official_submission.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMJYfysaREkb"
      },
      "source": [
        "# Notice: Execute on GPU P100 on kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sDN3yM4BSCv"
      },
      "source": [
        "# Explanation:\n",
        "generate_baseline_multihop - execute prompts without patching\n",
        "\n",
        "\n",
        "evaluate_attriburte_exraction_batch_multihop - the patching method\n",
        "\n",
        "\n",
        "run_experiment- call the patching for all the combinations of layers\n",
        "\n",
        "generate_CoT_data_v7 - create the prompts data set\n",
        "\n",
        "\n",
        "step_by_step_cot_baseline - the chain pf thought method\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJT3BloKBSCw",
        "outputId": "c4949aa7-e9cf-42f3-8ffe-173b17b706d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fatal: destination path 'interpretability' already exists and is not an empty directory.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/PAIR-code/interpretability.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b4sNkj4BSCy",
        "outputId": "b7221cf3-1df5-4f72-b705-ffea5cf3684f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/interpretability/patchscopes/code\n",
            "/content/interpretability/patchscopes/code\n"
          ]
        }
      ],
      "source": [
        "%cd interpretability/patchscopes/code\n",
        "!pwd\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygFkfC69BSCz",
        "outputId": "2f579e50-75f0-409b-b78a-cbf80f646c2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.23.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.6.2)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "print(torch.cuda.device_count())\n",
        "# Step 3: Install/upgrade necessary packages\n",
        "!pip install --upgrade transformers huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "UXL_KEDOwLON"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "SEED = 42\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "if torch.cuda.is_available():\n",
        "    torch.cuda.manual_seed_all(SEED)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4t0QHfgpBSCz"
      },
      "source": [
        "# Install all repo requirments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGJ3m51jBSC0",
        "outputId": "eda41e5f-bcc5-43ff-ec1a-2d470926f4dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "datasets is already installed\n"
          ]
        }
      ],
      "source": [
        "import pkg_resources\n",
        "import subprocess\n",
        "import sys\n",
        "\n",
        "# List of required packages and their versions\n",
        "required_packages = {\n",
        "    'datasets': 'datasets==2.14.7',\n",
        "}\n",
        "\n",
        "# Function to check and install the required package\n",
        "def install_package(package_name, package_version):\n",
        "    try:\n",
        "        # Only the package name should be used to check its presence, not the version\n",
        "        pkg_resources.get_distribution(package_name)\n",
        "        print(f\"{package_name} is already installed\")\n",
        "    except pkg_resources.VersionConflict as e:\n",
        "        print(f\"Version conflict: {e}. Attempting to install the correct version.\")\n",
        "        subprocess.check_call([sys.executable, '-m', 'pip', 'install', package_version])\n",
        "    except pkg_resources.DistributionNotFound:\n",
        "        # Package not found, so install it\n",
        "        subprocess.check_call([sys.executable, '-m', 'pip', 'install', package_version])\n",
        "\n",
        "# Loop through the required packages and install them if they are not installed\n",
        "for package, version in required_packages.items():\n",
        "    install_package(package, version)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "mdEmY4rDQ3ik"
      },
      "outputs": [],
      "source": [
        "from ast import literal_eval\n",
        "import functools\n",
        "import json\n",
        "import os\n",
        "import random\n",
        "import shutil\n",
        "\n",
        "# Scienfitic packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import torch\n",
        "import datasets\n",
        "from torch import cuda\n",
        "import tqdm\n",
        "torch.set_grad_enabled(False)\n",
        "\n",
        "# Visuals\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(context=\"notebook\",\n",
        "        rc={\"font.size\":16,\n",
        "            \"axes.titlesize\":16,\n",
        "            \"axes.labelsize\":16,\n",
        "            \"xtick.labelsize\": 16.0,\n",
        "            \"ytick.labelsize\": 16.0,\n",
        "            \"legend.fontsize\": 16.0})\n",
        "palette_ = sns.color_palette(\"Set1\")\n",
        "palette = palette_[2:5] + palette_[7:]\n",
        "sns.set_theme(style='whitegrid')\n",
        "\n",
        "# Utilities\n",
        "\n",
        "from general_utils import (\n",
        "  ModelAndTokenizer,\n",
        "  make_inputs,\n",
        "  decode_tokens,\n",
        "  find_token_range,\n",
        "  predict_from_input,\n",
        ")\n",
        "\n",
        "from patchscopes_utils import *\n",
        "\n",
        "from tqdm import tqdm\n",
        "tqdm.pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347,
          "referenced_widgets": [
            "fac9abb08abb4beeba99731f803f91e3",
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            "2b4338ec6b33431d811daf7e986f6568",
            "58d59742bee04a83a5fc86aea3a5b4ae",
            "c5b27010d5d74597b3bda5ff9e0131b9",
            "47aab83f2e1d459a878748bc8cdf77c1",
            "2a499fd96e544000b33f3be7146f708c",
            "e976abab299f4ad38ce21fee5d59b433",
            "f84168f982d3490abf1fc4cf997c23a0",
            "26fe1d053a4a4f1bade7810011e25466"
          ]
        },
        "id": "wHiCrdEZBSC1",
        "outputId": "d1af4dfa-28f1-4e54-cdce-36698d3b8872"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n",
            "cuda:0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:769: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:468: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fac9abb08abb4beeba99731f803f91e3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ModelAndTokenizer(model: LlamaForCausalLM [32 layers], tokenizer: LlamaTokenizerFast)\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from huggingface_hub import login\n",
        "import torch\n",
        "import general_utils\n",
        "\n",
        "\n",
        "# Step 1: Set the Hugging Face token as an environment variable\n",
        "token = \"INSERT_HUGGING_FACE_TOKEB\"\n",
        "os.environ[\"HUGGINGFACE_TOKEN\"] = token\n",
        "\n",
        "# Step 2: Authenticate\n",
        "login(token=os.environ[\"HUGGINGFACE_TOKEN\"])\n",
        "\n",
        "\n",
        "\n",
        "# Step 4: Set model and device\n",
        "model_name = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "my_device = torch.device(\"cuda:0\")\n",
        "\n",
        "# Step 5: Determine model precision\n",
        "if any(x in model_name for x in [\"13b\", \"12b\", \"7b\"]):\n",
        "    torch_dtype = torch.float16\n",
        "else:\n",
        "    torch_dtype = None\n",
        "\n",
        "my_device = torch.device(\"cuda:0\")\n",
        "print(my_device)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_auth_token=token)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=False, torch_dtype=torch_dtype, use_auth_token=token)\n",
        "\n",
        "\n",
        "mt = ModelAndTokenizer (\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    low_cpu_mem_usage=False,\n",
        "    torch_dtype=torch_dtype,\n",
        "    device=my_device,\n",
        ")\n",
        "mt.model.to(my_device)\n",
        "print(mt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "46DR2XTQBSC2"
      },
      "outputs": [],
      "source": [
        "def set_hs_patch_hooks_llama_batch_omer(\n",
        "    model,\n",
        "    hs_patch_config,\n",
        "    module=\"hs\",\n",
        "    patch_input=False,\n",
        "    generation_mode=False,\n",
        "):\n",
        "  \"\"\"LLAMA patch hooks - supporting batch.\"\"\"\n",
        "  # when using mode.generate() the hidden states in the input are cached after\n",
        "  # the first inference pass, and in the next steps the input/output are of\n",
        "  # size 1. In these cases we don't need to patch anymore the previous hidden\n",
        "  # states from the initial input, because they are cached, but we do need to\n",
        "  # handle these cases in this call because this hook wraps the generation call.\n",
        "  #\n",
        "  # NOTE: To use generation mode, we must patch a position that is not the\n",
        "  # first one. This is because in this case we don't know during generation if\n",
        "  # we are handling the initial input or a future step and thus don't know if\n",
        "  # a patching is needed or not.\n",
        "\n",
        "  # if generation_mode:\n",
        "  #     for i in hs_patch_config:\n",
