DecompX_Colab_Demo.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Install requirements / Clone repository"
      ],
      "metadata": {
        "id": "01oAbghNO_5h"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m4pCvymKOLkv"
      },
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      "source": [
        "! git clone \"https://github.com/mohsenfayyaz/DecompX\"\n",
        "! pip install datasets==1.18.3\n",
        "! pip install transformers==4.18.0"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Config (Change model and sentence here)"
      ],
      "metadata": {
        "id": "46T7VHabRQeS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm.auto import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "from IPython.display import display, HTML\n",
        "from transformers import AutoTokenizer\n",
        "from DecompX.src.decompx_utils import DecompXConfig\n",
        "from DecompX.src.modeling_bert import BertForSequenceClassification\n",
        "from DecompX.src.modeling_roberta import RobertaForSequenceClassification\n",
        "\n",
        "MODEL = \"WillHeld/roberta-base-sst2\"  # Only BERT or RoBERTa\n",
        "SENTENCES = [\n",
        "    \"A deep and meaningful film.\", \n",
        "    \"a good piece of work more often than not.\",\n",
        "]\n",
        "CONFIGS = {\n",
        "    \"DecompX\":\n",
        "        DecompXConfig(\n",
        "            include_biases=True,\n",
        "            bias_decomp_type=\"absdot\",\n",
        "            include_LN1=True,\n",
        "            include_FFN=True,\n",
        "            FFN_approx_type=\"GeLU_ZO\",\n",
        "            include_LN2=True,\n",
        "            aggregation=\"vector\",\n",
        "            include_classifier_w_pooler=True,\n",
        "            tanh_approx_type=\"ZO\",\n",
        "            output_all_layers=True,\n",
        "            output_attention=None,\n",
        "            output_res1=None,\n",
        "            output_LN1=None,\n",
        "            output_FFN=None,\n",
        "            output_res2=None,\n",
        "            output_encoder=None,\n",
        "            output_aggregated=\"norm\",\n",
        "            output_pooler=\"norm\",\n",
        "            output_classifier=True,\n",
        "        ),\n",
        "}"
      ],
      "metadata": {
        "id": "Uop2VstiQm02"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load corresponding model/tokenizer"
      ],
      "metadata": {
        "id": "NhO3vaddcZ-K"
      }
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      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
        "tokenized_sentence = tokenizer(SENTENCES, return_tensors=\"pt\", padding=True)\n",
        "batch_lengths = tokenized_sentence['attention_mask'].sum(dim=-1)\n",
        "if \"roberta\" in MODEL:\n",
        "    model = RobertaForSequenceClassification.from_pretrained(MODEL)\n",
        "elif \"bert\" in MODEL:\n",
        "    model = BertForSequenceClassification.from_pretrained(MODEL)\n",
        "else:\n",
        "    raise Exception(f\"Not implented model: {MODEL}\")"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compute DecompX"
      ],
      "metadata": {
        "id": "zZOpodv_cbUe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# logits ~ (8, 2)\n",
        "# hidden_states ~ (13, 8, 55, 768)\n",
        "# decompx_last_layer_outputs.aggregated ~ (1, 8, 55, 55)\n",
        "# decompx_last_layer_outputs.pooler ~ (1, 8, 55)\n",
        "# decompx_last_layer_outputs.classifier ~ (8, 55, 2)\n",
        "# decompx_all_layers_outputs.aggregated ~ (12, 8, 55, 55)\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    logits, hidden_states, decompx_last_layer_outputs, decompx_all_layers_outputs = model(\n",
        "        **tokenized_sentence, \n",
        "        output_attentions=False, \n",
        "        return_dict=False, \n",
        "        output_hidden_states=True, \n",
        "        decompx_config=CONFIGS[\"DecompX\"]\n",
        "    )\n",
        "decompx_outputs = {\n",
        "    \"tokens\": [tokenizer.convert_ids_to_tokens(tokenized_sentence[\"input_ids\"][i][:batch_lengths[i]]) for i in range(len(SENTENCES))],\n",
        "    \"logits\": logits.cpu().detach().numpy().tolist(),  # (batch, classes)\n",
        "    \"cls\": hidden_states[-1][:, 0, :].cpu().detach().numpy().tolist()# Last layer & only CLS -> (batch, emb_dim)\n",
        "}\n",
        "\n",
        "### decompx_last_layer_outputs.aggregated ~ (1, 8, 55, 55) ###\n",
        "importance = np.array([g.squeeze().cpu().detach().numpy() for g in decompx_last_layer_outputs.aggregated]).squeeze()  # (batch, seq_len, seq_len)\n",
        "importance = [importance[j][:batch_lengths[j],:batch_lengths[j]] for j in range(len(importance))]\n",
        "decompx_outputs[\"importance_last_layer_aggregated\"] = importance\n",
        "\n",
        "### decompx_last_layer_outputs.pooler ~ (1, 8, 55) ###\n",
        "importance = np.array([g.squeeze().cpu().detach().numpy() for g in decompx_last_layer_outputs.pooler]).squeeze()  # (batch, seq_len)\n",
