part_2/01_main-code/multihead-attention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f678e62-7bcb-4405-86ae-dce94f494303",
   "metadata": {},
   "source": [
    "# Multi-head Attention Plus Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac9b5847-0515-45cd-87b0-46541f6a1f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 2.2.2\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "from importlib.metadata import version\n",
    "\n",
    "print(\"torch version:\", version(\"torch\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "070000fc-a7b7-4c56-a2c0-a938d413a790",
   "metadata": {},
   "source": [
    "The complete chapter code is located in [Part 2: Coding Attention Mechanisms](Part%202%20Coding%20Attention%20Mechanisms.ipynb).\n",
    "\n",
    "This notebook contains the main takeaway, multihead-attention implementation (plus the data loading pipeline from part 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f60dc93-281d-447e-941f-aede0c7ff7fc",
   "metadata": {},
   "source": [
    "## Data Loader from Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ed4b7db-3b47-4fd3-a4a6-5f4ed5dd166e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class GPTDatasetV1(Dataset):\n",
    "    def __init__(self, txt, tokenizer, max_length, stride):\n",
    "        self.input_ids = []\n",
    "        self.target_ids = []\n",
    "\n",
    "        # Tokenize the entire text\n",
    "        token_ids = tokenizer.encode(txt, allowed_special={'<|endoftext|>'})\n",
    "\n",
    "        # Use a sliding window to chunk the book into overlapping sequences of max_length\n",
    "        for i in range(0, len(token_ids) - max_length, stride):\n",
    "            input_chunk = token_ids[i:i + max_length]\n",
    "            target_chunk = token_ids[i + 1: i + max_length + 1]\n",
    "            self.input_ids.append(torch.tensor(input_chunk))\n",
    "            self.target_ids.append(torch.tensor(target_chunk))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.input_ids)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.input_ids[idx], self.target_ids[idx]\n",
    "\n",
    "\n",
    "def create_dataloader(txt, batch_size=4, max_length=256, stride=128, shuffle=True):\n",
    "    # Initialize the tokenizer\n",
    "    tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
    "\n",
    "    # Create dataset\n",
    "    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)\n",
    "\n",
    "    # Create dataloader\n",
    "    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n",
    "\n",
    "    return dataloader\n",
    "\n",
    "\n",
    "with open(\"small-text-sample.txt\", \"r\", encoding=\"utf-8\") as f:\n",
    "    raw_text = f.read()\n",
    "\n",
    "tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
    "encoded_text = tokenizer.encode(raw_text)\n",
    "\n",
    "vocab_size = 50257\n",
    "output_dim = 256\n",
    "max_len = 1024\n",
    "context_length = max_len\n",
    "\n",
    "\n",
    "token_embedding_layer = nn.Embedding(vocab_size, output_dim)\n",
    "pos_embedding_layer = torch.nn.Embedding(context_length, output_dim)\n",
    "\n",
    "max_length = 4\n",
    "dataloader = create_dataloader(raw_text, batch_size=8, max_length=max_length, stride=max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "664397bc-6daa-4b88-90aa-e8fc1fbd5846",
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch in dataloader:\n",
    "    x, y = batch\n",
    "\n",
    "    token_embeddings = token_embedding_layer(x)\n",
    "    pos_embeddings = pos_embedding_layer(torch.arange(max_length))\n",
    "\n",
    "    input_embeddings = token_embeddings + pos_embeddings\n",
    "\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3664332-e6bb-447e-8b96-203aafde8b24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 4, 256])\n"
     ]
    }
   ],
   "source": [
    "print(input_embeddings.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd298bf4-e320-40c1-9084-6526d07e6d5d",
   "metadata": {},
   "source": [
    "# Multi-head Attention from Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b2297b-a001-49fd-994c-b1700866cd01",
   "metadata": {},
   "source": [
    "## Variant A: Simple implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a44e682d-1c3c-445d-85fa-b142f89f8503",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CausalSelfAttention(nn.Module):\n",
    "\n",
    "    def __init__(self, d_in, d_out, context_length, dropout, qkv_bias=False):\n",
    "        super().__init__()\n",
    "        self.d_out = d_out\n",
    "        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
    "        self.W_key   = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
    "        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
    "        self.dropout = nn.Dropout(dropout) # New\n",
    "        self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1)) # New\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, n_tokens, d_in = x.shape # New batch dimension b\n",
    "        keys = self.W_key(x)\n",
    "        queries = self.W_query(x)\n",
    "        values = self.W_value(x)\n",
