\n", + "
Code repository: https://github.com/rasbt/LLMs-from-scratch\n", + "\n", + "
![](\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\")
\n",
+ "| \n",
+ "\n",
+ "Supplementary code for the Build a Large Language Model From Scratch book by Sebastian Raschka \n", + " Code repository: https://github.com/rasbt/LLMs-from-scratch\n", + "\n", + " | \n",
+ "\n",
+ "\n",
+ " | \n",
+ "
![](\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/gpt2-to-llama2.webp?1\")
"
+ ]
+ },
+ {
+ "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",
+ "![](\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/settings.webp?1\")
\n",
+ "\n",
+ "- Then, create and copy the access token so you can copy & paste it into the next code cell\n",
+ "\n",
+ "![](\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/access-token.webp?1\")
"
+ ]
+ },
+ {
+ "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
+}
diff --git a/ch05/07_gpt_to_llama/previous_chapters.py b/ch05/07_gpt_to_llama/previous_chapters.py
new file mode 100644
index 00000000..1ca678c7
--- /dev/null
+++ b/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
diff --git a/ch05/07_gpt_to_llama/requirements-extra.txt b/ch05/07_gpt_to_llama/requirements-extra.txt
new file mode 100644
index 00000000..8646692b
--- /dev/null
+++ b/ch05/07_gpt_to_llama/requirements-extra.txt
@@ -0,0 +1,2 @@
+huggingface_hub>=0.24.7
+sentencepiece>=0.1.99
\ No newline at end of file
diff --git a/ch05/README.md b/ch05/README.md
index 428cd326..3a725194 100644
--- a/ch05/README.md
+++ b/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