From f8fb3480e0d2e807c74cc040b7460233d77a6cd0 Mon Sep 17 00:00:00 2001 From: Kevin Thomas Date: Sat, 30 Nov 2024 09:24:14 -0500 Subject: [PATCH] init commit --- RE-GPT.ipynb | 2306 ++++++++++++++++++++++ RE-GPT.jpeg | Bin 0 -> 200197 bytes README.md | 1660 +++++++++++++++- README_files/README_13_0.png | Bin 0 -> 33102 bytes README_files/README_18_0.png | Bin 0 -> 8090 bytes README_files/README_19_0.png | Bin 0 -> 20841 bytes README_files/README_1_0.jpg | Bin 0 -> 200197 bytes README_files/README_37_0.png | Bin 0 -> 14854 bytes README_files/README_38_0.png | Bin 0 -> 33718 bytes README_files/README_43_0.png | Bin 0 -> 6847 bytes README_files/README_48_0.png | Bin 0 -> 56771 bytes README_files/README_49_0.png | Bin 0 -> 19799 bytes README_files/README_53_0.png | Bin 0 -> 56771 bytes README_files/README_55_0.png | Bin 0 -> 77236 bytes README_files/README_58_0.png | Bin 0 -> 133765 bytes README_files/README_59_0.png | Bin 0 -> 56771 bytes README_files/README_63_0.png | Bin 0 -> 5580 bytes README_files/README_74_0.png | Bin 0 -> 5580 bytes README_files/README_75_0.png | Bin 0 -> 56771 bytes README_files/README_80_0.png | Bin 0 -> 49333 bytes README_files/README_8_0.png | Bin 0 -> 88592 bytes attention.png | Bin 0 -> 33102 bytes block.png | Bin 0 -> 56771 bytes convert_to_md.sh | 18 + cross-attention.png | Bin 0 -> 19799 bytes dropout.png | Bin 0 -> 49333 bytes ffn-formula.png | Bin 0 -> 6847 bytes layer-norm-formula.png | Bin 0 -> 5580 bytes multi-head-attention-formula.png | Bin 0 -> 14854 bytes multi-head-attention.png | Bin 0 -> 33718 bytes residual-blocks.png | Bin 0 -> 77236 bytes scaled-dot-product-attention-formula.png | Bin 0 -> 8090 bytes scaled-dot-product-attention.png | Bin 0 -> 20841 bytes transformer-model-arch.png | Bin 0 -> 88592 bytes types-of-residual-blocks.png | Bin 0 -> 133765 bytes 35 files changed, 3982 insertions(+), 2 deletions(-) create mode 100644 RE-GPT.ipynb create mode 100644 RE-GPT.jpeg create mode 100644 README_files/README_13_0.png create mode 100644 README_files/README_18_0.png create mode 100644 README_files/README_19_0.png create mode 100644 README_files/README_1_0.jpg create mode 100644 README_files/README_37_0.png create mode 100644 README_files/README_38_0.png create mode 100644 README_files/README_43_0.png create mode 100644 README_files/README_48_0.png create mode 100644 README_files/README_49_0.png create mode 100644 README_files/README_53_0.png create mode 100644 README_files/README_55_0.png create mode 100644 README_files/README_58_0.png create mode 100644 README_files/README_59_0.png create mode 100644 README_files/README_63_0.png create mode 100644 README_files/README_74_0.png create mode 100644 README_files/README_75_0.png create mode 100644 README_files/README_80_0.png create mode 100644 README_files/README_8_0.png create mode 100644 attention.png create mode 100644 block.png create mode 100755 convert_to_md.sh create mode 100644 cross-attention.png create mode 100644 dropout.png create mode 100644 ffn-formula.png create mode 100644 layer-norm-formula.png create mode 100644 multi-head-attention-formula.png create mode 100644 multi-head-attention.png create mode 100644 residual-blocks.png create mode 100644 scaled-dot-product-attention-formula.png create mode 100644 scaled-dot-product-attention.png create mode 100644 transformer-model-arch.png create mode 100644 types-of-residual-blocks.png diff --git a/RE-GPT.ipynb b/RE-GPT.ipynb new file mode 100644 index 0000000..598b761 --- /dev/null +++ b/RE-GPT.ipynb @@ -0,0 +1,2306 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'RE-GPT.jpeg')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wJpXpmjEYC_T" + }, + "source": [ + "# Reverse Engineering GPT\n", + "\n", + "#### Drawing inspiration from Andrej Karpathy’s iconic lecture, \"Let’s Build GPT: From Scratch, in Code, Spelled Out\", this project takes you on an immersive journey into the inner workings of GPT. Step-by-step, we’ll construct a GPT model from the ground up, demystifying its architecture and bringing its mechanics to life through hands-on coding.\n", + "\n", + "### [original video from Andrej Karpathy](https://www.youtube.com/watch?v=kCc8FmEb1nY)\n", + "\n", + "#### Credit: [Andrej Karpathy](mailto:karpathy@eurekalabs.ai)\n", + "#### Instructor: [Kevin Thomas](mailto:ket189@pitt.edu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Attention Is All You Need](https://arxiv.org/pdf/1706.03762)\n", + "#### Academic Paper" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: torch in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (2.5.1)\n", + "Requirement already satisfied: filelock in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (3.13.1)\n", + "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (4.11.0)\n", + "Requirement already satisfied: networkx in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (3.3)\n", + "Requirement already satisfied: jinja2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (3.1.4)\n", + "Requirement already satisfied: fsspec in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (2024.6.1)\n", + "Requirement already satisfied: setuptools in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (75.1.0)\n", + "Requirement already satisfied: sympy==1.13.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (1.13.1)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sympy==1.13.1->torch) (1.3.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from jinja2->torch) (2.1.3)\n" + ] + } + ], + "source": [ + "!pip install torch" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.nn import functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transformer Model Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": { + "image/png": { + "width": 400 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'transformer-model-arch.png', width=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Self-Attention in Simple Terms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When building a language model like GPT from scratch, we initially might use a uniform weight matrix `wei` based on a function like `torch.tril`. This matrix treats all previous tokens equally, which isn’t ideal because different words (tokens) in a sentence might be more or less important to each other. For example, if a vowel in a word is looking back at previous letters, it might be more interested in certain consonants rather than all past letters equally.\n", + "\n", + "Self-attention helps solve this problem by allowing each token to focus on specific other tokens in a data-dependent way. Here’s how it works: every token at each position generates two vectors—`query` and `key`. The `query` vector represents **“What am I looking for?”** and the `key` vector represents **“What do I contain?”**. By computing the dot product between a token’s query and the keys of all other tokens, we obtain a measure of similarity or “affinity”. This affinity tells us how much attention one token should pay to another.\n", + "\n", + "In code, we start by initializing linear layers for the keys and queries without biases:\n", + "```python\n", + "key = nn.Linear(input_size, head_size, bias=False)\n", + "query = nn.Linear(input_size, head_size, bias=False)\n", + "```\n", + "\n", + "We then compute the keys and queries by passing our input `x` (which contains all tokens) through these layers:\n", + "```python\n", + "k = key(x) # shape: (B, T, head_size)\n", + "q = query(x) # shape: (B, T, head_size)\n", + "```\n", + "\n", + "Here, `B` is the `batch_size`, `T` is the sequence length, and `head_size` is a hyperparameter (like 16). At this point, each token has independently produced its key and query vectors without any communication with other tokens.\n", + "\n", + "Next, we compute the affinities (similarities) between tokens by taking the dot product of queries and transposed keys:\n", + "```python\n", + "wei = q @ k.transpose(-2, -1) # shape: (B, T, T)\n", + "```\n", + "\n", + "This results in a matrix where each element tells us how much one token should pay attention to another. For example, `wei[0][8][4]` might represent how much the 8th token in the first batch should focus on the 4th token. These affinities are data-dependent, meaning they change based on the actual content of the tokens.\n", + "\n", + "However, when aggregating information, we don’t use the original tokens directly. Instead, each token also generates a value vector, which represents the information it wants to share:\n", + "```python\n", + "value = nn.Linear(input_size, head_size, bias=False)\n", + "v = value(x) # shape: (B, T, head_size)\n", + "```\n", + "\n", + "Finally, we use the affinities to compute a weighted sum of these values:\n", + "```python\n", + "output = wei @ v # shape: (B, T, head_size)\n", + "```\n", + "\n", + "This means each token gathers information from other tokens, weighted by how relevant they are (as determined by the affinities). So, a token effectively says, **“Based on what I’m interested in (my query) and what others contain (their keys), here’s the combined information (values) I should consider.”**\n", + "\n", + "By doing this, self-attention allows the model to dynamically focus on different parts of the input sequence, enabling it to capture complex patterns and relationships in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EDarxEWIRMKq", + "outputId": "07b587dd-a91c-4bb0-d7f1-e247cd5dacb5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 8, 16])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# version 4: self-attention!\n", + "torch.manual_seed(1337)\n", + "B, T, C = 4, 8, 32 # batch, time, channels\n", + "x = torch.randn(B,T,C)\n", + "\n", + "# let's see a single Head perform self-attention\n", + "head_size = 16\n", + "key = nn.Linear(C, head_size, bias=False)\n", + "query = nn.Linear(C, head_size, bias=False)\n", + "value = nn.Linear(C, head_size, bias=False)\n", + "k = key(x) # (B, T, 16)\n", + "q = query(x) # (B, T, 16)\n", + "wei = q @ k.transpose(-2, -1) # (B, T, 16) @ (B, 16, T) ---> (B, T, T)\n", + "\n", + "tril = torch.tril(torch.ones(T, T))\n", + "#wei = torch.zeros((T,T))\n", + "wei = wei.masked_fill(tril == 0, float('-inf'))\n", + "wei = F.softmax(wei, dim=-1)\n", + "\n", + "v = value(x)\n", + "out = wei @ v\n", + "#out = wei @ x\n", + "\n", + "out.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vT1hdtzXCjgL", + "outputId": "6d2c569b-7922-451f-9934-0fc564678d17" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.1574, 0.8426, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.2088, 0.1646, 0.6266, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.5792, 0.1187, 0.1889, 0.1131, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.0294, 0.1052, 0.0469, 0.0276, 0.7909, 0.0000, 0.0000, 0.0000],\n", + " [0.0176, 0.2689, 0.0215, 0.0089, 0.6812, 0.0019, 0.0000, 0.0000],\n", + " [0.1691, 0.4066, 0.0438, 0.0416, 0.1048, 0.2012, 0.0329, 0.0000],\n", + " [0.0210, 0.0843, 0.0555, 0.2297, 0.0573, 0.0709, 0.2423, 0.2391]],\n", + " grad_fn=)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wei[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": { + "image/png": { + "width": 400 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'attention.png', width=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Attention Mechanisms in Simple Terms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Attention is like a way for different parts (nodes) of a network to communicate with each other. Imagine these nodes as points in a directed graph where edges show which nodes are connected. Each node has some information stored as a vector, and it can gather information from other nodes it’s connected to by taking a weighted sum of their vectors. The weights are data-dependent, meaning they change based on the actual content at each node.\n", + "\n", + "In our case, we have a graph of 8 nodes because our `block_size` is 8, so there are always 8 tokens. The structure is such that the first node only looks at itself, the second node looks at itself and the first node, and so on, up to the 8th node, which looks at all previous nodes and itself. This setup ensures that future tokens don’t influence past ones, which is important in language modeling where we predict the next word based on previous words.\n", + "\n", + "One important thing to note is that attention doesn’t have a built-in sense of position or space. The nodes don’t inherently know where they are in the sequence. To fix this, we add positional encodings to our input vectors so that each token is aware of its position in the sequence. This is different from convolutional operations where the position is inherently part of the computation due to the structure of the convolution filters.\n", + "\n", + "In our model, we process multiple examples at once using batches. For instance, with a `batch_size` of 4, we have 4 separate groups of 8 nodes. These groups are processed independently and don’t share information with each other. This is handled efficiently using batched matrix multiplications that operate across the batch dimension `B`.\n", + "\n", + "When it comes to the code, if we wanted all the nodes to communicate with each other (like in tasks where future tokens can influence past ones), we’d use an encoder attention block. This involves removing the masking line in our code:\n", + "```python\n", + "wei = wei.masked_fill(tril == 0, float('-inf'))\n", + "```\n", + "\n", + "By deleting this line, we allow every node to attend to every other node without restrictions. However, for language modeling, we keep this line to prevent future tokens from influencing the computation of past tokens, creating what’s known as a **decoder attention block**.\n", + "\n", + "Lastly, in self-attention, the keys, queries, and values all come from the same source `x`. This means each node is attending to other nodes within the same set. In contrast, cross-attention involves keys and values coming from a different source than the queries, which is useful when integrating information from external data or another part of the network." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Scaled Attention in Simple Terms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scaled Dot-Product Attention Formula" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": { + "image/png": { + "width": 250 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'scaled-dot-product-attention.png', width=250)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the **“Attention Is All You Need”** paper, we’ve learned how to implement attention mechanisms using queries, keys, and values. We multiply the queries (`q`) and keys (`k`), apply the softmax function to the result, and then use these weights to aggregate the values (`v`). This process allows the model to focus on different parts of the input data.\n", + "\n", + "However, there’s an important step we haven’t included yet: dividing by the square root of the `head_size` (denoted as dk in the formula). This operation is known as scaled attention, and it’s a crucial normalization step in the attention mechanism.\n", + "\n", + "Here’s why scaling is important: if `q` and `k` are random variables drawn from a standard normal distribution (mean of 0 and standard deviation of 1), then their dot product will have a variance proportional to the `head_size` (which is 16 in our case). Without scaling, the `wei` (weights) would have a variance of about 16, causing the softmax function to produce very sharp (peaked) outputs.\n", + "\n", + "By multiplying by `head_size**-0.5` (which is the same as dividing by the square root of head_size), we adjust the variance of `wei` back to 1:\n", + "```python\n", + "wei = q @ k.transpose(-2, -1) * head_size ** -0.5\n", + "```\n", + "\n", + "This scaling ensures that when we apply the softmax function:\n", + "```python\n", + "wei = F.softmax(wei, dim=-1)\n", + "```\n", + "\n", + "The resulting weights are more evenly distributed (diffuse) rather than being overly concentrated. This is especially important during initialization because it allows the model to explore different parts of the input without being biased toward specific positions.\n", + "\n", + "In summary, including the scaling factor in our attention computation helps maintain stable gradients and prevents the softmax outputs from becoming too extreme. This makes the model more effective at learning and focusing on the relevant parts of the input data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "4SNbLq5z3oBw" + }, + "outputs": [], + "source": [ + "k = torch.randn(B,T,head_size)\n", + "q = torch.randn(B,T,head_size)\n", + "wei = q @ k.transpose(-2, -1) * head_size**-0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nl6I9n9IRTSo", + "outputId": "0c5b9cd0-af8a-4564-fbad-41d844e54822" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(1.0449)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "k.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T1tQx7oeRvtc", + "outputId": "3541ca1a-7447-4ef7-835e-81824aebc1b5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(1.0700)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MLb_odHU3iKM", + "outputId": "a687a222-5a2c-4cdb-c1bf-17cd05b45b69" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(1.0918)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wei.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding the Impact of Softmax in Attention Mechanisms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our attention mechanism, we use the softmax function to convert the raw attention weights (`wei`) into probabilities that sum up to one. However, there’s a problem when the values inside `wei` are very large or very small (both positive and negative). When `wei` contains very large positive and negative numbers, the softmax function tends to produce outputs that are extremely peaked, meaning it approaches one-hot vectors. This results in the model focusing almost entirely on one token and ignoring the rest, which isn’t always desirable.\n", + "\n", + "For example, if we have `wei` values like `[10, 20, 30]`, applying softmax will give us something close to `[0.0, 0.0, 1.0]`. This happens because the exponential function in softmax amplifies the differences between large numbers, making the largest value dominate the output. Conversely, if we apply softmax to values that are very close to zero, like `[0.1, 0.2, 0.3]`, the output will be more evenly distributed, such as `[0.30, 0.33, 0.37]`. This diffuse output means the model considers multiple tokens more equally.\n", + "\n", + "To prevent the softmax from becoming too sharp and focusing only on one token, we need to ensure that the values in `wei` are not too large in magnitude. This is where scaling comes in. By dividing `wei` by a scaling factor (specifically, the square root of the `head_size`), we reduce the variance of its values, keeping them closer to zero. This scaling ensures that the softmax function produces a more balanced output.\n", + "\n", + "In code, we implement this scaling as follows:\n", + "```python\n", + "wei = q @ k.transpose(-2, -1) * head_size ** -0.5\n", + "```\n", + "\n", + "By including the scaling factor `head_size ** -0.5`, we adjust the attention weights so that their variance is controlled, and the softmax function doesn’t saturate. This allows the model to consider information from multiple tokens rather than just one, improving its ability to learn and generalize from the data.\n", + "\n", + "Understanding this scaling is important because it highlights how mathematical operations in neural networks can significantly impact the model’s performance. By carefully managing the values passed into functions like softmax, we ensure that the attention mechanism works effectively, allowing our GPT model to capture complex patterns in language." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JB82yzt44REI", + "outputId": "f07da2f1-10bb-4a7a-bcaa-578587977d00" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.1925, 0.1426, 0.2351, 0.1426, 0.2872])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.softmax(torch.tensor([0.1, -0.2, 0.3, -0.2, 0.5]), dim=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mpt8569BB9_f", + "outputId": "5d8b910a-6192-44ba-ebb2-497d88e0b629" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.0326, 0.0030, 0.1615, 0.0030, 0.8000])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.softmax(torch.tensor([0.1, -0.2, 0.3, -0.2, 0.5])*8, dim=-1) # gets too peaky, converges to one-hot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding the Head Class in Self-Attention" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our journey to build GPT from scratch, the `Head` class is crucial because it implements a single head of self-attention. Self-attention allows the model to focus on different parts of the input sequence when generating each part of the output, which is essential for understanding context in language.\n", + "\n", + "When we initialize the `Head` class, we pass in a parameter called `head_size`. Inside the constructor (`__init__` method), we create three linear layers: `key`, `query`, and `value`. These layers are initialized without bias terms (`bias=False`) and are used to project our input `x` into different representations:\n", + "\n", + "```python\n", + "self.key = nn.Linear(input_size, head_size, bias=False)\n", + "self.query = nn.Linear(input_size, head_size, bias=False)\n", + "self.value = nn.Linear(input_size, head_size, bias=False)\n", + "```\n", + "\n", + "These linear layers apply matrix multiplications to the input data and are essential for computing the attention mechanism.