llm_architecture_intuition.ipynb

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        "**Notebook 2:** LLM Architecture Intuitions\n",
        "\n",
        "In the previous notebook, we explored the surface-level interface of HuggingFace &#x1F917; pipelines and peeked beneath the abstraction. You should now be familiar with the `preprocess -> forward -> postprocess` structure which simplifies the natural language interface for typical use.\n",
        "\n",
        "In this notebook, we’ll dig deeper into the mechanisms behind these abstractions, focusing on how the models handle data and reason about sequences at a fundamental level.\n",
        "\n",
        "#### **Learning Objectives:**\n",
        "\n",
        "- Understand tokenization and embedding: how input data is represented and what properties the network leverages.\n",
        "- Explore transformer encoder architectures for sequence-level reasoning in an $n$-sequence-in, $n$-sequence-out formulation."
      ]
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        "<hr>\n",
        "<br>\n",
        "\n",
        "## **Part 2.1:** Getting The Model Inputs\n",
        "\n",
        "We previously discussed that the pipeline converts data to and from tensor representations via the `preprocess` and `postprocess` stages. In `preprocess`, the tokenizer converts the input string into a series of ***tokens*** — symbolic representations of words, letters, or subwords — that function as basic building blocks of a sentence. This tokenization process is fundamental to all large language models since it gives us something to latch onto."
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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          "text": [
            "BertForMaskedLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
            "  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n",
            "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
            "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
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              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Tokenize: ['hello', 'world', '!']\n",
            "Encoding: [101, 7592, 2088, 999, 102]\n",
            "Decoding: [CLS] hello world! [SEP]\n"
          ]
        },
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            "text/plain": [
              "{'input_ids': [101, 7592, 2088, 999, 102, 2031, 1037, 2307, 2154, 999, 102], 'token_type_ids': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "unmasker = pipeline('fill-mask', model='bert-base-uncased')\n",
        "\n",
        "## Testing out the tokenizer\n",
        "msg = \"Hello world!\"\n",
        "print(\"Tokenize:\", unmasker.tokenizer.tokenize(msg))      ## See token boundaries\n",
        "print(\"Encoding:\", x := unmasker.tokenizer.encode(msg))   ## See special tokens at end\n",
        "print(\"Decoding:\", x := unmasker.tokenizer.decode(x))     ## See decoding\n",
        "\n",
        "## Specifying multiple sentences\n",
        "unmasker.tokenizer(\"Hello world!\", \"Have a great day!\")"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**When given our string, the tokenizer responds with several components:**\n",
        "- `input_ids`: These are just the IDs of the tokens that make up our sentence. Said tokens can be words, punctuation, letters, whatever. Just individual entries out of a set vocabulary, exactly like classes.\n",
        "- `token_type_ids`: A flag indicating whether a token is part of the first or second sentence, which is useful for BERT’s training and select tasks but will not affect us.\n",
        "- `attention_mask`: Regulates which sequence entry each other sequence entry can attend to during processing. We’ll explore this later.\n",
        "\n",
        "For our purposes, `input_ids` are the most crucial. These token ID sequences allow LLMs to reason about natural language by treating samples as an ordered sequence of tokens. This should feel somewhat familiar if you’ve worked with classification tasks in deep learning, although reasoning over sequences introduces additional complexity."
