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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Training"
   ],
   "metadata": {
    "id": "EWdNXqiie8eb"
   }
  },
  {
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   "source": [
    "## Installation"
   ],
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     "start_time": "2024-05-16T10:41:59.587010Z"
    }
   },
   "source": [
    "!pip install sentence-transformers"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
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     "text": [
      "Requirement already satisfied: sentence-transformers in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (2.7.0)\r\n",
      "Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (4.40.2)\r\n",
      "Requirement already satisfied: tqdm in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (4.66.4)\r\n",
      "Requirement already satisfied: torch>=1.11.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (2.3.0)\r\n",
      "Requirement already satisfied: numpy in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (1.26.4)\r\n",
      "Requirement already satisfied: scikit-learn in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (1.4.2)\r\n",
      "Requirement already satisfied: scipy in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (1.13.0)\r\n",
      "Requirement already satisfied: huggingface-hub>=0.15.1 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (0.23.0)\r\n",
      "Requirement already satisfied: Pillow in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sentence-transformers) (10.3.0)\r\n",
      "Requirement already satisfied: filelock in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (3.14.0)\r\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2024.5.0)\r\n",
      "Requirement already satisfied: packaging>=20.9 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (24.0)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (6.0.1)\r\n",
      "Requirement already satisfied: requests in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2.31.0)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (4.11.0)\r\n",
      "Requirement already satisfied: sympy in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (1.12)\r\n",
      "Requirement already satisfied: networkx in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (3.2.1)\r\n",
      "Requirement already satisfied: jinja2 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\r\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (8.9.2.26)\r\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (12.1.3.1)\r\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (11.0.2.54)\r\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (10.3.2.106)\r\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (11.4.5.107)\r\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (12.1.0.106)\r\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (2.20.5)\r\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (12.1.105)\r\n",
      "Requirement already satisfied: triton==2.3.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from torch>=1.11.0->sentence-transformers) (2.3.0)\r\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence-transformers) (12.4.127)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (2024.5.15)\r\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.19.1)\r\n",
      "Requirement already satisfied: safetensors>=0.4.1 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.4.3)\r\n",
      "Requirement already satisfied: joblib>=1.2.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from scikit-learn->sentence-transformers) (1.4.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from scikit-learn->sentence-transformers) (3.5.0)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.5)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.7)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2.2.1)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.2.2)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\r\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install pytrec_eval"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ruSXH9mOWoWx",
