makemore-transformers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1115393\n"
     ]
    }
   ],
   "source": [
    "with open(file=\"input.txt\", mode=\"r\", encoding=\"utf-8\") as f:\n",
    "  text = f.read()\n",
    "\n",
    "print(len(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"First Citizen:\\nBefore we proceed any further, hear me speak.\\n\\nAll:\\nSpeak, speak.\\n\\nFirst Citizen:\\nYou are all resolved rather to die than to famish?\\n\\nAll:\\nResolved. resolved.\\n\\nFirst Citizen:\\nFirst, you know Caius Marcius is chief enemy to the people.\\n\\nAll:\\nWe know't, we know't.\\n\\nFirst Citizen:\\nLet us kill him, and we'll have corn at our own price.\\nIs't a verdict?\\n\\nAll:\\nNo more talking on't; let it be done: away, away!\\n\\nSecond Citizen:\\nOne word, good citizens.\\n\\nFirst Citizen:\\nWe are accounted poor citizens, the patricians good.\\nWhat authority surfeits on would relieve us: if they\\nwould yield us but the superfluity, while it were\\nwholesome, we might guess they relieved us humanely;\\nbut they think we are too dear: the leanness that\\nafflicts us, the object of our misery, is as an\\ninventory to particularise their abundance; our\\nsufferance is a gain to them Let us revenge this with\\nour pikes, ere we become rakes: for the gods know I\\nspeak this in hunger for bread, not in thirst for revenge.\\n\\n\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\n",
      "65\n"
     ]
    }
   ],
   "source": [
    "# collecting all the unique characters in the corpus\n",
    "chars = sorted(list(set(text)))\n",
    "vocab_size = len(chars)\n",
    "print(\"\".join(chars))\n",
    "print(vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[51, 63, 1, 52, 39, 51, 43, 1, 47, 57, 1, 60, 47, 57, 46, 59, 1, 2]\n",
      "my name is vishu !\n"
     ]
    }
   ],
   "source": [
    "# creating a mapping from chars to integers\n",
    "stoi = {ch:i for i, ch in enumerate(chars)}\n",
    "itos = {i:ch for i, ch in enumerate(chars)}\n",
    "encode = lambda s: [stoi[c] for c in s] # string to char to index\n",
    "decode = lambda l: \"\".join([itos[i] for i in l]) # char to string then join\n",
    "\n",
    "print(encode(\"my name is vishu !\"))\n",
    "print(decode([51, 63, 1, 52, 39, 51, 43, 1, 47, 57, 1, 60, 47, 57, 46, 59, 1, 2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1115393]) torch.int64\n",
      "tensor([18, 47, 56, 57, 58,  1, 15, 47, 58, 47, 64, 43, 52, 10,  0, 14, 43, 44,\n",
      "        53, 56, 43,  1, 61, 43,  1, 54, 56, 53, 41, 43, 43, 42,  1, 39, 52, 63,\n",
      "         1, 44, 59, 56, 58, 46, 43, 56,  6,  1, 46, 43, 39, 56,  1, 51, 43,  1,\n",
      "        57, 54, 43, 39, 49,  8,  0,  0, 13, 50, 50, 10,  0, 31, 54, 43, 39, 49,\n",
      "         6,  1, 57, 54, 43, 39, 49,  8,  0,  0, 18, 47, 56, 57, 58,  1, 15, 47,\n",
      "        58, 47, 64, 43, 52, 10,  0, 37, 53, 59,  1, 39, 56, 43,  1, 39, 50, 50,\n",
      "         1, 56, 43, 57, 53, 50, 60, 43, 42,  1, 56, 39, 58, 46, 43, 56,  1, 58,\n",
      "        53,  1, 42, 47, 43,  1, 58, 46, 39, 52,  1, 58, 53,  1, 44, 39, 51, 47,\n",
      "        57, 46, 12,  0,  0, 13, 50, 50, 10,  0, 30, 43, 57, 53, 50, 60, 43, 42,\n",
      "         8,  1, 56, 43, 57, 53, 50, 60, 43, 42,  8,  0,  0, 18, 47, 56, 57, 58,\n",
      "         1, 15, 47, 58, 47, 64, 43, 52, 10,  0, 18, 47, 56, 57, 58,  6,  1, 63,\n",
      "        53, 59,  1, 49, 52, 53, 61,  1, 15, 39, 47, 59, 57,  1, 25, 39, 56, 41,\n",
      "        47, 59, 57,  1, 47, 57,  1, 41, 46, 47, 43, 44,  1, 43, 52, 43, 51, 63,\n",