      ]
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      "source": [
        "<hr>\n",
        "<br>\n",
        "\n",
        "## **Part 2.2:** Capturing Token Semantics\n",
        "\n",
        "Now that we understand natural language reasoning as working with an ordered sequence of tokens, how do we approach this? Let’s break it down using some familiar concepts from deep learning:\n",
        "\n",
        "- **On the output side**, we can predict a probability distribution over possible classes. For example, if we’re classifying among the tokens `cat`, `dog`, and `bird`, we output a vector like `<is_cat, is_dog, is_bird>`. The ground truth is represented as a one-hot vector, where the correct class is 1 and the rest are 0.\n",
        "\n",
        "- **On the input side**, while one-hot encoding could work, it’s more efficient to use an **Embedding Layer**. This is essentially a matrix where the class index points to a row of semantic information. Whether we use a one-hot vector or an embedding, the model maintains a record of class semantics (either in the weights of the first layer or in a lookup matrix).\n",
        "\n",
        "We can look over our unmasker's embedding strategy as follows:"
      ]
    },
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        "outputId": "cffd1ed9-9202-42b7-977a-deda61342325"
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          "data": {
            "text/plain": [
              "BertEmbeddings(\n",
              "  (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "  (position_embeddings): Embedding(512, 768)\n",
              "  (token_type_embeddings): Embedding(2, 768)\n",
              "  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "model = unmasker.model\n",
        "#dir(model)\n",
        "#dir(model.bert)\n",
        "model.bert.embeddings\n",
        "# model.bert.embeddings.word_embeddings\n",
        "# model.bert.embeddings.position_embeddings\n",
        "# model.bert.embeddings.token_type_embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgWJ8vhbGSJE"
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      "source": [
        "From this, we can identify the 3 components discussed in the presentation:\n",
        "- **Word Embeddings:** Semantic vectors representing the input tokens.\n",
        "- **Position Embeddings**: Semantic vectors representing the position of the words.\n",
        "- **Token Type Embedding**: Semantic vectors representing whether the token belongs to the first or second sentence.\n",
        "\n",
        "Notice how the `Embedding` component is constructed with the format:\n",
        "\n",
        "```\n",
        "Embedding(in_channel, out_channel)\n",
        "```\n",
        "\n",
        "We can see from this that BERT uses 768-dimensional embeddings, and can speculate on how they are obtained. The word embeddings seem to be coming from a 30,522-dimensional vector (the number of unique tokens in the vocabulary), the positional ones from 512, and the token types from just a few. Let's explore these a bit further."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiB9VTXsH8WB"
      },
      "source": [
        "#### **Investigating the Word Embeddings**\n",
        "\n",
        "Let’s start by examining the word embeddings of a sample sentence:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "GA3gjraKH9Kk",
        "tags": [],
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          "base_uri": "https://localhost:8080/"
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        "outputId": "be70e323-68ed-4560-a55d-87897de63092"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([13, 768])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0136, -0.0265, -0.0235,  ...,  0.0087,  0.0071,  0.0151],\n",
              "        [-0.0043, -0.0330, -0.0217,  ..., -0.0425, -0.0127, -0.0389],\n",
              "        [ 0.0244,  0.0322,  0.0024,  ..., -0.0424, -0.0054, -0.0284],\n",
              "        ...,\n",
              "        [-0.0149,  0.0124,  0.0091,  ..., -0.0445, -0.0193,  0.0234],\n",
              "        [ 0.0298, -0.0373, -0.0356,  ...,  0.0161,  0.0192,  0.0173],\n",
              "        [-0.0145, -0.0100,  0.0060,  ..., -0.0250,  0.0046, -0.0015]],\n",
              "       grad_fn=<EmbeddingBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "tokenizer = unmasker.tokenizer\n",
