tests and exercises

appendix-D/01_main-chapter-code/appendix-D.ipynb

@@ -199,8 +199,8 @@
     }
    ],
    "source": [
-    "total_steps = len(train_loader) * n_epochs * train_loader.batch_size\n",
-    "warmup_steps = int(0.1 * total_steps) # 10% warmup\n",
+    "total_steps = len(train_loader) * n_epochs\n",
+    "warmup_steps = int(0.2 * total_steps) # 20% warmup\n",
     "print(warmup_steps)"
    ]
   },
@@ -779,7 +779,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

ch06/01_main-chapter-code/exercise-solutions.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba450fb1-8a26-4894-ab7a-5d7bfefe90ce",
+   "metadata": {},
+   "source": [
+    "<font size=\"1\">\n",
+    "Supplementary code for \"Build a Large Language Model From Scratch\": <a href=\"https://www.manning.com/books/build-a-large-language-model-from-scratch\">https://www.manning.com/books/build-a-large-language-model-from-scratch</a> by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
+    "Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
+    "</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51c9672d-8d0c-470d-ac2d-1271f8ec3f14",
+   "metadata": {},
+   "source": [
+    "# Chapter 6 Exercise solutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fea8be3-30a1-4623-a6d7-b095c6c1092e",
+   "metadata": {},
+   "source": [
+    "## Exercise 6.1: Increasing the context length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5860ba9f-2db3-4480-b96b-4be1c68981eb",
+   "metadata": {},
+   "source": [
+    "We can pad the inputs to the maximum number of tokens to the maximum the model supports by setting the max length to\n",
+    "\n",
+    "```python\n",
+    "max_length = 1024\n",
+    "\n",
+    "train_dataset = SpamDataset(base_path / \"train.csv\", max_length=max_length, tokenizer=tokenizer)\n",
+    "val_dataset = SpamDataset(base_path / \"validation.csv\", max_length=max_length, tokenizer=tokenizer)\n",
+    "test_dataset = SpamDataset(base_path / \"test.csv\", max_length=max_length, tokenizer=tokenizer)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "or, equivalently, we can define the `max_length` via:\n",
+    "\n",
+    "```python\n",
+    "max_length = model.pos_emb.weight.shape[0]\n",
+    "```\n",
+    "\n",
+    "or\n",
+    "\n",
+    "```python\n",
+    "max_length = BASE_CONFIG[\"context_length\"]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b0f4d5d-17fd-4265-93d8-ea08a22fdaf8",
+   "metadata": {},
+   "source": [
+    "For convenience, you can run this experiment via\n",
+    "\n",
+    "```\n",
+    "python additional-experiments.py --context_length \"model_context_length\"\n",
+    "```\n",
+    "\n",
+    "using the code in the [../02_bonus_additional-experiments](../02_bonus_additional-experiments) folder, which results in a substantially worse test accuracy of 78.33% (versus the 95.67% in the main chapter)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a780455-f52a-48d1-ab82-6afd40bcad8b",
+   "metadata": {},
+   "source": [
+    "## Exercise 6.2: Finetuning the whole model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56aa5208-aa29-4165-a0ec-7480754e2a18",
+   "metadata": {},
+   "source": [
+    "Instead of finetuning just the final transformer block, we can finetune the entire model by removing the following lines from the code:\n",
+    "\n",
+    "```python\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "```\n",
+    "\n",
+    "For convenience, you can run this experiment via\n",
+    "\n",
+    "```\n",
+    "python additional-experiments.py --trainable_layers all\n",
+    "```\n",
+    "\n",
+    "using the code in the [../02_bonus_additional-experiments](../02_bonus_additional-experiments) folder, which results in a 1% improved test accuracy of 96.67% (versus the 95.67% in the main chapter)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2269bce3-f2b5-4a76-a692-5977c75a57b6",
+   "metadata": {},
+   "source": [
+    "## Exercise 6.3: Finetuning the first versus last token "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7418a629-51b6-4aa2-83b7-bc0261bc370f",
+   "metadata": {},
+   "source": [
+    "ther than finetuning the last output token, we can finetune the first output token by changing \n",
+    "\n",
+    "```python\n",
+    "model(input_batch)[:, -1, :]\n",
+    "```\n",
+    "\n",
+    "to\n",
+    "\n",
+    "```python\n",
+    "model(input_batch)[:, 0, :]\n",
+    "```\n",
+    "\n",
+    "everywhere in the code.\n",
+    "\n",
+    "For convenience, you can run this experiment via\n",
+    "\n",
+    "```\n",
+    "python additional-experiments.py --trainable_token first\n",
+    "```\n",
+    "\n",
+    "using the code in the [../02_bonus_additional-experiments](../02_bonus_additional-experiments) folder, which results in a substantially worse test accuracy of 75.00% (versus the 95.67% in the main chapter)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5e6188a-f182-4f26-b9e5-ccae3ecadae0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

ch06/01_main-chapter-code/tests.py

@@ -7,7 +7,6 @@
 
 
 import subprocess
-import pytest
 
 
 def test_gpt_class_finetune():
@@ -15,4 +14,3 @@ def test_gpt_class_finetune():
 
     result = subprocess.run(command, capture_output=True, text=True)
     assert result.returncode == 0, f"Script exited with errors: {result.stderr}"
-