LLM from scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aee500dc",
   "metadata": {},
   "source": [
    "# LLM from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ab15cf",
   "metadata": {},
   "source": [
    "The goal is to code from scratch (using Pytorch) a Transformer, more precisely a GPT-like model (using a decoder-only architecture). See discussion at the end for what's missing to build a full blown LLM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d1d489",
   "metadata": {},
   "source": [
    "Credits: a lot of the code is inspired by Andrej Karpathy's lectures, videos, and repositories, in particular\n",
    "https://github.com/karpathy/nn-zero-to-hero\n",
    "https://github.com/karpathy/makemore\n",
    "\n",
    "I did not know at the time I prepared this notebook, but a fantastic reference which goes in a lot more depth, is Sebastian Raschka's book and codebase:\n",
    "https://github.com/rasbt/LLMs-from-scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ef709fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from tqdm.notebook import trange"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37136931",
   "metadata": {},
   "source": [
    "What we will do here: train a generative model with political speeches (State of the Union).\n",
    "The goal is to generate more speeches!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a081d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# main model and data hyperparameters\n",
    "batch_size = 32 # number of sequences processed in parallel\n",
    "block_size = 16 # maximum context length for predictions\n",
    "n_token = 512 # number of tokens (used for world level tokenizer)\n",
    "blank_token = n_token\n",
    "\n",
    "# the rest of the hyper parameters are defined with the models\n",
    "n_embed = 64 # size of hidden layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19a695e",
   "metadata": {},
   "source": [
    "Note: the hyper parameters were **not** optimised at all! Also, the code runs on CPU, not GPU. It is mostly for educational purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f4014a",
   "metadata": {},
   "source": [
    "## Part 1: Playing with the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf30164",
   "metadata": {},
   "source": [
    "Let's download the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "109f64d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-30 18:40:07--  https://raw.githubusercontent.com/nathanael-fijalkow/LLM_from_scratch/main/sotu.txt\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8003::154, 2606:50c0:8002::154, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n",
      "HTTP request sent, awaiting response... 404 Not Found\n",
      "2024-03-30 18:40:07 ERROR 404: Not Found.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/nathanael-fijalkow/LLM_from_scratch/main/sotu.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec829a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sotu.txt', 'r', encoding='utf-8') as f:\n",
    "    text = f.read()\n",
    "\n",
    "text = text.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a711ea13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1279319"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_size = len(text)\n",
    "data_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "788ad1fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mr. speaker, mr. president, and distinguished members of the house and senate,\n",
      "honored guests, and fellow citizens: less than 3 weeks ago, i joined you on the\n",
      "west front of this very building and, looking over the monuments to our proud\n",
      "past, offered you my hand in filling the next page of american history with a\n",
      "story of extended prosperity and continued peace. and tonight i'm back to offer\n",
      "you my plans as well. the hand remains extended; the sleeves are rolled up;\n",
      "america is waiting; and now we must produce. together, we can build a better\n",
      "america.\n",
      "\n",
      "it is comforting to return to this historic chamber. here, 22 years ago, i first\n",
      "raised my hand to be sworn into public life. so, tonight i feel as if i'm\n",
      "returning home to friends. and i intend, in the months and years to come, to\n",
      "give you what friends deserve: frankness, respect, and my best judgment about\n",
      "ways to improve america's future. in return, i ask for an honest commitment to\n",
      "our common mission of progress. if we seize the oppor\n"
     ]
    }
   ],
   "source": [
    "print(text[:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e263f9",
   "metadata": {},
   "source": [
    "We will start with a basic character-level tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fac76bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: '\\n', 1: ' ', 2: '!', 3: '\"', 4: '$', 5: '%', 6: '&', 7: \"'\", 8: '(', 9: ')', 10: ',', 11: '-', 12: '.', 13: '/', 14: '0', 15: '1', 16: '2', 17: '3', 18: '4', 19: '5', 20: '6', 21: '7', 22: '8', 23: '9', 24: ':', 25: ';', 26: '?', 27: 'a', 28: 'b', 29: 'c', 30: 'd', 31: 'e', 32: 'f', 33: 'g', 34: 'h', 35: 'i', 36: 'j', 37: 'k', 38: 'l', 39: 'm', 40: 'n', 41: 'o', 42: 'p', 43: 'q', 44: 'r', 45: 's', 46: 't', 47: 'u', 48: 'v', 49: 'w', 50: 'x', 51: 'y', 52: 'z'}\n"
     ]
    }
   ],
   "source": [
    "# here are all the unique characters that occur in this text\n",
    "chars = sorted(list(set(text)))\n",
    "vocab_size = len(chars)\n",
    "# create a mapping from characters to integers\n",
    "stoi = { ch:i for i,ch in enumerate(chars) }\n",
    "itos = { i:ch for i,ch in enumerate(chars) }\n",
    "# encoder: take a string, output a list of integers\n",
    "encode = lambda s: [stoi[c] for c in s] \n",
    "# decoder: take a list of integers, output a string\n",
    "decode = lambda l: ''.join([itos[i] for i in l]) \n",
    "\n",
    "print(itos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "251577bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([34, 31, 38, 38, 41, 1, 49, 41, 44, 38, 30], 'hello world')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = \"hello world\"\n",
    "test_encoded = encode(test)\n",
    "test_encoded, decode(test_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8be15cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]),\n",
       " torch.Size([11, 53]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_encoded = torch.tensor(test_encoded)\n",
    "test_tensor = F.one_hot(test_encoded, num_classes=vocab_size).float()\n",
    "test_tensor, test_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5c6ed43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The first 5 characters code for \"hello\"\n",
    "plt.yticks([])\n",
    "plt.imshow(test_tensor[:5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affeb04f",
   "metadata": {},
   "source": [
    "We encode the entire text dataset and store it into a `torch.Tensor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bf906b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1279319])\n"
     ]
    }
   ],
   "source": [
    "data = torch.tensor(encode(text))\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac3c758",
   "metadata": {},
   "source": [
    "We split up the data into train and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80e1659e",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = int(0.9*len(data)) \n",
    "# first 90% goes to training\n",
    "train_data = data[:n]\n",
    "# the rest goes to validation\n",
    "val_data = data[n:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb181c59",
   "metadata": {},
   "source": [
    "`get_data` produces a random pair of consecutive characters, one-hot encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a831248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]]),\n",
       " tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_data(split):\n",
    "    data = train_data if split == 'train' else val_data\n",
    "    i = torch.randint(len(data) - 1, (1,))\n",
    "    x = F.one_hot(data[i], num_classes=vocab_size).float()\n",
    "    y = F.one_hot(data[i+1], num_classes=vocab_size).float()\n",
    "    return x,y\n",
    "        \n",
    "get_data(\"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4001f083",
   "metadata": {},
   "source": [
    "## Part 2: Bigram model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38881b74",
   "metadata": {},
   "source": [
    "We define our first model, a very basic bigram model: it is a Markov chain, which chooses the next character based only on the current character. It uses cross entropy loss, illustrated below.\n",
    "\n",
    "`W` is a `(vocab_size,vocab_size)` matrix. We use `nn.Parameter` to tell Pytorch that the entries of `W` are parameters of the model.\n",
    "\n",
    "To define a model, we typically write two functions:\n",
    "* `init`, which initialises the model,\n",
    "* `forward`, which is how the model is run. \n",
    "\n",
    "`forward` takes either only an input `x`, or both an input `x` and the expected target `y`. The model returns two tensors: `logits` and `loss`:\n",
    "* `logits` represents the predictions of the model on input `x`,\n",
    "* `loss` computes the loss of the model on `x` for predicting `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a43ef69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Bigram(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.W = nn.Parameter(torch.zeros((vocab_size, vocab_size)))\n",
    "\n",
    "    def forward(self, x, y=None):\n",
    "        logits = x @ self.W\n",
    "        \n",
    "        if y is None:\n",
    "            loss = None\n",
    "        else:\n",
    "            loss = F.cross_entropy(logits, y)\n",
    "\n",
    "        return logits, loss    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b809ca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2809  parameters\n"
     ]
    }
   ],
   "source": [
    "model = Bigram()\n",
    "print(sum(p.numel() for p in model.parameters()), ' parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c9af4e",
   "metadata": {},
   "source": [
    "The code below illustrates how cross entropy loss is computed.\n",
    "\n",
    "We consider some tensor `logits`, which is the output of the model on some (imaginary) input `x`. It assigns to each class (here, a class is a character) a float value. The `logits` are turned into a probabilistic distribution using a softmax (exponentiation and normalisation). For numerical stability we prefer to work with \"log probabilities\" as summing numbers is easier than multiplying them.\n",
    "\n",
    "The target is some `y`, representing a class (in our case, a character)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ab432fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The input x: \n",
      " tensor([-2.3578, -0.7919,  1.6708,  1.2615, -0.7398])\n",
      "After exponentiation: \n",
      " tensor([0.0946, 0.4530, 5.3162, 3.5308, 0.4772])\n",
      "After normalisation: \n",
      " tensor([0.0096, 0.0459, 0.5385, 0.3577, 0.0483])\n",
      "\n",
      "The two steps above are called softmax: \n",
      " tensor([0.0096, 0.0459, 0.5385, 0.3577, 0.0483])\n",
      "\n",
      "We work with log probabilities: \n",
      " tensor([4.6475, 3.0816, 0.6189, 1.0282, 3.0295])\n",
      "The target y:  1\n",
      "The cross entropy loss:  3.0816006660461426\n"
     ]
    }
   ],
   "source": [
    "number_classes_test = 5\n",
    "\n",
    "logits = torch.randn(number_classes_test)\n",