        "\n",
        "string = \"Hello World From Me, my cat and my dog!\"\n",
        "tokens = [tokenizer.convert_ids_to_tokens(x) for x in tokenizer.encode(string)]\n",
        "token_ids = torch.tensor(tokenizer(string)['input_ids'])\n",
        "embeddings = model.bert.embeddings.word_embeddings(token_ids)\n",
        "print(embeddings.shape)\n",
        "embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8LjH6fYIJzO"
      },
      "source": [
        "The output confirms that each token in our sentence is mapped to a 768-dimensional vector. These embeddings capture the meaning of each token. To explore this further, let’s define some helper functions to compute and visualize similarity between embeddings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "AIzRFOk_ILr0",
        "tags": []
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "def cosine_similarity(x1, x2):\n",
        "    \"\"\"Compute cosine similarity between two vectors.\"\"\"\n",
        "    x1_normalized = x1 / torch.norm(x1, dim=1, keepdim=True)\n",
        "    x2_normalized = x2 / torch.norm(x2, dim=1, keepdim=True)\n",
        "    return x1_normalized @ x2_normalized.T\n",
        "\n",
        "def scaled_dp_similarity(x1, x2):\n",
        "    \"\"\"Compute dot-product similarity between two vectors.\"\"\"\n",
        "    dot_product = x1 @ x2.T\n",
        "    d = torch.sqrt(torch.tensor(x1.shape[-1]))\n",
        "    return dot_product / d\n",
        "\n",
        "def softmax_similarity(x1, x2):\n",
        "    \"\"\"Compute softmaxed dp similarity between two vectors.\"\"\"\n",
        "    out = scaled_dp_similarity(x1, x2)\n",
        "    return torch.softmax(out, dim=1)\n",
        "\n",
        "def plot_mtx(matrix, name='', tokens=[]):\n",
        "    \"\"\"Compute similarity matrix for embeddings.\"\"\"\n",
        "    # Plot similarity matrix\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    label_dict = {} if tokens is None else {'xticklabels' : tokens, 'yticklabels': tokens}\n",
        "    sns.heatmap(\n",
        "        np.round(matrix.detach().numpy(), 3),\n",
        "        annot=True, cmap='coolwarm',\n",
        "        # vmin=-1, vmax=1,\n",
        "        **label_dict\n",
        "    )\n",
        "    plt.title(f\"Embedding {name} Matrix\")\n",
        "    plt.yticks(rotation=0)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uARirRUVIN-o"
      },
      "source": [
        "With these visualization and metric functions defined, we can view the similarities of the embeddings in different measurement spaces:\n",
        "\n",
        "#### **Basic Cosine Similarity**\n",
        "\n",
        "The following will compute the cosine similarity:\n",
        "\n",
        "```python\n",
        "plot_mtx(cosine_similarity(embeddings, embeddings), 'Cosine Sim', tokens)\n",
        "```\n",
        "\n",
        "This produces a normalized similarity matrix that shows the cosine similarity between each pair of token embeddings. While this captures angular relationships, it discards magnitude information.\n",
        "\n",
        "#### **Softmax Attention**\n",
        "\n",
        "As we'll soon see this idea being incorporated into the architecture, it's worth investigating what happens when we decide to transition to softmax-based similarity:\n",
        "\n",
        "```python\n",
        "plot_mtx(softmax_similarity(embeddings, embeddings), 'Softmax(x1) Sim', tokens)\n",
        "```\n",
        "You'll see that the matrix is no longer symmetric since we're applying softmax on a per-row basis, but it does have a nice intuitive analog when you format it as a matrix product: **Relative to the others, how much does a token contribute to every other token?** This formulation will come up later as \"attention.\"\n",
        "\n",
        "You'll also notice that the magnitudes are pretty small, but we can increase the magnitude of the embeddings and observe a much more polarizing similarity matrix.\n",
        "\n",
        "```python\n",
        "plot_mtx(softmax_similarity(embeddings*10, embeddings*10), 'Softmax(x10) Sim', tokens)\n",
        "```\n",
        "  \n",
        "Because the metric now factors magnitude into the decision process but keeps the output bounded and under control, this is a great choice when you want to inject similarity into optimization (again, foreshadowing).\n",
