mirror of
https://github.com/rasbt/LLMs-from-scratch.git
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Set up basic test gh worklows (#79)
* Set up basic test gh worklows * update file paths * env check * add env check * Update requirements.txt * simplify * upd
This commit is contained in:
Sebastian Raschka
GitHub
parent
9d6da22ebb
commit
ca96abac8a
41
.github/workflows/basic-tests.yml
vendored
Normal file
41
.github/workflows/basic-tests.yml
vendored
Normal file
@ -0,0 +1,41 @@
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name: Test Python Scripts and Notebooks
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on:
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push:
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branches: [ main ]
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paths:
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- '**/*.py' # Run workflow for changes in Python files
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- '**/*.ipynb' # Run workflow for changes in Jupyter notebooks
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pull_request:
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branches: [ main ]
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paths:
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- '**/*.py'
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- '**/*.ipynb'
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: '3.10'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest nbval
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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- name: Test Selected Python Scripts
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run: |
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pytest ch04/01_main-chapter-code/tests.py
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pytest appendix-A/02_installing-python-libraries/tests.py
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- name: Validate Selected Jupyter Notebooks
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run: |
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pytest --nbval ch02/01_main-chapter-code/dataloader.ipynb
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pytest --nbval ch03/01_main-chapter-code/multihead-attention.ipynb
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@ -11,13 +11,13 @@
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"output_type": "stream",
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"text": [
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"[OK] Your Python version is 3.10.12\n",
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"numpy >= 1.24.3 \n",
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"matplotlib >= 3.7.1. \n",
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"jupyterlab >= 4.0. \n",
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"tensorflow >= 2.15.0 \n",
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"torch >= 2.0.1 \n",
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"tqdm >= 4.66.1 \n",
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"tiktoken >= 0.5.1 \n"
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"[OK] numpy 1.26.0\n",
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"[OK] matplotlib 3.8.2\n",
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"[OK] jupyterlab 4.0.6\n",
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"[OK] tensorflow 2.15.0\n",
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"[OK] torch 2.2.1\n",
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"[OK] tqdm 4.66.1\n",
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"[OK] tiktoken 0.5.1\n"
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]
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}
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],
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@ -1,5 +1,6 @@
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# Sebastian Raschka, 2024
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import importlib
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from os.path import dirname, join, realpath
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from packaging.version import parse as version_parse
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import platform
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@ -16,28 +17,21 @@ def get_packages(pkgs):
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versions = []
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for p in pkgs:
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try:
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imported = __import__(p)
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imported = importlib.import_module(p)
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try:
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versions.append(imported.__version__)
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except AttributeError:
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try:
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versions.append(imported.version)
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except AttributeError:
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try:
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versions.append(imported.version_info)
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except AttributeError:
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try:
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import importlib
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import importlib_metadata
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imported = importlib.import_module(p)
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version = importlib_metadata.version(p)
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versions.append(version)
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except ImportError:
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version = "not installed"
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versions.append('0.0')
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version = (getattr(imported, '__version__', None) or
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getattr(imported, 'version', None) or
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getattr(imported, 'version_info', None))
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if version is None:
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# If common attributes don't exist, use importlib.metadata
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version = importlib.metadata.version(p)
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versions.append(version)
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except importlib.metadata.PackageNotFoundError:
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# Handle case where package is not installed
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versions.append('0.0')
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except ImportError:
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print(f'[FAIL]: {p} is not installed and/or cannot be imported.')