\n", + "\n", + "We also create a lower triangular matrix called tril using `torch.tril`, which stands for “triangle lower.” This matrix is registered as a buffer (not a parameter that the model learns) using register_buffer:\n", + "```python\n", + "self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n", + "```\n", + "\n", + "This matrix ensures that each position in the sequence can only attend to itself and previous positions, preventing information from “future” tokens from influencing the current token (which is important for language modeling where we predict the next word).\n", + "\n", + "In the forward method, which defines how the data flows through the model, we take an input `x` and extract its dimensions:\n", + "```python\n", + "B, T, C = x.shape # Batch size, Sequence length, Embedding size\n", + "```\n", + "\n", + "We compute the `key` and `query` matrices by passing`x` through their respective linear layers:\n", + "```python\n", + "k = self.key(x) # Shape: (B, T, C)\n", + "q = self.query(x) # Shape: (B, T, C)\n", + "```\n", + "\n", + "We calculate the attention weights (wei) by taking the dot product of `q` and the transposed `k`, and then we normalize it by dividing by the square root of `C` (this is known as scaled attention):\n", + "```python\n", + "wei = q @ k.transpose(-2, -1) * C ** -0.5 # Shape: (B, T, T)\n", + "```\n", + "\n", + "To ensure that future tokens do not influence the current token, we apply the lower triangular mask:\n", + "```python\n", + "wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # Shape: (B, T, T)\n", + "```\n", + "\n", + "We then apply the softmax function to turn these weights into probabilities that sum to one:\n", + "```python\n", + "wei = F.softmax(wei, dim=-1)\n", + "```\n", + "\n", + "Next, we compute the value matrix:\n", + "```python\n", + "v = self.value(x) # Shape: (B, T, C)\n", + "```\n", + "\n", + "Finally, we perform the weighted aggregation of the values by multiplying the attention weights `wei` with the values `v`:\n", + "```python\n", + "out = wei @ v # Shape: (B, T, C)\n", + "return out\n", + "```\n", + "\n", + "The result out is a new representation of the input sequence where each token has gathered information from the relevant tokens that came before it. This mechanism allows the model to capture dependencies and relationships in the data, which is fundamental for tasks like language modeling." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "class Head(nn.Module):\n", + " \"\"\"One head of self-attention.\"\"\"\n", + " \n", + " def __init__(self, head_size):\n", + " super().__init__()\n", + " self.key = nn.Linear(n_embd, head_size, bias=False)\n", + " self.query = nn.Linear(n_embd, head_size, bias=False)\n", + " self.value = nn.Linear(n_embd, head_size, bias=False)\n", + " self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(self, x):\n", + " B,T,C = x.shape\n", + " k = self.key(x) # (B,T,C)\n", + " q = self.query(x) # (B,T,C)\n", + " # compute attention scores (\"affinities\")\n", + " wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T) \n", + " wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T) \n", + " wei = F.softmax(wei, dim=-1) # (B, T, T)\n", + " wei = self.dropout(wei)\n", + " # perform the weighted aggregation of the values\n", + " v = self.value(x) # (B,T,C)\n", + " out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Self-Attention and Positional Embeddings in Our Language Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our language model called `BigramLanguageModel`, we incorporate self-attention mechanisms to help the model understand relationships between different tokens in a sequence. Within the constructor of our model, we create multiple attention blocks using the following code:\n", + "```python\n", + "self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n", + "```\n", + "\n", + "Here, `n_embd` represents the embedding size (the dimensionality of our token embeddings), and `n_head` is the number of attention heads we want to use. Each `Block` is essentially a self-attention head, and we’re stacking them together using `nn.Sequential`. The `head_size` for each attention head is set to `n_embd`.\n", + "\n", + "In the forward method, we first encode our input tokens by adding token embeddings and positional embeddings:\n", + "```python\n", + "x = tok_emb + pos_emb\n", + "```\n", + "\n", + "This means we take the embeddings of the tokens (`tok_emb`) and add positional information (`pos_emb`) so that the model knows the position of each token in the sequence. We then pass this combined embedding `x` through our self-attention blocks:\n", + "```python\n", + "x = self.blocks(x)\n", + "```\n", + "\n", + "The output from the attention blocks is then fed into the language modeling head to produce the `logits`, which are the unnormalized probabilities for the next token in the sequence:\n", + "```python\n", + "logits = self.lm_head(x)\n", + "```\n", + "\n", + "When generating new text with the generate method, we need to ensure that the input indices (`idx`) we feed into the model do not exceed the `block_size`. This is because our positional embedding table only has embeddings up to `block_size`, and we can’t provide positional embeddings for positions beyond that. To handle this, we crop the context to the last `block_size` tokens:\n", + "```python\n", + "idx_cond = idx[:, -block_size:]\n", + "```\n", + "\n", + "By doing this, we make sure we’re always using a valid range of positional embeddings, preventing any errors or out-of-scope issues. This allows the model to generate text effectively while respecting the limitations of our positional embedding setup." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class BigramLanguageModel(nn.Module):\n", + " \"\"\"Language model based on the Transformer architecture.\"\"\"\n", + " \n", + " def __init__(self):\n", + " super().__init__()\n", + " # each token directly reads off the logits for the next token from a lookup table\n", + " self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n", + " self.position_embedding_table = nn.Embedding(block_size, n_embd)\n", + " self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n", + " self.ln_f = nn.LayerNorm(n_embd) # final layer norm\n", + " self.lm_head = nn.Linear(n_embd, vocab_size)\n", + "\n", + " def forward(self, idx, targets=None):\n", + " B, T = idx.shape\n", + "\n", + " # idx and targets are both (B,T) tensor of integers\n", + " tok_emb = self.token_embedding_table(idx) # (B,T,C)\n", + " pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n", + " x = tok_emb + pos_emb # (B,T,C)\n", + " x = self.blocks(x) # (B,T,C)\n", + " x = self.ln_f(x) # (B,T,C)\n", + " logits = self.lm_head(x) # (B,T,vocab_size)\n", + "\n", + " if targets is None:\n", + " loss = None\n", + " else:\n", + " B, T, C = logits.shape\n", + " logits = logits.view(B*T, C)\n", + " targets = targets.view(B*T)\n", + " loss = F.cross_entropy(logits, targets)\n", + "\n", + " return logits, loss\n", + "\n", + " def generate(self, idx, max_new_tokens):\n", + " # idx is (B, T) array of indices in the current context\n", + " for _ in range(max_new_tokens):\n", + " # crop idx to the last block_size tokens\n", + " idx_cond = idx[:, -block_size:]\n", + " # get the predictions\n", + " logits, loss = self(idx_cond)\n", + " # focus only on the last time step\n", + " logits = logits[:, -1, :] # becomes (B, C)\n", + " # apply softmax to get probabilities\n", + " probs = F.softmax(logits, dim=-1) # (B, C)\n", + " # sample from the distribution\n", + " idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n", + " # append sampled index to the running sequence\n", + " idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n", + " return idx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Multi-Head Attention in Simple Terms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Head Attention Formula" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": { + "image/png": { + "width": 200 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'multi-head-attention.png', width=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our GPT model built from scratch, we use a concept called **Multi-Head Attention**. This means we have multiple self-attention mechanisms (called “heads”) running in parallel. Instead of relying on a single attention mechanism, we allow the model to focus on different aspects of the input simultaneously.\n", + "\n", + "In PyTorch, we specify the number of heads (`num_heads`) and the size of each head (`head_size`). Here’s how we create multiple heads:\n", + "```python\n", + "self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n", + "```\n", + "\n", + "Each Head is an instance of our self-attention mechanism. We process the input `x` through all these heads in parallel and then concatenate their outputs along the channel dimension (`dim=-1`):\n", + "```python\n", + "out = torch.cat([h(x) for h in self.heads], dim=-1)\n", + "```\n", + "\n", + "This concatenation combines the outputs of all the attention heads into a single tensor.\n", + "\n", + "Instead of having a single attention head with a `head_size` equal to the embedding size (`n_embd`), we divide the embedding size among multiple heads. For example, if `n_embd` is 32 and we have 4 heads, each head will have a `head_size` of 8. This means:\n", + "* We have 4 communication channels (heads) running in parallel.\n", + "* Each head processes an 8-dimensional vector.\n", + "* When we concatenate the outputs of all heads, we get back to the original embedding size of 32.\n", + "\n", + "Having multiple attention heads is beneficial because tokens (like words or characters) have a lot of different things to “talk” about. For instance, they might want to find vowels, consonants, or specific patterns at certain positions. By using multiple independent channels of communication, each head can focus on different types of information. This allows the model to gather a richer set of data before making predictions, leading to better performance in understanding and generating language." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class MultiHeadAttention(nn.Module):\n", + " \"\"\"Multiple self-attention heads in parallel.\"\"\"\n", + " \n", + " def __init__(self, num_heads, head_size):\n", + " super().__init__()\n", + " self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n", + " self.proj = nn.Linear(n_embd, n_embd)\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(self, x):\n", + " out = torch.cat([h(x) for h in self.heads], dim=-1)\n", + " out = self.dropout(self.proj(out))\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Computation at the Token Level" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Position-Wise Feed-Forward Network Formula" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": { + "image/png": { + "width": 350 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'ffn-formula.png', width=350)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far in our language model, we’ve implemented multi-headed self-attention, which allows tokens (like words or characters) to communicate with each other. This means each token can look at other tokens in the sequence and gather information from them. However, we’ve been moving a bit too quickly from this communication step to making predictions (calculating the `logits`) in our `BigramLanguageModel`.\n", + "\n", + "The problem is that while the tokens have looked at each other, they haven’t had much time to process or “think about” the information they’ve gathered from other tokens. To fix this, we’re going to add a small feed-forward neural network that operates on a per-token level. This means that after gathering information, each token will independently process that information to make better predictions.\n", + "\n", + "This feed-forward network is simply a linear layer followed by a ReLU (Rectified Linear Unit) activation function, which introduces non-linearity. In code, we implement it like this within our `Block` class:\n", + "```python\n", + "self.ffwd = FeedForward(n_embd)\n", + "```\n", + "\n", + "And we call it right after the self-attention layer in the forward method. The FeedForward class might look something like this:\n", + "```python\n", + "class FeedForward(nn.Module):\n", + " def __init__(self, n_embd):\n", + " super().__init__()\n", + " self.net = nn.Sequential(\n", + " nn.Linear(n_embd, n_embd),\n", + " nn.ReLU()\n", + " )\n", + "\n", + " def forward(self, x):\n", + " return self.net(x)\n", + "```\n", + "\n", + "Here, `n_embd` is the embedding size (the size of our token vectors). Each token processes its own vector independently through this network. The self-attention layer allows tokens to gather information from others (communication), and the feed-forward network allows each token to process that information individually (computation).\n", + "\n", + "By adding this computation step, we enable each token to make better use of the information it has received, leading to improved performance of the language model. This mirrors how, in human communication, we not only listen to others but also take time to think and process what we’ve heard before responding." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "class FeedFoward(nn.Module):\n", + " \"\"\"A simple feed-forward neural network.\"\"\"\n", + " \n", + " def __init__(self, n_embd):\n", + " super().__init__()\n", + " self.net = nn.Sequential(\n", + " nn.Linear(n_embd, 4 * n_embd),\n", + " nn.ReLU(),\n", + " nn.Linear(4 * n_embd, n_embd),\n", + " nn.Dropout(dropout),\n", + " )\n", + "\n", + " def forward(self, x):\n", + " return self.net(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Transformer Blocks and Their Role in GPT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In building our GPT (Generative Pre-trained Transformer) model from scratch, we’re now focusing on combining communication and computation within the network. This approach mirrors how Transformers work—they have blocks that allow tokens (like words or characters) to communicate with each other and then compute based on that information. These blocks are grouped and replicated multiple times to build a powerful model.\n", + "\n", + "The core of this mechanism is implemented in the `Block` class, which represents the main part of the Transformer decoder model (excluding cross-attention components that interact with an encoder in some architectures). The `Block` class interleaves communication and computation steps. The communication is handled by multi-headed self-attention:\n", + "```python\n", + "self.sa = MultiHeadAttention(n_head, head_size)\n", + "```\n", + "\n", + "This allows tokens to look at other tokens in the sequence and gather relevant information. After communication, each token independently processes the gathered information using a feed-forward neural network:\n", + "```python\n", + "self.ffwd = FeedForward(n_embd)\n", + "```\n", + "\n", + "In the constructor of the `Block` class, we specify `n_embd`, which is the size of our token embeddings (the embedding dimension), and `n_head`, the number of attention heads we want to use. These parameters determine how the tokens will communicate and compute within each block.\n", + "\n", + "Within our `BigramLanguageModel` class, we stack these blocks sequentially to build the depth of the model:\n", + "```python\n", + "self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n", + "```\n", + "\n", + "Here, `n_layer` specifies how many times we repeat the `Block`. This setup allows us to interleave communication and computation multiple times, enabling the model to capture complex patterns in language. Finally, in the forward method, after passing the data through all the blocks, we decode the output to generate the logits (the raw predictions before applying softmax) using:\n", + "```python\n", + "logits = self.lm_head(x)\n", + "```\n", + "\n", + "By interspersing communication and computation in this way, each token can gather information from others and then process it independently, which is crucial for understanding context and generating coherent text in language models like GPT." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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xTR6fOlLuv7+XhGMGzMzMBRc/JkFB/vL885Mlvk0jUOSKNvXAZuH3iDtWJoK7BwV7Yb2Tr7Rs3nhA2iJvT7QpZ5kbARnVwUlJZxXsa9askZ07d0KdmYBZMUPlejbHmTOn5Q9/+IMN5y4PeMbx8PAAzUEwyW4B9XYf6dSpow6krCyKqYYoy3j2DHUG9BS9KYasWL4DHDkendFHggK8UXEPeeKJ8fLBBzMhH16UoHaxcvbcZTTyUXnqqVHg1HEQYQigUmnTtqmMHdNf1q7ZjjYyFobLl22Vzl1aAkhd0JjueFosUdGeMmHiIJk9e7ku8pTRVmhVcmgvTx9M1Vvkiy8WYWA1l61bD0kjcNpGYcGqq6een/sBBgerkIH1q84EDpgxurdWkYJ5xcc3lm6YYThYONSAN3BNF1m0YBMGtSeAPUgiIoMx8ooxgznIyPsHytq1u2XFsm3StXMXiH6FmAnCZPCgnhIVGSIWZ+SShTzA4gcM6Ij1Q6w4u0IV6+oh7Ts0l0WLA6RP37YSHhGg9EZHhUmzptFoxwvQdhUC9DduR5OPWXD+ggWyfNlSNU+gmMNgyuYUVSnqpKSk6HNDXLkW9JyNabriDTHum2++lvbtO2BGe1o6dGiPdMaGJ/O0Z6gToGfnc4F3JT0DsuAW3fj5fPoX4ubKxQ0XtqngKIdk2/YD0rx5Ezl+7Kxy8sZxEWiwYnA+LG+RicWSJ62w+xuMmYGLqaycTIhBFyCiDBQfHws6HJoMxCsuzpGwEH/sI4RpW3LAsYENCBprAS78tmzeBzuiL6Vv344wshokK1dtgriwAPrqKHAnP/nHWzPk5y9PkcDAqjdomCt0F1JUXCBTHhkh27cdwmGTORIZEQVRDm9BjzEwiuXAwWPSsmUMZHUvK63cBS4Vb+jGu0OkopxPDkhS28THgQYvKUKdHIodsGtajHieEhkZirakaTeX/Nx0K0F+weLn64W8qNHBnokrVLQQgQoKbl6l6OLqggHWHwOsvWzcuBFaqqXg7GdUNieI+RMTEyP+/gHa5mxh26CzIDQ9l2ClSlGIg4PWuEePHkVf74Ztz2SZOvVxHQwcQPYEfp0APSvlBq60csNmVTU2jg2GjcdxBTYbips7wSF+smr1Thl+32DVxTs5OWJwcLVPdRhwgF+cop2goXHBZhafUd1HtZ9+dzSmSg4wBTmmTnd3F01n2xkcFMyrAGrDL75YrLL7A2OHSGgjX4gHw+QUtDYEe+cu8aqOfOXXU22TV/4Z+RVD7vX1c5Of/XyyvAh9/6LFayCLt1E6udBFDLVx8fB0VUBy34BjggOR4PX2xiyFB6wjAeCCNiED5FraGDTk9GgLfaDjQgcbBw33TFgnFmb9g3zQEBq0EOvnmv+xYNQ0jYuT2NhYGTRokEyaNEk2btqE9dcS2bF9OxbR2Xpa6Xe/+z3WZiG69mLZtoH9npGRpYNl27atMm/ePDl7Ngnrl/1y/vw/JOXiBZix/BQbnf5Kr9bBNoOb/HzHQc+2Z+exaxcv2ig9erSTSRNHibcPd0lLtLLOzm5y8FCCvPbaJ3Ii8aSEhPopx8rPM/TRbDzKuV6eblh8FoEb5moje3t5qKEbF7ZFkI256DVkV7aWu6SlZoET+RoIsWlAanzSoDtPPJUszzwzVgICXbC4ygadTvj+ADbMvpNZXy2RIcN660LSHCg2WVzz0QGVzIMI0KZNHDjYaJk5c4l10FFTQYbvjBnDXy5fypD8PAxeJ8xKqD/17e5u3nIOIh3biZtejE8wG7C9iqQyHHMqYMAfgxkwphFbn5vvjS839Zt1pjjKMgMCAqQnjk22B9cfPWokRM/dUDmvxhpoLRbdB1RO9/HxtQ64a+llHYcOGyKjRo+Sb7/5Rj799FO5eDEFm4efok+9sEZ5HjO1D8oyGNJNEWyTqA6AvhRc3g0cNFmOJpyRH/94PBZhUZiiwZ2sHUU+GBHpi8aYh6l0tzw2ZSzsZlxl3do94DZtoA2hLpsc0EPmg1ucT76kfezk5C6tWjaBcdROyL8DpUmTIORIOxgv2bRlGzRCJ6VFy2idMUxQECQqz2PAcNf0aMJp6devvwQGsKmKxdc7EmuCSMirC6FOTYGJgqeKLpxRKgYn7Dk4ORnnC8yuLioqwHnjQdAyJUBLMxvTezrAQ5m1FOV0gjj1NRaxKZhJ2oFOqvFg8gATiCVLNmJd0QwA80KejlDt0vbnaomkm6YMxm7s1ecWcH4+LxcZr3WmhAhom8fVVDX7xLQEIturpMQRm3muED9boJ3jhKLP7t27MStD3QuFADetKgukmz8BEIMG9B+A/mqJmd1DZs2aKampqdgYnCZxmFHGjBmNtRsH/K0Dv9ZAT45KAq8fyDEs8t/pi8E10WjNYpGEjaSCtiZXudbLG9qXrvLljCVY2PWVxx6/T974+xeSjp3O0WN6KLgWLtosu3cdhW7bR43NiksKoL8eIj//6Vvyhz++I48/dp80ahQgW7YmgBNtB6jdsCA1ZgmD3Rv0Uh7m0b8HHugv77z9lWRcKZAxY3uqXn/evI2yAQPvkUfuh4y/Xf7zn29l0uTBSrtZVxNI2dnYAIIJBeln0AGFWckddiw//NFD8vxz/wtdfDLqinVJcT64XRdwx13yu9/9G+LCcOnUpZlcupgpH380W2e2kaN6SWCQq6r1XFyxwVTG7TEcMYtRRUltkTmAWacCznyY6fjeNuTmYucUakyTNtt3VX22Lc9oLyMm68VA8POH4hcPYg8bNhT1z0XfcHDZjFAjWdlvztRmurDQMGiWfgm6ijAbfiUXcDjn448/0lmkGdSfxNWtBruD3pApjRU5iTNGJmXKykkll8vFtjVl2UmTIDuH+Sm3ggReloCqK5oQ3De8m5w9cx6iRg709z2gky6W5Su2Qec8TblMZGSYPPvsGNkH1V10bCjKLsKiMxzqxqegGVgt7743C/yUxnD+MnJkT+nbpy3EKHekxUwB+sBztHMoRhRgZ3PMmD7aGasxU/zlL9NAlwUiiJ889eRI6Q+14pCh3VV1SFGIA1Zz0HpS9i7FTmQHVSG6YifZzJ+cmLumzWC89ctfPiaLl2zCzEFNDTamIJ794hePyMyvlkHmXwu7oMXgbi54HyrP//AhadUqBvGKcWyvBQzq3IT5UoQnb3Fzc5aBAztBQ4Mdamtjl6D+EZGBeE41IHZjEQhItkvHjk0lPSMY9ee6hm+MuvNTxaD5WTmsRmUE6wemNXlbWbllIOYOrQcAXD0DZDozLXeaQ0JCse55CYvaBDCBddCabYUWbTUW6ZGo560fXLE76LUF0Ao8eCAAqyFvG9Z4FRtTv6PRyG0mTBgMGZc6eGxpQ6tQ1qr4RD5Je5SgQB/tfGdXHskrlhEje0ibdk0hZlyEyYBFIqNC0GB+kJtjIR5ZkIYzRhHUdnHQZQeBq17QqTYkOECiYqB25EKXMxLLVJVbWZeiGpiBIEI88GA/6dqlNTgORCaE8IgQWFoGYtHoCHVpCxyhI3DcNV/SWZYDPjz00EBDdKmQP/MuAl29ereFyNIUewcuuuimzUxQkLc88cRIOTWgi6RfgQUmtEi0Vg0I9NYBSPPtQYM6KUg4gPPQVhysFFcefnggBoKzlRaDUcTEhkB7M1BnF64pSB83k/r1b6ftTq0Z1Y82lONz+cD4LlhXFRQYJtBXQVpW23IJrr43uH+5l9f5wrTsi8aNY9F+D2Kj6zD2A5Kg1VsmI0aMwCCOMABxnXyqe20X0JNQgpuj2s/fHzKkC8SODIzSPVCttYU8TD1wFdMS2w1oCfAlhwIAGc9QW1xDN7mKH+KpYhugJ9fy9Q6SVi1aaVwnJ8h7jthQ8jXVZCyTzyDFe0BFGdVEx6TFgnkEqk6WS6Aa+WkW5X9h0Do4WMTfNxhc1uhgprVYeAQSeYMgb09/pLEVKcwsSsXPJxBfWEhldWfejmgb+hLiekApwTOoKL38JCQoHG2qRWDwYeOI6keDWPGF+lWDbb4gz98Pz/WZkZfmqdajmLq0DCMZn5floe1tPq/sLxfMpLEUewU85H9FI4UEB2OGdLHSVFm6W3vGug4aPES+mDFD9f179+6VM0lnwLzC0UZGX9xsCXYBPQunTEbQ9+jRU/W2aeAOH388F1v3hdKvb3vI2djBrIZYw96cPKv6UD4ehki5AUIgMQd2evm8mO4qNzPjGVGrIQsRMFwrSWvSWZ4ejV72S/UrKPZG601KCTKDXv6FWGJDpD7BL5tHGsfQ55SvN1+YWZk06zPzFx7aPufjcgGFZEMbxjXQN18v1X0CC2T0Ll27QI3qUy6qvb6QmXF2icZB9JZY2O4D4M9Dtk9MPKVOtXjwxmifmyvRbqBn8SR03Lhx2FVdDq6wVs4mpcgnH3+H7f5VahtTbeuafXy9elSMpwiwSVRVD9Y0nk1WZR+rScvBzjHG2URt8qsqvyyzCh+Yd2VpqimzQg61/pViIDViGRCtKC6OgjcEalpo78RQ3aC+WeLYrjytFhUZpSJeZmambmRxv8ME/c2WazfQm6MzKiIChmCvyt///ncctlgOQlOhejKNjW62CepuOsWmFbiVYbfuUl5zyjhTGItRge3RaBzueQXrjOiaZ3ALMemmj2pfBt1BLie+3VzGdgM9i+fI4+q7c+fO2Eh6DSq9obBH36pqpxJyxLsQFdugWaC/Tx9M9fFt2qh2wSpd3VyP1MFU7FeqErt06SJDhgyW2NhYiBfszIrTUS0Tz+LsUKRdQc8qU9YiV4hr0gRTU6SMHjkKZq05+qyWm+SOZD8Ojm0TYC8SDTuTX73yigLiZqfdO1KBGhTK+tAG3x8HQXhYv4gMzB7oq0HZtcEp7Q56NhCBTw8A9PMYGkZ9OZZYdhihNWqj2xyJIGBwxZnbmJhY3Y28zSTcluLMflWDtXo+Y9sd9OwByvdciHALmp+543qtTuG29FWtF2JqEcwZjvXm57uqvgA5rThpI0NGxv6sz8HuoCdH4KYQFx8+Pt4Kem6zG+q0+txUldNOXTsD683zverFmLNaPeeGWinzF+pD73WsIw+pU0tn2AvVz0raFfRsFDYI9fXcVZs3b76qLmk/YXDEu0/GOXv2jEIjMfEkbEZeUdn3bpLl2Kf07sYjfd27dZNRo8bAMtUP/QuPErp5V/+Ab1fQm7pVnph588031bb63LlzyvlNpnG3/S3EgWbgQi0CeRiaILnbAhkWRRqegV25cqX8D1SWzZo2UxOO+lhXu4GeDUNrOk5/BPzMmTOxZX1JZXtu5dMsVNFRH1upGpodYd/OwDlMjSnvssmMg7jQesIqD4e2586dC01cqfzpj3+EHVKEauXq20C3K+hpf7506VJwhMXYlLqolo+TRg+QQb07wp6EPldulguaSLpe+prGU5xaf9UkTXVxzHe2eVZFpxm3qve2edjr862VyS7jLuxmHNX87+xlcuFSmixYMB9HKPvK5MmTsCvLQ+U2Zh32IrsW87Eb6FX2g832anizpQxPXf3DYwbLz3/xmDSBhSMPM9xcQDozKRq32mAOquvFK5eJNf9q05QRUC6l8cV8h2/8qCRWRadZFlNWFYfv7BnMMm++PKbsNaiblGCh/vmXC+QSdtjXwuR3xIjhevNKtU1nz6rYKS+7gJ4jnapJLl4TE0+oWzcnHCUbDptzAl4tzXkg2QYfNaYfO3+0t2Cgqa8xW1TegaouxAEQCzrHxP/1ymGH8UY7mgobaWyJZDmwILUewGAdjTrYln81Punk4Kfd/bXxkBWSUfVHrwV67uB2AL9cmddrjcrfU5wJhWnziMHdZeGS9Qr68+d5rsEwAa9pW1ee++1/ahfQm2QT/GwIcnkeUvaDPxUn6K1pIajQsMWKmaiqv2Zc4gwWmiowU2NAwfkqzspSs2OccFwNCwtxQDwQUfauyg9EPADqCHt2Bxxpo9aFx/64AcP1iR69g62Hgx63w6CAKvZanJqEgiy4tGMemheLt6WT0TDbOcCWxAHiAhY75d9XSeQtvLDWz8EL541ZPw5eW5pqmLWKLzA687Oxg6FZCZ+zQfR9PUK+oWSuYeVvLBpaVwF6E63MpmQyUEfufSbxHGyqL0ohgFzOktgkCI3PQXY5NUNOIy7VadcL2mEAYR60L7QgLOBswnzgScEZp6MccQiFHQrkw7owGwc6MqRYO7mKnNHpKcgnDfbmGs+m2poMZRViMJ2DS5JsHKGrtB5VZH0zj3VfBGXmA+zJcICbC2exNwN4lq0si/Wxsd83Fq82lbwZIu9QmloAvU1DmBz+ZipHpIALp2bkym9f+1he+PU/5fip85qTwWGMTMlrsGCQdJxFffWvH8uPf/O2HEtMVlDpOyNa5b9hGnsUcd+aNlvOJKfSf7acTb4sy+B7JxXnSjlmS3Gaa8bctfLxV8vlYi68/qJOtuVrxqQV4ty7cKH39bz1coUnstAMV+PhPUSzS2lwzf3Wf2XX/hNShDRl9GmexJT1Cb5fE2oYh+lYrp5nhYlE0vlL8to7M+TI8TN6jKeszGsKuP6DW0l7/dxvXwy7ijf2JJsNTG6SB051BNx736FjMnJwT4mAgyYvHI0jYggNvWEazly3bNwva3A1TG4+PfzmKaBKuI5A4O6hwZn0q262UCPh4uEGh1D5cvj4OZhMwOU3Uh2GR4Yvvl0hjQJ8xRPH9JwBenecy3QAilVM054vD0qlFVz16MmzKkEUgCPaGiJwTLD8XBzS3ns4UUakZxnqTQ4MAJ3nfzmjcPCWgi7s+WBn17ouQRy60GMcB9YD8Uq4dmAcDCTbehVB/NJDLXhO8cOCNJxV9h89JRlZxiUSRgvc27/rLOi1W7TDS2C26yltWjaX9dsP4TB2T/GCGwyKIqUAkq4tIasuXb1NGsdGy2W4+dbDHBCFLFwLII4Dz+sCJBqQzoLDCY4+Xhg0hqrNAtEI0cTiFyzuQYEQo5AsMEJcsAh3gPPRcaP7IiYuaEBhgKUx2qzZ2f7hGoBiFoGoYCQy9TNiWf8yjvEOz0ALB5olNEDBDH/bOOGNdQkGekl6pjhSBqJIBxA74XieA2VyANvCs8SYTUrhKIkVQ44qBjrCc7AFh8s1HwwQcXWX4iOntTzWryEYLVB3QW+CFJ1FG/2hA7vJlp0HJPHEWYnwayFO6EVO4w44p3ns5Dk5Di7bH46iFuNYGxfSMHuUHTsPAyPF0hYXKniCcxJADpDZT2DmOIyLw3r06WjI1iwL+c3+brHMX7xe9sOx1NvvTJM+nVvJqOG9ZR+8BPP0ULcOzcSbHJjAryTwaSEAW4hBpketWR7opyjshF8FerkCvlhDCV5yAC5cuFEWLN0kF3H43B8OWB95aLB0x2F0F3J3AL4QbgtnwufPuo27dG3hAzOACWMGSN/ubcQDg0i1T9gHSTh+Vr6YvRKHqY/Dq5ub3D+8j8RFmH75zVIb/tZd0Fv7hmrCXPh5b9cqVpLh9WATNklatWkiQQAvZQSCft7KbRKMxWebFjGyFIeXaStC0K/cCHEnO19i4HvSHRoY3h9lgdx+5Php+fK7VdIEzpMIVHJx8ssCyNwZly7DQWquZKKszItwR4cF7YZtByQHa4uWrWPFE54YHOErp1wgshmQvy/ci7gGxohbMEQiol1Rj1IcXCXI6hBJ41pnnPcA5vm43qcbLm3o2jxKEs9elr/87TN59vHRMnxoN4DaUf7+75lyGCJKZzio9ffxkAQM8r+9/YWUPD9RBvZqK+5e7rJt9xH58xufSWRosAzq1hbyu4OsXL5Z1qK+WWgDzi4oUsnR8u/hX3Ue9OwbqiM9AfL7+neSr+evkwcupkkwvPU6QN+djYsZ1gHcDw7rKcH+3ohNGCOgkymv58DhUjmtC55Tk5EG8aHIqn/X+ADoYLjhLoacn/PVUpkKQLWBj3hfqCHJvfPhq8YYHPR9YBVRNCGVTBgyQBR3pJev3iypOEvq4camJS0G2Gi7ko5NnTPQ3uhMBBFlO7yczV+yQcaOHyqjcXmCL3znpGMZ4gOXgd/MWyNtO7WUZLgWXLl+tzz7zIMyFE5c3VFGBjwcp/zvB7Juy15p37WV+IEpvP/J99I4JgI+ecZI4zB4jEDoAa3Na29OlxQMZNLC8dcQ2F/1IJDb06anf4+26g/y8KGTks9FKkCyecchcLI8ad25pThA9CA3M4MuPCvpaD5SuRqAtA2BGDRh8K3jjlkiPCRAwvDd2er8VNcJiM87YZ0wMCzgrvoDj8KOEEE0AFXZUG/mwgQjAyrWK3AtfiU5RX8y8Tc15bKKahoX+wBLccVQPLwwDxncD57XQuAy21MawZvys4+OFCdw6JNHT4s/THp/9tx4GTS0nwQ2iRFPeGQOg28cT8xcnPlwu5McwqULZ89flodG9ZPWMY2w0LeIJ9YW8fCpM2xoD9xugrWNcvny9bWt+730uV5wegKUHoi9cRVO61ZxEDcOSRcMgEi4n16wYrPEt4iVEFx1eR5+J8s4vfWTzRgo61fqVq6qE8se6yKxlItIzAAO1GtzE8kdOeA/RaZSAO3oqQvy9vtfyXkAmIEeAX7x7MPSAVyZGqHu3dvL47gAws8LG14YgYQZhSFHDKSz4NqH4fVYBxzqc+pMsmzZtl+OnDiFE/5XVwq8o3bdln3SsUW0dH7uYYnGXsLalRskATd2F8LhEk3ctmzdJ7FwOgWhXw7jblcvDMRADAgHMgPOYCjbCXVpFxcpAX644BhpdA2Ev/d6qBegZyex0wiiEQM7y+tvz5Qr2IhygLbj4JFEeQHybzC4/gXtzavczJDU9WG5X9R2VBYUjBhgFAPMz2Y8pqD2xA8uswf2aC+ZuDiYzyi2hAb6YsrEghL0+cDxa3hYgATDTaEJeiWebsFxBxQ9ImtA3Ez4mm/VLFqG9Gyv6kfDh45R9sSR/eD9rIWcg6/LDz6eLb5Y8DaDuBUc6SfBuHQu8WKqZMGfeylUqXloBzW9AC1IzQzwl4MVznExo3AQNYSrLVBvQK8mARBxunVooRf47t2TIFcyMiUQrrabQTvjSlWgubljrR/BrRz6an3108W0LGgFqcPnUKoYCBgE6x/jiwFEctBAcM377++DlAQXgeWAm3Swk8uvAFspd3Yh/0s+HnBBjXjksJT7S7BTrBtQ+A7Wrn4u3fF3wNhBEobBpPGZERbKm7fsF1eYY38843sF7djJI6QF/FS64b0TNDwLl22SbNw5BbYukRCNeFlDAVSYjhhwDKwZdfrnLl7B7YIUgkhJQ2AL1HmZnkDSGwLRgVh5iqePpzwwrJfMxELvQyw4R+LWvchgP+1QDgwXgEgHCNL5Qm995NhpySp2EQsOqDvFRAHsJfLtgnW6mHVCfMr9dHtNrBEWXGSS4wrcBzoFGfpzXvRAX5GkhdHc8d4TsPJENP51ZHwEZ3BUGtppPMRl3qZens+4NiF9/MyMhsM198IVWyQp4ZS4BweJJ2j0DI+RDTsT5HdvTJczKVfkEHZS46JCcSdTKwmAi3AP1OHimUu4u2oN3Cda1Hy7H9SbtBdatHKrXHbC/kNEmFiiI+D72VE++WYZ9i7SlQ6DSiX1nv5Vtzk9gGG4oM7Saxe5GcWNmx6dWsg/P5wF7pYnsXHR4PK8F5X7NcXY6sdFX5RrMeX3xuL2q++WyfvvTZfBAzrpjud+uPJ2p4tucHpqhXjjSOoVeO6iHIx0jeDc9SJsxl//33dlSJ/2MuqhIZIOsYRusFUVWhVcQFtaRja4aq4xaCrGw/siDKh0XkTG2YDXbXZsKV2hqvzrP7+QgzCHiIsNk7MX0vTCh5646SS6WaTGmYUL4YrgyDW+ZRQWr2mSgIV8VKNGsn3fcdmwZjN2qrvLeNy1NX3WEslBNXrgjq0C1Gfzhj3Y4aW7RU9tE7YRWvCeD3UW9MoNsf3uA1l9wqj+0rJJhDiDHXOh2TgmTJ54eBg4pqM0iwsXCwcDABsG2XrSyD7QvATqQrQj9Pk/hqpvGfT4Mz6dg51dL1zC0ESemjJCjmFzygfigwu8HD+IO2qp7iyF+BEHM4fnptwvGzfvkQwaj8FqtHfHFpIPkOrGFLi6we+vYsegtVjGQO3I/QJPzBwElxnPfO8HeX8iDtXEQcNiASiD/H3k5Z9Mlpkwe9iCTbWNy3GrOG476YL9gynjh0DkcZNHcctgIejasX6H7NqwTQHcpXNrefC+njJ34VophXq0GAvdiWP7oz1Etm47KDMPHoHphIdE4EK1H0wdJYcPJUoEd32Besa510MdBj26BvYjvpB1fwTgckHmjB5jn1kAvKm4FIEd6A6RQpdp4Njs2GcfHykeFCGgd3eGOPHQ2IG41aO1JEGFyJu2mzWJEm9oYbpjtgjAlr2jl5tMnTTUSANO7IbNp3EPDpL7hnRHekg5mEWGwAEtpxI3ikFVsUsMuskAKEUaIx4RZsBLgYb3AdA2Pf/kA8biUgdwgTTHZcc/+8lENZKjQVoAzCMaN46QQCx8HaHhiWoUKD97YaLaH6XBZicUNkExGDTe2LdoirRcB3hR/MIAnoQ7sfr07iCnz/GGFDdpEhuOOrpLV6gu3dBOhgh3r0Meu+N1vQkoB/ui47j7qgs9EOzAjsYmDbUzjvhsootyt7eLu2GjQjkb79zB3ppD6xHXPEa1KU7g2Ezvhus6HTGoGLwwGHQRrGlwARvy9Qjw0fhUW3pALalByzI+XvsbMj7z4aCoIh7rwpsCtSzE4QzgCC7uh0HaAXdRFWNQWfgcC3ba3PO9A8QTXwC8E670LEF6C2jW8wKY8fxpW4RArRIX0G4YZJypYuOidNBbkDfTu4IubT/WryHUfdBT7VYKjmecNLL2GMBQys7nV2Wj1uccGJSXrSxNxQqkd8yhwVmuEVfjIyWud2FaalZK9S5Ygy3rbz4rOxXEsjiwINbYlmUtstwfAIw2YlXGQ740FDPpY1oOBFqDOhbismCWwX+gn//0PWlEvo4FVJEa77lRxjJKeaiF8fCZ37mgLoHdjwVmFAzU3lCVWor8q6RJY95bv+o8pzc6zwCAbddQjKgslBsciMDO1h9rZDOVCSrbd2Z+xjMzpmKKOZmvq/xbsezKIlYWR7VNCunKS2EarhLIp22pAMzLFUG6ub4x+bkZ16xrucj38Jd6AXp79I8JAHvkZZc8SJCJTmuG16Pxeu9Numoaz4x/r/0tzyrugtpz80cPclcAVFnVOENUREVlz8oS2OFDJfmXYAOqUlMIOxTXkEX1LXB3cXrK4jw8QZBVsZgsxgKRwfB+YKC/smfVN9uNva00f9UwYWRSzm8It7UF7hrQK9cE2B0BJobSQgNQ5Zg6AOYIXTnHhOC8q8oXlT1jBvYKyN+CMlWUgTbFDFzA6sBsAL3ZJLft710j3hDHDtCp/+vD7+T3OCB+FhtLtoBXbEHXfw7mvklnUsQ4x4pEUBOe5bMkXLcJ3T71I2VBxRLrdy3A5h0jmV/LxcNDHVUGzmG/ABv6FDmHfYJCHirBopTnft/5aK7swI5qAQgr4/XWdFq+bZ4mQeZ786/SYBJhRmr4e70WuCs4vVonQpd97ESyzFm6WY6eSJIuHVpLEEwPXAAQjmw9uAGfmt8v3KBuMSaMGyABOCBisbjInIXrpQRqwYcfGiDB4Mq0mae6T32yQ91ngpvwMg5sG0BTuZyQxX8jf3zEmoIqRVo98lkp8vl27ho1HnsQlzGHYIOKdjI79p/Ezmy4xBPxIFAPiGMw8Cu1MHrKixtK1t1dikHFoJGaHB761t0JazweEKcDqYZQsxa4K0CvGy/YQJoL1x0d27WUkOBAWb15n3To2lrCcTCDOn1HelDwC5fzWbi2Hg5JS70biSvOlwvugU3JhNiBCwdKfGDS4A5g5cHOBvp+Rx4g98VpLIBKkQ/dfXFquu4IUw3oiE0fUy/PQ9nQFxrxYKcDIxxD1AoMl3MZeXCUhFe+4cgTG2PFmfLKzx6REBz9cwGIS2FuwYFrgd2PAw+Gm8wbvnZKuceAOByEFj/cvo3NNQs2zizw0GCuXUrTrhh7GbYzAIprCJW3QL0HPcUWHp6+cumKbNq6X56ZdJ+C5B/wZZOcdFHCW0KGx+I2A4Zgew5ukuM4sEFXGStXrJX4xqGSWVCqzzgwli9dJb3axkpMqL+4YOc0N69QFuOg9YGDx+HWxkn69eogHds3FzfdFBIYpqUrQMnVF4ObHzuWKL44tD1iQFeYADSSjLRM2b9/kyQmnoYtjIssW7Za+rRrLJGwz6FDqqJiAJesHfRZUN72XYdlw6bdcuVKpkTDZn408gnCzjG9k3F2OIFDMhGNguXY3mOyesNOSYVxXSvYEg3DAXd/DkDu5lbezw1PbVqg3oOe19w74DjcBhy9o3+bqKaREoWjfu99Pl927j4Mg7QI8YfRWiYuAF46b5ns3bUfYANnzUqXrL7d5PSZ87Jv9wHltLlpl8VfRkijiL5yCXbof3t7BryfXYYxGkQSpHkNZ1UfGN1fxo3qK/6w2Vmzab/sOQTHTdAIZcO2n+dD9u89jgG1VX77P09IEDwULJ6zGM8OQ7R3ksyUi9LIdawEdW4j0z6dL2OHdsdAihc3XEH/+ZeLZS6sKf0wM/kg7xWHT8mKZVvkpz98WNrHN5F0nAH4/Mul4oXbXZJxzhbymI6Xaet2y254ffjJsw/BSwRmJeUCNj3c8PGaFqj3oNfdUwDyexyqaIOjcf4QD2hL0yk+Tlas2ymDBncTf8j7PNE0atz9kpiaJUUwBXgAHpVbxgRLB4jsSekwB4ZZwuhRg+BfJ1JcAdAPPpsviThY/ciUkdIFnhhQhCxftV1mzV6BY3rBuAW9g1xIzZRZc1bKEzjgMWn8IAmHXf8ZPPvtH9+X7+aslmefGitjJ42SExczcJzXRYaP6Cct42MVrOcvXsbZ3hx4UHOTZet2yXScDRgCy8kxMBMOhE3NScxc7743Sw98/+ZXT4g3ZPZNOw9iYDvLlMdGSN8OzcUTs8MqDMS3/v219IEpsrmGaeD21+C83IN6rb2hmtIBYsMRODTKyMyV4Q8NlbCWjSEbh8hTT4+DVrJQTkEkyIftjDsGQscubaQxDlc0hsltt14d9ahe565tYbUYLrGR4dKjVyeJhn1+0il6VDspL/5iqox8YKTEtG0NdyGt5dFnn5Uu3TrJDtjkp0L0ccJgiokKkZEPDpA2rRqrFWU7LKDp3fcY8siHuNGpezuJimqkngp69O4sjcJDdaFKlaV6Ycbi9Ku5q6Vj22YyfvxwWIFGwuTYWzrhhNhvfvaonMBMtH/nEV1HsL4DereToTjsHR0ZKiEwTR4DU+VwWGKewLHCHG54cXFLkakhVNkC9ZrTU+PhAGvLRWu3y0WoKA8cSIA4Aj+WUG24AFQpuK18ObhoWxzWCABALfmZuoil0yVLXiY2stzgdSAbi8B8nPDDwjUHi1Q/Zz2tdCYpWb79apGsmr8GnBn/kKnF01PWbNgKM10nGTt5mGpc2sc31QVpKV0JQu62uOdKALwEK/IAaKfsLOQP+R2LUUsuvJa5cEVraHt4YuvKhctQoZ6XMTA5DneB1ghrDwr6DvlXYDocqWbER+HgqhNEHHp6a4/B5U8DuTzjEIwbZgpveF3m3a7lXJ1oKQ2/KmuBegt6FV0BmozLGbIempqinGxZ9v0KQ92Hl7zpzxEewugb5pHkoRKEhaXuwmJu0yN8SKu+IbEOUBEJHFL9xkO00ZNUOOeaA62IM7Q4BD3jCBaYdMgUD48MQRCjePDbCYOLNvbM03AzaHJaPEQSc+eX6VX9SE0MYY20FuwrXMG5VgLWF7b9wDwUQMbky0UpQ3CAnx78LgYX5yF0Gp7RclLLM+lCXsyTPw3h+i1Qb0GvC1h3D1m/dpdkgzs+/fwkCQ/1M2psBWERxJs//t+nsn3nIYgvweJL4AFUPCZYnJeHmzWw8iRgCFiArIi+9QFyHjaJjGgkz/zoEWka7Kvg5uxRgsMn+XAglY9D2J4Qq0zwloMbBwcCJQz+MBDw6oQ1H2WCK5vPi8Gx/XHml/sC2TjeSDHMqRTqVaShNok7uTxYEgQZX02QQTfINcBtzcSgQYtp+FXDFqi3oCe2yKkXrdoGl39NpP+AbhKmjl2BTr4kp4Quu/vK7bIGngUGQg4m6Onvnk6VXPwjxcWNvsp4QMs4cO4SFAXxwyLt4UeHAyk1JU18O7cV11LYrXNguHnJ//3lffFAosk4rEHOzUPjLK4sAIy8asjWozAHlBc2pVwDIlVP7wC34jwgzs0vH3gjCwsPkRXrd0m7nl0lIgCiEQareHvL8UPHJQFrkjEDu4orPKYxT72/tVyB2EbA7KRmDbZ0lBHU8KFiCxhzacWndfw7ObUDAL1rx2HZDh+O9HwW6ojpPz1DLDjk7QQxxAE/lqwseQBgP3D4pOyBJ7Q8XdC6y1qcf509ZyH8Q56EGQCOHCIvuvmeM3uRHNl/VBpBFOrZNV7+/o/p8vU3S+Q8TBRS8POft6fLvAVrJRyLSC+sEehZLR/Ol0DO1QDg8eA3/W+STo4IV/rVxBnX2cj/5MnTagWajfd5PFCCQTP14aFaj3+//6UcPQK32jj3ugHOWn/5+/ekGY78tYEjWUjxutfAQ+9qZ3S1RPX+xssXOAs0hOu3QD3l9AA9wJIA9x49oLpr2boJ1IyGqELZgAyPsjRPKXVu01T6YGc2Df4v86CrHz6wCzwfn4b8v0QicL4uGgfOR+DZqZNJshTPIsHGI6GReW7qaNW/z561VBZzrYB/6Ug/FQfS+0Nd6Q5uHoVD5c4l4Np0z822JgfGrBEW6C9tm0er/02MChk9pJuch/3NEujs4/xcJLhrO2nbLEpCuLOKRXVv+Kj8EdSb1NP//s9H4XsWPjpzi/DeV54BHdFBfpKFja72yDPQ11NlekOKB8qB9NZwUBvVKEgPzisZ/NUQqmyBegl6ncrhdq8bDnx3hX46HF4QKCgr8KxV1c8QKzwgh7/0/Hh9547v7VrGyK9+OVVOwQCsSWiguIFztoezqF//8nE5df6SxOGZK2aEKOzKvvLSZNmK3c9TiUlqBtCieay0pRaFdi7QzPTvGY9FptV9N8uHbO4A680uHVpKXNNo9YVJ0Hdq3wxlPi5JcAXYDK4J3bAeeHrqSAnE5pUedseMMHpkX2mKc7zc/aU/zOCwYOTTQiJQN2fQ7YOF7nNwzkrbHVpUQNgyBhk2xh6dNBxuTVzgvxI2OGVCf5V9fs+/qF3QkxHVVhODo0bTczH/gaMrCCqWBc7L860xEEcYHCkrY0UaGxEiUfA4wIPTFooFAFUs8oqCitB8VoKFpT82fwbCU3IhfsjFXQAoR6o7KV4hPzp8paxPrwUa+BCDyB87o3509UfbfaRxRvwmWAPEYLfYgsFqQZxYrgngeMoBi1mW74p47TD4WuKAuKHZQXm4ucQB6k4GyvN0feKItHo5gz7FL6SNjgzTw+581xCu3wJ2Bz1neDOoTMsvfGZv9ANs9GVJgJDDVhVIjzpjJRlIowwS8jQPi+t3RKAHA3JoR2hmVDVJICNPXoVDJ6i8AEIDQc/41u/0WKDVMt8zErkwZHq4F9MBwUdG/jywjUekgfFBAwjAD79Ch8+MsHh2M/NivfBP4zMTfNc0FeuK+KyfJjPTMr69A+jUtQTpQGBRtVmcvcm3zc+uoGejaMNYS3CkJ15wHz3NZG0s28Jv9TPBogAEtzO6ovIcCR7+J0dl4Hd1EIVE3HQq9wxfzHjsVerEr2bOzzZ1ZPk2eWhGmhnpwgcbulimPrPGN/K1yQvRdeiWVQQZsDguhq0EaBqbPFkUA/NmFLMuxlP7/VbFARbsV++/Negy+tpaL/sVV+s52QX0Jufj5gldyPEv75O9eCVHiiAiUKzQKZlAsGuwIkTRVF3GjEcQmQRY0/GZ/jBtFc80CX/Zvmd8ayh7bz4w/yJ+WXl8ZpvezI9/NQNrIjOOGR/v9LUZh+/Nz9Yk+sd8Xtk723g397kYI9sZjmsv4cbFfIqSqJcj1KRmv99crnculV1AT/Kp6+aWeNNmTcVjjYeq0aZ/s1yat46TTpBT6S66HAbuXJ0bSr6RFsB44jGB4wlJMgPq2xQYwjHEREeDwdHZlDFb1acBYDfQsyHIZ4YPH4ELy76TK9jC37B1j7z6/96T9m2awV0eNl1qhxGx6IZQSy1ARsVNtAOHT8jGHQf0ImlvbJwNGTJEfH39KO3VO45vN9Bz4ceDEd26dpWnn35G3nrrTblw4YKs3bJbNuNWwKqcM9VSXzVka68WAOgpu/OWFW6Acff3ySeflv79++lnvqtPXJ7NYjfQMzMuuixQIj/55JPi7eMjX331lezatRNurmHRSJZQg6BGVRCFCnE6qaZpapBtrUZxxSxGevWcbK2WZJ/M3WEsVwAAV+t6vEJRrGO7du1l7Nix8vhjj0tgYJDWt74BntWyK+ipAuQWPKe/xx97VLp27SL79+2XVNyYURNA0DLy4kVcUoZBEhkJOxWrxWGF9q9zXy+cPy/+uErTxQUiXD0IiYknJRhnDjxhKl2TQObjjRNbrVu1ktatWyOdB9Zw2GOop4s0u4KeDUgxh1yPf9u1bSfxaKSaAN5s/O++my179+6VsaNHwxwg0nxcZ//y1sPXXntNBg4apHVVg7A6Sy22AuDc9Xevviq9e/eSLl26qAn29cjlJM39Ame4A6eYQ6VFfV6gVb2rc72WqOa9buygpfJgvssDzdeTbIz3vPqmVJYsXSozZ34pp06dsqaDrr9mklE1FNXGK4Pe3bt34cD3Ulm9ahVEBtwpBVrrolhGoLriFvGdO7bLhg3rZcWKFTChwMGXGtBLhk7GlYuDMsyHl2HUVy5PJNQK6JkxG4XyOQcAg9G4lf9lg5KLHDhwQPbt3SPJOP20Zt1aycnFGVJsHhFE1aW/E+9IM+lasmSJnD17FlfdL5Tz55OvW9c7QSvLJFA5KOfPWyBnk87KosWL5MzpM0ovufb16GIc2/60JqyXf2oN9LatwQFQ1Q8HhTlAloLLJyefN8C0eLGcA5jYWWacqvK43c9NetxgkrwYdGbgHqndu3fLzp27wAlx7NBap9tNV1XlcXByIXrk6FHcP7sVIk62HDkMs+zt2yGu0ITB0CVXld58btun9fnzbQF9dQ1EjskLiM+dOyerV6+W9HS440MnHDx4ULZt26HrA8wPOhCqy+d2vlOaAaLlEBGOHDmiU38OgMQBcOWKsXlTt0QcaNVgar1i+XLMSknK1bnuIpNJTU29nU1XJ8qqE6CnBeG69evkJLQKhszooGYMixYvBIjStJPqRGuBCIKZ03xGerp88MH7WLPA6AzP+LMKcn3CsWNKqsk97zTdpMvZ2UVSUlIU9AR5CS078Xw92vzQoUP6ua7Qezva646Cng1PANFOh7LxpYuXtM6mvLxx40aA6LiKOJQp60IwZiZniDI7VUQgTawHRZrLly/JhvUbJCcH5pTW5/rhDv4ivVQFb9q8GZ7cjqs4w2cEeVpaGoC/AUcjs5RC1uNeCHcU9Gx8N2yUbN+xU+VLLlrZGWx8dzhXTU29jEuCF0lmZgb6om6IOBykpG/u93N1wW0rv3OWmjNnNkQ1rkWMdcydBBHp5A4qF7DffvuNipAUJUkznzOQ3jNnztQJem9XW90x0LND2Pjk8gvmz5OOHTrK5MlTsNMXqHXv33+APPvsD3T6PQEOxXCnp2AOUp533bJlq+zbtw/2J0Nxk3cLrYefnx+8oT2qa5BVWJvkQl3LwHreqcCySS9nzHTYQk155FGJjo7RdgwNDZUXX3pJAoODZe3adTo7YZzeUXpvVzvZfXOqpoQTwAQ9RYEePXpKK+z2rVy5EovBhZpF+/Yd5NlnnoYD1P3YAaR7YQP0dxJE5ixEzvn8D54H3d1l0qTJSltoWJj86Ec/lDTIzPSkUAyfO+qB+A6KZaSXaw5XV3f56U9/pmYHm7ds1oU3wT/lkSnyAMwK0nGgngOa4/NOMxZtzFr+dcdAz3qxoTndDhs2VBdbb/3jLVX/cTBEhOPi34AAvBsG7mnYcN9JwJNeAoK0tGvXTrp166rc/Pjx48odaTYRHBQsnTt1gpeEbHF2wj23d47Jk1yll5uDbdrASSzEyLf+8Q8saC8ova1bx0toaAh+2uqmE9v8Xgl1oqaUL0+ehJuOPXuwIZULu5BgtfGwwH0l5O90AAAJaElEQVQeO41ydF0JHHju7u6QiZ3lo48+xiBNVxC1b9dBfGCfQktTN+x81gWaTU5Pu/eks+ewc7xMF6/83qdPb9hI+WDXvEDl+3uBw5sYumOgZyMTQFz8EfSzvp4lJ09AZYnpuEfPXhIbG6viDxewnBHqQjA4faFqQyh2fQ2aGXx9faVXr556aowc0xAV7gybJ40mgLleouaGKsqPPvwQxn/7dFHbs2dP6YQZie2O6HWmfW9XH9+yeGM28I0SbJoOe8ENxlJwoFmzZunGlJeXlzz44INlC1rTzuNOiDa2dSOQKcuTS1IG/te7/5IE7HCSriFDh0rbtm0UbPxum872M9/VZj3M/GkBSWbBtmX506ZN0wGajr0FXyy4H33sMYmIjNAus6XvRvuwvsZ3IvhYcfPHbDjbCvGgtOHZwNjUsH13I5+Zt8kFPaCSpDy/bt16ef311+GA6YRqPh566CHpAwtAckzOAgykrTbBUl0dSC9/yBW9ccVOGrQgb7zxhiyGKjUfFpaNGoXLY48+pqa6Jq3mX9LM9rWthwGy2tlzMC0hPT2dtf2SYGPz+fTP5ZOPP8FFckm6qH10ymMycOBA65qj/ACtrh3upnfoS4PZm2A3gWlbSQLQAYeDHeFll8HoOCPGjSyAuBVOWZfpM7OyoSOeK++++y42enaoRWbHjp3kueeek5AQw4d7xbKMEm/fb9LpAlfg/MufXbt2gWt+KHPnzpFLly7pYHjxxRdVi8N2YNuZfzkrMA0Ht1nn20F5AQ5uJyaeUktK7nGsWbMGi9eLoBUXyT08QZ5//ge40idQb14hffdicNq0aZM0b95c/P39tXPYcWwMA+hGo3Abm4PDxcUN79hMZmMZMjnTXC8wDk1TqT3YBeMsmrZyF5ZGZeSkHTt1lj//6c/SvXt31eQYHWKWc73ca+c9jccI7qNHE+BbcoMsg63Krl27dfHKWWoCQPQYRAV6gGBgm5lmCTygQfOKy5dTda+BnJZ1p4q2Ju11MzXKx0C7fOmymmXv378P5aUoM6FhHAH/85//XBo3jtUTbne2ZW+mdvZL4/Tyyy9jwyJa1YM07zUBv3btWoA0VztoxpczsGu6Xd/hQVnp7Lx82GQTtNcLvD2P+bFTEhKOaodwocVRRLCQo9LcdcnSxVpEXegUngegacHp06flBMQvWlNSXImIiMSRyKdk6tTHoLHx0fqz3fiO9aBFY0JCAgy6lslWWDXyM0+E0ZiOg6K2AruGHJ1lkBb2D5nJxAkTcMvJOBXFzPINplJblNTtfJ1oXspp25azs0EISDYQG24VNo3WYRAog7+KedTsxhZmzIvyLfM1G50DhmXvBg3ULmj25cq4cw1IswjSR3pJt4+PL/YN+uiG1ODBg8XH10c9BbAujOMOFyhcRM6cOVPPB2/btg1ATwcAcV0nwFgT5nBrtXVQdWpERAR2ilvpXsLgwTjRFU89vXu5dr+1cup3aqfOnTvrIodcjB1LYDKYf83PBICBSH1d9qsInWwbt+xFhQ/AhYpPFJP0ol+Uo+n4AoGl1j4otKga/SI9zqCVYl9kZJS0bNkSsnsP6Lf74A6pKCWYtwpSXifgufmTBdfg7733b/nss0+1TSnXE2xUDzI901E7pW4Ire1cI2JqGIlty5knHBt7cXFNsPEUpmsKDjrSSObSEHAw/M03DVcd2dhFNEFPzjV9+nQshjaoTDj1iSfUtQfdX5frK3yhHFkj0KO1KQebHL5i4/N5HWHwZaS5QNzzx72wBA8By80ngpyeBNgQ1JZwoFIs5Mz4Btpy+uefq2EXn/ft20+9B1AvHhsbqwexjRm1rAi7f2D+xsKZZ5UL0H/w24ln9RXwtngxcHbrKHHi4WB2UEUuS7GH0zM7sy+428iRI5WbGb1kStzk1nxSE0KoAamuj6t9WV3CWntHek3AUGwh5ybXJKjMynAGJOg/xObPlzNw7yw8I1Cmp/HZ0089hZ3lVipysJ24MDZUv7VDMkHBHWzSyEDAKK21U1yt5mqA3RAbTYmA2kPbQXCzBOCesKvH9ZghwW/+NTl4AW7f4zM26L0UWH+CleBnMAeA8c34TZOEjdCAffrpp+DwyRrnBz/4gbzwwgvqxoRiBbVWbD/+1HZgGfWVq1dsGzIUHnohs2FHcLPNUJtfj4FWzKn8d7iYNOzDzQ4xO8f8bkS/eiiYQLB9Zw6M8tlW/c02bdWx6sabinWtjKq8vFyV4ROOJWjnjB8/HibRz0JT0gizJNx/W8WNytI2PKu6BYjLDNzCzuOYFL3pSykiMlKcMKveatCVTUUgVvxusjo+r/jOfFbTv7dK8O1MX7GulZW9e/ce2Qr7+hx0DLUmzzzzjC58KQ6Zs2hl6RqeVd0CnB0pMtL/0fHjPO1VKE2axElsbIy6WL9RRluxpIblfMUWucHv69atgw4+RcXC++8fqTI8szCZwA1md09HN5kMmQXXPzztlZR0Rtu2V69e0ghnFkohITaA/g7DhNaWVFVSM9W3b2/V5Tdw+Jp3iskcuNwhR+cP3QbOnfs9TKGXa9uGhoXCG/Z9wtNp1JmYg6PmpZSPectWluWzu/e+8XwpF1rUjcfExGqHVNSE3XutUvMak2sb2sNSBTvFGu7M//PtfyqX5/sJEyZKx44dVVyk9qsB9DVv31qJyU09dlpQUJBuDJHLs6NutWNqhdg6mCkXrAQ69zyuXElXT3Gv/+11OQFZnmYg/fv3l0eh/vX3D0A726ddGzj9LQKhhDvVCBRveAjbuJ3jqprzFrO/65NTw0VjPO4L0QCRhoinT59SRtK1azd55ZVXsJvdQlXHaFW7tEcD6G+xGemTn5ydRmlvvvmW7uDeYpb3THI0G9y7ZOoO9mG4GUxOPqdWqJw5hwwZKjSG5G42Z09jA7QB9HUCHAQ8A29dmTHjC2sH1QnS6gURBDjNX7iA5ee4uDiZMHGijBs3TlrBXunqDrZ9AM9GaeD0doIGsc8dawcHijvGQLBT1nd1NjTZCA+PUAM5Gub1xPnoPn37iLtahfIyXvuB3WzIBtCbLXGTf/UCZqSl5uYp2NrQk4PJ/W8yy3sqGddAISHBONMRJWHQw/PwjWnnZIg19m+OBtDfYptij1o1NcHBQTDKux927M1vMcd7Kzm1XNTgkKNzJ/Z2WIU2gP5WMWadfanFIWcyjPIo3th/Wr5VUutiemNWpBhjGPQZA6B2Kf3/+EPy5CgemqQAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": { + "image/png": { + "width": 125 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'cross-attention.png', width=125)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "class Block(nn.Module):\n", + " \"\"\"Transformer block: communication followed by computation.\"\"\"\n", + " \n", + " def __init__(self, n_embd, n_head):\n", + " # n_embd: embedding dimension, n_head: the number of heads we'd like\n", + " super().__init__()\n", + " head_size = n_embd // n_head\n", + " self.sa = MultiHeadAttention(n_head, head_size)\n", + " self.ffwd = FeedFoward(n_embd)\n", + " self.ln1 = nn.LayerNorm(n_embd)\n", + " self.ln2 = nn.LayerNorm(n_embd)\n", + "\n", + " def forward(self, x):\n", + " x = x + self.sa(self.ln1(x))\n", + " x = x + self.ffwd(self.ln2(x))\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Improving Deep Neural Networks with Residual Connections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this stage of building our GPT model from scratch, we’re noticing that the performance isn’t as good as we’d like. One reason is that our neural network is becoming quite deep, and deep neural networks often face optimization issues. This means they can be hard to train effectively because the gradients used in learning can either vanish or explode as they pass through many layers.\n", + "\n", + "To tackle this problem, we can borrow an idea from the **“Attention Is All You Need”** paper. The paper introduces two optimizations that significantly help deep networks remain trainable. The first optimization is the use of skip connections, also known as residual connections. These connections allow the model to bypass certain layers by adding the input of a layer directly to its output. This helps preserve the original information and makes it easier for the network to learn.\n", + "\n", + "In simple terms, instead of just passing data through a transformation (like a neural network layer), we also add the original data back into the output. This means that if the transformation doesn’t learn anything useful, the network can still pass the original information forward. This helps prevent the network from getting worse as it gets deeper.\n", + "\n", + "Here’s how we can implement residual connections in our `Block` class:\n", + "```python\n", + "class Block(nn.Module):\n", + " def __init__(self, n_embd, n_head):\n", + " super().__init__()\n", + " head_size = n_embd // n_head\n", + " self.sa = MultiHeadAttention(n_head, head_size)\n", + " self.ffwd = FeedForward(n_embd)\n", + "\n", + " def forward(self, x):\n", + " x = x + self.sa(x) # residual connection after self-attention\n", + " x = x + self.ffwd(x) # residual connection after feed-forward network\n", + " return x\n", + "```\n", + "\n", + "In this code:\n", + "* Self-Attention Residual Connection: We compute `self.sa(x)`, which is the output of the self-attention layer, and add it to the original input `x`.\n", + "```python\n", + "x = x + self.sa(x)\n", + "```\n", + "* Feed-Forward Residual Connection: Similarly, we compute `self.ffwd(x)`, which processes each token independently, and add it to the result of the previous step.\n", + "```python\n", + "x = x + self.ffwd(x)\n", + "```\n", + "\n", + "By adding these residual connections, we’re effectively allowing the network to “skip” layers if needed, making it easier to train deeper models. The residual connections help the gradients flow backward through the network during training, which addresses the optimization issues associated with deep neural networks.\n", + "\n", + "In summary, residual connections are a simple yet powerful idea that helps deep neural networks learn more effectively. By incorporating them into our model, we’re borrowing a successful strategy from advanced architectures like Transformers, ensuring that our GPT model can train successfully even as it becomes deeper and more complex." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": { + "image/png": { + "width": 300 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'residual-blocks.png', width=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Residual Connections in the GPT Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When building deep neural networks like our GPT model, we can run into problems because deep networks are harder to train effectively. One powerful solution is using residual connections. Think of a residual connection as a shortcut path for information to flow through the network without getting distorted by too many layers. In our model, the computation flows from top to bottom, and there’s a central pathway called the residual pathway, represented by a black line in diagrams.\n", + "\n", + "At certain points, we “fork off” from this residual pathway to perform some computations—like self-attention or feed-forward processing—and then we add the result back to the main pathway. This is implemented using addition operations. Here’s why this helps: during training, when the network learns by backpropagation, gradients (which update the network’s weights) can flow directly through these addition points. This creates a “gradient superhighway” that allows learning signals to pass unimpeded from the output back to the input layers, making training more efficient.\n", + "\n", + "To implement residual connections in our code, we modify the forward method of the `Block` class like this:\n", + "```python\n", + "def forward(self, x):\n", + " x = x + self.sa(self.ln1(x))\n", + " x = x + self.ffwd(self.ln2(x))\n", + " return x\n", + "```\n", + "\n", + "In this code:\n", + "* `self.ln1(x)` and `self.ln2(x)` apply layer normalization to stabilize the inputs.