Sparse_Autoencoder (2).ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!python3 -V"
      ],
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        },
        "id": "rlGPTRmTknIY",
        "outputId": "83342d4b-f43b-4980-e37f-4d80992eaf04"
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          "text": [
            "Python 3.10.12\n"
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      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dependencies"
      ],
      "metadata": {
        "id": "kcSFok0OAsjs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "text": [
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          ]
        }
      ],
      "source": [
        "\n",
        "!pip install transformer_lens\n",
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformer_lens\n",
        "from transformer_lens import HookedTransformer, utils\n",
        "import torch\n",
        "import numpy as np\n",
        "import gradio as gr\n",
        "import pprint\n",
        "import json\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from datasets import load_dataset\n",
        "from huggingface_hub import HfApi\n",
        "from IPython.display import HTML\n",
        "from functools import partial\n",
        "import tqdm.notebook as tqdm\n",
        "import plotly.express as px\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "6uagRYLkCC6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining the Autoencoder"
      ],
      "metadata": {
        "id": "iXl91Y1dAtyT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cfg = {\n",
        "    \"seed\": 49,\n",
        "    \"batch_size\": 4096,\n",
        "    \"buffer_mult\": 384,\n",
        "    \"lr\": 1e-4,\n",
        "    \"num_tokens\": int(2e9),\n",
        "    \"l1_coeff\": 3e-4,\n",
        "    \"beta1\": 0.9,\n",
        "    \"beta2\": 0.99,\n",
        "    \"dict_mult\": 8,\n",
        "    \"seq_len\": 128,\n",
        "    \"d_mlp\": 2048,\n",
        "    \"enc_dtype\":\"fp32\",\n",
        "    \"remove_rare_dir\": False,\n",
        "}\n",
        "cfg[\"model_batch_size\"] = 64\n",
        "cfg[\"buffer_size\"] = cfg[\"batch_size\"] * cfg[\"buffer_mult\"]\n",
        "cfg[\"buffer_batches\"] = cfg[\"buffer_size\"] // cfg[\"seq_len\"]"
      ],
      "metadata": {
        "id": "qCF9odNdAvKX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DTYPES = {\"fp32\": torch.float32, \"fp16\": torch.float16, \"bf16\": torch.bfloat16}\n",
        "class AutoEncoder(nn.Module):\n",
        "    def __init__(self, cfg):\n",
        "        super().__init__()\n",
        "        d_hidden = cfg[\"d_mlp\"] * cfg[\"dict_mult\"]\n",
        "        d_mlp = cfg[\"d_mlp\"]\n",
        "        l1_coeff = cfg[\"l1_coeff\"]\n",
        "        dtype = DTYPES[cfg[\"enc_dtype\"]]\n",
        "        torch.manual_seed(cfg[\"seed\"])\n",
        "        self.W_enc = nn.Parameter(torch.nn.init.kaiming_uniform_(torch.empty(d_mlp, d_hidden, dtype=dtype)))\n",
        "        self.W_dec = nn.Parameter(torch.nn.init.kaiming_uniform_(torch.empty(d_hidden, d_mlp, dtype=dtype)))\n",
        "        self.b_enc = nn.Parameter(torch.zeros(d_hidden, dtype=dtype))\n",
        "        self.b_dec = nn.Parameter(torch.zeros(d_mlp, dtype=dtype))\n",
        "\n",
        "        self.W_dec.data[:] = self.W_dec / self.W_dec.norm(dim=-1, keepdim=True)\n",
        "\n",
        "        self.d_hidden = d_hidden\n",
        "        self.l1_coeff = l1_coeff\n",
        "\n",
        "        self.to(\"cuda\")\n",
        "\n",
        "    def forward(self, x):\n",
        "        x_cent = x - self.b_dec\n",
        "        acts = F.relu(x_cent @ self.W_enc + self.b_enc)\n",
        "        x_reconstruct = acts @ self.W_dec + self.b_dec\n",
        "        l2_loss = (x_reconstruct.float() - x.float()).pow(2).sum(-1).mean(0)\n",
        "        l1_loss = self.l1_coeff * (acts.float().abs().sum())\n",
        "        loss = l2_loss + l1_loss\n",
        "        return loss, x_reconstruct, acts, l2_loss, l1_loss\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def remove_parallel_component_of_grads(self):\n",
        "        W_dec_normed = self.W_dec / self.W_dec.norm(dim=-1, keepdim=True)\n",
        "        W_dec_grad_proj = (self.W_dec.grad * W_dec_normed).sum(-1, keepdim=True) * W_dec_normed\n",
        "        self.W_dec.grad -= W_dec_grad_proj\n",
        "\n",
        "    # def get_version(self):\n",
        "    #     return 1+max([int(file.name.split(\".\")[0]) for file in list(SAVE_DIR.iterdir()) if \"pt\" in str(file)])\n",
        "\n",
        "    # def save(self):\n",
        "    #     version = self.get_version()\n",
        "    #     torch.save(self.state_dict(), SAVE_DIR/(str(version)+\".pt\"))\n",
        "    #     with open(SAVE_DIR/(str(version)+\"_cfg.json\"), \"w\") as f:\n",
        "    #         json.dump(cfg, f)\n",
        "    #     print(\"Saved as version\", version)\n",
        "\n",
        "    # def load(cls, version):\n",
        "    #     cfg = (json.load(open(SAVE_DIR/(str(version)+\"_cfg.json\"), \"r\")))\n",
        "    #     pprint.pprint(cfg)\n",
        "    #     self = cls(cfg=cfg)\n",
        "    #     self.load_state_dict(torch.load(SAVE_DIR/(str(version)+\".pt\")))\n",
        "    #     return self\n",
        "\n",
        "    @classmethod\n",
        "    def load_from_hf(cls, version):\n",
        "        \"\"\"\n",
        "        Loads the saved autoencoder from HuggingFace.\n",
        "\n",
        "        Version is expected to be an int, or \"run1\" or \"run2\"\n",
        "\n",
        "        version 25 is the final checkpoint of the first autoencoder run,\n",