        "  #         for position_, _ in hs_patch_config[i]:\n",
        "  #             assert position_ > 0\n",
        "\n",
        "  if module != \"hs\":\n",
        "    raise ValueError(\"Module %s not yet supported\", module)\n",
        "\n",
        "  def patch_hs(name, position_hs, patch_input, generation_mode):\n",
        "    def pre_hook(module, inp):\n",
        "      # inp[0]: (batch, sequence, hidden_state)\n",
        "      idx_, position_, hs_ = (\n",
        "          position_hs[\"batch_idx\"],\n",
        "          position_hs[\"position_target\"],\n",
        "          position_hs[\"hidden_rep\"],\n",
        "      )\n",
        "      input_len = len(inp[0][idx_])\n",
        "      if generation_mode and input_len == 1:\n",
        "        return\n",
        "      inp[0][idx_][position_] = hs_\n",
        "\n",
        "    def post_hook(module, inp, output):\n",
        "      idx_, position_, hs_ = (\n",
        "          position_hs[\"batch_idx\"],\n",
        "          position_hs[\"position_target\"],\n",
        "          position_hs[\"hidden_rep\"],\n",
        "      )\n",
        "      if \"skip_ln\" in name:\n",
        "        # output: (batch, sequence, hidden_state)\n",
        "        output_len = len(output[idx_])\n",
        "        if generation_mode and output_len == 1:\n",
        "          return\n",
        "        output[idx_][position_] = hs_\n",
        "      else:\n",
        "        # output[0]: (batch, sequence, hidden_state)\n",
        "        output_len = len(output[0][idx_])\n",
        "        if generation_mode and output_len == 1:\n",
        "          return\n",
        "        output[0][idx_][position_] = hs_\n",
        "    if patch_input:\n",
        "      return pre_hook\n",
        "    else:\n",
        "      return post_hook\n",
        "\n",
        "  hooks = []\n",
        "\n",
        "  for item in hs_patch_config:\n",
        "    i = item[\"layer_target\"]\n",
        "    skip_final_ln = item[\"skip_final_ln\"]\n",
        "    if patch_input:\n",
        "      hooks.append(\n",
        "          model.model.layers[i].register_forward_pre_hook(\n",
        "              patch_hs(f\"patch_hs_{i}\", item, patch_input, generation_mode)\n",
        "          )\n",
        "      )\n",
        "    else:\n",
        "      # when patching a last-layer representation to the last layer of the same\n",
        "      # model, the final layer norm is not needed because it was already applied\n",
        "      # (assuming that the representation for patching was obtained by setting\n",
        "      # output_hidden_representations to True).\n",
        "      if skip_final_ln and i == len(model.model.layers) - 1:\n",
        "        hooks.append(\n",
        "            model.model.norm.register_forward_hook(\n",
        "                patch_hs(\n",
        "                    f\"patch_hs_{i}_skip_ln\", item, patch_input, generation_mode\n",
        "                )\n",
        "            )\n",
        "        )\n",
        "      else:\n",
        "        hooks.append(\n",
        "            model.model.layers[i].register_forward_hook(\n",
        "                patch_hs(f\"patch_hs_{i}\", item, patch_input, generation_mode)\n",
        "            )\n",
        "        )\n",
        "\n",
        "  return hooks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11t5JybGBSC2",
        "outputId": "efe0b795-e64c-4d9e-e264-f1d4baf7be17"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(32000, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaSdpaAttention(\n",
              "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm()\n",
              "        (post_attention_layernorm): LlamaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              ")"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mt.set_hs_patch_hooks = set_hs_patch_hooks_llama_batch_omer\n",
        "mt.model.eval()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OuSKs6L-BSC3"
      },
      "source": [
        "# Auto Patch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ck8sAJqBSC3"
      },
      "source": [
        "# Download dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "O9rfqazpBSC3"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "def clean_memory():\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "clean_memory()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JAfvEAPBSC4"
      },
      "outputs": [],
      "source": [
        "!pip install gdown"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fzKa2bcBSC4"
      },
      "source": [
        "## The repo of the data:\n",
        "https://github.com/StonyBrookNLP/musique/blob/main/download_data.sh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U21PyYj9BSC5",
        "outputId": "70f3ce63-ff82-4bb1-d5ef-7216e1e61319"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1tGdADlNjWFaHLeZZGShh2IRcpO6Lv24h\n",
            "From (redirected): https://drive.google.com/uc?id=1tGdADlNjWFaHLeZZGShh2IRcpO6Lv24h&confirm=t&uuid=73a8082a-5250-4f96-9dde-d493fdc7b131\n",
            "To: /content/interpretability/patchscopes/code/musique_v1.0.zip\n",
            "100%|██████████| 272M/272M [00:11<00:00, 24.1MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Import required libraries\n",
        "import os\n",
        "import gdown\n",
        "import zipfile\n",
        "\n",
        "# Set variables\n",
        "zip_name = \"musique_v1.0.zip\"\n",
        "file_id = \"1tGdADlNjWFaHLeZZGShh2IRcpO6Lv24h\"\n",
        "\n",
        "# Download the file from Google Drive\n",
        "gdown.download(f\"https://drive.google.com/uc?id={file_id}\", zip_name, quiet=False)\n",
        "\n",
        "# Unzip the file\n",
        "with zipfile.ZipFile(zip_name, 'r') as zip_ref:\n",
        "    zip_ref.extractall()\n",
        "\n",
        "# Remove the zip file\n",
        "os.remove(zip_name)\n",
        "\n",
        "# Remove __MACOSX directory if it exists\n",
        "macosx_dir = \"__MACOSX\"\n",
        "if os.path.exists(macosx_dir):\n",
        "    os.system(f'rm -rf {macosx_dir}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdMZXPMrBSC5",
        "outputId": "a827d203-6e3e-4cb6-e9e4-0bc807613558"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dev_test_singlehop_questions_v1.0.json\tmusique_ans_v1.0_train.jsonl  musique_full_v1.0_train.jsonl\n",
            "musique_ans_v1.0_dev.jsonl\t\tmusique_full_v1.0_dev.jsonl\n",
            "musique_ans_v1.0_test.jsonl\t\tmusique_full_v1.0_test.jsonl\n"
          ]
        }
      ],
      "source": [
        "!ls data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWFFnxttBSC6"
      },
      "source": [
        "# Create dataset manually"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "ugg4t5H4BKyy"
      },
      "outputs": [],
      "source": [
        "TRAIN_START_RANGE = 0\n",
        "TRAIN_END_RANGE = 1024\n",
        "TEST_START_RANGE = 4 * 1024\n",
        "TEST_END_RANGE =  TEST_START_RANGE + 1024\n",
        "TARGET_LAYER = 15"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "osATH3oJBSC6"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "def generate_baseline_multihop(\n",
        "    mt, df, batch_size=16, max_gen_len=10,\n",
        "):\n",
        "    def _generate_baseline_single_batch(batch_df):\n",
        "        batch_size = len(batch_df)\n",
        "        cases = [\n",
        "                 (\"baseline_multihop3\", \"hop3\"),\n",
        "                ]\n",
        "        results = {}\n",
        "        for target_col, object_col in cases:\n",
        "\n",
        "            target_baseline_batch = np.array(batch_df[target_col])\n",
        "            object_batch = np.array(batch_df[object_col])\n",
        "\n",
        "\n",
        "            # Step 0: run the the model on target prompt baseline (having the subject token in input rather than patched)\n",
        "            # The goal of this step is to calculate whether the model works correctly by default, and to calculate surprisal\n",
        "            inp_target_baseline = make_inputs(mt.tokenizer, target_baseline_batch, mt.device)\n",
        "            seq_len_target_baseline = len(inp_target_baseline[\"input_ids\"][0])\n",
        "            output_target_baseline_toks = mt.model.generate(\n",
        "                inp_target_baseline[\"input_ids\"],\n",
        "                max_length=seq_len_target_baseline + max_gen_len,\n",
        "                pad_token_id=mt.model.generation_config.eos_token_id,\n",
        "            )[:, seq_len_target_baseline:]\n",
        "            generations_baseline = decode_tokens(mt.tokenizer, output_target_baseline_toks)\n",
        "            generations_baseline_txt = np.array([\" \".join(sample_gen) for sample_gen in generations_baseline])\n",
        "\n",
        "\n",
        "            is_correct_baseline = np.array([\n",
        "                (object_batch[i] in generations_baseline_txt[i] or\n",
        "                 object_batch[i].replace(\" \", \"\") in generations_baseline_txt[i].replace(\" \", \"\"))\n",
        "                for i in range(batch_size)\n",