\n", + "* `self.sa` is the multi-head self-attention operation.\n", + "* `self.ffwd` is the feed-forward neural network.\n", + "* We add the output of these operations back to the original input `x`, creating the residual connections.\n", + "\n", + "In the `MultiHeadAttention` class, we need to make sure the output dimensions match so we can add them back to `x`. We do this by introducing a projection layer:\n", + "```python\n", + "self.proj = nn.Linear(n_embd, n_embd)\n", + "```\n", + "\n", + "After combining the outputs of all attention heads:\n", + "```python\n", + "out = torch.cat([h(x) for h in self.heads], dim=-1)\n", + "out = self.proj(out)\n", + "return out\n", + "```\n", + "\n", + "* We concatenate the outputs from all heads along the last dimension.\n", + "* We then project this combined output back to the original embedding size (`n_embd`) using `self.proj(out)`.\n", + "\n", + "Similarly, in the `FeedForward` class, we adjust the network to have a larger inner layer, which increases its capacity to learn complex patterns:\n", + "```python\n", + "self.net = nn.Sequential(\n", + " nn.Linear(n_embd, 4 * n_embd),\n", + " nn.ReLU(),\n", + " nn.Linear(4 * n_embd, n_embd),\n", + ")\n", + "```\n", + "\n", + "* The first linear layer expands the size from `n_embd` to `4 * n_embd`.\n", + "* After applying the ReLU activation function, the second linear layer brings it back to `n_embd`, allowing us to add it back to `x`.\n", + "\n", + "By using these residual connections and appropriately sized projection layers, we allow the model to add new computations without losing the original information. This helps the gradients flow smoothly during training, making it much easier to optimize deep networks like our GPT model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": { + "image/png": { + "width": 125 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'block.png', width=125)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Layer Normalization in Deep Neural Networks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As our GPT model becomes deeper, we encounter difficulties in training it effectively. Deep neural networks can suffer from optimization issues, making it hard for the model to learn from the data. To overcome this, we use two important techniques from the **“Attention Is All You Need”** paper. We’ve already added residual connections to help information flow through the network. The second optimization is called layer normalization, often shown as “Norm” next to the “Add” operations in diagrams.\n", + "\n", + "Layer normalization is similar to batch normalization, which you might have heard of. In batch normalization, we ensure that each neuron’s output has a mean of zero and a standard deviation of one across the entire batch of data (`B`). This helps stabilize the learning process by keeping the outputs of neurons on a similar scale.\n", + "\n", + "However, layer normalization works a bit differently. Instead of normalizing across the batch, layer normalization normalizes across the features (the elements within each data point). This means that for each individual example in the batch, we compute the mean and variance of its features and adjust them so that they have a mean of zero and a standard deviation of one. This is especially helpful in models like Transformers because it doesn’t depend on the batch size and works well with variable-length sequences.\n", + "\n", + "Here’s how we incorporate layer normalization into our `Block` class:\n", + "```python\n", + "import torch.nn as nn\n", + "\n", + "class Block(nn.Module):\n", + " def __init__(self, n_embd, n_head):\n", + " super().__init__()\n", + " head_size = n_embd // n_head\n", + " self.ln1 = nn.LayerNorm(n_embd) # layer normalization before self-attention\n", + " self.sa = MultiHeadAttention(n_head, head_size)\n", + " self.ln2 = nn.LayerNorm(n_embd) # layer normalization before feed-forward network\n", + " self.ffwd = FeedForward(n_embd)\n", + "\n", + " def forward(self, x):\n", + " x = x + self.sa(self.ln1(x)) # residual connection with self-attention\n", + " x = x + self.ffwd(self.ln2(x)) # residual connection with feed-forward network\n", + " return x\n", + "```\n", + "\n", + "In this code:\n", + "* Layer Normalization Layers: We introduce `self.ln1` and `self.ln2` using `nn.LayerNorm(n_embd)`. These layers normalize the inputs to the self-attention and feed-forward networks.\n", + "* Residual Connections: We maintain our residual connections by adding the output of the self-attention and feed-forward networks back to the original input `x`.\n", + "* Forward Method: In the `forward` method, we apply layer normalization before each main operation. This helps stabilize the inputs to those layers.\n", + "\n", + "By using layer normalization, we ensure that the activations (outputs of each layer) have consistent statistics throughout the network. This makes the deep network easier to train because it reduces the internal changes that the network has to adapt to during learning. Combined with residual connections, layer normalization greatly improves the optimization of very deep neural networks like our GPT model." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# SOURCE: https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": { + "image/png": { + "width": 225 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'layer-norm-formula.png', width=225)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2Num7sX9CKOH", + "outputId": "929ceb78-a639-41d6-aac7-12997b5c93f0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([32, 100])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class LayerNorm1d: # (used to be BatchNorm1d)\n", + " \"\"\"Implements 1D Layer Normalization to stabilize and normalize input activations.\"\"\"\n", + " \n", + " def __init__(self, dim, eps=1e-5, momentum=0.1):\n", + " self.eps = eps\n", + " self.gamma = torch.ones(dim)\n", + " self.beta = torch.zeros(dim)\n", + " \n", + " def __call__(self, x):\n", + " # calculate the forward pass\n", + " xmean = x.mean(1, keepdim=True) # batch mean\n", + " xvar = x.var(1, keepdim=True) # batch variance\n", + " xhat = (x - xmean) / torch.sqrt(xvar + self.eps) # normalize to unit variance\n", + " self.out = self.gamma * xhat + self.beta\n", + " return self.out\n", + " \n", + " def parameters(self):\n", + " return [self.gamma, self.beta]\n", + "\n", + "\n", + "torch.manual_seed(1337)\n", + "module = LayerNorm1d(100)\n", + "x = torch.randn(32, 100) # batch size 32 of 100-dimensional vectors\n", + "x = module(x)\n", + "x.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding How Layer Normalization Works in Our GPT Mode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our GPT model, we use layer normalization to help stabilize and improve the training of our deep neural network. Let’s consider an example where we have a `batch_size` of 32, and each input vector has 100 dimensions. This means we have 32 samples (vectors), each with 100 features.\n", + "\n", + "When we pass these vectors through a layer normalization layer, we ensure that each feature within a sample is normalized. Specifically, for each individual sample in the batch, we compute the mean and standard deviation across its features and adjust them to have a mean of zero and a standard deviation of one.\n", + "\n", + "Here’s how we implement layer normalization:\n", + "```python\n", + "# x has a shape of (batch_size, num_features), e.g., (32, 100)\n", + "xmean = x.mean(1, keepdim=True) # compute the mean across features for each sample\n", + "xvar = x.var(1, keepdim=True) # compute the variance across features for each sample\n", + "x_normalized = (x - xmean) / torch.sqrt(xvar + 1e-5) # normalize each sample\n", + "```\n", + "\n", + "In this code:\n", + "* `xmean` is calculated by taking the mean of `x` across dimension 1, which corresponds to the feature dimension. We use `keepdim=True` to maintain the dimensionality for broadcasting.\n", + "* `xvar` is the variance computed similarly across the features of each sample.\n", + "* `x_normalized` is the result of subtracting the mean and dividing by the standard deviation (square root of variance plus a small epsilon to prevent division by zero).\n", + "\n", + "By changing the dimension from 0 to 1 in the `mean` and `var` functions, we’re computing the statistics across the features of each individual sample rather than across the batch. This means we’re normalizing each sample independently, and the normalization does not depend on other samples in the batch.\n", + "\n", + "Initially, if we had used:\n", + "```python\n", + "xmean = x.mean(0, keepdim=True) # mean across the batch for each feature\n", + "xvar = x.var(0, keepdim=True) # variance across the batch for each feature\n", + "```\n", + "\n", + "This would have computed the mean and variance across the batch dimension for each feature (column). In this case, we would be normalizing each feature across all samples in the batch, which is what batch normalization does.\n", + "\n", + "However, since we’re implementing layer normalization, we use:\n", + "```python\n", + "xmean = x.mean(1, keepdim=True) # mean across features for each sample\n", + "xvar = x.var(1, keepdim=True) # variance across features for each sample\n", + "```\n", + "\n", + "With layer normalization, the columns (features) are not normalized across the batch. Instead, each sample’s features are normalized based on that sample’s own mean and variance. This ensures that the normalization is independent of the batch size and the data in other samples.\n", + "\n", + "By normalizing each sample individually, we help the model to perform consistently regardless of the batch composition, which is particularly useful in models like Transformers where sequences can have varying lengths, and batching can be complex.\n", + "\n", + "In summary, layer normalization adjusts the activations (outputs) of each sample so that they have a mean of zero and a standard deviation of one across their features. This helps the network learn more effectively by preventing internal covariate shift and ensuring that the scale of the inputs to each layer remains consistent." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "633T2cmnW1uk", + "outputId": "7720fa58-0478-4e8a-86a7-502d4cce9443" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor(0.1469), tensor(0.8803))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[:,0].mean(), x[:,0].std() # mean,std of one feature across all batch inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LN9cK9BoXCYb", + "outputId": "6368ece0-600e-417d-8a91-7c1e5d750ba8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor(-3.5763e-09), tensor(1.0000))" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[0,:].mean(), x[0,:].std() # mean,std of a single input from the batch, of its features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding the Pre-Norm Formulation in Transformer Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the original Transformer model described in the **“Attention Is All You Need”** paper, the **Add & Norm** (addition and layer normalization) steps are applied after the main transformations like self-attention and feed-forward networks. However, in more recent implementations, it’s common to apply layer normalization before these transformations. This approach is called the **Pre-Norm Formulation**.\n", + "\n", + "Applying layer normalization before the transformations helps stabilize the training of deep neural networks. It ensures that the inputs to each layer have a consistent scale and distribution, which makes it easier for the network to learn effectively.\n", + "\n", + "In our `Block` class, which represents a single Transformer block, we implement this by adding two layer normalization layers in the constructor:\n", + "```python\n", + "self.ln1 = nn.LayerNorm(n_embd) # first layer norm for self-attention\n", + "self.ln2 = nn.LayerNorm(n_embd) # second layer norm for feed-forward network\n", + "```\n", + "\n", + "Here, `n_embd` is the embedding dimension—the size of the vector that represents each token (like a word or character) in our sequence.\n", + "\n", + "In the `forward` method of the `Block` class, we apply the layer norms before passing the data to the self-attention and feed-forward layers:\n", + "```python\n", + "def forward(self, x):\n", + " x = x + self.sa(self.ln1(x)) # apply layer norm before self-attention\n", + " x = x + self.ffwd(self.ln2(x)) # apply layer norm before feed-forward network\n", + " return x\n", + "```\n", + "\n", + "By normalizing `x` before each transformation, we help the model learn better and more stable representations. This change reflects modern best practices in training Transformer models, allowing our deep neural network to train more effectively, leading to improved performance in tasks like language modeling." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding Layer Normalization and Scaling Up Our GPT Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our GPT model, we set the embedding size `n_embd` to 32. This means each token in our sequence is represented by a vector of 32 numbers. When we apply layer normalization, we normalize these features by calculating the mean and variance over these 32 numbers for each token. The batch size (`B`) and the sequence length (`T`) act as batch dimensions, so the normalization happens per token independently. This ensures that each token’s features have a mean of zero and a standard deviation of one at initialization.\n", + "\n", + "Layer normalization includes trainable parameters called gamma (γ) and beta (β), which allow the model to scale and shift the normalized outputs during training. In our implementation, we initialize them as follows:\n", + "```python\n", + "self.gamma = torch.ones(dim)\n", + "self.beta = torch.zeros(dim)\n", + "```\n", + "\n", + "Here, dim is the embedding dimension (`n_embd`). While the initial output after normalization might be unit Gaussian, the optimization process during training adjusts these parameters to find the best scale and shift for the data.\n", + "\n", + "In the `BigramLanguageModel` class, we add a final layer normalization layer at the end of the Transformer, right before the last linear layer that decodes the embeddings into logits for the vocabulary. This is done in the constructor:\n", + "```python\n", + "self.ln_f = nn.LayerNorm(n_embd) # final layer norm\n", + "```\n", + "\n", + "To scale up our model and make it more powerful, we introduce the variable `n_layer` in the `BigramLanguageModel` constructor. This variable specifies how many layers of `Block` modules we stack together:\n", + "```python\n", + "self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n", + "```\n", + "\n", + "Each `Block` consists of multi-head self-attention and a feed-forward neural network, along with residual connections and layer normalization. We also introduce `n_head` which specifies the number of attention heads in our multi-head attention mechanism. By increasing `n_layer` and `n_head`, we can make our model deeper and allow it to capture more complex patterns in the data.\n", + "\n", + "In summary, by properly applying layer normalization and scaling up the model with more layers (`n_layer`) and attention heads (`n_head`), we enhance the model’s ability to learn and generalize from the data. This approach ensures our deep neural network remains stable and effective during training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Layer Norm Formula" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": { + "image/png": { + "width": 125 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'block.png', width=125)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Dropout to Improve the GPT Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In our GPT model, we introduce a technique called dropout to prevent overfitting and improve the model’s ability to generalize to new data. Dropout works by randomly “turning off” or setting to zero a subset of neurons during each training pass. This means that every time the model processes data during training, it uses a slightly different network configuration. At test time, all neurons are active, and the model benefits from the combined knowledge of these different configurations.\n", + "\n", + "We add dropout layers at specific points in our model to enhance regularization:\n", + "1.\tIn the `FeedForward` class constructor, we add `dropout` right before connecting back to the residual pathway. This ensures that some neurons in the feed-forward network are randomly ignored during training:\n", + "```python\n", + "self.dropout = nn.Dropout(dropout)\n", + "```\n", + "\n", + "2.\tIn the `MultiHeadAttention` class constructor, we include `dropout` after the attention heads have been processed. This helps prevent the model from becoming too dependent on any single attention pathway:\n", + "```python\n", + "self.dropout = nn.Dropout(dropout)\n", + "```\n", + "\n", + "3. In the `Head` class constructor, we add `dropout` after calculating the attention weights (affinities) and applying the softmax function. This randomly prevents some nodes from communicating, adding a layer of regularization to the attention mechanism:\n", + "```python\n", + "self.dropout = nn.Dropout(dropout)\n", + "```\n", + "\n", + "By incorporating dropout in these areas, we effectively train an ensemble of smaller sub-networks within our larger network. Each sub-network learns slightly different patterns, and when combined, they make the overall model more robust. This technique is especially useful when scaling up models, as it reduces the risk of overfitting to the training data and improves performance on unseen data.\n", + "\n", + "In summary, dropout enhances our GPT model by:\n", + "* Randomly disabling neurons during training, which prevents the model from relying too heavily on any single neuron.\n", + "* Encouraging the network to learn more generalized features that are useful across different subsets of the data.\n", + "* Improving the model’s ability to generalize to new, unseen inputs by reducing overfitting.\n", + "\n", + "This addition ensures that our model remains effective and reliable as it becomes more complex." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dropout Layer" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": { + "image/png": { + "width": 425 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename = 'dropout.png', width=425)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZcvKeBXoZFOY" + }, + "source": [ + "## Full Finished Code\n", + "You may want to refer directly to the git repo instead though." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hoelkOrFY8bN", + "outputId": "961304cd-e379-40d4-dd56-8de0b91d2861" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.209729 M parameters\n", + "step 0: train loss 4.4116, val loss 4.4022\n", + "step 100: train loss 2.6568, val loss 2.6670\n", + "step 200: train loss 2.5091, val loss 2.5060\n", + "step 300: train loss 2.4196, val loss 2.4336\n", + "step 400: train loss 2.3503, val loss 2.3565\n", + "step 500: train loss 2.2965, val loss 2.3127\n", + "step 600: train loss 2.2410, val loss 2.2501\n", + "step 700: train loss 2.2048, val loss 2.2186\n", + "step 800: train loss 2.1636, val loss 2.1864\n", + "step 900: train loss 2.1242, val loss 2.1504\n", + "step 1000: train loss 2.1024, val loss 2.1291\n", + "step 1100: train loss 2.0690, val loss 2.1176\n", + "step 1200: train loss 2.0377, val loss 2.0795\n", + "step 1300: train loss 2.0229, val loss 2.0622\n", + "step 1400: train loss 1.9922, val loss 2.0357\n", + "step 1500: train loss 1.9706, val loss 2.0315\n", + "step 1600: train loss 1.9618, val loss 2.0465\n", + "step 1700: train loss 1.9409, val loss 2.0130\n", + "step 1800: train loss 1.9077, val loss 1.9936\n", + "step 1900: train loss 1.9078, val loss 1.9855\n", + "step 2000: train loss 1.8825, val loss 1.9938\n", + "step 2100: train loss 1.8711, val loss 1.9750\n", + "step 2200: train loss 1.8579, val loss 1.9596\n", + "step 2300: train loss 1.8543, val loss 1.9528\n", + "step 2400: train loss 1.8401, val loss 1.9418\n", + "step 2500: train loss 1.8150, val loss 1.9439\n", + "step 2600: train loss 1.8234, val loss 1.9347\n", + "step 2700: train loss 1.8118, val loss 1.9318\n", + "step 2800: train loss 1.8048, val loss 1.9225\n", + "step 2900: train loss 1.8070, val loss 1.9296\n", + "step 3000: train loss 1.7953, val loss 1.9239\n", + "step 3100: train loss 1.7688, val loss 1.9158\n", + "step 3200: train loss 1.7511, val loss 1.9081\n", + "step 3300: train loss 1.7580, val loss 1.9045\n", + "step 3400: train loss 1.7561, val loss 1.8935\n", + "step 3500: train loss 1.7398, val loss 1.8928\n", + "step 3600: train loss 1.7244, val loss 1.8893\n", + "step 3700: train loss 1.7305, val loss 1.8828\n", + "step 3800: train loss 1.7180, val loss 1.8852\n", + "step 3900: train loss 1.7196, val loss 1.8693\n", + "step 4000: train loss 1.7148, val loss 1.8605\n", + "step 4100: train loss 1.7127, val loss 1.8744\n", + "step 4200: train loss 1.7071, val loss 1.8654\n", + "step 4300: train loss 1.7023, val loss 1.8460\n", + "step 4400: train loss 1.7052, val loss 1.8656\n", + "step 4500: train loss 1.6899, val loss 1.8512\n", + "step 4600: train loss 1.6862, val loss 1.8300\n", + "step 4700: train loss 1.6828, val loss 1.8413\n", + "step 4800: train loss 1.6659, val loss 1.8388\n", + "step 4900: train loss 1.6686, val loss 1.8351\n", + "step 4999: train loss 1.6622, val loss 1.8221\n", + "\n", + "Foast.