      "         1, 58, 53,  1, 58, 46, 43,  1, 54, 43, 53, 54, 50, 43,  8,  0,  0, 13,\n",
      "        50, 50, 10,  0, 35, 43,  1, 49, 52, 53, 61,  5, 58,  6,  1, 61, 43,  1,\n",
      "        49, 52, 53, 61,  5, 58,  8,  0,  0, 18, 47, 56, 57, 58,  1, 15, 47, 58,\n",
      "        47, 64, 43, 52, 10,  0, 24, 43, 58,  1, 59, 57,  1, 49, 47, 50, 50,  1,\n",
      "        46, 47, 51,  6,  1, 39, 52, 42,  1, 61, 43,  5, 50, 50,  1, 46, 39, 60,\n",
      "        43,  1, 41, 53, 56, 52,  1, 39, 58,  1, 53, 59, 56,  1, 53, 61, 52,  1,\n",
      "        54, 56, 47, 41, 43,  8,  0, 21, 57,  5, 58,  1, 39,  1, 60, 43, 56, 42,\n",
      "        47, 41, 58, 12,  0,  0, 13, 50, 50, 10,  0, 26, 53,  1, 51, 53, 56, 43,\n",
      "         1, 58, 39, 50, 49, 47, 52, 45,  1, 53, 52,  5, 58, 11,  1, 50, 43, 58,\n",
      "         1, 47, 58,  1, 40, 43,  1, 42, 53, 52, 43, 10,  1, 39, 61, 39, 63,  6,\n",
      "         1, 39, 61, 39, 63,  2,  0,  0, 31, 43, 41, 53, 52, 42,  1, 15, 47, 58,\n",
      "        47, 64, 43, 52, 10,  0, 27, 52, 43,  1, 61, 53, 56, 42,  6,  1, 45, 53,\n",
      "        53, 42,  1, 41, 47, 58, 47, 64, 43, 52, 57,  8,  0,  0, 18, 47, 56, 57,\n",
      "        58,  1, 15, 47, 58, 47, 64, 43, 52, 10,  0, 35, 43,  1, 39, 56, 43,  1,\n",
      "        39, 41, 41, 53, 59, 52, 58, 43, 42,  1, 54, 53, 53, 56,  1, 41, 47, 58,\n",
      "        47, 64, 43, 52, 57,  6,  1, 58, 46, 43,  1, 54, 39, 58, 56, 47, 41, 47,\n",
      "        39, 52, 57,  1, 45, 53, 53, 42,  8,  0, 35, 46, 39, 58,  1, 39, 59, 58,\n",
      "        46, 53, 56, 47, 58, 63,  1, 57, 59, 56, 44, 43, 47, 58, 57,  1, 53, 52,\n",
      "         1, 61, 53, 59, 50, 42,  1, 56, 43, 50, 47, 43, 60, 43,  1, 59, 57, 10,\n",
      "         1, 47, 44,  1, 58, 46, 43, 63,  0, 61, 53, 59, 50, 42,  1, 63, 47, 43,\n",
      "        50, 42,  1, 59, 57,  1, 40, 59, 58,  1, 58, 46, 43,  1, 57, 59, 54, 43,\n",
      "        56, 44, 50, 59, 47, 58, 63,  6,  1, 61, 46, 47, 50, 43,  1, 47, 58,  1,\n",
      "        61, 43, 56, 43,  0, 61, 46, 53, 50, 43, 57, 53, 51, 43,  6,  1, 61, 43,\n",
      "         1, 51, 47, 45, 46, 58,  1, 45, 59, 43, 57, 57,  1, 58, 46, 43, 63,  1,\n",
      "        56, 43, 50, 47, 43, 60, 43, 42,  1, 59, 57,  1, 46, 59, 51, 39, 52, 43,\n",
      "        50, 63, 11,  0, 40, 59, 58,  1, 58, 46, 43, 63,  1, 58, 46, 47, 52, 49,\n",
      "         1, 61, 43,  1, 39, 56, 43,  1, 58, 53, 53,  1, 42, 43, 39, 56, 10,  1,\n",
      "        58, 46, 43,  1, 50, 43, 39, 52, 52, 43, 57, 57,  1, 58, 46, 39, 58,  0,\n",
      "        39, 44, 44, 50, 47, 41, 58, 57,  1, 59, 57,  6,  1, 58, 46, 43,  1, 53,\n",
      "        40, 48, 43, 41, 58,  1, 53, 44,  1, 53, 59, 56,  1, 51, 47, 57, 43, 56,\n",
      "        63,  6,  1, 47, 57,  1, 39, 57,  1, 39, 52,  0, 47, 52, 60, 43, 52, 58,\n",
      "        53, 56, 63,  1, 58, 53,  1, 54, 39, 56, 58, 47, 41, 59, 50, 39, 56, 47,\n",
      "        57, 43,  1, 58, 46, 43, 47, 56,  1, 39, 40, 59, 52, 42, 39, 52, 41, 43,\n",
      "        11,  1, 53, 59, 56,  0, 57, 59, 44, 44, 43, 56, 39, 52, 41, 43,  1, 47,\n",
      "        57,  1, 39,  1, 45, 39, 47, 52,  1, 58, 53,  1, 58, 46, 43, 51,  1, 24,\n",
      "        43, 58,  1, 59, 57,  1, 56, 43, 60, 43, 52, 45, 43,  1, 58, 46, 47, 57,\n",
      "         1, 61, 47, 58, 46,  0, 53, 59, 56,  1, 54, 47, 49, 43, 57,  6,  1, 43,\n",
      "        56, 43,  1, 61, 43,  1, 40, 43, 41, 53, 51, 43,  1, 56, 39, 49, 43, 57,\n",
      "        10,  1, 44, 53, 56,  1, 58, 46, 43,  1, 45, 53, 42, 57,  1, 49, 52, 53,\n",
