demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# SAE-VIS demo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This notebook was created to demo my open-source sparse autoencoder visualizer, as can be seen [here](https://www.perfectlynormal.co.uk/blog-sae). Other useful links:\n",
        "\n",
        "* [GitHub repo](https://github.com/callummcdougall/sae_vis)\n",
        "* [Dev guide](https://docs.google.com/document/d/10ctbiIskkkDc5eztqgADlvTufs7uzx5Wj8FE_y5petk/edit#heading=h.t3sp1uj6qghd), for people looking to understand the codebase so they can contribute\n",
        "* [User guide](https://docs.google.com/document/d/1QGjDB3iFJ5Y0GGpTwibUVsvpnzctRSHRLI-0rm6wt_k/edit#heading=h.t3sp1uj6qghd), for people looking to understand how to use all the features of this codebase (although obviously reading through this notebook is another option, it should be mostly self-explanatory)\n",
        "\n",
        "In this notebook, we demo two different types of vis:\n",
        "\n",
        "1. **Feature-centric vis**, where you look at a single feature and see e.g. which sequences in a large dataset this feature fires strongest on.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/callummcdougall/computational-thread-art/master/example_images/misc/sae-snip-1.png\" width=\"800\">\n",
        "\n",
        "2. **Prompt-centric vis**, where you input a custom prompt and see which features score highest on that prompt, according to a variety of possible metrics.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/callummcdougall/computational-thread-art/master/example_images/misc/sae-snip-2.png\" width=\"800\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1teONZ2QO8p"
      },
      "source": [
        "# Imports & Installs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4fV8Lls2N_WF"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    import google.colab # type: ignore\n",
        "    COLAB = True\n",
        "    %pip install sae-lens\n",
        "    %pip install transformer_lens\n",
        "    %pip install sae-vis\n",
        "except:\n",
        "    COLAB = False\n",
        "    from IPython import get_ipython # type: ignore\n",
        "    ipython = get_ipython(); assert ipython is not None\n",
        "    ipython.run_line_magic(\"load_ext\", \"autoreload\")\n",
        "    ipython.run_line_magic(\"autoreload\", \"2\")\n",
        "\n",
        "# Standard imports\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "import webbrowser\n",
        "import os\n",
        "from transformer_lens import utils, HookedTransformer\n",
        "from datasets.arrow_dataset import Dataset\n",
        "from huggingface_hub import hf_hub_download\n",
        "import time\n",
        "\n",
        "# Library imports\n",
        "from sae_vis.utils_fns import get_device\n",
        "from sae_vis.model_fns import AutoEncoder\n",
        "from sae_vis.data_storing_fns import SaeVisData\n",
        "from sae_vis.data_config_classes import SaeVisConfig\n",
        "from sae_lens.training.sparse_autoencoder import SparseAutoencoder\n",
        "\n",
        "device = get_device()\n",
        "torch.set_grad_enabled(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "feJOqPeoPjvX"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Autoencoders\n",
        "\n",
        "<!-- We're being a bit lazy here, and slicing our autoencoder so that we only take the first 2048 features (i.e. `dict_mult = 1`) rather than all 16384 features. This is literally just to avoid OOMs; you can increase the `DICT_MULT` parameter up to 8 if you'd like. -->\n",
        "\n",
        "We set up our autoencoder here. You can use your own autoencoder, as long as it has the same parameters `W_enc`, `W_dec`, `b_enc` and `b_dec` (used in the same way) and has a `cfg` attribute which itself is a dataclass with attributes `d_mlp` and `dict_mult`. The forward pass method doesn't matter; we only ever use the weights directly in this codebase."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "W_enc: (2048, 16384)\n",
            "W_dec: (16384, 2048)\n",
            "b_enc: (16384,)\n",
            "b_dec: (2048,)\n"
          ]
        }
      ],
      "source": [
        "encoder = AutoEncoder.load_from_hf(version=\"run1\").to(device)\n",
        "encoder_B = AutoEncoder.load_from_hf(version=\"run2\").to(device)\n",
        "\n",
        "for k, v in encoder.named_parameters():\n",
        "    print(f\"{k}: {tuple(v.shape)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Models\n",
        "\n",
        "This library will eventually support non-transformerlens models, but it's not there currently. If you're interested in this, please reach out!\n",
        "\n",
        "<!-- This library supports non-transformerlens models, provided you apply a wrapper around your model with a few specific methods (e.g. a modified `forward` function which returns a tuple of `(logits, activations, resid)`). However, it's much easier to just use a TransformerLens model in most cases! -->\n",
        "\n",
        "<!-- The code below loads in our GELU-1l transformer model. You can create your transformer model any way you like; all that matters is that:\n",
        "\n",
        "* Your model has a `forward` method which takes `tokens` and returns a tuple of `(logits, residual, post_activations)`.\n",
        "* This forward method has a parameter `return_logits`, which is by default `True`, and when `False` it only returns `(residual, post_activations)`.\n",
        "\n",
        "Provided this is the case, all other code here (including calculating the effect of ablating certain features) doesn't rely on any specific implementation details of the model.\n",
        "\n",
        "If you're trying to use a particular model, we recommend **creating a wrapper class around your model which has an altered `forward` method** to match the required behaviour. In the case of this notebook, to make it clear that a `HookedTransformer` model is not necessary, we're using a `DemoTransformer` model (code in this repository), which is a very minimal version of the `HookedTransformer` model lacking the features like hooks, caches, etc. -->"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained model gelu-1l into HookedTransformer\n",
            "Moving model to device:  cuda\n"
          ]
        }
      ],
      "source": [
        "model = HookedTransformer.from_pretrained(\"gelu-1l\")\n",