    "outputId": "02051fcb-13bf-4dfa-f812-11a5cdcdb420",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:42:11.696905Z",
     "start_time": "2024-05-16T10:42:07.307608Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pytrec_eval in /home/noshamedevil/miniconda3/envs/pytorch/lib/python3.9/site-packages (0.5)\r\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Imports"
   ],
   "metadata": {
    "id": "ZJgkbRJZAN78"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from sentence_transformers import LoggingHandler, util\n",
    "from sentence_transformers.cross_encoder import CrossEncoder\n",
    "from sentence_transformers.cross_encoder.evaluation import CERerankingEvaluator\n",
    "from sentence_transformers import InputExample\n",
    "from datetime import datetime\n",
    "import gzip\n",
    "import os\n",
    "import tarfile\n",
    "import tqdm\n",
    "import logging\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "import sys\n",
    "import pytrec_eval\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.DEBUG)\n",
    "logging.basicConfig(format='%(asctime)s - %(message)s',datefmt='%Y-%m-%d %H:%M:%S')"
   ],
   "metadata": {
    "id": "lt_IhRat3Dc7",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:42:11.705707Z",
     "start_time": "2024-05-16T10:42:11.698624Z"
    }
   },
   "outputs": [],
   "execution_count": 24
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training preparation"
   ],
   "metadata": {
    "id": "rVS4in0cC1wP"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Initialize hyperparameters (e.g., batch size, etc)"
   ],
   "metadata": {
    "id": "ttbCVh96A8vo"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### To prevent from losing the trained model because of getting disconnected from google colab, we suggest you to store trained model on your google drive. In below we do that by loading google.colab and set the path.\n"
   ],
   "metadata": {
    "id": "knYh7WcUR8vq"
   }
  },
  {
   "cell_type": "code",
   "source": "base_path = \"D:\\Downloads\\Leiden University\\Information Retrieval\\IR-A1/models\"",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ExC_PBArSQY_",
    "outputId": "c0379381-1637-42ee-83b9-45ab779653db",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:42:11.713967Z",
     "start_time": "2024-05-16T10:42:11.709081Z"
    }
   },
   "outputs": [],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "source": [
    "!mkdir -p $base_path"
   ],
   "metadata": {
    "id": "ljIKjL57SQ2E",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:42:13.053831Z",
     "start_time": "2024-05-16T10:42:11.716026Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "source": [
    "train_batch_size = 32\n",
    "num_epochs = 1\n",
    "pos_neg_ration = 4\n",
    "max_train_samples = 5e6 "
   ],
   "metadata": {
    "id": "ZFyApJNH3h_R",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:42:13.059692Z",
     "start_time": "2024-05-16T10:42:13.055776Z"
    }
   },
   "outputs": [],
   "execution_count": 27
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load model (cross-encoder/ms-marco-MiniLM-L-2-v2)"
   ],
   "metadata": {
    "id": "laRe4iRBBL4U"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "#We set num_labels=1, which predicts a continous score between 0 and 1\n",
    "#model_name = 'cross-encoder/ms-marco-MiniLM-L-2-v2'\n",
    "#model_name = 'cross-encoder/ms-marco-TinyBERT-L-2-v2'\n",
    "model_name = 'distilroberta-base'\n",
    "model = CrossEncoder(model_name, num_labels=1, max_length=512)\n",
    "model_save_path = base_path  +'finetuned_models/cross-encoder-'+model_name.replace(\"/\", \"-\")+'-'+datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 944,
     "referenced_widgets": [
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      "ff759660211941a79fa836f5223a0a73",
      "1bd626b52dd84b5da9f845c2aca44782",
      "f0917317ae1f44efa60b88fe514f7c6e",
      "18513541a1a84cbeb4ca02ad8bbe1bc5",
      "0aa9c967e2d0403ba5a0f9ba5a5ec02f",
      "f9197fe03c284e5ea46162d940644746",