        "\n",
        "Regardless, the takehome message for word embeddings is roughly **\"learned vector representation for each token based on its meaning and usage in sentences.\"**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3aFe3KrNSt2j"
      },
      "outputs": [],
      "source": [
        "plot_mtx(cosine_similarity(embeddings, embeddings), 'Cosine Sim', tokens)\n",
        "\n",
        "# plot_mtx(scaled_dp_similarity(embeddings, embeddings), 'Scaled DP Sim', tokens)\n",
        "# plot_mtx(scaled_dp_similarity(embeddings*10, embeddings), 'Scaled DP (x10, x1) Sim', tokens)\n",
        "# plot_mtx(scaled_dp_similarity(embeddings*10, embeddings*10), 'Scaled DP (x10, x10) Sim', tokens)\n",
        "\n",
        "# plot_mtx(softmax_similarity(embeddings, embeddings), 'Softmax(x1) Sim', tokens)\n",
        "# plot_mtx(softmax_similarity(embeddings*10, embeddings*10), 'Softmax(x10) Sim', tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sReNjJpXIlSP"
      },
      "source": [
        "#### **Investigating the Positional Embeddings**\n",
        "\n",
        "Now that we've seen the word embeddings, we can take a look at the positional embeddings:\n",
        "\n",
        "```python\n",
        "model.bert.embeddings.position_embeddings ## -> Embedding(512, 768)\n",
        "```\n",
        "\n",
        "In contrast to the word embeddings, these are 512-dimensional and capture the semantics of the different positions in the input sequence. That way, our aggregate embedding can reason about both the words themselves and where they are in the sentence."
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "cQ0n60xhIrro"
      },
      "source": [
        "You'll notice that the positional embedding has a more predictable and uniform cosine similarity plots compared to the word embeddings, which are all actually pretty consistent with a few key exceptions.\n",
        "\n",
        "**You're free to visualize a subset of the positional embeddings matrix below.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "aW1lxTkoIvVx",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "outputId": "5397368b-33af-413c-ce50-311b277a624f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pos_options = torch.arange(len(tokens)) ## 0, 1, ..., n\n",
        "pos_embeddings = model.bert.embeddings.position_embeddings(pos_options)\n",
        "plot_mtx(cosine_similarity(pos_embeddings, pos_embeddings), 'Cosine Sim', tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXwGMOOhI-WK"
      },
      "source": [
        "#### **Wrapping Up The Embeddings**\n",
        "\n",
        "To wrap up our embedding discussions, we do still have our **token_type_embedding** embeddings, but they follow roughly the same logic; take in some extra semantic information about the sentence structure, encode it to some compatable representation, and incorporate it into the input. The authors saw this extra information as useful for supervising the model's training routine, so the overall embedding definition for BERT is:\n",
        "\n",
        "`embed = WordEmbed[token] + PosEmbed[pos] + TypeEmbed[pos]`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_R3BjEOkJBhs",
        "tags": []
      },
      "outputs": [],
      "source": [
        "model.bert.embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qKG1domJDdU"
      },
      "source": [
        "Then at the end, the LayerNorm section and Dropout section are also included, and these will permeate your architectures going forward. A light discussion is sufficient to motivate them:\n",
        "\n",
        "- The [**LayerNorm layer**](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html) normalizes the data flowing through it so that each minibatch subscribes to a similar distribution. You've probably seen [**BatchNorm**](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html) from computer vision; this has a similar logic, but now the normalization covers the layer outputs instead of the batch. For some more discussion, we found the [**PowerNorm paper**](https://arxiv.org/abs/2003.07845) helpful.\n",
        "- The [**Dropout layer**](https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html) just masks out some of the values during training. You've probably seen this before, and the logic is the same as usual; prevent over-reliance on a selection of features and distribute the logic throughout the network.\n",