        "model.to(device);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "\n",
        "Obviously you can replace this code with your own data loading code. You should eventually have a 2D tensor of token ids."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "cgAJQMFhPlew",
        "outputId": "562ef5df-9cfb-44b2-e952-95bd2e526363"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([215402, 128])\n"
          ]
        }
      ],
      "source": [
        "SEQ_LEN = 128\n",
        "\n",
        "# Load in the data (it's a Dataset object)\n",
        "data = load_dataset(\"NeelNanda/c4-code-20k\", split=\"train\")\n",
        "assert isinstance(data, Dataset)\n",
        "\n",
        "# Tokenize the data (using a utils function) and shuffle it\n",
        "tokenized_data = utils.tokenize_and_concatenate(data, model.tokenizer, max_length=SEQ_LEN) # type: ignore\n",
        "tokenized_data = tokenized_data.shuffle(42)\n",
        "\n",
        "# Get the tokens as a tensor\n",
        "all_tokens = tokenized_data[\"tokens\"]\n",
        "assert isinstance(all_tokens, torch.Tensor)\n",
        "\n",
        "print(all_tokens.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a45zvAjpPuZX"
      },
      "source": [
        "# Feature-centric vis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here's a minimal example, which generates data for the first 64 features, and produces the corresponding vis.\n",
        "\n",
        "Once you open it, you can hover over tokens to see the feature activation magnitudes / most boosted tokens, and you can also use the dropdown in the top left to navigate between features!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "model_id": "0b2d913ee620445cae0dfecf3ff1ca86",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Forward passes to cache data for vis:   0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting vis data from cached data:   0%|          | 0/64 [00:00<?, ?it/s]"
            ]
          },
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          "output_type": "display_data"
        },
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Task                                           </span>┃<span style=\"font-weight: bold\"> Time  </span>┃<span style=\"font-weight: bold\"> Pct % </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
              "│ (1) Initialization                             │ 0.98s │ 11.4% │\n",
              "│ (2) Forward passes to gather model activations │ 0.42s │ 4.9%  │\n",
              "│ (3) Computing feature acts from model acts     │ 4.80s │ 55.9% │\n",
              "│ (4) Getting data for tables                    │ 0.15s │ 1.7%  │\n",
              "│ (5) Getting data for histograms                │ 0.11s │ 1.3%  │\n",
              "│ (6) Getting data for sequences                 │ 2.05s │ 23.9% │\n",
              "│ (7) Getting data for quantiles                 │ 0.07s │ 0.9%  │\n",
              "└────────────────────────────────────────────────┴───────┴───────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mTask                                          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mPct %\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
              "│ (1) Initialization                             │ 0.98s │ 11.4% │\n",
              "│ (2) Forward passes to gather model activations │ 0.42s │ 4.9%  │\n",
              "│ (3) Computing feature acts from model acts     │ 4.80s │ 55.9% │\n",
              "│ (4) Getting data for tables                    │ 0.15s │ 1.7%  │\n",
              "│ (5) Getting data for histograms                │ 0.11s │ 1.3%  │\n",
              "│ (6) Getting data for sequences                 │ 2.05s │ 23.9% │\n",
              "│ (7) Getting data for quantiles                 │ 0.07s │ 0.9%  │\n",
              "└────────────────────────────────────────────────┴───────┴───────┘\n"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
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            },
            "text/plain": [
              "Saving feature-centric vis:   0%|          | 0/64 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Specify the hook point you're using, the features you're analyzing, and the batch size for gathering activations\n",
        "sae_vis_config = SaeVisConfig(\n",
        "    hook_point = utils.get_act_name(\"post\", 0),\n",
        "    features = range(64),\n",
        "    batch_size = 2048,\n",
        "    verbose = True,\n",
        ")\n",
        "\n",
        "# Gather the feature data\n",
        "sae_vis_data = SaeVisData.create(\n",
        "    encoder = encoder,\n",
        "    encoder_B = encoder_B,\n",
        "    model = model,\n",
        "    tokens = all_tokens, # type: ignore\n",
        "    cfg = sae_vis_config,\n",
        ")\n",
        "\n",
        "# Save as HTML file & open in browser (or not, if in Colab)\n",
        "filename = \"_feature_vis_demo.html\"\n",
        "sae_vis_data.save_feature_centric_vis(filename, feature_idx=8)\n",
        "\n",
        "if COLAB: print(f\"Download the HTML file and open it in your browser: {filename}\")\n",
        "else: webbrowser.open(filename);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Feature-centric vis: customizing layout"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To customize our view, we pass an `SaeVisLayoutConfig` object into our main `SaeVisConfig`. This works by specifying a list of components for each column, with each component being customized by its associated config object.\n",
        "\n",
        "There's also a `help` method which explains all of these arguments. Run the cell below to see the output for the default layout (i.e. the one that produced the vis from the cell above)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">SaeVisLayoutConfig\n",
              "\n",
              "Key: \n",
              "  the tree shows which components will be displayed in each column (from left to right)\n",
              "  arguments are <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">green</span>\n",
              "  arguments changed from their default are <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">orange</span>, with default in brackets\n",
              "  argument descriptions are in <span style=\"font-style: italic\">italics</span>\n",
              "\n",
              "├── Column 0\n",