    "print(\"The input x: \\n\", logits)\n",
    "logits_exped = logits.exp()\n",
    "print(\"After exponentiation: \\n\", logits_exped)\n",
    "probs = logits_exped / logits_exped.sum()\n",
    "print(\"After normalisation: \\n\", probs)\n",
    "print(\"\\nThe two steps above are called softmax: \\n\", F.softmax(logits, 0))\n",
    "logprobs = -probs.log()\n",
    "print(\"\\nWe work with log probabilities: \\n\", logprobs)\n",
    "\n",
    "y = torch.randint(number_classes_test, (), dtype=torch.int64)\n",
    "print(\"The target y: \", y.item())\n",
    "\n",
    "loss = F.cross_entropy(logits, y)\n",
    "print(\"The cross entropy loss: \", loss.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4270c0f4",
   "metadata": {},
   "source": [
    "Now we will write some boilerplate code for running, evaluating, and training models. Since this is still a warm-up we're not doing batches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325b8249",
   "metadata": {},
   "source": [
    "The code below shows how to use the model to generate text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "158bfb33",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_bigram(model, max_new_tokens = 200):\n",
    "    idx = F.one_hot(torch.tensor(stoi[\"\\n\"]), num_classes=vocab_size).float()\n",
    "    out = []\n",
    "    for _ in range(max_new_tokens):\n",
    "        # get the predictions\n",
    "        logits, _ = model(idx)\n",
    "        # logits.shape = (vocab_size)\n",
    "        # apply softmax to get probabilities\n",
    "        probs = F.softmax(logits, dim=0) \n",
    "        # probs.shape = (vocab_size)\n",
    "        # sample from the distribution\n",
    "        idx_next = torch.multinomial(probs, num_samples=1) \n",
    "        # idx_next.shape = (1)\n",
    "        # append sampled index to the output sequence\n",
    "        out.append(idx_next.item())\n",
    "        idx = F.one_hot(idx_next.view(()), num_classes=vocab_size).float()\n",
    "\n",
    "    return decode(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d74900",
   "metadata": {},
   "source": [
    "Since the model was not trained for now it produces garbage (it is a uniform distribution)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "322f1bc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.m?vf0xq9th(b2oc:0yrl46y99.\"\n",
      "ki6\"-vj.pc(p2nm74 u'mkdmgfi\n",
      "1s\"q&)5j4-3)s.,b;f'1?%y/q5c&cg\".7nhsq9'm7 g. 6lx/;f.tdu6eav?,gq) &7:)rw\";6rm19i.'-g:\"vzzl:;n,n,-w/k6,81advrr.&34-w(!1(eg&hzd/c$c'g e &p.(&ej5b\n"
     ]
    }
   ],
   "source": [
    "print(generate_bigram(model))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fc6587",
   "metadata": {},
   "source": [
    "The code below makes an estimation of the loss by averaging on `eval_iters` samples of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfacbf2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def estimate_loss_no_batch(model, eval_iters = 100):\n",
    "    out = {}\n",
    "    for split in ['train', 'val']:\n",
    "        losses = torch.zeros(eval_iters)\n",
    "        for k in range(eval_iters):\n",
    "            x, y = get_data(split)\n",
    "            _, loss = model(x, y)\n",
    "            losses[k] = loss.item()\n",
    "        out[split] = losses.mean()\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d24cc8f",
   "metadata": {},
   "source": [
    "The code below for training is very generic. We iterate a number of times:\n",
    "* sample data `(x,y)`\n",
    "* forward pass: compute the loss on `(x,y)`\n",
    "* reset the gradients for all parameters\n",
    "* backward pass: compute the gradients, using `loss.backward()`\n",
    "* run one step of gradient descent (here, the refined Adam optimizer, which adapts the learning rate)\n",
    "\n",
    "When doing the forward pass, Pytorch creates computation graphs which are then used to compute the gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "72a0971b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_no_batch(model, n_iterations = 20000, learning_rate = 1e-3, eval_interval = 1000, eval_iters = 100):\n",
    "    # create a PyTorch optimizer\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "    for iter in range(n_iterations):\n",
    "        # every once in a while evaluate the loss on training and validation sets\n",
    "        if iter % eval_interval == 0 or iter == n_iterations - 1:\n",
    "            losses = estimate_loss_no_batch(model, eval_iters)\n",
    "            print(f\"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}\")\n",
    "\n",
    "        x,y = get_data(\"train\")\n",
    "        _, loss = model(x, y)\n",
    "        optimizer.zero_grad(set_to_none=True)\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d180a524",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0: train loss 3.9703, val loss 3.9703\n",
      "step 2000: train loss 3.6700, val loss 3.6479\n",
      "step 4000: train loss 3.3513, val loss 3.3616\n",
      "step 6000: train loss 3.1573, val loss 3.1824\n",
      "step 8000: train loss 3.0685, val loss 3.0020\n",
      "step 10000: train loss 2.8461, val loss 2.8435\n",
      "step 12000: train loss 2.7789, val loss 2.8211\n",
      "step 14000: train loss 2.7506, val loss 2.6983\n",
      "step 16000: train loss 2.7127, val loss 2.6846\n",
      "step 18000: train loss 2.7043, val loss 2.6432\n",
      "step 20000: train loss 2.5786, val loss 2.7071\n",
      "step 22000: train loss 2.7645, val loss 2.5532\n",
      "step 24000: train loss 2.6096, val loss 2.5636\n",
      "step 26000: train loss 2.6663, val loss 2.5475\n",
      "step 28000: train loss 2.5029, val loss 2.5812\n",
      "step 30000: train loss 2.3959, val loss 2.4697\n",
      "step 32000: train loss 2.7020, val loss 2.4483\n",
      "step 34000: train loss 2.4368, val loss 2.6042\n",
      "step 36000: train loss 2.4400, val loss 2.4085\n",
      "step 38000: train loss 2.5458, val loss 2.6689\n",
      "step 39999: train loss 2.3896, val loss 2.5130\n"
     ]
    }
   ],
   "source": [
    "train_no_batch(model, n_iterations = 40000, learning_rate = 1e-3, eval_interval = 2000, eval_iters = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d530a1",
   "metadata": {},
   "source": [
    "The output of the trained bigram model should be a bit better than random..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e33474d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spr s the esil6urino s itean in ctis.\"jwhathe hese dindidomo te wefonoun elive andz!/und7jin amorowa\n",
      "halerefese alud, s mbto\n",
      "\n",
      "steregeathastw)arime foield se, intie tece mongr re ton l tearn ns cin\n",
      "fru\n"
     ]
    }
   ],
   "source": [
    "print(generate_bigram(model))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4acdbc",
   "metadata": {},
   "source": [
    "Let's try to understand what the bigram model learned. For this we will plot the \"idealised\" matrix, representing frequencies of apparition of consecutive characters. For readability we restrict ourselves to letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4cdd7d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = torch.zeros((26, 26), dtype=torch.int32)\n",
    "shift = stoi['a']\n",
    "\n",
    "for i in range(len(text) - 1):\n",
    "    if text[i].isalpha() and text[i+1].isalpha():\n",
    "        N[stoi[text[i]] - shift, stoi[text[i+1]]  - shift] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "79e4ccea",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "plt.imshow(N, cmap='Blues')\n",
    "for i in range(26):\n",
    "    for j in range(26):\n",
    "        chstr = itos[i + shift] + itos[j + shift]\n",
    "        plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n",
    "        plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd44bf33",
   "metadata": {},
   "source": [
    "Now we plot in a similar way the matrix `model.W`. From far, the two matrices look similar, suggesting that the bigram model is indeed convering towards the frequency matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c657dd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shift = stoi['a']\n",
    "\n",
    "@torch.no_grad()\n",
    "def plot_W(model):\n",
    "    plt.figure(figsize=(16,16))\n",
    "    W_letters = model.W[shift:shift + 26, shift:shift + 26]\n",
    "    plt.imshow(W_letters, cmap='Blues')\n",
    "    for i in range(26):\n",
    "        for j in range(26):\n",
    "            chstr = itos[i + shift] + itos[j + shift]\n",
    "            plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n",
    "    plt.axis('off');\n",
    "    \n",
    "plot_W(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e138c669",
   "metadata": {},
   "source": [
    "Before moving on to more expressive models, let us write the bigram model in a more Pytorch-friendly way.\n",
    "\n",
    "Also, we add to the loss a regularisation term: `0.1 * (self.net.weights**2).mean()`. By adding this to the loss, we force the weights to be as small as possible, which helps generalisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "980c6a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BigramProper(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.net = nn.Linear(vocab_size, vocab_size, bias=False)\n",
    "\n",
    "    def forward(self, x, y=None):\n",
    "        logits = self.net(x)\n",
    "        \n",
    "        if y is None:\n",
    "            loss = None\n",
    "        else:\n",
    "            loss = F.cross_entropy(logits, y) + 0.1 * (self.net.weight**2).mean()\n",
    "\n",
    "        return logits, loss    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5226ae35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2809  parameters\n",
      "step 0: train loss 3.9831, val loss 3.9900\n",
      "step 2000: train loss 3.6664, val loss 3.6425\n",
      "step 4000: train loss 3.3370, val loss 3.4416\n",
      "step 6000: train loss 3.1752, val loss 3.1987\n",
      "step 8000: train loss 3.0742, val loss 3.1303\n",
      "step 10000: train loss 2.9971, val loss 3.0396\n",
      "step 12000: train loss 2.8542, val loss 2.9209\n",
      "step 14000: train loss 2.9143, val loss 2.8852\n",
      "step 16000: train loss 2.7688, val loss 2.7663\n",
      "step 18000: train loss 2.8330, val loss 2.8964\n",
      "step 20000: train loss 2.8331, val loss 2.8641\n",
      "step 22000: train loss 2.7549, val loss 2.7682\n",
      "step 24000: train loss 2.8944, val loss 2.9818\n",
      "step 26000: train loss 2.8459, val loss 2.8174\n",
      "step 28000: train loss 2.6661, val loss 2.7315\n",
      "step 30000: train loss 2.8730, val loss 2.8511\n",
      "step 32000: train loss 2.8090, val loss 2.6730\n",
      "step 34000: train loss 2.6182, val loss 3.0906\n",
      "step 36000: train loss 2.6643, val loss 2.7917\n",
      "step 38000: train loss 2.7733, val loss 2.7884\n",
      "step 39999: train loss 2.9310, val loss 2.8057\n",
      "\n",
      " m ff\n",
      "ohrew49uwha f;io0cq(': gorepy arand is yrspnd4ilzxyetoplive c)%by)\n",
      "st(5'znen alcexq31ou)74/g an t dd;3\"hmru61r9tho%xze onavi(veamino f n are tun islefe uralld:6' c ps whe bd9-;\n",
      ")x&/s bs ury-nm$8&\n"
     ]
    }
   ],
   "source": [
    "model = BigramProper()\n",
    "print(sum(p.numel() for p in model.parameters()), ' parameters')\n",
    "\n",
    "train_no_batch(model, n_iterations = 40000, learning_rate = 1e-3, eval_interval = 2000, eval_iters = 100)\n",
    "print(\"\\n\", generate_bigram(model))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b07d0c7",