      "        61,  1, 21,  0, 57, 54, 43, 39, 49,  1, 58, 46, 47, 57,  1, 47, 52,  1,\n",
      "        46, 59, 52, 45, 43, 56,  1, 44, 53, 56,  1, 40, 56, 43, 39, 42,  6,  1,\n",
      "        52, 53, 58,  1, 47, 52,  1, 58, 46, 47, 56, 57, 58,  1, 44, 53, 56,  1,\n",
      "        56, 43, 60, 43, 52, 45, 43,  8,  0,  0])\n"
     ]
    }
   ],
   "source": [
    "data = torch.tensor(encode(text), dtype=torch.long)\n",
    "print(data.shape, data.dtype)\n",
    "print(data[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = int(0.9 * len(data))\n",
    "train_data = data[:n]\n",
    "val_data = data[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([18, 47, 56, 57, 58,  1, 15, 47, 58])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block_size = 8\n",
    "train_data[:block_size+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When context is tensor([18]), the target is 47\n",
      "When context is tensor([18, 47]), the target is 56\n",
      "When context is tensor([18, 47, 56]), the target is 57\n",
      "When context is tensor([18, 47, 56, 57]), the target is 58\n",
      "When context is tensor([18, 47, 56, 57, 58]), the target is 1\n",
      "When context is tensor([18, 47, 56, 57, 58,  1]), the target is 15\n",
      "When context is tensor([18, 47, 56, 57, 58,  1, 15]), the target is 47\n",
      "When context is tensor([18, 47, 56, 57, 58,  1, 15, 47]), the target is 58\n"
     ]
    }
   ],
   "source": [
    "x = train_data[:block_size]\n",
    "y = train_data[1:block_size+1]\n",
    "for t in range(block_size):\n",
    "  context = x[:t+1]\n",
    "  target = y[t]\n",
    "  print(f\"When context is {context}, the target is {target}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inputs:\n",
      "torch.Size([4, 8])\n",
      "tensor([[53, 59,  6,  1, 58, 56, 47, 40],\n",
      "        [49, 43, 43, 54,  1, 47, 58,  1],\n",
      "        [13, 52, 45, 43, 50, 53,  8,  0],\n",
      "        [ 1, 39,  1, 46, 53, 59, 57, 43]])\n",
      "target:\n",
      "torch.Size([4, 8])\n",
      "tensor([[59,  6,  1, 58, 56, 47, 40, 59],\n",
      "        [43, 43, 54,  1, 47, 58,  1, 58],\n",
      "        [52, 45, 43, 50, 53,  8,  0, 26],\n",
      "        [39,  1, 46, 53, 59, 57, 43,  0]])\n",
      "------------------\n",
      "When input is [53], target is 59\n",
      "When input is [53, 59], target is 6\n",
      "When input is [53, 59, 6], target is 1\n",
      "When input is [53, 59, 6, 1], target is 58\n",
      "When input is [53, 59, 6, 1, 58], target is 56\n",
      "When input is [53, 59, 6, 1, 58, 56], target is 47\n",
      "When input is [53, 59, 6, 1, 58, 56, 47], target is 40\n",
      "When input is [53, 59, 6, 1, 58, 56, 47, 40], target is 59\n",
      "When input is [49], target is 43\n",
      "When input is [49, 43], target is 43\n",
      "When input is [49, 43, 43], target is 54\n",
      "When input is [49, 43, 43, 54], target is 1\n",
      "When input is [49, 43, 43, 54, 1], target is 47\n",
      "When input is [49, 43, 43, 54, 1, 47], target is 58\n",
      "When input is [49, 43, 43, 54, 1, 47, 58], target is 1\n",
      "When input is [49, 43, 43, 54, 1, 47, 58, 1], target is 58\n",
      "When input is [13], target is 52\n",
      "When input is [13, 52], target is 45\n",
      "When input is [13, 52, 45], target is 43\n",
      "When input is [13, 52, 45, 43], target is 50\n",
      "When input is [13, 52, 45, 43, 50], target is 53\n",
      "When input is [13, 52, 45, 43, 50, 53], target is 8\n",
      "When input is [13, 52, 45, 43, 50, 53, 8], target is 0\n",
