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

.github/workflows/basic-tests.yml

@@ -0,0 +1,41 @@
+name: Test Python Scripts and Notebooks
+
+on:
+  push:
+    branches: [ main ]
+    paths:
+      - '**/*.py'   # Run workflow for changes in Python files
+      - '**/*.ipynb' # Run workflow for changes in Jupyter notebooks
+  pull_request:
+    branches: [ main ]
+    paths:
+      - '**/*.py'
+      - '**/*.ipynb'
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.10'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest nbval
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+
+    - name: Test Selected Python Scripts
+      run: |
+        pytest ch04/01_main-chapter-code/tests.py
+        pytest appendix-A/02_installing-python-libraries/tests.py
+
+    - name: Validate Selected Jupyter Notebooks
+      run: |
+        pytest --nbval ch02/01_main-chapter-code/dataloader.ipynb
+        pytest --nbval ch03/01_main-chapter-code/multihead-attention.ipynb

appendix-A/02_installing-python-libraries/python_environment_check.ipynb

@@ -11,13 +11,13 @@
      "output_type": "stream",
      "text": [
       "[OK] Your Python version is 3.10.12\n",
-      "numpy >= 1.24.3       \n",
+      "[OK] numpy 1.26.0\n",
-      "matplotlib >= 3.7.1.  \n",
+      "[OK] matplotlib 3.8.2\n",
-      "jupyterlab >= 4.0.    \n",
+      "[OK] jupyterlab 4.0.6\n",
-      "tensorflow >= 2.15.0  \n",
+      "[OK] tensorflow 2.15.0\n",
-      "torch >= 2.0.1        \n",
+      "[OK] torch 2.2.1\n",
-      "tqdm >= 4.66.1        \n",
+      "[OK] tqdm 4.66.1\n",
-      "tiktoken >= 0.5.1     \n"
+      "[OK] tiktoken 0.5.1\n"
      ]
     }
    ],

appendix-A/02_installing-python-libraries/python_environment_check.py

@@ -1,5 +1,6 @@
 # Sebastian Raschka, 2024
 
+import importlib
 from os.path import dirname, join, realpath
 from packaging.version import parse as version_parse
 import platform
@@ -16,28 +17,21 @@ def get_packages(pkgs):
     versions = []
     for p in pkgs:
         try:
-            imported = __import__(p)
+            imported = importlib.import_module(p)
             try:
-                versions.append(imported.__version__)
+                version = (getattr(imported, '__version__', None) or
-            except AttributeError:
+                           getattr(imported, 'version', None) or
-                try:
+                           getattr(imported, 'version_info', None))
-                    versions.append(imported.version)
+                if version is None:
-                except AttributeError:
+                    # If common attributes don't exist, use importlib.metadata
-                    try:
+                    version = importlib.metadata.version(p)
-                        versions.append(imported.version_info)
+                versions.append(version)
-                    except AttributeError:
+            except importlib.metadata.PackageNotFoundError:
-                        try:
+                # Handle case where package is not installed
-                            import importlib
+                versions.append('0.0')
-                            import importlib_metadata
-                            imported = importlib.import_module(p)
-                            version = importlib_metadata.version(p)
-                            versions.append(version)
-                        except ImportError:
-                            version = "not installed"
-                            versions.append('0.0')
         except ImportError:
-            print(f'[FAIL]: {p} is not installed and/or cannot be imported.')
+            # Fallback if importlib.import_module fails for unexpected reasons
-            versions.append('N/A')
+            versions.append('0.0')
     return versions
 
 
@@ -50,11 +44,9 @@ def get_requirements_dict():
         for line in f:
             if not line.strip():
                 continue
-            line = line.split("#")[0]
+            line = line.split("#")[0].strip()
-            print(line)
             line = line.split(" ")
-            if not line[0].strip() or not line[-1].strip():
+            line = [l.strip() for l in line]
-                continue
             d[line[0]] = line[-1]
     return d
 
@@ -72,6 +64,10 @@ def check_packages(d):
             print(f'[OK] {pkg_name} {actual_ver}')
 