        "importance = [importance[j][:batch_lengths[j]] for j in range(len(importance))]\n",
        "decompx_outputs[\"importance_last_layer_pooler\"] = importance\n",
        "\n",
        "### decompx_last_layer_outputs.classifier ~ (8, 55, 2) ###\n",
        "importance = np.array([g.squeeze().cpu().detach().numpy() for g in decompx_last_layer_outputs.classifier]).squeeze()  # (batch, seq_len, classes)\n",
        "importance = [importance[j][:batch_lengths[j], :] for j in range(len(importance))]\n",
        "decompx_outputs[\"importance_last_layer_classifier\"] = importance\n",
        "\n",
        "### decompx_all_layers_outputs.aggregated ~ (12, 8, 55, 55) ###\n",
        "importance = np.array([g.squeeze().cpu().detach().numpy() for g in decompx_all_layers_outputs.aggregated])  # (layers, batch, seq_len, seq_len)\n",
        "importance = np.einsum('lbij->blij', importance)  # (batch, layers, seq_len, seq_len)\n",
        "importance = [importance[j][:, :batch_lengths[j], :batch_lengths[j]] for j in range(len(importance))]\n",
        "decompx_outputs[\"importance_all_layers_aggregated\"] = importance\n",
        "\n",
        "decompx_outputs_df = pd.DataFrame(decompx_outputs)\n",
        "decompx_outputs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "JVg5RXtzS1Vo",
        "outputId": "e486300d-b51b-4798-ca64-654944928a96"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              tokens  \\\n",
              "0  [<s>, A, Ġdeep, Ġand, Ġmeaningful, Ġfilm, ., <...   \n",
              "1  [<s>, a, Ġgood, Ġpiece, Ġof, Ġwork, Ġmore, Ġof...   \n",
              "\n",
              "                                     logits  \\\n",
              "0  [-2.926377773284912, 2.6316184997558594]   \n",
              "1  [-2.792595863342285, 2.5693302154541016]   \n",
              "\n",
              "                                                 cls  \\\n",
              "0  [0.4278368651866913, -0.44068610668182373, -0....   \n",
              "1  [0.3009447455406189, -0.26642340421676636, -0....   \n",
              "\n",
              "                    importance_last_layer_aggregated  \\\n",
              "0  [[9.912518, 15.123899, 9.198907, 7.3025403, 29...   \n",
              "1  [[10.204507, 3.1018076, 45.00585, 12.329525, 1...   \n",
              "\n",
              "                        importance_last_layer_pooler  \\\n",
              "0  [5.2680273, 8.650479, 5.2628765, 4.173719, 17....   \n",
              "1  [5.43212, 1.8558518, 26.258024, 7.356039, 0.78...   \n",
              "\n",
              "                    importance_last_layer_classifier  \\\n",
              "0  [[-0.122595854, 0.09062181], [1.8409867, -1.55...   \n",
              "1  [[-0.42901182, 0.29831558], [-0.37757882, 0.31...   \n",
              "\n",
              "                    importance_all_layers_aggregated  \n",
              "0  [[[19.39683, 1.1414818, 0.7298583, 0.71953577,...  \n",
              "1  [[[19.514332, 0.64310545, 0.39037097, 0.218365...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf8450d8-48de-4e5c-9ec6-39cd3455df16\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tokens</th>\n",
              "      <th>logits</th>\n",
              "      <th>cls</th>\n",
              "      <th>importance_last_layer_aggregated</th>\n",
              "      <th>importance_last_layer_pooler</th>\n",
              "      <th>importance_last_layer_classifier</th>\n",
              "      <th>importance_all_layers_aggregated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[&lt;s&gt;, A, Ġdeep, Ġand, Ġmeaningful, Ġfilm, ., &lt;...</td>\n",
              "      <td>[-2.926377773284912, 2.6316184997558594]</td>\n",
              "      <td>[0.4278368651866913, -0.44068610668182373, -0....</td>\n",
              "      <td>[[9.912518, 15.123899, 9.198907, 7.3025403, 29...</td>\n",
              "      <td>[5.2680273, 8.650479, 5.2628765, 4.173719, 17....</td>\n",
              "      <td>[[-0.122595854, 0.09062181], [1.8409867, -1.55...</td>\n",
              "      <td>[[[19.39683, 1.1414818, 0.7298583, 0.71953577,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[&lt;s&gt;, a, Ġgood, Ġpiece, Ġof, Ġwork, Ġmore, Ġof...</td>\n",
              "      <td>[-2.792595863342285, 2.5693302154541016]</td>\n",
              "      <td>[0.3009447455406189, -0.26642340421676636, -0....</td>\n",
              "      <td>[[10.204507, 3.1018076, 45.00585, 12.329525, 1...</td>\n",
              "      <td>[5.43212, 1.8558518, 26.258024, 7.356039, 0.78...</td>\n",
              "      <td>[[-0.42901182, 0.29831558], [-0.37757882, 0.31...</td>\n",
              "      <td>[[[19.514332, 0.64310545, 0.39037097, 0.218365...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf8450d8-48de-4e5c-9ec6-39cd3455df16')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bf8450d8-48de-4e5c-9ec6-39cd3455df16 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bf8450d8-48de-4e5c-9ec6-39cd3455df16');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization"
      ],
      "metadata": {