      "When input is [13, 52, 45, 43, 50, 53, 8, 0], target is 26\n",
      "When input is [1], target is 39\n",
      "When input is [1, 39], target is 1\n",
      "When input is [1, 39, 1], target is 46\n",
      "When input is [1, 39, 1, 46], target is 53\n",
      "When input is [1, 39, 1, 46, 53], target is 59\n",
      "When input is [1, 39, 1, 46, 53, 59], target is 57\n",
      "When input is [1, 39, 1, 46, 53, 59, 57], target is 43\n",
      "When input is [1, 39, 1, 46, 53, 59, 57, 43], target is 0\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(1337)\n",
    "\n",
    "batch_size = 4 # how many independent sequences to process ?\n",
    "block_size = 8 # maximum length of the context length to predict ?\n",
    "\n",
    "def get_batch(split):\n",
    "  # generate batches of data for input (x) and output (y)\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",
    "\n",
    "  return x, y\n",
    "\n",
    "xb, yb = get_batch(\"train\")\n",
    "print(\"inputs:\")\n",
    "print(xb.shape)\n",
    "print(xb)\n",
    "print(\"target:\")\n",
    "print(yb.shape)\n",
    "print(yb)\n",
    "\n",
    "print(\"------------------\")\n",
    "\n",
    "for b in range(batch_size):\n",
    "  for t in range(block_size):\n",
    "    context = xb[b, :t+1]\n",
    "    target = yb[b, t]\n",
    "    print(f\"When input is {context.tolist()}, target is {target}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 65])\n",
      "tensor(4.8948, grad_fn=<NllLossBackward0>)\n",
      "\n",
      "SKIcLT;AcELMoTbvZv C?nq-QE33:CJqkOKH-q;:la!oiywkHjgChzbQ?u!3bLIgwevmyFJGUGp\n",
      "wnYWmnxKWWev-tDqXErVKLgJ\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "torch.manual_seed(1337)\n",
    "\n",
    "class BigramLanguageModel(nn.Module):\n",
    "\n",
    "  def __init__(self, vocab_size):\n",
    "    super().__init__()\n",
    "    self.token_embedding_table = nn.Embedding(vocab_size, vocab_size)\n",
    "\n",
    "  def forward(self, idx, targets=None):\n",
    "\n",
    "    # each token directly reads off the logits for the next token from the lookup table\n",
    "    logits = self.token_embedding_table(idx)\n",
    "\n",
    "    if targets is None:\n",
    "      loss = None\n",
    "\n",
    "    else:\n",
    "      B, T, C = logits.shape\n",
    "      logits = logits.view(B*T, C)\n",
    "      targets = targets.view(B*T)\n",
    "      loss = F.cross_entropy(logits, targets)\n",
    "\n",
    "    return logits, loss\n",
    "\n",
    "  def generate(self, idx, max_new_tokens):\n",
    "    for _ in range(max_new_tokens):\n",
    "      \n",
    "      logits, loss = self(idx)\n",
    "      logits = logits[:, -1, :]\n",
    "      probs = F.softmax(logits, dim=-1)\n",
    "      idx_next = torch.multinomial(probs, num_samples=1)\n",
    "      idx = torch.cat((idx, idx_next), dim=1)\n",
    "\n",
    "    return idx\n",
    "\n",
    "m = BigramLanguageModel(vocab_size)\n",
    "logits, loss = m(xb, yb)\n",
    "print(logits.shape)\n",
    "print(loss)\n",
    "\n",
    "print(decode(m.generate(idx = torch.zeros((1,1), dtype=torch.long), max_new_tokens=100)[0].tolist()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.AdamW(m.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.452850580215454\n",
      "2.544088125228882\n",
      "2.5024654865264893\n",
      "2.559384822845459\n",
      "2.479424238204956\n",
      "2.3964293003082275\n",
      "2.4621920585632324\n",
      "2.590568780899048\n",
      "2.523573637008667\n",
      "2.440568685531616\n",
      "2.5325677394866943\n",
      "2.567152500152588\n",