\n", + "\n", + "MENENIUS:\n", + "Prave is your niews? I cank, COmine. I well torms, beary.\n", + "\n", + "HENRY WARWORDriown:\n", + "The Papoinst proy way as home\n", + "but exfulings begt as liht;\n", + "Lyief, away, friom is of bulb.\n", + "\n", + "HENRY BOLINA:\n", + "What\n", + "Than what you suffect toogny!\n", + "That prope of so pity this badoggent;\n", + "Stame deck untiless,\n", + "Their laters you\n", + "Is you\n", + "Tow my such in mamy that prongmanoe,\n", + "Anjoth then your usequind, my would wontimn;\n", + "Thou prove to day them as it?\n", + "\n", + "SITUS:\n", + "Yeas staw his Kingdeed our chall:\n", + "But now this dray.\n", + "\n", + "ROMEO:\n", + "O, upon to death! him not this bornorow-prince.\n", + "My sunder's like us.\n", + "But you wilerss armiss brond,\n", + "Stayle my becul'st I say, your bear shalle I mone faults not fleathms ell spraver of it\n", + "she wongrame and broth of his it.\n", + "But reven. \n", + "WARY HARDONTIO:\n", + "Qumper! what voishmes!\n", + "Good liff tumbuntincaed up us.\n", + "\n", + "AUCHIOPOM:\n", + "Therefort them, but In to sproved.\n", + "\n", + "KING RICHARD II:\n", + "Come, dreivide, But twas oot, for and sirring to to a\n", + "but mantore your bond wedaus thee.\n", + "\n", + "VORK:\n", + "For which his lictless me, gurse?\n", + "Uhould dried:\n", + "To now, alm? I wherse fortune deque;\n", + "To least my not thinged weouly entount.\n", + "Cewle ther, Nont loung, you Vilive:\n", + "Let thou beves thou one true toges amont;\n", + "There twfined me. If your cause with and\n", + "Thost the will langed! So morman, mad the'e noccust to knot\n", + "Hench when is the underer you: if\n", + "The I hom blidess one lip\n", + "We is maid weak'd a bed'sime, maday,\n", + "And then you pringent, and what, for there is a gring,\n", + "And is ear aftiffed where diswer.\n", + "Make slendow to nit,\n", + "You loved, my tonte mind hath dels in wor flords.\n", + "\n", + "ISABELLA:\n", + "Whult bear your sont\n", + "On is Sup\n", + "Where not: I bust ma! part you bring,\n", + "thou met dincedts them thee towly him,\n", + "But a frust those, if you would kingt.\n", + "\n", + "TROM\n", + "\n", + "First:\n", + "It,\n", + "Jurets both our too right or lmed of hide\n", + "not these dut o' the ploss you.\n", + "And I known, the piors, time say as day BI thy God came to time. I'll would is bring; Lorde,\n", + "What, his arm he nobt\n", + "That boved fireive, what evert togen\n", + "our whus.\n", + "\n", + "ISABELLA:\n", + "You our loverd would let before elcome see,\n", + "Which ha\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.nn import functional as F\n", + "\n", + "# hyperparameters\n", + "batch_size = 16 # number of independent sequences to process in parallel\n", + "block_size = 32 # maximum context length for predictions\n", + "max_iters = 5000 # total number of training iterations\n", + "eval_interval = 100 # interval for evaluating the model on validation set\n", + "learning_rate = 1e-3 # learning rate for the optimizer\n", + "device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' # device to run the model on\n", + "eval_iters = 200 # number of iterations to estimate loss\n", + "n_embd = 64 # embedding dimension\n", + "n_head = 4 # number of attention heads\n", + "n_layer = 4 # number of transformer blocks\n", + "dropout = 0.0 # dropout rate for regularization\n", + "# ------------\n", + "\n", + "torch.manual_seed(1337) # for reproducibility\n", + "\n", + "# load the dataset\n", + "# make sure to have 'input.txt' file in your working directory\n", + "# you can download it using: wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\n", + "with open('input.txt', 'r', encoding='utf-8') as f:\n", + " text = f.read()\n", + "\n", + "# create a mapping from characters to integers\n", + "chars = sorted(list(set(text)))\n", + "vocab_size = len(chars)\n", + "# create a mapping from characters to indices and vice versa\n", + "stoi = { ch:i for i,ch in enumerate(chars) } # string to index\n", + "itos = { i:ch for i,ch in enumerate(chars) } # index to string\n", + "encode = lambda s: [stoi[c] for c in s] # encoder: string to list of integers\n", + "decode = lambda l: ''.join([itos[i] for i in l]) # decoder: list of integers to string\n", + "\n", + "# prepare the dataset\n", + "data = torch.tensor(encode(text), dtype=torch.long)\n", + "n = int(0.9 * len(data)) # split 90% for training, 10% for validation\n", + "train_data = data[:n]\n", + "val_data = data[n:]\n", + "\n", + "\n", + "# function to generate a batch of data\n", + "def get_batch(split):\n", + " \"\"\"\n", + " Generate a batch of input and target sequences for training.\n", + "\n", + " Args:\n", + " split (str): 'train' or 'val' to select the dataset split.\n", + "\n", + " Returns:\n", + " x (torch.Tensor): Input tensor of shape (batch_size, block_size).\n", + " y (torch.Tensor): Target tensor of shape (batch_size, block_size).\n", + " \"\"\"\n", + " # select the appropriate data split\n", + " data = train_data if split == 'train' else val_data\n", + " # randomly choose starting indices for each sequence in the batch\n", + " ix = torch.randint(len(data) - block_size, (batch_size,))\n", + " # collect sequences of length 'block_size' starting from each index\n", + " x = torch.stack([data[i:i+block_size] for i in ix])\n", + " y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n", + " # move data to the appropriate device\n", + " x, y = x.to(device), y.to(device)\n", + " return x, y\n", + "\n", + "\n", + "# function to estimate the loss on training and validation sets\n", + "@torch.no_grad()\n", + "def estimate_loss():\n", + " \"\"\"\n", + " Estimate the average loss over several iterations for both\n", + " training and validation datasets.\n", + "\n", + " Returns:\n", + " out (dict): Dictionary containing average losses for 'train' and 'val'.\n", + " \"\"\"\n", + " out = {}\n", + " model.eval() # set the model to evaluation mode\n", + " for split in ['train', 'val']:\n", + " losses = torch.zeros(eval_iters)\n", + " for k in range(eval_iters):\n", + " X, Y = get_batch(split) # get a batch of data\n", + " logits, loss = model(X, Y) # forward pass\n", + " losses[k] = loss.item() # store the loss\n", + " out[split] = losses.mean() # compute the average loss\n", + " model.train() # set the model back to training mode\n", + " return out\n", + "\n", + "\n", + "class Head(nn.Module):\n", + " \"\"\"One head of self-attention.\"\"\"\n", + "\n", + " def __init__(self, head_size):\n", + " \"\"\"\n", + " Initialize the self-attention head.\n", + "\n", + " Args:\n", + " head_size (int): Dimensionality of the key, query, and value vectors.\n", + " \"\"\"\n", + " super().__init__()\n", + " # linear projections for keys, queries, and values\n", + " self.key = nn.Linear(n_embd, head_size, bias=False)\n", + " self.query = nn.Linear(n_embd, head_size, bias=False)\n", + " self.value = nn.Linear(n_embd, head_size, bias=False)\n", + " # register a lower triangular matrix for masking future positions\n", + " self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n", + " # dropout layer for regularization\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Perform the forward pass of the self-attention head.\n", + "\n", + " Args:\n", + " x (torch.Tensor): Input tensor of shape (B, T, C).\n", + "\n", + " Returns:\n", + " out (torch.Tensor): Output tensor of shape (B, T, head_size).\n", + " \"\"\"\n", + " B, T, C = x.shape\n", + " # compute keys, queries, and values\n", + " k = self.key(x) # (B, T, head_size)\n", + " q = self.query(x) # (B, T, head_size)\n", + " v = self.value(x) # (B, T, head_size)\n", + "\n", + " # compute attention scores using scaled dot-product\n", + " wei = q @ k.transpose(-2, -1) * C**-0.5 # (B, T, T)\n", + " # apply causal mask to prevent attending to future positions\n", + " wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))\n", + " # convert scores to probabilities\n", + " wei = F.softmax(wei, dim=-1) # (B, T, T)\n", + " wei = self.dropout(wei) # apply dropout\n", + "\n", + " # compute the weighted sum of values\n", + " out = wei @ v # (B, T, head_size)\n", + " return out\n", + "\n", + "\n", + "class MultiHeadAttention(nn.Module):\n", + " \"\"\"Multiple self-attention heads in parallel.\"\"\"\n", + "\n", + " def __init__(self, num_heads, head_size):\n", + " \"\"\"\n", + " Initialize the multi-head attention module.\n", + "\n", + " Args:\n", + " num_heads (int): Number of attention heads.\n", + " head_size (int): Size of each head.\n", + " \"\"\"\n", + " super().__init__()\n", + " # create a list of attention heads\n", + " self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n", + " # linear projection to combine the outputs of all heads\n", + " self.proj = nn.Linear(n_embd, n_embd)\n", + " # dropout layer\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Perform the forward pass of multi-head attention.\n", + "\n", + " Args:\n", + " x (torch.Tensor): Input tensor of shape (B, T, C).\n", + "\n", + " Returns:\n", + " out (torch.Tensor): Output tensor of shape (B, T, C).\n", + " \"\"\"\n", + " # concatenate the outputs from all attention heads\n", + " out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, C)\n", + " # apply linear projection and dropout\n", + " out = self.dropout(self.proj(out))\n", + " return out\n", + "\n", + "\n", + "class FeedForward(nn.Module):\n", + " \"\"\"A simple feed-forward neural network.\"\"\"\n", + "\n", + " def __init__(self, n_embd):\n", + " \"\"\"\n", + " Initialize the feed-forward network.\n", + "\n", + " Args:\n", + " n_embd (int): Embedding dimension.\n", + " \"\"\"\n", + " super().__init__()\n", + " # define a two-layer MLP\n", + " self.net = nn.Sequential(\n", + " nn.Linear(n_embd, 4 * n_embd), # expand dimensionality\n", + " nn.ReLU(), # non-linearity\n", + " nn.Linear(4 * n_embd, n_embd), # project back to original size\n", + " nn.Dropout(dropout), # dropout for regularization\n", + " )\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Perform the forward pass of the feed-forward network.\n", + "\n", + " Args:\n", + " x (torch.Tensor): Input tensor of shape (B, T, C).\n", + "\n", + " Returns:\n", + " torch.Tensor: Output tensor of the same shape.\n", + " \"\"\"\n", + " return self.net(x)\n", + "\n", + "\n", + "class Block(nn.Module):\n", + " \"\"\"Transformer block: communication followed by computation.\"\"\"\n", + "\n", + " def __init__(self, n_embd, n_head):\n", + " \"\"\"\n", + " Initialize the transformer block.\n", + "\n", + " Args:\n", + " n_embd (int): Embedding dimension.\n", + " n_head (int): Number of attention heads.\n", + " \"\"\"\n", + " super().__init__()\n", + " head_size = n_embd // n_head # size of each attention head\n", + " # multi-head self-attention\n", + " self.sa = MultiHeadAttention(n_head, head_size)\n", + " # feed-forward network\n", + " self.ffwd = FeedForward(n_embd)\n", + " # layer normalizations\n", + " self.ln1 = nn.LayerNorm(n_embd)\n", + " self.ln2 = nn.LayerNorm(n_embd)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Perform the forward pass of the transformer block.\n", + "\n", + " Args:\n", + " x (torch.Tensor): Input tensor of shape (B, T, C).\n", + "\n", + " Returns:\n", + " torch.Tensor: Output tensor of the same shape.\n", + " \"\"\"\n", + " # apply layer norm and self-attention, then add residual connection\n", + " x = x + self.sa(self.ln1(x))\n", + " # apply layer norm and feed-forward network, then add residual connection\n", + " x = x + self.ffwd(self.ln2(x))\n", + " return x\n", + "\n", + "\n", + "class BigramLanguageModel(nn.Module):\n", + " \"\"\"Language model based on the Transformer architecture.\"\"\"\n", + "\n", + " def __init__(self):\n", + " \"\"\"\n", + " Initialize the language model.\n", + "\n", + " The model consists of token embeddings, positional embeddings,\n", + " multiple transformer blocks, and a final linear layer to produce logits.\n", + " \"\"\"\n", + " super().__init__()\n", + " # token embedding table: maps token indices to embedding vectors\n", + " self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n", + " # positional embedding table: learns embeddings for positions in the sequence\n", + " self.position_embedding_table = nn.Embedding(block_size, n_embd)\n", + " # stack of transformer blocks\n", + " self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n", + " # final layer normalization\n", + " self.ln_f = nn.LayerNorm(n_embd)\n", + " # linear layer to project embeddings to vocabulary logits\n", + " self.lm_head = nn.Linear(n_embd, vocab_size)\n", + "\n", + " def forward(self, idx, targets=None):\n", + " \"\"\"\n", + " Perform the forward pass of the language model.\n", + "\n", + " Args:\n", + " idx (torch.Tensor): Input tensor of token indices with shape (B, T).\n", + " targets (torch.Tensor, optional): Target tensor for computing loss.\n", + "\n", + " Returns:\n", + " logits (torch.Tensor): Logits tensor of shape (B, T, vocab_size).\n", + " loss (torch.Tensor or None): Cross-entropy loss if targets are provided.\n", + " \"\"\"\n", + " B, T = idx.shape\n", + "\n", + " # get token embeddings for each token in the sequence\n", + " tok_emb = self.token_embedding_table(idx) # (B, T, C)\n", + " # get positional embeddings for each position in the sequence\n", + " pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T, C)\n", + " # add token and positional embeddings to get the input to transformer blocks\n", + " x = tok_emb + pos_emb # (B, T, C)\n", + " # pass through the stack of transformer blocks\n", + " x = self.blocks(x) # (B, T, C)\n", + " # apply final layer normalization\n", + " x = self.ln_f(x) # (B, T, C)\n", + " # compute logits for the next token prediction\n", + " logits = self.lm_head(x) # (B, T, vocab_size)\n", + "\n", + " # if targets are provided, compute the loss\n", + " if targets is None:\n", + " loss = None\n", + " else:\n", + " # reshape logits and targets for computing cross-entropy loss\n", + " B, T, C = logits.shape\n", + " logits = logits.view(B*T, C)\n", + " targets = targets.view(B*T)\n", + " # compute the loss\n", + " loss = F.cross_entropy(logits, targets)\n", + "\n", + " return logits, loss\n", + "\n", + " def generate(self, idx, max_new_tokens):\n", + " \"\"\"\n", + " Generate new text by sampling from the language model.\n", + "\n", + " Args:\n", + " idx (torch.Tensor): Input tensor of shape (B, T) containing the context.\n", + " max_new_tokens (int): Number of new tokens to generate.\n", + "\n", + " Returns:\n", + " idx (torch.Tensor): Tensor of shape (B, T + max_new_tokens) with generated tokens.\n", + " \"\"\"\n", + " for _ in range(max_new_tokens):\n", + " # ensure the context does not exceed the block size\n", + " idx_cond = idx[:, -block_size:]\n", + " # get the predictions\n", + " logits, _ = self(idx_cond)\n", + " # focus on the last time step\n", + " logits = logits[:, -1, :] # (B, vocab_size)\n", + " # convert logits to probabilities\n", + " probs = F.softmax(logits, dim=-1) # (B, vocab_size)\n", + " # sample the next token from the probability distribution\n", + " idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n", + " # append the new token to the sequence\n", + " idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n", + " return idx\n", + "\n", + "\n", + "# instantiate the model and move it to the appropriate device\n", + "model = BigramLanguageModel().to(device)\n", + "# print the number of parameters (in millions)\n", + "print(sum(p.numel() for p in model.parameters())/1e6, 'M parameters')\n", + "\n", + "# create an optimizer for updating the model parameters\n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n", + "\n", + "# training loop\n", + "for iter in range(max_iters):\n", + "\n", + " # every eval_interval iterations, evaluate the model on the validation set\n", + " if iter % eval_interval == 0 or iter == max_iters - 1:\n", + " losses = estimate_loss()\n", + " print(f\"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}\")\n", + "\n", + " # get a batch of training data\n", + " xb, yb = get_batch('train')\n", + "\n", + " # compute the loss and gradients\n", + " logits, loss = model(xb, yb)\n", + " optimizer.zero_grad(set_to_none=True)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + "# generate text from the model\n", + "context = torch.zeros((1, 1), dtype=torch.long, device=device) # starting token (e.g., )\n", + "generated_sequence = model.generate(context, max_new_tokens=2000)[0].tolist()\n", + "# decode and print the generated text\n", + "print(decode(generated_sequence))" + ] + } + ], + "metadata": { + "colab": { + "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": 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"Let’s Build GPT: From Scratch, in Code, Spelled Out", this project takes you on an immersive journey into the inner workings of GPT. Step-by-step, we’ll construct a GPT model from the ground up, demystifying its architecture and bringing its mechanics to life through hands-on coding. +```python +from IPython.display import Image +``` + + +```python +Image(filename = 'RE-GPT.jpeg') +``` + + + + + +![jpeg](README_files/README_1_0.jpg) + + + + +# Reverse Engineering GPT + +#### Drawing inspiration from Andrej Karpathy’s iconic lecture, "Let’s Build GPT: From Scratch, in Code, Spelled Out", this project takes you on an immersive journey into the inner workings of GPT. Step-by-step, we’ll construct a GPT model from the ground up, demystifying its architecture and bringing its mechanics to life through hands-on coding. + +### [original video from Andrej Karpathy](https://www.youtube.com/watch?v=kCc8FmEb1nY) + +#### Credit: [Andrej Karpathy](mailto:karpathy@eurekalabs.ai) +#### Instructor: [Kevin Thomas](mailto:ket189@pitt.edu) + +## [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) +#### Academic Paper + + +```python +!pip install torch +``` + + Requirement already satisfied: torch in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (2.5.1) + Requirement already satisfied: filelock in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (3.13.1) + Requirement already satisfied: typing-extensions>=4.8.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (4.11.0) + Requirement already satisfied: networkx in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (3.3) + Requirement already satisfied: jinja2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (3.1.4) + Requirement already satisfied: fsspec in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (2024.6.1) + Requirement already satisfied: setuptools in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (75.1.0) + Requirement already satisfied: sympy==1.13.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch) (1.13.1) + Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sympy==1.13.1->torch) (1.3.0) + Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from jinja2->torch) (2.1.3) + + + +```python +import torch +import torch.nn as nn +from torch.nn import functional as F +``` + + +```python +from IPython.display import Image +``` + +## Transformer Model Architecture + + +```python +Image(filename = 'transformer-model-arch.png', width=400) +``` + + + + + +![png](README_files/README_8_0.png) + + + + +## Understanding Self-Attention in Simple Terms + +When building a language model like GPT from scratch, we initially might use a uniform weight matrix `wei` based on a function like `torch.tril`. This matrix treats all previous tokens equally, which isn’t ideal because different words (tokens) in a sentence might be more or less important to each other. For example, if a vowel in a word is looking back at previous letters, it might be more interested in certain consonants rather than all past letters equally. + +Self-attention helps solve this problem by allowing each token to focus on specific other tokens in a data-dependent way. Here’s how it works: every token at each position generates two vectors—`query` and `key`. The `query` vector represents **“What am I looking for?”** and the `key` vector represents **“What do I contain?”**. By computing the dot product between a token’s query and the keys of all other tokens, we obtain a measure of similarity or “affinity”. This affinity tells us how much attention one token should pay to another. + +In code, we start by initializing linear layers for the keys and queries without biases: +```python +key = nn.Linear(input_size, head_size, bias=False) +query = nn.Linear(input_size, head_size, bias=False) +``` + +We then compute the keys and queries by passing our input `x` (which contains all tokens) through these layers: +```python +k = key(x) # shape: (B, T, head_size) +q = query(x) # shape: (B, T, head_size) +``` + +Here, `B` is the `batch_size`, `T` is the sequence length, and `head_size` is a hyperparameter (like 16). At this point, each token has independently produced its key and query vectors without any communication with other tokens. + +Next, we compute the affinities (similarities) between tokens by taking the dot product of queries and transposed keys: +```python +wei = q @ k.transpose(-2, -1) # shape: (B, T, T) +``` + +This results in a matrix where each element tells us how much one token should pay attention to another. For example, `wei[0][8][4]` might represent how much the 8th token in the first batch should focus on the 4th token. These affinities are data-dependent, meaning they change based on the actual content of the tokens. + +However, when aggregating information, we don’t use the original tokens directly. Instead, each token also generates a value vector, which represents the information it wants to share: +```python +value = nn.Linear(input_size, head_size, bias=False) +v = value(x) # shape: (B, T, head_size) +``` + +Finally, we use the affinities to compute a weighted sum of these values: +```python +output = wei @ v # shape: (B, T, head_size) +``` + +This means each token gathers information from other tokens, weighted by how relevant they are (as determined by the affinities). So, a token effectively says, **“Based on what I’m interested in (my query) and what others contain (their keys), here’s the combined information (values) I should consider.”