ch04/01_main-chapter-code/exercise-solutions.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "51c9672d-8d0c-470d-ac2d-1271f8ec3f14",
   "metadata": {},
   "source": [
    "# Chapter 4 Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fea8be3-30a1-4623-a6d7-b095c6c1092e",
   "metadata": {},
   "source": [
    "# Exercise 4.1: Parameters in the feed forward versus attention module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2751b0e5-ffd3-4be2-8db3-e20dd4d61d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gpt import TransformerBlock\n",
    "\n",
    "GPT_CONFIG_124M = {\n",
    "    \"vocab_size\": 50257,\n",
    "    \"ctx_len\": 1024,\n",
    "    \"emb_dim\": 768,\n",
    "    \"n_heads\": 12,\n",
    "    \"n_layers\": 12,\n",
    "    \"drop_rate\": 0.1,\n",
    "    \"qkv_bias\": False\n",
    "}\n",
    "\n",
    "block = TransformerBlock(GPT_CONFIG_124M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1bcaffd1-0cf6-4f8f-bd53-ab88a37f443e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of parameters in feed forward module: 4,722,432\n"
     ]
    }
   ],
   "source": [
    "total_params = sum(p.numel() for p in block.ff.parameters())\n",
    "print(f\"Total number of parameters in feed forward module: {total_params:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1dd06c1-ab6c-4df7-ba73-f9cd54b31138",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of parameters in attention module: 2,360,064\n"
     ]
    }
   ],
   "source": [
    "total_params = sum(p.numel() for p in block.att.parameters())\n",
    "print(f\"Total number of parameters in attention module: {total_params:,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15463dec-520a-47b4-b3ad-e180394fd076",
   "metadata": {},
   "source": [
    "- The results above are for a single transformer block\n",
    "- Optionally multiply by 12 to capture all transformer blocks in the 124M GPT model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7b7c7f-0fa1-4d30-ab44-e499edd55b6d",
   "metadata": {},
   "source": [
    "# Exercise 4.2: Initialize larger GPT models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310b2e05-3ec8-47fc-afd9-83bf03d4aad8",
   "metadata": {},
   "source": [
    "- **GPT2-small** (the 124M configuration we already implemented):\n",
    "    - \"emb_dim\" = 768\n",
    "    - \"n_layers\" = 12\n",
    "    - \"n_heads\" = 12\n",
    "\n",
    "- **GPT2-medium:**\n",
    "    - \"emb_dim\" = 1024\n",
    "    - \"n_layers\" = 24\n",
    "    - \"n_heads\" = 16\n",
    "\n",
    "- **GPT2-large:**\n",
    "    - \"emb_dim\" = 1280\n",
    "    - \"n_layers\" = 36\n",
    "    - \"n_heads\" = 20\n",
    "\n",
    "- **GPT2-XL:**\n",
    "    - \"emb_dim\" = 1600\n",
    "    - \"n_layers\" = 48\n",
    "    - \"n_heads\" = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90185dea-81ca-4cdc-aef7-4aaf95cba946",
   "metadata": {},
   "outputs": [],
   "source": [
    "GPT_CONFIG_124M = {\n",
    "    \"vocab_size\": 50257,\n",
    "    \"ctx_len\": 1024,\n",
    "    \"emb_dim\": 768,\n",
    "    \"n_heads\": 12,\n",
    "    \"n_layers\": 12,\n",
    "    \"drop_rate\": 0.1,\n",
    "    \"qkv_bias\": False\n",
    "}\n",
    "\n",
    "\n",
    "def get_config(base_config, model_name=\"gpt2-small\"):\n",
    "    GPT_CONFIG = base_config.copy()\n",
    "\n",
    "    if model_name == \"gpt2-small\":\n",
    "        GPT_CONFIG[\"emb_dim\"] = 768\n",
    "        GPT_CONFIG[\"n_layers\"] = 12\n",
    "        GPT_CONFIG[\"n_heads\"] = 12\n",