        "            ])\n",
        "            results.update(\n",
        "                {\n",
        "                f\"generations_{target_col}\": generations_baseline_txt,\n",
        "                f\"is_correct_{target_col}\": is_correct_baseline,\n",
        "                }\n",
        "            )\n",
        "\n",
        "        return results\n",
        "\n",
        "    results = {}\n",
        "    n_batches = len(df) // batch_size\n",
        "    if len(df)%batch_size !=0:\n",
        "        n_batches +=1\n",
        "    for i in tqdm.tqdm(range(n_batches)):\n",
        "        cur_df = df.iloc[batch_size * i : batch_size * (i + 1)]\n",
        "        batch_results = _generate_baseline_single_batch(cur_df)\n",
        "        for key, value in batch_results.items():\n",
        "            if key in results:\n",
        "                results[key] = np.concatenate((results[key], value))\n",
        "            else:\n",
        "                results[key] = value\n",
        "\n",
        "    return results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "8Ffe7CAiBSC6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "def generate_CoT_data_v7(fname_in=\"./outputs/preprocessed_data_LRE_CoT/factual_multihop/combined_multihop.pkl\",\n",
        "                         fdir_out=\"./outputs/preprocessed_data_LRE_CoT/factual_multihop\", batch_size=512, max_gen_len=20):\n",
        "    if not os.path.exists(fdir_out):\n",
        "        os.makedirs(fdir_out)\n",
        "\n",
        "    print(\"Step 1: Read multihop dataset created using LRE data prep...\")\n",
        "\n",
        "    data = []\n",
        "\n",
        "    # Load the JSONL file\n",
        "    with open(fname_in, 'r') as f:\n",
        "        for line in f:\n",
        "            json_data = json.loads(line)\n",
        "            data.append(json_data)\n",
        "\n",
        "    # Create a list to store the formatted data\n",
        "    formatted_data = []\n",
        "\n",
        "    # Loop through each entry in the data\n",
        "    for entry_index in range(len(data)):\n",
        "        entry = data[entry_index]\n",
        "        hops = entry.get('question_decomposition', [])\n",
        "        first_hop_question = hops[0].get('question', None)\n",
        "        first_hop_answer = hops[0].get('answer', None)\n",
        "        second_hop_question = hops[1].get('question', None)\n",
        "        second_hop_answer = hops[1].get('answer', None)\n",
        "\n",
        "        formatted_entry = {\n",
        "            'Unnamed: 0': 0,  # This seems to be a placeholder\n",
        "            'sample_id': entry_index,\n",
        "            'prompt_source': entry.get('question', None),\n",
        "            'prompt_target': entry.get('question', None),\n",
        "            'position_source': -1,  # isn't used\n",
        "            'position_target': -1,  # isn't used\n",
        "            'baseline_hop2': first_hop_question, # for visualization\n",
        "            'baseline_hop3': second_hop_question, # for visualization\n",
        "            'baseline_multihop3': entry.get('question', None),\n",
        "            'hop1': first_hop_answer,\n",
        "            'hop2': second_hop_answer,\n",
        "            'hop3': entry.get('answer', [None])[0]\n",
        "        }\n",
        "        formatted_data.append(formatted_entry)\n",
        "\n",
        "    df = pd.DataFrame(formatted_data)\n",
        "\n",
        "    df = df.iloc[TRAIN_START_RANGE:TRAIN_END_RANGE]\n",
        "    df['hop3'] = df['hop3'].astype(str)\n",
        "    print(f\"len of df is {len(df)}\")\n",
        "\n",
        "    # Step 2: Compute baseline generations\n",
        "    print(\"Step 2: Compute baseline generations...\")\n",
        "    eval_results = generate_baseline_multihop(mt, df, batch_size=batch_size, max_gen_len=max_gen_len)\n",
        "    for key, value in eval_results.items():\n",
        "        df[key] = list(value)\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIILRbS_BSC7"
      },
      "source": [
        "300"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62dZQvtQBSC7",
        "outputId": "31d885ed-18e6-4cec-c87f-946e08578515"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Step 1: Read multihop dataset created using LRE data prep...\n",
            "len of df is 1024\n",
            "Step 2: Compute baseline generations...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 32/32 [01:02<00:00,  1.96s/it]\n"
          ]
        }
      ],
      "source": [
        "df = generate_CoT_data_v7(fname_in=\"./data/musique_full_v1.0_dev.jsonl\",\n",
        "                     fdir_out=\"./outputs/preprocessed_data_LRE_CoT/factual_multihop\",\n",
        "                     batch_size=32, max_gen_len=20)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "luM0S5bcBSC8"
      },
      "outputs": [],
      "source": [
        "def create_batches(df):\n",
        "    batches = []\n",
        "    for layer_source in tqdm.tqdm(range(TARGET_LAYER, TARGET_LAYER + 1), desc=\"Layer Source Loop\"):\n",
        "        for _, row in df.iterrows():\n",
        "            target_baseline_batch = np.array([row[\"prompt_target\"]])\n",
        "            # Step 0: run the the model on target prompt baseline (having the subject token in input rather than patched)\n",
        "            # The goal of this step is to calculate whether the model works correctly by default, and to calculate surprisal\n",
        "            inp_target_baseline = make_inputs(mt.tokenizer, target_baseline_batch, mt.device)\n",
        "            seq_length = len(inp_target_baseline[\"input_ids\"][0])\n",
        "            for position_source in range(seq_length):\n",
        "                # for position_target in range(seq_length):\n",
        "                item = dict(row)\n",
        "                item.update({\n",
        "                    \"layer_source\": layer_source,\n",
        "                    \"layer_target\": layer_source - 7,\n",
        "                    \"position_source\": position_source,\n",
        "                    \"position_target\": position_source\n",
        "                })\n",
        "                batches.append(item)\n",
        "    return batches"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "uiFz0NebBSC8"
      },
      "outputs": [],
      "source": [
        "def evaluate_attriburte_exraction_batch_multihop(\n",
        "    mt, df, batch_size=32, max_gen_len=10, transform=None\n",
        "):\n",
        "    def _evaluate_attriburte_exraction_single_batch(batch_df):\n",
        "        batch_size = len(batch_df)\n",
        "        prompt_source_batch = np.array(batch_df[\"prompt_source\"])\n",
        "        prompt_target_batch = np.array(batch_df[\"prompt_target\"])\n",
        "        layer_source_batch = np.array(batch_df[\"layer_source\"])\n",
        "        layer_target_batch = np.array(batch_df[\"layer_target\"])\n",
        "        position_source_batch = np.array(batch_df[\"position_source\"])\n",
        "        position_target_batch = np.array(batch_df[\"position_target\"])\n",
        "\n",
        "        object_batch = np.array(batch_df[\"hop3\"])\n",
        "\n",
        "        prompt = make_inputs(mt.tokenizer, prompt_source_batch, mt.device)\n",
        "\n",
        "        # Adjust position_target to be absolute rather than relative\n",
        "        inp_target = prompt\n",
        "        for i in range(batch_size):\n",
        "            if position_target_batch[i] < 0:\n",
        "                position_target_batch[i] += len(inp_target[\"input_ids\"][i])\n",
        "\n",
        "        # Step 1: run the the model on source without patching and get the hidden representations.\n",
        "        inp_source = prompt\n",
        "        output_orig = mt.model(**inp_source, output_hidden_states=True)\n",
        "\n",
        "        # hidden_states size (n_layers, n_sample, seq_len, hidden_dim)\n",
        "        hidden_rep = [\n",
        "            output_orig.hidden_states[layer_source_batch[i] + 1][i][\n",
        "                position_source_batch[i]\n",
        "            ]\n",
        "            for i in range(batch_size)\n",
        "        ]\n",
        "\n",
        "        if transform is not None:\n",
        "            for i in range(batch_size):\n",
        "                print(f\"type of hidden_rep[i]: {hidden_rep[i]}\")\n",
        "                hidden_rep[i] = transform(hidden_rep[i])\n",
        "\n",
        "        # Step 2: do second run on target prompt, while patching the input hidden state.\n",
        "        hs_patch_config = [\n",
        "            {\n",
        "                \"batch_idx\": i,\n",
        "                \"layer_target\": layer_target_batch[i],\n",
        "                \"position_target\": position_target_batch[i],\n",
        "                \"hidden_rep\": hidden_rep[i],\n",
        "                \"skip_final_ln\": (\n",
        "                    layer_source_batch[i]\n",
        "                    == layer_target_batch[i]\n",
