Appendix E: Parameter-efficient Finetuning with LoRA (#142)

README.md

@@ -58,7 +58,7 @@ Alternatively, you can view this and other files on GitHub at [https://github.co
 | Appendix B: References and Further Reading                 | No code                                                                                                                         | -                             |
 | Appendix C: Exercise Solutions                             | No code                                                                                                                         | -                             |
 | Appendix D: Adding Bells and Whistles to the Training Loop | - [appendix-D.ipynb](appendix-D/01_main-chapter-code/appendix-D.ipynb)                                                          | [./appendix-D](./appendix-D)  |
-| Appendix E: Parameter-efficient Finetuning with LoRA       | - Q2 2024                                                                                                                       | ...                           |
+| Appendix E: Parameter-efficient Finetuning with LoRA       | - [appendix-E.ipynb](appendix-E/01_main-chapter-code/appendix-E.ipynb)                                                 | [./appendix-E](./appendix-E) |
 
 
 

appendix-E/01_main-chapter-code/gpt_download.py

@@ -0,0 +1,99 @@
+# 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

appendix-E/01_main-chapter-code/previous_chapters.py

@@ -0,0 +1,542 @@
+# 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 2-6.
+# This file can be run as a standalone script.
+
+import os
+from pathlib import Path
+import urllib
+import zipfile
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+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 text_to_token_ids(text, tokenizer):
+    encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
+    encoded_tensor = torch.tensor(encoded).unsqueeze(0)  # add batch dimension
+    return encoded_tensor
+
+
+def token_ids_to_text(token_ids, tokenizer):
+    flat = token_ids.squeeze(0)  # remove batch dimension
+    return tokenizer.decode(flat.tolist())
+
+
+def calc_loss_loader(data_loader, model, device, num_batches=None):
+    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)
+            total_loss += loss.item()
+        else:
+            break
+    return total_loss / num_batches
+
+
+def evaluate_model(model, train_loader, val_loader, device, eval_iter):
+    model.eval()
+    with torch.no_grad():
+        train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
+        val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
+    model.train()
+    return train_loss, val_loss
+
+
+#####################################
+# Chapter 6
+#####################################
+
+
+def download_and_unzip(url, zip_path, extracted_path, data_file_path):
+    if data_file_path.exists():
+        print(f"{data_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(extracted_path)
+
+    # Add .tsv file extension
+    original_file_path = Path(extracted_path) / "SMSSpamCollection"
+    os.rename(original_file_path, data_file_path)
+    print(f"File downloaded and saved as {data_file_path}")
+
+
+def create_balanced_dataset(df):
+
+    # Count the instances of "spam"
+    num_spam = df[df["Label"] == "spam"].shape[0]
+
+    # Randomly sample "ham' instances to match the number of 'spam' instances
+    ham_subset = df[df["Label"] == "ham"].sample(num_spam, random_state=123)
+
+    # Combine ham "subset" with "spam"
+    balanced_df = pd.concat([ham_subset, df[df["Label"] == "spam"]])
+
+    return balanced_df
+
+
+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
+
+
+class SpamDataset(Dataset):
+    def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
+        self.data = pd.read_csv(csv_file)
+
+        # Pre-tokenize texts
+        self.encoded_texts = [
+            tokenizer.encode(text) for text in self.data["Text"]
+        ]
+
+        if max_length is None:
+            self.max_length = self._longest_encoded_length()
+        else:
+            self.max_length = max_length
+            # Truncate sequences if they are longer than max_length
+            self.encoded_texts = [
+                encoded_text[:self.max_length]
+                for encoded_text in self.encoded_texts
+            ]
+
+        # Pad sequences to the longest sequence
+        self.encoded_texts = [
+            encoded_text + [pad_token_id] * (self.max_length - len(encoded_text))
+            for encoded_text 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):
+        max_length = 0
+        for encoded_text in self.encoded_texts:
+            encoded_length = len(encoded_text)
+            if encoded_length > max_length:
+                max_length = encoded_length
+        return max_length
+
+
+@torch.no_grad()  # Disable gradient tracking for efficiency
+def calc_accuracy_loader(data_loader, model, device, num_batches=None):
+    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)[:, -1, :]  # 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 calc_loss_batch(input_batch, target_batch, model, device):
+    input_batch, target_batch = input_batch.to(device), target_batch.to(device)
+    logits = model(input_batch)[:, -1, :]  # Logits of last ouput token
+    loss = torch.nn.functional.cross_entropy(logits, target_batch)
+    return loss
+
+
+# Overall the same as `train_model_simple` in chapter 5
+def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
+                            eval_freq, eval_iter, tokenizer):
+    # 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)
+            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)
+                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}")
+
+        # Calculate accuracy after each epoch
+        train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter)
+        val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter)
+        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)
+
+    return train_losses, val_losses, train_accs, val_accs, examples_seen
+
+
+def plot_values(epochs_seen, examples_seen, train_values, val_values, label="loss"):
+    fig, ax1 = plt.subplots(figsize=(5, 3))
+
+    # Plot training and validation loss against epochs
+    ax1.plot(epochs_seen, train_values, label=f"Training {label}")
+    ax1.plot(epochs_seen, val_values, linestyle="-.", label=f"Validation {label}")
+    ax1.set_xlabel("Epochs")
+    ax1.set_ylabel(label.capitalize())
+    ax1.legend()
+
+    # Create a second x-axis for tokens seen
+    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis
+    ax2.plot(examples_seen, train_values, alpha=0)  # Invisible plot for aligning ticks
+    ax2.set_xlabel("Examples seen")
+
+    fig.tight_layout()  # Adjust layout to make room
+    plt.savefig(f"{label}-plot.pdf")
+    plt.show()

appendix-E/README.md

@@ -0,0 +1,3 @@
+# Appendix E: Parameter-efficient Finetuning with LoRA
+
+- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code.