finetune_multivers_on_climate_fever.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aaalexlit/omdena_climate_change_challenge_notebooks/blob/main/finetune_multivers_on_climate_fever.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finetune pretrained MultiVerS model on modified Climate-FEVER dataset\n",
        "\n",
        "Following [these instructions](https://github.com/dwadden/multivers/blob/main/doc/training.md)\n",
        "\n",
        "The idea is to re-use the code from the instruction but replace the content of `covidfact` folder with the modified CLIMATE-FEVER "
      ],
      "metadata": {
        "id": "YTrSqQaVNUCI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!git clone https://github.com/dwadden/multivers.git\n",
        "!git clone https://github.com/allenai/scifact-evaluator.git\n",
        "!pip install virtualenv\n",
        "!virtualenv multivers\n",
        "!source /content/multivers/bin/activate; pip install -r /content/multivers/requirements.txt"
      ],
      "metadata": {
        "id": "UKvsScigNK3R"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python /content/multivers/script/get_checkpoint.py longformer_large_science\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4q7Gi_wNaOY",
        "outputId": "6a32a03a-ff3c-4ae4-b97b-450f7b946253"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-25 04:38:01--  https://scifact.s3.us-west-2.amazonaws.com/longchecker/latest/checkpoints/longformer_large_science.ckpt\n",
            "Resolving scifact.s3.us-west-2.amazonaws.com (scifact.s3.us-west-2.amazonaws.com)... 52.92.208.170, 52.218.184.201, 3.5.79.119, ...\n",
            "Connecting to scifact.s3.us-west-2.amazonaws.com (scifact.s3.us-west-2.amazonaws.com)|52.92.208.170|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1646843693 (1.5G) [binary/octet-stream]\n",
            "Saving to: ‘checkpoints/longformer_large_science.ckpt’\n",
            "\n",
            "checkpoints/longfor 100%[===================>]   1.53G  35.4MB/s    in 41s     \n",
            "\n",
            "2023-04-25 04:38:42 (38.5 MB/s) - ‘checkpoints/longformer_large_science.ckpt’ saved [1646843693/1646843693]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/checkpoints /content/multivers/checkpoints"
      ],
      "metadata": {
        "id": "42qDM1RcNb6I"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the train data.\n",
        "\n",
        "!gdown https://drive.google.com/uc?id=1uiG3RCA366nbLX9NoEbYX5BAi2NFXiKG\n",
        "!tar -xf data_train.tar.gz\n",
        "!rm data_train.tar.gz\n",
        "\n",
        "!mv /content/data_train /content/multivers/data_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5FhSYiNX-xx",
        "outputId": "0095a589-9ee0-4d84-e9df-97a7c50c1865"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uiG3RCA366nbLX9NoEbYX5BAi2NFXiKG\n",
            "To: /content/data_train.tar.gz\n",
            "100% 417M/417M [00:02<00:00, 184MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "v04Ula66Ewyd"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install datasets\n",
        "!pip install jsonlines"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import jsonlines\n",
        "\n",
        "ds_orig = load_dataset(\"climate_fever\", split='test')\n",
        "df = ds_orig.to_pandas()\n",
        "\n",
        "mv_label_dict = {0: 'SUPPORT', 1: 'CONTRADICT'}\n",
        "\n",
        "docs = {}\n",
        "doc_id = 0\n",
        "indexed_evidence_sents = set()\n",
        "\n",
        "counter = 0\n",
        "\n",
        "with jsonlines.open('claims_comb_train.jsonl', 'w') as claims_train_writer, \\\n",
        "    jsonlines.open('claims_comb_test.jsonl', 'w') as claims_test_writer, \\\n",
        "    jsonlines.open('claims_comb_dev.jsonl', 'w') as claims_dev_writer:\n",
        "    for claim_row in df.to_dict('records'):\n",
        "        counter += 1\n",
        "        evidences = claim_row['evidences']\n",
        "        cur_evidence_dict = {}\n",
        "        cur_claim_doc_ids = set()\n",
        "        for evidence in evidences:\n",