        "                    == mt.num_layers - 1\n",
        "                ),\n",
        "            }\n",
        "            for i in range(batch_size)\n",
        "        ]\n",
        "        patch_hooks = mt.set_hs_patch_hooks(\n",
        "            mt.model, hs_patch_config, patch_input=False, generation_mode=True\n",
        "        )\n",
        "\n",
        "\n",
        "        output = mt.model(**prompt)\n",
        "\n",
        "        # NOTE: inputs are left padded,\n",
        "        # and sequence length is the same across batch\n",
        "        seq_len = len(inp_target[\"input_ids\"][0])\n",
        "        output_toks = mt.model.generate(\n",
        "            inp_target[\"input_ids\"],\n",
        "            max_length=seq_len + max_gen_len,\n",
        "            pad_token_id=mt.model.generation_config.eos_token_id,\n",
        "        )[:, seq_len:]\n",
        "        generations_patched = decode_tokens(mt.tokenizer, output_toks)\n",
        "        generations_patched_txt = np.array([\n",
        "            \" \".join(generations_patched[i])\n",
        "            for i in range(batch_size)\n",
        "        ])\n",
        "        is_correct_patched = np.array([\n",
        "            (object_batch[i] in generations_patched_txt[i]\n",
        "             or object_batch[i].replace(\" \", \"\") in generations_patched_txt[i].replace(\" \", \"\"))\n",
        "            for i in range(batch_size)\n",
        "        ])\n",
        "\n",
        "        # remove patching hooks\n",
        "        remove_hooks(patch_hooks)\n",
        "\n",
        "        cpu_hidden_rep = np.array([hidden_rep[i].detach().cpu().numpy() for i in range(batch_size)])\n",
        "        if True in is_correct_patched:\n",
        "            print(\"is_correct_patched\")\n",
        "\n",
        "        results = {\n",
        "            \"generations_patched\": generations_patched,\n",
        "            \"is_correct_patched\": is_correct_patched,\n",
        "            \"hidden_rep\": cpu_hidden_rep,\n",
        "        }\n",
        "\n",
        "        clean_memory()\n",
        "        del generations_patched_txt, output_toks, output, output_orig, inp_source, inp_target, prompt, hidden_rep\n",
        "\n",
        "        return results\n",
        "\n",
        "    results = {}\n",
        "    n_batches = len(df) // batch_size\n",
        "    if len(df)%batch_size !=0:\n",
        "        n_batches +=1\n",
        "    for i in tqdm.tqdm(range(len(df) // batch_size)):\n",
        "        clean_memory()\n",
        "        cur_df = df.iloc[batch_size * i : batch_size * (i + 1)]\n",
        "        batch_results = _evaluate_attriburte_exraction_single_batch(cur_df)\n",
        "        for key, value in batch_results.items():\n",
        "            if key in results:\n",
        "                results[key] = np.concatenate((results[key], value))\n",
        "            else:\n",
        "                results[key] = value\n",
        "\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "o3tcvb_wBSC9"
      },
      "outputs": [],
      "source": [
        "def run_experiment(df, fname_in, fdir_out, fname_out = \"multihop\", batch_size=512, n_samples=-1,\n",
        "                   save_output=True, replace=False):\n",
        "    print(f\"\\tNumber of samples: {len(df)}\")\n",
        "\n",
        "    # BATCHED\n",
        "    batch = create_batches(df)\n",
        "    experiment_df = pd.DataFrame.from_records(batch)\n",
        "\n",
        "    if n_samples > 0 and n_samples<len(experiment_df):\n",
        "        experiment_df = experiment_df.sample(n=n_samples, replace=False, random_state=42).reset_index(drop=True)\n",
        "\n",
        "    print(f\"\\tNumber of datapoints for patching experiment: {len(experiment_df)}\")\n",
        "\n",
        "    eval_results = evaluate_attriburte_exraction_batch_multihop(mt, experiment_df, batch_size=batch_size)\n",
        "\n",
        "    results_df = experiment_df.head(len(eval_results[\"is_correct_patched\"]))\n",
        "    for key, value in eval_results.items():\n",
        "        results_df[key] = list(value)\n",
        "\n",
        "    return results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jyOdVMrBSC9",
        "outputId": "95444e9b-06ad-404e-fad8-d26113b3aea6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\tNumber of samples: 1024\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Layer Source Loop: 100%|██████████| 1/1 [00:01<00:00,  1.27s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\tNumber of datapoints for patching experiment: 24919\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 3/3114 [00:03<1:01:17,  1.18s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
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            "  0%|          | 11/3114 [00:12<59:54,  1.16s/it]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
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            "  0%|          | 13/3114 [00:15<1:00:43,  1.17s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  1%|          | 20/3114 [00:23<59:20,  1.15s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
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            "  1%|          | 23/3114 [00:26<1:00:12,  1.17s/it]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\r  1%|          | 24/3114 [00:27<1:00:53,  1.18s/it]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
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            "\r  1%|          | 25/3114 [00:29<1:01:14,  1.19s/it]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
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        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
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            "\r 99%|█████████▉| 3088/3114 [1:05:13<00:32,  1.27s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 99%|█████████▉| 3089/3114 [1:05:14<00:31,  1.27s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 99%|█████████▉| 3094/3114 [1:05:20<00:25,  1.26s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 99%|█████████▉| 3095/3114 [1:05:22<00:23,  1.24s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 99%|█████████▉| 3096/3114 [1:05:23<00:22,  1.24s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "100%|█████████▉| 3099/3114 [1:05:27<00:18,  1.25s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r100%|█████████▉| 3100/3114 [1:05:28<00:17,  1.26s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r100%|█████████▉| 3101/3114 [1:05:29<00:16,  1.27s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r100%|█████████▉| 3102/3114 [1:05:30<00:15,  1.28s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r100%|█████████▉| 3103/3114 [1:05:32<00:14,  1.28s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "is_correct_patched\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3114/3114 [1:05:46<00:00,  1.27s/it]\n",
            "<ipython-input-37-62e39204e041>:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  results_df[key] = list(value)\n",
            "<ipython-input-37-62e39204e041>:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  results_df[key] = list(value)\n",
            "<ipython-input-37-62e39204e041>:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  results_df[key] = list(value)\n"
          ]
        }
      ],
      "source": [
        "import tqdm\n",
        "cot_correct_baseline = run_experiment(df,\n",
        "    f\"./outputs/preprocessed_data_LRE_CoT/factual_multihop/combined_multihop_CoT_only_correct_True.pkl\",\n",
        "    \"./outputs/results_LRE_CoT/factual_multihop\",\n",
        "    fname_out = f\"combined_multihop_CoT_only_correct_True\", batch_size=8, n_samples=-1,\n",
        "    save_output=True, replace=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxvpBrMlBSC_"
      },
      "source": [
        "# Train the classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2OiD-ydBSDA",
        "outputId": "e4b28e94-a8e9-4fa2-ef76-36cd9f7856a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification report for layer_source: 15\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.84      0.92      0.88      3808\n",
            "        True       0.64      0.45      0.53      1175\n",
            "\n",
            "    accuracy                           0.81      4983\n",
            "   macro avg       0.74      0.69      0.70      4983\n",
            "weighted avg       0.80      0.81      0.80      4983\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "classifiers = []\n",
        "\n",
        "layers_with_classifiers = [15]\n",
        "for layer_source in layers_with_classifiers:\n",
        "    # Filter the DataFrame for the current layer_source\n",
        "    filtered_df = cot_correct_baseline[cot_correct_baseline['layer_source'] == layer_source]\n",
        "\n",
        "    # Convert the 'hidden_rep' lists into a DataFrame of features\n",
        "    features = pd.DataFrame(filtered_df['hidden_rep'].tolist())\n",
        "\n",
        "    # Prepare the target variable\n",
        "    labels = filtered_df['is_correct_patched']\n",