        "        version 47 is the final checkpoint of the second autoencoder run.\n",
        "        \"\"\"\n",
        "        if version==\"run1\":\n",
        "            version = 25\n",
        "        elif version==\"run2\":\n",
        "            version = 47\n",
        "\n",
        "        cfg = utils.download_file_from_hf(\"NeelNanda/sparse_autoencoder\", f\"{version}_cfg.json\")\n",
        "        pprint.pprint(cfg)\n",
        "        self = cls(cfg=cfg)\n",
        "        self.load_state_dict(utils.download_file_from_hf(\"NeelNanda/sparse_autoencoder\", f\"{version}.pt\", force_is_torch=True))\n",
        "        return self\n"
      ],
      "metadata": {
        "id": "Kn1E_44gCa-Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Utils"
      ],
      "metadata": {
        "id": "ysejlJ4ZLrl7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get Reconstruction Loss"
      ],
      "metadata": {
        "id": "gjpJCFxGLtoK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def replacement_hook(mlp_post, hook, encoder):\n",
        "    mlp_post_reconstr = encoder(mlp_post)[1]\n",
        "    return mlp_post_reconstr\n",
        "\n",
        "def mean_ablate_hook(mlp_post, hook):\n",
        "    mlp_post[:] = mlp_post.mean([0, 1])\n",
        "    return mlp_post\n",
        "\n",
        "def zero_ablate_hook(mlp_post, hook):\n",
        "    mlp_post[:] = 0.\n",
        "    return mlp_post\n",
        "\n",
        "@torch.no_grad()\n",
        "def get_recons_loss(num_batches=5, local_encoder=None):\n",
        "    if local_encoder is None:\n",
        "        local_encoder = encoder\n",
        "    loss_list = []\n",
        "    for i in range(num_batches):\n",
        "        tokens = all_tokens[torch.randperm(len(all_tokens))[:cfg[\"model_batch_size\"]]]\n",
        "        loss = model(tokens, return_type=\"loss\")\n",
        "        recons_loss = model.run_with_hooks(tokens, return_type=\"loss\", fwd_hooks=[(utils.get_act_name(\"post\", 0), partial(replacement_hook, encoder=local_encoder))])\n",
        "        # mean_abl_loss = model.run_with_hooks(tokens, return_type=\"loss\", fwd_hooks=[(utils.get_act_name(\"post\", 0), mean_ablate_hook)])\n",
        "        zero_abl_loss = model.run_with_hooks(tokens, return_type=\"loss\", fwd_hooks=[(utils.get_act_name(\"post\", 0), zero_ablate_hook)])\n",
        "        loss_list.append((loss, recons_loss, zero_abl_loss))\n",
        "    losses = torch.tensor(loss_list)\n",
        "    loss, recons_loss, zero_abl_loss = losses.mean(0).tolist()\n",
        "\n",
        "    print(f\"loss: {loss:.4f}, recons_loss: {recons_loss:.4f}, zero_abl_loss: {zero_abl_loss:.4f}\")\n",
        "    score = ((zero_abl_loss - recons_loss)/(zero_abl_loss - loss))\n",
        "    print(f\"Reconstruction Score: {score:.2%}\")\n",
        "    # print(f\"{((zero_abl_loss - mean_abl_loss)/(zero_abl_loss - loss)).item():.2%}\")\n",
        "    return score, loss, recons_loss, zero_abl_loss"
      ],
      "metadata": {
        "id": "pXH4pos0LvAs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get Frequencies"
      ],
      "metadata": {
        "id": "7vz9UyVGBdWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Frequency\n",
        "@torch.no_grad()\n",
        "def get_freqs(num_batches=25, local_encoder=None):\n",
        "    if local_encoder is None:\n",
        "        local_encoder = encoder\n",
        "    act_freq_scores = torch.zeros(local_encoder.d_hidden, dtype=torch.float32).cuda()\n",
        "    total = 0\n",
        "    for i in tqdm.trange(num_batches):\n",
        "        tokens = all_tokens[torch.randperm(len(all_tokens))[:cfg[\"model_batch_size\"]]]\n",
        "\n",
        "        _, cache = model.run_with_cache(tokens, stop_at_layer=1, names_filter=utils.get_act_name(\"post\", 0))\n",
        "        mlp_acts = cache[utils.get_act_name(\"post\", 0)]\n",
        "        mlp_acts = mlp_acts.reshape(-1, d_mlp)\n",
        "\n",
        "        hidden = local_encoder(mlp_acts)[2]\n",
        "\n",
        "        act_freq_scores += (hidden > 0).sum(0)\n",
        "        total+=hidden.shape[0]\n",
        "    act_freq_scores /= total\n",
        "    num_dead = (act_freq_scores==0).float().mean()\n",
        "    print(\"Num dead\", num_dead)\n",
        "    return act_freq_scores"
      ],
      "metadata": {
        "id": "NeslkII9Bqa3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualise Feature Utils"
      ],
      "metadata": {
        "id": "7ZiWeLhaBrFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from html import escape\n",
        "import colorsys\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "SPACE = \"·\"\n",
        "NEWLINE=\"↩\"\n",
        "TAB = \"→\"\n",
        "\n",
        "def create_html(strings, values, max_value=None, saturation=0.5, allow_different_length=False, return_string=False):\n",
        "    # escape strings to deal with tabs, newlines, etc.\n",
        "    escaped_strings = [escape(s, quote=True) for s in strings]\n",
        "    processed_strings = [\n",
        "        s.replace(\"\\n\", f\"{NEWLINE}<br/>\").replace(\"\\t\", f\"{TAB}&emsp;\").replace(\" \", \"&nbsp;\")\n",
        "        for s in escaped_strings\n",
        "    ]\n",
        "\n",
        "    if isinstance(values, torch.Tensor) and len(values.shape)>1:\n",
        "        values = values.flatten().tolist()\n",
        "\n",
        "    if not allow_different_length:\n",
        "        assert len(processed_strings) == len(values)\n",
        "\n",
        "    # scale values\n",
        "    if max_value is None:\n",
        "        max_value = max(max(values), -min(values))+1e-3\n",