    "\n",
    "        attn_scores = queries @ keys.transpose(1, 2) # Changed transpose\n",
    "        attn_scores.masked_fill_(  # New, _ ops are in-place\n",
    "            self.mask.bool()[:n_tokens, :n_tokens], -torch.inf) \n",
    "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
    "        attn_weights = self.dropout(attn_weights) # New\n",
    "\n",
    "        context_vec = attn_weights @ values\n",
    "        return context_vec\n",
    "\n",
    "\n",
    "class MultiHeadAttentionWrapper(nn.Module):\n",
    "    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):\n",
    "        super().__init__()\n",
    "        self.heads = nn.ModuleList(\n",
    "            [CausalSelfAttention(d_in, d_out, context_length, dropout, qkv_bias) \n",
    "             for _ in range(num_heads)]\n",
    "        )\n",
    "        self.out_proj = nn.Linear(d_out*num_heads, d_out*num_heads)\n",
    "\n",
    "    def forward(self, x):\n",
    "        context_vec = torch.cat([head(x) for head in self.heads], dim=-1)\n",
    "        return self.out_proj(context_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "context_vecs.shape: torch.Size([8, 4, 256])\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(123)\n",
    "\n",
    "context_length = max_length\n",
    "d_in = output_dim\n",
    "\n",
    "num_heads=2\n",
    "d_out = d_in // num_heads\n",
    "\n",
    "mha = MultiHeadAttentionWrapper(d_in, d_out, context_length, 0.0, num_heads)\n",
    "\n",
    "batch = input_embeddings\n",
    "context_vecs = mha(batch)\n",
    "\n",
    "print(\"context_vecs.shape:\", context_vecs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e288239-5146-424d-97fe-74024ae711b9",
   "metadata": {},
   "source": [
    "## Variant B: Alternative implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2773c09d-c136-4372-a2be-04b58d292842",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):\n",
    "        super().__init__()\n",
    "        assert d_out % num_heads == 0, \"d_out must be divisible by num_heads\"\n",
    "\n",
    "        self.d_out = d_out\n",
    "        self.num_heads = num_heads\n",
    "        self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim\n",
    "\n",
    "        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
    "        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
    "        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
    "        self.out_proj = nn.Linear(d_out, d_out)  # Linear layer to combine head outputs\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, num_tokens, d_in = x.shape\n",
    "\n",
    "        keys = self.W_key(x) # Shape: (b, num_tokens, d_out)\n",
    "        queries = self.W_query(x)\n",
    "        values = self.W_value(x)\n",
    "\n",
    "        # We implicitly split the matrix by adding a `num_heads` dimension\n",
    "        # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)\n",
    "        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim) \n",
    "        values = values.view(b, num_tokens, self.num_heads, self.head_dim)\n",
    "        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)\n",
    "\n",
    "        # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)\n",
    "        keys = keys.transpose(1, 2)\n",
    "        queries = queries.transpose(1, 2)\n",
    "        values = values.transpose(1, 2)\n",
    "\n",
    "        # Compute scaled dot-product attention (aka self-attention) with a causal mask\n",
    "        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head\n",
    "        \n",
    "        # Original mask truncated to the number of tokens and converted to boolean\n",
    "        mask_bool = self.mask.bool()[:num_tokens, :num_tokens]\n",
    "\n",
    "        # Use the mask to fill attention scores\n",
    "        attn_scores.masked_fill_(mask_bool, -torch.inf)\n",
    "        \n",
    "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
    "        attn_weights = self.dropout(attn_weights)\n",
    "\n",
    "        # Shape: (b, num_tokens, num_heads, head_dim)\n",
    "        context_vec = (attn_weights @ values).transpose(1, 2) \n",
    "        \n",
    "        # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
    "        context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)\n",
    "        context_vec = self.out_proj(context_vec) # optional projection\n",
    "\n",
    "        return context_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "779fdd04-0152-4308-af08-840800a7f395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "context_vecs.shape: torch.Size([8, 4, 256])\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(123)\n",
    "\n",
    "context_length = max_length\n",
    "d_in = output_dim\n",
    "d_out = d_in\n",
    "\n",
    "mha = MultiHeadAttention(d_in, d_out, context_length, 0.0, num_heads=2)\n",
    "\n",
    "batch = input_embeddings\n",
    "context_vecs = mha(batch)\n",
    "\n",
    "print(\"context_vecs.shape:\", context_vecs.shape)"
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}