        "\n",
        "    # Split the data into training and testing sets\n",
        "    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "    from imblearn.combine import SMOTETomek\n",
        "    sm = SMOTETomek(sampling_strategy=0.5, random_state=42)\n",
        "    X_train_sm, y_train_sm = sm.fit_resample(X_train, y_train)\n",
        "\n",
        "    # Standardize features by removing the mean and scaling to unit variance\n",
        "    scaler = StandardScaler()\n",
        "    X_train_sm = scaler.fit_transform(X_train_sm)\n",
        "    X_test = scaler.transform(X_test)\n",
        "\n",
        "    # Create and train the SVM model with RBF (Gaussian) kernel\n",
        "    classifier = SVC(kernel='rbf', C=10, gamma='scale')\n",
        "    classifier.fit(X_train_sm, y_train_sm)\n",
        "\n",
        "    # Make predictions and evaluate the model\n",
        "    predictions = classifier.predict(X_test)\n",
        "    print(f\"Classification report for layer_source: {layer_source}\")\n",
        "    print(classification_report(y_test, predictions))\n",
        "\n",
        "    classifiers.append(classifier)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_LHVNxxnBSDB"
      },
      "source": [
        " # The patching using classifiers expirement"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "2ByBfAQfBSDC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "def generate_CoT_data_v7_in_range(min_value ,max_value, fname_in=\"./outputs/preprocessed_data_LRE_CoT/factual_multihop/combined_multihop.pkl\",\n",
        "                         fdir_out=\"./outputs/preprocessed_data_LRE_CoT/factual_multihop\", batch_size=512, max_gen_len=20):\n",
        "    if not os.path.exists(fdir_out):\n",
        "        os.makedirs(fdir_out)\n",
        "\n",
        "    print(\"Step 1: Read multihop dataset created using LRE data prep...\")\n",
        "\n",
        "    data = []\n",
        "\n",
        "    # Load the JSONL file\n",
        "    with open(fname_in, 'r') as f:\n",
        "        for line in f:\n",
        "            json_data = json.loads(line)\n",
        "            data.append(json_data)\n",
        "\n",
        "    # Create a list to store the formatted data\n",
        "    formatted_data = []\n",
        "\n",
        "    # Loop through each entry in the data\n",
        "    for entry_index in range(len(data)):\n",
        "        entry = data[entry_index]\n",
        "        hops = entry.get('question_decomposition', [])\n",
        "        first_hop_question = hops[0].get('question', None)\n",
        "        first_hop_answer = hops[0].get('answer', None)\n",
        "        second_hop_question = hops[1].get('question', None)\n",
        "        second_hop_answer = hops[1].get('answer', None)\n",
        "\n",
        "        formatted_entry = {\n",
        "            'Unnamed: 0': 0,  # This seems to be a placeholder\n",
        "            'sample_id': entry_index,\n",
        "            'prompt_source': entry.get('question', None),\n",
        "            'prompt_target': entry.get('question', None),\n",
        "            'position_source': -1,\n",
        "            'position_target': -1,\n",
        "            'baseline_hop2': first_hop_question, # for visualization\n",
        "            'baseline_hop3': second_hop_question, # for visualization\n",
        "            'baseline_multihop3': entry.get('question', None),\n",
        "            'hop1': first_hop_answer,\n",
        "            'hop2': second_hop_answer,\n",
        "            'hop3': entry.get('answer', [None])[0]\n",
        "        }\n",
        "        formatted_data.append(formatted_entry)\n",
        "\n",
        "    df = pd.DataFrame(formatted_data)\n",
        "\n",
        "    df = df.iloc[min_value : max_value]\n",
        "    df['hop3'] = df['hop3'].astype(str)\n",
        "    print(f\"len of df is {len(df)}\")\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "tiemj_fjBSDC"
      },
      "outputs": [],
      "source": [
        "def evaluate_attriburte_exraction_batch_multihop_with_classifiers(\n",
        "    classifiers, mt, df, batch_size=32, max_gen_len=10, transform=None\n",
        "):\n",
        "    def _evaluate_attriburte_exraction_single_batch(batch_df):\n",
        "        batch_size = len(batch_df)\n",
        "        prompt_source_batch = np.array(batch_df[\"prompt_source\"])\n",
        "        prompt_target_batch = np.array(batch_df[\"prompt_target\"])\n",
        "\n",
        "        object_batch = np.array(batch_df[\"hop3\"])\n",
        "\n",
        "\n",
        "        # Adjust position_target to be absolute rather than relative\n",
        "        inp_target = make_inputs(mt.tokenizer, prompt_target_batch, mt.device)\n",
        "\n",
        "        # Step 1: run the the model on source without patching and get the hidden representations.\n",
        "        inp_source = make_inputs(mt.tokenizer, prompt_source_batch, mt.device)\n",
        "        output_orig = mt.model(**inp_source, output_hidden_states=True)\n",
        "        layers_with_classifiers = [TARGET_LAYER]\n",
        "        # hidden_states size (n_layers, n_sample, seq_len, hidden_dim)\n",
        "        def get_layer_hidden_states(layerNum, index_in_batch):\n",
        "          return output_orig.hidden_states[layerNum + 1][index_in_batch]\n",
        "\n",
        "        if transform is not None:\n",
        "            for i in range(batch_size):\n",
        "                print(f\"type of hidden_rep[i]: {hidden_rep[i]}\")\n",
        "                hidden_rep[i] = transform(hidden_rep[i])\n",
        "\n",
        "#         # Step 2: do second run on target prompt, while patching the input hidden state.\n",
        "        # Initialize the hs_patch_config list\n",
        "        hs_patch_config = []\n",
        "\n",
        "        # Iterate over the range of batch_size\n",
        "        for i in range(batch_size):\n",
        "            for classified_layer in layers_with_classifiers:\n",
        "                hidden_states = []\n",
        "                hidden_rep_np = get_layer_hidden_states(classified_layer, i)\n",
        "                for hidden_state_index in range(len(hidden_rep_np)):\n",
        "                    hidden_state = hidden_rep_np[hidden_state_index]\n",
        "                    should_patched_by_the_classifier = classifiers[layers_with_classifiers.index(classified_layer)].predict(hidden_state.cpu().numpy().reshape(1, -1))\n",
        "\n",
        "\n",
        "                    if should_patched_by_the_classifier:\n",
        "                      hidden_states.append(hidden_state_index)\n",
        "                      for layer_diff in [0]:\n",
        "                        source_layer_index = classified_layer + layer_diff\n",
        "                        state_to_patch = get_layer_hidden_states(source_layer_index, i)[hidden_state_index]\n",
        "                        config_dict = {\n",
        "                              \"batch_idx\": i,\n",
        "                              \"layer_target\": source_layer_index - 7,\n",
        "                              \"position_target\": hidden_state_index,\n",
        "                              \"hidden_rep\": state_to_patch,\n",
        "                              \"skip_final_ln\": (\n",
        "                                  source_layer_index\n",
        "                                  == source_layer_index - 7\n",
        "                                  == mt.num_layers - 1\n",
        "                              ),\n",
        "                            }\n",
        "                        hs_patch_config.append(config_dict)\n",
        "            print(f\"hidden_states : {hidden_states}\")\n",
        "\n",
        "        patch_hooks = mt.set_hs_patch_hooks(\n",
        "            mt.model, hs_patch_config, patch_input=False, generation_mode=True\n",
        "        )\n",
        "\n",
        "        output = mt.model(**inp_target)\n",
        "\n",
        "        # NOTE: inputs are left padded,\n",
        "        # and sequence length is the same across batch\n",
        "        seq_len = len(inp_target[\"input_ids\"][0])\n",
        "        output_toks = mt.model.generate(\n",
        "            inp_target[\"input_ids\"],\n",
        "            max_length=seq_len + max_gen_len,\n",
        "            pad_token_id=mt.model.generation_config.eos_token_id,\n",
        "        )[:, seq_len:]\n",
        "        generations_patched = decode_tokens(mt.tokenizer, output_toks)\n",
        "        generations_patched_txt = np.array([\n",
        "            \" \".join(generations_patched[i])\n",
        "            for i in range(batch_size)\n",
        "        ])\n",
        "        is_correct_patched = np.array([\n",
        "            (object_batch[i] in generations_patched_txt[i]\n",
        "             or object_batch[i].replace(\" \", \"\") in generations_patched_txt[i].replace(\" \", \"\"))\n",
        "            for i in range(batch_size)\n",
        "        ])\n",
        "\n",
