GPT to Llama (#368)

* GPT to Llama

* fix urls

.gitignore

@@ -34,6 +34,8 @@ ch05/01_main-chapter-code/model.pth
 ch05/01_main-chapter-code/model_and_optimizer.pth
 ch05/03_bonus_pretraining_on_gutenberg/model_checkpoints
 ch05/06_user_interface/gpt2
+ch05/07_gpt_to_llama/models--meta-llama--Llama-2-7b
+ch05/07_gpt_to_llama/models--meta-llama--Llama-2-7b-chat
 
 ch06/01_main-chapter-code/gpt2
 ch06/02_bonus_additional-experiments/gpt2

README.md

@@ -116,6 +116,7 @@ Several folders contain optional materials as a bonus for interested readers:
   - [Adding Bells and Whistles to the Training Loop](ch05/04_learning_rate_schedulers)
   - [Optimizing Hyperparameters for Pretraining](ch05/05_bonus_hparam_tuning)
   - [Building a User Interface to Interact With the Pretrained LLM](ch05/06_user_interface)
+  - [Converting GPT to Llama](ch05/07_gpt_to_llama)
 - **Chapter 6:**
   - [Additional experiments finetuning different layers and using larger models](ch06/02_bonus_additional-experiments)
   - [Finetuning different models on 50k IMDB movie review dataset](ch06/03_bonus_imdb-classification)

ch05/07_gpt_to_llama/README.md

@@ -0,0 +1,7 @@
+# Converting GPT to Llama
+
+
+
+This folder contains code for converting the GPT implementation from chapter 4 and 5 to Meta AI's Llama architecture:
+
+- [converting-gpt-to-llama2.ipynb](converting-gpt-to-llama2.ipynb): contains code to convert GPT to Llama 2 7B step by step and loads pretrained weights from Meta AI

ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb

@@ -0,0 +1,1568 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0_xya1nyDHfY",
+   "metadata": {
+    "id": "0_xya1nyDHfY"
+   },
+   "source": [
+    "<table style=\"width:100%\">\n",
+    "<tr>\n",
+    "<td style=\"vertical-align:middle; text-align:left;\">\n",
+    "<font size=\"2\">\n",
+    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
+    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
+    "</font>\n",
+    "</td>\n",
+    "<td style=\"vertical-align:middle; text-align:left;\">\n",
+    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
+    "</td>\n",
+    "</tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "l62zIRRSBy_R",
+   "metadata": {
+    "id": "l62zIRRSBy_R"
+   },
+   "source": [
+    "# Converting a From-Scratch GPT Architecture to Llama 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aFmxTQbwCUMl",
+   "metadata": {
+    "id": "aFmxTQbwCUMl"
+   },
+   "source": [
+    "- In this notebook, we convert the original GPT and GPT-2 architecture into a Llama 2 model step by step\n",
+    "- Why not Llama 1 or Llama 3?\n",
+    " - The Llama 1 architecture is similar to Llama 2, except that Llama 2 has a larger context window (which is nice); the Llama 1 weights are not readily available and have more usage restrictions, so it makes more sense to focus on Llama 2\n",
+    " - Regarding Llama 3, I will share a separate notebook to convert Llama 2 to Llama 3 (there are only a few small additional changes)\n",
+    "- The explanations are purposefully kept minimal in this notebook not to bloat it unnecessarily and focus on the main code\n",
+    "- For more information, please see the Llama 2 paper: [Llama 2: Open Foundation and Fine-Tuned Chat Models (2023)](https://arxiv.org/abs/2307.09288)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ohhMKUWvGm9z",
+   "metadata": {
+    "id": "ohhMKUWvGm9z"
+   },
+   "source": [
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/gpt2-to-llama2.webp?1\" width=\"800px\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "JBpQwU89ETA1",
+   "metadata": {
+    "id": "JBpQwU89ETA1"
+   },
+   "source": [
+    "- Packages that are being used in this notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "34a9a440-84c2-42cc-808b-38677cb6af8a",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "34a9a440-84c2-42cc-808b-38677cb6af8a",
+    "outputId": "d0fc89be-74a3-40d0-bc4d-7f6f1febf2cd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "huggingface_hub version: 0.24.7\n",
+      "sentencepiece version: 0.1.99\n",
+      "torch version: 2.4.1+cu121\n"
+     ]
+    }
+   ],
+   "source": [
+    "from importlib.metadata import version\n",
+    "\n",
+    "pkgs = [\n",
+    "    \"huggingface_hub\",  # to download pretrained weights\n",
+    "    \"sentencepiece\",    # to implement the tokenizer\n",
+    "    \"torch\",            # to implement the model\n",
+    "]\n",
+    "for p in pkgs:\n",
+    "    print(f\"{p} version: {version(p)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "UJJneXpTEg4W",
+   "metadata": {
+    "id": "UJJneXpTEg4W"
+   },
+   "source": [
+    "## 1. Convert the GPT model implementation step by step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "v1zpfX2GHBKa",
+   "metadata": {
+    "id": "v1zpfX2GHBKa"
+   },
+   "source": [
+    "- In this section, we go through the GPT model code from [chapter 4](../../ch04/01_main-chapter-code/ch04.ipynb) and modify it step by step to implement the Llama 2 architecture\n",
+    "- Later, we load the original Llama 2 weights shared by Meta AI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "979c7b6d-1370-4da1-8bfb-a2b27537bf2f",
+   "metadata": {
+    "id": "979c7b6d-1370-4da1-8bfb-a2b27537bf2f"
+   },
+   "source": [
+    "### 1.2 Replace LayerNorm with RMSNorm layer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8b27fc8-23a1-4e0e-a1ea-792e0428e5e6",
+   "metadata": {
+    "id": "f8b27fc8-23a1-4e0e-a1ea-792e0428e5e6"
+   },
+   "source": [
+    "- First, we replace LayerNorm by Root Mean Square Layer Normalization (RMSNorm)\n",
+    "- LayerNorm normalizes inputs using mean and variance, while RMSNorm uses only the root mean square, which improves computational efficiency\n",
+    "- The RMSNorm operation is as follows, where $x$ is the input $\\gamma$ is a trainable parameter (vector), and $\\epsilon$ is a small constant to avoid zero-division errors:\n",
+    "\n",
+    "$$y = \\frac{x}{\\sqrt{\\text{RMS}[x]} + \\epsilon} * \\gamma$$\n",
+    "\n",
+    "- For more details, please see the paper [Root Mean Square Layer Normalization (2019)](https://arxiv.org/abs/1910.07467)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d7094381-9499-4e9e-93f9-b79470da3771",
+   "metadata": {
+    "id": "d7094381-9499-4e9e-93f9-b79470da3771"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "\n",
+    "#####################################\n",
+    "# Chapter 4\n",
+    "#####################################\n",
+    "\n",
+    "# class LayerNorm(nn.Module):\n",
+    "#     def __init__(self, emb_dim):\n",
+    "#         super().__init__()\n",
+    "#         self.eps = 1e-5\n",
+    "#         self.scale = nn.Parameter(torch.ones(emb_dim))\n",
+    "#         self.shift = nn.Parameter(torch.zeros(emb_dim))\n",
+    "\n",
+    "#     def forward(self, x):\n",
+    "#         mean = x.mean(dim=-1, keepdim=True)\n",
+    "#         var = x.var(dim=-1, keepdim=True, unbiased=False)\n",
+    "#         norm_x = (x - mean) / torch.sqrt(var + self.eps)\n",
+    "#         return self.scale * norm_x + self.shift\n",
+    "\n",
+    "\n",
+    "class RMSNorm(nn.Module):\n",
+    "    def __init__(self, emb_dim, eps=1e-6):\n",
+    "        super().__init__()\n",
+    "        self.eps = eps\n",
+    "        self.emb_dim = emb_dim\n",
+    "        self.weight = nn.Parameter(torch.ones(emb_dim)).float()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        means = x.pow(2).mean(dim=-1, keepdim=True)\n",
+    "        x_normed = x * torch.rsqrt(means + self.eps)\n",
+    "        return (x_normed * self.weight).to(dtype=x.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mtWC8DOmIu0F",
+   "metadata": {
+    "id": "mtWC8DOmIu0F"
+   },
+   "source": [
+    "- The following code cell checks that this implementation works the same as PyTorch's built-in implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e41ade7a-bf06-48b1-8b7e-0e4037d5753f",
+   "metadata": {
+    "id": "e41ade7a-bf06-48b1-8b7e-0e4037d5753f"
+   },
+   "outputs": [],
+   "source": [
+    "torch.manual_seed(123)\n",
+    "\n",
+    "example_batch = torch.randn(2, 3, 4)\n",
+    "\n",
+    "rms_norm = RMSNorm(emb_dim=example_batch.shape[-1])\n",
+    "rmsnorm_pytorch = torch.nn.RMSNorm(example_batch.shape[-1], eps=1e-6)\n",
+    "\n",
+    "assert torch.allclose(rms_norm(example_batch), rmsnorm_pytorch(example_batch))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5eb81f83-c38c-46a4-b763-aa630a32e357",
+   "metadata": {
+    "id": "5eb81f83-c38c-46a4-b763-aa630a32e357"
+   },
+   "source": [