        "    scaled_values = [v / max_value * saturation for v in values]\n",
        "\n",
        "    # create html\n",
        "    html = \"\"\n",
        "    for i, s in enumerate(processed_strings):\n",
        "        if i<len(scaled_values):\n",
        "            v = scaled_values[i]\n",
        "        else:\n",
        "            v = 0\n",
        "        if v < 0:\n",
        "            hue = 0  # hue for red in HSV\n",
        "        else:\n",
        "            hue = 0.66  # hue for blue in HSV\n",
        "        rgb_color = colorsys.hsv_to_rgb(\n",
        "            hue, v, 1\n",
        "        )  # hsv color with hue 0.66 (blue), saturation as v, value 1\n",
        "        hex_color = \"#%02x%02x%02x\" % (\n",
        "            int(rgb_color[0] * 255),\n",
        "            int(rgb_color[1] * 255),\n",
        "            int(rgb_color[2] * 255),\n",
        "        )\n",
        "        html += f'<span style=\"background-color: {hex_color}; border: 1px solid lightgray; font-size: 16px; border-radius: 3px;\">{s}</span>'\n",
        "    if return_string:\n",
        "        return html\n",
        "    else:\n",
        "        display(HTML(html))\n",
        "\n",
        "def basic_feature_vis(text, feature_index, max_val=0):\n",
        "    feature_in = encoder.W_enc[:, feature_index]\n",
        "    feature_bias = encoder.b_enc[feature_index]\n",
        "    _, cache = model.run_with_cache(text, stop_at_layer=1, names_filter=utils.get_act_name(\"post\", 0))\n",
        "    mlp_acts = cache[utils.get_act_name(\"post\", 0)][0]\n",
        "    feature_acts = F.relu((mlp_acts - encoder.b_dec) @ feature_in + feature_bias)\n",
        "    if max_val==0:\n",
        "        max_val = max(1e-7, feature_acts.max().item())\n",
        "        # print(max_val)\n",
        "    # if min_val==0:\n",
        "    #     min_val = min(-1e-7, feature_acts.min().item())\n",
        "    return basic_token_vis_make_str(text, feature_acts, max_val)\n",
        "def basic_token_vis_make_str(strings, values, max_val=None):\n",
        "    if not isinstance(strings, list):\n",
        "        strings = model.to_str_tokens(strings)\n",
        "    values = utils.to_numpy(values)\n",
        "    if max_val is None:\n",
        "        max_val = values.max()\n",
        "    # if min_val is None:\n",
        "    #     min_val = values.min()\n",
        "    header_string = f\"<h4>Max Range <b>{values.max():.4f}</b> Min Range: <b>{values.min():.4f}</b></h4>\"\n",
        "    header_string += f\"<h4>Set Max Range <b>{max_val:.4f}</b></h4>\"\n",
        "    # values[values>0] = values[values>0]/ma|x_val\n",
        "    # values[values<0] = values[values<0]/abs(min_val)\n",
        "    body_string = create_html(strings, values, max_value=max_val, return_string=True)\n",
        "    return header_string + body_string\n",
        "# display(HTML(basic_token_vis_make_str(tokens[0, :10], mlp_acts[0, :10, 7], 0.1)))\n",
        "# # %%\n",
        "# The `with gr.Blocks() as demo:` syntax just creates a variable called demo containing all these components\n",
        "import gradio as gr\n",
        "try:\n",
        "    demos[0].close()\n",
        "except:\n",
        "    pass\n",
        "demos = [None]\n",
        "def make_feature_vis_gradio(feature_id, starting_text=None, batch=None, pos=None):\n",
        "    if starting_text is None:\n",
        "        starting_text = model.to_string(all_tokens[batch, 1:pos+1])\n",
        "    try:\n",
        "        demos[0].close()\n",
        "    except:\n",
        "        pass\n",
        "    with gr.Blocks() as demo:\n",
        "        gr.HTML(value=f\"Hacky Interactive Neuroscope for gelu-1l\")\n",
        "        # The input elements\n",
        "        with gr.Row():\n",
        "            with gr.Column():\n",
        "                text = gr.Textbox(label=\"Text\", value=starting_text)\n",
        "                # Precision=0 makes it an int, otherwise it's a float\n",
        "                # Value sets the initial default value\n",
        "                feature_index = gr.Number(\n",
        "                    label=\"Feature Index\", value=feature_id, precision=0\n",
        "                )\n",
        "                # # If empty, these two map to None\n",
        "                max_val = gr.Number(label=\"Max Value\", value=None)\n",
        "                # min_val = gr.Number(label=\"Min Value\", value=None)\n",
        "                inputs = [text, feature_index, max_val]\n",
        "        with gr.Row():\n",
        "            with gr.Column():\n",
        "                # The output element\n",
        "                out = gr.HTML(label=\"Neuron Acts\", value=basic_feature_vis(starting_text, feature_id))\n",
        "        for inp in inputs:\n",
        "            inp.change(basic_feature_vis, inputs, out)\n",
        "    demo.launch(share=True)\n",
        "    demos[0] = demo"
      ],
      "metadata": {
        "id": "zkOsX7DvBs0u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inspecting Top Logits"
      ],
      "metadata": {
        "id": "xgSWkew4SYWz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SPACE = \"·\"\n",
        "NEWLINE=\"↩\"\n",
        "TAB = \"→\"\n",
        "def process_token(s):\n",
        "    if isinstance(s, torch.Tensor):\n",
        "        s = s.item()\n",
        "    if isinstance(s, np.int64):\n",
        "        s = s.item()\n",
        "    if isinstance(s, int):\n",
        "        s = model.to_string(s)\n",
        "    s = s.replace(\" \", SPACE)\n",
        "    s = s.replace(\"\\n\", NEWLINE+\"\\n\")\n",
        "    s = s.replace(\"\\t\", TAB)\n",
