LLMs-from-scratch/appendix-A/01_main-chapter-code/exercise-solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"width:100%\">\n",
    "<tr>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<font size=\"2\">\n",
    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
    "</font>\n",
    "</td>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
    "</td>\n",
    "</tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise A.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Python Setup Tips](../../setup/01_optional-python-setup-preferences/README.md) document in this repository contains additional recommendations and tips to set up your Python environment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise A.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Installing Libraries Used In This Book document](../../setup/02_installing-python-libraries/README.md) and [directory](../../setup/02_installing-python-libraries/) contains utilities to check whether your environment is set up correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise A.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "class NeuralNetwork(torch.nn.Module):\n",
    "    def __init__(self, num_inputs, num_outputs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.layers = torch.nn.Sequential(\n",
    "                \n",
    "            # 1st hidden layer\n",
    "            torch.nn.Linear(num_inputs, 30),\n",
    "            torch.nn.ReLU(),\n",
    "\n",
    "            # 2nd hidden layer\n",
    "            torch.nn.Linear(30, 20),\n",
    "            torch.nn.ReLU(),\n",
    "\n",
    "            # output layer\n",
    "            torch.nn.Linear(20, num_outputs),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of trainable model parameters: 752\n"
     ]
    }
   ],
   "source": [
    "model = NeuralNetwork(2, 2)\n",
    "\n",
    "num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(\"Total number of trainable model parameters:\", num_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise A.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qGgnamiyLJxp"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "a = torch.rand(100, 200)\n",
    "b = torch.rand(200, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CvGvIeVkLzXE",
    "outputId": "44d027be-0787-4348-9c06-4e559d94d0e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.8 µs ± 8.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit a @ b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "OmRtZLa9L2ZG"
   },
   "outputs": [],
   "source": [
    "a, b = a.to(\"cuda\"), b.to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "duLEhXDPL6k0",
    "outputId": "3486471d-fd62-446f-9855-2d01f41fd101"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.8 µs ± 425 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit a @ b"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "V100",
   "machine_shape": "hm",
   "provenance": []
  },
  "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": 4
}