        "            evidence_label = evidence['evidence_label']\n",
        "            evidence_article = evidence['article']\n",
        "            evidence_sent = evidence['evidence']\n",
        "            if evidence_article in docs:\n",
        "                doc = docs.get(evidence_article)\n",
        "            else:\n",
        "                doc = {\"doc_id\": doc_id,\n",
        "                       \"title\": evidence_article,\n",
        "                       \"abstract\": []}\n",
        "                docs[evidence_article] = doc\n",
        "                doc_id += 1\n",
        "            cur_doc_id = doc[\"doc_id\"]\n",
        "            cur_claim_doc_ids.add(cur_doc_id)\n",
        "            abstract = doc[\"abstract\"]\n",
        "            # Don't allow abstracts longer than 4096 otherwise longformer won't work\n",
        "            if len(''.join(abstract).split()) > 2900:\n",
        "                continue\n",
        "            if not evidence_sent in indexed_evidence_sents:\n",
        "                abstract.append(evidence_sent)\n",
        "                indexed_evidence_sents.add(evidence_sent)\n",
        "                sent_ind = len(abstract) - 1\n",
        "            else:\n",
        "                sent_ind = abstract.index(evidence_sent)\n",
        "\n",
        "            # add only evidences to the documents that are not NEI\n",
        "            if evidence_label != 2:\n",
        "                curr_sent = {\n",
        "                    \"sentences\": [sent_ind],\n",
        "                    \"label\": mv_label_dict[evidence_label]\n",
        "                }\n",
        "                if f\"{cur_doc_id}\" in cur_evidence_dict:\n",
        "                    exist_sents = cur_evidence_dict[f\"{cur_doc_id}\"]\n",
        "                    for s in exist_sents:\n",
        "                        if s['label'] == curr_sent['label']:\n",
        "                            s['sentences'].append(sent_ind)\n",
        "                else:\n",
        "                    cur_evidence_dict[f\"{cur_doc_id}\"] = [curr_sent]\n",
        "\n",
        "        claim_doc = {\n",
        "            'id': int(claim_row['claim_id']),\n",
        "            'claim': claim_row['claim'],\n",
        "            'cited_doc_ids': list(cur_claim_doc_ids),\n",
        "            'evidence': cur_evidence_dict\n",
        "        }\n",
        "        if counter % 4 == 0:\n",
        "            claims_test_writer.write(claim_doc)\n",
        "        else:\n",
        "            claims_train_writer.write(claim_doc)\n",
        "        if counter % 20 == 0:\n",
        "          claims_dev_writer.write(claim_doc)\n",
        "\n",
        "\n",
        "with jsonlines.open('corpus_comb_for_training.jsonl', 'w') as corpus_writer:\n",
        "    for doc in docs.values():\n",
        "        if doc[\"abstract\"]:\n",
        "            corpus_writer.write(doc)\n"
      ],
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            "63cb49baf3814c0c9d6ff864c496ec0d",
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            "08a7a9f01eaf485b9e8fefd01cc31880",
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            "a71945d84cb14d198c9a5ac7fff553b6",
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            "9e1953de4e174122845cc23d9694496e",
            "e0ea111499c04e96bd499d7345060e3f",
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            "912f8c89d5cc477fb702e6b314654b01",
            "014afa76fd914285b5dac5b79d3f0e28",
            "33952c4b7c504ca2aabcf6112bff06fd",
            "1ac75ab2c69d4a9f98a49208721d85e6",
            "ce89c7cb6b4a497e8119e56573f9ffc0",
            "24bc1fc335c94d6cb856e679d29c1302"
          ]
        },