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versions.append('N/A')
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# Fallback if importlib.import_module fails for unexpected reasons
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versions.append('0.0')
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return versions
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@ -50,11 +44,9 @@ def get_requirements_dict():
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for line in f:
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if not line.strip():
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continue
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line = line.split("#")[0]
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print(line)
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line = line.split("#")[0].strip()
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line = line.split(" ")
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if not line[0].strip() or not line[-1].strip():
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continue
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line = [l.strip() for l in line]
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d[line[0]] = line[-1]
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return d
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@ -72,6 +64,10 @@ def check_packages(d):
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print(f'[OK] {pkg_name} {actual_ver}')
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if __name__ == '__main__':
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def main():
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d = get_requirements_dict()
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check_packages(d)
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if __name__ == '__main__':
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main()
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7
appendix-A/02_installing-python-libraries/tests.py
Normal file
7
appendix-A/02_installing-python-libraries/tests.py
Normal file
@ -0,0 +1,7 @@
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from python_environment_check import main
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def test_main(capsys):
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main()
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captured = capsys.readouterr()
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assert "FAIL" not in captured.out
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@ -18,31 +18,6 @@
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"This notebook contains the main takeaway, the data loading pipeline without the intermediate steps."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "93804da5-372b-45ff-9ef4-8398ba1dd78e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch version: 2.0.1\n",
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"tiktoken version: 0.5.1\n"
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]
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}
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],
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"source": [
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"from importlib.metadata import version\n",
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"\n",
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"import tiktoken\n",
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"import torch\n",
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"\n",
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"print(\"torch version:\", version(\"torch\"))\n",
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"print(\"tiktoken version:\", version(\"tiktoken\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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@ -164,7 +139,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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@ -10,7 +10,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "d70bae22-b540-4a13-ab01-e748cb9d55c9",
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"metadata": {},
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"outputs": [],
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@ -146,8 +146,8 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Fetching encoder.json: 1.04Mit [00:00, 3.03Mit/s] \n",
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"Fetching vocab.bpe: 457kit [00:00, 2.36Mit/s] \n"
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"Fetching encoder.json: 1.04Mit [00:00, 3.14Mit/s] \n",
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"Fetching vocab.bpe: 457kit [00:00, 1.67Mit/s] \n"
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]
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}
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],
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@ -215,25 +215,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 12,
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"id": "e9077bf4-f91f-42ad-ab76-f3d89128510e",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/sebastian/miniforge3/envs/book/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'4.33.2'"
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"'4.34.0'"
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]
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},
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"execution_count": 13,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -246,10 +238,81 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 13,
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"id": "a9839137-b8ea-4a2c-85fc-9a63064cf8c8",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e4df871bb797435787143a3abe6b0231",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Downloading tokenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f11b27a4aabf43af9bf57f929683def6",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Downloading vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d3aa9a24aacc43108ef2ed72e7bacd33",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Downloading merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f9341bc23b594bb68dcf8954bff6d9bd",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Downloading tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c5f55f2f1dbc4152acc9b2061167ee0a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Downloading config.json: 0%| | 0.00/665 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from transformers import GPT2Tokenizer\n",
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"\n",
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@ -258,7 +321,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 14,
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"id": "222cbd69-6a3d-4868-9c1f-421ffc9d5fe1",
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"metadata": {},
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"outputs": [
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@ -268,7 +331,7 @@
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"[15496, 11, 995, 13, 1148, 428, 438, 257, 1332, 30]"
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]
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},
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"execution_count": 15,
|
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -287,7 +350,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 15,
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"id": "a61bb445-b151-4a2f-8180-d4004c503754",
|
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"metadata": {},
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"outputs": [],
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@ -298,7 +361,7 @@
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},
|
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 16,
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"id": "57f7c0a3-c1fd-4313-af34-68e78eb33653",
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"metadata": {},
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"outputs": [
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@ -306,7 +369,7 @@
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"4.12 ms ± 41.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
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"4.29 ms ± 46.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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]
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}
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],
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@ -316,7 +379,7 @@
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},
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{
|
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 17,
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"id": "036dd628-3591-46c9-a5ce-b20b105a8062",