        "    return s\n",
        "\n",
        "def process_tokens(l):\n",
        "    if isinstance(l, str):\n",
        "        l = model.to_str_tokens(l)\n",
        "    elif isinstance(l, torch.Tensor) and len(l.shape)>1:\n",
        "        l = l.squeeze(0)\n",
        "    return [process_token(s) for s in l]\n",
        "\n",
        "def process_tokens_index(l):\n",
        "    if isinstance(l, str):\n",
        "        l = model.to_str_tokens(l)\n",
        "    elif isinstance(l, torch.Tensor) and len(l.shape)>1:\n",
        "        l = l.squeeze(0)\n",
        "    return [f\"{process_token(s)}/{i}\" for i,s in enumerate(l)]\n",
        "\n",
        "def create_vocab_df(logit_vec, make_probs=False, full_vocab=None):\n",
        "    if full_vocab is None:\n",
        "        full_vocab = process_tokens(model.to_str_tokens(torch.arange(model.cfg.d_vocab)))\n",
        "    vocab_df = pd.DataFrame({\"token\": full_vocab, \"logit\": utils.to_numpy(logit_vec)})\n",
        "    if make_probs:\n",
        "        vocab_df[\"log_prob\"] = utils.to_numpy(logit_vec.log_softmax(dim=-1))\n",
        "        vocab_df[\"prob\"] = utils.to_numpy(logit_vec.softmax(dim=-1))\n",
        "    return vocab_df.sort_values(\"logit\", ascending=False)"
      ],
      "metadata": {
        "id": "zzY9rOh-SaUm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Make Token DataFrame"
      ],
      "metadata": {
        "id": "ZqU0S8NcT-Aj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def list_flatten(nested_list):\n",
        "    return [x for y in nested_list for x in y]\n",
        "def make_token_df(tokens, len_prefix=5, len_suffix=1):\n",
        "    str_tokens = [process_tokens(model.to_str_tokens(t)) for t in tokens]\n",
        "    unique_token = [[f\"{s}/{i}\" for i, s in enumerate(str_tok)] for str_tok in str_tokens]\n",
        "\n",
        "    context = []\n",
        "    batch = []\n",
        "    pos = []\n",
        "    label = []\n",
        "    for b in range(tokens.shape[0]):\n",
        "        # context.append([])\n",
        "        # batch.append([])\n",
        "        # pos.append([])\n",
        "        # label.append([])\n",
        "        for p in range(tokens.shape[1]):\n",
        "            prefix = \"\".join(str_tokens[b][max(0, p-len_prefix):p])\n",
        "            if p==tokens.shape[1]-1:\n",
        "                suffix = \"\"\n",
        "            else:\n",
        "                suffix = \"\".join(str_tokens[b][p+1:min(tokens.shape[1]-1, p+1+len_suffix)])\n",
        "            current = str_tokens[b][p]\n",
        "            context.append(f\"{prefix}|{current}|{suffix}\")\n",
        "            batch.append(b)\n",
        "            pos.append(p)\n",
        "            label.append(f\"{b}/{p}\")\n",
        "    # print(len(batch), len(pos), len(context), len(label))\n",
        "    return pd.DataFrame(dict(\n",
        "        str_tokens=list_flatten(str_tokens),\n",
        "        unique_token=list_flatten(unique_token),\n",
        "        context=context,\n",
        "        batch=batch,\n",
        "        pos=pos,\n",
        "        label=label,\n",
        "    ))"
      ],
      "metadata": {
        "id": "ISRtTzfJT_x2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the Model"
      ],
      "metadata": {
        "id": "riilpvs-CgoQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = HookedTransformer.from_pretrained(\"gelu-1l\").to(DTYPES[cfg[\"enc_dtype\"]])\n",
        "n_layers = model.cfg.n_layers\n",
        "d_model = model.cfg.d_model\n",
        "n_heads = model.cfg.n_heads\n",
        "d_head = model.cfg.d_head\n",
        "d_mlp = model.cfg.d_mlp\n",
        "d_vocab = model.cfg.d_vocab"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2LkEWyACgP8",
        "outputId": "d4863a36-6455-4117-beb9-008f43ae3e21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded pretrained model gelu-1l into HookedTransformer\n",
            "Changing model dtype to torch.float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Data"
      ],
      "metadata": {
        "id": "N3D_0qDmBY5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_dataset(\"NeelNanda/c4-code-20k\", split=\"train\")\n",
        "tokenized_data = utils.tokenize_and_concatenate(data, model.tokenizer, max_length=128)\n",
        "tokenized_data = tokenized_data.shuffle(42)\n",
        "all_tokens = tokenized_data[\"tokens\"]"
      ],
      "metadata": {
        "id": "2TiJcyRWF3wn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis"
      ],
      "metadata": {
        "id": "MYrIYDEfBtbL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the Autoencoder\n",
        "\n",
        "There are two runs on separate random seeds, along with a bunch of intermediate checkpoints"
      ],
      "metadata": {
        "id": "3JfuCFyCFjGG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auto_encoder_run = \"run1\" # @param [\"run1\", \"run2\"]\n",
        "encoder = AutoEncoder.load_from_hf(auto_encoder_run)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yyuf42IGFku3",
        "outputId": "939b989d-87c5-40c3-e07a-906421727f00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'batch_size': 4096,\n",
            " 'beta1': 0.9,\n",
            " 'beta2': 0.99,\n",
            " 'buffer_batches': 12288,\n",
            " 'buffer_mult': 384,\n",
            " 'buffer_size': 1572864,\n",
            " 'd_mlp': 2048,\n",
            " 'dict_mult': 8,\n",
            " 'enc_dtype': 'fp32',\n",
            " 'l1_coeff': 0.0003,\n",
            " 'lr': 0.0001,\n",
            " 'model_batch_size': 512,\n",
            " 'num_tokens': 2000000000,\n",
            " 'seed': 52,\n",
