len_dataset
- assert dm.pool_size == len_dataset - dm.train_size
- assert dm.has_labelled_data is True
- assert dm.has_unlabelled_data is False
- assert dm.train_dataset.indices == list(range(len_dataset))
-
- with pytest.raises(ValueError):
- assert dm.reset_at_labelling_step(100)
diff --git a/tests/unit/mcdropout/test_replacement.py b/tests/unit/mcdropout/test_replacement.py
deleted file mode 100644
index eb01702..0000000
--- a/tests/unit/mcdropout/test_replacement.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from typing import List, Tuple
-
-import pytest
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from torch import nn
-
-from energizer.utilities.mcdropout import replace_dropout_layers, replace_energizer_dropout_layers
-
-
-def get_dropout_modules(module: nn.Module) -> List[Tuple[str, float]]:
- return [
- (m.__class__.__name__, m.p) for _, m in module.named_modules() if isinstance(m, nn.modules.dropout._DropoutNd)
- ]
-
-
-def test_replace_dropout(dropout_module):
- """Checks that replacement of layers works.
-
- In particular, it checks that the layer type is change but the original
- probabilty for each layer is preserved.
- """
-
- original_target = get_dropout_modules(dropout_module)
- new_target = [(f"MC{m}", p) for (m, p) in original_target]
-
- # silly check to see whether it works
- assert get_dropout_modules(dropout_module) == original_target
-
- # now replace torch dropout with energizer dropout
- replace_dropout_layers(dropout_module)
- assert get_dropout_modules(dropout_module) == new_target
-
- # now replace energizer dropout with torch dropout
- replace_energizer_dropout_layers(dropout_module)
- assert get_dropout_modules(dropout_module) == original_target
-
- # change probability
- replace_dropout_layers(dropout_module, 0.9)
- assert get_dropout_modules(dropout_module) == [(m, 0.9) for (m, _) in new_target]
-
- # change probability
- replace_energizer_dropout_layers(dropout_module, 0.9)
- assert get_dropout_modules(dropout_module) == [(m, 0.9) for (m, _) in original_target]
-
-
-def test_consistent_inputs(dropout_module):
- # checks inputs
- with pytest.raises(MisconfigurationException):
- replace_dropout_layers(dropout_module, consistent=True)
-
- with pytest.raises(AssertionError):
- replace_dropout_layers(dropout_module, consistent=True, num_inference_iters=10, seeds=[0])
-
-
-def test_consistent_dropout(dropout_module):
- replace_dropout_layers(dropout_module, consistent=True, num_inference_iters=10)
-
- # outputs =
diff --git a/tests/utils.py b/tests/utils.py
deleted file mode 100644
index a9d76ec..0000000
--- a/tests/utils.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from typing import Dict, Tuple, Union
-
-import torch
-from pytorch_lightning import LightningModule
-from torch import Tensor
-from torch.utils.data import Dataset
-
-NUM_CLASSES = 2
-NUM_FEATURES = 100
-
-
-class RandomSupervisedDataset(Dataset):
- """Generate a dummy dataset with inputs and labels."""
-
- def __init__(
- self, num_classes: int = NUM_CLASSES, num_features: int = NUM_FEATURES, num_samples: int = 250
- ) -> None:
- """
- It generates a dataset where each instance is a Tuple[Tensor, Tensor]
- where the first element is a tensor of inputs of size `(num_samples, num_features)`
- and the second element is a tensor of labels of size `(num_samples,)` and `num_labels`
- unique elements.
-
- Args:
- num_classes (int): The number of classes.
- num_features (int): The number of features.
- num_samples (int): The number of instances.
- """
- self.len = num_samples
- self.x = torch.randn(size=(num_samples, num_features))
- self.y = torch.randint(low=0, high=num_classes, size=(num_samples,))
-
- def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor]:
- return self.x[idx], self.y
-
- def __len__(self):
- return self.len
-
-
-class BoringModel(LightningModule):
- """A simple LightningModule with a linear layer."""
-
- def __init__(
- self, backbone: torch.nn.Module, num_classes: int = NUM_CLASSES, num_features: int = NUM_FEATURES
- ) -> None:
- super().__init__()
- self.backbone = backbone
- self.head = torch.nn.Linear(num_features, num_classes)
- self.loss = torch.nn.CrossEntropyLoss()
-
- def forward(self, x: Tensor) -> Tensor:
- return self.head(self.backbone(x))
-
- def step(self, batch: Union[Tuple[Tensor, Tensor], Dict[str, Tensor]]) -> Tensor:
- if isinstance(batch, dict):
- batch = (batch["inputs"], batch["labels"])
- inputs, targets = batch
- preds = self(inputs)
- return self.loss(preds, targets)
-
- def training_step(self, batch, *args, **kwargs) -> Dict[str, Tensor]:
- return {"loss": self.step(batch)}
-
- def validation_step(self, batch, *args, **kwargs) -> Dict[str, Tensor]:
- return {"loss": self.step(batch)}
-
- def test_step(self, batch, *args, **kwargs) -> Dict[str, Tensor]:
- return {"loss": self.step(batch)}
-
- def configure_optimizers(self):
- return torch.optim.SGD(self.layer.parameters(), lr=0.01)