      "61729ac853c94a8fa4cbe2615ffe7bdc",
      "9198ce724ceb40cebdba02841f6dc4d8",
      "65f275359bc4453783ea26df791cc23f",
      "47f7fa5a87e747848bf0b7ba175bf598",
      "706e502f797743f08cb7ee14a3a3cc8a",
      "3748b35ff7c6498d86cc6650eca45fcf",
      "784d5e47864140a19d93c80e8e186420",
      "8fd9f37f6adc4d329fdd81cc789981b5",
      "aea9c25a4fda494fa772fd752a070a07",
      "c962d36b0065408e86a8699e1170c0f4",
      "71be71cc24a045879ef1f62fce3ca104",
      "a9541d2d5e3e4fe79c07740512a79dad",
      "fa3e00f566bc443d9989be518b2be12f",
      "c961530a7613473d8341b5bb68d3a38c",
      "5d3926d094df41099c118e6fbb92a281",
      "057ff68eec9c449db9d4c998e39c91de",
      "65d7bd236a7745c39d2ddf9b97205932",
      "b4c4af37d017415b8d74141d0fade1c9",
      "288facb3f21440f7aa65eedb091f1bc6",
      "4ee1c2066140442894c0788349796538",
      "b35ede02816e4c0099243a24131326da",
      "d682a9fd27554d8db876e82c6ce78be1",
      "59d5b5ac261742ac9489feae6d6ca7ed",
      "7a2ea69d28bc45af8305d384dd966291",
      "914a9189c5d3454bb8d27ac7c7c721cb",
      "4d683c85590d4fb78181f7199cb5c15f",
      "2efff77b625841949c63ecc959b23381",
      "2052130b3459488a91d4c1b57356ba9a",
      "0158c81685a84d12a787cdb403a3ddf0",
      "796b6440ecc94d588fb869f1de8e1e20",
      "a44d1d7ac4e44c2c925c5f0c55bb5994",
      "9cb73d5ea78b439285149c8fcdab3c76",
      "c927a851e1494463a862e9c98c1fa6e4",
      "e1bbf9d0ffb74825ac7d7eda678d9a42",
      "3f0b339146ce4675b22d9d9dad626861",
      "beaf8e841aa8442d9abeff05fe0d74cc",
      "bed4aceec05d42e4a36efd4d7cf73779",
      "038a561a4996444aa8b18ac05b2b786c",
      "ce84cd4952cc4aafa845f962e02e334f",
      "f9303e2023bf402eb0d47a25f8fc403e",
      "10400aff13b14d30b6cf6ebebdf566f0",
      "7567c4e2b9904933835bc22d7304cc9c",
      "fe2c610cd990497bb3e57f0470bd9db6",
      "f8a3990fdb1342bc9908c7240f7adfc8",
      "176b37379ab44fb79ca40f05bcd5b9b5"
     ]
    },
    "id": "vlDzdeUYBNOC",
    "outputId": "33711965-9916-4504-9914-e27f17b817d8",
    "ExecuteTime": {
     "end_time": "2024-05-26T17:12:49.554759Z",
     "start_time": "2024-05-26T17:12:48.984429Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'CrossEncoder' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[1], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m model_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdistilroberta-base\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m----> 2\u001B[0m model \u001B[38;5;241m=\u001B[39m CrossEncoder(model_name, num_labels\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m, max_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m512\u001B[39m)\n\u001B[0;32m      3\u001B[0m model_save_path \u001B[38;5;241m=\u001B[39m base_path  \u001B[38;5;241m+\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfinetuned_models/cross-encoder-\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m+\u001B[39mmodel_name\u001B[38;5;241m.\u001B[39mreplace(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m-\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m+\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m-\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m+\u001B[39mdatetime\u001B[38;5;241m.\u001B[39mnow()\u001B[38;5;241m.\u001B[39mstrftime(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m%\u001B[39m\u001B[38;5;124mY-\u001B[39m\u001B[38;5;124m%\u001B[39m\u001B[38;5;124mm-\u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[38;5;124m_\u001B[39m\u001B[38;5;124m%\u001B[39m\u001B[38;5;124mH-\u001B[39m\u001B[38;5;124m%\u001B[39m\u001B[38;5;124mM-\u001B[39m\u001B[38;5;124m%\u001B[39m\u001B[38;5;124mS\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'CrossEncoder' is not defined"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Download MSMARCO data + BM25 initial ranking run file"
   ],
   "metadata": {
    "id": "Z1eRAicXBUrT"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!wget https://msmarco.z22.web.core.windows.net/msmarcoranking/queries.tar.gz\n",
    "!tar -xvzf  queries.tar.gz"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NS-bOlb4WB_m",
    "outputId": "d48a4d33-0ae8-4a17-cd1d-310c2ee2e4eb",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:42:38.787570Z",