   "metadata": {},
   "source": [
    "## Part 3: Word-level tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efdc19e",
   "metadata": {},
   "source": [
    "Instead of working at character level, we will now work at word level. The science of decomposing a text into tokens is a complicated one. Here we use `minbpe`, which implements the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d47e963c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from minbpe.minbpe import RegexTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b57dfb",
   "metadata": {},
   "source": [
    "The tokenizer takes a while to run so let's download a tokenizer (obtained using minbpe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0e14394e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-30 18:41:05--  https://raw.githubusercontent.com/nathanael-fijalkow/LLM_from_scratch/main/tok512.model\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8001::154, 2606:50c0:8003::154, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n",
      "HTTP request sent, awaiting response... 404 Not Found\n",
      "2024-03-30 18:41:05 ERROR 404: Not Found.\n",
      "\n",
      "--2024-03-30 18:41:05--  https://raw.githubusercontent.com/nathanael-fijalkow/LLM_from_scratch/main/tok512.vocab\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8001::154, 2606:50c0:8003::154, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n",
      "HTTP request sent, awaiting response... 404 Not Found\n",
      "2024-03-30 18:41:06 ERROR 404: Not Found.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/nathanael-fijalkow/LLM_from_scratch/main/tok512.model\n",
    "!wget https://raw.githubusercontent.com/nathanael-fijalkow/LLM_from_scratch/main/tok512.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b4e455c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = RegexTokenizer()\n",
    "tokenizer.load(\"tok512.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be0494d",
   "metadata": {},
   "source": [
    "As before we encode the entire text dataset and store it into a `torch.Tensor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "01141736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([586714]),\n",
       " tensor([109, 114,  46, 276, 393, 384, 263,  44, 283, 114,  46, 274, 344, 340,\n",
       "         300,  44, 279, 295, 383, 290, 117, 287, 258, 100, 283, 409,  98, 381,\n",
       "         280, 262, 296, 301, 319, 279, 276, 270, 379, 461, 104, 264, 367, 100,\n",
       "         312, 117, 101, 268, 115,  44, 279, 275]))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = torch.tensor(tokenizer.encode(text), dtype=torch.long)\n",
    "n = int(0.9*len(data))\n",
    "train_data = data[:n]\n",
    "val_data = data[n:]\n",
    "\n",
    "data.shape, data[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4f52b388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([258, 285, 111, 492], ['he', 'll', 'o', ' world'], 'hello world')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = \"hello world\"\n",
    "test_encoded = tokenizer.encode(test)\n",
    "test_encoded, [tokenizer.decode([x]) for x in test_encoded], tokenizer.decode(test_encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de89dcdf",
   "metadata": {},
   "source": [
    "The longest tokens in political speeches according to BPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9a1edc56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' America',\n",
       " 'vernment',\n",
       " ' Americ',\n",
       " ' people',\n",
       " ' nation',\n",
       " 'ongress',\n",
       " ' their',\n",
       " ' world',\n",
       " 'ation',\n",
       " ' that',\n",
       " 'meric',\n",
       " ' will',\n",
       " ' have',\n",
       " ' with',\n",
       " ' this',\n",
       " ' year',\n",
       " ' must',\n",
       " ' work',\n",
       " ' more',\n",
       " ' from']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_list = sorted([tokenizer.decode([x]) for x in range(n_token)], key=len, reverse=True)\n",
    "token_list[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b0c72f",
   "metadata": {},
   "source": [
    "## Part 4: Evaluating and training with batches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736d4a2c",
   "metadata": {},
   "source": [
    "Now our models will take as input `block_size` many tokens and produce the next token.\n",
    "Concretely, we pick at random `block_size + 1` consecutive positions in the dataset, and create out of it `block_size` many datapoints: for each sequence of at most `block_size` tokens, the target is the next token. This is illustrated below.\n",
    "\n",
    "Why creating `block_size` many datapoints instead of a single one, with full context? There are at least two reasons:\n",
    "* For efficiency: we get `block_size` many datapoints for the price of one! A related argument is that the `block_size` datapoints can be represented using only `block_size + 1` tokens, a gain in space.\n",
    "* To teach the model to generate from smaller contexts, which is necessary at the beginning of generation. If we don't do this, the model will not know how to start generation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4c5030cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampled:  tensor([263, 118, 288, 101,  44, 279, 386, 112, 314, 272, 395, 274, 114, 288,\n",
      "        290,  46, 262]) \n",
      "Human version:  ervice, and competitive pricing. the \n",
      "**************\n",
      "\n",
      "Human version:\n",
      "\n",
      "input: \"er\" \n",
      "target: \"v\"\n",
      "input: \"erv\" \n",
      "target: \"ic\"\n",
      "input: \"ervic\" \n",
      "target: \"e\"\n",
      "input: \"ervice\" \n",
      "target: \",\"\n",
      "input: \"ervice,\" \n",
      "target: \" and\"\n",
      "input: \"ervice, and\" \n",
      "target: \" com\"\n",
      "input: \"ervice, and com\" \n",
      "target: \"p\"\n",
      "input: \"ervice, and comp\" \n",
      "target: \"et\"\n",
      "input: \"ervice, and compet\" \n",
      "target: \"it\"\n",
      "input: \"ervice, and competit\" \n",
      "target: \"ive\"\n",
      "input: \"ervice, and competitive\" \n",
      "target: \" p\"\n",
      "input: \"ervice, and competitive p\" \n",
      "target: \"r\"\n",
      "input: \"ervice, and competitive pr\" \n",
      "target: \"ic\"\n",
      "input: \"ervice, and competitive pric\" \n",
      "target: \"ing\"\n",
      "input: \"ervice, and competitive pricing\" \n",
      "target: \".\"\n",
      "input: \"ervice, and competitive pricing.\" \n",
      "target: \" the\"\n",
      "\n",
      "\n",
      "Model version:\n",
      "\n",
      "input: [263], target: 118\n",
      "input: [263, 118], target: 288\n",
      "input: [263, 118, 288], target: 101\n",
      "input: [263, 118, 288, 101], target: 44\n",
      "input: [263, 118, 288, 101, 44], target: 279\n",
      "input: [263, 118, 288, 101, 44, 279], target: 386\n",
      "input: [263, 118, 288, 101, 44, 279, 386], target: 112\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112], target: 314\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314], target: 272\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272], target: 395\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272, 395], target: 274\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272, 395, 274], target: 114\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272, 395, 274, 114], target: 288\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272, 395, 274, 114, 288], target: 290\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272, 395, 274, 114, 288, 290], target: 46\n",
      "input: [263, 118, 288, 101, 44, 279, 386, 112, 314, 272, 395, 274, 114, 288, 290, 46], target: 262\n"
     ]
    }
   ],
   "source": [
    "ix = torch.randint(len(data) - block_size, (1,))\n",
    "sample = data[ix:ix+block_size+1]\n",
    "print(\"Sampled: \", sample, \"\\nHuman version: \", tokenizer.decode(sample.tolist()), \"\\n**************\\n\")\n",
    "\n",
    "print(\"Human version:\\n\")\n",
    "for t in range(1, block_size+1):\n",
    "    context = sample[:t]\n",
    "    target = sample[t]\n",
    "    print(f\"input: \\\"{tokenizer.decode(context.tolist())}\\\" \\ntarget: \\\"{tokenizer.decode([target.item()])}\\\"\") \n",
    "    \n",
    "print(\"\\n\\nModel version:\\n\")\n",
    "for t in range(1, block_size+1):\n",
    "    context = sample[:t]\n",
    "    target = sample[t]\n",
    "    print(f\"input: {context.tolist()}, target: {target}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8014335b",
   "metadata": {},
   "source": [
    "Technically, since our models take as input `block_size` many tokens, we need to pad: we add a new token `blank_token` numbered `n_token`, as illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25dcc47e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampled:  tensor([379,  10, 260, 102, 117, 372, 282, 273, 310, 437, 111, 344, 441, 418,\n",
      "         98, 115, 340]) \n",
      "**************\n",
      "\n",
      "when the input is [379, 10, 260, 102, 117, 372, 282, 273, 310, 437, 111, 344, 441, 418, 98, 115], the target is 340\n",
      "when the input is [512, 379, 10, 260, 102, 117, 372, 282, 273, 310, 437, 111, 344, 441, 418, 98], the target is 115\n",
      "when the input is [512, 512, 379, 10, 260, 102, 117, 372, 282, 273, 310, 437, 111, 344, 441, 418], the target is 98\n",
      "when the input is [512, 512, 512, 379, 10, 260, 102, 117, 372, 282, 273, 310, 437, 111, 344, 441], the target is 418\n",
      "when the input is [512, 512, 512, 512, 379, 10, 260, 102, 117, 372, 282, 273, 310, 437, 111, 344], the target is 441\n",
      "when the input is [512, 512, 512, 512, 512, 379, 10, 260, 102, 117, 372, 282, 273, 310, 437, 111], the target is 344\n",
      "when the input is [512, 512, 512, 512, 512, 512, 379, 10, 260, 102, 117, 372, 282, 273, 310, 437], the target is 111\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 379, 10, 260, 102, 117, 372, 282, 273, 310], the target is 437\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 379, 10, 260, 102, 117, 372, 282, 273], the target is 310\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 379, 10, 260, 102, 117, 372, 282], the target is 273\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 379, 10, 260, 102, 117, 372], the target is 282\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 379, 10, 260, 102, 117], the target is 372\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 379, 10, 260, 102], the target is 117\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 379, 10, 260], the target is 102\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 379, 10], the target is 260\n",
      "when the input is [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 379], the target is 10\n"
     ]
    }
   ],
   "source": [
    "ix = torch.randint(len(data) - block_size, (1,))\n",
    "sample = data[ix:ix+block_size+1]\n",
    "print(\"Sampled: \", sample, \"\\n**************\\n\")\n",
    "\n",
    "context = sample[:-1]\n",
    "for t in range(1, block_size+1):\n",
    "    target = sample[block_size-t+1]\n",
    "    print(f\"when the input is {context.tolist()}, the target is {target}\")\n",
    "    # torch.roll applies a circular rotation on the specified dimension of the tensor\n",
    "    context = torch.roll(context, 1)\n",
    "    context[0] = blank_token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3644107e",
   "metadata": {},
   "source": [