    "\n",
    "    elif model_name == \"gpt2-medium\":\n",
    "        GPT_CONFIG[\"emb_dim\"] = 1024\n",
    "        GPT_CONFIG[\"n_layers\"] = 24\n",
    "        GPT_CONFIG[\"n_heads\"] = 16\n",
    "\n",
    "    elif model_name == \"gpt2-large\":\n",
    "        GPT_CONFIG[\"emb_dim\"] = 1280\n",
    "        GPT_CONFIG[\"n_layers\"] = 36\n",
    "        GPT_CONFIG[\"n_heads\"] = 20\n",
    "\n",
    "    elif model_name == \"gpt2-xl\":\n",
    "        GPT_CONFIG[\"emb_dim\"] = 1600\n",
    "        GPT_CONFIG[\"n_layers\"] = 48\n",
    "        GPT_CONFIG[\"n_heads\"] = 25\n",
    "\n",
    "    else:\n",
    "        raise ValueError(f\"Incorrect model name {model_name}\")\n",
    "\n",
    "    return GPT_CONFIG\n",
    "\n",
    "\n",
    "def calculate_size(model): # based on chapter code\n",
    "    \n",
    "    total_params = sum(p.numel() for p in model.parameters())\n",
    "    print(f\"Total number of parameters: {total_params:,}\")\n",
    "\n",
    "    total_params_gpt2 =  total_params - sum(p.numel() for p in model.out_head.parameters())\n",
    "    print(f\"Number of trainable parameters considering weight tying: {total_params_gpt2:,}\")\n",
    "    \n",
    "    # Calculate the total size in bytes (assuming float32, 4 bytes per parameter)\n",
    "    total_size_bytes = total_params * 4\n",
    "    \n",
    "    # Convert to megabytes\n",
    "    total_size_mb = total_size_bytes / (1024 * 1024)\n",
    "    \n",
    "    print(f\"Total size of the model: {total_size_mb:.2f} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2587e011-78a4-479c-a8fd-961cc40a5fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "gpt2-small:\n",
      "Total number of parameters: 163,009,536\n",
      "Number of trainable parameters considering weight tying: 124,412,160\n",
      "Total size of the model: 621.83 MB\n",
      "\n",
      "\n",
      "gpt2-medium:\n",
      "Total number of parameters: 406,212,608\n",
      "Number of trainable parameters considering weight tying: 354,749,440\n",
      "Total size of the model: 1549.58 MB\n",
      "\n",
      "\n",
      "gpt2-large:\n",
      "Total number of parameters: 838,220,800\n",
      "Number of trainable parameters considering weight tying: 773,891,840\n",
      "Total size of the model: 3197.56 MB\n",
      "\n",
      "\n",
      "gpt2-xl:\n",
      "Total number of parameters: 1,637,792,000\n",
      "Number of trainable parameters considering weight tying: 1,557,380,800\n",
      "Total size of the model: 6247.68 MB\n"
     ]
    }
   ],
   "source": [
    "from gpt import GPTModel\n",
    "\n",
    "\n",
    "for model_abbrev in (\"small\", \"medium\", \"large\", \"xl\"):\n",
    "    model_name = f\"gpt2-{model_abbrev}\"\n",
    "    CONFIG = get_config(GPT_CONFIG_124M, model_name=model_name)\n",
    "    model = GPTModel(CONFIG)\n",
    "    print(f\"\\n\\n{model_name}:\")\n",
    "    calculate_size(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5f2306e-5dc8-498e-92ee-70ae7ec37ac1",
   "metadata": {},
   "source": [
    "# Exercise 4.3: Using separate dropout parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5fee2cf5-61c3-4167-81b5-44ea155bbaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "GPT_CONFIG_124M = {\n",
    "    \"vocab_size\": 50257,\n",
    "    \"ctx_len\": 1024,\n",
    "    \"emb_dim\": 768,\n",
    "    \"n_heads\": 12,\n",
    "    \"n_layers\": 12,\n",
    "    \"drop_rate_emb\": 0.1,    # NEW: dropout for embedding layers\n",