+    "## Replace GELU with SiLU activation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b8aa702-f118-4ff6-9135-90725ec8756c",
+   "metadata": {
+    "id": "0b8aa702-f118-4ff6-9135-90725ec8756c"
+   },
+   "source": [
+    "- Llama uses the SiLU activation function (instead of GELU), which is also known as the Swish function:\n",
+    "\n",
+    "$$\n",
+    "\\text{silu}(x) = x \\cdot \\sigma(x), \\quad \\text{where} \\quad \\sigma(x) \\text{ is the logistic sigmoid.}\n",
+    "$$\n",
+    "\n",
+    "- For more information, see the SiLU paper: [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning (2017)](https://arxiv.org/abs/1702.03118)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a74f3757-c634-4a3a-a8f3-6334cde454fe",
+   "metadata": {
+    "id": "a74f3757-c634-4a3a-a8f3-6334cde454fe"
+   },
+   "outputs": [],
+   "source": [
+    "#####################################\n",
+    "# Chapter 4\n",
+    "#####################################\n",
+    "\n",
+    "# class GELU(nn.Module):\n",
+    "#     def __init__(self):\n",
+    "#         super().__init__()\n",
+    "\n",
+    "#     def forward(self, x):\n",
+    "#         return 0.5 * x * (1 + torch.tanh(\n",
+    "#             torch.sqrt(torch.tensor(2.0 / torch.pi)) *\n",
+    "#             (x + 0.044715 * torch.pow(x, 3))\n",
+    "#         ))\n",
+    "\n",
+    "\n",
+    "class SiLU(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(SiLU, self).__init__()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return x * torch.sigmoid(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "72ecbe2e-b6b7-4319-972b-1a7fefa3368c",
+   "metadata": {
+    "id": "72ecbe2e-b6b7-4319-972b-1a7fefa3368c"
+   },
+   "outputs": [],
+   "source": [
+    "silu = SiLU()\n",
+    "\n",
+    "assert torch.allclose(silu(example_batch), torch.nn.functional.silu(example_batch))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f9b5167-1da9-46c8-9964-8036b3b1deb9",
+   "metadata": {
+    "id": "4f9b5167-1da9-46c8-9964-8036b3b1deb9"
+   },
+   "source": [
+    "## Update the FeedForward module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a381e7a-b807-472e-91c9-3e4e3fc5ad91",
+   "metadata": {
+    "id": "3a381e7a-b807-472e-91c9-3e4e3fc5ad91"
+   },
+   "source": [
+    "- In fact, Llama uses a \"Gates Linear Unit\" (GLU) variant of SiLU called SwiGLU, which essentially results in a slightly differently structured `FeedForward` module\n",
+    "- SwiGLU uses a gating mechanism in the feedforward layer, with the formula:\n",
+    "\n",
+    "$$\\text{SwiGLU}(x) = (\\text{Linear}_1(x) * \\text{SiLU}(\\text{Linear}_2(x)))$$\n",
+    "\n",
+    "- Here, $\\text{Linear}_1$ and $\\text{Linear}_2$ are two linear layers, and $*$ denotes element-wise multiplication\n",
+    "- The third linear layer, $\\text{Linear}_3$, is applied after this gated activation\n",
+    "\n",
+    "- For more information, see SwiGLU paper: [GLU Variants Improve Transformer (2020)](https://arxiv.org/abs/2002.05202)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d25fbe3d-b7c9-4772-ad67-bc0527e4e20a",
+   "metadata": {
+    "id": "d25fbe3d-b7c9-4772-ad67-bc0527e4e20a"
+   },
+   "outputs": [],
+   "source": [
+    "#####################################\n",
+    "# Chapter 4\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",
+    "#         )\n",
+    "\n",
+    "#     def forward(self, x):\n",
+    "#         return self.layers(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "477568cb-03cd-4510-b663-a42ce3ec64a2",
+   "metadata": {
+    "id": "477568cb-03cd-4510-b663-a42ce3ec64a2"
+   },
+   "outputs": [],
+   "source": [
+    "class FeedForward(nn.Module):\n",
+    "    def __init__(self, cfg):\n",
+    "        super().__init__()\n",
+    "        self.fc1 = nn.Linear(cfg[\"emb_dim\"], cfg[\"hidden_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
+    "        self.fc2 = nn.Linear(cfg[\"emb_dim\"], cfg[\"hidden_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
+    "        self.fc3 = nn.Linear(cfg[\"hidden_dim\"], cfg[\"emb_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
+    "        self.silu = SiLU()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x_fc1 = self.fc1(x)\n",
+    "        x_fc2 = self.fc2(x)\n",
+    "        x = self.silu(x_fc1) * x_fc2\n",
+    "        return self.fc3(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qcD8LSHNhBRW",
+   "metadata": {
+    "id": "qcD8LSHNhBRW"
+   },
+   "source": [
+    "- Note that we also added a `dtype=cfg[\"dtype\"]` setting above, which will allow us to load the model directly in lower precision formats later to save memory (versus instantiating it in the original 32-bit precision format and then converting it)\n",
+    "- We also set `bias=False` since Llama doesn't use any bias units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6b7bf4f-99d0-42c1-807c-5074d2cc1949",
+   "metadata": {
+    "id": "f6b7bf4f-99d0-42c1-807c-5074d2cc1949"
+   },
+   "source": [
+    "## Implement RoPE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3487a6f-0373-49d8-b2eb-f8ee05d42884",
+   "metadata": {
+    "id": "d3487a6f-0373-49d8-b2eb-f8ee05d42884"
+   },
+   "source": [
+    "- In the GPT model, the positional embeddings are implemented as follows:\n",
+    "\n",
+    "```python\n",
+    "self.pos_emb = nn.Embedding(cfg[\"context_length\"], cfg[\"emb_dim\"])\n",
+    "```\n",
+    "\n",
+    "- Instead of these absolute positional embeddings, Llama uses relative positional embeddings, called rotary position embeddings (RoPE for short)\n",
+    "- The reference paper for RoPE is [RoFormer: Enhanced Transformer with Rotary Position Embedding (2021)](https://arxiv.org/abs/2104.09864)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a34180fb-448f-44e9-a244-0c736051687b",
+   "metadata": {
+    "id": "a34180fb-448f-44e9-a244-0c736051687b"
+   },
+   "outputs": [],
+   "source": [
+    "def precompute_rope_params(head_dim, context_length=4096):\n",
+    "    assert head_dim % 2 == 0, \"Embedding dimension must be even\"\n",
+    "\n",
+    "    # Compute the inverse frequencies\n",
+    "    inv_freq = 1.0 / (10000 ** (torch.arange(0, head_dim, 2) / head_dim))\n",
+    "\n",
+    "    # Generate position indices\n",
+    "    positions = torch.arange(context_length)\n",
+    "\n",
+    "    # Compute the angles using inverse frequencies and positions\n",
+    "    angles = positions[:, None] * inv_freq[None, :]  # Shape: (context_length, emb_dim // 2)\n",
+    "\n",
+    "    # Precompute sine and cosine of the angles\n",
+    "    sin = torch.sin(angles)  # Shape: (context_length, emb_dim // 2)\n",
+    "    cos = torch.cos(angles)  # Shape: (context_length, emb_dim // 2)\n",
+    "\n",
+    "    return sin, cos\n",
+    "\n",
+    "\n",
+    "def compute_rope(x, sin, cos):\n",
+    "    # x: (batch_size, num_heads, seq_len, head_dim)\n",
+    "    batch_size, num_heads, seq_len, head_dim = x.shape\n",
+    "    assert head_dim % 2 == 0, \"Head dimension must be even\"\n",
+    "\n",
+    "    # Split x into even and odd parts\n",
+    "    x1 = x[..., ::2]  # Shape: (batch_size, num_heads, seq_len, head_dim // 2)\n",
+    "    x2 = x[..., 1::2]\n",
+    "\n",