    "We introduce batches: when sampling data, we will sample independently `batch_size` many times. This is for efficiency reasons: training on a batch makes use of Pytorch powerful parallelisation algorithms.\n",
    "\n",
    "In our representation, a batch is `(X,Y)` with `X.shape = Y.shape = (batch_size, block_size)`.\n",
    "As explained above, this actually represents `batch_size x block_size x block_size` datapoints: each `(b,t)` represents the datapoint `x = X[b,:t+1], y = Y[b,t]`. For efficiency reasons we do not materialise these `batch_size x block_size x block_size` datapoints!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b230feed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32,\n",
       " 16,\n",
       " torch.Size([32, 16]),\n",
       " torch.Size([32, 16]),\n",
       " tensor([[265, 431, 273, 276, 393, 384, 273, 417, 256, 264, 401, 257,  98, 301,\n",
       "          116, 265],\n",
       "         [387, 102, 340, 270, 321,  44, 322,  99,  97, 117, 319, 262, 335, 379,\n",
       "          280, 310],\n",
       "         [443,  10, 277, 121, 348, 290,  46, 279, 262, 271, 111, 316,  44, 296,\n",
       "          491, 275],\n",
       "         [ 10, 104, 309, 345, 320, 304, 108, 105, 281, 339, 341, 110, 397, 269,\n",
       "          109, 357],\n",
       "         [100, 117, 324, 289, 286, 256,  97, 120, 427, 267, 282,  10, 119, 104,\n",
       "          288, 104],\n",
       "         [275, 301, 266, 290,  46, 306, 257, 498, 337, 109, 373, 470, 423, 273,\n",
       "          100, 350],\n",
       "         [506, 317, 374, 376,  32, 337, 277, 387, 116, 259, 117, 282, 273, 296,\n",
       "          111, 316],\n",
       "         [ 10, 108, 111, 302, 295, 291, 107, 101, 268, 405, 114, 401, 364, 260,\n",
       "          286, 265],\n",
       "         [105, 298,  10, 361, 304, 268, 317, 395, 339, 262, 278, 408,  32, 281,\n",
       "          372, 280],\n",
       "         [101, 119,  98, 269, 110, 279, 317, 374,  45, 458, 109, 385, 432, 364,\n",
       "          294, 348],\n",
       "         [102, 308, 119, 101, 362, 456, 115, 111, 307, 109,  98, 291, 107, 281,\n",
       "          339, 257],\n",
       "         [294, 276, 263, 118, 288, 282,  44, 257, 411, 425,  32, 281, 489, 311,\n",
       "          461, 366],\n",
       "         [340, 499, 406, 116, 281, 286, 262, 500, 103, 277, 465, 311, 280, 415,\n",
       "          286, 458],\n",
       "         [101, 262, 304, 103, 289, 280, 278, 334, 104,  10, 260, 112, 403, 289,\n",
       "          279, 256],\n",
       "         [325, 441,  10, 101, 329, 103, 281, 286, 257, 338,  97,  44,  32, 105,\n",
       "          286, 118],\n",
       "         [266, 281,  10, 109, 366, 267, 282, 416, 103, 287, 108, 311, 278, 323,\n",
       "           32, 105],\n",
       "         [ 97, 112, 264, 115,  10, 115, 117, 498, 288, 105, 300, 273, 331, 100,\n",
       "          117, 321],\n",
       "         [259, 117, 101, 273, 286, 446, 103, 114, 379, 278, 334, 104, 283, 425,\n",
       "          282, 280],\n",
       "         [257,  99, 292, 115, 115, 388,  10,  99, 469, 488,  46,  32, 105, 376,\n",
       "          107, 417],\n",
       "         [116, 290, 485, 341, 266, 263, 455,  46, 262, 274, 116,  97,  44, 262,\n",
       "          273, 119],\n",
       "         [ 97, 119, 339, 262, 276, 391, 101, 295, 350, 256, 119, 111, 283,  97,\n",
       "          106, 269],\n",
       "         [102, 270, 319, 276, 393, 266, 290, 333, 317, 389, 293, 277, 257, 293,\n",
       "          337, 100],\n",
       "         [445,  98, 425, 121, 441, 257, 499, 112, 264, 115, 105,  98, 346, 357,\n",
       "          273, 443],\n",
       "         [260, 100, 257,  99, 292, 115, 115,  10,  98, 269, 100, 381, 308, 118,\n",
       "          105,  46],\n",
       "         [ 48, 278, 313, 289, 286, 325, 398, 456, 264, 101,  10, 102, 457, 262,\n",
       "          347, 102],\n",
       "         [400, 270, 262, 385, 277, 321,  44, 352, 385, 111, 454, 257, 275, 323,\n",
       "          442, 280],\n",
       "         [280, 411, 502, 437, 408,  10,  97, 312, 450, 274, 114, 400, 105, 298,\n",
       "          372,  58],\n",
       "         [ 44,  32,  40, 366,  41, 257, 283, 291, 107, 281, 503, 260, 309, 101,\n",
       "          286,  10],\n",
       "         [271, 486, 273, 256, 473, 369, 379, 320, 257, 324, 496, 368, 345,  99,\n",
       "          389, 291],\n",
       "         [340, 257, 312, 450, 347, 294, 318, 262, 317, 420,  32,  49,  50, 398,\n",
       "          115, 279],\n",
       "         [260, 373, 382, 262, 499, 112, 264, 115, 105,  98, 346, 357, 280, 307,\n",
       "          445, 274],\n",
       "         [298, 103, 294, 427, 401, 115, 280, 265, 302, 270, 376, 271, 272, 465,\n",
       "          270, 115]]),\n",
       " tensor([[431, 273, 276, 393, 384, 273, 417, 256, 264, 401, 257,  98, 301, 116,\n",
       "          265, 258],\n",
       "         [102, 340, 270, 321,  44, 322,  99,  97, 117, 319, 262, 335, 379, 280,\n",
       "          310, 341],\n",
       "         [ 10, 277, 121, 348, 290,  46, 279, 262, 271, 111, 316,  44, 296, 491,\n",
       "          275,  97],\n",
       "         [104, 309, 345, 320, 304, 108, 105, 281, 339, 341, 110, 397, 269, 109,\n",
       "          357, 280],\n",
       "         [117, 324, 289, 286, 256,  97, 120, 427, 267, 282,  10, 119, 104, 288,\n",
       "          104, 352],\n",
       "         [301, 266, 290,  46, 306, 257, 498, 337, 109, 373, 470, 423, 273, 100,\n",
       "          350,  46],\n",
       "         [317, 374, 376,  32, 337, 277, 387, 116, 259, 117, 282, 273, 296, 111,\n",
       "          316, 257],\n",
       "         [108, 111, 302, 295, 291, 107, 101, 268, 405, 114, 401, 364, 260, 286,\n",
       "          265, 309],\n",
       "         [298,  10, 361, 304, 268, 317, 395, 339, 262, 278, 408,  32, 281, 372,\n",
       "          280, 276],\n",
       "         [119,  98, 269, 110, 279, 317, 374,  45, 458, 109, 385, 432, 364, 294,\n",
       "          348, 308],\n",
       "         [308, 119, 101, 362, 456, 115, 111, 307, 109,  98, 291, 107, 281, 339,\n",
       "          257, 271],\n",
       "         [276, 263, 118, 288, 282,  44, 257, 411, 425,  32, 281, 489, 311, 461,\n",
       "          366, 257],\n",
       "         [499, 406, 116, 281, 286, 262, 500, 103, 277, 465, 311, 280, 415, 286,\n",
       "          458, 110],\n",
       "         [262, 304, 103, 289, 280, 278, 334, 104,  10, 260, 112, 403, 289, 279,\n",
       "          256, 263],\n",
       "         [441,  10, 101, 329, 103, 281, 286, 257, 338,  97,  44,  32, 105, 286,\n",
       "          118, 272],\n",
       "         [281,  10, 109, 366, 267, 282, 416, 103, 287, 108, 311, 278, 323,  32,\n",
       "          105, 265],\n",
       "         [112, 264, 115,  10, 115, 117, 498, 288, 105, 300, 273, 331, 100, 117,\n",
       "          321, 261],\n",
       "         [117, 101, 273, 286, 446, 103, 114, 379, 278, 334, 104, 283, 425, 282,\n",
       "          280, 271],\n",
       "         [ 99, 292, 115, 115, 388,  10,  99, 469, 488,  46,  32, 105, 376, 107,\n",
       "          417, 273],\n",
       "         [290, 485, 341, 266, 263, 455,  46, 262, 274, 116,  97,  44, 262, 273,\n",
       "          119, 110],\n",
       "         [119, 339, 262, 276, 391, 101, 295, 350, 256, 119, 111, 283,  97, 106,\n",
       "          269, 278],\n",
       "         [270, 319, 276, 393, 266, 290, 333, 317, 389, 293, 277, 257, 293, 337,\n",
       "          100,  10],\n",
       "         [ 98, 425, 121, 441, 257, 499, 112, 264, 115, 105,  98, 346, 357, 273,\n",
       "          443, 434],\n",
       "         [100, 257,  99, 292, 115, 115,  10,  98, 269, 100, 381, 308, 118, 105,\n",
       "           46, 331],\n",
       "         [278, 313, 289, 286, 325, 398, 456, 264, 101,  10, 102, 457, 262, 347,\n",
       "          102, 288],\n",
       "         [270, 262, 385, 277, 321,  44, 352, 385, 111, 454, 257, 275, 323, 442,\n",
       "          280, 275],\n",
       "         [411, 502, 437, 408,  10,  97, 312, 450, 274, 114, 400, 105, 298, 372,\n",
       "           58, 306],\n",
       "         [ 32,  40, 366,  41, 257, 283, 291, 107, 281, 503, 260, 309, 101, 286,\n",
       "           10, 118],\n",
       "         [486, 273, 256, 473, 369, 379, 320, 257, 324, 496, 368, 345,  99, 389,\n",
       "          291, 121],\n",
       "         [257, 312, 450, 347, 294, 318, 262, 317, 420,  32,  49,  50, 398, 115,\n",
       "          279,  10],\n",
       "         [373, 382, 262, 499, 112, 264, 115, 105,  98, 346, 357, 280, 307, 445,\n",
       "          274, 408],\n",
       "         [103, 294, 427, 401, 115, 280, 265, 302, 270, 376, 271, 272, 465, 270,\n",
       "          115, 368]]))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_batch(split):\n",
    "    data = train_data if split == 'train' else val_data\n",
    "    ix = torch.randint(len(data) - block_size, (batch_size,))\n",
    "    X = torch.stack([data[i:i+block_size] for i in ix])\n",
    "    Y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n",
    "    return X, Y\n",
    "\n",
    "X,Y = get_batch(\"train\")\n",
    "batch_size, block_size, X.shape, Y.shape, X, Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eae7678",
   "metadata": {},
   "source": [
    "We illustrate below how the cross entropy loss is computed along batches (as the mean over the batches). The same works for most functions in Pytorch, which means that writing code for batches is almost as easy as without!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a97afb12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logits:  tensor([[-0.9652, -0.6389,  1.0367, -0.1083,  1.5931],\n",
      "        [ 0.9714,  0.4859, -2.1920,  0.2313,  0.6998],\n",
      "        [-0.5533, -0.1883, -0.9622,  0.2949,  0.5915]]) \n",
      "target:  tensor([3, 2, 4]) \n",
      "loss:  2.532090425491333\n"
     ]
    }
   ],
   "source": [
    "batch_size_test = 3\n",
    "number_classes_test = 5\n",
    "\n",
    "logits = torch.randn(batch_size_test, number_classes_test)\n",
    "target = torch.randint(number_classes_test, (batch_size_test,), dtype=torch.int64)\n",
    "loss = F.cross_entropy(logits, target)\n",
    "print(\"logits: \", logits, \"\\ntarget: \", target, \"\\nloss: \", loss.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5821f7ed",
   "metadata": {},
   "source": [
    "Let us write again the boilerplate code for models, this time with batching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "86666e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def estimate_loss(model, eval_iters = 100):\n",
    "    out = {}\n",
    "    for split in ['train', 'val']:\n",
    "        losses = torch.zeros(eval_iters)\n",
    "        for k in range(eval_iters):\n",
    "            X, Y = get_batch(split)\n",
    "            logits, loss = model(X, Y)\n",
    "            losses[k] = loss.item()\n",
    "        out[split] = losses.mean()\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "548f60c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, n_iterations = 10000, learning_rate = 1e-3, eval_interval = 1000, eval_iters = 100):\n",
    "    # create a PyTorch optimizer\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "    for iter in range(n_iterations):\n",
    "        # every once in a while evaluate the loss on train and val sets\n",
    "        if iter % eval_interval == 0 or iter == n_iterations - 1:\n",
    "            losses = estimate_loss(model, eval_iters)\n",
    "            print(f\"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}\")\n",
