Chapter 6 ablation studies (#127)

* Chapter 6 ablation studies

* add table

* formatting

* formatting

* formatting

ch06/01_main-chapter-code/README.md

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+In progress.

ch06/02_additional-experiments/README.md

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+# Additional Experiments
+
+| Model              | Trainable token | Trainable layers | CPU/GPU | Training time | Training acc | Validation acc | Test acc |
+|--------------------|-----------------|------------------|---------|---------------|--------------|----------------|----------|
+| gpt2-small (124M)  | last            | last_block       | V100    | 0.39 min      | 96.63%       | 97.99%         | 94.33%   |
+| gpt2-small (124M)  | first           | last_block       | V100    | 0.37 min      | 78.46%       | 80.54%         | 75.00%   |
+| gpt2-small (124M)  | last            | last_layer       | V100    | 0.33 min      | 78.65%       | 87.25%         | 78.33%   |
+| gpt2-small (124M)  | last            | all              | V100    | 0.94 min      | 99.62%       | 96.64%         | 96.33%   |
+| gpt2-medium (355M) | last            | last_block       | V100    | 0.91 min      | 87.50%       | 51.01%         | 56.67%   |
+| gpt2-large (774M)  | last            | last_block       | V100    | 1.91 min      | 99.52%       | 98.66%         | 96.67%   |