      "2.5133845806121826\n",
      "2.3936336040496826\n",
      "2.466322183609009\n",
      "2.368321657180786\n",
      "2.562734603881836\n",
      "2.3920631408691406\n",
      "2.45816707611084\n",
      "2.6093900203704834\n",
      "2.423999309539795\n",
      "2.4549763202667236\n",
      "2.448296546936035\n",
      "2.49141263961792\n",
      "2.497774839401245\n",
      "2.4092857837677\n",
      "2.44084095954895\n",
      "2.3384525775909424\n",
      "2.560450553894043\n",
      "2.477339744567871\n",
      "2.530503511428833\n",
      "2.5151102542877197\n",
      "2.578629732131958\n",
      "2.4296977519989014\n",
      "2.3904428482055664\n",
      "2.3276515007019043\n",
      "2.4791228771209717\n",
      "2.5043091773986816\n",
      "2.4057867527008057\n",
      "2.426945924758911\n",
      "2.4725310802459717\n",
      "2.521289825439453\n",
      "2.389709949493408\n",
      "2.540320634841919\n",
      "2.4795777797698975\n",
      "2.3668618202209473\n",
      "2.358574867248535\n",
      "2.4156880378723145\n",
      "2.378319025039673\n",
      "2.506113290786743\n",
      "2.4738049507141113\n",
      "2.4161417484283447\n",
      "2.4293627738952637\n",
      "2.3913564682006836\n",
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      "2.497671127319336\n",
      "2.3736894130706787\n",
      "2.5017526149749756\n",
      "2.493495225906372\n",
      "2.58750057220459\n",
      "2.3868257999420166\n",
      "2.484487295150757\n",
      "2.5707151889801025\n",
      "2.4754135608673096\n",
      "2.3779549598693848\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "\n",
    "for steps in range(10000):\n",
    "\n",
    "  xb, yb = get_batch(split='train')\n",
    "\n",
    "  logits, loss = m(xb, yb)\n",
    "  optimizer.zero_grad(set_to_none=True)\n",
    "  loss.backward()\n",
    "  optimizer.step()\n",
    "\n",
    "  print(loss.item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ALo, warawoofe, M: atre deseeshen tar me ifukeshaceweag t io, d at.\n",
      "KE: co ctisefang he t d knerde, t thises;\n",
      "Bund wiemetiarele hen\n",
      "le, be ad, jush we, withindire INENGRe' thovexpu a PESAn stlis wilur\n"
     ]
    }
   ],
   "source": [
    "print(decode(m.generate(idx = torch.zeros((1,1), dtype=torch.long), max_new_tokens=200)[0].tolist()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Math in self-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 8, 2])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(seed=1337)\n",
    "B, T, C = 4, 8, 2\n",
    "x = torch.randn(B, T, C)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean from start to (t-1)th context\n",
    "xbow = torch.zeros((B, T, C))\n",
    "for b in range(B):\n",
    "  for t in range(T):\n",
    "\n",
    "    xprev = x[b, :t+1]\n",
    "    xbow[b, t] = torch.mean(xprev, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.0000, 0.0000, 0.0000],\n",
      "        [0.5000, 0.5000, 0.0000],\n",
      "        [0.3333, 0.3333, 0.3333]])\n",
      "tensor([[2., 7.],\n",
      "        [6., 4.],\n",
      "        [6., 5.]])\n",
      "tensor([[2.0000, 7.0000],\n",
      "        [4.0000, 5.5000],\n",
      "        [4.6667, 5.3333]])\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(seed=42)\n",
    "a = torch.tril(torch.ones(size=(3, 3)))\n",
    "a = a / torch.sum(a, 1, keepdim=True)\n",
    "b = torch.randint(low=0, high=10, size=(3, 2)).float()\n",
    "c = a @ b\n",
    "print(a)\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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