        "        # remove patching hooks\n",
        "        remove_hooks(patch_hooks)\n",
        "\n",
        "        results = {\n",
        "            \"generations_patched\": generations_patched,\n",
        "            \"is_correct_patched\": is_correct_patched,\n",
        "        }\n",
        "        clean_memory()\n",
        "        del generations_patched_txt, output_toks, output, output_orig, inp_source, inp_target\n",
        "        return results\n",
        "\n",
        "    results = {}\n",
        "    n_batches = len(df) // batch_size\n",
        "    if len(df)%batch_size !=0:\n",
        "        n_batches +=1\n",
        "    for i in tqdm.tqdm(range(len(df) // batch_size)):\n",
        "        clean_memory()\n",
        "        cur_df = df.iloc[batch_size * i : batch_size * (i + 1)]\n",
        "        batch_results = _evaluate_attriburte_exraction_single_batch(cur_df)\n",
        "\n",
        "        for key, value in batch_results.items():\n",
        "            if key in results:\n",
        "                results[key] = np.concatenate((results[key], value))\n",
        "            else:\n",
        "                results[key] = value\n",
        "\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "QqgRIp72BSDD"
      },
      "outputs": [],
      "source": [
        "def run_experiment_with_classifiers(classifiers, df, fname_in, fdir_out, fname_out = \"multihop\", batch_size=512, n_samples=-1,\n",
        "                   save_output=True, replace=False):\n",
        "    print(f\"\\tNumber of samples: {len(df)}\")\n",
        "\n",
        "    experiment_df = df.copy()\n",
        "    print(f\"\\tNumber of datapoints for patching experiment: {len(experiment_df)}\")\n",
        "    eval_results = evaluate_attriburte_exraction_batch_multihop_with_classifiers(classifiers, mt, experiment_df, batch_size=batch_size)\n",
        "    results_df = experiment_df.head(len(eval_results[\"is_correct_patched\"]))\n",
        "\n",
        "    for key, value in eval_results.items():\n",
        "        results_df[key] = list(value)\n",
        "\n",
        "    if save_output:\n",
        "        if not os.path.exists(fdir_out):\n",
        "            os.makedirs(fdir_out)\n",
        "        results_df.to_csv(f\"{fdir_out}/{fname_out}.tsv\", sep=\"\\t\")\n",
        "        results_df.to_pickle(f\"{fdir_out}/{fname_out}.pkl\")\n",
        "\n",
        "    return results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDmyk6JUBSDD",
        "outputId": "8c7901de-8f7c-4100-cbe0-3c7aa056e327"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Step 1: Read multihop dataset created using LRE data prep...\n",
            "len of df is 1024\n"
          ]
        }
      ],
      "source": [
        "test_df = generate_CoT_data_v7_in_range(TRAIN_START_RANGE,\n",
        "TRAIN_END_RANGE, fname_in=\"./data/musique_full_v1.0_dev.jsonl\",\n",
        "                     fdir_out=\"./outputs/preprocessed_data_LRE_CoT/factual_multihop\",\n",
        "                     batch_size=4, max_gen_len=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEjV7_Q4BSDE",
        "outputId": "9c929abe-5ac3-42ba-9954-a1b47df88936"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\tNumber of samples: 1024\n",
            "\tNumber of datapoints for patching experiment: 1024\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/128 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hidden_states : [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n",
            "hidden_states : [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  1%|          | 1/128 [00:12<26:50, 12.68s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hidden_states : [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]\n",
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            "\r 26%|██▌       | 33/128 [08:49<24:34, 15.52s/it]"
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            "\r 27%|██▋       | 34/128 [09:02<23:09, 14.79s/it]"
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            "\r 27%|██▋       | 35/128 [09:17<22:54, 14.78s/it]"
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            "\r 28%|██▊       | 36/128 [09:38<25:48, 16.83s/it]"
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            "\r 90%|████████▉ | 115/128 [30:59<03:27, 15.93s/it]"
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            "hidden_states : [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 128/128 [34:16<00:00, 16.06s/it]\n",
            "<ipython-input-45-282d4ffb0362>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  results_df[key] = list(value)\n",
            "<ipython-input-45-282d4ffb0362>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  results_df[key] = list(value)\n"
          ]
        }
      ],
      "source": [
        "import tqdm\n",
        "df_classifier = test_df.copy()\n",
        "cot_correct_baseline_with_classifiers = run_experiment_with_classifiers(classifiers, test_df, \"./outputs/factual/multihop_product_company_ceo.pkl\",\n",
        "               \"./outputs/results/factual\",\n",
        "               fname_out = \"multihop_product_company_ceo\", batch_size=8, n_samples=-1,\n",
        "               save_output=True, replace=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "id": "CYre7uqnBSDE",
        "outputId": "16213782-2da5-4b0d-f657-0f132d063a05"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>1022</th>\n",
              "      <td>0</td>\n",
              "      <td>1022</td>\n",
              "      <td>Where did the author of The Canon of Medicine ...</td>\n",
              "      <td>Where did the author of The Canon of Medicine ...</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>Who wrote The Canon of Medicine?</td>\n",
              "      <td>Where did #1 think Venus was in relation to th...</td>\n",
              "      <td>Where did the author of The Canon of Medicine ...</td>\n",
              "      <td>Avicenna</td>\n",
              "      <td>below the Sun</td>\n",
              "      <td>b</td>\n",
              "      <td>Let's think step by step.</td>\n",
              "      <td>\\n The Can on of Medicine , written by Av ic e...</td>\n",
              "      <td>True</td>\n",
              "      <td>[\\n, (, Note, :, The, author, of, The, Can, on]</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1023</th>\n",
              "      <td>0</td>\n",
              "      <td>1023</td>\n",
              "      <td>When was the region immediately north of the r...</td>\n",
              "      <td>When was the region immediately north of the r...</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>Urim &gt;&gt; country</td>\n",
              "      <td>Where is #1 located?</td>\n",
              "      <td>When was the region immediately north of the r...</td>\n",
              "      <td>Israel</td>\n",
              "      <td>Middle East</td>\n",
              "      <td>1</td>\n",
              "      <td>Let's think step by step.</td>\n",
              "      <td>\\n \\n The region immediately north of U rim is...</td>\n",
              "      <td>False</td>\n",
              "      <td>[\\n, \\n, U, rim, ', s, country, is, located, in]</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024 rows × 17 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cot_correct_baseline_with_classifiers')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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            ],
            "text/plain": [
              "      Unnamed: 0  sample_id  \\\n",
              "0              0          0   \n",
              "1              0          1   \n",
              "2              0          2   \n",
              "3              0          3   \n",
              "4              0          4   \n",
              "...          ...        ...   \n",
              "1019           0       1019   \n",
              "1020           0       1020   \n",
              "1021           0       1021   \n",
              "1022           0       1022   \n",
              "1023           0       1023   \n",
              "\n",
              "                                          prompt_source  \\\n",
              "0     Who developed the eponymous character from the...   \n",
              "1     What county is the city that shares a border w...   \n",
              "2     In what region of the country containing A Lướ...   \n",
              "3     Where do Greyhound buses leave from in the cit...   \n",
              "4     Which county does Lloyd Dane's birthplace belo...   \n",
              "...                                                 ...   \n",
              "1019  Who runs the school system in the country on t...   \n",
              "1020  How many square miles is the city where the Yo...   \n",
              "1021  What county is the city that shares a border w...   \n",
              "1022  Where did the author of The Canon of Medicine ...   \n",
              "1023  When was the region immediately north of the r...   \n",
              "\n",
              "                                          prompt_target  position_source  \\\n",