              "│   └── FeatureTables\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">n_rows: 3</span> \n",
              "│       │   <span style=\"font-style: italic\">Number of rows to show for each feature table.</span>\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">neuron_alignment_table: True</span> \n",
              "│       │   <span style=\"font-style: italic\">Whether to show the neuron alignment table.</span>\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">correlated_neurons_table: True</span> \n",
              "│       │   <span style=\"font-style: italic\">Whether to show the correlated neurons table.</span>\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">correlated_features_table: True</span> \n",
              "│       │   <span style=\"font-style: italic\">Whether to show the (pairwise) correlated features table.</span>\n",
              "│       └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">correlated_b_features_table: False</span> \n",
              "│           <span style=\"font-style: italic\">Whether to show the correlated encoder-B features table.</span>\n",
              "│           \n",
              "├── Column 1\n",
              "│   ├── ActsHistogram\n",
              "│   │   └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">n_bins: 50</span> \n",
              "│   │       <span style=\"font-style: italic\">Number of bins for the histogram.</span>\n",
              "│   │       \n",
              "│   ├── LogitsTable\n",
              "│   │   └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">n_rows: 10</span> \n",
              "│   │       <span style=\"font-style: italic\">Number of top/bottom logits to show in the table.</span>\n",
              "│   │       \n",
              "│   └── LogitsHistogram\n",
              "│       └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">n_bins: 50</span> \n",
              "│           <span style=\"font-style: italic\">Number of bins for the histogram.</span>\n",
              "│           \n",
              "└── Column 2\n",
              "    └── Sequences\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">buffer: (5, 5)</span> \n",
              "        │   <span style=\"font-style: italic\">How many tokens to add as context to each sequence. The tokens chosen for the top acts / quantile </span>\n",
              "        │   <span style=\"font-style: italic\">groups can't be outside the buffer range.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">compute_buffer: True</span> \n",
              "        │   <span style=\"font-style: italic\">If False, then we don't compute the loss effect, activations, or any other data for tokens other than </span>\n",
              "        │   <span style=\"font-style: italic\">the bold tokens in our sequences (saving time).</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">n_quantiles: 10</span> \n",
              "        │   <span style=\"font-style: italic\">Number of quantile groups for the sequences. If zero, we only show top activations, no quantile groups.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">top_acts_group_size: 20</span> \n",
              "        │   <span style=\"font-style: italic\">Number of sequences in the 'top activating sequences' group.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">quantile_group_size: 5</span> \n",
              "        │   <span style=\"font-style: italic\">Number of sequences in each of the sequence quantile groups.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">top_logits_hoverdata: 5</span> \n",
              "        │   <span style=\"font-style: italic\">Number of top/bottom logits to show in the hoverdata for each token.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">stack_mode: 'stack-all'</span> \n",
              "        │   <span style=\"font-style: italic\">How to stack the sequence groups.</span>\n",
              "        │   <span style=\"font-style: italic\">  'stack-all' = all groups are stacked in a single column (scrolls vertically if it overflows)</span>\n",
              "        │   <span style=\"font-style: italic\">  'stack-quantiles' = first col contains top acts, second col contains all quantile groups</span>\n",
              "        │   <span style=\"font-style: italic\">  'stack-none' = we stack in a way which ensures no vertical scrolling.</span>\n",
              "        └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">hover_below: True</span> \n",
              "            <span style=\"font-style: italic\">Whether the hover information about a token appears below or above the token.</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "SaeVisLayoutConfig\n",
              "\n",
              "Key: \n",
              "  the tree shows which components will be displayed in each column (from left to right)\n",
              "  arguments are \u001b[1;38;2;0;170;0mgreen\u001b[0m\n",
              "  arguments changed from their default are \u001b[1;38;5;208morange\u001b[0m, with default in brackets\n",
              "  argument descriptions are in \u001b[3mitalics\u001b[0m\n",
              "\n",
              "├── Column 0\n",
              "│   └── FeatureTables\n",
              "│       ├── \u001b[1;38;2;0;170;0mn_rows: 3\u001b[0m \n",
              "│       │   \u001b[3mNumber of rows to show for each feature table.\u001b[0m\n",
              "│       ├── \u001b[1;38;2;0;170;0mneuron_alignment_table: True\u001b[0m \n",
              "│       │   \u001b[3mWhether to show the neuron alignment table.\u001b[0m\n",
              "│       ├── \u001b[1;38;2;0;170;0mcorrelated_neurons_table: True\u001b[0m \n",
              "│       │   \u001b[3mWhether to show the correlated neurons table.\u001b[0m\n",
              "│       ├── \u001b[1;38;2;0;170;0mcorrelated_features_table: True\u001b[0m \n",
              "│       │   \u001b[3mWhether to show the (pairwise) correlated features table.\u001b[0m\n",
              "│       └── \u001b[1;38;2;0;170;0mcorrelated_b_features_table: False\u001b[0m \n",
              "│           \u001b[3mWhether to show the correlated encoder-B features table.\u001b[0m\n",
              "│           \n",
              "├── Column 1\n",
              "│   ├── ActsHistogram\n",
              "│   │   └── \u001b[1;38;2;0;170;0mn_bins: 50\u001b[0m \n",
              "│   │       \u001b[3mNumber of bins for the histogram.\u001b[0m\n",
              "│   │       \n",
              "│   ├── LogitsTable\n",