** + +By doing this, self-attention allows the model to dynamically focus on different parts of the input sequence, enabling it to capture complex patterns and relationships in the data. + + +```python +# version 4: self-attention! +torch.manual_seed(1337) +B, T, C = 4, 8, 32 # batch, time, channels +x = torch.randn(B,T,C) + +# let's see a single Head perform self-attention +head_size = 16 +key = nn.Linear(C, head_size, bias=False) +query = nn.Linear(C, head_size, bias=False) +value = nn.Linear(C, head_size, bias=False) +k = key(x) # (B, T, 16) +q = query(x) # (B, T, 16) +wei = q @ k.transpose(-2, -1) # (B, T, 16) @ (B, 16, T) ---> (B, T, T) + +tril = torch.tril(torch.ones(T, T)) +#wei = torch.zeros((T,T)) +wei = wei.masked_fill(tril == 0, float('-inf')) +wei = F.softmax(wei, dim=-1) + +v = value(x) +out = wei @ v +#out = wei @ x + +out.shape +``` + + + + + torch.Size([4, 8, 16]) + + + + +```python +wei[0] +``` + + + + + tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [0.1574, 0.8426, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [0.2088, 0.1646, 0.6266, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [0.5792, 0.1187, 0.1889, 0.1131, 0.0000, 0.0000, 0.0000, 0.0000], + [0.0294, 0.1052, 0.0469, 0.0276, 0.7909, 0.0000, 0.0000, 0.0000], + [0.0176, 0.2689, 0.0215, 0.0089, 0.6812, 0.0019, 0.0000, 0.0000], + [0.1691, 0.4066, 0.0438, 0.0416, 0.1048, 0.2012, 0.0329, 0.0000], + [0.0210, 0.0843, 0.0555, 0.2297, 0.0573, 0.0709, 0.2423, 0.2391]], + grad_fn=) + + + + +```python +Image(filename = 'attention.png', width=400) +``` + + + + + +![png](README_files/README_13_0.png) + + + + +## Understanding Attention Mechanisms in Simple Terms + +Attention is like a way for different parts (nodes) of a network to communicate with each other. Imagine these nodes as points in a directed graph where edges show which nodes are connected. Each node has some information stored as a vector, and it can gather information from other nodes it’s connected to by taking a weighted sum of their vectors. The weights are data-dependent, meaning they change based on the actual content at each node. + +In our case, we have a graph of 8 nodes because our `block_size` is 8, so there are always 8 tokens. The structure is such that the first node only looks at itself, the second node looks at itself and the first node, and so on, up to the 8th node, which looks at all previous nodes and itself. This setup ensures that future tokens don’t influence past ones, which is important in language modeling where we predict the next word based on previous words. + +One important thing to note is that attention doesn’t have a built-in sense of position or space. The nodes don’t inherently know where they are in the sequence. To fix this, we add positional encodings to our input vectors so that each token is aware of its position in the sequence. This is different from convolutional operations where the position is inherently part of the computation due to the structure of the convolution filters. + +In our model, we process multiple examples at once using batches. For instance, with a `batch_size` of 4, we have 4 separate groups of 8 nodes. These groups are processed independently and don’t share information with each other. This is handled efficiently using batched matrix multiplications that operate across the batch dimension `B`. + +When it comes to the code, if we wanted all the nodes to communicate with each other (like in tasks where future tokens can influence past ones), we’d use an encoder attention block. This involves removing the masking line in our code: +```python +wei = wei.masked_fill(tril == 0, float('-inf')) +``` + +By deleting this line, we allow every node to attend to every other node without restrictions. However, for language modeling, we keep this line to prevent future tokens from influencing the computation of past tokens, creating what’s known as a **decoder attention block**. + +Lastly, in self-attention, the keys, queries, and values all come from the same source `x`. This means each node is attending to other nodes within the same set. In contrast, cross-attention involves keys and values coming from a different source than the queries, which is useful when integrating information from external data or another part of the network. + +## Understanding Scaled Attention in Simple Terms + +### Scaled Dot-Product Attention Formula + + +```python +Image(filename = 'scaled-dot-product-attention-formula.png', width=350) +``` + + + + + +![png](README_files/README_18_0.png) + + + + + +```python +Image(filename = 'scaled-dot-product-attention.png', width=250) +``` + + + + + +![png](README_files/README_19_0.png) + + + + +In the **“Attention Is All You Need”** paper, we’ve learned how to implement attention mechanisms using queries, keys, and values. We multiply the queries (`q`) and keys (`k`), apply the softmax function to the result, and then use these weights to aggregate the values (`v`). This process allows the model to focus on different parts of the input data. + +However, there’s an important step we haven’t included yet: dividing by the square root of the `head_size` (denoted as dk in the formula). This operation is known as scaled attention, and it’s a crucial normalization step in the attention mechanism. + +Here’s why scaling is important: if `q` and `k` are random variables drawn from a standard normal distribution (mean of 0 and standard deviation of 1), then their dot product will have a variance proportional to the `head_size` (which is 16 in our case). Without scaling, the `wei` (weights) would have a variance of about 16, causing the softmax function to produce very sharp (peaked) outputs. + +By multiplying by `head_size**-0.5` (which is the same as dividing by the square root of head_size), we adjust the variance of `wei` back to 1: +```python +wei = q @ k.transpose(-2, -1) * head_size ** -0.5 +``` + +This scaling ensures that when we apply the softmax function: +```python +wei = F.softmax(wei, dim=-1) +``` + +The resulting weights are more evenly distributed (diffuse) rather than being overly concentrated. This is especially important during initialization because it allows the model to explore different parts of the input without being biased toward specific positions. + +In summary, including the scaling factor in our attention computation helps maintain stable gradients and prevents the softmax outputs from becoming too extreme. This makes the model more effective at learning and focusing on the relevant parts of the input data. + + +```python +k = torch.randn(B,T,head_size) +q = torch.randn(B,T,head_size) +wei = q @ k.transpose(-2, -1) * head_size**-0.5 +``` + + +```python +k.var() +``` + + + + + tensor(1.0449) + + + + +```python +q.var() +``` + + + + + tensor(1.0700) + + + + +```python +wei.var() +``` + + + + + tensor(1.0918) + + + +## Understanding the Impact of Softmax in Attention Mechanisms + +In our attention mechanism, we use the softmax function to convert the raw attention weights (`wei`) into probabilities that sum up to one. However, there’s a problem when the values inside `wei` are very large or very small (both positive and negative). When `wei` contains very large positive and negative numbers, the softmax function tends to produce outputs that are extremely peaked, meaning it approaches one-hot vectors. This results in the model focusing almost entirely on one token and ignoring the rest, which isn’t always desirable. + +For example, if we have `wei` values like `[10, 20, 30]`, applying softmax will give us something close to `[0.0, 0.0, 1.0]`. This happens because the exponential function in softmax amplifies the differences between large numbers, making the largest value dominate the output. Conversely, if we apply softmax to values that are very close to zero, like `[0.1, 0.2, 0.3]`, the output will be more evenly distributed, such as `[0.30, 0.33, 0.37]`. This diffuse output means the model considers multiple tokens more equally. + +To prevent the softmax from becoming too sharp and focusing only on one token, we need to ensure that the values in `wei` are not too large in magnitude. This is where scaling comes in. By dividing `wei` by a scaling factor (specifically, the square root of the `head_size`), we reduce the variance of its values, keeping them closer to zero. This scaling ensures that the softmax function produces a more balanced output. + +In code, we implement this scaling as follows: +```python +wei = q @ k.transpose(-2, -1) * head_size ** -0.5 +``` + +By including the scaling factor `head_size ** -0.5`, we adjust the attention weights so that their variance is controlled, and the softmax function doesn’t saturate. This allows the model to consider information from multiple tokens rather than just one, improving its ability to learn and generalize from the data. + +Understanding this scaling is important because it highlights how mathematical operations in neural networks can significantly impact the model’s performance. By carefully managing the values passed into functions like softmax, we ensure that the attention mechanism works effectively, allowing our GPT model to capture complex patterns in language. + + +```python +torch.softmax(torch.tensor([0.1, -0.2, 0.3, -0.2, 0.5]), dim=-1) +``` + + + + + tensor([0.1925, 0.1426, 0.2351, 0.1426, 0.2872]) + + + + +```python +torch.softmax(torch.tensor([0.1, -0.2, 0.3, -0.2, 0.5])*8, dim=-1) # gets too peaky, converges to one-hot +``` + + + + + tensor([0.0326, 0.0030, 0.1615, 0.0030, 0.8000]) + + + +## Understanding the Head Class in Self-Attention + +In our journey to build GPT from scratch, the `Head` class is crucial because it implements a single head of self-attention. Self-attention allows the model to focus on different parts of the input sequence when generating each part of the output, which is essential for understanding context in language. + +When we initialize the `Head` class, we pass in a parameter called `head_size`. Inside the constructor (`__init__` method), we create three linear layers: `key`, `query`, and `value`. These layers are initialized without bias terms (`bias=False`) and are used to project our input `x` into different representations: + +```python +self.key = nn.Linear(input_size, head_size, bias=False) +self.query = nn.Linear(input_size, head_size, bias=False) +self.value = nn.Linear(input_size, head_size, bias=False) +``` + +These linear layers apply matrix multiplications to the input data and are essential for computing the attention mechanism. + +We also create a lower triangular matrix called tril using `torch.tril`, which stands for “triangle lower.” This matrix is registered as a buffer (not a parameter that the model learns) using register_buffer: +```python +self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size))) +``` + +This matrix ensures that each position in the sequence can only attend to itself and previous positions, preventing information from “future” tokens from influencing the current token (which is important for language modeling where we predict the next word). + +In the forward method, which defines how the data flows through the model, we take an input `x` and extract its dimensions: +```python +B, T, C = x.shape # Batch size, Sequence length, Embedding size +``` + +We compute the `key` and `query` matrices by passing`x` through their respective linear layers: +```python +k = self.key(x) # Shape: (B, T, C) +q = self.query(x) # Shape: (B, T, C) +``` + +We calculate the attention weights (wei) by taking the dot product of `q` and the transposed `k`, and then we normalize it by dividing by the square root of `C` (this is known as scaled attention): +```python +wei = q @ k.transpose(-2, -1) * C ** -0.5 # Shape: (B, T, T) +``` + +To ensure that future tokens do not influence the current token, we apply the lower triangular mask: +```python +wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # Shape: (B, T, T) +``` + +We then apply the softmax function to turn these weights into probabilities that sum to one: +```python +wei = F.softmax(wei, dim=-1) +``` + +Next, we compute the value matrix: +```python +v = self.value(x) # Shape: (B, T, C) +``` + +Finally, we perform the weighted aggregation of the values by multiplying the attention weights `wei` with the values `v`: +```python +out = wei @ v # Shape: (B, T, C) +return out +``` + +The result out is a new representation of the input sequence where each token has gathered information from the relevant tokens that came before it. This mechanism allows the model to capture dependencies and relationships in the data, which is fundamental for tasks like language modeling. + + +```python +class Head(nn.Module): + """One head of self-attention.""" + + def __init__(self, head_size): + super().__init__() + self.key = nn.Linear(n_embd, head_size, bias=False) + self.query = nn.Linear(n_embd, head_size, bias=False) + self.value = nn.Linear(n_embd, head_size, bias=False) + self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size))) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + B,T,C = x.shape + k = self.key(x) # (B,T,C) + q = self.query(x) # (B,T,C) + # compute attention scores ("affinities") + wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T) + wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T) + wei = F.softmax(wei, dim=-1) # (B, T, T) + wei = self.dropout(wei) + # perform the weighted aggregation of the values + v = self.value(x) # (B,T,C) + out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C) + return out +``` + +## Understanding Self-Attention and Positional Embeddings in Our Language Model + +In our language model called `BigramLanguageModel`, we incorporate self-attention mechanisms to help the model understand relationships between different tokens in a sequence. Within the constructor of our model, we create multiple attention blocks using the following code: +```python +self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) +``` + +Here, `n_embd` represents the embedding size (the dimensionality of our token embeddings), and `n_head` is the number of attention heads we want to use. Each `Block` is essentially a self-attention head, and we’re stacking them together using `nn.Sequential`. The `head_size` for each attention head is set to `n_embd`. + +In the forward method, we first encode our input tokens by adding token embeddings and positional embeddings: +```python +x = tok_emb + pos_emb +``` + +This means we take the embeddings of the tokens (`tok_emb`) and add positional information (`pos_emb`) so that the model knows the position of each token in the sequence. We then pass this combined embedding `x` through our self-attention blocks: +```python +x = self.blocks(x) +``` + +The output from the attention blocks is then fed into the language modeling head to produce the `logits`, which are the unnormalized probabilities for the next token in the sequence: +```python +logits = self.lm_head(x) +``` + +When generating new text with the generate method, we need to ensure that the input indices (`idx`) we feed into the model do not exceed the `block_size`. This is because our positional embedding table only has embeddings up to `block_size`, and we can’t provide positional embeddings for positions beyond that. To handle this, we crop the context to the last `block_size` tokens: +```python +idx_cond = idx[:, -block_size:] +``` + +By doing this, we make sure we’re always using a valid range of positional embeddings, preventing any errors or out-of-scope issues. This allows the model to generate text effectively while respecting the limitations of our positional embedding setup. + + +```python +class BigramLanguageModel(nn.Module): + """Language model based on the Transformer architecture.""" + + def __init__(self): + super().__init__() + # each token directly reads off the logits for the next token from a lookup table + self.token_embedding_table = nn.Embedding(vocab_size, n_embd) + self.position_embedding_table = nn.Embedding(block_size, n_embd) + self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) + self.ln_f = nn.LayerNorm(n_embd) # final layer norm + self.lm_head = nn.Linear(n_embd, vocab_size) + + def forward(self, idx, targets=None): + B, T = idx.shape + + # idx and targets are both (B,T) tensor of integers + tok_emb = self.token_embedding_table(idx) # (B,T,C) + pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C) + x = tok_emb + pos_emb # (B,T,C) + x = self.blocks(x) # (B,T,C) + x = self.ln_f(x) # (B,T,C) + logits = self.lm_head(x) # (B,T,vocab_size) + + if targets is None: + loss = None + else: + B, T, C = logits.shape + logits = logits.view(B*T, C) + targets = targets.view(B*T) + loss = F.cross_entropy(logits, targets) + + return logits, loss + + def generate(self, idx, max_new_tokens): + # idx is (B, T) array of indices in the current context + for _ in range(max_new_tokens): + # crop idx to the last block_size tokens + idx_cond = idx[:, -block_size:] + # get the predictions + logits, loss = self(idx_cond) + # focus only on the last time step + logits = logits[:, -1, :] # becomes (B, C) + # apply softmax to get probabilities + probs = F.softmax(logits, dim=-1) # (B, C) + # sample from the distribution + idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) + # append sampled index to the running sequence + idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) + return idx +``` + +## Understanding Multi-Head Attention in Simple Terms + +### Multi-Head Attention Formula + + +```python +Image(filename = 'multi-head-attention-formula.png', width=450) +``` + + + + + +![png](README_files/README_37_0.png) + + + + + +```python +Image(filename = 'multi-head-attention.png', width=200) +``` + + + + + +![png](README_files/README_38_0.png) + + + + +In our GPT model built from scratch, we use a concept called **Multi-Head Attention**. This means we have multiple self-attention mechanisms (called “heads”) running in parallel. Instead of relying on a single attention mechanism, we allow the model to focus on different aspects of the input simultaneously. + +In PyTorch, we specify the number of heads (`num_heads`) and the size of each head (`head_size`). Here’s how we create multiple heads: +```python +self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) +``` + +Each Head is an instance of our self-attention mechanism. We process the input `x` through all these heads in parallel and then concatenate their outputs along the channel dimension (`dim=-1`): +```python +out = torch.cat([h(x) for h in self.heads], dim=-1) +``` + +This concatenation combines the outputs of all the attention heads into a single tensor. + +Instead of having a single attention head with a `head_size` equal to the embedding size (`n_embd`), we divide the embedding size among multiple heads. For example, if `n_embd` is 32 and we have 4 heads, each head will have a `head_size` of 8. This means: +* We have 4 communication channels (heads) running in parallel. +* Each head processes an 8-dimensional vector. +* When we concatenate the outputs of all heads, we get back to the original embedding size of 32. + +Having multiple attention heads is beneficial because tokens (like words or characters) have a lot of different things to “talk” about. For instance, they might want to find vowels, consonants, or specific patterns at certain positions. By using multiple independent channels of communication, each head can focus on different types of information. This allows the model to gather a richer set of data before making predictions, leading to better performance in understanding and generating language. + + +```python +class MultiHeadAttention(nn.Module): + """Multiple self-attention heads in parallel.""" + + def __init__(self, num_heads, head_size): + super().__init__() + self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) + self.proj = nn.Linear(n_embd, n_embd) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + out = torch.cat([h(x) for h in self.heads], dim=-1) + out = self.dropout(self.proj(out)) + return out +``` + +## Adding Computation at the Token Level + +### Position-Wise Feed-Forward Network Formula + + +```python +Image(filename = 'ffn-formula.png', width=350) +``` + + + + + +![png](README_files/README_43_0.png) + + + + +So far in our language model, we’ve implemented multi-headed self-attention, which allows tokens (like words or characters) to communicate with each other. This means each token can look at other tokens in the sequence and gather information from them. However, we’ve been moving a bit too quickly from this communication step to making predictions (calculating the `logits`) in our `BigramLanguageModel`. + +The problem is that while the tokens have looked at each other, they haven’t had much time to process or “think about” the information they’ve gathered from other tokens. To fix this, we’re going to add a small feed-forward neural network that operates on a per-token level. This means that after gathering information, each token will independently process that information to make better predictions. + +This feed-forward network is simply a linear layer followed by a ReLU (Rectified Linear Unit) activation function, which introduces non-linearity. In code, we implement it like this within our `Block` class: +```python +self.ffwd = FeedForward(n_embd) +``` + +And we call it right after the self-attention layer in the forward method. The FeedForward class might look something like this: +```python +class FeedForward(nn.Module): + def __init__(self, n_embd): + super().