ch06/02_additional-experiments/additional-experiments.py

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+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+
+import argparse
+import os
+from pathlib import Path
+import time
+import urllib.request
+import zipfile
+
+import pandas as pd
+import tiktoken
+import torch
+from torch.utils.data import DataLoader
+from torch.utils.data import Dataset
+
+from gpt_download import download_and_load_gpt2
+from previous_chapters import GPTModel, load_weights_into_gpt
+
+
+class SpamDataset(Dataset):
+    def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
+        self.data = pd.read_csv(csv_file)
+        self.max_length = max_length if max_length is not None else self._longest_encoded_length(tokenizer)
+
+        # Pre-tokenize texts
+        self.encoded_texts = [
+            tokenizer.encode(text)[:self.max_length]
+            for text in self.data["Text"]
+        ]
+        # Pad sequences to the longest sequence
+        self.encoded_texts = [
+            et + [pad_token_id] * (self.max_length - len(et))
+            for et in self.encoded_texts
+        ]
+
+    def __getitem__(self, index):
+        encoded = self.encoded_texts[index]
+        label = self.data.iloc[index]["Label"]
+        return torch.tensor(encoded, dtype=torch.long), torch.tensor(label, dtype=torch.long)
+
+    def __len__(self):
+        return len(self.data)
+
+    def _longest_encoded_length(self, tokenizer):
+        max_length = 0
+        for text in self.data["Text"]:
+            encoded_length = len(tokenizer.encode(text))
+            if encoded_length > max_length:
+                max_length = encoded_length
+        return max_length
+
+
+def download_and_unzip(url, zip_path, extract_to, new_file_path):
+    if new_file_path.exists():
+        print(f"{new_file_path} already exists. Skipping download and extraction.")
+        return
+
+    # Downloading the file
+    with urllib.request.urlopen(url) as response:
+        with open(zip_path, "wb") as out_file:
+            out_file.write(response.read())
+
+    # Unzipping the file
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_to)
+
+    # Renaming the file to indicate its format
+    original_file = Path(extract_to) / "SMSSpamCollection"
+    os.rename(original_file, new_file_path)
+    print(f"File downloaded and saved as {new_file_path}")
+
+
+def random_split(df, train_frac, validation_frac):
+    # Shuffle the entire DataFrame
+    df = df.sample(frac=1, random_state=123).reset_index(drop=True)
+
+    # Calculate split indices
+    train_end = int(len(df) * train_frac)
+    validation_end = train_end + int(len(df) * validation_frac)
+
+    # Split the DataFrame
+    train_df = df[:train_end]
+    validation_df = df[train_end:validation_end]
+    test_df = df[validation_end:]
+
+    return train_df, validation_df, test_df
+
+
+def create_dataset_csvs(data_file_path):
+    df = pd.read_csv(new_file_path, sep="\t", header=None, names=["Label", "Text"])
+
+    # Create balanced dataset
+    n_spam = df[df["Label"] == "spam"].shape[0]
+    ham_sampled = df[df["Label"] == "ham"].sample(n_spam, random_state=123)
+    balanced_df = pd.concat([ham_sampled, df[df["Label"] == "spam"]])
+    balanced_df = balanced_df.sample(frac=1, random_state=123).reset_index(drop=True)
+    balanced_df["Label"] = balanced_df["Label"].map({"ham": 0, "spam": 1})
+
+    # Sample and save csv files
+    train_df, validation_df, test_df = random_split(balanced_df, 0.7, 0.1)
+    train_df.to_csv("train.csv", index=None)
+    validation_df.to_csv("validation.csv", index=None)
+    test_df.to_csv("test.csv", index=None)
+
+
+def instantiate_model(choose_model):
+
+    BASE_CONFIG = {
+        "vocab_size": 50257,     # Vocabulary size
+        "context_length": 1024,  # Context length
+        "drop_rate": 0.0,        # Dropout rate
+        "qkv_bias": True         # Query-key-value bias
+    }
+
+    model_configs = {
+        "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
+        "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
+        "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
+        "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
+    }
+
+    BASE_CONFIG.update(model_configs[choose_model])
+
+    model_size = choose_model.split(" ")[-1].lstrip("(").rstrip(")")
+    settings, params = download_and_load_gpt2(model_size=model_size, models_dir="gpt2")
+
+    model = GPTModel(BASE_CONFIG)
+    load_weights_into_gpt(model, params)
+    model.eval()
+    return model
+
+
+def calc_loss_batch(input_batch, target_batch, model, device, trainable_token=-1):
+    input_batch, target_batch = input_batch.to(device), target_batch.to(device)
