Add alternative weight loading strategy as backup (#82)

.gitignore

@@ -1,3 +1,4 @@
+ch05/02_alternative_weight_loading/checkpoints
 ch05/01_main-chapter-code/the-verdict.txt
 
 .DS_Store

ch05/01_main-chapter-code/README.md

@@ -0,0 +1,7 @@
+# Chapter 5: Pretraining on Unlabeled Data
+
+- [ch05.ipynb](ch05.ipynb) contains all the code as it appears in the chapter
+- [previous_chapters.py](previous_chapters.py) is a Python module that contains the `MultiHeadAttention` module from the previous chapter, which we import in [ch05.ipynb](ch05.ipynb) to pretrain the GPT model
+- [train.py](train.py) is a standalone Python script file with the code that we implemented in [ch05.ipynb](ch05.ipynb) to train the GPT model
+- [generate.py](generate.py) is a standalone Python script file with the code that we implemented in [ch05.ipynb](ch05.ipynb) to load and use the pretrained model weights from OpenAI
+

ch05/01_main-chapter-code/generate.py

@@ -199,16 +199,17 @@ def main(gpt_config, input_prompt, model_size):
     gpt = GPTModel(gpt_config)
     load_weights_into_gpt(gpt, params)
     gpt.to(device)
+    gpt.eval()
 
     tokenizer = tiktoken.get_encoding("gpt2")
 
     token_ids = generate(
         model=gpt,
         idx=text_to_token_ids(input_prompt, tokenizer),
-        max_new_tokens=65,
+        max_new_tokens=30,
         context_size=gpt_config["ctx_len"],
-        top_k=50,
-        temperature=1.5
+        top_k=1,
+        temperature=1.0
     )
 
     print("Output text:\n", token_ids_to_text(token_ids, tokenizer))
@@ -219,7 +220,7 @@ if __name__ == "__main__":
     torch.manual_seed(123)
 
     CHOOSE_MODEL = "gpt2-small"
-    INPUT_PROMPT = "Every effort moves you"
+    INPUT_PROMPT = "Every effort moves"
 
     BASE_CONFIG = {
         "vocab_size": 50257,  # Vocabulary size

ch05/02_alternative_weight_loading/README.md

@@ -0,0 +1,5 @@
+# Alternative Weight Loading
+
+This folder contains alternative weight loading strategies in case the weights become unavailable from Open AI.
+
+- [weight-loading-hf-transformers.ipynb](weight-loading-hf-transformers.ipynb): contains code to load the weights from the Hugging Face Model Hub via the `transformers` library

ch05/02_alternative_weight_loading/previous_chapters.py

@@ -0,0 +1,287 @@
+# This file collects all the relevant code that we covered thus far
+# throughout Chapters 2-4.
+# This file can be run as a standalone script.
+
+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, block_size, 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(block_size, block_size), 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"]),
+            nn.Dropout(cfg["drop_rate"])
+        )
+
+    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"],
+            block_size=cfg["ctx_len"],
+            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["ctx_len"], 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 text_to_token_ids(text, tokenizer):
+    encoded = tokenizer.encode(text)
+    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 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

ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb

@@ -0,0 +1,312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6d6bc54f-2b16-4b0f-be69-957eed5d112f",
+   "metadata": {},
+   "source": [
+    "<font size=\"1\">\n",
+    "Supplementary code for \"Build a Large Language Model From Scratch\": <a href=\"https://www.manning.com/books/build-a-large-language-model-from-scratch\">https://www.manning.com/books/build-a-large-language-model-from-scratch</a> by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
+    "Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
+    "</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72953590-5363-4398-85ce-54bde07f3d8a",
+   "metadata": {},
+   "source": [
+    "# Bonus Code for Chapter 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a4ab5ee-e7b9-45d3-a82b-a12bcfc0945a",
+   "metadata": {},
+   "source": [
+    "## Alternative Weight Loading from Hugging Face Model Hub using Transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2feea87-49f0-48b9-b925-b8f0dda4096f",
+   "metadata": {},
+   "source": [
+    "- In the main chapter, we loaded the GPT model weights directly from OpenAI\n",
+    "- This notebook provides alternative weight loading code to load the model weights from the [Hugging Face Model Hub](https://huggingface.co/docs/hub/en/models-the-hub) using the `transformers` Python library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99b77109-5215-4d07-a618-4d10eff1a488",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0467eff-b43c-4a38-93e8-5ed87a5fc2b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "numpy version: 1.25.2\n",
+      "torch version: 2.2.1\n",
+      "transformers version: 4.33.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from importlib.metadata import version\n",
+    "\n",
+    "pkgs = [\"numpy\", \"torch\", \"transformers\"]\n",
+    "for p in pkgs:\n",
+    "    print(f\"{p} version: {version(p)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ffc17d7d-bcd8-42ee-82a9-04fd55acf15d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sebastian/miniforge3/envs/book/lib/python3.11/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+      "  torch.utils._pytree._register_pytree_node(\n",
+      "/Users/sebastian/miniforge3/envs/book/lib/python3.11/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+      "  torch.utils._pytree._register_pytree_node(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GPT2Model(\n",
+       "  (wte): Embedding(50257, 768)\n",
+       "  (wpe): Embedding(1024, 768)\n",
+       "  (drop): Dropout(p=0.1, inplace=False)\n",
+       "  (h): ModuleList(\n",
+       "    (0-11): 12 x GPT2Block(\n",
+       "      (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+       "      (attn): GPT2Attention(\n",
+       "        (c_attn): Conv1D()\n",
+       "        (c_proj): Conv1D()\n",
+       "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
+       "        (resid_dropout): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+       "      (mlp): GPT2MLP(\n",
+       "        (c_fc): Conv1D()\n",
+       "        (c_proj): Conv1D()\n",
+       "        (act): NewGELUActivation()\n",
+       "        (dropout): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import GPT2Model\n",
+    "\n",
+    "\n",
+    "# allowed model names\n",
+    "model_names = {\n",
+    "    \"gpt2-small\": \"openai-community/gpt2\",         # 124M\n",
+    "    \"gpt2-medium\": \"openai-community/gpt2-medium\", # 355M\n",
+    "    \"gpt2-large\": \"openai-community/gpt2-large\",   # 774M\n",
+    "    \"gpt2-xl\": \"openai-community/gpt2-xl\"          # 1558M\n",
+    "}\n",
+    "\n",
+    "CHOOSE_MODEL = \"gpt2-small\"\n",
+    "\n",
+    "gpt_hf = GPT2Model.from_pretrained(model_names[CHOOSE_MODEL], cache_dir=\"checkpoints\")\n",
+    "gpt_hf.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9ea9b1bc-7881-46ad-9555-27a9cf23faa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_CONFIG = {\n",
+    "    \"vocab_size\": 50257,  # Vocabulary size\n",
+    "    \"ctx_len\": 1024,      # Context length\n",
+    "    \"drop_rate\": 0.0,     # Dropout rate\n",
+    "    \"qkv_bias\": True      # Query-key-value bias\n",
+    "}\n",
+    "\n",
+    "model_configs = {\n",
+    "    \"gpt2-small\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
+    "    \"gpt2-medium\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
+    "    \"gpt2-large\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
+    "    \"gpt2-xl\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
+    "}\n",
+    "\n",
+    "\n",
+    "BASE_CONFIG.update(model_configs[CHOOSE_MODEL])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4e2a4cf4-a54e-4307-9141-fb9f288e4dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def assign_check(left, right):\n",
+    "    if left.shape != right.shape:\n",
+    "        raise ValueError(f\"Shape mismatch. Left: {left.shape}, Right: {right.shape}\")\n",
+    "    return torch.nn.Parameter(torch.tensor(right))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "75be3077-f141-44bb-af88-62580ffd224c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def load_weights(gpt, gpt_hf):\n",