            " 'seq_len': 128}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using the Autoencoder"
      ],
      "metadata": {
        "id": "hmgJ6u2hBvLu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We run the model and replace the MLP activations with those reconstructed from the autoencoder, and get 91% loss recovered"
      ],
      "metadata": {
        "id": "SP9qP-SiM0Sk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_ = get_recons_loss(num_batches=5, local_encoder=encoder)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWlP-98EBygL",
        "outputId": "9cf2bfd6-d835-4368-af23-dbb3dd7bb72a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss: 5.1872, recons_loss: 5.5907, zero_abl_loss: 9.9854\n",
            "Reconstruction Score: 91.59%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rare Features Are All The Same"
      ],
      "metadata": {
        "id": "x9Iok8SZB4fg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For each feature we can get the frequency at which it's non-zero (per token, averaged across a bunch of batches), and plot a histogram"
      ],
      "metadata": {
        "id": "skvpaPClM7Nb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "freqs = get_freqs(num_batches = 50, local_encoder = encoder)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "8876b02d18874eca93ee2848935b03d0",
            "9e201992276c466b9adb947fae1dfe3f",
            "ac53f3b61b9c4559886ed9e9ca375821",
            "40cc5c9d586443aa9796e4659461cc69",
            "7b41033bb79c40409217456d42830afe",
            "60051a06fb6d4b8fb3361b38ca2fabc7",
            "37d5b1e0e4c04b84aef2918beab06c6a",
            "3a838f4f61d04ae4b2727bc070daccf1",
            "38b2e75f618a4cccaa45f322101dc3e2",
            "cb7437297d6c4987a38728dcd8cc650f",
            "437ce801cada49a0884830cdf079984a"
          ]
        },
        "id": "5SECGnWLNAlg",
        "outputId": "edf3a6a9-35b5-4e31-b2dc-abf738a63967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8876b02d18874eca93ee2848935b03d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num dead tensor(0., device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add 1e-6.5 so that dead features show up as log_freq -6.5\n",
        "log_freq = (freqs + 10**-6.5).log10()\n",
        "px.histogram(utils.to_numpy(log_freq), title=\"Log Frequency of Features\", histnorm='percent')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "_Zn7g7WgNTQD",
        "outputId": "4fc62139-2398-4648-c127-19965fc152a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "We see that it's clearly bimodal! Let's define rare features as those with freq < 1e-4, and look at the cosine sim of each feature with the average rare feature - we see that almost all rare features correspond to this feature!"
      ],
      "metadata": {
        "id": "c612eWjYNpdv"
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      "source": [
        "is_rare = freqs < 1e-4\n",
        "rare_enc = encoder.W_enc[:, is_rare]\n",
        "rare_mean = rare_enc.mean(-1)\n",
        "px.histogram(utils.to_numpy(rare_mean @ encoder.W_enc / rare_mean.norm() / encoder.W_enc.norm(dim=0)), title=\"Cosine Sim with Ave Rare Feature\", color=utils.to_numpy(is_rare), labels={\"color\": \"is_rare\", \"count\": \"percent\", \"value\": \"cosine_sim\"}, marginal=\"box\", histnorm=\"percent\", barmode='overlay')"
      ],
      "metadata": {
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          "height": 542
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        "id": "GbM9UZsJN0Uy",
        "outputId": "73b0c800-30b2-48c7-ce0f-77f3c5244685"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "## Interpreting A Feature"
      ],
      "metadata": {
        "id": "eZCqUIB9Bzx_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's go and investigate a non rare feature, feature 7"
      ],
      "metadata": {
        "id": "3m_u8-zXRW2J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_id = 7 # @param {type:\"number\"}\n",
        "batch_size = 128 # @param {type:\"number\"}\n",
        "\n",
        "print(f\"Feature freq: {freqs[7].item():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HrsTqVIUKC0",
        "outputId": "e5333995-dae8-4188-f72d-907ce29b3d2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature freq: 0.0026\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's run the model on some text and then use the autoencoder to process the MLP activations"
      ],
      "metadata": {
        "id": "AYoGLyl2VpA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = all_tokens[:batch_size]\n",
        "_, cache = model.run_with_cache(tokens, stop_at_layer=1, names_filter=utils.get_act_name(\"post\", 0))\n",
        "mlp_acts = cache[utils.get_act_name(\"post\", 0)]\n",
        "mlp_acts_flattened = mlp_acts.reshape(-1, cfg[\"d_mlp\"])\n",
        "loss, x_reconstruct, hidden_acts, l2_loss, l1_loss = encoder(mlp_acts_flattened)\n",
        "# This is equivalent to:\n",
        "# hidden_acts = F.relu((mlp_acts_flattened - encoder.b_dec) @ encoder.W_enc + encoder.b_enc)\n",
        "print(\"hidden_acts.shape\", hidden_acts.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBKY9P_PULJs",
        "outputId": "ae219a6a-0eb6-481e-8c0b-b330ac029406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hidden_acts.shape torch.Size([16384, 16384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now sort and display the top tokens, and we see that this feature activates on text like \" and I\" (ditto for other connectives and pronouns)! It seems interpretable!\n",
        "\n",
        "**Aside:** Note on how to read the context column:\n",
        "\n",
