ch4 exercise solutions

README.md

@@ -36,7 +36,7 @@ Alternatively, you can view this and other files on GitHub at [https://github.co
 | Ch 1: Understanding Large Language Models      | No code                                                                                                                         | No code                       |
 | Ch 2: Working with Text Data                   | - [ch02.ipynb](ch02/01_main-chapter-code/ch02.ipynb)<br/>- [dataloader.ipynb](ch02/01_main-chapter-code/dataloader.ipynb) (summary)<br/>- [exercise-solutions.ipynb](ch02/01_main-chapter-code/exercise-solutions.ipynb) | [./ch02](./ch02)              |
 | Ch 3: Coding Attention Mechanisms              | - [ch03.ipynb](ch03/01_main-chapter-code/ch03.ipynb)<br/>- [multihead-attention.ipynb](ch03/01_main-chapter-code/multihead-attention.ipynb) (summary) <br/>- [exercise-solutions.ipynb](ch03/01_main-chapter-code/exercise-solutions.ipynb)| [./ch03](./ch03)              |
-| Ch 4: Implementing a GPT Model from Scratch    | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary) | [./ch04](./ch04)           |
+| Ch 4: Implementing a GPT Model from Scratch    | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary)<br/>- [exercise-solutions.ipynb](ch04/01_main-chapter-code/exercise-solutions.ipynb) | [./ch04](./ch04)           |
 | Ch 5: Pretraining on Unlabeled Data            | Q1 2024                                                                                                                         | ...                           |
 | Ch 6: Finetuning for Text Classification       | Q2 2024                                                                                                                         | ...                           |
 | Ch 7: Finetuning with Human Feedback           | Q2 2024                                                                                                                         | ...                           |

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

@@ -942,12 +942,11 @@
     "        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\"])\n",
     "        \n",
-    "        # Use a placeholder for TransformerBlock\n",
     "        self.trf_blocks = nn.Sequential(\n",
     "            *[TransformerBlock(cfg) for _ in range(cfg[\"n_layers\"])])\n",
     "        \n",
-    "        # Use a placeholder for LayerNorm\n",
     "        self.final_norm = LayerNorm(cfg[\"emb_dim\"])\n",
     "        self.out_head = nn.Linear(\n",
     "            cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False\n",
@@ -1210,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 26,
    "id": "c9b428a9-8764-4b36-80cd-7d4e00595ba6",
    "metadata": {},
    "outputs": [],
@@ -1264,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 27,
    "id": "bb3ffc8e-f95f-4a24-a978-939b8953ea3e",
    "metadata": {},
    "outputs": [
@@ -1282,7 +1281,7 @@
        "            0.0000], grad_fn=<SoftmaxBackward0>)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1299,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 28,
    "id": "3d7e3e94-df0f-4c0f-a6a1-423f500ac1d3",
    "metadata": {},
    "outputs": [
@@ -1324,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 29,
    "id": "a72a9b60-de66-44cf-b2f9-1e638934ada4",
    "metadata": {},
    "outputs": [
@@ -1332,9 +1331,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Output: tensor([[15496,    11,   314,   716, 27018, 24086, 47843, 30961, 42348,  7267,\n",
+      "Output: tensor([[15496,    11,   314,   716, 27018, 24086, 47843, 30961, 42348,  7267]])\n",
-      "         49706, 43231, 47062, 34657]])\n",
+      "Output length: 10\n"
-      "Output length: 14\n"
      ]
     }
    ],
@@ -1344,7 +1342,7 @@
     "out = generate_text_simple(\n",
     "    model=model,\n",
     "    idx=encoded_tensor, \n",
-    "    max_new_tokens=10, \n",
+    "    max_new_tokens=6, \n",
     "    context_size=GPT_CONFIG_124M[\"ctx_len\"]\n",
     ")\n",
     "\n",
@@ -1362,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "053d99f6-5710-4446-8d52-117fb34ea9f6",
    "metadata": {},
    "outputs": [
@@ -1370,7 +1368,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hello, I am Featureiman Byeswickattribute argue logger Normandy Compton analogous\n"
+      "Hello, I am Featureiman Byeswickattribute argue\n"
      ]
     }
    ],

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

@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51c9672d-8d0c-470d-ac2d-1271f8ec3f14",
+   "metadata": {},
+   "source": [
+    "# Chapter 4 Exercise solutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33dfa199-9aee-41d4-a64b-7e3811b9a616",
+   "metadata": {},
+   "source": [
+    "# Exercise 4.1: Using separate dropout parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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": 2,
+   "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.trf_blocks(x)\n",
+    "        x = self.final_norm(x)\n",
+    "        logits = self.out_head(x)\n",
+    "        return logits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1d013d32-c275-4f42-be21-9010f1537227",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import tiktoken\n",
+    "\n",
+    "torch.manual_seed(123)\n",
+    "model = GPTModel(GPT_CONFIG_124M)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fea8be3-30a1-4623-a6d7-b095c6c1092e",
+   "metadata": {},
+   "source": [
+    "# Exercise 4.2: Parameters in the feed forward versus attention module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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",
+    "model = TransformerBlock(GPT_CONFIG_124M)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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": 27,
+   "id": "c1dd06c1-ab6c-4df7-ba73-f9cd54b31138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of parameters in feed forward 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 4.3: 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": 12,
+   "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": 13,
+   "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)"
+   ]
+  }
+ ],
+ "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
+}

ch04/01_main-chapter-code/gpt.py

@@ -187,12 +187,11 @@ class GPTModel(nn.Module):
         super().__init__()
         self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
         self.pos_emb = nn.Embedding(cfg["ctx_len"], cfg["emb_dim"])
+        self.drop_emb = nn.Dropout(cfg["drop_rate"])
 
-        # Use a placeholder for TransformerBlock
         self.trf_blocks = nn.Sequential(
             *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
 
-        # Use a placeholder for LayerNorm
         self.final_norm = LayerNorm(cfg["emb_dim"])
         self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)