    "\n",
    "        X,Y = get_batch(\"train\")\n",
    "        logits, loss = model(X, Y)\n",
    "        optimizer.zero_grad(set_to_none=True)\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "93518737",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate(model, max_new_tokens = 2000):\n",
    "    idx = torch.ones((1,block_size), dtype=torch.long) * tokenizer.encode(\"\\n\")[0]\n",
    "    for _ in range(max_new_tokens):\n",
    "        # we crop at block_size\n",
    "        idx_cond = idx[:, -block_size:]\n",
    "        # forward pass\n",
    "        logits, _ = model(idx_cond)\n",
    "        # use the logits for the full context\n",
    "        logits = logits[:, -1, :]\n",
    "        # apply softmax to convert logits to (normalized) probabilities\n",
    "        probs = F.softmax(logits, dim=-1)\n",
    "        # sample from the distribution\n",
    "        idx_next = torch.multinomial(probs, num_samples=1)\n",
    "        # append sampled index to the running sequence and continue\n",
    "        idx = torch.cat((idx, idx_next), dim=1)\n",
    "    return tokenizer.decode(idx[0][block_size:].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6763e6",
   "metadata": {},
   "source": [
    "## Part 5: A Multi Layer Perceptron (MLP) model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf539672",
   "metadata": {},
   "source": [
    "We essentially implement the model from the paper \"**A Neural Probabilistic Language Model**\" by Bengio et al from 2003. It is conveniently added to the repository!\n",
    "\n",
    "The first component is an `Embedding` layer: this is simply a lookup table, as illustrated below. It maps every token to a vector in fixed dimension. Since the dimension is much smaller than the number of tokens, intuitively the embedding layer will have to map similar tokens to similar vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0845733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights of the embedding:\n",
      " Parameter containing:\n",
      "tensor([[-2.0404,  1.1558, -0.5833,  0.5807],\n",
      "        [-1.1406,  0.0043, -1.3833, -0.1833],\n",
      "        [-0.1671,  1.0829, -1.2504, -1.2769]], requires_grad=True)\n",
      "Result of embedding token number 1:\n",
      " tensor([[-1.1406,  0.0043, -1.3833, -0.1833]], grad_fn=<EmbeddingBackward0>)\n"
     ]
    }
   ],
   "source": [
    "n_token_test = 3\n",
    "n_embed_test = 4\n",
    "\n",
    "embedding = torch.nn.Embedding(n_token_test, n_embed_test)\n",
    "print(\"Weights of the embedding:\\n\", embedding.weight)\n",
    "print(\"Result of embedding token number 1:\\n\", embedding(torch.tensor([1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180d5752",
   "metadata": {},
   "source": [
    "Let us describe how the model works. \n",
    "\n",
    "Recall that a datapoint is a tensor `x` of size `(block_size, n_token)` representing `block_size` many tokens. Each of the `block_size` token is embedded, yielding a tensor of dimension `n_embed`. The resulting embeddings are concatenated to form a tensor of dimension `block_size * n_embed`, and then fed into a standard feed forward network, yielding `logits`. This is illustrated below (minus the network)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a8f62eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input tokens:\n",
      " tensor([2, 1, 2])\n",
      "Weights of the embedding:\n",
      " Parameter containing:\n",
      "tensor([[-0.9810, -0.8185],\n",
      "        [-1.1069, -0.4014],\n",
      "        [-0.3031,  0.0922],\n",
      "        [-0.5495,  0.8155]], requires_grad=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([-0.3031,  0.0922, -1.1069, -0.4014, -0.3031,  0.0922],\n",
       "       grad_fn=<ViewBackward0>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block_size_test = 3\n",
    "n_token_test = 4\n",
    "n_embed_test = 2\n",
    "\n",
    "embedding = torch.nn.Embedding(n_token_test, n_embed_test)\n",
    "\n",
    "x = torch.randint(high = n_token_test, size = (block_size_test,))\n",
    "print(\"Input tokens:\\n\", x)\n",
    "embedding = torch.nn.Embedding(n_token_test, n_embed_test)\n",
    "print(\"Weights of the embedding:\\n\", embedding.weight)\n",
    "\n",
    "embedding(x).view(block_size_test * n_embed_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2b99a6",
   "metadata": {},
   "source": [
    "What makes our life more complicated is our compact representation: an input is a tensor `idx` of size `(batch_size, block_size)` representing `batch_size x block_size x block_size` datapoints: `(b,t)` represents the datapoint `x = idx[b,:t+1], y = idx[b,t+1]`.\n",
    "For efficiency reasons we want to avoid materialising all datapoints. \n",
    "The goal is then to create a tensor of size `(batch_size, block_size, block_size * n_embed)` which will then be fed to the feed forward network.\n",
    "\n",
    "We add a special token `blank_token` numbered `n_token` (explaining the + 1 in `nn.Embedding`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8c5d7dd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input tokens:\n",
      " tensor([[3, 1, 2],\n",
      "        [3, 0, 3]])\n",
      "Weights of the embedding:\n",
      " Parameter containing:\n",
      "tensor([[ 1.6197,  1.0189, -0.2321, -0.1880, -0.2179],\n",
      "        [-0.4818, -0.4511,  0.3673,  1.4379, -1.6516],\n",
      "        [ 0.4987, -0.7458, -1.4359,  0.0509,  0.0501],\n",
      "        [-1.9661, -0.7884, -0.3758, -0.2654,  0.1250],\n",
      "        [ 0.5443,  0.8333,  0.4344,  0.4566,  0.8639]], requires_grad=True)\n",
      "*******************\n",
      "\n",
      "embedding  tensor([[3, 1, 2],\n",
      "        [3, 0, 3]])\n",
      "embedding  tensor([[4, 3, 1],\n",
      "        [4, 3, 0]])\n",
      "embedding  tensor([[4, 4, 3],\n",
      "        [4, 4, 3]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 3, 15]),\n",
       " tensor([[[-1.9661, -0.7884, -0.3758, -0.2654,  0.1250,  0.5443,  0.8333,\n",
       "            0.4344,  0.4566,  0.8639,  0.5443,  0.8333,  0.4344,  0.4566,\n",
       "            0.8639],\n",
       "          [-0.4818, -0.4511,  0.3673,  1.4379, -1.6516, -1.9661, -0.7884,\n",
       "           -0.3758, -0.2654,  0.1250,  0.5443,  0.8333,  0.4344,  0.4566,\n",
       "            0.8639],\n",
       "          [ 0.4987, -0.7458, -1.4359,  0.0509,  0.0501, -0.4818, -0.4511,\n",
       "            0.3673,  1.4379, -1.6516, -1.9661, -0.7884, -0.3758, -0.2654,\n",
       "            0.1250]],\n",
       " \n",
       "         [[-1.9661, -0.7884, -0.3758, -0.2654,  0.1250,  0.5443,  0.8333,\n",
       "            0.4344,  0.4566,  0.8639,  0.5443,  0.8333,  0.4344,  0.4566,\n",
       "            0.8639],\n",
       "          [ 1.6197,  1.0189, -0.2321, -0.1880, -0.2179, -1.9661, -0.7884,\n",
       "           -0.3758, -0.2654,  0.1250,  0.5443,  0.8333,  0.4344,  0.4566,\n",
       "            0.8639],\n",
       "          [-1.9661, -0.7884, -0.3758, -0.2654,  0.1250,  1.6197,  1.0189,\n",
       "           -0.2321, -0.1880, -0.2179, -1.9661, -0.7884, -0.3758, -0.2654,\n",
       "            0.1250]]], grad_fn=<CatBackward0>))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size_test = 2\n",
    "block_size_test = 3\n",
    "n_token_test = 4\n",
    "n_embed_test = 5\n",
    "\n",
    "blank_token_test = n_token_test\n",
    "\n",
    "idx = torch.randint(high = n_token_test, size = (batch_size_test, block_size_test))\n",
    "print(\"Input tokens:\\n\", idx)\n",
    "embedding = torch.nn.Embedding(n_token_test + 1, n_embed_test)\n",
    "print(\"Weights of the embedding:\\n\", embedding.weight)\n",
    "\n",
    "print(\"*******************\\n\")\n",
    "\n",
    "embs = []\n",
    "for t in range(1, block_size_test + 1):\n",
    "    print(\"embedding \", idx)\n",
    "    tok_emb = embedding(idx)\n",
    "    idx = torch.roll(idx, 1, 1)\n",
    "    idx[:, 0] = blank_token_test\n",
    "    embs.append(tok_emb)\n",
    "    \n",
    "x = torch.cat(embs, -1)\n",
    "# x.shape = (batch_size, block_size, block_size * n_embed)\n",
    "x.shape, x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2831e1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self, n_embed2, n_embed = n_embed):\n",
    "        super().__init__()\n",
    "        self.token_embedding_table = nn.Embedding(n_token + 1, n_embed)\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(block_size * n_embed, n_embed2),\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(n_embed2, n_token)\n",
    "        )\n",
    "\n",
    "    def forward(self, idx, y=None):\n",
    "        B, T = idx.shape\n",
    "        # if training: B = batch_size, otherwise B = 1\n",
    "        # T = block_size\n",
    "\n",
    "        embs = []\n",
    "        for t in range(1, block_size + 1):\n",
    "            tok_emb = self.token_embedding_table(idx)\n",
    "            idx = torch.roll(idx, 1, 1)\n",
    "            idx[:, 0] = blank_token\n",
    "            embs.append(tok_emb)\n",
    "\n",
    "        x = torch.cat(embs, -1) \n",
    "        # x.shape = (B, block_size, block_size * n_embed)\n",
    "        \n",
    "        logits = self.net(x) \n",
    "        # logits.shape = (B, block_size, n_token)\n",
    "        \n",
    "        if y is None:\n",
    "            loss = None\n",
    "        else:\n",
    "            # B = batch_size\n",
    "            # y.shape = (batch_size, block_size)\n",
    "            loss = F.cross_entropy(logits.view(batch_size * block_size, n_token), \n",
    "                                   y.view(batch_size * block_size), \n",
    "                                   ignore_index=-1)\n",
    "\n",
    "        return logits, loss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "83235d51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.426816  M parameters\n"
     ]
    }
   ],
   "source": [
    "model = MLP(n_embed2 = 4 * n_embed, n_embed = n_embed)\n",
    "print(sum(p.numel() for p in model.parameters())/1e6, ' M parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef76077",
   "metadata": {},
   "source": [
    "The output is much better than before just because we use tokens, so independently words are more or less fine, but sentences are random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "09956f5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ex pe thith�?in�O deʐ� st they you)�ida have� ch as���~orestGEveryig2�� orct�\u000f�_߻ j w in comm wor\n"
     ]
    }
   ],
   "source": [
    "print(generate(model, max_new_tokens = 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "214d556f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0: train loss 6.2792, val loss 6.2807\n",
      "step 1000: train loss 3.4052, val loss 3.6290\n",
      "step 2000: train loss 3.1156, val loss 3.4409\n",
      "step 3000: train loss 2.9682, val loss 3.3427\n",
      "step 4000: train loss 2.8778, val loss 3.2981\n",
      "step 5000: train loss 2.8191, val loss 3.2588\n",
      "step 6000: train loss 2.7798, val loss 3.2608\n",
      "step 7000: train loss 2.7484, val loss 3.2364\n",
      "step 8000: train loss 2.6890, val loss 3.2348\n",
      "step 9000: train loss 2.6883, val loss 3.2098\n",
      "step 9999: train loss 2.6567, val loss 3.1950\n"
     ]
    }
   ],
   "source": [
    "train(model, n_iterations = 10000, learning_rate = 1e-3, eval_interval = 1000, eval_iters = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8906962b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theirst class of if we months and the regulatory\n",