     "start_time": "2024-05-16T10:42:14.383578Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-05-16 12:42:16--  https://msmarco.z22.web.core.windows.net/msmarcoranking/queries.tar.gz\r\n",
      "Resolving msmarco.z22.web.core.windows.net (msmarco.z22.web.core.windows.net)... 20.150.34.1\r\n",
      "Connecting to msmarco.z22.web.core.windows.net (msmarco.z22.web.core.windows.net)|20.150.34.1|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 18882551 (18M) [application/gzip]\r\n",
      "Saving to: ‘queries.tar.gz.2’\r\n",
      "\r\n",
      "queries.tar.gz.2     24%[===>                ]   4.36M   219KB/s    eta 59s    ^C\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "queries.dev.tsv\r\n",
      "queries.eval.tsv\r\n",
      "queries.train.tsv\r\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "source": [
    "data_folder = 'msmarco-data'\n",
    "os.makedirs(data_folder, exist_ok=True)\n",
    "\n",
    "corpus = {}\n",
    "collection_filepath = os.path.join(data_folder, 'collection.tsv')\n",
    "if not os.path.exists(collection_filepath):\n",
    "    tar_filepath = os.path.join(data_folder, 'collection.tar.gz')\n",
    "    if not os.path.exists(tar_filepath):\n",
    "        logging.info(\"Download collection.tar.gz\")\n",
    "        util.http_get('https://msmarco.z22.web.core.windows.net/msmarcoranking/collection.tar.gz', tar_filepath)\n",
    "\n",
    "    with tarfile.open(tar_filepath, \"r:gz\") as tar:\n",
    "        tar.extractall(path=data_folder)\n",
    "\n",
    "with open(collection_filepath, 'r', encoding='utf8') as fIn:\n",
    "    for line in fIn:\n",
    "        pid, passage = line.strip().split(\"\\t\")\n",
    "        corpus[pid] = passage\n",
    "\n",
    "queries = {}\n",
    "queries_filepath = os.path.join('queries.train.tsv')\n",
    "with open(queries_filepath, 'r', encoding='utf8') as fIn:\n",
    "    for line in fIn:\n",
    "        qid, query = line.strip().split(\"\\t\")\n",
    "        queries[qid] = query\n",
    "\n",
    "train_samples = []\n",
    "dev_samples = {}\n",
    "\n",
    "num_dev_queries = 200\n",
    "num_max_dev_negatives = 200\n",
    "\n",
    "train_eval_filepath = os.path.join(data_folder, 'msmarco-qidpidtriples.rnd-shuf.train-eval.tsv.gz')\n",
    "if not os.path.exists(train_eval_filepath):\n",
    "    logging.info(\"Download \"+os.path.basename(train_eval_filepath))\n",
    "    util.http_get('https://sbert.net/datasets/msmarco-qidpidtriples.rnd-shuf.train-eval.tsv.gz', train_eval_filepath)\n",
    "\n",
    "with gzip.open(train_eval_filepath, 'rt') as fIn:\n",
    "    for line in fIn:\n",
    "        qid, pos_id, neg_id = line.strip().split()\n",
    "\n",
    "        if qid not in dev_samples and len(dev_samples) < num_dev_queries:\n",
    "            dev_samples[qid] = {'query': queries[qid], 'positive': set(), 'negative': set()}\n",
    "\n",
    "        if qid in dev_samples:\n",
    "            dev_samples[qid]['positive'].add(corpus[pos_id])\n",
    "\n",
    "            if len(dev_samples[qid]['negative']) < num_max_dev_negatives:\n",
    "                dev_samples[qid]['negative'].add(corpus[neg_id])\n",
    "\n",
    "\n",
    "train_filepath = os.path.join(data_folder, 'msmarco-qidpidtriples.rnd-shuf.train.tsv.gz')\n",
    "if not os.path.exists(train_filepath):\n",
    "    logging.info(\"Download \"+os.path.basename(train_filepath))\n",
    "    util.http_get('https://sbert.net/datasets/msmarco-qidpidtriples.rnd-shuf.train.tsv.gz', train_filepath)\n",
    "\n",
    "cnt = 0\n",
    "with gzip.open(train_filepath, 'rt') as fIn:\n",
    "    for line in tqdm.tqdm(fIn, unit_scale=True):\n",
    "        qid, pos_id, neg_id = line.strip().split()\n",
    "\n",
    "        if qid in dev_samples:\n",
    "            continue\n",
    "\n",
    "        query = queries[qid]\n",
    "        if (cnt % (pos_neg_ration+1)) == 0:\n",
    "            passage = corpus[pos_id]\n",
    "            label = 1\n",
    "        else:\n",
    "            passage = corpus[neg_id]\n",