        "A line like \"·himself·as·democratic·socialist·and|·he|·favors\" means that the preceding 5 tokens are \" himself as democratic socialist and\", the current token is \" he\" and the next token is \" favors\".  · are spaces, ↩ is a newline.\n",
        "\n",
        "This gets a bit confusing for this feature, since the pipe separators look a lot like a capital I\n"
      ],
      "metadata": {
        "id": "D0-vxYAIVtxv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "token_df = make_token_df(tokens)\n",
        "token_df[\"feature\"] = utils.to_numpy(hidden_acts[:, feature_id])\n",
        "token_df.sort_values(\"feature\", ascending=False).head(20).style.background_gradient(\"coolwarm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "id": "6LQet6vaU60H",
        "outputId": "db109e09-efc7-4fb5-9808-6f6e976c8cd8"
      },
      "execution_count": null,
      "outputs": [
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              "<table id=\"T_b2254\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b2254_level0_col0\" class=\"col_heading level0 col0\" >str_tokens</th>\n",
              "      <th id=\"T_b2254_level0_col1\" class=\"col_heading level0 col1\" >unique_token</th>\n",
              "      <th id=\"T_b2254_level0_col2\" class=\"col_heading level0 col2\" >context</th>\n",
              "      <th id=\"T_b2254_level0_col3\" class=\"col_heading level0 col3\" >batch</th>\n",
              "      <th id=\"T_b2254_level0_col4\" class=\"col_heading level0 col4\" >pos</th>\n",
              "      <th id=\"T_b2254_level0_col5\" class=\"col_heading level0 col5\" >label</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row0\" class=\"row_heading level0 row0\" >5998</th>\n",
              "      <td id=\"T_b2254_row0_col0\" class=\"data row0 col0\" >·I</td>\n",
              "      <td id=\"T_b2254_row0_col1\" class=\"data row0 col1\" >·I/110</td>\n",
              "      <td id=\"T_b2254_row0_col2\" class=\"data row0 col2\" >Your·so·popular…·and|·I|·like</td>\n",
              "      <td id=\"T_b2254_row0_col3\" class=\"data row0 col3\" >46</td>\n",
              "      <td id=\"T_b2254_row0_col4\" class=\"data row0 col4\" >110</td>\n",
              "      <td id=\"T_b2254_row0_col5\" class=\"data row0 col5\" >46/110</td>\n",
              "      <td id=\"T_b2254_row0_col6\" class=\"data row0 col6\" >1.669495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row1\" class=\"row_heading level0 row1\" >6240</th>\n",
              "      <td id=\"T_b2254_row1_col0\" class=\"data row1 col0\" >·he</td>\n",
              "      <td id=\"T_b2254_row1_col1\" class=\"data row1 col1\" >·he/96</td>\n",
              "      <td id=\"T_b2254_row1_col2\" class=\"data row1 col2\" >·himself·as·democratic·socialist·and|·he|·favors</td>\n",
              "      <td id=\"T_b2254_row1_col3\" class=\"data row1 col3\" >48</td>\n",
              "      <td id=\"T_b2254_row1_col4\" class=\"data row1 col4\" >96</td>\n",
              "      <td id=\"T_b2254_row1_col5\" class=\"data row1 col5\" >48/96</td>\n",
              "      <td id=\"T_b2254_row1_col6\" class=\"data row1 col6\" >1.301398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row2\" class=\"row_heading level0 row2\" >3710</th>\n",
              "      <td id=\"T_b2254_row2_col0\" class=\"data row2 col0\" >·he</td>\n",
              "      <td id=\"T_b2254_row2_col1\" class=\"data row2 col1\" >·he/126</td>\n",
              "      <td id=\"T_b2254_row2_col2\" class=\"data row2 col2\" >·a·months’·notice·and|·he|</td>\n",
              "      <td id=\"T_b2254_row2_col3\" class=\"data row2 col3\" >28</td>\n",
              "      <td id=\"T_b2254_row2_col4\" class=\"data row2 col4\" >126</td>\n",
              "      <td id=\"T_b2254_row2_col5\" class=\"data row2 col5\" >28/126</td>\n",
              "      <td id=\"T_b2254_row2_col6\" class=\"data row2 col6\" >1.235980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row3\" class=\"row_heading level0 row3\" >6444</th>\n",
              "      <td id=\"T_b2254_row3_col0\" class=\"data row3 col0\" >·she</td>\n",
              "      <td id=\"T_b2254_row3_col1\" class=\"data row3 col1\" >·she/44</td>\n",
              "      <td id=\"T_b2254_row3_col2\" class=\"data row3 col2\" >·1965·and|·she|·registered</td>\n",
              "      <td id=\"T_b2254_row3_col3\" class=\"data row3 col3\" >50</td>\n",
              "      <td id=\"T_b2254_row3_col4\" class=\"data row3 col4\" >44</td>\n",
              "      <td id=\"T_b2254_row3_col5\" class=\"data row3 col5\" >50/44</td>\n",
              "      <td id=\"T_b2254_row3_col6\" class=\"data row3 col6\" >1.006057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row4\" class=\"row_heading level0 row4\" >6502</th>\n",
              "      <td id=\"T_b2254_row4_col0\" class=\"data row4 col0\" >·she</td>\n",
              "      <td id=\"T_b2254_row4_col1\" class=\"data row4 col1\" >·she/102</td>\n",
              "      <td id=\"T_b2254_row4_col2\" class=\"data row4 col2\" >·1941·and|·she|·registered</td>\n",
              "      <td id=\"T_b2254_row4_col3\" class=\"data row4 col3\" >50</td>\n",
              "      <td id=\"T_b2254_row4_col4\" class=\"data row4 col4\" >102</td>\n",
              "      <td id=\"T_b2254_row4_col5\" class=\"data row4 col5\" >50/102</td>\n",
              "      <td id=\"T_b2254_row4_col6\" class=\"data row4 col6\" >0.873914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row5\" class=\"row_heading level0 row5\" >8254</th>\n",
              "      <td id=\"T_b2254_row5_col0\" class=\"data row5 col0\" >·i</td>\n",
              "      <td id=\"T_b2254_row5_col1\" class=\"data row5 col1\" >·i/62</td>\n",
              "      <td id=\"T_b2254_row5_col2\" class=\"data row5 col2\" >0].lower()·and|·i|[</td>\n",