              "│   │   └── \u001b[1;38;2;0;170;0mn_rows: 10\u001b[0m \n",
              "│   │       \u001b[3mNumber of top/bottom logits to show in the table.\u001b[0m\n",
              "│   │       \n",
              "│   └── LogitsHistogram\n",
              "│       └── \u001b[1;38;2;0;170;0mn_bins: 50\u001b[0m \n",
              "│           \u001b[3mNumber of bins for the histogram.\u001b[0m\n",
              "│           \n",
              "└── Column 2\n",
              "    └── Sequences\n",
              "        ├── \u001b[1;38;2;0;170;0mbuffer: (5, 5)\u001b[0m \n",
              "        │   \u001b[3mHow many tokens to add as context to each sequence. The tokens chosen for the top acts / quantile \u001b[0m\n",
              "        │   \u001b[3mgroups can't be outside the buffer range.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mcompute_buffer: True\u001b[0m \n",
              "        │   \u001b[3mIf False, then we don't compute the loss effect, activations, or any other data for tokens other than \u001b[0m\n",
              "        │   \u001b[3mthe bold tokens in our sequences (saving time).\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mn_quantiles: 10\u001b[0m \n",
              "        │   \u001b[3mNumber of quantile groups for the sequences. If zero, we only show top activations, no quantile groups.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mtop_acts_group_size: 20\u001b[0m \n",
              "        │   \u001b[3mNumber of sequences in the 'top activating sequences' group.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mquantile_group_size: 5\u001b[0m \n",
              "        │   \u001b[3mNumber of sequences in each of the sequence quantile groups.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mtop_logits_hoverdata: 5\u001b[0m \n",
              "        │   \u001b[3mNumber of top/bottom logits to show in the hoverdata for each token.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mstack_mode: 'stack-all'\u001b[0m \n",
              "        │   \u001b[3mHow to stack the sequence groups.\u001b[0m\n",
              "        │   \u001b[3m  'stack-all' = all groups are stacked in a single column (scrolls vertically if it overflows)\u001b[0m\n",
              "        │   \u001b[3m  'stack-quantiles' = first col contains top acts, second col contains all quantile groups\u001b[0m\n",
              "        │   \u001b[3m  'stack-none' = we stack in a way which ensures no vertical scrolling.\u001b[0m\n",
              "        └── \u001b[1;38;2;0;170;0mhover_below: True\u001b[0m \n",
              "            \u001b[3mWhether the hover information about a token appears below or above the token.\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sae_vis.data_config_classes import (\n",
        "    SaeVisLayoutConfig,\n",
        "    Column,\n",
        "    FeatureTablesConfig,\n",
        "    ActsHistogramConfig,\n",
        "    LogitsTableConfig,\n",
        "    LogitsHistogramConfig,\n",
        "    SequencesConfig,\n",
        "    PromptConfig, # this one is only used for the prompt-centric vis\n",
        ")\n",
        "\n",
        "layout = SaeVisLayoutConfig(\n",
        "    columns = [\n",
        "        Column(FeatureTablesConfig()),\n",
        "        Column(ActsHistogramConfig(), LogitsTableConfig(), LogitsHistogramConfig()),\n",
        "        Column(SequencesConfig()),\n",
        "    ]\n",
        ")\n",
        "layout.help()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here's an example of how you might customize the layout, by changing around a few components & altering how they look. Note that we have to pass this `layout` object into the `SaeVisData.create` method, because some of the arguments determine *what data* we gather.\n",
        "\n",
        "<!-- Note, you can change the layout object via `multi_feature_data.fvc.layout = layout` (here `fvc` stands for feature vis config, this is how the `FeatureVisConfig` object is stored), and then re-run the `get_html` method. However, this might not work if your `SaeVisData` object doesn't contain as much data as you'd need to create the layout (an obvious example would be if you generated your data using only 3 rows in the feature tables, then created a vis using 5 tables - obviously you'd need to create that data again!). Where possible, we've tried to make the error messages explicit, so if you change the layout in a way which requires data you don't have, the error message should make it clear what's happened. If anything seems ambiguous, please make a PR! -->"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">SaeVisLayoutConfig\n",
              "\n",
              "Key: \n",
              "  the tree shows which components will be displayed in each column (from left to right)\n",
              "  arguments are <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">green</span>\n",
              "  arguments changed from their default are <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">orange</span>, with default in brackets\n",
              "  argument descriptions are in <span style=\"font-style: italic\">italics</span>\n",
              "\n",
              "├── Column 0\n",
              "│   └── Sequences\n",
              "│       ├── <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">buffer: (10, 5)</span> ((5, 5)) \n",
              "│       │   <span style=\"font-style: italic\">How many tokens to add as context to each sequence. The tokens chosen for the top acts / quantile </span>\n",
              "│       │   <span style=\"font-style: italic\">groups can't be outside the buffer range.</span>\n",
              "│       ├── <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">compute_buffer: False</span> (True) \n",
              "│       │   <span style=\"font-style: italic\">If False, then we don't compute the loss effect, activations, or any other data for tokens other than </span>\n",
              "│       │   <span style=\"font-style: italic\">the bold tokens in our sequences (saving time).</span>\n",
              "│       ├── <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">n_quantiles: 0</span> (10) \n",
              "│       │   <span style=\"font-style: italic\">Number of quantile groups for the sequences. If zero, we only show top activations, no quantile groups.</span>\n",
              "│       ├── <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">top_acts_group_size: 30</span> (20) \n",