    "            label = 0\n",
    "\n",
    "        train_samples.append(InputExample(texts=[query, passage], label=label))\n",
    "        cnt += 1\n",
    "\n",
    "        if cnt >= max_train_samples:\n",
    "            break"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292,
     "referenced_widgets": [
      "cf94ef1d41b143d5a669acbbe318b556",
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      "a3b47da69dec46239f08446b13a43758",
      "62d3f34c0d0249d7964eb520663d0df5",
      "efcbdf28210947f1a0e78555fb8544e7",
      "442ee5c38e06434d86a3251f547bc572",
      "794ed7d181d544c5badcbf5184da93bb",
      "239a8aa01ba34c9d80623506e4e4d0ef",
      "93820caa5cf347f988bb6e5d3f275f3f",
      "7a614cacca8a49e8ae50781bf0947bd5",
      "95c50ac0275a494ba3a058e5657cb98b",
      "d07393955f224ab8bee9d531983e148d",
      "4fd08b245cf74e508bc00f743afe2a1e",
      "746aef3fdbd14cf1be637667903d31ff",
      "843d5bf335734c0087342a2df88a06c4",
      "8dc665d15bbf47c08e9df7d2bb914ab4",
      "ee15f2e7446b48209cdc38c0ea6e9bf5",
      "c29d9ba08dec4d17a5ec35be6abbf118",
      "e12400ddcac646308637a54dfbcfcf27",
      "ed1f1112d5c54b83a907774c0c54c274",
      "7d91c0e6df5f4de8a7a77e07d8092e9b"
     ]
    },
    "id": "jjcxJ0td3BDr",
    "outputId": "8a62ce8d-2ac5-44c3-c506-e032d256dea5",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:44:17.571486Z",
     "start_time": "2024-05-16T10:43:13.629535Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5.00Mit [00:38, 130kit/s] \n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Initialize dataloader"
   ],
   "metadata": {
    "id": "9i65_aSjBh_k"
   }
  },
  {
   "cell_type": "code",
   "source": "train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)",
   "metadata": {
    "id": "b6vNH0mwBk93",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:44:19.871483Z",
     "start_time": "2024-05-16T10:44:17.575470Z"
    }
   },
   "outputs": [],
   "execution_count": 32
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Initialize CERerankingEvaluator Class\n",
    "### The CERerankingEvaluator class evaluates the model after every 1k steps of training on the validation set\n",
    "### Currently, CERerankingEvaluator computes MRR@10 on the valiadion set. You need to change MRR@10 to NDCG@10 for Exercise 4.\n",
    "###For that, you can download the CERerankingEvaluator class ([link](https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/cross_encoder/evaluation/CERerankingEvaluator.py)) and upload the modified implementation to the brightspace.\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "id": "BF2-sWTABoI5"
   }
  },
  {
   "cell_type": "code",
   "source": "evaluator = CERerankingEvaluator(dev_samples, name='train-eval')",
   "metadata": {
    "id": "3Fbxj6hwB5jt",
    "ExecuteTime": {
     "end_time": "2024-05-16T10:44:19.913514Z",
     "start_time": "2024-05-16T10:44:19.875201Z"
    }
   },
   "outputs": [],
   "execution_count": 33
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train the model\n",
    "### You can stop the training after one hour by stopping the run"
   ],
   "metadata": {
    "id": "F-su_NdICr6r"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model.fit(\n",
    "    train_dataloader=train_dataloader,\n",
    "    evaluator=evaluator,\n",
    "    epochs=num_epochs,\n",
    "    evaluation_steps=1000,\n",
    "    warmup_steps=5000,\n",
    "    output_path=model_save_path,\n",
    "    use_amp=True,\n",
    ")\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 438,
     "referenced_widgets": [
      "253f5adc4f3549b597502bfbd638455b",
      "930f813205ab4a9dbf29d37a7343e505",
      "7e257d0de690439887c98628d256af6f",
      "195afa805d8a4f6eb615c316c5a51d65",
      "760afcefba63407e98e4cae484902559",
      "4e9b186d0c6644648b449c9af0ee3aa3",
      "08aa012af24040409772d064d5a19fcd",
      "968f9a7fdd584f90af08753bb03e8dc5",
      "a8fcdfb306ca4ef0bb05c196d1c762c0",
      "88b496b08316400a8f23b8222764369f",
      "546f6108ff7d4fb4a556b59c873c1a83",
      "17a777d5c5de440383543c5fdf1b2345",
      "edbbc87acdf047d6afd95570185e18b7",
      "b9a7cab5677e4b20886346100458ee07",