+    "    # Ensure sin and cos have correct shapes\n",
+    "    sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)  # Shape: (1, 1, seq_len, head_dim // 2)\n",
+    "    cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)\n",
+    "\n",
+    "    # Apply the rotary transformation\n",
+    "    x_rotated_0 = x1 * cos - x2 * sin\n",
+    "    x_rotated_1 = x1 * sin + x2 * cos\n",
+    "\n",
+    "    # Interleave x_rotated_0 and x_rotated_1\n",
+    "    x_rotated = torch.stack((x_rotated_0, x_rotated_1), dim=-1)\n",
+    "    x_rotated = x_rotated.flatten(-2)\n",
+    "\n",
+    "    return x_rotated.to(dtype=x.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e841b8e-75aa-49db-b1a7-d5c2116dc299",
+   "metadata": {
+    "id": "8e841b8e-75aa-49db-b1a7-d5c2116dc299"
+   },
+   "source": [
+    "- The following is an example of applying RoPE to the `q` and `k` tensors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8c89f022-7167-4001-8c21-8e012878733f",
+   "metadata": {
+    "id": "8c89f022-7167-4001-8c21-8e012878733f"
+   },
+   "outputs": [],
+   "source": [
+    "# Settings\n",
+    "batch_size = 2\n",
+    "context_len = 5\n",
+    "num_heads = 4\n",
+    "head_dim = 16\n",
+    "\n",
+    "# Instantiate RoPE parameters\n",
+    "sin, cos = precompute_rope_params(head_dim=head_dim, context_length=context_len)\n",
+    "\n",
+    "# Dummy query and key tensors\n",
+    "torch.manual_seed(123)\n",
+    "queries = torch.randn(batch_size, context_len, num_heads, head_dim)\n",
+    "keys = torch.randn(batch_size, context_len, num_heads, head_dim)\n",
+    "\n",
+    "# Apply rotary position embeddings\n",
+    "queries_rot = compute_rope(queries, sin, cos)\n",
+    "keys_rot = compute_rope(keys, sin, cos)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f78127b0-dda2-4c5a-98dd-bae8f5fe8297",
+   "metadata": {
+    "id": "f78127b0-dda2-4c5a-98dd-bae8f5fe8297"
+   },
+   "source": [
+    "## Add RoPE to MultiHeadAttention module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "RnmSHROLhhR3",
+   "metadata": {
+    "id": "RnmSHROLhhR3"
+   },
+   "source": [
+    "- It's important to note that GPT applies the positional embeddings to the inputs, whereas Llama applies rotations to the query and key vectors in the self-attention mechanism itself\n",
+    "- Here, we modify the `MultiHeadAttention` class with the appropriate RoPE code\n",
+    "- In addition, we remove the `qkv_bias` option and hardcode the `bias=False` setting\n",
+    "- Also, we add a dtype setting to be able to instantiate the model with a lower precision later\n",
+    " - Tip: since the `TransformerBlock's (in the next section) are repeated exactly, we could simplify the code and only initialize the buffers once instead for each `MultiHeadAttention` module; however, we add the precomputed RoPE parameters to the `MultiHeadAttention` class so that it can function as a standalone module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d81a441e-0b79-4a8b-8291-ea7f55d58c84",
+   "metadata": {
+    "id": "d81a441e-0b79-4a8b-8291-ea7f55d58c84"
+   },
+   "outputs": [],
+   "source": [
+    "#####################################\n",
+    "# Chapter 3\n",
+    "#####################################\n",
+    "class MultiHeadAttention(nn.Module):\n",
+    "    def __init__(self, d_in, d_out, context_length, num_heads, dtype=None):  # ,dropout, num_heads, qkv_bias=False):\n",
+    "        super().__init__()\n",
+    "        assert d_out % num_heads == 0, \"d_out must be divisible by n_heads\"\n",
+    "\n",
+    "        self.d_out = d_out\n",
+    "        self.num_heads = num_heads\n",
+    "        self.head_dim = d_out // num_heads  # Reduce the projection dim to match desired output dim\n",
+    "\n",
+    "        ################################### NEW ###################################\n",
+    "        # Set bias=False and dtype=dtype for all linear layers below\n",
+    "        ###########################################################################\n",
+    "        self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)\n",
+    "        self.W_key = nn.Linear(d_in, d_out, bias=False, dtype=dtype)\n",
+    "        self.W_value = nn.Linear(d_in, d_out, bias=False, dtype=dtype)\n",
+    "        self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)  # Linear layer to combine head outputs\n",
+    "        # self.dropout = nn.Dropout(dropout)\n",
+    "        self.register_buffer(\"mask\", torch.triu(torch.ones(context_length, context_length), diagonal=1))\n",
+    "\n",
+    "        ################################### NEW ###################################\n",
+    "        sin, cos = precompute_rope_params(head_dim=self.head_dim, context_length=context_length)\n",
+    "        self.register_buffer(\"sin\", sin)\n",
+    "        self.register_buffer(\"cos\", cos)\n",
+    "        ###########################################################################\n",
+    "\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "\n",
+    "        b, num_tokens, d_in = x.shape\n",
+    "\n",
+    "        keys = self.W_key(x)  # Shape: (b, num_tokens, d_out)\n",
+    "        queries = self.W_query(x)\n",
+    "        values = self.W_value(x)\n",
+    "\n",
+    "        # We implicitly split the matrix by adding a `num_heads` dimension\n",
+    "        # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)\n",
+    "        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)\n",
+    "        values = values.view(b, num_tokens, self.num_heads, self.head_dim)\n",
+    "        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)\n",
+    "\n",
+    "        # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)\n",
+    "        keys = keys.transpose(1, 2)\n",
+    "        queries = queries.transpose(1, 2)\n",
+    "        values = values.transpose(1, 2)\n",
+    "\n",
+    "        ################################### NEW ###################################\n",
+    "        keys = compute_rope(keys, self.sin, self.cos)\n",
+    "        queries = compute_rope(queries, self.sin, self.cos)\n",
+    "        ###########################################################################\n",
+    "\n",
+    "        # Compute scaled dot-product attention (aka self-attention) with a causal mask\n",
+    "        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head\n",
+    "\n",
+    "        # Original mask truncated to the number of tokens and converted to boolean\n",
+    "        mask_bool = self.mask.bool()[:num_tokens, :num_tokens]\n",
+    "\n",
+    "        # Use the mask to fill attention scores\n",
+    "        attn_scores.masked_fill_(mask_bool, -torch.inf)\n",
+    "\n",
+    "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
+    "        # attn_weights = self.dropout(attn_weights)\n",
+    "\n",
+    "        # Shape: (b, num_tokens, num_heads, head_dim)\n",
+    "        context_vec = (attn_weights @ values).transpose(1, 2)\n",
+    "\n",
+    "        # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
+    "        context_vec = context_vec.reshape(b, num_tokens, self.d_out)\n",
+    "        context_vec = self.out_proj(context_vec)  # optional projection\n",