        "id": "lyOIqbryc9c-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def print_importance(importance, tokenized_text, discrete=False, prefix=\"\", no_cls_sep=False):\n",
        "    \"\"\"\n",
        "    importance: (sent_len)\n",
        "    \"\"\"\n",
        "    if no_cls_sep:\n",
        "        importance = importance[1:-1]\n",
        "        tokenized_text = tokenized_text[1:-1]\n",
        "    importance = importance / np.abs(importance).max() / 1.5  # Normalize\n",
        "    if discrete:\n",
        "        importance = np.argsort(np.argsort(importance)) / len(importance) / 1.6\n",
        "    \n",
        "    html = \"<pre style='color:black; padding: 3px;'>\"+prefix\n",
        "    for i in range(len(tokenized_text)):\n",
        "        if importance[i] >= 0:\n",
        "            rgba = matplotlib.colormaps.get_cmap('Greens')(importance[i])   # Wistia\n",
        "        else:\n",
        "            rgba = matplotlib.colormaps.get_cmap('Reds')(np.abs(importance[i]))   # Wistia\n",
        "        text_color = \"color: rgba(255, 255, 255, 1.0); \" if np.abs(importance[i]) > 0.9 else \"\"\n",
        "        color = f\"background-color: rgba({rgba[0]*255}, {rgba[1]*255}, {rgba[2]*255}, {rgba[3]}); \" + text_color\n",
        "        html += (f\"<span style='\"\n",
        "                 f\"{color}\"\n",
        "                 f\"border-radius: 5px; padding: 3px;\"\n",
        "                 f\"font-weight: {int(800)};\"\n",
        "                 \"'>\")\n",
        "        html += tokenized_text[i].replace('<', \"[\").replace(\">\", \"]\")\n",
        "        html += \"</span> \"\n",
        "    display(HTML(html))\n",
        "#     print(html)\n",
        "    return html\n",
        "\n",
        "def print_preview(idx=0, discrete=False):\n",
        "    NO_CLS_SEP = False\n",
        "    df = decompx_outputs_df\n",
        "    for col in [\"importance_last_layer_aggregated\", \"importance_last_layer_classifier\"]:\n",
        "        if col in df and df[col][idx] is not None:\n",
        "            if \"aggregated\" in col:\n",
        "                sentence_importance = df[col].iloc[idx][0, :]\n",
        "            if \"classifier\" in col:\n",
        "                for label in range(df[col].iloc[idx].shape[-1]):\n",
        "                    sentence_importance = df[col].iloc[idx][:, label]\n",
        "                    print_importance(\n",
        "                        sentence_importance,\n",
        "                        df[\"tokens\"].iloc[idx], \n",
        "                        prefix=f\"{col.split('_')[-1]} Label{label}:\".ljust(20),\n",
        "                        no_cls_sep=NO_CLS_SEP,\n",
        "                        discrete=False\n",
        "                    )\n",
        "                break\n",
        "                sentence_importance = df[col].iloc[idx][:, df[\"label\"].iloc[idx]]\n",
        "            if \"pooler\" in col:\n",
        "                sentence_importance = df[col].iloc[idx]\n",
        "            print_importance(\n",
        "                sentence_importance,\n",
        "                df[\"tokens\"].iloc[idx], \n",
        "                prefix=f\"{col.split('_')[-1]}:\".ljust(20),\n",
        "                no_cls_sep=NO_CLS_SEP,\n",
        "                discrete=discrete\n",
        "            )\n",
        "    print(\"------------------------------------\")\n",
        "    return df\n",
        "\n",
        "for i in range(len(SENTENCES)):\n",
        "    print_preview(idx=i)"
      ],
      "metadata": {
        "id": "rytX6bB6Wd1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "outputId": "b6d94eb8-aa03-4e71-9be2-7917799a9c64"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<pre style='color:black; padding: 3px;'>aggregated:         <span style='background-color: rgba(206.2941176470588, 235.91764705882355, 199.78039215686275, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>[s]</span> <span style='background-color: rgba(172.47450980392156, 221.8313725490196, 166.17254901960786, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>A</span> <span style='background-color: rgba(210.05882352941177, 237.4235294117647, 203.79607843137254, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>Ġdeep</span> <span style='background-color: rgba(220.41176470588235, 241.56470588235294, 214.8392156862745, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>Ġand</span> <span style='background-color: rgba(55.0, 160.33333333333334, 85.0, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>Ġmeaningful</span> <span style='background-color: rgba(218.52941176470588, 240.81176470588235, 212.8313725490196, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>Ġfilm</span> <span style='background-color: rgba(195.12549019607843, 231.3686274509804, 188.22745098039215, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>.</span> <span style='background-color: rgba(210.05882352941177, 237.4235294117647, 203.79607843137254, 1.0); border-radius: 5px; padding: 3px;font-weight: 800;'>[/s]</span> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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