+    logits = model(input_batch)[:, trainable_token, :]  # Logits of last ouput token
+    loss = torch.nn.functional.cross_entropy(logits, target_batch)
+    return loss
+
+
+def calc_loss_loader(data_loader, model, device, num_batches=None, trainable_token=-1):
+    total_loss = 0.
+    if len(data_loader) == 0:
+        return float("nan")
+    elif num_batches is None:
+        num_batches = len(data_loader)
+    else:
+        # Reduce the number of batches to match the total number of batches in the data loader
+        # if num_batches exceeds the number of batches in the data loader
+        num_batches = min(num_batches, len(data_loader))
+    for i, (input_batch, target_batch) in enumerate(data_loader):
+        if i < num_batches:
+            loss = calc_loss_batch(input_batch, target_batch, model, device, trainable_token=trainable_token)
+            total_loss += loss.item()
+        else:
+            break
+    return total_loss / num_batches
+
+
+@torch.no_grad()  # Disable gradient tracking for efficiency
+def calc_accuracy_loader(data_loader, model, device, num_batches=None, trainable_token=-1):
+    model.eval()
+    correct_predictions, num_examples = 0, 0
+
+    if num_batches is None:
+        num_batches = len(data_loader)
+    else:
+        num_batches = min(num_batches, len(data_loader))
+    for i, (input_batch, target_batch) in enumerate(data_loader):
+        if i < num_batches:
+            input_batch, target_batch = input_batch.to(device), target_batch.to(device)
+            logits = model(input_batch)[:, trainable_token, :]  # Logits of last ouput token
+            predicted_labels = torch.argmax(logits, dim=-1)
+
+            num_examples += predicted_labels.shape[0]
+            correct_predictions += (predicted_labels == target_batch).sum().item()
+        else:
+            break
+    return correct_predictions / num_examples
+
+
+def evaluate_model(model, train_loader, val_loader, device, eval_iter, trainable_token=-1):
+    model.eval()
+    with torch.no_grad():
+        train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter, trainable_token=trainable_token)
+        val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter, trainable_token=trainable_token)
+    model.train()
+    return train_loss, val_loss
+
+
+def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
+                            eval_freq, eval_iter, tokenizer, max_steps=None, trainable_token=-1):
+    # Initialize lists to track losses and tokens seen
+    train_losses, val_losses, train_accs, val_accs = [], [], [], []
+    examples_seen, global_step = 0, -1
+
+    # Main training loop
+    for epoch in range(num_epochs):
+        model.train()  # Set model to training mode
+
+        for input_batch, target_batch in train_loader:
+            optimizer.zero_grad()  # Reset loss gradients from previous epoch
+            loss = calc_loss_batch(input_batch, target_batch, model, device, trainable_token=trainable_token)
+            loss.backward()  # Calculate loss gradients
+            optimizer.step()  # Update model weights using loss gradients
+            examples_seen += input_batch.shape[0]  # New: track examples instead of tokens
+            global_step += 1
+
+            # Optional evaluation step
+            if global_step % eval_freq == 0:
+                train_loss, val_loss = evaluate_model(
+                    model, train_loader, val_loader, device, eval_iter, trainable_token=trainable_token)
+                train_losses.append(train_loss)
+                val_losses.append(val_loss)
+                print(f"Ep {epoch+1} (Step {global_step:06d}): "
+                      f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
+
+            if max_steps is not None and global_step > max_steps:
+                break
+
+        # New: Calculate accuracy after each epoch
+        train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter, trainable_token=trainable_token)
+        val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter, trainable_token=trainable_token)
+        print(f"Training accuracy: {train_accuracy*100:.2f}% | ", end="")
+        print(f"Validation accuracy: {val_accuracy*100:.2f}%")
+        train_accs.append(train_accuracy)
+        val_accs.append(val_accuracy)
+
+        if max_steps is not None and global_step > max_steps:
+            break
+
+    return train_losses, val_losses, train_accs, val_accs, examples_seen
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_size",
+        type=str,
+        default="gpt2-small (124M)",
+        help=(
+            "Which GPT model to use. Options: 'gpt2-small (124M)', 'gpt2-medium (355M)',"
+            " 'gpt2-large (774M)', 'gpt2-xl (1558M)'."