              "│       │   <span style=\"font-style: italic\">Number of sequences in the 'top activating sequences' group.</span>\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">quantile_group_size: 5</span> \n",
              "│       │   <span style=\"font-style: italic\">Number of sequences in each of the sequence quantile groups.</span>\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">top_logits_hoverdata: 5</span> \n",
              "│       │   <span style=\"font-style: italic\">Number of top/bottom logits to show in the hoverdata for each token.</span>\n",
              "│       ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">stack_mode: 'stack-all'</span> \n",
              "│       │   <span style=\"font-style: italic\">How to stack the sequence groups.</span>\n",
              "│       │   <span style=\"font-style: italic\">  'stack-all' = all groups are stacked in a single column (scrolls vertically if it overflows)</span>\n",
              "│       │   <span style=\"font-style: italic\">  'stack-quantiles' = first col contains top acts, second col contains all quantile groups</span>\n",
              "│       │   <span style=\"font-style: italic\">  'stack-none' = we stack in a way which ensures no vertical scrolling.</span>\n",
              "│       └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">hover_below: True</span> \n",
              "│           <span style=\"font-style: italic\">Whether the hover information about a token appears below or above the token.</span>\n",
              "│           \n",
              "└── Column 1\n",
              "    ├── ActsHistogram\n",
              "    │   └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">n_bins: 50</span> \n",
              "    │       <span style=\"font-style: italic\">Number of bins for the histogram.</span>\n",
              "    │       \n",
              "    └── FeatureTables\n",
              "        ├── <span style=\"color: #ff8700; text-decoration-color: #ff8700; font-weight: bold\">n_rows: 5</span> (3) \n",
              "        │   <span style=\"font-style: italic\">Number of rows to show for each feature table.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">neuron_alignment_table: True</span> \n",
              "        │   <span style=\"font-style: italic\">Whether to show the neuron alignment table.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">correlated_neurons_table: True</span> \n",
              "        │   <span style=\"font-style: italic\">Whether to show the correlated neurons table.</span>\n",
              "        ├── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">correlated_features_table: True</span> \n",
              "        │   <span style=\"font-style: italic\">Whether to show the (pairwise) correlated features table.</span>\n",
              "        └── <span style=\"color: #00aa00; text-decoration-color: #00aa00; font-weight: bold\">correlated_b_features_table: False</span> \n",
              "            <span style=\"font-style: italic\">Whether to show the correlated encoder-B features table.</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "SaeVisLayoutConfig\n",
              "\n",
              "Key: \n",
              "  the tree shows which components will be displayed in each column (from left to right)\n",
              "  arguments are \u001b[1;38;2;0;170;0mgreen\u001b[0m\n",
              "  arguments changed from their default are \u001b[1;38;5;208morange\u001b[0m, with default in brackets\n",
              "  argument descriptions are in \u001b[3mitalics\u001b[0m\n",
              "\n",
              "├── Column 0\n",
              "│   └── Sequences\n",
              "│       ├── \u001b[1;38;5;208mbuffer: (10, 5)\u001b[0m ((5, 5)) \n",
              "│       │   \u001b[3mHow many tokens to add as context to each sequence. The tokens chosen for the top acts / quantile \u001b[0m\n",
              "│       │   \u001b[3mgroups can't be outside the buffer range.\u001b[0m\n",
              "│       ├── \u001b[1;38;5;208mcompute_buffer: False\u001b[0m (True) \n",
              "│       │   \u001b[3mIf False, then we don't compute the loss effect, activations, or any other data for tokens other than \u001b[0m\n",
              "│       │   \u001b[3mthe bold tokens in our sequences (saving time).\u001b[0m\n",
              "│       ├── \u001b[1;38;5;208mn_quantiles: 0\u001b[0m (10) \n",
              "│       │   \u001b[3mNumber of quantile groups for the sequences. If zero, we only show top activations, no quantile groups.\u001b[0m\n",
              "│       ├── \u001b[1;38;5;208mtop_acts_group_size: 30\u001b[0m (20) \n",
              "│       │   \u001b[3mNumber of sequences in the 'top activating sequences' group.\u001b[0m\n",
              "│       ├── \u001b[1;38;2;0;170;0mquantile_group_size: 5\u001b[0m \n",
              "│       │   \u001b[3mNumber of sequences in each of the sequence quantile groups.\u001b[0m\n",
              "│       ├── \u001b[1;38;2;0;170;0mtop_logits_hoverdata: 5\u001b[0m \n",
              "│       │   \u001b[3mNumber of top/bottom logits to show in the hoverdata for each token.\u001b[0m\n",
              "│       ├── \u001b[1;38;2;0;170;0mstack_mode: 'stack-all'\u001b[0m \n",
              "│       │   \u001b[3mHow to stack the sequence groups.\u001b[0m\n",
              "│       │   \u001b[3m  'stack-all' = all groups are stacked in a single column (scrolls vertically if it overflows)\u001b[0m\n",
              "│       │   \u001b[3m  'stack-quantiles' = first col contains top acts, second col contains all quantile groups\u001b[0m\n",
              "│       │   \u001b[3m  'stack-none' = we stack in a way which ensures no vertical scrolling.\u001b[0m\n",
              "│       └── \u001b[1;38;2;0;170;0mhover_below: True\u001b[0m \n",
              "│           \u001b[3mWhether the hover information about a token appears below or above the token.\u001b[0m\n",
              "│           \n",
              "└── Column 1\n",
              "    ├── ActsHistogram\n",
              "    │   └── \u001b[1;38;2;0;170;0mn_bins: 50\u001b[0m \n",
              "    │       \u001b[3mNumber of bins for the histogram.\u001b[0m\n",
              "    │       \n",
              "    └── FeatureTables\n",
              "        ├── \u001b[1;38;5;208mn_rows: 5\u001b[0m (3) \n",