+    "\n",
+    "        return context_vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "-lt9SfnVioB3",
+   "metadata": {
+    "id": "-lt9SfnVioB3"
+   },
+   "source": [
+    "- Below is an example using the `MultiHeadAttention` module on an example input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "03f15755-0083-483f-963b-99b599651638",
+   "metadata": {
+    "id": "03f15755-0083-483f-963b-99b599651638"
+   },
+   "outputs": [],
+   "source": [
+    "# Settings\n",
+    "batch_size = 1\n",
+    "context_len = 100\n",
+    "max_context_len = 4096\n",
+    "embed_dim = 128\n",
+    "num_heads = 4\n",
+    "\n",
+    "\n",
+    "example_batch = torch.randn((batch_size, context_len, embed_dim))\n",
+    "\n",
+    "mha = MultiHeadAttention(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim,\n",
+    "    context_length=max_context_len,\n",
+    "    num_heads=num_heads\n",
+    ")\n",
+    "\n",
+    "mha(example_batch)\n",
+    "\n",
+    "del mha  # delete to safe memory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5a1a272-a038-4b8f-aaaa-f4b241e7f23f",
+   "metadata": {
+    "id": "e5a1a272-a038-4b8f-aaaa-f4b241e7f23f"
+   },
+   "source": [
+    "## Update the TransformerBlock module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "255f70ac-9c2e-4328-8af7-1c298b8d4a18",
+   "metadata": {
+    "id": "255f70ac-9c2e-4328-8af7-1c298b8d4a18"
+   },
+   "source": [
+    "- At this stage, most of the hard work is already done; we can now update the `TransformerBlock` to use the code we implemented above\n",
+    "- This means we\n",
+    " - replace LayerNorm with RMSNorm\n",
+    " - remove dropout\n",
+    " - remove the `qkv_bias` setting\n",
+    " - add the `dtype` setting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2e110721-bf2b-42b3-989a-1635b1658af0",
+   "metadata": {
+    "id": "2e110721-bf2b-42b3-989a-1635b1658af0"
+   },
+   "outputs": [],
+   "source": [
+    "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",
+    "            context_length=cfg[\"context_length\"],\n",
+    "            num_heads=cfg[\"n_heads\"],\n",
+    "            dtype=cfg[\"dtype\"]  # NEW\n",
+    "            # dropout=cfg[\"drop_rate\"],\n",
+    "            # qkv_bias=cfg[\"qkv_bias\"]\n",
+    "        )\n",
+    "        self.ff = FeedForward(cfg)\n",
+    "\n",
+    "        ################################### NEW ###################################\n",
+    "        # self.norm1 = LayerNorm(cfg[\"emb_dim\"])\n",
+    "        # self.norm2 = LayerNorm(cfg[\"emb_dim\"])\n",
+    "        self.norm1 = RMSNorm(cfg[\"emb_dim\"])\n",
+    "        self.norm2 = RMSNorm(cfg[\"emb_dim\"])\n",
+    "        ###########################################################################\n",
+    "\n",
+    "        # self.drop_shortcut = nn.Dropout(cfg[\"drop_rate\"])\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_shortcut(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_shortcut(x)\n",
+    "        x = x + shortcut  # Add the original input back\n",
+    "\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ada953bc-e2c0-4432-a32d-3f7efa3f6e0f",
+   "metadata": {
+    "id": "ada953bc-e2c0-4432-a32d-3f7efa3f6e0f"
+   },
+   "source": [
+    "## Update the model class"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "aa79780d-74a8-4ee0-934a-9ad63205a02e",
+   "metadata": {
+    "id": "aa79780d-74a8-4ee0-934a-9ad63205a02e"
+   },
+   "source": [
+    "- As you may recall from [chapter 5](../01_main-chapter-code/ch05.ipynb), the `TransformerBlock` is a repeated block within the main model\n",
+    "- Our Llama model is almost complete; we just have to update the model code surrounding the `TransformerBlock`\n",
+    "- This means we\n",
+    "  - remove absolute positional embeddings since we have RoPE embeddings now\n",
+    "  - replace LayerNorm with RMSNorm\n",
+    "  - remove dropout\n",
+    "  - add the dtype setting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cf8240fe-5d7f-4e7e-b1ac-e0755aab5e79",
+   "metadata": {
+    "id": "cf8240fe-5d7f-4e7e-b1ac-e0755aab5e79"
+   },
+   "outputs": [],
+   "source": [
+    "# class GPTModel(nn.Module):\n",
+    "class Llama2Model(nn.Module):\n",
+    "    def __init__(self, cfg):\n",
+    "        super().__init__()\n",
+    "        self.tok_emb = nn.Embedding(cfg[\"vocab_size\"], cfg[\"emb_dim\"], dtype=cfg[\"dtype\"])\n",
+    "        # self.pos_emb = nn.Embedding(cfg[\"context_length\"], cfg[\"emb_dim\"])\n",
+    "        # self.drop_emb = nn.Dropout(cfg[\"drop_rate\"])\n",
+    "\n",
+    "        self.trf_blocks = nn.Sequential(\n",
+    "            *[TransformerBlock(cfg) for _ in range(cfg[\"n_layers\"])])\n",
+    "\n",
+    "        ################################### NEW ###################################\n",
+    "        # self.final_norm = LayerNorm(cfg[\"emb_dim\"])\n",
+    "        self.final_norm = RMSNorm(cfg[\"emb_dim\"])\n",
+    "        ###########################################################################\n",
+    "        self.out_head = nn.Linear(cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False, dtype=cfg[\"dtype\"])\n",
+    "\n",
+    "    def forward(self, in_idx):\n",
+    "        batch_size, seq_len = in_idx.shape\n",
+    "        tok_embeds = self.tok_emb(in_idx)\n",
+    "        # pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))\n",
+    "        x = tok_embeds  # + pos_embeds  # Shape [batch_size, num_tokens, emb_size]\n",
+    "        # x = self.drop_emb(x)\n",
+    "        x = self.trf_blocks(x)\n",
+    "        x = self.final_norm(x)\n",
+    "        logits = self.out_head(x)\n",
+    "        return logits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc94940-aaeb-45b9-9399-3a69b8043e60",
+   "metadata": {
+    "id": "4bc94940-aaeb-45b9-9399-3a69b8043e60"
+   },
+   "source": [
+    "## Initialize model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bG--zY-Ljj1f",
+   "metadata": {
+    "id": "bG--zY-Ljj1f"
+   },
+   "source": [
+    "- The model code is now complete, and we are ready to initialize it\n",
+    "- In [chapter 5](../01_main-chapter-code/ch05.ipynb), we used the following config file to specify the 124M-parameter GPT model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4b7428df-3d02-4ccd-97b5-a629bdabbe8f",
+   "metadata": {
+    "id": "4b7428df-3d02-4ccd-97b5-a629bdabbe8f"
+   },
+   "outputs": [],
+   "source": [
+    "GPT_CONFIG_124M = {\n",
+    "    \"vocab_size\": 50257,     # Vocabulary size\n",
+    "    \"context_length\": 1024,  # Context length\n",
+    "    \"emb_dim\": 768,          # Embedding dimension\n",
+    "    \"n_heads\": 12,           # Number of attention heads\n",
+    "    \"n_layers\": 12,          # Number of layers\n",
+    "    \"drop_rate\": 0.1,        # Dropout rate\n",
+    "    \"qkv_bias\": False        # Query-Key-Value bias\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bVi8uiBjw2T",
+   "metadata": {
+    "id": "8bVi8uiBjw2T"
+   },
+   "source": [
+    "- For reference, the 1.5B parameter GPT model config is shown below as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "tAOojV_mkEnd",