+        )
+    )
+    parser.add_argument(
+        "--trainable_layers",
+        type=str,
+        default="last_block",
+        help=(
+            "Which layers to train. Options: 'all', 'last_block', 'last_layer'."
+        )
+    )
+    parser.add_argument(
+        "--trainable_token",
+        type=str,
+        default="last",
+        help=(
+            "Which token to train. Options: 'first', 'last'."
+        )
+    )
+
+    args = parser.parse_args()
+
+    if args.trainable_token == "first":
+        args.trainable_token = 0
+    elif args.trainable_token == "last":
+        args.trainable_token = -1
+    else:
+        raise ValueError("Invalid --trainable_token argument")
+
+    ###############################
+    # Instantiate dataloaders
+    ###############################
+
+    url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip"
+    zip_path = "sms_spam_collection.zip"
+    extract_to = "sms_spam_collection"
+    new_file_path = Path(extract_to) / "SMSSpamCollection.tsv"
+
+    base_path = Path(".")
+    file_names = ["train.csv", "validation.csv", "test.csv"]
+    all_exist = all((base_path / file_name).exists() for file_name in file_names)
+
+    if not all_exist:
+        download_and_unzip(url, zip_path, extract_to, new_file_path)
+        create_dataset_csvs(new_file_path)
+
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    train_dataset = SpamDataset(base_path / "train.csv", max_length=None, tokenizer=tokenizer)
+    val_dataset = SpamDataset(base_path / "validation.csv", max_length=None, tokenizer=tokenizer)
+    test_dataset = SpamDataset(base_path / "test.csv", max_length=None, tokenizer=tokenizer)
+
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    num_workers = 0
+    batch_size = 8
+
+    train_loader = DataLoader(
+        dataset=train_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        drop_last=True,
+    )
+
+    val_loader = DataLoader(
+        dataset=val_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+    )
+
+    test_loader = DataLoader(
+        dataset=test_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        drop_last=False,
+    )
+
+    ###############################
+    # Load model
+    ###############################
+
+    model = instantiate_model(args.model_size)
+    for param in model.parameters():
+        param.requires_grad = False
+
+    if args.model_size == "gpt2-small (124M)":
+        in_features = 768
+    elif args.model_size == "gpt2-medium (355M)":
+        in_features = 1024
+    elif args.model_size == "gpt2-large (774M)":
+        in_features = 1280
+    elif args.model_size == "gpt2-xl (1558M)":
+        in_features = 1280
+    else:
+        raise ValueError("Invalid --model_size argument")
+
+    torch.manual_seed(123)
+    print(model.out_head.weight.shape)
+    model.out_head = torch.nn.Linear(in_features=in_features, out_features=2)
+
+    if args.trainable_layers == "last_layer":
+        pass
+    elif args.trainable_layers == "last_block":
+        for param in model.trf_blocks[-1].parameters():
+            param.requires_grad = True
+        for param in model.final_norm.parameters():
+            param.requires_grad = True
+    elif args.trainable_layers == "all":
+        for param in model.parameters():
+            param.requires_grad = True
+    else:
+        raise ValueError("Invalid --trainable_layers argument.")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+
+    ###############################
+    # Train model
+    ###############################
+
+    start_time = time.time()
+    torch.manual_seed(123)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.1)
+
+    num_epochs = 5
+    train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
+        model, train_loader, val_loader, optimizer, device,
+        num_epochs=num_epochs, eval_freq=50, eval_iter=5,
+        tokenizer=tokenizer, max_steps=None, trainable_token=args.trainable_token
+    )
+
+    end_time = time.time()
+    execution_time_minutes = (end_time - start_time) / 60
+    print(f"Training completed in {execution_time_minutes:.2f} minutes.")
+
+    ###############################
+    # Evaluate model
+    ###############################
+
+    train_accuracy = calc_accuracy_loader(train_loader, model, device, trainable_token=args.trainable_token)
+    val_accuracy = calc_accuracy_loader(val_loader, model, device, trainable_token=args.trainable_token)
+    test_accuracy = calc_accuracy_loader(test_loader, model, device, trainable_token=args.trainable_token)
+
+    print(f"Training accuracy: {train_accuracy*100:.2f}%")
+    print(f"Validation accuracy: {val_accuracy*100:.2f}%")
+    print(f"Test accuracy: {test_accuracy*100:.2f}%")