              "        │   \u001b[3mNumber of rows to show for each feature table.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mneuron_alignment_table: True\u001b[0m \n",
              "        │   \u001b[3mWhether to show the neuron alignment table.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mcorrelated_neurons_table: True\u001b[0m \n",
              "        │   \u001b[3mWhether to show the correlated neurons table.\u001b[0m\n",
              "        ├── \u001b[1;38;2;0;170;0mcorrelated_features_table: True\u001b[0m \n",
              "        │   \u001b[3mWhether to show the (pairwise) correlated features table.\u001b[0m\n",
              "        └── \u001b[1;38;2;0;170;0mcorrelated_b_features_table: False\u001b[0m \n",
              "            \u001b[3mWhether to show the correlated encoder-B features table.\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create custom layout\n",
        "layout = SaeVisLayoutConfig(\n",
        "    columns = [\n",
        "        Column(SequencesConfig(stack_mode='stack-all', buffer=(10, 5), compute_buffer=False, n_quantiles=0, top_acts_group_size=30), width=650),\n",
        "        Column(ActsHistogramConfig(), FeatureTablesConfig(n_rows=5), width=500),\n",
        "    ],\n",
        "    height = 1000,\n",
        ")\n",
        "layout.help()\n",
        "\n",
        "# Set all config parameter\n",
        "feature_vis_config_custom_layout = SaeVisConfig(\n",
        "    hook_point = utils.get_act_name(\"post\", 0),\n",
        "    features = range(64),\n",
        "    batch_size = 2048,\n",
        "    feature_centric_layout = layout,\n",
        ")\n",
        "\n",
        "# Generate data\n",
        "multi_feature_data_custom = SaeVisData.create(\n",
        "    encoder = encoder,\n",
        "    encoder_B = encoder_B,\n",
        "    model = model,\n",
        "    tokens = all_tokens, # type: ignore\n",
        "    cfg = feature_vis_config_custom_layout,\n",
        ")\n",
        "\n",
        "# Save data (and open it using webbrowser)\n",
        "filename = \"_feature_vis_demo_custom.html\"\n",
        "multi_feature_data_custom.save_feature_centric_vis(filename, feature_idx=8)\n",
        "webbrowser.open(filename);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Feature-centric vis: multi-layer models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We've currently only worked with 1-layer models. Let's try and see what happens when we use a multi-layer model. Thankfully, Joseph Bloom has trained some excellent SAEs on GPT2-small, so we can use one of them.\n",
        "\n",
        "Note - we're planning closer integration with Joseph Bloom's library at some point in the near future. Currently, this extends to supporting his models `SparseAutoencoder` (which work pretty similarly to transformerlens' `HookedTransformer` object, if you're familiar with that). You don't have to understand this here though, just run the code to get your model & autoencoder!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded pretrained model gpt2-small into HookedTransformer\n",
            "Moving model to device:  cuda\n"
          ]
        }
      ],
      "source": [
        "# Get gpt2 model\n",
        "gpt2 = HookedTransformer.from_pretrained(\"gpt2-small\")\n",
        "gpt2.to(device);\n",
        "\n",
        "# Get autoencoder\n",
        "hook_point = \"blocks.0.hook_resid_pre\"\n",
        "sae_path = hf_hub_download(\n",
        "    repo_id = \"jbloom/GPT2-Small-SAEs-Reformatted\",\n",
        "    filename = f\"{hook_point}/sae_weights.safetensors\"\n",
        ")\n",
        "gpt2_sae = SparseAutoencoder.load_from_pretrained(os.path.dirname(sae_path))\n",
        "gpt2_sae.to(device);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "state_dict = {\n",
        "    \"W_dec\": gpt2_sae.state_dict()[\"W_enc\"].T,\n",
        "    \"b_dec\": gpt2_sae.state_dict()[\"b_dec\"],\n",
        "    \"W_enc\": gpt2_sae.state_dict()[\"W_dec\"].T,\n",
        "    \"b_enc\": gpt2_sae.state_dict()[\"b_enc\"],\n",
        "}\n",
        "gpt2_sae.load_state_dict(state_dict);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "And now let's get our vis. Feel the force!\n",
        "\n",
        "Note that the time complexity here is dominated by the forward passes, because we're only getting one feature. Also note that the vis has no dropdown to select features, because we only have one feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "495b7473fc35400b9bf79d67c6697211",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Forward passes to cache data for vis:   0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c7020cfa6e964adf90a1a5aedba45535",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting vis data from cached data:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Task                                           </span>┃<span style=\"font-weight: bold\"> Time   </span>┃<span style=\"font-weight: bold\"> Pct % </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩\n",
              "│ (1) Initialization                             │ 0.00s  │ 0.0%  │\n",
              "│ (2) Forward passes to gather model activations │ 0.94s  │ 5.9%  │\n",
              "│ (3) Computing feature acts from model acts     │ 14.75s │ 93.0% │\n",
              "│ (4) Getting data for tables                    │ 0.01s  │ 0.1%  │\n",
              "│ (5) Getting data for histograms                │ 0.05s  │ 0.3%  │\n",
              "│ (6) Getting data for sequences                 │ 0.10s  │ 0.6%  │\n",
              "│ (7) Getting data for quantiles                 │ 0.01s  │ 0.1%  │\n",
              "└────────────────────────────────────────────────┴────────┴───────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mTask                                          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mPct %\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩\n",
              "│ (1) Initialization                             │ 0.00s  │ 0.0%  │\n",
              "│ (2) Forward passes to gather model activations │ 0.94s  │ 5.9%  │\n",
              "│ (3) Computing feature acts from model acts     │ 14.75s │ 93.0% │\n",
              "│ (4) Getting data for tables                    │ 0.01s  │ 0.1%  │\n",
              "│ (5) Getting data for histograms                │ 0.05s  │ 0.3%  │\n",
              "│ (6) Getting data for sequences                 │ 0.10s  │ 0.6%  │\n",
              "│ (7) Getting data for quantiles                 │ 0.01s  │ 0.1%  │\n",