        "\n",
        "HuggingFace, being open-source, provides insight into these components. You can find the BERT implementation in [`transformers/models/bert/modeling_bert.py`](https://github.com/huggingface/transformers/blob/0a365c3e6a0e174302debff4023182838607acf1/src/transformers/models/bert/modeling_bert.py#L180C11-L180C11), which may shed light on some lingering questions like \"is this addition or concatenation\" (it's addition) or \"do we need more considerations to make this scheme work in practice\" (yes)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lj7nG585JOnt"
      },
      "source": [
        "<hr>\n",
        "<br>\n",
        "\n",
        "## **Part 2.3:** From Token-Level to Sequence-Level Reasoning\n",
        "\n",
        "**To summarize the key points of the LLM intake strategy:**\n",
        "\n",
        "- A passage is **tokenized** into an ordered sequence of tokens (unique class instances).\n",
        "- The tokens are **embedded** into a vector that captures useful semantic features (meaning, position, etc).\n",
        "\n",
        "\n",
        "**With this, we have some obvious options for how to reason about our data:**\n",
        "\n",
        "1. **Token-by-token reasoning**: Treat each token in isolation (like classification tasks).\n",
        "    - **Problem**: Tokens need to understand the context of other tokens in the sequence.\n",
        "2. **Dense layer approach**: Reason about the sequence as a whole by combining all tokens at once.\n",
        "    - **Problem**: Creates an intractable neural network.\n",
        "\n",
        "The LLM solution is to do something between those two options:\n",
        "> Allow reasoning to be done **on each token *(Token-Level Reasoning)***, but also allow for small opportunities in which the network can **consider the sequence as a whole *(Sequence-Level Reasoning)***!\n",
        "\n",
        "That's where the **transformer** components come in!\n",
        "\n",
        "#### **Transformer Attention**\n",
        "\n",
        "Transformers, introduced in the 2017 paper [*Attention Is All You Need*](https://arxiv.org/abs/1706.03762), have become central to state-of-the-art language models. This architecture uses an ***attention mechanism*** to create an interface where the other entries of the sequence can communicate semantic information to other tokens in the series.\n",
        "\n",
        "The formulation goes as follows: If we have semantic and positional information present in our embeddings, we can train a mapping from our embeddings into three semantic spaces $K$, $Q$, and $V$:\n",
        "\n",
        "- **$K$ (Key)** and **$Q$ (Query)** are arguments to a similarity function (recall scaled softmax attention) to guage how much weight should be given between any pair of sequence entries in the input.\n",
        "\n",
        "- **$V$ (Value)** is the information that should pass through to the next component, and is weighted by `SoftmaxAttention(Key, Query)` to produce an output that is positionally and semantically motivated.\n",
        "\n",
        "\n",
        "**In other words:** Given a semantic/position-rich sequence of $d_k$-element embeddings ($S$) and three dense layers ($K$, $Q$, and $V$) that operate per-sequence-entry, we can train a neural network to make semantic/position-driven predictions via the forward pass equation:\n",
        "\n",
        "$$\\text{Self-Attention} = \\text{SoftmaxAttention}(K_s, Q_s) \\cdot V_s$$$$= \\frac{K_s Q_s ^T}{\\sqrt{d_k}}V_s$$\n",
        "\n",
        "**This effectively lets us produce:**\n",
        "- **$K(E),V(E),Q(E)$:** Three different embeddings derived from $E$.\n",
        "- **$\\sigma(A)$:** Gives intuition of how each embedding relates to all the other embeddings.\n",
        "- **$\\sigma(A)V$:** Embedding which considers both (pre-interface) $V$ and (post-interface) attention.\n",
        "\n",
        "<div><img src=\"imgs/attention-logic.png\" width=\"1000\"/></div>\n",
        "\n",
        "This mechanism is known as **self-attention** because the `Key`, `Query`, and `Value` vectors are derived from the same sequence. Other types of attention will be introduced later."