ch06/02_additional-experiments/gpt_download.py

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+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+
+
+import os
+import requests
+import json
+import numpy as np
+import tensorflow as tf
+from tqdm import tqdm
+
+
+def download_and_load_gpt2(model_size, models_dir):
+    # Validate model size
+    allowed_sizes = ("124M", "355M", "774M", "1558M")
+    if model_size not in allowed_sizes:
+        raise ValueError(f"Model size not in {allowed_sizes}")
+
+    # Define paths
+    model_dir = os.path.join(models_dir, model_size)
+    base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
+    filenames = [
+        "checkpoint", "encoder.json", "hparams.json",
+        "model.ckpt.data-00000-of-00001", "model.ckpt.index",
+        "model.ckpt.meta", "vocab.bpe"
+    ]
+
+    # Download files
+    os.makedirs(model_dir, exist_ok=True)
+    for filename in filenames:
+        file_url = os.path.join(base_url, model_size, filename)
+        file_path = os.path.join(model_dir, filename)
+        download_file(file_url, file_path)
+
+    # Load settings and params
+    tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
+    settings = json.load(open(os.path.join(model_dir, "hparams.json")))
+    params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
+
+    return settings, params
+
+
+def download_file(url, destination):
+    # Send a GET request to download the file in streaming mode
+    response = requests.get(url, stream=True)
+
+    # Get the total file size from headers, defaulting to 0 if not present
+    file_size = int(response.headers.get("content-length", 0))
+
+    # Check if file exists and has the same size
+    if os.path.exists(destination):
+        file_size_local = os.path.getsize(destination)
+        if file_size == file_size_local:
+            print(f"File already exists and is up-to-date: {destination}")
+            return
+
+    # Define the block size for reading the file
+    block_size = 1024  # 1 Kilobyte
+
+    # Initialize the progress bar with total file size
+    progress_bar_description = url.split("/")[-1]  # Extract filename from URL
+    with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
+        # Open the destination file in binary write mode
+        with open(destination, "wb") as file:
+            # Iterate over the file data in chunks
+            for chunk in response.iter_content(block_size):
+                progress_bar.update(len(chunk))  # Update progress bar
+                file.write(chunk)  # Write the chunk to the file
+
+
+def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
+    # Initialize parameters dictionary with empty blocks for each layer
+    params = {"blocks": [{} for _ in range(settings["n_layer"])]}
+
+    # Iterate over each variable in the checkpoint
+    for name, _ in tf.train.list_variables(ckpt_path):
+        # Load the variable and remove singleton dimensions
+        variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
+
+        # Process the variable name to extract relevant parts
+        variable_name_parts = name.split("/")[1:]  # Skip the 'model/' prefix
+
+        # Identify the target dictionary for the variable
+        target_dict = params
+        if variable_name_parts[0].startswith("h"):
+            layer_number = int(variable_name_parts[0][1:])
+            target_dict = params["blocks"][layer_number]
+
+        # Recursively access or create nested dictionaries
+        for key in variable_name_parts[1:-1]:
+            target_dict = target_dict.setdefault(key, {})
+
+        # Assign the variable array to the last key
+        last_key = variable_name_parts[-1]
+        target_dict[last_key] = variable_array
+
+    return params