      "06b84afaea8e4d719f32be100e383960",
      "5146e6dfb0914c1e944e1bd9e2432418",
      "208256c40d2b41aa8825a9c7afb18d5f",
      "e4c2a6a8f813444394dd3d374e19f6ac",
      "7d2465c36a33481fac33c0855b23a79d",
      "2e4e4effb18b47b0b60460e5f033f33c",
      "b3681639554b4b88bdfba4d55298b71b",
      "911dbbec59bb486187aed76342edaf6d"
     ]
    },
    "id": "qtvow-8rAJY3",
    "outputId": "3627a9bc-7e17-46ce-eeb6-ee33ea6439d8",
    "ExecuteTime": {
     "end_time": "2024-05-16T11:44:41.254197Z",
     "start_time": "2024-05-16T10:44:21.734822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f288a63b63674f0aaef6537cb0fda4b1"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Iteration:   0%|          | 0/156250 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "107f1320596042b4bd9f15ed573035f2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-16 13:17:35 - CERerankingEvaluator: Evaluating the model on train-eval dataset in epoch 0 after 1000 steps:\n",
      "2024-05-16 13:42:06 - Queries: 200 \t Positives: Min 1.0, Mean 1.1, Max 3.0 \t Negatives: Min 100.0, Mean 199.1, Max 200.0\n",
      "2024-05-16 13:42:06 - MRR@10: 38.83\n",
      "2024-05-16 13:42:06 - NDCG@10: 45.73\n",
      "2024-05-16 13:42:06 - Save model to /home/noshamedevil/Documents/Assignments/IR/modelsfinetuned_models/cross-encoder-distilroberta-base-2024-05-16_12-42-14\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[34], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# Train the model\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m      3\u001B[0m \u001B[43m    \u001B[49m\u001B[43mtrain_dataloader\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtrain_dataloader\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      4\u001B[0m \u001B[43m    \u001B[49m\u001B[43mevaluator\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mevaluator\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      5\u001B[0m \u001B[43m    \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnum_epochs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      6\u001B[0m \u001B[43m    \u001B[49m\u001B[43mevaluation_steps\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1000\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      7\u001B[0m \u001B[43m    \u001B[49m\u001B[43mwarmup_steps\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m5000\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      8\u001B[0m \u001B[43m    \u001B[49m\u001B[43moutput_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel_save_path\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      9\u001B[0m \u001B[43m    \u001B[49m\u001B[43muse_amp\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m     10\u001B[0m \u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/pytorch/lib/python3.9/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py:253\u001B[0m, in \u001B[0;36mCrossEncoder.fit\u001B[0;34m(self, train_dataloader, evaluator, epochs, loss_fct, activation_fct, scheduler, warmup_steps, optimizer_class, optimizer_params, weight_decay, evaluation_steps, output_path, save_best_model, max_grad_norm, use_amp, callback, show_progress_bar)\u001B[0m\n\u001B[1;32m    251\u001B[0m scaler\u001B[38;5;241m.\u001B[39munscale_(optimizer)\n\u001B[1;32m    252\u001B[0m torch\u001B[38;5;241m.\u001B[39mnn\u001B[38;5;241m.\u001B[39mutils\u001B[38;5;241m.\u001B[39mclip_grad_norm_(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel\u001B[38;5;241m.\u001B[39mparameters(), max_grad_norm)\n\u001B[0;32m--> 253\u001B[0m \u001B[43mscaler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstep\u001B[49m\u001B[43m(\u001B[49m\u001B[43moptimizer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    254\u001B[0m scaler\u001B[38;5;241m.\u001B[39mupdate()\n\u001B[1;32m    256\u001B[0m skip_scheduler \u001B[38;5;241m=\u001B[39m scaler\u001B[38;5;241m.\u001B[39mget_scale() \u001B[38;5;241m!=\u001B[39m scale_before_step\n",