              "└────────────────────────────────────────────────┴────────┴───────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b349bbd643f54b1bb4ee22a04d1fcbfd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving feature-centric vis:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "torch.cuda.empty_cache()\n",
        "import gc\n",
        "gc.collect()\n",
        "\n",
        "test_feature_idx_gpt = 7650\n",
        "\n",
        "feature_vis_config_gpt = SaeVisConfig(\n",
        "    hook_point = hook_point,\n",
        "    features = test_feature_idx_gpt,\n",
        "    batch_size = 2048,\n",
        "    verbose = True,\n",
        ")\n",
        "\n",
        "sae_vis_data_gpt = SaeVisData.create(\n",
        "    encoder = gpt2_sae,\n",
        "    model = gpt2,\n",
        "    tokens = all_tokens, # type: ignore\n",
        "    cfg = feature_vis_config_gpt,\n",
        ")\n",
        "\n",
        "filename = \"_feature_vis_demo_gpt.html\"\n",
        "sae_vis_data_gpt.save_feature_centric_vis(filename)\n",
        "\n",
        "if COLAB: print(f\"Download the HTML file and open it in your browser: {filename}\")\n",
        "else: webbrowser.open(filename);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Prompt-centric vis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this vis, we pick a prompt see which features score highest on that prompt, according to a variety of possible metrics.\n",
        "\n",
        "Let's first do the minimal thing, which just involves the same steps as above, except we call `save_prompt_centric_vis` on our `sae_vis_data` object instead. This also requires passing in our prompt, plus optionally a `seq_pos` and `metric` argument which determine which dropdown options are selected by default when the page loads.\n",
        "\n",
        "Note that the actual `create` method is just the same as for the feature centric vis, in other words we could use the same object for both if we wanted (since the `create` method is the one which takes the longest to run by far)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "998115b437234c14bc3e97bd741c40fb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Forward passes to cache data for vis:   0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b31b545466f34c5bbbc8b490a2919cbc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting vis data from cached data:   0%|          | 0/256 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Task                                           </span>┃<span style=\"font-weight: bold\"> Time  </span>┃<span style=\"font-weight: bold\"> Pct % </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
              "│ (1) Initialization                             │ 0.01s │ 0.1%  │\n",
              "│ (2) Forward passes to gather model activations │ 0.28s │ 1.8%  │\n",
              "│ (3) Computing feature acts from model acts     │ 5.18s │ 32.4% │\n",
              "│ (4) Getting data for tables                    │ 0.10s │ 0.6%  │\n",
              "│ (5) Getting data for histograms                │ 0.43s │ 2.7%  │\n",
              "│ (6) Getting data for sequences                 │ 9.19s │ 57.4% │\n",
              "│ (7) Getting data for quantiles                 │ 0.80s │ 5.0%  │\n",
              "└────────────────────────────────────────────────┴───────┴───────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mTask                                          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mPct %\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
              "│ (1) Initialization                             │ 0.01s │ 0.1%  │\n",
              "│ (2) Forward passes to gather model activations │ 0.28s │ 1.8%  │\n",
              "│ (3) Computing feature acts from model acts     │ 5.18s │ 32.4% │\n",
              "│ (4) Getting data for tables                    │ 0.10s │ 0.6%  │\n",
              "│ (5) Getting data for histograms                │ 0.43s │ 2.7%  │\n",
              "│ (6) Getting data for sequences                 │ 9.19s │ 57.4% │\n",
              "│ (7) Getting data for quantiles                 │ 0.80s │ 5.0%  │\n",
              "└────────────────────────────────────────────────┴───────┴───────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "torch.cuda.empty_cache()\n",
        "import gc\n",
        "gc.collect()\n",
        "\n",
        "# Specify the hook point you're using, the features you're analyzing, and the batch size for gathering activations\n",
        "sae_vis_config = SaeVisConfig(\n",
        "    hook_point = utils.get_act_name(\"post\", 0),\n",
        "    features = range(256),\n",
        "    batch_size = 2048,\n",
        "    verbose = True,\n",
        ")\n",
        "\n",
        "# Gather the feature data\n",
        "sae_vis_data = SaeVisData.create(\n",
        "    encoder = encoder,\n",
        "    encoder_B = encoder_B,\n",
        "    model = model,\n",
        "    tokens = all_tokens, # type: ignore\n",
        "    cfg = sae_vis_config,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "prompt = \"'first_name': ('django.db.models.fields\"\n",
        "\n",
        "seq_pos = model.tokenizer.tokenize(prompt).index(\"Ġ('\") # type: ignore\n",
        "metric = 'act-quantiles'\n",
        "\n",
        "filename = \"_prompt_vis_demo.html\"\n",
        "\n",
        "sae_vis_data.save_prompt_centric_vis(\n",
        "    prompt = prompt,\n",
        "    filename = filename,\n",
        "    seq_pos = seq_pos, # optional argument, to determine the default option when the page loads\n",
        "    metric = metric, # optional argument, to determine the default option when the page loads\n",
        ")\n",
        "if COLAB: print(f\"Download the HTML file and open it in your browser: {filename}\")\n",
        "else: webbrowser.open(filename);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Just like the feature-centric vis, we can also customize the view. The feature-centric view was customized by the `sae_vis_data.feature_centric_layout` object, which defaulted to:\n",
        "\n",
        "```python\n",
        "SaeVisLayoutConfig(\n",
        "    columns = [\n",
        "        Column(FeatureTablesConfig()),\n",
        "        Column(ActsHistogramConfig(), LogitsTableConfig(), LogitsHistogramConfig()),\n",
        "        Column(SequencesConfig(stack_mode='stack-none')),\n",
        "    ],\n",