ch06/02_additional-experiments/previous_chapters.py

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+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+#
+# This file collects all the relevant code that we covered thus far
+# throughout Chapters 2-5.
+# This file can be run as a standalone script.
+
+import numpy as np
+import tiktoken
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+#####################################
+# Chapter 2
+#####################################
+
+
+class GPTDatasetV1(Dataset):
+    def __init__(self, txt, tokenizer, max_length, stride):
+        self.tokenizer = tokenizer
+        self.input_ids = []
+        self.target_ids = []
+
+        # Tokenize the entire text
+        token_ids = tokenizer.encode(txt)
+
+        # Use a sliding window to chunk the book into overlapping sequences of max_length
+        for i in range(0, len(token_ids) - max_length, stride):
+            input_chunk = token_ids[i:i + max_length]
+            target_chunk = token_ids[i + 1: i + max_length + 1]
+            self.input_ids.append(torch.tensor(input_chunk))
+            self.target_ids.append(torch.tensor(target_chunk))
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, idx):
+        return self.input_ids[idx], self.target_ids[idx]
+
+
+def create_dataloader_v1(txt, batch_size=4, max_length=256,
+                         stride=128, shuffle=True, drop_last=True):
+    # Initialize the tokenizer
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    # Create dataset
+    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
+
+    # Create dataloader
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+
+    return dataloader
+
+
+#####################################
+# Chapter 3
+#####################################
+class MultiHeadAttention(nn.Module):
+    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
+        super().__init__()
+        assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
+
+        self.d_out = d_out
+        self.num_heads = num_heads
+        self.head_dim = d_out // num_heads  # Reduce the projection dim to match desired output dim
+
+        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.out_proj = nn.Linear(d_out, d_out)  # Linear layer to combine head outputs
+        self.dropout = nn.Dropout(dropout)
+        self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
+
+    def forward(self, x):
+        b, num_tokens, d_in = x.shape
+
+        keys = self.W_key(x)  # Shape: (b, num_tokens, d_out)
+        queries = self.W_query(x)
+        values = self.W_value(x)
+
+        # We implicitly split the matrix by adding a `num_heads` dimension
+        # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
+        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
+        values = values.view(b, num_tokens, self.num_heads, self.head_dim)
+        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
+
+        # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
+        keys = keys.transpose(1, 2)
+        queries = queries.transpose(1, 2)
+        values = values.transpose(1, 2)
+
+        # Compute scaled dot-product attention (aka self-attention) with a causal mask
+        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head
+
+        # Original mask truncated to the number of tokens and converted to boolean
+        mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
+
+        # Use the mask to fill attention scores
+        attn_scores.masked_fill_(mask_bool, -torch.inf)
+
+        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+
+        # Shape: (b, num_tokens, num_heads, head_dim)
+        context_vec = (attn_weights @ values).transpose(1, 2)
+
+        # Combine heads, where self.d_out = self.num_heads * self.head_dim
+        context_vec = context_vec.reshape(b, num_tokens, self.d_out)
+        context_vec = self.out_proj(context_vec)  # optional projection
+
+        return context_vec
+
+
+#####################################
+# Chapter 4
+#####################################
+class LayerNorm(nn.Module):
+    def __init__(self, emb_dim):
+        super().__init__()
+        self.eps = 1e-5
+        self.scale = nn.Parameter(torch.ones(emb_dim))
+        self.shift = nn.Parameter(torch.zeros(emb_dim))
+
+    def forward(self, x):
+        mean = x.mean(dim=-1, keepdim=True)
+        var = x.var(dim=-1, keepdim=True, unbiased=False)
+        norm_x = (x - mean) / torch.sqrt(var + self.eps)
+        return self.scale * norm_x + self.shift
+
+
+class GELU(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return 0.5 * x * (1 + torch.tanh(
+            torch.sqrt(torch.tensor(2.0 / torch.pi)) *
+            (x + 0.044715 * torch.pow(x, 3))
+        ))
+
+
+class FeedForward(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
+            GELU(),
+            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.att = MultiHeadAttention(
+            d_in=cfg["emb_dim"],
+            d_out=cfg["emb_dim"],
+            context_length=cfg["context_length"],
+            num_heads=cfg["n_heads"],
+            dropout=cfg["drop_rate"],
+            qkv_bias=cfg["qkv_bias"])
+        self.ff = FeedForward(cfg)
+        self.norm1 = LayerNorm(cfg["emb_dim"])
+        self.norm2 = LayerNorm(cfg["emb_dim"])
+        self.drop_resid = nn.Dropout(cfg["drop_rate"])
+
+    def forward(self, x):
+        # Shortcut connection for attention block
+        shortcut = x
+        x = self.norm1(x)
+        x = self.att(x)   # Shape [batch_size, num_tokens, emb_size]
+        x = self.drop_resid(x)
+        x = x + shortcut  # Add the original input back
+
+        # Shortcut connection for feed-forward block
+        shortcut = x
+        x = self.norm2(x)
+        x = self.ff(x)
+        x = self.drop_resid(x)
+        x = x + shortcut  # Add the original input back
+
+        return x
+
+
+class GPTModel(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
+        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