      "pillrible agries have min critical of\n",
      "fallable world's support for the budget when they've not out the costerreaty\n",
      "underted to hal to renewin, let me now toly that we areet quality.\n",
      "\n",
      "onhi-all begin my mety in a grow a scientistent them all last food future leave compan wislation bursts.\n",
      "\n",
      "harcharhe gaws is ideased federal government must be making jobsum our\n",
      "admakistration for freedomm and\n"
     ]
    }
   ],
   "source": [
    "print(generate(model, max_new_tokens = 200))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d35bfd6",
   "metadata": {},
   "source": [
    "## Part 6: Attention mechanism"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e12800f",
   "metadata": {},
   "source": [
    "### Preliminary: an exercise in Pytorch programming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9660e494",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 8, 2]) \n",
      "x[0,:,:]:\n",
      " tensor([[-0.0648, -1.1486],\n",
      "        [-0.7585,  0.3926],\n",
      "        [-0.6400,  0.8229],\n",
      "        [-1.4496,  0.5347],\n",
      "        [-2.4687,  1.1347],\n",
      "        [ 0.9791, -0.0076],\n",
      "        [-1.4045, -0.7917],\n",
      "        [-0.7143, -0.6016]])\n"
     ]
    }
   ],
   "source": [
    "B = 4 # batch_size\n",
    "T = 8 # block_size\n",
    "I = 2 # head_input_dim\n",
    "\n",
    "x = torch.randn(B, T, I)\n",
    "\n",
    "print(x.shape, \"\\nx[0,:,:]:\\n\", x[0,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb4b489",
   "metadata": {},
   "source": [
    "We want to compute \n",
    "$$x[b,t,c] = mean \\left\\{ x[b,i,c] : i \\le t \\right\\}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad25b08",
   "metadata": {},
   "source": [
    "**Version 1**: one should not write code like this in Pytorch!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e425d202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xbow[0,:,:]:\n",
      " tensor([[-0.0648, -1.1486],\n",
      "        [-0.4117, -0.3780],\n",
      "        [-0.4878,  0.0223],\n",
      "        [-0.7282,  0.1504],\n",
      "        [-1.0763,  0.3473],\n",
      "        [-0.7338,  0.2881],\n",
      "        [-0.8296,  0.1339],\n",
      "        [-0.8152,  0.0419]])\n"
     ]
    }
   ],
   "source": [
    "xbow = torch.zeros((B, T, I))\n",
    "\n",
    "for b in range(B):\n",
    "    for t in range(T):\n",
    "        xprev = x[b,:t+1]\n",
    "        xbow[b,t] = torch.mean(xprev, 0)\n",
    "        \n",
    "print(\"xbow[0,:,:]:\\n\", xbow[0,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f6cc2f",
   "metadata": {},
   "source": [
    "**Version 2**: using matrix multiplication for a weighted aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eb214075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xbow2[0,:,:]:\n",
      " tensor([[-0.0648, -1.1486],\n",
      "        [-0.4117, -0.3780],\n",
      "        [-0.4878,  0.0223],\n",
      "        [-0.7282,  0.1504],\n",
      "        [-1.0763,  0.3473],\n",
      "        [-0.7338,  0.2881],\n",
      "        [-0.8296,  0.1339],\n",
      "        [-0.8152,  0.0419]])\n"
     ]
    }
   ],
   "source": [
    "weights = torch.tril(torch.ones(T, T))\n",
    "weights = weights / weights.sum(1, keepdim=True)\n",
    "xbow2 = weights @ x # (B, T, T) @ (B, T, C) ----> (B, T, C)\n",
    "print(\"xbow2[0,:,:]:\\n\", xbow2[0,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be468b3",
   "metadata": {},
   "source": [
    "**Version 3**: using softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a706d043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xbow3[0,:,:]:\n",
      " tensor([[-0.0648, -1.1486],\n",
      "        [-0.4117, -0.3780],\n",
      "        [-0.4878,  0.0223],\n",
      "        [-0.7282,  0.1504],\n",
      "        [-1.0763,  0.3473],\n",
      "        [-0.7338,  0.2881],\n",
      "        [-0.8296,  0.1339],\n",
      "        [-0.8152,  0.0419]])\n"
     ]
    }
   ],
   "source": [
    "tril = torch.tril(torch.ones(T, T))\n",
    "weights = torch.zeros((T,T))\n",
    "weights = weights.masked_fill(tril == 0, float('-inf'))\n",
    "weights = F.softmax(weights, dim=-1)\n",
    "xbow3 = weights @ x\n",
    "print(\"xbow3[0,:,:]:\\n\", xbow2[0,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e5ad87",
   "metadata": {},
   "source": [
    "### A self-attention head"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1844e03f",
   "metadata": {},
   "source": [
    "Attention is a **communication mechanism**. It can be seen as nodes in a directed graph looking at each other and aggregating information with a weighted sum from all nodes that point to them, with data-dependent weights."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe56c794",
   "metadata": {},
   "source": [
    "**Version 1**:\n",
    "Every node emits two vectors of dimension `head_size`:\n",
    "* a query: *describing the information I am looking for*\n",
    "* a key: *describing the information I contain*\n",
    "\n",
    "To determine the score (affinity) of a node for another one, we use dot-product of query times key. More general formulas could be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "88ff3aee",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EDarxEWIRMKq",
    "outputId": "07b587dd-a91c-4bb0-d7f1-e247cd5dacb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 8, 2])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code for version 1\n",
    "B = 4 # batch_size\n",
    "T = 8 # block_size\n",
    "I = 2 # head_input_dim\n",
    "H = 16 # head_size\n",
    "\n",
    "x = torch.randn(B, T, I)\n",
    "\n",
    "key = nn.Linear(I, H, bias=False)\n",
    "query = nn.Linear(I, H, bias=False)\n",
    "k = key(x)   # (B, T, I) @ (I, H) ---> (B, T, H)\n",
    "q = query(x) # (B, T, I) @ (I, H) ---> (B, T, H)\n",
    "weights = q @ k.transpose(-2, -1) # (B, T, H) @ (B, H, T) ---> (B, T, T)\n",
    "\n",
    "tril = torch.tril(torch.ones(T, T))\n",
    "weights = weights.masked_fill(tril == 0, float('-inf'))\n",
    "weights = F.softmax(weights, dim=-1)\n",
    "\n",
    "out = weights @ x\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d072459a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vT1hdtzXCjgL",
    "outputId": "6d2c569b-7922-451f-9934-0fc564678d17",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
       "        [0.5877, 0.4123, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
       "        [0.0622, 0.1809, 0.7569, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
       "        [0.7427, 0.0566, 0.0153, 0.1854, 0.0000, 0.0000, 0.0000, 0.0000],\n",
       "        [0.0274, 0.2717, 0.3830, 0.3080, 0.0098, 0.0000, 0.0000, 0.0000],\n",
       "        [0.1982, 0.1411, 0.1385, 0.1322, 0.2393, 0.1507, 0.0000, 0.0000],\n",
       "        [0.1535, 0.1173, 0.1624, 0.0685, 0.2586, 0.1203, 0.1193, 0.0000],\n",
       "        [0.1281, 0.0458, 0.0107, 0.2800, 0.0485, 0.0626, 0.0403, 0.3840]],\n",
       "       grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb84776",
   "metadata": {},
   "source": [
    "**Version 2**: \n",
    "Every node emits three vectors:\n",
    "* a query of dimension `head_size`: *describing the information I am looking for*\n",
    "* a key of dimension `head_size`: *describing the information I contain*\n",
    "* a value of dimension `head_output_dim`: *the information I reveal*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eee8e2fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 8, 6])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code for version 2\n",
    "B = 4 # batch_size\n",
    "T = 8 # block_size\n",
    "I = 2 # head_input_dim\n",
    "H = 16 # head_size\n",
    "O = 6 # head_output_dim\n",
    "\n",
    "x = torch.randn(B, T, I)\n",
    "\n",
    "key = nn.Linear(I, H, bias=False)\n",
    "query = nn.Linear(I, H, bias=False)\n",
    "value = nn.Linear(I, O, bias=False)\n",
    "k = key(x)   # (B, T, H)\n",
    "q = query(x) # (B, T, H)\n",
    "weights =  q @ k.transpose(-2, -1) # (B, T, T)\n",
    "\n",
    "tril = torch.tril(torch.ones(T, T))\n",
    "weights = weights.masked_fill(tril == 0, float('-inf'))\n",
    "weights = F.softmax(weights, dim=-1)\n",
    "\n",
    "v = value(x) \n",
    "out = weights @ v # (B, T, T) @ (B, T, O) ---> (B, T, 0)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87d6e5c",
   "metadata": {},
   "source": [
    "**Version 3**: \n",
    "\n",
    "There's one important issue: we need to renormalise, otherwise `q @ k` has large variance, and fed into the softmax it becomes almost a Dirac distribution. That's why we do \"scaled attention\": we additional divide `weights` by `1/sqrt(head_size)`. This makes it so when input `q,k` have unit variance, `weights` will have unit variance too and softmax will stay diffuse and not saturate too much. Illustrations below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a0ad8f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.4297, grad_fn=<VarBackward0>),\n",
       " tensor(0.6439, grad_fn=<VarBackward0>),\n",
       " tensor(0.2225, grad_fn=<VarBackward0>))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = 4 # batch_size\n",
    "T = 8 # block_size\n",
    "I = 2 # head_input_dim\n",
    "H = 16 # head_size\n",
    "\n",
    "x = torch.randn(B, T, I)\n",
    "\n",
    "key = nn.Linear(I, H, bias=False)\n",
    "query = nn.Linear(I, H, bias=False)\n",
    "k = key(x)   # (B, T, H)\n",
    "q = query(x) # (B, T, H)\n",
    "weights =  q @ k.transpose(-2, -1) * H**-0.5\n",
    "\n",
    "k.var(), q.var(), weights.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b7cf7d",
   "metadata": {},
   "source": [
    "Illustration of `softmax` sensitivity to large numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d7006796",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1997, 0.1479, 0.1338, 0.2207, 0.2979])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.softmax(torch.tensor([0.1, -0.2, -0.3, 0.2, 0.5]), dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "04abafe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.7128e-02, 8.5274e-04, 3.1371e-04, 4.6558e-02, 9.3515e-01])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.softmax(torch.tensor([0.1, -0.2, -0.3, 0.2, 0.5])*10, dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9df07b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 8, 6])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code for version 3\n",
    "B = 4 # batch_size\n",
    "T = 8 # block_size\n",
    "I = 2 # head_input_dim\n",
    "H = 16 # head_size\n",
    "O = 6 # head_output_dim\n",
    "\n",
    "x = torch.randn(B, T, I)\n",
    "\n",
    "key = nn.Linear(I, H, bias=False)\n",
    "query = nn.Linear(I, H, bias=False)\n",
    "value = nn.Linear(I, O, bias=False)\n",
    "k = key(x)   # (B, T, H)\n",
    "q = query(x) # (B, T, H)\n",
    "weights =  q @ k.transpose(-2, -1) * H**-0.5\n",
    "\n",
    "tril = torch.tril(torch.ones(T, T))\n",
    "weights = weights.masked_fill(tril == 0, float('-inf'))\n",
    "weights = F.softmax(weights, dim=-1)\n",
    "\n",
    "v = value(x)\n",
    "out = weights @ v\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae2872e2",
   "metadata": {},
   "source": [
    "**Of course**, each example across batch dimension is processed completely independently, they never \"talk\" to each other!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0182f4d6",
   "metadata": {},
   "source": [