        "outputId": "bcba755b-c910-4f22-fa96-53a82bf2f3ce"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.13k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "673fce952c1e401dbdb22c3092e59556"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/2.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9fa2b25904ed42a38011138ce07bd1e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.00k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63cb49baf3814c0c9d6ff864c496ec0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset climate_fever/default to /root/.cache/huggingface/datasets/climate_fever/default/1.0.1/9091d2d01d327631e4880d42fda9e8d69da1fb59b53b12d68867cee6e5770ba4...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/686k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4653d3a1363d4a72ad6ff2a0395d53c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1535 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e1953de4e174122845cc23d9694496e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset climate_fever downloaded and prepared to /root/.cache/huggingface/datasets/climate_fever/default/1.0.1/9091d2d01d327631e4880d42fda9e8d69da1fb59b53b12d68867cee6e5770ba4. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm /content/multivers/data_train/target/covidfact/*\n",
        "!mv /content/claims_comb_test.jsonl /content/multivers/data_train/target/covidfact/claims_test.jsonl\n",
        "!mv /content/claims_comb_train.jsonl /content/multivers/data_train/target/covidfact/claims_train.jsonl\n",
        "!mv /content/claims_comb_dev.jsonl /content/multivers/data_train/target/covidfact/claims_dev.jsonl\n",
        "!mv /content/corpus_comb_for_training.jsonl /content/multivers/data_train/target/covidfact/corpus.jsonl"
      ],
      "metadata": {
        "id": "8a04JCpLZrZp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "manually  change line 59 of /content/multivers/script/train_target.py\n",
        "to become `        \"multivers/multivers/train.py\",\n",
        "`"
      ],
      "metadata": {
        "id": "NXoPXKIwkCLE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# to continue training\n",
        "manually change this line https://github.com/dwadden/multivers/blob/main/script/train_target.py#L65 to start from available checkpoint\n",
        "in /content/checkpoints_user/covidfact/checkpoint/last.ckpt\n",
        "\n",
        "and lower number of epochs by 1 on ine 51 of the same file"
      ],
      "metadata": {
        "id": "xlo5wafUTV-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile /content/multivers/script/train_target.py\n",
        "\"\"\"\n",
        "Kickoff training on target datasets.\n",
        "\n",
        "NOTE: Training right now doesn't work with multiple GPU's and DDP. This is known issue;\n",
        "see for instance https://lightning.ai/forums/t/gradient-checkpointing-ddp-nan/398/7.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "import argparse\n",
        "import subprocess\n",
        "\n",
        "\n",
        "def get_args():\n",
        "    help_gpus = \"\"\"GPU's used for training.\n",
        "    If a single int, specifies the number of GPU's.\n",
        "    If a comma-separated list, specifies the specific device ID's.\n",
        "    For a single specific device, write it as `[device-num],`\n",
        "    \"\"\"\n",
        "\n",
        "    parser = argparse.ArgumentParser(\"Kick off model training.\")\n",
        "    parser.add_argument(\n",
        "        \"--dataset\",\n",
        "        type=str,\n",
        "        help=\"The dataset to train on.\",\n",
        "        choices=[\"scifact_20\", \"scifact_10\", \"healthver\", \"covidfact\"],\n",
        "    )\n",
        "    parser.add_argument(\"--gpus\", type=str, help=help_gpus)\n",
        "    parser.add_argument(\n",
        "        \"--gradient_checkpointing\",\n",
        "        action=\"store_true\",\n",
        "        help=\"Turning this on decreases memory usage at the cost of slower training\",\n",
        "    )\n",
        "    args = parser.parse_args()\n",
        "\n",
        "    return args\n",
        "\n",
        "\n",
        "def main():\n",
        "    args = get_args()\n",
        "    gpus = args.gpus\n",
        "\n",
        "    # Deal with case of specific devides\n",
        "    if \",\" in gpus:\n",
        "        n_gpus = len([x for x in gpus.split(\",\") if x])\n",