+        self.drop_emb = nn.Dropout(cfg["drop_rate"])
+
+        self.trf_blocks = nn.Sequential(
+            *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
+
+        self.final_norm = LayerNorm(cfg["emb_dim"])
+        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
+
+    def forward(self, in_idx):
+        batch_size, seq_len = in_idx.shape
+        tok_embeds = self.tok_emb(in_idx)
+        pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
+        x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]
+        x = self.drop_emb(x)
+        x = self.trf_blocks(x)
+        x = self.final_norm(x)
+        logits = self.out_head(x)
+        return logits
+
+
+def generate_text_simple(model, idx, max_new_tokens, context_size):
+    # idx is (B, T) array of indices in the current context
+    for _ in range(max_new_tokens):
+
+        # Crop current context if it exceeds the supported context size
+        # E.g., if LLM supports only 5 tokens, and the context size is 10
+        # then only the last 5 tokens are used as context
+        idx_cond = idx[:, -context_size:]
+
+        # Get the predictions
+        with torch.no_grad():
+            logits = model(idx_cond)
+
+        # Focus only on the last time step
+        # (batch, n_token, vocab_size) becomes (batch, vocab_size)
+        logits = logits[:, -1, :]
+
+        # Get the idx of the vocab entry with the highest logits value
+        idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch, 1)
+
+        # Append sampled index to the running sequence
+        idx = torch.cat((idx, idx_next), dim=1)  # (batch, n_tokens+1)
+
+    return idx
+
+
+#####################################
+# Chapter 5
+#####################################
+def assign(left, right):
+    if left.shape != right.shape:
+        raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
+    return torch.nn.Parameter(torch.tensor(right))
+
+
+def load_weights_into_gpt(gpt, params):
+    gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
+    gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
+
+    for b in range(len(params["blocks"])):
+        q_w, k_w, v_w = np.split(
+            (params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
+        gpt.trf_blocks[b].att.W_query.weight = assign(
+            gpt.trf_blocks[b].att.W_query.weight, q_w.T)
+        gpt.trf_blocks[b].att.W_key.weight = assign(
+            gpt.trf_blocks[b].att.W_key.weight, k_w.T)
+        gpt.trf_blocks[b].att.W_value.weight = assign(
+            gpt.trf_blocks[b].att.W_value.weight, v_w.T)
+
+        q_b, k_b, v_b = np.split(
+            (params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
+        gpt.trf_blocks[b].att.W_query.bias = assign(
+            gpt.trf_blocks[b].att.W_query.bias, q_b)
+        gpt.trf_blocks[b].att.W_key.bias = assign(
+            gpt.trf_blocks[b].att.W_key.bias, k_b)
+        gpt.trf_blocks[b].att.W_value.bias = assign(
+            gpt.trf_blocks[b].att.W_value.bias, v_b)
+
+        gpt.trf_blocks[b].att.out_proj.weight = assign(
+            gpt.trf_blocks[b].att.out_proj.weight,
+            params["blocks"][b]["attn"]["c_proj"]["w"].T)
+        gpt.trf_blocks[b].att.out_proj.bias = assign(
+            gpt.trf_blocks[b].att.out_proj.bias,
+            params["blocks"][b]["attn"]["c_proj"]["b"])
+
+        gpt.trf_blocks[b].ff.layers[0].weight = assign(
+            gpt.trf_blocks[b].ff.layers[0].weight,
+            params["blocks"][b]["mlp"]["c_fc"]["w"].T)
+        gpt.trf_blocks[b].ff.layers[0].bias = assign(
+            gpt.trf_blocks[b].ff.layers[0].bias,
+            params["blocks"][b]["mlp"]["c_fc"]["b"])
+        gpt.trf_blocks[b].ff.layers[2].weight = assign(
+            gpt.trf_blocks[b].ff.layers[2].weight,
+            params["blocks"][b]["mlp"]["c_proj"]["w"].T)
+        gpt.trf_blocks[b].ff.layers[2].bias = assign(
+            gpt.trf_blocks[b].ff.layers[2].bias,
+            params["blocks"][b]["mlp"]["c_proj"]["b"])
+
+        gpt.trf_blocks[b].norm1.scale = assign(
+            gpt.trf_blocks[b].norm1.scale,
+            params["blocks"][b]["ln_1"]["g"])
+        gpt.trf_blocks[b].norm1.shift = assign(
+            gpt.trf_blocks[b].norm1.shift,
+            params["blocks"][b]["ln_1"]["b"])
+        gpt.trf_blocks[b].norm2.scale = assign(
+            gpt.trf_blocks[b].norm2.scale,
+            params["blocks"][b]["ln_2"]["g"])
+        gpt.trf_blocks[b].norm2.shift = assign(
+            gpt.trf_blocks[b].norm2.shift,
+            params["blocks"][b]["ln_2"]["b"])
+
+    gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
+    gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
+    gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
+
+
+def generate(model, idx, max_new_tokens, context_size, temperature, top_k=None):
+    # For-loop is the same as before: Get logits, and only focus on last time step
+    for _ in range(max_new_tokens):
+        idx_cond = idx[:, -context_size:]
+        with torch.no_grad():
+            logits = model(idx_cond)
+        logits = logits[:, -1, :]
+
+        # New: Filter logits with top_k sampling
+        if top_k is not None:
+            # Keep only top_k values
+            top_logits, _ = torch.topk(logits, top_k)
+            min_val = top_logits[:, -1]
+            logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
+
+        # New: Apply temperature scaling
+        if temperature > 0.0:
+            logits = logits / temperature
+
+            # Apply softmax to get probabilities
+            probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)
+
+            # Sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)
+
+        # Otherwise same as before: get idx of the vocab entry with the highest logits value
+        else:
+            idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)
+
+        # Same as before: append sampled index to the running sequence
+        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)
+
+    return idx