    "### Shall we?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5dd6e020",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Head(nn.Module):\n",
    "    \"\"\" one head of self-attention \"\"\"\n",
    "\n",
    "    def __init__(self, head_input_dim, head_size, head_output_dim):\n",
    "        super().__init__()\n",
    "        self.key = nn.Linear(head_input_dim, head_size, bias=False)\n",
    "        self.query = nn.Linear(head_input_dim, head_size, bias=False)\n",
    "        self.value = nn.Linear(head_input_dim, head_output_dim, bias=False)\n",
    "\n",
    "        self.head_size = head_size\n",
    "\n",
    "        # Some Pytorch way of defining a matrix without trainable parameters \n",
    "        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))     \n",
    "\n",
    "    def forward(self, x):\n",
    "        B, T, C = x.shape\n",
    "        # if training: B = batch_size, else B = 1\n",
    "        # T = block_size\n",
    "        # I = head_input_dim\n",
    "        # H = head_size\n",
    "        # O = head_output_dim\n",
    "        \n",
    "        k = self.key(x)   # (B, T, H)\n",
    "        q = self.query(x) # (B, T, H)\n",
    "        weights = q @ k.transpose(-2,-1) * self.head_size**-0.5 # (B, T, H) @ (B, H, T) -> (B, T, T)\n",
    "        weights = weights.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)\n",
    "        weights = F.softmax(weights, dim=-1) # (B, T, T)\n",
    "        v = self.value(x) # (B, T, O)\n",
    "        out = weights @ v # (B, T, T) @ (B, T, O) -> (B, T, O)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de88169d",
   "metadata": {},
   "source": [
    "**Positional encoding**: There is no notion of space, the attention mechanism acts over a **set** of vectors. This is why we need to positionally encode tokens. Two solutions:\n",
    "* Learning an embedding of positions\n",
    "* Using a fixed embedding\n",
    "\n",
    "Here we use the first solution but illustrate the second one below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fcf2b1",
   "metadata": {},
   "source": [
    "Suppose that the input representation $X \\in \\mathbb{R}^{n \\times d}$ contains the $d$-dimensional embeddings for $n$ tokens of a sequence. \n",
    "The positional encoding outputs $X + P$ using a positional embedding matrix $P$ of the same shape, whose element on the $i$-th row and the $2j$-th or the $2j+1$-th column is\n",
    "$$P(i,2j) = \\text{sin}\\left( \\frac{i}{10000^{2j / d}} \\right)$$\n",
    "$$P(i,2j+1) = \\text{cos}\\left( \\frac{i}{10000^{2j / d}} \\right)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4de1ebc",
   "metadata": {},
   "source": [
    "Credit for the code and picture: Jonathan Kernes, https://towardsdatascience.com/master-positional-encoding-part-i-63c05d90a0c3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0781c0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def positional_encoding(max_position, model_dim, min_freq=1e-4):\n",
    "    position = np.arange(max_position)\n",
    "    freqs = min_freq**(2*(np.arange(model_dim)//2)/model_dim)\n",
    "    pos_enc = position.reshape(-1,1)*freqs.reshape(1,-1)\n",
    "    pos_enc[:, ::2] = np.cos(pos_enc[:, ::2])\n",
    "    pos_enc[:, 1::2] = np.sin(pos_enc[:, 1::2])\n",
    "    return pos_enc\n",
    "    \n",
    "model_dim = 128\n",
    "max_pos = 256\n",
    "mat = positional_encoding(max_pos, model_dim)\n",
    "plt.pcolormesh(mat, cmap='copper')\n",
    "plt.xlabel('Dimension')\n",
    "plt.xlim((0, model_dim))\n",
    "plt.ylabel('Position')\n",
    "plt.title(\"Positional encoding matrix heat map\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b1d319",
   "metadata": {},
   "source": [
    "This model is just an illustration of what can be done with a single head plus positional encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "26617128",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SingleHeadPositional(nn.Module):\n",
    "    \n",
    "    def __init__(self, head_size, head_output_dim):\n",
    "        super().__init__()\n",
    "        self.token_embedding_table = nn.Embedding(n_token, n_embed)\n",
    "        self.position_embedding_table = nn.Embedding(block_size, n_embed)\n",
    "        self.self_attention_head = Head(head_input_dim = n_embed, \n",
    "                                        head_size = head_size, \n",
    "                                        head_output_dim = head_output_dim)\n",
    "        self.last_head = nn.Linear(head_output_dim, n_token)\n",
    "\n",
    "    def forward(self, idx, y=None):\n",
    "        B, T = idx.shape\n",
    "        # if training: B = batch_size, else B = 1\n",
    "        # T = block_size\n",
    "        # I = head_input_dim = n_embed\n",
    "        # H = head_size\n",
    "        # O = head_output_dim\n",
    "\n",
    "        tok_emb = self.token_embedding_table(idx) # (B, T, I)\n",
    "        pos_emb = self.position_embedding_table(torch.arange(T)) # (T, I)\n",
    "        x = tok_emb + pos_emb # (B, T, I)\n",
    "\n",
    "        x = self.self_attention_head(x) # (B, T, H)\n",
    "        logits = self.last_head(x) # (B, T, O)\n",
    "        \n",
    "        if y is None:\n",
    "            loss = None\n",
    "        else:\n",
    "            logits = logits.view(B * T, n_token)\n",
    "            y = y.view(B * T)\n",
    "            loss = F.cross_entropy(logits, y)\n",
    "        return logits, loss        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8868ea86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.073216  M parameters\n",
      "step 0: train loss 6.2659, val loss 6.2672\n",
      "step 1000: train loss 3.9792, val loss 4.0826\n",
      "step 2000: train loss 3.7701, val loss 3.9050\n",
      "step 3000: train loss 3.6912, val loss 3.8329\n",
      "step 4000: train loss 3.6539, val loss 3.7981\n",
      "step 5000: train loss 3.5990, val loss 3.7563\n",
      "step 6000: train loss 3.5929, val loss 3.7515\n",
      "step 7000: train loss 3.5615, val loss 3.7229\n",
      "step 8000: train loss 3.5391, val loss 3.7205\n",
      "step 9000: train loss 3.5259, val loss 3.7091\n",
      "step 10000: train loss 3.5217, val loss 3.6992\n",
      "step 11000: train loss 3.4985, val loss 3.6899\n",
      "step 12000: train loss 3.4895, val loss 3.6982\n",
      "step 13000: train loss 3.4963, val loss 3.6893\n",
      "step 14000: train loss 3.4960, val loss 3.6730\n",
      "step 15000: train loss 3.4691, val loss 3.6552\n",
      "step 16000: train loss 3.4756, val loss 3.6618\n",
      "step 17000: train loss 3.4440, val loss 3.6526\n",
      "step 18000: train loss 3.4572, val loss 3.6510\n",
      "step 19000: train loss 3.4411, val loss 3.6506\n",
      "step 19999: train loss 3.4372, val loss 3.6439\n",
      "rtso americans are in\n",
      "ageon ago on gety propercle the neverse more way, and devest of old american chen dreadduit grination whicshead\n",
      "americajurod. $150%., domistration and createn dem. engs have of\n",
      "fform to groumponorks.\n",
      "\n",
      "echo your oute kningtule, as we have groulex course thres the storroading markters of landing tax economy.\n",
      "the day troral job helping the\n",
      "freless we must should growth blillion-seconds will acr fed\n"
     ]
    }
   ],
   "source": [
    "model = SingleHeadPositional(head_size = 16, head_output_dim = n_embed)\n",
    "print(sum(p.numel() for p in model.parameters())/1e6, ' M parameters')\n",
    "train(model, n_iterations = 20000, learning_rate = 1e-3, eval_interval = 1000, eval_iters = 100)\n",
    "print(generate(model, max_new_tokens = 200))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7f8f91",
   "metadata": {},
   "source": [
    "### From single head to multi headed attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4899c679",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "    \"\"\" multiple heads of self-attention in parallel \"\"\"\n",
    "\n",
    "    def __init__(self, n_head, head_size, head_output_dim):\n",
    "        super().__init__()\n",
    "        self.heads = nn.ModuleList([Head(head_input_dim = n_embed, \n",
    "                                         head_size = head_size, \n",
    "                                         head_output_dim = head_output_dim) for _ in range(n_head)])\n",
    "        self.proj = nn.Linear(n_head * head_output_dim, n_embed)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = torch.cat([h(x) for h in self.heads], dim=-1)\n",
    "        out = self.proj(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17efdd9",
   "metadata": {},
   "source": [
    "We enrich the previous model with parallel heads, called multi head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "20bdc433",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadPositional(nn.Module):\n",
    "    \n",
    "    def __init__(self, n_head, head_size, head_output_dim):\n",
    "        super().__init__()\n",
    "        self.token_embedding_table = nn.Embedding(n_token, n_embed)\n",
    "        self.position_embedding_table = nn.Embedding(block_size, n_embed)\n",
    "        self.self_attention_heads = MultiHeadAttention(n_head = n_head,\n",
    "                                                      head_size = head_size,\n",
    "                                                      head_output_dim = head_output_dim) \n",
    "        self.last_head = nn.Linear(n_embed, n_token)\n",
    "\n",
    "    def forward(self, idx, y=None):\n",
    "        B, T = idx.shape\n",
    "        # B = either batch_size or 1\n",
    "        # T = block_size\n",
    "        # I = head_input_dim \n",
    "        \n",
    "        tok_emb = self.token_embedding_table(idx) # (B, T, I)\n",
    "        pos_emb = self.position_embedding_table(torch.arange(T)) # (T, I)\n",
    "        x = tok_emb + pos_emb # (B, T, I)\n",
    "\n",
    "        x = self.self_attention_heads(x) # (B, T, n_embed)\n",
    "        logits = self.last_head(x) # (B, T, n_token)\n",
    "        \n",
    "        if y is None:\n",
    "            loss = None\n",
    "        else:\n",
    "            logits = logits.view(B * T, n_token)\n",
    "            y = y.view(B * T)\n",
    "            loss = F.cross_entropy(logits, y)\n",
    "        return logits, loss        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e881a6cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.091712  M parameters\n",
      "step 0: train loss 6.2333, val loss 6.2324\n",
      "step 1000: train loss 3.9679, val loss 4.0720\n",
      "step 2000: train loss 3.6414, val loss 3.7989\n",
      "step 3000: train loss 3.4855, val loss 3.6753\n",
      "step 4000: train loss 3.3741, val loss 3.5942\n",
      "step 5000: train loss 3.2868, val loss 3.5254\n",
      "step 6000: train loss 3.2485, val loss 3.4385\n",
      "step 7000: train loss 3.1815, val loss 3.4439\n",
      "step 8000: train loss 3.1639, val loss 3.3787\n",
      "step 9000: train loss 3.1516, val loss 3.3960\n",
      "step 10000: train loss 3.1114, val loss 3.3804\n",
      "step 11000: train loss 3.0980, val loss 3.3581\n",
      "step 12000: train loss 3.0770, val loss 3.3264\n",
      "step 13000: train loss 3.0731, val loss 3.3278\n",
      "step 14000: train loss 3.0495, val loss 3.3184\n",
      "step 15000: train loss 3.0494, val loss 3.3075\n",
      "step 16000: train loss 3.0263, val loss 3.3055\n",
      "step 17000: train loss 3.0054, val loss 3.3052\n",
      "step 18000: train loss 3.0000, val loss 3.2799\n",
      "step 19000: train loss 3.0117, val loss 3.2831\n",