      ]
    },
    {
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      "metadata": {
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      "source": [
        "#### **Seeing Attention in the BERT Encoder**\n",
        "\n",
        "Now that we understand self-attention, let's look at how BERT’s encoder handles our embeddings:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "unmasker.model.bert.encoder"
      ]
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      "source": [
        "Those interested can dig much deeper into the implementation details, a high-level overview should be sufficient to see the big picture:\n",
        "- `BertSdpa(ScaledDotProductAttention)SelfAttention`: This component takes a sequence of vectors (let's call it `x`) as input and gets the `Q`, `K`, and `V` components via `query(x)`, `key(x)`, and `value(x)`, respectively. As these are all $768$-dimensional vectors - and are thereby multiplicatively compatible under transpose - the layer performs the attention computation with a few key modifications:\n",
        "    - **Multi-Headed Attention:** $K$, $Q$, and $V$ are all slices up along the embedding dimension such that we get 12 trios with dimension $768/12 = 64$. This will give us 12 different attention results, allowing the network to distribute attention in a variety of ways. After all 12 results are obtained, just concatenate embedding-wise and you'll be back up to 768-feature vectors.\n",
        "    - **Masked Attention:** This is less useful for BERT but explains what the `attention_mask` input is doing. Essentially, it's a true-or-false \"should-I-add-negative-infinity-to-the-attention\" mask to keep the model from attending to things it shouldn't. This will be more useful in later architectures.\n",
        "    - **Residual Connections:** To help the network keep the token-level information propagating through the network and improve the overall gradient flow, most architectures add residual connections around the transformer components.\n",
        "\n",
        "- `BertSelfOutput -> BertIntermediate -> BertOutput`: These are all just token-level dense layers with non-linear activations and some `LayerNorm`/`Dropout` layers mixed in for regularization. Each element in the sequence is thereby fed through an MLP *(multi-later perceptron, or multi-layer dense network)* with dimensions progressing through $768 \\to 768 \\to 3072 \\to 768$ to create a new data representation.\n",
        "\n",
        "BERT stacks 12 such layers in sequence. Each layer performs attention, followed by token-wise transformations, resulting in deep reasoning about the input tokens. Not too bad, right?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "St9sku1kSt2l"
      },
      "source": [
        "<div><img src=\"imgs/bert-construction.png\" width=\"800\"/></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00X5bjwcNueo"
      },
      "source": [
        "#### **Visualizing The Attention Mechanism In Action**\n",
        "\n",
        "We can request the realized attention values computed at each `SelfAttention` layer by specifying `output_attentions=True`:"
      ]
    },
    {
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      "execution_count": null,
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        "id": "nSYojpqSN3Qk",
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        "import torch\n",
        "\n",
        "## Tokenization Efforts\n",
        "string = \"Hello Mr. Bert! How is it [MASK]?\"\n",
        "input_tensors = unmasker.preprocess(string)\n",
        "input_ids = unmasker.tokenizer.encode(string)\n",
        "input_tokens = unmasker.tokenizer.convert_ids_to_tokens(input_ids)\n",
        "\n",
        "## Encoding Efforts\n",
        "embeddings = unmasker.model.bert.embeddings(input_tensors['input_ids'])\n",
        "x = unmasker.model.bert.encoder(embeddings, input_tensors['attention_mask'], output_attentions=True)\n",
        "\n",
        "print(\"From encoder.forward():\")\n",
        "for k,v in x.items():\n",
        "    v = v if hasattr(v, \"shape\") else torch.stack(v)\n",
        "    print(f\" > '{k}' : {v.shape}\")"
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "BrQ5n7S5N0rt"
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      "source": [
        "To visualize this, we can use the [**`BertViz` package**](https://github.com/jessevig/bertviz) to display the attention associations from our last forward pass in an interactive grid! Please feel free to test this out with other input strings to see what changes.\n",
        "- See what happens to the dimensionality when you increase the number of tokens.\n",
        "- See what happens to the connections, and see if you see any patterns worth noting.\n",
        "- Why do you think the CLS and SEP tokens get so much attention in many of the attention heads?"
      ]
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      "execution_count": null,
      "metadata": {
        "id": "z7FjdryUN6FY",
        "tags": []
      },
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      "source": [
        "from bertviz import model_view\n",
        "\n",
        "model_view(x['attentions'], input_tokens)  # Display model view"
      ]
    }
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