+   "metadata": {
+    "id": "tAOojV_mkEnd"
+   },
+   "outputs": [],
+   "source": [
+    "GPT_CONFIG_1558M = {\n",
+    "    \"vocab_size\": 50257,     # Vocabulary size\n",
+    "    \"context_length\": 1024,  # Context length\n",
+    "    \"emb_dim\": 1600,         # Embedding dimension\n",
+    "    \"n_heads\": 25,           # Number of attention heads\n",
+    "    \"n_layers\": 48,          # Number of layers\n",
+    "    \"drop_rate\": 0.1,        # Dropout rate\n",
+    "    \"qkv_bias\": False        # Query-Key-Value bias\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "HoGGRAGykQTE",
+   "metadata": {
+    "id": "HoGGRAGykQTE"
+   },
+   "source": [
+    "- Similarly, we can define a Llama 2 config file for the 7B model (we ignore the other larger models for simplicity here):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e0564727-2d35-4f0c-b0fc-cde1e9134a18",
+   "metadata": {
+    "id": "e0564727-2d35-4f0c-b0fc-cde1e9134a18"
+   },
+   "outputs": [],
+   "source": [
+    "LLAMA2_CONFIG_7B = {\n",
+    "    \"vocab_size\": 32000,     # Vocabulary size\n",
+    "    \"context_length\": 4096,  # Context length\n",
+    "    \"emb_dim\": 4096,         # Embedding dimension\n",
+    "    \"n_heads\": 32,           # Number of attention heads\n",
+    "    \"n_layers\": 32,          # Number of layers\n",
+    "    \"hidden_dim\": 11008,     # NEW: Size of the intermediate dimension in FeedForward\n",
+    "    \"dtype\": torch.bfloat16  # NEW: Lower-precision dtype to save memory\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "FAP7fiBzkaBz",
+   "metadata": {
+    "id": "FAP7fiBzkaBz"
+   },
+   "source": [
+    "- Using these settings, we can now initialize a Llama 2 7B model (note that this requires ~26 GB of memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7004d785-ac9a-4df5-8760-6807fc604686",
+   "metadata": {
+    "id": "7004d785-ac9a-4df5-8760-6807fc604686"
+   },
+   "outputs": [],
+   "source": [
+    "model = Llama2Model(LLAMA2_CONFIG_7B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6079f747-8f20-4c6b-8d38-7156f1101729",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6079f747-8f20-4c6b-8d38-7156f1101729",
+    "outputId": "78ab929e-ac78-4b16-ddb1-704d45ee69a8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of parameters: 6,738,415,616\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_params = sum(p.numel() for p in model.parameters())\n",
+    "print(f\"Total number of parameters: {total_params:,}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Bx14NtzWk2wj",
+   "metadata": {
+    "id": "Bx14NtzWk2wj"
+   },
+   "source": [
+    "- As shown above, the model contains 6.7 billion parameters (commonly rounded and referred to as a 7B model)\n",
+    "- Additionally, we can calculate the memory requirements for this model using the code below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0df1c79e-27a7-4b0f-ba4e-167fe107125a",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0df1c79e-27a7-4b0f-ba4e-167fe107125a",
+    "outputId": "c0cbdcc8-dc46-44f7-a800-fbe888a3f9e9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "float32 (PyTorch default): 52.27 GB\n",
+      "bfloat16: 26.13 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "def model_memory_size(model, input_dtype=torch.float32):\n",
+    "    total_params = 0\n",
+    "    total_grads = 0\n",
+    "    for param in model.parameters():\n",
+    "        # Calculate total number of elements per parameter\n",
+    "        param_size = param.numel()\n",
+    "        total_params += param_size\n",
+    "        # Check if gradients are stored for this parameter\n",
+    "        if param.requires_grad:\n",
+    "            total_grads += param_size\n",
+    "\n",
+    "    # Calculate buffer size (non-parameters that require memory)\n",
+    "    total_buffers = sum(buf.numel() for buf in model.buffers())\n",
+    "\n",
+    "    # Size in bytes = (Number of elements) * (Size of each element in bytes)\n",
+    "    # We assume parameters and gradients are stored in the same type as input dtype\n",
+    "    element_size = torch.tensor(0, dtype=input_dtype).element_size()\n",
+    "    total_memory_bytes = (total_params + total_grads + total_buffers) * element_size\n",
+    "\n",
+    "    # Convert bytes to gigabytes\n",
+    "    total_memory_gb = total_memory_bytes / (1024**3)\n",
+    "\n",
+    "    return total_memory_gb\n",
+    "\n",
+    "print(f\"float32 (PyTorch default): {model_memory_size(model, input_dtype=torch.float32):.2f} GB\")\n",
+    "print(f\"bfloat16: {model_memory_size(model, input_dtype=torch.bfloat16):.2f} GB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "zudd-5PulKFL",
+   "metadata": {
+    "id": "zudd-5PulKFL"
+   },
+   "source": [
+    "- Lastly, we can also transfer the model to an NVIDIA or Apple Silicon GPU if applicable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a4c50e19-1402-45b6-8ccd-9077b2ba836d",
+   "metadata": {
+    "id": "a4c50e19-1402-45b6-8ccd-9077b2ba836d"
+   },
+   "outputs": [],
+   "source": [
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "elif torch.backends.mps.is_available():\n",
+    "    device = torch.device(\"mps\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "\n",
+    "model.to(device);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc64a06-27dc-46ec-9e6d-1700a8227d34",
+   "metadata": {
+    "id": "5dc64a06-27dc-46ec-9e6d-1700a8227d34"
+   },
+   "source": [
+    "## Load tokenizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb30f0c-6144-4bed-87d9-6b2bac377005",
+   "metadata": {
+    "id": "0eb30f0c-6144-4bed-87d9-6b2bac377005"
+   },
+   "source": [
+    "- In this section, we are going to load the tokenizer for the model\n",
+    "- Llama 2 uses Google's [SentencePiece](https://github.com/google/sentencepiece) tokenizer instead of OpenAI's [Tiktoken](https://github.com/openai/tiktoken) (but Llama 3 uses Tiktoken)\n",
+    "- Meta AI shared the original Llama 2 model weights and tokenizer vocabulary on the Hugging Face Hub\n",
+    "- We will download the tokenizer vocabulary from the Hub and load it into SentencePiece\n",
+    "- Uncomment and run the following code to install the required libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "768989ea-dc60-4dc8-ae84-cbb3fd224422",
+   "metadata": {
+    "id": "768989ea-dc60-4dc8-ae84-cbb3fd224422"
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install huggingface_hub sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "KbnlzsbYmJU6",
+   "metadata": {
+    "id": "KbnlzsbYmJU6"
+   },
+   "source": [
+    "- Please note that Meta AI requires that you accept the Llama 2 licensing terms before you can download the files; to do this, you have to create a Hugging Face Hub account and visit the [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) repository to accept the terms\n",
+    "- Next, you will need to create an access token; to generate an access token, click on the profile picture in the upper right and click on \"Settings\"\n",
+    "\n",
+    "\n",
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/settings.webp?1\" width=\"300px\">\n",