              "0     Who developed the eponymous character from the...               -1   \n",
              "1     What county is the city that shares a border w...               -1   \n",
              "2     In what region of the country containing A Lướ...               -1   \n",
              "3     Where do Greyhound buses leave from in the cit...               -1   \n",
              "4     Which county does Lloyd Dane's birthplace belo...               -1   \n",
              "...                                                 ...              ...   \n",
              "1019  Who runs the school system in the country on t...               -1   \n",
              "1020  How many square miles is the city where the Yo...               -1   \n",
              "1021  What county is the city that shares a border w...               -1   \n",
              "1022  Where did the author of The Canon of Medicine ...               -1   \n",
              "1023  When was the region immediately north of the r...               -1   \n",
              "\n",
              "      position_target                                      baseline_hop2  \\\n",
              "0                  -1  What series is Mickey's Safari in Letterland f...   \n",
              "1                  -1          In which state is Zubly Cemetery located?   \n",
              "2                  -1                                  A Lưới >> country   \n",
              "3                  -1                          Arna Selznick >> employer   \n",
              "4                  -1                       Lloyd Dane >> place of birth   \n",
              "...               ...                                                ...   \n",
              "1019               -1                         Who hosted the tournament?   \n",
              "1020               -1                    Who was the edict addressed to?   \n",
              "1021               -1                   Andrew Deveaux >> place of birth   \n",
              "1022               -1                   Who wrote The Canon of Medicine?   \n",
              "1023               -1                                    Urim >> country   \n",
              "\n",
              "                                          baseline_hop3  \\\n",
              "0                                    Who developed #1 ?   \n",
              "1            What city became the state capital of #1 ?   \n",
              "2                           John Phan >> place of birth   \n",
              "3                           #1 >> headquarters location   \n",
              "4     #1 >> located in the administrative territoria...   \n",
              "...                                                 ...   \n",
              "1019                                   A Don >> country   \n",
              "1020        Where did the Yongle Emperor greet the #1 ?   \n",
              "1021         What city became the state capital of #1 ?   \n",
              "1022  Where did #1 think Venus was in relation to th...   \n",
              "1023                               Where is #1 located?   \n",
              "\n",
              "                                     baseline_multihop3            hop1  \\\n",
              "0     Who developed the eponymous character from the...    Mickey Mouse   \n",
              "1     What county is the city that shares a border w...  South Carolina   \n",
              "2     In what region of the country containing A Lướ...         Vietnam   \n",
              "3     Where do Greyhound buses leave from in the cit...         Nelvana   \n",
              "4     Which county does Lloyd Dane's birthplace belo...           Eldon   \n",
              "...                                                 ...             ...   \n",
              "1019  Who runs the school system in the country on t...        Thailand   \n",
              "1020  How many square miles is the city where the Yo...     the Karmapa   \n",
              "1021  What county is the city that shares a border w...  South Carolina   \n",
              "1022  Where did the author of The Canon of Medicine ...        Avicenna   \n",
              "1023  When was the region immediately north of the r...          Israel   \n",
              "\n",
              "               hop2 hop3                  cot_prefix  \\\n",
              "0       Walt Disney    W  Let's think step by step.    \n",
              "1          Columbia    R  Let's think step by step.    \n",
              "2           Da Nang    S  Let's think step by step.    \n",
              "3           Toronto    T  Let's think step by step.    \n",
              "4     Miller County    M  Let's think step by step.    \n",
              "...             ...  ...                         ...   \n",
              "1019           Laos    t  Let's think step by step.    \n",
              "1020        Nanjing    2  Let's think step by step.    \n",
              "1021       Columbia    R  Let's think step by step.    \n",
              "1022  below the Sun    b  Let's think step by step.    \n",
              "1023    Middle East    1  Let's think step by step.    \n",
              "\n",
              "            step_by_step_generations_baseline_multihop3  \\\n",
              "0      1 . The character from the series that contai...   \n",
              "1     \\n \\n Answer : The county where the city that ...   \n",
              "2     \\n \\n John Ph an is a Vietnam ese - American e...   \n",
              "3     Is it a major bus station ? Is it a small bus ...   \n",
              "4     \\n \\n L loyd min ster , D ane ' s birth place ...   \n",
              "...                                                 ...   \n",
              "1019  \\n \\n Answer : The school system in the countr...   \n",
              "1020  \\n \\n The ed ict was addressed to the person w...   \n",
              "1021  \\n \\n Answer : The city that shares a border w...   \n",
              "1022  \\n The Can on of Medicine , written by Av ic e...   \n",
              "1023  \\n \\n The region immediately north of U rim is...   \n",
              "\n",
              "      step_by_step_is_correct_baseline_multihop3  \\\n",
              "0                                          False   \n",
              "1                                          False   \n",
              "2                                          False   \n",
              "3                                          False   \n",
              "4                                          False   \n",
              "...                                          ...   \n",
              "1019                                        True   \n",
              "1020                                       False   \n",
              "1021                                       False   \n",
              "1022                                        True   \n",
              "1023                                       False   \n",
              "\n",
              "                                    generations_patched  is_correct_patched  \n",
              "0           [\\n, \\n, A, ., Roger, H, arg, re, aves, \\n]               False  \n",
              "1      [\\n, \\n, Z, ub, ly, C, emetery, is, located, in]               False  \n",
              "2               [\\n, J, OH, N, P, H, AN, was, born, in]               False  \n",
              "3     [\\n, \\n, Ar, na, Sel, zn, ick, is, the, protagon]               False  \n",
              "4        [\\n, n, Lloyd, D, ane, ', s, birth, place, is]               False  \n",
              "...                                                 ...                 ...  \n",
              "1019   [\\n, \\n, A, Don, is, a, character, in, a, novel]                True  \n",
              "1020      [\\n, \\n, Answer, :, , 1, 0, square, miles, .]               False  \n",
              "1021     [\\n, \\n, And, rew, De, ve, aux, was, born, in]               False  \n",
              "1022    [\\n, (, Note, :, The, author, of, The, Can, on]               False  \n",
              "1023   [\\n, \\n, U, rim, ', s, country, is, located, in]               False  \n",
              "\n",
              "[1024 rows x 17 columns]"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cot_correct_baseline_with_classifiers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6b9C9r8BSDF",
        "outputId": "742e326a-7f66-4f4a-9257-5bc65b38b269"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "General Patching MultiHop Accuracy (all source layer x target layer):  0.236328125\n"
          ]
        }
      ],
      "source": [
        "print(\"General Patching MultiHop Accuracy (all source layer x target layer): \",\n",
        "      cot_correct_baseline_with_classifiers[\"is_correct_patched\"].mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmKZES55BSDG"
      },
      "source": [
        "# Comparing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAMuxlCoBSDG"
      },
      "source": [
        "Compare with COT:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "5-csNa9pBSDG"
      },
      "outputs": [],
      "source": [