__init__() + self.net = nn.Sequential( + nn.Linear(n_embd, n_embd), + nn.ReLU() + ) + + def forward(self, x): + return self.net(x) +``` + +Here, `n_embd` is the embedding size (the size of our token vectors). Each token processes its own vector independently through this network. The self-attention layer allows tokens to gather information from others (communication), and the feed-forward network allows each token to process that information individually (computation). + +By adding this computation step, we enable each token to make better use of the information it has received, leading to improved performance of the language model. This mirrors how, in human communication, we not only listen to others but also take time to think and process what we’ve heard before responding. + + +```python +class FeedFoward(nn.Module): + """A simple feed-forward neural network.""" + + def __init__(self, n_embd): + super().__init__() + self.net = nn.Sequential( + nn.Linear(n_embd, 4 * n_embd), + nn.ReLU(), + nn.Linear(4 * n_embd, n_embd), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) +``` + +## Understanding Transformer Blocks and Their Role in GPT + +In building our GPT (Generative Pre-trained Transformer) model from scratch, we’re now focusing on combining communication and computation within the network. This approach mirrors how Transformers work—they have blocks that allow tokens (like words or characters) to communicate with each other and then compute based on that information. These blocks are grouped and replicated multiple times to build a powerful model. + +The core of this mechanism is implemented in the `Block` class, which represents the main part of the Transformer decoder model (excluding cross-attention components that interact with an encoder in some architectures). The `Block` class interleaves communication and computation steps. The communication is handled by multi-headed self-attention: +```python +self.sa = MultiHeadAttention(n_head, head_size) +``` + +This allows tokens to look at other tokens in the sequence and gather relevant information. After communication, each token independently processes the gathered information using a feed-forward neural network: +```python +self.ffwd = FeedForward(n_embd) +``` + +In the constructor of the `Block` class, we specify `n_embd`, which is the size of our token embeddings (the embedding dimension), and `n_head`, the number of attention heads we want to use. These parameters determine how the tokens will communicate and compute within each block. + +Within our `BigramLanguageModel` class, we stack these blocks sequentially to build the depth of the model: +```python +self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) +``` + +Here, `n_layer` specifies how many times we repeat the `Block`. This setup allows us to interleave communication and computation multiple times, enabling the model to capture complex patterns in language. Finally, in the forward method, after passing the data through all the blocks, we decode the output to generate the logits (the raw predictions before applying softmax) using: +```python +logits = self.lm_head(x) +``` + +By interspersing communication and computation in this way, each token can gather information from others and then process it independently, which is crucial for understanding context and generating coherent text in language models like GPT. + + +```python +Image(filename = 'block.png', width=120) +``` + + + + + +![png](README_files/README_48_0.png) + + + + + +```python +Image(filename = 'cross-attention.png', width=125) +``` + + + + + +![png](README_files/README_49_0.png) + + + + + +```python +class Block(nn.Module): + """Transformer block: communication followed by computation.""" + + def __init__(self, n_embd, n_head): + # n_embd: embedding dimension, n_head: the number of heads we'd like + super().__init__() + head_size = n_embd // n_head + self.sa = MultiHeadAttention(n_head, head_size) + self.ffwd = FeedFoward(n_embd) + self.ln1 = nn.LayerNorm(n_embd) + self.ln2 = nn.LayerNorm(n_embd) + + def forward(self, x): + x = x + self.sa(self.ln1(x)) + x = x + self.ffwd(self.ln2(x)) + return x +``` + +## Improving Deep Neural Networks with Residual Connections + +At this stage of building our GPT model from scratch, we’re noticing that the performance isn’t as good as we’d like. One reason is that our neural network is becoming quite deep, and deep neural networks often face optimization issues. This means they can be hard to train effectively because the gradients used in learning can either vanish or explode as they pass through many layers. + +To tackle this problem, we can borrow an idea from the **“Attention Is All You Need”** paper. The paper introduces two optimizations that significantly help deep networks remain trainable. The first optimization is the use of skip connections, also known as residual connections. These connections allow the model to bypass certain layers by adding the input of a layer directly to its output. This helps preserve the original information and makes it easier for the network to learn. + +In simple terms, instead of just passing data through a transformation (like a neural network layer), we also add the original data back into the output. This means that if the transformation doesn’t learn anything useful, the network can still pass the original information forward. This helps prevent the network from getting worse as it gets deeper. + +Here’s how we can implement residual connections in our `Block` class: +```python +class Block(nn.Module): + def __init__(self, n_embd, n_head): + super().__init__() + head_size = n_embd // n_head + self.sa = MultiHeadAttention(n_head, head_size) + self.ffwd = FeedForward(n_embd) + + def forward(self, x): + x = x + self.sa(x) # residual connection after self-attention + x = x + self.ffwd(x) # residual connection after feed-forward network + return x +``` + +In this code: +* Self-Attention Residual Connection: We compute `self.sa(x)`, which is the output of the self-attention layer, and add it to the original input `x`. +```python +x = x + self.sa(x) +``` +* Feed-Forward Residual Connection: Similarly, we compute `self.ffwd(x)`, which processes each token independently, and add it to the result of the previous step. +```python +x = x + self.ffwd(x) +``` + +By adding these residual connections, we’re effectively allowing the network to “skip” layers if needed, making it easier to train deeper models. The residual connections help the gradients flow backward through the network during training, which addresses the optimization issues associated with deep neural networks. + +In summary, residual connections are a simple yet powerful idea that helps deep neural networks learn more effectively. By incorporating them into our model, we’re borrowing a successful strategy from advanced architectures like Transformers, ensuring that our GPT model can train successfully even as it becomes deeper and more complex. + + +```python +Image(filename = 'block.png', width=125) +``` + + + + + +![png](README_files/README_53_0.png) + + + + + +```python +# SOURCE: https://towardsdatascience.com/residual-blocks-building-blocks-of-resnet-fd90ca15d6ec +``` + + +```python +Image(filename = 'residual-blocks.png', width=300) +``` + + + + + +![png](README_files/README_55_0.png) + + + + +## Understanding Residual Connections in the GPT Model + +When building deep neural networks like our GPT model, we can run into problems because deep networks are harder to train effectively. One powerful solution is using residual connections. Think of a residual connection as a shortcut path for information to flow through the network without getting distorted by too many layers. In our model, the computation flows from top to bottom, and there’s a central pathway called the residual pathway, represented by a black line in diagrams. + +At certain points, we “fork off” from this residual pathway to perform some computations—like self-attention or feed-forward processing—and then we add the result back to the main pathway. This is implemented using addition operations. Here’s why this helps: during training, when the network learns by backpropagation, gradients (which update the network’s weights) can flow directly through these addition points. This creates a “gradient superhighway” that allows learning signals to pass unimpeded from the output back to the input layers, making training more efficient. + +To implement residual connections in our code, we modify the forward method of the `Block` class like this: +```python +def forward(self, x): + x = x + self.sa(self.ln1(x)) + x = x + self.ffwd(self.ln2(x)) + return x +``` + +In this code: +* `self.ln1(x)` and `self.ln2(x)` apply layer normalization to stabilize the inputs. +* `self.sa` is the multi-head self-attention operation. +* `self.ffwd` is the feed-forward neural network. +* We add the output of these operations back to the original input `x`, creating the residual connections. + +In the `MultiHeadAttention` class, we need to make sure the output dimensions match so we can add them back to `x`. We do this by introducing a projection layer: +```python +self.proj = nn.Linear(n_embd, n_embd) +``` + +After combining the outputs of all attention heads: +```python +out = torch.cat([h(x) for h in self.heads], dim=-1) +out = self.proj(out) +return out +``` + +* We concatenate the outputs from all heads along the last dimension. +* We then project this combined output back to the original embedding size (`n_embd`) using `self.proj(out)`. + +Similarly, in the `FeedForward` class, we adjust the network to have a larger inner layer, which increases its capacity to learn complex patterns: +```python +self.net = nn.Sequential( + nn.Linear(n_embd, 4 * n_embd), + nn.ReLU(), + nn.Linear(4 * n_embd, n_embd), +) +``` + +* The first linear layer expands the size from `n_embd` to `4 * n_embd`. +* After applying the ReLU activation function, the second linear layer brings it back to `n_embd`, allowing us to add it back to `x`. + +By using these residual connections and appropriately sized projection layers, we allow the model to add new computations without losing the original information. This helps the gradients flow smoothly during training, making it much easier to optimize deep networks like our GPT model. + + +```python +Image(filename = 'types-of-residual-blocks.png', width=500) +``` + + + + + +![png](README_files/README_58_0.png) + + + + + +```python +Image(filename = 'block.png', width=125) +``` + + + + + +![png](README_files/README_59_0.png) + + + + +## Understanding Layer Normalization in Deep Neural Networks + +As our GPT model becomes deeper, we encounter difficulties in training it effectively. Deep neural networks can suffer from optimization issues, making it hard for the model to learn from the data. To overcome this, we use two important techniques from the **“Attention Is All You Need”** paper. We’ve already added residual connections to help information flow through the network. The second optimization is called layer normalization, often shown as “Norm” next to the “Add” operations in diagrams. + +Layer normalization is similar to batch normalization, which you might have heard of. In batch normalization, we ensure that each neuron’s output has a mean of zero and a standard deviation of one across the entire batch of data (`B`). This helps stabilize the learning process by keeping the outputs of neurons on a similar scale. + +However, layer normalization works a bit differently. Instead of normalizing across the batch, layer normalization normalizes across the features (the elements within each data point). This means that for each individual example in the batch, we compute the mean and variance of its features and adjust them so that they have a mean of zero and a standard deviation of one. This is especially helpful in models like Transformers because it doesn’t depend on the batch size and works well with variable-length sequences. + +Here’s how we incorporate layer normalization into our `Block` class: +```python +import torch.nn as nn + +class Block(nn.Module): + def __init__(self, n_embd, n_head): + super().__init__() + head_size = n_embd // n_head + self.ln1 = nn.LayerNorm(n_embd) # layer normalization before self-attention + self.sa = MultiHeadAttention(n_head, head_size) + self.ln2 = nn.LayerNorm(n_embd) # layer normalization before feed-forward network + self.ffwd = FeedForward(n_embd) + + def forward(self, x): + x = x + self.sa(self.ln1(x)) # residual connection with self-attention + x = x + self.ffwd(self.ln2(x)) # residual connection with feed-forward network + return x +``` + +In this code: +* Layer Normalization Layers: We introduce `self.ln1` and `self.ln2` using `nn.LayerNorm(n_embd)`. These layers normalize the inputs to the self-attention and feed-forward networks. +* Residual Connections: We maintain our residual connections by adding the output of the self-attention and feed-forward networks back to the original input `x`. +* Forward Method: In the `forward` method, we apply layer normalization before each main operation. This helps stabilize the inputs to those layers. + +By using layer normalization, we ensure that the activations (outputs of each layer) have consistent statistics throughout the network. This makes the deep network easier to train because it reduces the internal changes that the network has to adapt to during learning. Combined with residual connections, layer normalization greatly improves the optimization of very deep neural networks like our GPT model. + + +```python +# SOURCE: https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html +``` + + +```python +Image(filename = 'layer-norm-formula.png', width=225) +``` + + + + + +![png](README_files/README_63_0.png) + + + + + +```python +class LayerNorm1d: # (used to be BatchNorm1d) + """Implements 1D Layer Normalization to stabilize and normalize input activations.""" + + def __init__(self, dim, eps=1e-5, momentum=0.1): + self.eps = eps + self.gamma = torch.ones(dim) + self.beta = torch.zeros(dim) + + def __call__(self, x): + # calculate the forward pass + xmean = x.mean(1, keepdim=True) # batch mean + xvar = x.var(1, keepdim=True) # batch variance + xhat = (x - xmean) / torch.sqrt(xvar + self.eps) # normalize to unit variance + self.out = self.gamma * xhat + self.beta + return self.out + + def parameters(self): + return [self.gamma, self.beta] + + +torch.manual_seed(1337) +module = LayerNorm1d(100) +x = torch.randn(32, 100) # batch size 32 of 100-dimensional vectors +x = module(x) +x.shape +``` + + + + + torch.Size([32, 100]) + + + +## Understanding How Layer Normalization Works in Our GPT Mode + +In our GPT model, we use layer normalization to help stabilize and improve the training of our deep neural network. Let’s consider an example where we have a `batch_size` of 32, and each input vector has 100 dimensions. This means we have 32 samples (vectors), each with 100 features. + +When we pass these vectors through a layer normalization layer, we ensure that each feature within a sample is normalized. Specifically, for each individual sample in the batch, we compute the mean and standard deviation across its features and adjust them to have a mean of zero and a standard deviation of one. + +Here’s how we implement layer normalization: +```python +# x has a shape of (batch_size, num_features), e.g., (32, 100) +xmean = x.mean(1, keepdim=True) # compute the mean across features for each sample +xvar = x.var(1, keepdim=True) # compute the variance across features for each sample +x_normalized = (x - xmean) / torch.sqrt(xvar + 1e-5) # normalize each sample +``` + +In this code: +* `xmean` is calculated by taking the mean of `x` across dimension 1, which corresponds to the feature dimension. We use `keepdim=True` to maintain the dimensionality for broadcasting. +* `xvar` is the variance computed similarly across the features of each sample. +* `x_normalized` is the result of subtracting the mean and dividing by the standard deviation (square root of variance plus a small epsilon to prevent division by zero). + +By changing the dimension from 0 to 1 in the `mean` and `var` functions, we’re computing the statistics across the features of each individual sample rather than across the batch. This means we’re normalizing each sample independently, and the normalization does not depend on other samples in the batch. + +Initially, if we had used: +```python +xmean = x.mean(0, keepdim=True) # mean across the batch for each feature +xvar = x.var(0, keepdim=True) # variance across the batch for each feature +``` + +This would have computed the mean and variance across the batch dimension for each feature (column). In this case, we would be normalizing each feature across all samples in the batch, which is what batch normalization does. + +However, since we’re implementing layer normalization, we use: +```python +xmean = x.mean(1, keepdim=True) # mean across features for each sample +xvar = x.var(1, keepdim=True) # variance across features for each sample +``` + +With layer normalization, the columns (features) are not normalized across the batch. Instead, each sample’s features are normalized based on that sample’s own mean and variance. This ensures that the normalization is independent of the batch size and the data in other samples. + +By normalizing each sample individually, we help the model to perform consistently regardless of the batch composition, which is particularly useful in models like Transformers where sequences can have varying lengths, and batching can be complex. + +In summary, layer normalization adjusts the activations (outputs) of each sample so that they have a mean of zero and a standard deviation of one across their features. This helps the network learn more effectively by preventing internal covariate shift and ensuring that the scale of the inputs to each layer remains consistent. + + +```python +x[:,0].mean(), x[:,0].std() # mean,std of one feature across all batch inputs +``` + + + + + (tensor(0.1469), tensor(0.8803)) + + + + +```python +x[0,:].mean(), x[0,:].std() # mean,std of a single input from the batch, of its features +``` + + + + + (tensor(-3.5763e-09), tensor(1.0000)) + + + +## Understanding the Pre-Norm Formulation in Transformer Models + +In the original Transformer model described in the **“Attention Is All You Need”** paper, the **Add & Norm** (addition and layer normalization) steps are applied after the main transformations like self-attention and feed-forward networks. However, in more recent implementations, it’s common to apply layer normalization before these transformations. This approach is called the **Pre-Norm Formulation**. + +Applying layer normalization before the transformations helps stabilize the training of deep neural networks. It ensures that the inputs to each layer have a consistent scale and distribution, which makes it easier for the network to learn effectively. + +In our `Block` class, which represents a single Transformer block, we implement this by adding two layer normalization layers in the constructor: +```python +self.ln1 = nn.LayerNorm(n_embd) # first layer norm for self-attention +self.ln2 = nn.LayerNorm(n_embd) # second layer norm for feed-forward network +``` + +Here, `n_embd` is the embedding dimension—the size of the vector that represents each token (like a word or character) in our sequence. + +In the `forward` method of the `Block` class, we apply the layer norms before passing the data to the self-attention and feed-forward layers: +```python +def forward(self, x): + x = x + self.sa(self.ln1(x)) # apply layer norm before self-attention + x = x + self.ffwd(self.ln2(x)) # apply layer norm before feed-forward network + return x +``` + +By normalizing `x` before each transformation, we help the model learn better and more stable representations. This change reflects modern best practices in training Transformer models, allowing our deep neural network to train more effectively, leading to improved performance in tasks like language modeling. + +## Understanding Layer Normalization and Scaling Up Our GPT Model + +In our GPT model, we set the embedding size `n_embd` to 32. This means each token in our sequence is represented by a vector of 32 numbers. When we apply layer normalization, we normalize these features by calculating the mean and variance over these 32 numbers for each token. The batch size (`B`) and the sequence length (`T`) act as batch dimensions, so the normalization happens per token independently. This ensures that each token’s features have a mean of zero and a standard deviation of one at initialization. + +Layer normalization includes trainable parameters called gamma (γ) and beta (β), which allow the model to scale and shift the normalized outputs during training. In our implementation, we initialize them as follows: +```python +self.gamma = torch.ones(dim) +self.beta = torch.zeros(dim) +``` + +Here, dim is the embedding dimension (`n_embd`). While the