|
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"metadata": {},
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"outputs": [
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@ -324,7 +387,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"1.75 ms ± 8.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
|
||||
"1.4 ms ± 9.71 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
|
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]
|
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}
|
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],
|
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@ -334,7 +397,7 @@
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 19,
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"execution_count": 18,
|
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"id": "b9c85b58-bfbc-465e-9a7e-477e53d55c90",
|
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"metadata": {},
|
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"outputs": [
|
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@ -349,7 +412,7 @@
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
||||
"9.12 ms ± 856 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||||
"8.46 ms ± 48.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
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]
|
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}
|
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],
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@ -359,7 +422,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
|
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"execution_count": 19,
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"id": "7117107f-22a6-46b4-a442-712d50b3ac7a",
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"metadata": {},
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"outputs": [
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@ -367,7 +430,7 @@
|
||||
"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"8.63 ms ± 247 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||||
"8.36 ms ± 184 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||||
]
|
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}
|
||||
],
|
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@ -392,7 +455,7 @@
|
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"name": "python",
|
||||
"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
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"version": "3.11.4"
|
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"version": "3.10.12"
|
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}
|
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},
|
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"nbformat": 4,
|
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|
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1
ch02/02_bonus_bytepair-encoder/gpt2_model/encoder.json
Normal file
1
ch02/02_bonus_bytepair-encoder/gpt2_model/encoder.json
Normal file
File diff suppressed because one or more lines are too long
50001
ch02/02_bonus_bytepair-encoder/gpt2_model/vocab.bpe
Normal file
50001
ch02/02_bonus_bytepair-encoder/gpt2_model/vocab.bpe
Normal file
File diff suppressed because it is too large
Load Diff
@ -26,13 +26,12 @@
|
||||
"name": "stdout",
|
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"output_type": "stream",
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"text": [
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||||
"torch version: 2.1.0\n"
|
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"torch version: 2.2.1\n"
|
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]
|
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}
|
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],
|
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"source": [
|
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"from importlib.metadata import version\n",
|
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"import torch\n",
|
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"\n",
|
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"print(\"torch version:\", version(\"torch\"))"
|
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]
|
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@ -1992,7 +1991,7 @@
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
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"version": "3.10.12"
|
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}
|
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},
|
||||
"nbformat": 4,
|
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|
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@ -8,6 +8,26 @@
|
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"# Multi-head Attention Plus Data Loading"
|
||||
]
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 1,
|
||||
"id": "ac9b5847-0515-45cd-87b0-46541f6a1f79",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"torch version: 2.2.1\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from importlib.metadata import version\n",
|
||||
"\n",
|
||||
"print(\"torch version:\", version(\"torch\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "070000fc-a7b7-4c56-a2c0-a938d413a790",
|
||||
@ -28,7 +48,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 2,
|
||||
"id": "0ed4b7db-3b47-4fd3-a4a6-5f4ed5dd166e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -96,7 +116,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 3,
|
||||
"id": "664397bc-6daa-4b88-90aa-e8fc1fbd5846",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -114,7 +134,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"id": "d3664332-e6bb-447e-8b96-203aafde8b24",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -148,7 +168,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 5,
|
||||
"id": "a44e682d-1c3c-445d-85fa-b142f89f8503",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -196,7 +216,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 6,
|
||||
"id": "7898551e-f582-48ac-9f66-3632abe2a93f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -235,7 +255,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 7,
|
||||
"id": "2773c09d-c136-4372-a2be-04b58d292842",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -298,7 +318,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 8,
|
||||
"id": "779fdd04-0152-4308-af08-840800a7f395",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -324,14 +344,6 @@
|
||||
"\n",
|
||||
"print(\"context_vecs.shape:\", context_vecs.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3ac01b16-8ac6-4487-a6f2-fd9cf33a9fe4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@ -350,7 +362,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -235,8 +235,7 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
|
||||
return idx
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
def main():
|
||||
GPT_CONFIG_124M = {
|
||||
"vocab_size": 50257, # Vocabulary size
|
||||
"ctx_len": 1024, # Context length
|
||||
@ -274,3 +273,7 @@ if __name__ == "__main__":
|
||||
print("\nOutput:", out)
|
||||
print("Output length:", len(out[0]))
|
||||
print("Output text:", decoded_text)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
33
ch04/01_main-chapter-code/tests.py
Normal file
33
ch04/01_main-chapter-code/tests.py
Normal file
@ -0,0 +1,33 @@
|
||||
from gpt import main
|
||||
|
||||
expected = """
|
||||
==================================================
|
||||
IN
|
||||
==================================================
|
||||
|
||||
Input text: Hello, I am
|
||||
Encoded input text: [15496, 11, 314, 716]
|
||||
encoded_tensor.shape: torch.Size([1, 4])
|
||||
|
||||
|
||||
==================================================
|
||||
OUT
|
||||
==================================================
|
||||
|
||||
Output: tensor([[15496, 11, 314, 716, 27018, 24086, 47843, 30961, 42348, 7267,
|
||||
49706, 43231, 47062, 34657]])
|
||||
Output length: 14
|
||||
Output text: Hello, I am Featureiman Byeswickattribute argue logger Normandy Compton analogous
|
||||
"""
|
||||
|
||||
|
||||
def test_main(capsys):
|
||||
main()
|
||||
captured = capsys.readouterr()
|
||||
|
||||
# Normalize line endings and strip trailing whitespace from each line
|
||||
normalized_expected = '\n'.join(line.rstrip() for line in expected.splitlines())
|
||||
normalized_output = '\n'.join(line.rstrip() for line in captured.out.splitlines())
|
||||
|
||||
# Compare normalized strings
|
||||
assert normalized_output == normalized_expected
|
||||
@ -1,7 +1,7 @@
|
||||
numpy >= 1.24.3 # ch05
|
||||
matplotlib >= 3.7.1. # ch04, ch05
|
||||
jupyterlab >= 4.0. # all
|
||||
matplotlib >= 3.7.1 # ch04, ch05
|
||||
jupyterlab >= 4.0 # all
|
||||
tensorflow >= 2.15.0 # ch05
|
||||
torch >= 2.0.1 # all
|
||||
tqdm >= 4.66.1 # ch05
|
||||
tiktoken >= 0.5.1 # ch02, ch04, ch05
|
||||
tiktoken >= 0.5.1 # ch02, ch04, ch05
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user