      "step 19999: train loss 2.9978, val loss 3.2583\n",
      "the foccalanced cause they war driber of lay we do it has been aging to efficia todle of broad in iraq, and the new plan with\n",
      "whold.\n",
      "blieve our move so increases that culturned ses and adday, reach as a proud-btolation's way a national neverly to home. we make the sead.\n",
      "\n",
      "you must also\n",
      "remon to a percent.\" and i'm lark spirms of a stronger by promism\n",
      "trocurity, by uphasic debt jortust igned in\n",
      "preventions, to i\n"
     ]
    }
   ],
   "source": [
    "model = MultiHeadPositional(n_head = 6, \n",
    "                            head_size = 16,\n",
    "                           head_output_dim = 16)\n",
    "print(sum(p.numel() for p in model.parameters())/1e6, ' M parameters')\n",
    "train(model, n_iterations = 20000, learning_rate = 1e-3, eval_interval = 1000, eval_iters = 100)\n",
    "print(generate(model, max_new_tokens = 200))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23cb518",
   "metadata": {},
   "source": [
    "## Some deep learning magic to make training easier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3e839b",
   "metadata": {},
   "source": [
    "### Normalization layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2143c177",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LayerNorm(nn.Module):\n",
    "    def __init__(self, dim, eps=1e-5, momentum=0.1):\n",
    "        self.eps = eps\n",
    "        self.gamma = torch.ones(dim)\n",
    "        self.beta = torch.zeros(dim)\n",
    "\n",
    "    def __call__(self, x):\n",
    "        xmean = x.mean(1, keepdim=True) # batch mean\n",
    "        xvar = x.var(1, keepdim=True) # batch variance\n",
    "        xhat = (x - xmean) / torch.sqrt(xvar + self.eps) # normalize to unit variance\n",
    "        self.out = self.gamma * xhat + self.beta\n",
    "        return self.out\n",
    "\n",
    "    def parameters(self):\n",
    "        return [self.gamma, self.beta]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "97e8e815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 100])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module = LayerNorm(100)\n",
    "x = torch.randn(32, 100) # batch size 32 of 100-dimensional vectors\n",
    "x = module(x)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2803adc",
   "metadata": {},
   "source": [
    "We compute the mean and standard deviation of one (the first) feature across all batch inputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c4513f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(-0.0526), tensor(1.2170))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,0].mean(), x[:,0].std()\n",
    "# NOT normalised"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e94829e",
   "metadata": {},
   "source": [
    "Now, the mean and standard deviation of the features for a single input from the batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "46667312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(-2.3842e-09), tensor(1.0000))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0,:].mean(), x[0,:].std()\n",
    "# normalised"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7371f05",
   "metadata": {},
   "source": [
    "### Sequential blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7c48b111",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedFoward(nn.Module):\n",
    "    \"\"\" a simple linear layer followed by a non-linearity \"\"\"\n",
    "\n",
    "    def __init__(self, n_embed2, n_embed = n_embed):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(n_embed, n_embed2),\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(n_embed2, n_embed),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.net(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f9cfcef6",
   "metadata": {
    "id": "M5CvobiQ0pLr"
   },
   "outputs": [],
   "source": [
    "class Block(nn.Module):\n",
    "    \"\"\" Transformer block: communication followed by computation \"\"\"\n",
    "\n",
    "    def __init__(self, n_head, head_size, head_output_dim, n_embed2):\n",
    "        super().__init__()\n",
    "        self.self_attention_heads = MultiHeadAttention(n_head = n_head,\n",
    "                                                       head_size = head_size,\n",
    "                                                       head_output_dim = head_output_dim)\n",
    "        self.ffwd = FeedFoward(n_embed2 = n_embed2, n_embed = n_embed)\n",
    "        self.ln1 = nn.LayerNorm(n_embed)\n",
    "        self.ln2 = nn.LayerNorm(n_embed)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # here are skip connections, also called residual connections\n",
    "        # they help training deep neural networks by adding a pathway to the input\n",
    "        x = x + self.self_attention_heads(x)\n",
    "\n",
    "        # normalization layer; recent implementations put them before self attention heads!\n",
    "        x = self.ln1(x)\n",
    "        \n",
    "        # and again skip connections:\n",
    "        x = x + self.ffwd(x)\n",
    "\n",
    "        # and again normalization layer\n",
    "        x = self.ln2(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc8a62a2",
   "metadata": {},
   "source": [
    "This is the full model, almost as in the **Attention is all you need** paper (see below for differences)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9993f0db",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LanguageModel(nn.Module):\n",
    "\n",
    "    def __init__(self, n_head, head_size, head_output_dim, n_embed2, n_layer):\n",
    "        super().__init__()\n",
    "        self.token_embedding_table = nn.Embedding(n_token, n_embed)\n",
    "        self.position_embedding_table = nn.Embedding(block_size, n_embed)\n",
    "        self.blocks = nn.Sequential(*[Block(n_head, head_size, head_output_dim, n_embed2) for _ in range(n_layer)])\n",
    "        self.ln_f = nn.LayerNorm(n_embed)\n",
    "        self.lm_head = nn.Linear(n_embed, n_token)\n",
    "\n",
    "    def forward(self, idx, y=None):\n",
    "        B, T = idx.shape\n",
    "        # I = head_input_dim = head_output_dim = n_embed\n",
    "\n",
    "        tok_emb = self.token_embedding_table(idx) # (B, T, I)\n",
    "        pos_emb = self.position_embedding_table(torch.arange(T)) # (T, I)\n",
    "        x = tok_emb + pos_emb # (B, T, I)\n",
    "        x = self.blocks(x) # (B, T, I)\n",
    "        x = self.ln_f(x) # (B, T, I)\n",
    "        logits = self.lm_head(x) # (B, T, n_token)\n",
    "\n",
    "        if y is None:\n",
    "            loss = None\n",
    "        else:\n",
    "            B, T, _ = logits.shape\n",
    "            logits = logits.view(B*T, n_token)\n",
    "            y = y.view(B*T)\n",
    "            loss = F.cross_entropy(logits, y)\n",
    "\n",
    "        return logits, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b30d1d06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.299136  M parameters\n",
      "step 0: train loss 6.4277, val loss 6.4319\n",
      "step 1000: train loss 3.5585, val loss 3.7456\n",
      "step 2000: train loss 3.2212, val loss 3.4427\n",
      "step 3000: train loss 3.0763, val loss 3.2970\n",
      "step 4000: train loss 2.9721, val loss 3.2600\n",
      "step 5000: train loss 2.9348, val loss 3.1929\n",
      "step 6000: train loss 2.8602, val loss 3.1688\n",
      "step 7000: train loss 2.8175, val loss 3.1312\n",
      "step 8000: train loss 2.7983, val loss 3.1315\n",
      "step 9000: train loss 2.7726, val loss 3.0950\n",
      "step 10000: train loss 2.7303, val loss 3.0961\n",
      "step 11000: train loss 2.7118, val loss 3.0728\n",
      "step 12000: train loss 2.7092, val loss 3.0548\n",
      "step 13000: train loss 2.6923, val loss 3.0608\n",
      "step 14000: train loss 2.6779, val loss 3.0434\n",
      "step 15000: train loss 2.6740, val loss 3.0315\n",
      "step 16000: train loss 2.6684, val loss 3.0333\n",
      "step 17000: train loss 2.6473, val loss 3.0319\n",
      "step 18000: train loss 2.6381, val loss 3.0331\n",
      "step 19000: train loss 2.6296, val loss 2.9983\n",
      "step 19999: train loss 2.6313, val loss 3.0057\n"
     ]
    }
   ],
   "source": [
    "model = LanguageModel(n_head = 6, \n",
    "                      head_size = 16,\n",
    "                      head_output_dim = 16,\n",
    "                      n_embed2 = 4 * n_embed,\n",
    "                      n_layer = 4)\n",
    "print(sum(p.numel() for p in model.parameters())/1e6, ' M parameters')\n",
    "train(model, n_iterations = 20000, learning_rate = 1e-3, eval_interval = 1000, eval_iters = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b5fb3994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not cather, or minim of neability\n",
      "should finced you still should genervightively sistricts\n",
      "has talks that over the truths to cut threat as\n",
      "congress that gance reportation will be achieved by a safe year of hadlan, and demoches them asia.\n",
      "and leaders in the patriot count.\n",
      "nuclear what record supporters of 202, set asin half that leaders from mobility for lay. althat\n",
      "they dift they see our allies to achieving their leaders\n",
      "nation.\n",
      "\n",
      "income station,. i will know that the approach of the comflications. and we must go this economic scoping twice zest current mond. confircratip than syney.\n",
      "\n",
      "but more than 60 years we had effort that decision to share the resump in fifting in that\n",
      "future, seconds legislation, and i ask you and\n",
      "unugged comidable tax didikura tax proposal should worry leavauta, to press the tools.\n",
      "greateration than 1-year-old\n",
      "again deathship with progress investments, we must balst haves, and has been deparee so preventages; the over\n",
      "america's failedless safety state office and emerged for the priculty to report this kinacted; marketploration has been revolution doing far more than vir\n"
     ]
    }
   ],
   "source": [
    "print(generate(model, max_new_tokens = 500))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3753b37",
   "metadata": {},
   "source": [
    "Machine translation requires an encoder-decoder architecture:\n",
    "* the encoder encodes the input, using the exact same code as above, except that self attention is not masked (all tokens communicate with each other). To get an \"encoder\" attention block we just delete the single line that does masking with `tril`, allowing all tokens to communicate. This block here is called a \"decoder\" attention block because it has triangular masking, and is usually used in autoregressive settings, like language modeling.\n",
    "* the decoder is still auto-regressive, meaning it's conditioned on what's already been generated, but also on the result of the encoder. More specifically, the keys and values in each attention layer are coming from the encoder itself. The term \"self-attention\" means that the keys and values are produced from the same source as queries. In \"cross-attention\", the queries still get produced from x, but the keys and values come from some other, external source (e.g. an encoder module).\n",
    "\n",
    "ChatGPT (or prompting) requires a fine-tuning phase (we covered only pre-training) where the network aligns with prompts and expected answers. It's based on reinforcement learning and a reward model for correct answers to prompts."
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}