        "def step_by_step_cot_baseline(df,\n",
        "    fname_in=f\"./preprocessed_data/factual_multihop/combined_multihop_CoT_only_correct_True.pkl\",\n",
        "    fdir_out=\"./outputs/results_CoT/factual_multihop\",\n",
        "    fname_out = f\"combined_multihop_CoT_only_correct_True_step_by_step\",\n",
        "    batch_size=8,\n",
        "    max_gen_len=20,\n",
        "    rewrite=False,\n",
        "    target_col = \"baseline_multihop3\",\n",
        "    object_col = \"hop3\",\n",
        "    cot_prefix = \"Let's think step by step. \"):\n",
        "\n",
        "    df[\"cot_prefix\"] = cot_prefix\n",
        "\n",
        "    def _generate_baseline_single_batch(batch_df):\n",
        "        batch_size = len(batch_df)\n",
        "\n",
        "        results = {}\n",
        "        target_baseline_batch = np.array(batch_df[target_col])\n",
        "        target_baseline_batch = np.core.defchararray.add(cot_prefix, target_baseline_batch.astype(str))\n",
        "        object_batch = np.array(batch_df[object_col])\n",
        "\n",
        "        inp_target_baseline = make_inputs(mt.tokenizer, target_baseline_batch, mt.device)\n",
        "        seq_len_target_baseline = len(inp_target_baseline[\"input_ids\"][0])\n",
        "        output_target_baseline_toks = mt.model.generate(\n",
        "            inp_target_baseline[\"input_ids\"],\n",
        "            max_length=seq_len_target_baseline + max_gen_len,\n",
        "            pad_token_id=mt.model.generation_config.eos_token_id,\n",
        "        )[:, seq_len_target_baseline:]\n",
        "        generations_baseline = decode_tokens(mt.tokenizer, output_target_baseline_toks)\n",
        "        generations_baseline_txt = np.array([\" \".join(sample_gen) for sample_gen in generations_baseline])\n",
        "\n",
        "\n",
        "        is_correct_baseline = np.array([\n",
        "            (object_batch[i] in generations_baseline_txt[i] or\n",
        "             object_batch[i].replace(\" \", \"\") in generations_baseline_txt[i].replace(\" \", \"\"))\n",
        "            for i in range(batch_size)\n",
        "        ])\n",
        "        results.update(\n",
        "            {\n",
        "            f\"step_by_step_generations_{target_col}\": generations_baseline_txt,\n",
        "            f\"step_by_step_is_correct_{target_col}\": is_correct_baseline,\n",
        "            }\n",
        "        )\n",
        "\n",
        "        return results\n",
        "\n",
        "    results = {}\n",
        "    n_batches = len(df) // batch_size\n",
        "    if len(df)%batch_size !=0:\n",
        "        n_batches +=1\n",
        "    for i in tqdm.tqdm(range(n_batches)):\n",
        "        cur_df = df.iloc[batch_size * i : batch_size * (i + 1)]\n",
        "        batch_results = _generate_baseline_single_batch(cur_df)\n",
        "        for key, value in batch_results.items():\n",
        "            if key in results:\n",
        "                results[key] = np.concatenate((results[key], value))\n",
        "            else:\n",
        "                results[key] = value\n",
        "\n",
        "    for key, value in results.items():\n",
        "        df[key] = list(value)\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcPVHHbaBSDH",
        "outputId": "288781c1-2887-415b-b730-12cf8e5464ef"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 128/128 [02:52<00:00,  1.35s/it]\n"
          ]
        }
      ],
      "source": [
        "cot_correct_baseline_step_by_step_baseline = step_by_step_cot_baseline(test_df,\n",
        "    fname_in=f\"./outputs/preprocessed_data_LRE_CoT/factual_multihop/combined_multihop_CoT_only_correct_True.pkl\",\n",
        "    fdir_out=\"./outputs/results_LRE_CoT/factual_multihop\",\n",
        "    fname_out = f\"combined_multihop_CoT_only_correct_True_step_by_step\",\n",
        "    batch_size=8,\n",
        "    max_gen_len=20,\n",
        "    target_col = \"baseline_multihop3\",\n",
        "    object_col = \"hop3\",\n",
        "    cot_prefix = \"Let's think step by step. \",\n",
        "    rewrite=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RDd_5SuFBSDH",
        "outputId": "be8731fa-758d-4ffa-a50b-b3ecc3aa3a99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Canonical CoT ('Let's think step by step. ') MultiHop Accuracy:  0.2744140625\n"
          ]
        }
      ],
      "source": [
        "print(\"Canonical CoT ('Let's think step by step. ') MultiHop Accuracy: \",\n",
        "      cot_correct_baseline_step_by_step_baseline.groupby(['sample_id'])[\"step_by_step_is_correct_baseline_multihop3\"].max().reset_index()[\"step_by_step_is_correct_baseline_multihop3\"].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "tvp7a9TcBSDI"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "def generate_baseline_multihop(\n",
        "    mt, df, batch_size=256, max_gen_len=10,\n",
        "):\n",
        "    def _generate_baseline_single_batch(batch_df):\n",
        "        batch_size = len(batch_df)\n",
        "        cases = [\n",
        "                 (\"baseline_multihop3\", \"hop3\"),\n",
        "                ]\n",
        "        results = {}\n",
        "        for target_col, object_col in cases:\n",
        "\n",
        "            target_baseline_batch = np.array(batch_df[target_col])\n",
        "            object_batch = np.array(batch_df[object_col])\n",
        "\n",
        "\n",
        "            # Step 0: run the the model on target prompt baseline (having the subject token in input rather than patched)\n",
        "            # The goal of this step is to calculate whether the model works correctly by default, and to calculate surprisal\n",
        "            inp_target_baseline = make_inputs(mt.tokenizer, target_baseline_batch, mt.device)\n",
        "            seq_len_target_baseline = len(inp_target_baseline[\"input_ids\"][0])\n",
        "            output_target_baseline_toks = mt.model.generate(\n",
        "                inp_target_baseline[\"input_ids\"],\n",
        "                max_length=seq_len_target_baseline + max_gen_len,\n",
        "                pad_token_id=mt.model.generation_config.eos_token_id,\n",
        "            )[:, seq_len_target_baseline:]\n",
        "            generations_baseline = decode_tokens(mt.tokenizer, output_target_baseline_toks)\n",
        "            generations_baseline_txt = np.array([\" \".join(sample_gen) for sample_gen in generations_baseline])\n",
        "\n",
        "\n",
        "            is_correct_baseline = np.array([\n",
        "                (object_batch[i] in generations_baseline_txt[i] or\n",
        "                 object_batch[i].replace(\" \", \"\") in generations_baseline_txt[i].replace(\" \", \"\"))\n",
        "                for i in range(batch_size)\n",
        "            ])\n",
        "            results.update(\n",
        "                {\n",
        "                f\"generations_{target_col}\": generations_baseline_txt,\n",
        "                f\"is_correct_{target_col}\": is_correct_baseline,\n",
        "                }\n",
        "            )\n",
        "\n",
        "        return results\n",
        "\n",
        "    results = {}\n",
        "    n_batches = len(df) // batch_size\n",
        "    if len(df)%batch_size !=0:\n",
        "        n_batches +=1\n",
        "    for i in tqdm.tqdm(range(n_batches)):\n",
        "        cur_df = df.iloc[batch_size * i : batch_size * (i + 1)]\n",
        "        batch_results = _generate_baseline_single_batch(cur_df)\n",
        "        for key, value in batch_results.items():\n",
        "            if key in results:\n",
        "                results[key] = np.concatenate((results[key], value))\n",
        "            else:\n",
        "                results[key] = value\n",
        "\n",
        "    return results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VWxyu23BSDI",
        "outputId": "dc0ad14f-d399-4779-c231-4f76150fd5e7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 128/128 [01:31<00:00,  1.40it/s]\n"
          ]
        }
      ],
      "source": [
        "df_base = test_df.copy()\n",
        "max_gen_len = 10\n",
        "batch_size = 8\n",
        "eval_results = generate_baseline_multihop(mt, df_base, batch_size=batch_size, max_gen_len=max_gen_len)\n",
        "for key, value in eval_results.items():\n",
        "    df_base[key] = list(value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6LJnKglBSDJ",
        "outputId": "7afa1d5c-b6d8-4a1d-f882-7d352cfd0afe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Base MultiHop Accuracy:  0.1845703125\n"
          ]
        }
      ],
      "source": [
        "print(\"Base MultiHop Accuracy: \",\n",
        "      df_base.groupby(['sample_id'])[\"is_correct_baseline_multihop3\"].max().reset_index()[\"is_correct_baseline_multihop3\"].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
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        "id": "24FgtHf0UAzf"
      },
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      "source": []
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