        "    else:\n",
        "        n_gpus = int(gpus)\n",
        "\n",
        "    if n_gpus not in [1, 2, 4, 8]:\n",
        "        raise ValueError(\"The number of GPU's must be a power of 2.\")\n",
        "\n",
        "    epochs = 3\n",
        "    workers_per_gpu = 4  # Number of CPU's per gpu.\n",
        "    effective_batch_size = 8  # Desired effective batch size.\n",
        "    accumulate_grad_batches = effective_batch_size // n_gpus\n",
        "    num_workers = workers_per_gpu * n_gpus\n",
        "\n",
        "    cmd = [\n",
        "        \"python\",\n",
        "        \"multivers/multivers/train.py\",\n",
        "        \"--result_dir\",\n",
        "        \"checkpoints_user\",\n",
        "        \"--datasets\",\n",
        "        args.dataset,\n",
        "        \"--starting_checkpoint\",\n",
        "        \"/content/checkpoints/last.ckpt\",\n",
        "        \"--experiment_name\",\n",
        "        args.dataset,\n",
        "        \"--num_workers\",\n",
        "        num_workers,\n",
        "        \"--gpus\",\n",
        "        gpus,\n",
        "        \"--accumulate_grad_batches\",\n",
        "        accumulate_grad_batches,\n",
        "        \"--lr\",\n",
        "        \"1e-5\",\n",
        "        \"--precision\",\n",
        "        16,\n",
        "        \"--max_epochs\",\n",
        "        epochs,\n",
        "        \"--scheduler_total_epochs\",\n",
        "        epochs,\n",
        "        \"--train_batch_size\",\n",
        "        1,\n",
        "        \"--eval_batch_size\",\n",
        "        2,\n",
        "        \"--encoder_name\",\n",
        "        \"longformer-large-science\",\n",
        "        \"--no_reweight_labels\",\n",
        "    ]\n",
        "\n",
        "    # If training on more than 1 gpu, use DDP accelerator.\n",
        "    if n_gpus > 1:\n",
        "        cmd.extend([\"--accelerator\", \"ddp\"])\n",
        "\n",
        "    # Turn on gradient checkpointing if requested.\n",
        "    if args.gradient_checkpointing:\n",
        "        cmd.append(\"--gradient_checkpointing\")\n",
        "\n",
        "    subprocess.call(map(str, cmd))\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsF8UkOVHk6Z",
        "outputId": "579ee1cc-1c17-403b-eeb3-5e7f2bb7816f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting /content/multivers/script/train_target.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# To start training"
      ],
      "metadata": {
        "id": "R-24P-BbQepg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !source /content/multivers/bin/activate; python /content/multivers/script/train_target.py \\\n",
        "#         --dataset=covidfact --gpus=1 --gradient_checkpointing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BGL--8q8QydA",
        "outputId": "d8e5b1f6-2b09-4807-b7da-92e7f4bd1997"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Global seed set to 76\n",
            "fatal: not a git repository (or any of the parent directories): .git\n",
            "GPU available: True, used: True\n",
            "TPU available: None, using: 0 TPU cores\n",
            "Using native 16bit precision.\n",
            "/content/multivers/lib/python3.9/site-packages/pytorch_lightning/utilities/distributed.py:50: UserWarning: Experiment logs directory ./checkpoints_user/covidfact exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
            "  warnings.warn(*args, **kwargs)\n",
            "\n",
            "  | Name                 | Type            | Params\n",
            "---------------------------------------------------------\n",
            "0 | encoder              | LongformerModel | 434 M \n",
            "1 | dropout              | Dropout         | 0     \n",
            "2 | label_classifier     | FeedForward     | 1.1 M \n",
            "3 | rationale_classifier | FeedForward     | 2.1 M \n",
            "4 | metrics              | ModuleDict      | 0     \n",
            "---------------------------------------------------------\n",