+    "\n",
+    "    d = gpt_hf.state_dict()\n",
+    "\n",
+    "    gpt.pos_emb.weight = assign_check(gpt.pos_emb.weight, d[\"wpe.weight\"])\n",
+    "    gpt.tok_emb.weight = assign_check(gpt.tok_emb.weight, d[\"wte.weight\"])\n",
+    "    \n",
+    "    for b in range(BASE_CONFIG[\"n_layers\"]):\n",
+    "        q_w, k_w, v_w = np.split(d[f\"h.{b}.attn.c_attn.weight\"], 3, axis=-1)\n",
+    "        gpt.trf_blocks[b].att.W_query.weight = assign_check(gpt.trf_blocks[b].att.W_query.weight, q_w.T)\n",
+    "        gpt.trf_blocks[b].att.W_key.weight = assign_check(gpt.trf_blocks[b].att.W_key.weight, k_w.T)\n",
+    "        gpt.trf_blocks[b].att.W_value.weight = assign_check(gpt.trf_blocks[b].att.W_value.weight, v_w.T)\n",
+    "    \n",
+    "        q_b, k_b, v_b = np.split(d[f\"h.{b}.attn.c_attn.bias\"], 3, axis=-1)\n",
+    "        gpt.trf_blocks[b].att.W_query.bias = assign_check(gpt.trf_blocks[b].att.W_query.bias, q_b)\n",
+    "        gpt.trf_blocks[b].att.W_key.bias = assign_check(gpt.trf_blocks[b].att.W_key.bias, k_b)\n",
+    "        gpt.trf_blocks[b].att.W_value.bias = assign_check(gpt.trf_blocks[b].att.W_value.bias, v_b)\n",
+    "    \n",
+    "    \n",
+    "        gpt.trf_blocks[b].att.out_proj.weight = assign_check(gpt.trf_blocks[b].att.out_proj.weight, d[f\"h.{b}.attn.c_proj.weight\"].T)\n",
+    "        gpt.trf_blocks[b].att.out_proj.bias = assign_check(gpt.trf_blocks[b].att.out_proj.bias, d[f\"h.{b}.attn.c_proj.bias\"])\n",
+    "    \n",
+    "        gpt.trf_blocks[b].ff.layers[0].weight = assign_check(gpt.trf_blocks[b].ff.layers[0].weight, d[f\"h.{b}.mlp.c_fc.weight\"].T)\n",
+    "        gpt.trf_blocks[b].ff.layers[0].bias = assign_check(gpt.trf_blocks[b].ff.layers[0].bias, d[f\"h.{b}.mlp.c_fc.bias\"])\n",
+    "        gpt.trf_blocks[b].ff.layers[2].weight = assign_check(gpt.trf_blocks[b].ff.layers[2].weight, d[f\"h.{b}.mlp.c_proj.weight\"].T)\n",
+    "        gpt.trf_blocks[b].ff.layers[2].bias = assign_check(gpt.trf_blocks[b].ff.layers[2].bias, d[f\"h.{b}.mlp.c_proj.bias\"])\n",
+    "    \n",
+    "        gpt.trf_blocks[b].norm1.scale = assign_check(gpt.trf_blocks[b].norm1.scale, d[f\"h.{b}.ln_1.weight\"])\n",
+    "        gpt.trf_blocks[b].norm1.shift = assign_check(gpt.trf_blocks[b].norm1.shift, d[f\"h.{b}.ln_1.bias\"])\n",
+    "        gpt.trf_blocks[b].norm2.scale = assign_check(gpt.trf_blocks[b].norm2.scale, d[f\"h.{b}.ln_2.weight\"])\n",
+    "        gpt.trf_blocks[b].norm2.shift = assign_check(gpt.trf_blocks[b].norm2.shift, d[f\"h.{b}.ln_2.bias\"])\n",
+    "    \n",
+    "        gpt.final_norm.scale = assign_check(gpt.final_norm.scale, d[f\"ln_f.weight\"])\n",
+    "        gpt.final_norm.shift = assign_check(gpt.final_norm.shift, d[f\"ln_f.bias\"])\n",
+    "        gpt.out_head.weight = assign_check(gpt.out_head.weight, d[\"wte.weight\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cda44d37-92c0-4c19-a70a-15711513afce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/jg/tpqyh1fd5js5wsr1d138k3n40000gn/T/ipykernel_32618/3877979348.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  return torch.nn.Parameter(torch.tensor(right))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from previous_chapters import GPTModel\n",
+    "\n",
+    "\n",
+    "gpt = GPTModel(BASE_CONFIG)\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "load_weights(gpt, gpt_hf)\n",
+    "gpt.to(device);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4ddd0d51-3ade-4890-9bab-d63f141d095f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output text:\n",
+      " Every effort moves forward, but it's not enough.\n",
+      "\n",
+      "\"I'm not going to sit here and say, 'I'm not going to do this,'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tiktoken\n",
+    "from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
+    "\n",
+    "torch.manual_seed(123)\n",
+    "\n",
+    "tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
+    "\n",
+    "token_ids = generate(\n",
+    "    model=gpt,\n",
+    "    idx=text_to_token_ids(\"Every effort moves\", tokenizer),\n",
+    "    max_new_tokens=30,\n",
+    "    context_size=BASE_CONFIG[\"ctx_len\"],\n",
+    "    top_k=1,\n",
+    "    temperature=1.0\n",
+    ")\n",
+    "\n",
+    "print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

ch05/README.md

@@ -1,4 +1,6 @@
 # Chapter 5: Pretraining on Unlabeled Data
 
-- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code.
-- [02_hparam_tuning](02_hparam_tuning) contains an optional hyperparameter tuning script
+- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code
+- [02_alternative_weight_loading](02_alternative_weight_loading) contains code to load the GPT model weights from alternative places in case the model weights become unavailable from OpenAI
+- [03_bonus_pretraining_on_gutenberg](03_bonus_pretraining_on_gutenberg) contains code to pretrain the LLM longer on the whole corpus of books from Project Gutenberg
+- [04_hparam_tuning](04_hparam_tuning) contains an optional hyperparameter tuning script