 
-if __name__ == '__main__':
+def main():
     d = get_requirements_dict()
     check_packages(d)
+
+
+if __name__ == '__main__':
+    main()

appendix-A/02_installing-python-libraries/tests.py

@@ -0,0 +1,7 @@
+from python_environment_check import main
+
+
+def test_main(capsys):
+    main()
+    captured = capsys.readouterr()
+    assert "FAIL" not in captured.out

ch02/01_main-chapter-code/dataloader.ipynb

@@ -18,31 +18,6 @@
     "This notebook contains the main takeaway, the data loading pipeline without the intermediate steps."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "93804da5-372b-45ff-9ef4-8398ba1dd78e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch version: 2.0.1\n",
-      "tiktoken version: 0.5.1\n"
-     ]
-    }
-   ],
-   "source": [
-    "from importlib.metadata import version\n",
-    "\n",
-    "import tiktoken\n",
-    "import torch\n",
-    "\n",
-    "print(\"torch version:\", version(\"torch\"))\n",
-    "print(\"tiktoken version:\", version(\"tiktoken\"))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -164,7 +139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

ch02/02_bonus_bytepair-encoder/compare-bpe-tiktoken.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d70bae22-b540-4a13-ab01-e748cb9d55c9",
    "metadata": {},
    "outputs": [],
@@ -146,8 +146,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fetching encoder.json: 1.04Mit [00:00, 3.03Mit/s]                                                   \n",
+      "Fetching encoder.json: 1.04Mit [00:00, 3.14Mit/s]                                                   \n",
-      "Fetching vocab.bpe: 457kit [00:00, 2.36Mit/s]                                                       \n"
+      "Fetching vocab.bpe: 457kit [00:00, 1.67Mit/s]                                                       \n"
      ]
     }
    ],
@@ -215,25 +215,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "e9077bf4-f91f-42ad-ab76-f3d89128510e",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/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",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "'4.33.2'"
+       "'4.34.0'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,10 +238,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "a9839137-b8ea-4a2c-85fc-9a63064cf8c8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4df871bb797435787143a3abe6b0231",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f11b27a4aabf43af9bf57f929683def6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3aa9a24aacc43108ef2ed72e7bacd33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9341bc23b594bb68dcf8954bff6d9bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5f55f2f1dbc4152acc9b2061167ee0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from transformers import GPT2Tokenizer\n",
     "\n",
@@ -258,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "222cbd69-6a3d-4868-9c1f-421ffc9d5fe1",
    "metadata": {},
    "outputs": [
@@ -268,7 +331,7 @@
        "[15496, 11, 995, 13, 1148, 428, 438, 257, 1332, 30]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -287,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "a61bb445-b151-4a2f-8180-d4004c503754",
    "metadata": {},
    "outputs": [],
@@ -298,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "57f7c0a3-c1fd-4313-af34-68e78eb33653",
    "metadata": {},
    "outputs": [
@@ -306,7 +369,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.12 ms ± 41.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "4.29 ms ± 46.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -316,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "036dd628-3591-46c9-a5ce-b20b105a8062",
    "metadata": {},
    "outputs": [
@@ -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"
      ]
     }
    ],
@@ -334,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "b9c85b58-bfbc-465e-9a7e-477e53d55c90",
    "metadata": {},
    "outputs": [
@@ -349,7 +412,7 @@
      "name": "stdout",
      "output_type": "stream",
      "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"
      ]
     }
    ],
@@ -359,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "7117107f-22a6-46b4-a442-712d50b3ac7a",
    "metadata": {},
    "outputs": [
@@ -367,7 +430,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
      ]
     }
    ],
@@ -392,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

ch03/01_main-chapter-code/ch03.ipynb

@@ -26,13 +26,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch version: 2.1.0\n"
+      "torch version: 2.2.1\n"
      ]
     }
    ],
    "source": [
     "from importlib.metadata import version\n",
-    "import torch\n",
     "\n",
     "print(\"torch version:\", version(\"torch\"))"
    ]
@@ -1992,7 +1991,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

ch03/01_main-chapter-code/multihead-attention.ipynb

@@ -8,6 +8,26 @@
     "# Multi-head Attention Plus Data Loading"
    ]
   },
+  {
+   "cell_type": "code",
+   "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,

ch04/01_main-chapter-code/gpt.py

@@ -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()

ch04/01_main-chapter-code/tests.py

@@ -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

requirements.txt

@@ -1,6 +1,6 @@
 numpy >= 1.24.3       # ch05
-matplotlib >= 3.7.1.  # ch04, ch05
+matplotlib >= 3.7.1   # ch04, ch05
-jupyterlab >= 4.0.    # all
+jupyterlab >= 4.0     # all
 tensorflow >= 2.15.0  # ch05
 torch >= 2.0.1        # all
 tqdm >= 4.66.1        # ch05