initial output after normalization might be unit Gaussian, the optimization process during training adjusts these parameters to find the best scale and shift for the data. + +In the `BigramLanguageModel` class, we add a final layer normalization layer at the end of the Transformer, right before the last linear layer that decodes the embeddings into logits for the vocabulary. This is done in the constructor: +```python +self.ln_f = nn.LayerNorm(n_embd) # final layer norm +``` + +To scale up our model and make it more powerful, we introduce the variable `n_layer` in the `BigramLanguageModel` constructor. This variable specifies how many layers of `Block` modules we stack together: +```python +self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) +``` + +Each `Block` consists of multi-head self-attention and a feed-forward neural network, along with residual connections and layer normalization. We also introduce `n_head` which specifies the number of attention heads in our multi-head attention mechanism. By increasing `n_layer` and `n_head`, we can make our model deeper and allow it to capture more complex patterns in the data. + +In summary, by properly applying layer normalization and scaling up the model with more layers (`n_layer`) and attention heads (`n_head`), we enhance the model’s ability to learn and generalize from the data. This approach ensures our deep neural network remains stable and effective during training. + +### Layer Norm Formula + + +```python +Image(filename = 'layer-norm-formula.png', width=225) +``` + + + + + +![png](README_files/README_74_0.png) + + + + + +```python +Image(filename = 'block.png', width=125) +``` + + + + + +![png](README_files/README_75_0.png) + + + + +## Adding Dropout to Improve the GPT Model + +In our GPT model, we introduce a technique called dropout to prevent overfitting and improve the model’s ability to generalize to new data. Dropout works by randomly “turning off” or setting to zero a subset of neurons during each training pass. This means that every time the model processes data during training, it uses a slightly different network configuration. At test time, all neurons are active, and the model benefits from the combined knowledge of these different configurations. + +We add dropout layers at specific points in our model to enhance regularization: +1. In the `FeedForward` class constructor, we add `dropout` right before connecting back to the residual pathway. This ensures that some neurons in the feed-forward network are randomly ignored during training: +```python +self.dropout = nn.Dropout(dropout) +``` + +2. In the `MultiHeadAttention` class constructor, we include `dropout` after the attention heads have been processed. This helps prevent the model from becoming too dependent on any single attention pathway: +```python +self.dropout = nn.Dropout(dropout) +``` + +3. In the `Head` class constructor, we add `dropout` after calculating the attention weights (affinities) and applying the softmax function. This randomly prevents some nodes from communicating, adding a layer of regularization to the attention mechanism: +```python +self.dropout = nn.Dropout(dropout) +``` + +By incorporating dropout in these areas, we effectively train an ensemble of smaller sub-networks within our larger network. Each sub-network learns slightly different patterns, and when combined, they make the overall model more robust. This technique is especially useful when scaling up models, as it reduces the risk of overfitting to the training data and improves performance on unseen data. + +In summary, dropout enhances our GPT model by: +* Randomly disabling neurons during training, which prevents the model from relying too heavily on any single neuron. +* Encouraging the network to learn more generalized features that are useful across different subsets of the data. +* Improving the model’s ability to generalize to new, unseen inputs by reducing overfitting. + +This addition ensures that our model remains effective and reliable as it becomes more complex. + +### Dropout Layer + + +```python +# https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf +``` + + +```python +Image(filename = 'dropout.png', width=425) +``` + + + + + +![png](README_files/README_80_0.png) + + + + +## Full Finished Code +You may want to refer directly to the git repo instead though. + + +```python +import torch +import torch.nn as nn +from torch.nn import functional as F + +# hyperparameters +batch_size = 16 # number of independent sequences to process in parallel +block_size = 32 # maximum context length for predictions +max_iters = 5000 # total number of training iterations +eval_interval = 100 # interval for evaluating the model on validation set +learning_rate = 1e-3 # learning rate for the optimizer +device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' # device to run the model on +eval_iters = 200 # number of iterations to estimate loss +n_embd = 64 # embedding dimension +n_head = 4 # number of attention heads +n_layer = 4 # number of transformer blocks +dropout = 0.0 # dropout rate for regularization +# ------------ + +torch.manual_seed(1337) # for reproducibility + +# load the dataset +# make sure to have 'input.txt' file in your working directory +# you can download it using: wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt +with open('input.txt', 'r', encoding='utf-8') as f: + text = f.read() + +# create a mapping from characters to integers +chars = sorted(list(set(text))) +vocab_size = len(chars) +# create a mapping from characters to indices and vice versa +stoi = { ch:i for i,ch in enumerate(chars) } # string to index +itos = { i:ch for i,ch in enumerate(chars) } # index to string +encode = lambda s: [stoi[c] for c in s] # encoder: string to list of integers +decode = lambda l: ''.join([itos[i] for i in l]) # decoder: list of integers to string + +# prepare the dataset +data = torch.tensor(encode(text), dtype=torch.long) +n = int(0.9 * len(data)) # split 90% for training, 10% for validation +train_data = data[:n] +val_data = data[n:] + + +# function to generate a batch of data +def get_batch(split): + """ + Generate a batch of input and target sequences for training. + + Args: + split (str): 'train' or 'val' to select the dataset split. + + Returns: + x (torch.Tensor): Input tensor of shape (batch_size, block_size). + y (torch.Tensor): Target tensor of shape (batch_size, block_size). + """ + # select the appropriate data split + data = train_data if split == 'train' else val_data + # randomly choose starting indices for each sequence in the batch + ix = torch.randint(len(data) - block_size, (batch_size,)) + # collect sequences of length 'block_size' starting from each index + x = torch.stack([data[i:i+block_size] for i in ix]) + y = torch.stack([data[i+1:i+block_size+1] for i in ix]) + # move data to the appropriate device + x, y = x.to(device), y.to(device) + return x, y + + +# function to estimate the loss on training and validation sets +@torch.no_grad() +def estimate_loss(): + """ + Estimate the average loss over several iterations for both + training and validation datasets. + + Returns: + out (dict): Dictionary containing average losses for 'train' and 'val'. + """ + out = {} + model.eval() # set the model to evaluation mode + for split in ['train', 'val']: + losses = torch.zeros(eval_iters) + for k in range(eval_iters): + X, Y = get_batch(split) # get a batch of data + logits, loss = model(X, Y) # forward pass + losses[k] = loss.item() # store the loss + out[split] = losses.mean() # compute the average loss + model.train() # set the model back to training mode + return out + + +class Head(nn.Module): + """One head of self-attention.""" + + def __init__(self, head_size): + """ + Initialize the self-attention head. + + Args: + head_size (int): Dimensionality of the key, query, and value vectors. + """ + super().__init__() + # linear projections for keys, queries, and values + self.key = nn.Linear(n_embd, head_size, bias=False) + self.query = nn.Linear(n_embd, head_size, bias=False) + self.value = nn.Linear(n_embd, head_size, bias=False) + # register a lower triangular matrix for masking future positions + self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size))) + # dropout layer for regularization + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + """ + Perform the forward pass of the self-attention head. + + Args: + x (torch.Tensor): Input tensor of shape (B, T, C). + + Returns: + out (torch.Tensor): Output tensor of shape (B, T, head_size). + """ + B, T, C = x.shape + # compute keys, queries, and values + k = self.key(x) # (B, T, head_size) + q = self.query(x) # (B, T, head_size) + v = self.value(x) # (B, T, head_size) + + # compute attention scores using scaled dot-product + wei = q @ k.transpose(-2, -1) * C**-0.5 # (B, T, T) + # apply causal mask to prevent attending to future positions + wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) + # convert scores to probabilities + wei = F.softmax(wei, dim=-1) # (B, T, T) + wei = self.dropout(wei) # apply dropout + + # compute the weighted sum of values + out = wei @ v # (B, T, head_size) + return out + + +class MultiHeadAttention(nn.Module): + """Multiple self-attention heads in parallel.""" + + def __init__(self, num_heads, head_size): + """ + Initialize the multi-head attention module. + + Args: + num_heads (int): Number of attention heads. + head_size (int): Size of each head. + """ + super().__init__() + # create a list of attention heads + self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) + # linear projection to combine the outputs of all heads + self.proj = nn.Linear(n_embd, n_embd) + # dropout layer + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + """ + Perform the forward pass of multi-head attention. + + Args: + x (torch.Tensor): Input tensor of shape (B, T, C). + + Returns: + out (torch.Tensor): Output tensor of shape (B, T, C). + """ + # concatenate the outputs from all attention heads + out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, C) + # apply linear projection and dropout + out = self.dropout(self.proj(out)) + return out + + +class FeedForward(nn.Module): + """A simple feed-forward neural network.""" + + def __init__(self, n_embd): + """ + Initialize the feed-forward network. + + Args: + n_embd (int): Embedding dimension. + """ + super().__init__() + # define a two-layer MLP + self.net = nn.Sequential( + nn.Linear(n_embd, 4 * n_embd), # expand dimensionality + nn.ReLU(), # non-linearity + nn.Linear(4 * n_embd, n_embd), # project back to original size + nn.Dropout(dropout), # dropout for regularization + ) + + def forward(self, x): + """ + Perform the forward pass of the feed-forward network. + + Args: + x (torch.Tensor): Input tensor of shape (B, T, C). + + Returns: + torch.Tensor: Output tensor of the same shape. + """ + return self.net(x) + + +class Block(nn.Module): + """Transformer block: communication followed by computation.""" + + def __init__(self, n_embd, n_head): + """ + Initialize the transformer block. + + Args: + n_embd (int): Embedding dimension. + n_head (int): Number of attention heads. + """ + super().__init__() + head_size = n_embd // n_head # size of each attention head + # multi-head self-attention + self.sa = MultiHeadAttention(n_head, head_size) + # feed-forward network + self.ffwd = FeedForward(n_embd) + # layer normalizations + self.ln1 = nn.LayerNorm(n_embd) + self.ln2 = nn.LayerNorm(n_embd) + + def forward(self, x): + """ + Perform the forward pass of the transformer block. + + Args: + x (torch.Tensor): Input tensor of shape (B, T, C). + + Returns: + torch.Tensor: Output tensor of the same shape. + """ + # apply layer norm and self-attention, then add residual connection + x = x + self.sa(self.ln1(x)) + # apply layer norm and feed-forward network, then add residual connection + x = x + self.ffwd(self.ln2(x)) + return x + + +class BigramLanguageModel(nn.Module): + """Language model based on the Transformer architecture.""" + + def __init__(self): + """ + Initialize the language model. + + The model consists of token embeddings, positional embeddings, + multiple transformer blocks, and a final linear layer to produce logits. + """ + super().__init__() + # token embedding table: maps token indices to embedding vectors + self.token_embedding_table = nn.Embedding(vocab_size, n_embd) + # positional embedding table: learns embeddings for positions in the sequence + self.position_embedding_table = nn.Embedding(block_size, n_embd) + # stack of transformer blocks + self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) + # final layer normalization + self.ln_f = nn.LayerNorm(n_embd) + # linear layer to project embeddings to vocabulary logits + self.lm_head = nn.Linear(n_embd, vocab_size) + + def forward(self, idx, targets=None): + """ + Perform the forward pass of the language model. + + Args: + idx (torch.Tensor): Input tensor of token indices with shape (B, T). + targets (torch.Tensor, optional): Target tensor for computing loss. + + Returns: + logits (torch.Tensor): Logits tensor of shape (B, T, vocab_size). + loss (torch.Tensor or None): Cross-entropy loss if targets are provided. + """ + B, T = idx.shape + + # get token embeddings for each token in the sequence + tok_emb = self.token_embedding_table(idx) # (B, T, C) + # get positional embeddings for each position in the sequence + pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T, C) + # add token and positional embeddings to get the input to transformer blocks + x = tok_emb + pos_emb # (B, T, C) + # pass through the stack of transformer blocks + x = self.blocks(x) # (B, T, C) + # apply final layer normalization + x = self.ln_f(x) # (B, T, C) + # compute logits for the next token prediction + logits = self.lm_head(x) # (B, T, vocab_size) + + # if targets are provided, compute the loss + if targets is None: + loss = None + else: + # reshape logits and targets for computing cross-entropy loss + B, T, C = logits.shape + logits = logits.view(B*T, C) + targets = targets.view(B*T) + # compute the loss + loss = F.cross_entropy(logits, targets) + + return logits, loss + + def generate(self, idx, max_new_tokens): + """ + Generate new text by sampling from the language model. + + Args: + idx (torch.Tensor): Input tensor of shape (B, T) containing the context. + max_new_tokens (int): Number of new tokens to generate. + + Returns: + idx (torch.Tensor): Tensor of shape (B, T + max_new_tokens) with generated tokens. + """ + for _ in range(max_new_tokens): + # ensure the context does not exceed the block size + idx_cond = idx[:, -block_size:] + # get the predictions + logits, _ = self(idx_cond) + # focus on the last time step + logits = logits[:, -1, :] # (B, vocab_size) + # convert logits to probabilities + probs = F.softmax(logits, dim=-1) # (B, vocab_size) + # sample the next token from the probability distribution + idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) + # append the new token to the sequence + idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) + return idx + + +# instantiate the model and move it to the appropriate device +model = BigramLanguageModel().to(device) +# print the number of parameters (in millions) +print(sum(p.numel() for p in model.parameters())/1e6, 'M parameters') + +# create an optimizer for updating the model parameters +optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + +# training loop +for iter in range(max_iters): + + # every eval_interval iterations, evaluate the model on the validation set + if iter % eval_interval == 0 or iter == max_iters - 1: + losses = estimate_loss() + print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}") + + # get a batch of training data + xb, yb = get_batch('train') + + # compute the loss and gradients + logits, loss = model(xb, yb) + optimizer.zero_grad(set_to_none=True) + loss.backward() + optimizer.step() + +# generate text from the model +context = torch.zeros((1, 1), dtype=torch.long, device=device) # starting token (e.g., ) +generated_sequence = model.generate(context, max_new_tokens=2000)[0].tolist() +# decode and print the generated text +print(decode(generated_sequence)) +``` + + 0.209729 M parameters + step 0: train loss 4.4116, val loss 4.4022 + step 100: train loss 2.6568, val loss 2.6670 + step 200: train loss 2.5091, val loss 2.5060 + step 300: train loss 2.4196, val loss 2.4336 + step 400: train loss 2.3503, val loss 2.3565 + step 500: train loss 2.2965, val loss 2.3127 + step 600: train loss 2.2410, val loss 2.2501 + step 700: train loss 2.2048, val loss 2.2186 + step 800: train loss 2.1636, val loss 2.1864 + step 900: train loss 2.1242, val loss 2.1504 + step 1000: train loss 2.1024, val loss 2.1291 + step 1100: train loss 2.0690, val loss 2.1176 + step 1200: train loss 2.0377, val loss 2.0795 + step 1300: train loss 2.0229, val loss 2.0622 + step 1400: train loss 1.9922, val loss 2.0357 + step 1500: train loss 1.9706, val loss 2.0315 + step 1600: train loss 1.9618, val loss 2.0465 + step 1700: train loss 1.9409, val loss 2.0130 + step 1800: train loss 1.9077, val loss 1.9936 + step 1900: train loss 1.9078, val loss 1.9855 + step 2000: train loss 1.8825, val loss 1.9938 + step 2100: train loss 1.8711, val loss 1.9750 + step 2200: train loss 1.8579, val loss 1.9596 + step 2300: train loss 1.8543, val loss 1.9528 + step 2400: train loss 1.8401, val loss 1.9418 + step 2500: train loss 1.8150, val loss 1.9439 + step 2600: train loss 1.8234, val loss 1.9347 + step 2700: train loss 1.8118, val loss 1.9318 + step 2800: train loss 1.8048, val loss 1.9225 + step 2900: train loss 1.8070, val loss 1.9296 + step 3000: train loss 1.7953, val loss 1.9239 + step 3100: train loss 1.7688, val loss 1.9158 + step 3200: train loss 1.7511, val loss 1.9081 + step 3300: train loss 1.7580, val loss 1.9045 + step 3400: train loss 1.7561, val loss 1.8935 + step 3500: train loss 1.7398, val loss 1.8928 + step 3600: train loss 1.7244, val loss 1.8893 + step 3700: train loss 1.7305, val loss 1.8828 + step 3800: train loss 1.7180, val loss 1.8852 + step 3900: train loss 1.7196, val loss 1.8693 + step 4000: train loss 1.7148, val loss 1.8605 + step 4100: train loss 1.7127, val loss 1.8744 + step 4200: train loss 1.7071, val loss 1.8654 + step 4300: train loss 1.7023, val loss 1.8460 + step 4400: train loss 1.7052, val loss 1.8656 + step 4500: train loss 1.6899, val loss 1.8512 + step 4600: train loss 1.6862, val loss 1.8300 + step 4700: train loss 1.6828, val loss 1.8413 + step 4800: train loss 1.6659, val loss 1.8388 + step 4900: train loss 1.6686, val loss 1.8351 + step 4999: train loss 1.6622, val loss 1.8221 + + Foast. + + MENENIUS: + Prave is your niews? I cank, COmine. I well torms, beary. + + HENRY WARWORDriown: + The Papoinst proy way as home + but exfulings begt as liht; + Lyief, away, friom is of bulb. + + HENRY BOLINA: + What + Than what you suffect toogny! + That prope of so pity this badoggent; + Stame deck untiless, + Their laters you + Is you + Tow my such in mamy that prongmanoe, + Anjoth then your usequind, my would wontimn; + Thou prove to day them as it? + + SITUS: + Yeas staw his Kingdeed our chall: + But now this dray. + + ROMEO: + O, upon to death! him not this bornorow-prince. + My sunder's like us. + But you wilerss armiss brond, + Stayle my becul'st I say, your bear shalle I mone faults not fleathms ell spraver of it + she wongrame and broth of his it. + But reven. + WARY HARDONTIO: + Qumper! what voishmes! + Good liff tumbuntincaed up us. + + AUCHIOPOM: + Therefort them, but In to sproved. + + KING RICHARD II: + Come, dreivide, But twas oot, for and sirring to to a + but mantore your bond wedaus thee. + + VORK: + For which his lictless me, gurse? + Uhould dried: + To now, alm? I wherse fortune deque; + To least my not thinged weouly entount. + Cewle ther, Nont loung, you Vilive: + Let thou beves thou one true toges amont; + There twfined me. If your cause with and + Thost the will langed! So morman, mad the'e noccust to knot + Hench when is the underer you: if + The I hom blidess one lip + We is maid weak'd a bed'sime, maday, + And then you pringent, and what, for there is a gring, + And is ear aftiffed where diswer. + Make slendow to nit, + You loved, my tonte mind hath dels in wor flords. + + ISABELLA: + Whult bear your sont + On is Sup + Where not: I bust ma! part you bring, + thou met dincedts them thee towly him, + But a frust those, if you would kingt. + + TROM + + First: + It, + Jurets both our too right or lmed of hide + not these dut o' the ploss you. + And I known, the piors, time say as day BI thy God came to time. I'll would is bring; Lorde, + What, his arm he nobt + That boved fireive, what evert togen + our whus. + + ISABELLA: + You our loverd would let before elcome see, + Which ha + diff --git a/README_files/README_13_0.png b/README_files/README_13_0.png new file mode 100644 index 0000000000000000000000000000000000000000..029515c44aab4ea81d80c384a2bd9028e10cace2 GIT binary patch literal 33102 zcma&NWmH>T*9Dpcg1Z-YcPs8vv`BIHV8xw6@L~muyGwD36?Z7b-CDdraVc&$Jg002N}000mb2?3VUXurw~`=f6w zEv=>?ElsKB>SS%}U~JLTkl&RHSIMWUdQM!L8B6w&4A7c z@oe+palYfm{rvm z)ynF<8^p<7w0V*M*3`W1J6(5O6=fj{Cr5TOODA(Hc5g@Lmnr}eZz0&Fqm{cErMIKQ zdp99(QR;t62*Ivjk~yd;|0&{bFG{VeqDCq0ac&J)DU-fXCV#_FE1~4FCKO$R~rs4K|w(dPHqlvZZ=p6HaDO5?q=R>@7-wr)yRL^ zk+E{KaJ6-Iw{?0?`O>bLxs!*xC^hvSi;(WRq(n ze!sN#*zox6`L&La-e-_PP?3ZNB~v`!8x&TFpf&yV<_D&K{?UT}m6Nvl_|I<9J=^OGlDF3Zws*XZys+%O^Bl+*{ zUviL%J|q6mm~@k1DFk%t#xKMBPfbwx_WzCf|L<^4SE7v*U?WbE2wI#ubcypbY`K4a zI(gQf-E1Bbe>(den7ftnEpNf=%pfNbv{fW=T4#fRxwWvap1bvY!+v~Qwf5t3QQ)3A 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