              "      <td id=\"T_b2254_row5_col3\" class=\"data row5 col3\" >64</td>\n",
              "      <td id=\"T_b2254_row5_col4\" class=\"data row5 col4\" >62</td>\n",
              "      <td id=\"T_b2254_row5_col5\" class=\"data row5 col5\" >64/62</td>\n",
              "      <td id=\"T_b2254_row5_col6\" class=\"data row5 col6\" >0.624549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row6\" class=\"row_heading level0 row6\" >2324</th>\n",
              "      <td id=\"T_b2254_row6_col0\" class=\"data row6 col0\" >·this</td>\n",
              "      <td id=\"T_b2254_row6_col1\" class=\"data row6 col1\" >·this/20</td>\n",
              "      <td id=\"T_b2254_row6_col2\" class=\"data row6 col2\" >·little·bit·challenging,·and|·this|·is</td>\n",
              "      <td id=\"T_b2254_row6_col3\" class=\"data row6 col3\" >18</td>\n",
              "      <td id=\"T_b2254_row6_col4\" class=\"data row6 col4\" >20</td>\n",
              "      <td id=\"T_b2254_row6_col5\" class=\"data row6 col5\" >18/20</td>\n",
              "      <td id=\"T_b2254_row6_col6\" class=\"data row6 col6\" >0.554155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row7\" class=\"row_heading level0 row7\" >8309</th>\n",
              "      <td id=\"T_b2254_row7_col0\" class=\"data row7 col0\" >·i</td>\n",
              "      <td id=\"T_b2254_row7_col1\" class=\"data row7 col1\" >·i/117</td>\n",
              "      <td id=\"T_b2254_row7_col2\" class=\"data row7 col2\" >j[3]·or|·i|[</td>\n",
              "      <td id=\"T_b2254_row7_col3\" class=\"data row7 col3\" >64</td>\n",
              "      <td id=\"T_b2254_row7_col4\" class=\"data row7 col4\" >117</td>\n",
              "      <td id=\"T_b2254_row7_col5\" class=\"data row7 col5\" >64/117</td>\n",
              "      <td id=\"T_b2254_row7_col6\" class=\"data row7 col6\" >0.532984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row8\" class=\"row_heading level0 row8\" >8623</th>\n",
              "      <td id=\"T_b2254_row8_col0\" class=\"data row8 col0\" >·it</td>\n",
              "      <td id=\"T_b2254_row8_col1\" class=\"data row8 col1\" >·it/47</td>\n",
              "      <td id=\"T_b2254_row8_col2\" class=\"data row8 col2\" >·with·so·many·requests·and|·it|·is</td>\n",
              "      <td id=\"T_b2254_row8_col3\" class=\"data row8 col3\" >67</td>\n",
              "      <td id=\"T_b2254_row8_col4\" class=\"data row8 col4\" >47</td>\n",
              "      <td id=\"T_b2254_row8_col5\" class=\"data row8 col5\" >67/47</td>\n",
              "      <td id=\"T_b2254_row8_col6\" class=\"data row8 col6\" >0.512638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row9\" class=\"row_heading level0 row9\" >8299</th>\n",
              "      <td id=\"T_b2254_row9_col0\" class=\"data row9 col0\" >·i</td>\n",
              "      <td id=\"T_b2254_row9_col1\" class=\"data row9 col1\" >·i/107</td>\n",
              "      <td id=\"T_b2254_row9_col2\" class=\"data row9 col2\" >j[2]·or|·i|[</td>\n",
              "      <td id=\"T_b2254_row9_col3\" class=\"data row9 col3\" >64</td>\n",
              "      <td id=\"T_b2254_row9_col4\" class=\"data row9 col4\" >107</td>\n",
              "      <td id=\"T_b2254_row9_col5\" class=\"data row9 col5\" >64/107</td>\n",
              "      <td id=\"T_b2254_row9_col6\" class=\"data row9 col6\" >0.492926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row10\" class=\"row_heading level0 row10\" >8153</th>\n",
              "      <td id=\"T_b2254_row10_col0\" class=\"data row10 col0\" >·they</td>\n",
              "      <td id=\"T_b2254_row10_col1\" class=\"data row10 col1\" >·they/89</td>\n",
              "      <td id=\"T_b2254_row10_col2\" class=\"data row10 col2\" >·recruit·those·buyers,·but|·they|·also</td>\n",
              "      <td id=\"T_b2254_row10_col3\" class=\"data row10 col3\" >63</td>\n",
              "      <td id=\"T_b2254_row10_col4\" class=\"data row10 col4\" >89</td>\n",
              "      <td id=\"T_b2254_row10_col5\" class=\"data row10 col5\" >63/89</td>\n",
              "      <td id=\"T_b2254_row10_col6\" class=\"data row10 col6\" >0.487142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row11\" class=\"row_heading level0 row11\" >7325</th>\n",
              "      <td id=\"T_b2254_row11_col0\" class=\"data row11 col0\" >·that</td>\n",
              "      <td id=\"T_b2254_row11_col1\" class=\"data row11 col1\" >·that/29</td>\n",
              "      <td id=\"T_b2254_row11_col2\" class=\"data row11 col2\" >↩\n",
              "········#·downloaded·and|·that|·the</td>\n",
              "      <td id=\"T_b2254_row11_col3\" class=\"data row11 col3\" >57</td>\n",
              "      <td id=\"T_b2254_row11_col4\" class=\"data row11 col4\" >29</td>\n",
              "      <td id=\"T_b2254_row11_col5\" class=\"data row11 col5\" >57/29</td>\n",
              "      <td id=\"T_b2254_row11_col6\" class=\"data row11 col6\" >0.294558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row12\" class=\"row_heading level0 row12\" >1778</th>\n",
              "      <td id=\"T_b2254_row12_col0\" class=\"data row12 col0\" >lic</td>\n",
              "      <td id=\"T_b2254_row12_col1\" class=\"data row12 col1\" >lic/114</td>\n",
              "      <td id=\"T_b2254_row12_col2\" class=\"data row12 col2\" >#License-open-|lic|>`</td>\n",
              "      <td id=\"T_b2254_row12_col3\" class=\"data row12 col3\" >13</td>\n",
              "      <td id=\"T_b2254_row12_col4\" class=\"data row12 col4\" >114</td>\n",
              "      <td id=\"T_b2254_row12_col5\" class=\"data row12 col5\" >13/114</td>\n",