            "437 M     Trainable params\n",
            "0         Non-trainable params\n",
            "437 M     Total params\n",
            "1,751.052 Total estimated model params size (MB)\n",
            "Validation sanity check:   0% 0/2 [00:00<?, ?it/s]/content/multivers/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
            "Epoch 0:  97% 3520/3624 [2:26:22<04:19,  2.49s/it, loss=3.12, v_num=fact]\n",
            "Validating: 0it [00:00, ?it/s]\u001b[A\n",
            "Validating:   0% 0/110 [00:00<?, ?it/s]\u001b[A\n",
            "Epoch 0:  98% 3540/3624 [2:27:08<03:29,  2.49s/it, loss=3.12, v_num=fact]\n",
            "Epoch 0:  98% 3560/3624 [2:27:35<02:39,  2.49s/it, loss=3.12, v_num=fact]\n",
            "Epoch 0:  99% 3580/3624 [2:28:09<01:49,  2.48s/it, loss=3.12, v_num=fact]\n",
            "Epoch 0:  99% 3600/3624 [2:28:45<00:59,  2.48s/it, loss=3.12, v_num=fact]\n",
            "Epoch 0: 100% 3620/3624 [2:29:25<00:09,  2.48s/it, loss=3.12, v_num=fact]\n",
            "Epoch 0: 100% 3624/3624 [2:29:46<00:00,  2.48s/it, loss=3.05, v_num=fact]\n",
            "                                                 \u001b[A/content/multivers/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n",
            "Epoch 1:   3% 100/3624 [03:49<2:14:32,  2.29s/it, loss=3.34, v_num=fact]Traceback (most recent call last):\n",
            "  File \"/content/multivers/script/train_target.py\", line 103, in <module>\n",
            "    main()\n",
            "  File \"/content/multivers/script/train_target.py\", line 99, in main\n",
            "    subprocess.call(map(str, cmd))\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 351, in call\n",
            "    return p.wait(timeout=timeout)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1189, in wait\n",
            "    return self._wait(timeout=timeout)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1917, in _wait\n",
            "    (pid, sts) = self._try_wait(0)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1875, in _try_wait\n",
            "    (pid, sts) = os.waitpid(self.pid, wait_flags)\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !source /content/multivers/bin/activate; python /content/multivers/script/train_target.py \\\n",
        "#         --dataset=covidfact --gpus=1 --gradient_checkpointing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "4hRCRp0h3bV2",
        "outputId": "68cb684c-384b-4018-da4a-fed7dc60150e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Global seed set to 76\n",
            "fatal: not a git repository (or any of the parent directories): .git\n",
            "Downloading: 100% 803/803 [00:00<00:00, 628kB/s]\n",
            "Downloading: 100% 899k/899k [00:00<00:00, 5.65MB/s]\n",
            "Downloading: 100% 456k/456k [00:00<00:00, 3.67MB/s]\n",
            "Downloading: 100% 1.36M/1.36M [00:00<00:00, 12.8MB/s]\n",
            "Downloading: 100% 1.74G/1.74G [00:45<00:00, 37.9MB/s]\n",
            "GPU available: True, used: True\n",
            "TPU available: None, using: 0 TPU cores\n",
            "Using native 16bit precision.\n",
            "/content/multivers/lib/python3.9/site-packages/pytorch_lightning/utilities/distributed.py:50: UserWarning: Experiment logs directory ./checkpoints_user/covidfact_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
            "  warnings.warn(*args, **kwargs)\n",
            "\n",
            "  | Name                 | Type            | Params\n",
            "---------------------------------------------------------\n",
            "0 | encoder              | LongformerModel | 434 M \n",
            "1 | dropout              | Dropout         | 0     \n",
            "2 | label_classifier     | FeedForward     | 1.1 M \n",
            "3 | rationale_classifier | FeedForward     | 2.1 M \n",
            "4 | metrics              | ModuleDict      | 0     \n",
            "---------------------------------------------------------\n",
            "437 M     Trainable params\n",
            "0         Non-trainable params\n",
            "437 M     Total params\n",