      "File \u001B[0;32m~/miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/amp/grad_scaler.py:453\u001B[0m, in \u001B[0;36mGradScaler.step\u001B[0;34m(self, optimizer, *args, **kwargs)\u001B[0m\n\u001B[1;32m    447\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39munscale_(optimizer)\n\u001B[1;32m    449\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m (\n\u001B[1;32m    450\u001B[0m     \u001B[38;5;28mlen\u001B[39m(optimizer_state[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfound_inf_per_device\u001B[39m\u001B[38;5;124m\"\u001B[39m]) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[1;32m    451\u001B[0m ), \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo inf checks were recorded for this optimizer.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m--> 453\u001B[0m retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_maybe_opt_step\u001B[49m\u001B[43m(\u001B[49m\u001B[43moptimizer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptimizer_state\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    455\u001B[0m optimizer_state[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstage\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m OptState\u001B[38;5;241m.\u001B[39mSTEPPED\n\u001B[1;32m    457\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m retval\n",
      "File \u001B[0;32m~/miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/amp/grad_scaler.py:350\u001B[0m, in \u001B[0;36mGradScaler._maybe_opt_step\u001B[0;34m(self, optimizer, optimizer_state, *args, **kwargs)\u001B[0m\n\u001B[1;32m    342\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_maybe_opt_step\u001B[39m(\n\u001B[1;32m    343\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m    344\u001B[0m     optimizer: torch\u001B[38;5;241m.\u001B[39moptim\u001B[38;5;241m.\u001B[39mOptimizer,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    347\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: Any,\n\u001B[1;32m    348\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Optional[\u001B[38;5;28mfloat\u001B[39m]:\n\u001B[1;32m    349\u001B[0m     retval: Optional[\u001B[38;5;28mfloat\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m--> 350\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;43msum\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitem\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43moptimizer_state\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfound_inf_per_device\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[1;32m    351\u001B[0m         retval \u001B[38;5;241m=\u001B[39m optimizer\u001B[38;5;241m.\u001B[39mstep(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m    352\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m retval\n",
      "File \u001B[0;32m~/miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/amp/grad_scaler.py:350\u001B[0m, in \u001B[0;36m<genexpr>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m    342\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_maybe_opt_step\u001B[39m(\n\u001B[1;32m    343\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m    344\u001B[0m     optimizer: torch\u001B[38;5;241m.\u001B[39moptim\u001B[38;5;241m.\u001B[39mOptimizer,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    347\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: Any,\n\u001B[1;32m    348\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Optional[\u001B[38;5;28mfloat\u001B[39m]:\n\u001B[1;32m    349\u001B[0m     retval: Optional[\u001B[38;5;28mfloat\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m--> 350\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28msum\u001B[39m(\u001B[43mv\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitem\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m v \u001B[38;5;129;01min\u001B[39;00m optimizer_state[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfound_inf_per_device\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mvalues()):\n\u001B[1;32m    351\u001B[0m         retval \u001B[38;5;241m=\u001B[39m optimizer\u001B[38;5;241m.\u001B[39mstep(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m    352\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m retval\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": 34
  }
 ]
}