              "      <td id=\"T_b2254_row12_col6\" class=\"data row12 col6\" >0.285223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row13\" class=\"row_heading level0 row13\" >7068</th>\n",
              "      <td id=\"T_b2254_row13_col0\" class=\"data row13 col0\" >·you</td>\n",
              "      <td id=\"T_b2254_row13_col1\" class=\"data row13 col1\" >·you/28</td>\n",
              "      <td id=\"T_b2254_row13_col2\" class=\"data row13 col2\" >·bit·more·adventure?·Maybe|·you|’</td>\n",
              "      <td id=\"T_b2254_row13_col3\" class=\"data row13 col3\" >55</td>\n",
              "      <td id=\"T_b2254_row13_col4\" class=\"data row13 col4\" >28</td>\n",
              "      <td id=\"T_b2254_row13_col5\" class=\"data row13 col5\" >55/28</td>\n",
              "      <td id=\"T_b2254_row13_col6\" class=\"data row13 col6\" >0.274137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row14\" class=\"row_heading level0 row14\" >3593</th>\n",
              "      <td id=\"T_b2254_row14_col0\" class=\"data row14 col0\" >·Michael</td>\n",
              "      <td id=\"T_b2254_row14_col1\" class=\"data row14 col1\" >·Michael/9</td>\n",
              "      <td id=\"T_b2254_row14_col2\" class=\"data row14 col2\" >·fellow·winners,·Susan·and|·Michael|·Cross</td>\n",
              "      <td id=\"T_b2254_row14_col3\" class=\"data row14 col3\" >28</td>\n",
              "      <td id=\"T_b2254_row14_col4\" class=\"data row14 col4\" >9</td>\n",
              "      <td id=\"T_b2254_row14_col5\" class=\"data row14 col5\" >28/9</td>\n",
              "      <td id=\"T_b2254_row14_col6\" class=\"data row14 col6\" >0.273158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2254_level0_row15\" class=\"row_heading level0 row15\" >1775</th>\n",
              "      <td id=\"T_b2254_row15_col0\" class=\"data row15 col0\" >-</td>\n",
              "      <td id=\"T_b2254_row15_col1\" class=\"data row15 col1\" >-/111</td>\n",
              "      <td id=\"T_b2254_row15_col2\" class=\"data row15 col2\" >6/License#License|-|open</td>\n",
              "      <td id=\"T_b2254_row15_col3\" class=\"data row15 col3\" >13</td>\n",
              "      <td id=\"T_b2254_row15_col4\" class=\"data row15 col4\" >111</td>\n",
              "      <td id=\"T_b2254_row15_col5\" class=\"data row15 col5\" >13/111</td>\n",
              "      <td id=\"T_b2254_row15_col6\" class=\"data row15 col6\" >0.253741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td id=\"T_b2254_row16_col0\" class=\"data row16 col0\" >·I</td>\n",
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              "      <td id=\"T_b2254_row16_col2\" class=\"data row16 col2\" >·a·stranger·or·two,|·I|'ve</td>\n",
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              "      <td id=\"T_b2254_row16_col5\" class=\"data row16 col5\" >115/90</td>\n",
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              "      <td id=\"T_b2254_row18_col2\" class=\"data row18 col2\" >·sister's·wine·glass·while|·she|·snapped</td>\n",
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              "      <td id=\"T_b2254_row18_col5\" class=\"data row18 col5\" >85/118</td>\n",
              "      <td id=\"T_b2254_row18_col6\" class=\"data row18 col6\" >0.207402</td>\n",
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              "      <td id=\"T_b2254_row19_col2\" class=\"data row19 col2\" >,·a·sprawling·and|·se|rene</td>\n",
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              "      <td id=\"T_b2254_row19_col4\" class=\"data row19 col4\" >58</td>\n",
              "      <td id=\"T_b2254_row19_col5\" class=\"data row19 col5\" >85/58</td>\n",
              "      <td id=\"T_b2254_row19_col6\" class=\"data row19 col6\" >0.198278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's easy to misread evidence like the above, so it's useful to take some text and edit it and see how this changes the model's activations. Here's a hacky interactive tool to play around with some text."
      ],
      "metadata": {
        "id": "H_SsYbIhWm43"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "starting_text = \"Hero and I will head to Samantha and Mark's, then he and she will. Then I or you\" # @param {type:\"string\"}\n",
        "make_feature_vis_gradio(feature_id, starting_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "HbgjmwTnQman",
        "outputId": "3023b510-8ef1-4eb4-e7ce-f224c836f49d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://87a07b6372fc0e6128.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://87a07b6372fc0e6128.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A final piece of evidence: This is a one layer model, so the neurons can only matter by directly impacting the final logits! We can directly look at how the decoder weights for this feature affect the logits, and see that it boosts `'ll`! This checks out, I and he'll etc is a common construction."
      ],
      "metadata": {
        "id": "MjquxRjkQlpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logit_effect = encoder.W_dec[feature_id] @ model.W_out[0] @ model.W_U\n",
        "create_vocab_df(logit_effect).head(20).style.background_gradient(\"coolwarm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "JUtGmqx8Si-G",
        "outputId": "d5c96886-0fed-4aca-a079-d44af4ef40d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7b40d4afb010>"
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              "    <tr>\n",
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              "      <td id=\"T_be3c1_row15_col0\" class=\"data row15 col0\" >eled</td>\n",
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              "    <tr>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LBb6AwBLSoQU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}