        "    height = 750,\n",
        ")\n",
        "```\n",
        "\n",
        "By contrast, the prompt-centric view is customized by the `sae_vis_data.prompt_centric_layout` object, which defaults to:\n",
        "\n",
        "```python\n",
        "SaeVisLayoutConfig(\n",
        "    columns = [\n",
        "        Column(\n",
        "            PromptConfig(),\n",
        "            ActsHistogramConfig(),\n",
        "            LogitsTableConfig(n_rows=5),\n",
        "            SequencesConfig(top_acts_group_size=10, n_quantiles=0),\n",
        "            width=450\n",
        "        ),\n",
        "    ],\n",
        "    height = 1000,\n",
        ")\n",
        "```\n",
        "\n",
        "You can create your own of these just like for the feature-centric view, although there are 2 extra conditions:\n",
        "\n",
        "1. **You must have only one column** (since in this vis, we dedicate one column to each of the top features).\n",
        "2. **The prompt-centric vis must not require more data than the feature-centric vis.** This is because the data contained in `sae_vis_data` is determined by the prompt-centric vis layout, and when generating the prompt-centric vis we can't use data that's not there. As a simple example, if we used `LogitsTableConfig(n_rows=10)` in our feature-centric config, we couldn't use `LogitsTableConfig(n_rows=15)` when generating our prompt-centric vis.\n",
        "\n",
        "Both these conditions will be checked when you initialize your config objects, before you've gathered any data.\n",
        "\n",
        "Here's an example customized prompt-centric layout. Note, this example shows how you can manually change `sae_vis_data.cfg.prompt_centric_layout` without re-generating the data (and the same is true for the feature data). This is a bit dangerous sometimes because you'll get an error if this config object requires more data than you originally gathered, but here it's fine because we're being careful not to do that!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SaeVisLayoutConfig(columns={0: <sae_vis.data_config_classes.Column object at 0x00000255D7CA3F80>}, height=1200, seq_cfg=SequencesConfig(buffer=(5, 5), compute_buffer=True, n_quantiles=10, top_acts_group_size=20, quantile_group_size=5, top_logits_hoverdata=5, stack_mode='stack-all', hover_below=True), act_hist_cfg=ActsHistogramConfig(n_bins=50), logits_hist_cfg=LogitsHistogramConfig(n_bins=50), logits_table_cfg=LogitsTableConfig(n_rows=10), feature_tables_cfg=None, prompt_cfg=PromptConfig())"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "SaeVisLayoutConfig(\n",
        "    columns = [\n",
        "        Column(PromptConfig(), LogitsTableConfig(), LogitsHistogramConfig(), ActsHistogramConfig(), SequencesConfig(n_quantiles=10), width=550),\n",
        "    ],\n",
        "    height = 1200,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "sae_vis_data.cfg.prompt_centric_layout = SaeVisLayoutConfig(\n",
        "    columns = [\n",
        "        Column(PromptConfig(), LogitsTableConfig(), LogitsHistogramConfig(), ActsHistogramConfig(), SequencesConfig(n_quantiles=10), width=550),\n",
        "    ],\n",
        "    height = 1200,\n",
        ")\n",
        "\n",
        "filename = \"_prompt_vis_demo_custom.html\"\n",
        "\n",
        "sae_vis_data.save_prompt_centric_vis(\n",
        "    prompt = prompt,\n",
        "    filename = filename,\n",
        "    seq_pos = seq_pos,\n",
        "    metric = metric,\n",
        ")\n",
        "\n",
        "if COLAB: print(f\"Download the HTML file and open it in your browser: {filename}\")\n",
        "else: webbrowser.open(filename);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Saving data as JSON"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can save your data as a JSON file, as follows. Note that we don't actually save much storage space by doing this, because the HTML is already stored very compactly (essentially the JSON is dumped directly into the HTML page; most of the extra size comes from the JavaScript functions which populate the empty HTML elements with the data)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved in 6.17 seconds\n",
            "Loaded in 11.63 seconds\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d06acfa08ea54b56902e83a9499a4e83",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving feature-centric vis:   0%|          | 0/256 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Size of JSON: 14.703 MB\n",
            "Size of HTML: 17.440 MB\n",
            "Saved some space, but not much, because the HTML files already pack the data pretty efficiently!\n"
          ]
        }
      ],
      "source": [
        "json_filepath = \"_feature_vis_demo_for_saving.json\"\n",
        "html_filepath = \"_feature_vis_demo_for_saving.html\"\n",
        "\n",
        "# Save\n",
        "t0 = time.time()\n",
        "sae_vis_data.save_json(filename=json_filepath)\n",
        "print(f\"Saved in {time.time() - t0:.2f} seconds\")\n",
        "\n",
        "# Load back in (supplying our config, model & encoders which aren't saved)\n",
        "t0 = time.time()\n",
        "sae_vis_data_loaded = SaeVisData.load_json(\n",
        "    filename=json_filepath,\n",
        "    cfg=sae_vis_data.cfg,\n",
        "    model=model,\n",
        "    encoder=encoder,\n",
        "    encoder_B=encoder_B,\n",
        ")\n",
        "assert isinstance(sae_vis_data_loaded, SaeVisData)\n",
        "print(f\"Loaded in {time.time() - t0:.2f} seconds\\n\")\n",
        "\n",
        "# Check we can still use it\n",
        "sae_vis_data_loaded.save_feature_centric_vis(html_filepath, feature_idx=8)\n",
        "webbrowser.open(html_filepath);\n",
        "\n",
        "# Print out sizes, to see how much we save using json\n",
        "print(f\"Size of JSON: {os.path.getsize(json_filepath) / 1e6:.3f} MB\")\n",
        "print(f\"Size of HTML: {os.path.getsize(html_filepath) / 1e6:.3f} MB\")\n",
        "print(\"Saved some space, but not much, because the HTML files already pack the data pretty efficiently!\")\n",
        "\n",
        "# Delete files\n",
        "os.remove(json_filepath)\n",
        "os.remove(html_filepath)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "J1teONZ2QO8p",
        "feJOqPeoPjvX"
      ],
      "gpuType": "V100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
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