+    "\n",
+    "- Then, create and copy the access token so you can copy & paste it into the next code cell\n",
+    "\n",
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/access-token.webp?1\" width=\"600px\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3357a230-b678-4691-a238-257ee4e80185",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3357a230-b678-4691-a238-257ee4e80185",
+    "outputId": "d326d32c-fa8d-4f2b-84d5-a1b8f35dd387"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
+      "Token is valid (permission: read).\n",
+      "Your token has been saved to /root/.cache/huggingface/token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "\n",
+    "login(token=\"hf_...\")  # Insert your token here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "IxGh6ZYQo0VN",
+   "metadata": {
+    "id": "IxGh6ZYQo0VN"
+   },
+   "source": [
+    "- After login via the access token, which is necessary to verify that we accepted the Llama 2 licensing terms, we can now download the tokenizer vocabulary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "69714ea8-b9b8-4687-8392-f3abb8f93a32",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "69714ea8-b9b8-4687-8392-f3abb8f93a32",
+    "outputId": "82bc5037-c86c-46c2-b374-269f9d09599a"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "tokenizer_file = hf_hub_download(\n",
+    "    repo_id=\"meta-llama/Llama-2-7b\",\n",
+    "    filename=\"tokenizer.model\",\n",
+    "    cache_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gp7iQ8cXAJLv",
+   "metadata": {
+    "id": "gp7iQ8cXAJLv"
+   },
+   "source": [
+    "- To provide a more familiar interface for the tokenizer, we define a small `LlamaTokenizer` wrapper class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "Ef4WxhjOBOOc",
+   "metadata": {
+    "id": "Ef4WxhjOBOOc"
+   },
+   "outputs": [],
+   "source": [
+    "import sentencepiece as spm\n",
+    "\n",
+    "\n",
+    "class LlamaTokenizer:\n",
+    "    def __init__(self, filepath):\n",
+    "        sp = spm.SentencePieceProcessor()\n",
+    "        sp.load(tokenizer_file)\n",
+    "        self.tokenizer = sp\n",
+    "\n",
+    "    def encode(self, text):\n",
+    "        return self.tokenizer.encode_as_ids(text)\n",
+    "\n",
+    "    def decode(self, ids):\n",
+    "        return self.tokenizer.decode_pieces(ids)\n",
+    "\n",
+    "\n",
+    "tokenizer = LlamaTokenizer(tokenizer_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "NVhmFeX3pT_M",
+   "metadata": {
+    "id": "NVhmFeX3pT_M"
+   },
+   "source": [
+    "- We can now use the `generate` function to have the Llama 2 model generate new text:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e0a2b5cd-6cba-4d72-b8ff-04d8315d483e",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e0a2b5cd-6cba-4d72-b8ff-04d8315d483e",
+    "outputId": "d733bc0a-5136-4c33-d70d-36056f1e8329"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output text:\n",
+      " Every effort movesαllRadius deletingpretccappedRadius zas Parte Material Ку términчной herousztusllRadiusotto кра liberotto siguientesagnost#{ (@topicquez restored log\n"
+     ]
+    }
+   ],
+   "source": [
+    "from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
+    "\n",
+    "\n",
+    "torch.manual_seed(123)\n",
+    "\n",
+    "token_ids = generate(\n",
+    "    model=model,\n",
+    "    idx=text_to_token_ids(\"Every effort moves\", tokenizer).to(device),\n",
+    "    max_new_tokens=30,\n",
+    "    context_size=LLAMA2_CONFIG_7B[\"context_length\"],\n",
+    "    top_k=1,\n",
+    "    temperature=1.0\n",
+    ")\n",
+    "\n",
+    "print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93WTtAA5paYV",
+   "metadata": {
+    "id": "93WTtAA5paYV"
+   },
+   "source": [
+    "- Of course, as we can see above, the text is nonsensical since we haven't trained the Llama 2 model yet\n",
+    "- In the next section, instead of training it ourselves, which would cost tens to hundreds of thousands of dollars, we load the pretrained weights from Meta AI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f63cc248-1d27-4eb6-aa50-173b436652f8",
+   "metadata": {
+    "id": "f63cc248-1d27-4eb6-aa50-173b436652f8"
+   },
+   "source": [
+    "## Load pretrained weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aKeN7rUfqZMI",
+   "metadata": {
+    "id": "aKeN7rUfqZMI"
+   },
+   "source": [
+    "- We are loading the [\"meta-llama/Llama-2-7b\"](https://huggingface.co/meta-llama/Llama-2-7b) base model below, which is a simple text completion model before finetuning\n",
+    "- Alternatively, you can load the instruction-finetuned and aligned [\"meta-llama/Llama-2-7b-chat\"](https://huggingface.co/meta-llama/Llama-2-7b-chat) model by modifying the string in the next code cell accordingly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5fa9c06c-7a53-4b4d-9ce4-acc027322ee4",
+   "metadata": {
+    "id": "5fa9c06c-7a53-4b4d-9ce4-acc027322ee4"
+   },
+   "outputs": [],
+   "source": [
+    "weights_file = hf_hub_download(\n",
+    "   repo_id=\"meta-llama/Llama-2-7b\",\n",
+    "   filename=\"consolidated.00.pth\",\n",
+    "   cache_dir=\".\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e67cca5c-ba4b-4be5-85c7-fdceae8a5701",
+   "metadata": {
+    "id": "e67cca5c-ba4b-4be5-85c7-fdceae8a5701"
+   },
+   "outputs": [],
+   "source": [
+    "weights = torch.load(weights_file, weights_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "-15SJ7btq2zE",
+   "metadata": {
+    "id": "-15SJ7btq2zE"
+   },
+   "source": [
+    "- The `weights` contains the following tensors (only the first 15 are shown for simplicity):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ee26bd0b-fea9-4924-97f7-409c14f28e49",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ee26bd0b-fea9-4924-97f7-409c14f28e49",
+    "outputId": "01721809-ace1-4a7a-ab54-8fad2e8f54a6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['tok_embeddings.weight',\n",
+       " 'norm.weight',\n",
+       " 'output.weight',\n",
+       " 'layers.0.attention.wq.weight',\n",
+       " 'layers.0.attention.wk.weight',\n",
+       " 'layers.0.attention.wv.weight',\n",
+       " 'layers.0.attention.wo.weight',\n",
+       " 'layers.0.feed_forward.w1.weight',\n",
+       " 'layers.0.feed_forward.w2.weight',\n",
+       " 'layers.0.feed_forward.w3.weight',\n",
+       " 'layers.0.attention_norm.weight',\n",
+       " 'layers.0.ffn_norm.weight',\n",
+       " 'layers.1.attention.wq.weight',\n",
+       " 'layers.1.attention.wk.weight',\n",
+       " 'layers.1.attention.wv.weight']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(weights.keys())[:15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "UeeSpnunrDFB",
+   "metadata": {
+    "id": "UeeSpnunrDFB"
+   },
+   "source": [
+    "- The following function, modeled after the `load_weights_into_gpt` function in [chapter 5](../01_main-chapter-code/ch05.ipynb), loads the pretrained weights into our Llama 2 model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "3820e2a7-4f26-41bc-953b-f3879b0aff65",
+   "metadata": {
+    "id": "3820e2a7-4f26-41bc-953b-f3879b0aff65"
+   },
+   "outputs": [],