    "    \"drop_rate_ffn\": 0.1,    # NEW: dropout for feed forward module\n",
    "    \"drop_rate_attn\": 0.1,   # NEW: dropout for multi-head attention  \n",
    "    \"drop_rate_resid\": 0.1,   # NEW: dropout for residual connections  \n",
    "    \"qkv_bias\": False\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5aa1b0c1-d78a-48fc-ad08-4802458b43f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from gpt import MultiHeadAttention, LayerNorm, GELU\n",
    "\n",
    "class FeedForward(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Linear(cfg[\"emb_dim\"], 4 * cfg[\"emb_dim\"]),\n",
    "            GELU(),\n",
    "            nn.Linear(4 * cfg[\"emb_dim\"], cfg[\"emb_dim\"]),\n",
    "            nn.Dropout(cfg[\"drop_rate_ffn\"]) # NEW: dropout for feed forward module\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.layers(x)\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.att = MultiHeadAttention(\n",
    "            d_in=cfg[\"emb_dim\"],\n",
    "            d_out=cfg[\"emb_dim\"],\n",
    "            block_size=cfg[\"ctx_len\"],\n",
    "            num_heads=cfg[\"n_heads\"], \n",
    "            dropout=cfg[\"drop_rate_attn\"], # NEW: dropout for multi-head attention\n",
    "            qkv_bias=cfg[\"qkv_bias\"])\n",
    "        self.ff = FeedForward(cfg)\n",
    "        self.norm1 = LayerNorm(cfg[\"emb_dim\"])\n",
    "        self.norm2 = LayerNorm(cfg[\"emb_dim\"])\n",
    "        self.drop_resid = nn.Dropout(cfg[\"drop_rate_resid\"])\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Shortcut connection for attention block\n",
    "        shortcut = x\n",
    "        x = self.norm1(x)\n",
    "        x = self.att(x)  # Shape [batch_size, num_tokens, emb_size]\n",
    "        x = self.drop_resid(x)\n",
    "        x = x + shortcut  # Add the original input back\n",
    "\n",
    "        # Shortcut connection for feed-forward block\n",
    "        shortcut = x\n",
    "        x = self.norm2(x)\n",
    "        x = self.ff(x)\n",
    "        x = self.drop_resid(x)\n",
    "        x = x + shortcut  # Add the original input back\n",
    "\n",
    "        return x\n",
    "\n",
    "\n",
    "class GPTModel(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.tok_emb = nn.Embedding(cfg[\"vocab_size\"], cfg[\"emb_dim\"])\n",
    "        self.pos_emb = nn.Embedding(cfg[\"ctx_len\"], cfg[\"emb_dim\"])\n",
    "        self.drop_emb = nn.Dropout(cfg[\"drop_rate_emb\"]) # NEW: dropout for embedding layers\n",
    "\n",
    "        self.trf_blocks = nn.Sequential(\n",
    "            *[TransformerBlock(cfg) for _ in range(cfg[\"n_layers\"])])\n",
    "\n",
    "        self.final_norm = LayerNorm(cfg[\"emb_dim\"])\n",
    "        self.out_head = nn.Linear(cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False)\n",
    "\n",
    "    def forward(self, in_idx):\n",
    "        batch_size, seq_len = in_idx.shape\n",
    "        tok_embeds = self.tok_emb(in_idx)\n",
    "        pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))\n",
    "        x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]\n",
    "        x = self.drop_emb(x)\n",
    "        x = self.trf_blocks(x)\n",
    "        x = self.final_norm(x)\n",
    "        logits = self.out_head(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d013d32-c275-4f42-be21-9010f1537227",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "torch.manual_seed(123)\n",
    "model = GPTModel(GPT_CONFIG_124M)"
   ]
  }
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