+   "source": [
+    "def assign(left, right):\n",
+    "    if left.shape != right.shape:\n",
+    "        raise ValueError(f\"Shape mismatch. Left: {left.shape}, Right: {right.shape}\")\n",
+    "\n",
+    "    if isinstance(right, torch.Tensor):\n",
+    "        return torch.nn.Parameter(right.clone().detach())\n",
+    "    else:\n",
+    "        return torch.nn.Parameter(torch.tensor(right))\n",
+    "\n",
+    "\n",
+    "def load_weights_into_llama(model, param_config, params):\n",
+    "    model.tok_emb.weight = assign(model.tok_emb.weight, params[\"tok_embeddings.weight\"])\n",
+    "\n",
+    "    for l in range(param_config[\"n_layers\"]):\n",
+    "\n",
+    "        # Load attention weights\n",
+    "        model.trf_blocks[l].att.W_query.weight = assign(\n",
+    "            model.trf_blocks[l].att.W_query.weight,\n",
+    "            params[f\"layers.{l}.attention.wq.weight\"]\n",
+    "        )\n",
+    "        model.trf_blocks[l].att.W_key.weight = assign(\n",
+    "            model.trf_blocks[l].att.W_key.weight,\n",
+    "            params[f\"layers.{l}.attention.wk.weight\"]\n",
+    "        )\n",
+    "        model.trf_blocks[l].att.W_value.weight = assign(\n",
+    "            model.trf_blocks[l].att.W_value.weight,\n",
+    "            params[f\"layers.{l}.attention.wv.weight\"]\n",
+    "        )\n",
+    "        model.trf_blocks[l].att.out_proj.weight = assign(\n",
+    "            model.trf_blocks[l].att.out_proj.weight,\n",
+    "            params[f\"layers.{l}.attention.wo.weight\"]\n",
+    "        )\n",
+    "        model.trf_blocks[l].norm1.weight = assign(\n",
+    "            model.trf_blocks[l].norm1.weight,\n",
+    "            params[f\"layers.{l}.attention_norm.weight\"]\n",
+    "        )\n",
+    "\n",
+    "        # Load FeedForward weights\n",
+    "        model.trf_blocks[l].ff.fc1.weight = assign(\n",
+    "            model.trf_blocks[l].ff.fc1.weight,\n",
+    "            params[f\"layers.{l}.feed_forward.w1.weight\"]\n",
+    "        )\n",
+    "        # For some reason w2 and w3 are provided in the wrong order in the weights file\n",
+    "        model.trf_blocks[l].ff.fc2.weight = assign(\n",
+    "            model.trf_blocks[l].ff.fc2.weight,\n",
+    "            params[f\"layers.{l}.feed_forward.w3.weight\"]\n",
+    "        )\n",
+    "        model.trf_blocks[l].ff.fc3.weight = assign(\n",
+    "            model.trf_blocks[l].ff.fc3.weight,\n",
+    "            params[f\"layers.{l}.feed_forward.w2.weight\"]\n",
+    "        )\n",
+    "        model.trf_blocks[l].norm2.weight = assign(\n",
+    "            model.trf_blocks[l].norm2.weight,\n",
+    "            params[f\"layers.{l}.ffn_norm.weight\"]\n",
+    "        )\n",
+    "\n",
+    "    # Load output layer weights\n",
+    "    model.final_norm.weight = assign(model.final_norm.weight, params[\"norm.weight\"])\n",
+    "    model.out_head.weight = assign(model.out_head.weight, params[\"output.weight\"])\n",
+    "\n",
+    "\n",
+    "load_weights_into_llama(model, LLAMA2_CONFIG_7B, weights)\n",
+    "model.to(device);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "TDuv_Us2rNvk",
+   "metadata": {
+    "id": "TDuv_Us2rNvk"
+   },
+   "source": [
+    "- Next, we are ready to use the model for text generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "240987e8-a023-462e-9376-9edfb27559ec",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "240987e8-a023-462e-9376-9edfb27559ec",
+    "outputId": "59830005-42af-406b-c836-38a8f2d7b961"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output text:\n",
+      " Every effort has been made to ensure that the information contained in this website is accurate and up to date. However, the information is provided without any warranty\n"
+     ]
+    }
+   ],
+   "source": [
+    "torch.manual_seed(123)\n",
+    "\n",
+    "token_ids = generate(\n",
+    "    model=model,\n",
+    "    idx=text_to_token_ids(\"Every effort\", tokenizer).to(device),\n",
+    "    max_new_tokens=30,\n",
+    "    context_size=LLAMA2_CONFIG_7B[\"context_length\"],\n",
+    "    top_k=1,\n",
+    "    temperature=0.\n",
+    ")\n",
+    "\n",
+    "print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "akyo7WNyF_YL",
+   "metadata": {
+    "id": "akyo7WNyF_YL"
+   },
+   "source": [
+    "- Tip: as mentioned earlier, this is the pretrained base model; if you want to use a model capable of following instructions, use the `\"meta-llama/Llama-2-7b-chat\"` model instead"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "nbvAV7vaz6yc",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "nbvAV7vaz6yc",
+    "outputId": "faa930dc-0db2-4095-b395-f97baef08903"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output text:\n",
+      " What do llamas eat?\n",
+      "Llamas are herbivores, which means they eat plants. They eat grass, leaves, and hay.\n"
+     ]
+    }
+   ],
+   "source": [
+    "del model  # to free up memory\n",
+    "\n",
+    "weights_file = hf_hub_download(\n",
+    "   repo_id=\"meta-llama/Llama-2-7b-chat\",\n",
+    "   filename=\"consolidated.00.pth\",\n",
+    "   cache_dir=\".\"\n",
+    ")\n",
+    "\n",
+    "model = Llama2Model(LLAMA2_CONFIG_7B)\n",
+    "load_weights_into_llama(model, LLAMA2_CONFIG_7B, weights)\n",
+    "model.to(device);\n",
+    "\n",
+    "torch.manual_seed(123)\n",
+    "\n",
+    "token_ids = generate(\n",
+    "    model=model,\n",
+    "    idx=text_to_token_ids(\"What do llamas eat?\", tokenizer).to(device),\n",
+    "    max_new_tokens=25,\n",
+    "    context_size=LLAMA2_CONFIG_7B[\"context_length\"],\n",
+    "    top_k=1,\n",
+    "    temperature=0.\n",
+    ")\n",
+    "\n",
+    "print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "A100",
+   "provenance": []
+  },
+  "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
+}

ch05/07_gpt_to_llama/previous_chapters.py

@@ -0,0 +1,63 @@
+# 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 2-4.
+# This file can be run as a standalone script.
+
+import torch
+
+
+#####################################
+# 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=0.0, top_k=None, eos_id=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)
+
+        if idx_next == eos_id:  # Stop generating early if end-of-sequence token is encountered and eos_id is specified
+            break
+
+        # 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

ch05/07_gpt_to_llama/requirements-extra.txt

@@ -0,0 +1,2 @@
+huggingface_hub>=0.24.7
+sentencepiece>=0.1.99

ch05/README.md

@@ -11,3 +11,4 @@
 - [04_learning_rate_schedulers](04_learning_rate_schedulers) contains code implementing a more sophisticated training function including learning rate schedulers and gradient clipping
 - [05_bonus_hparam_tuning](05_bonus_hparam_tuning) contains an optional hyperparameter tuning script
 - [06_user_interface](06_user_interface) implements an interactive user interface to interact with the pretrained LLM
+- [07_gpt_to_llama](07_gpt_to_llama) contains a step-by-step guide for converting a GPT architecture implementation to Llama and loads pretrained weights from Meta AI