            "1,751.052 Total estimated model params size (MB)\n",
            "Validation sanity check:   0% 0/2 [00:00<?, ?it/s]/content/multivers/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
            "Epoch 0:  97% 3520/3624 [2:19:23<04:07,  2.38s/it, loss=2.05, v_num=ct_0]\n",
            "Validating: 0it [00:00, ?it/s]\u001b[A\n",
            "Validating:   0% 0/110 [00:00<?, ?it/s]\u001b[A\n",
            "Epoch 0:  98% 3540/3624 [2:20:06<03:19,  2.37s/it, loss=2.05, v_num=ct_0]\n",
            "Epoch 0:  98% 3560/3624 [2:20:31<02:31,  2.37s/it, loss=2.05, v_num=ct_0]\n",
            "Epoch 0:  99% 3580/3624 [2:21:03<01:44,  2.36s/it, loss=2.05, v_num=ct_0]\n",
            "Epoch 0:  99% 3600/3624 [2:21:37<00:56,  2.36s/it, loss=2.05, v_num=ct_0]\n",
            "Epoch 0: 100% 3620/3624 [2:22:14<00:09,  2.36s/it, loss=2.05, v_num=ct_0]\n",
            "Epoch 0: 100% 3624/3624 [2:22:33<00:00,  2.36s/it, loss=2.32, v_num=ct_0]\n",
            "                                                 \u001b[A/content/multivers/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n",
            "Epoch 1:  44% 1580/3624 [1:02:45<1:21:11,  2.38s/it, loss=2.84, v_num=ct_0]Traceback (most recent call last):\n",
            "  File \"/content/multivers/script/train_target.py\", line 103, in <module>\n",
            "    main()\n",
            "  File \"/content/multivers/script/train_target.py\", line 99, in main\n",
            "    subprocess.call(map(str, cmd))\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 351, in call\n",
            "    return p.wait(timeout=timeout)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1189, in wait\n",
            "    return self._wait(timeout=timeout)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1917, in _wait\n",
            "    (pid, sts) = self._try_wait(0)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1875, in _try_wait\n",
            "    (pid, sts) = os.waitpid(self.pid, wait_flags)\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download the last checkpoit "
      ],
      "metadata": {
        "id": "hZD_y_H1ISk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gsutil cp gs://cc-evidences-data/multivers_checkpoints/covidfact/checkpoint/last.ckpt /content/checkpoints/last.ckpt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "YL2yV1SyIW7y",
        "outputId": "9f7cfaa0-d5c9-4b79-d57b-b91ca04211da"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Copying gs://cc-evidences-data/multivers_checkpoints/covidfact/checkpoint/last.ckpt...\n",
            "/ [0 files][    0.0 B/  4.9 GiB]                                                \r==> NOTE: You are downloading one or more large file(s), which would\n",
            "run significantly faster if you enabled sliced object downloads. This\n",
            "feature is enabled by default but requires that compiled crcmod be\n",
            "installed (see \"gsutil help crcmod\").\n",
            "\n",
            "\\ [1 files][  4.9 GiB/  4.9 GiB]   23.9 MiB/s                                   \n",
            "Operation completed over 1 objects/4.9 GiB.                                      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Continue training"
      ],
      "metadata": {
        "id": "pZ0VXjkXI4V8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !source /content/multivers/bin/activate; python /content/multivers/script/train_target.py \\\n",
        "#         --dataset=covidfact --gpus=1 --gradient_checkpointing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsT-t_3CIzUU",
        "outputId": "e2dd8ad8-ea4b-4b3d-db88-458252c25231"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Global seed set to 76\n",
            "fatal: not a git repository (or any of the parent directories): .git\n",
            "Downloading: 100% 803/803 [00:00<00:00, 501kB/s]\n",
            "Downloading: 100% 899k/899k [00:00<00:00, 5.13MB/s]\n",
            "Downloading: 100% 456k/456k [00:00<00:00, 9.87MB/s]\n",
            "Downloading: 100% 1.36M/1.36M [00:00<00:00, 4.34MB/s]\n",
            "Downloading: 100% 1.74G/1.74G [00:32<00:00, 53.4MB/s]\n",
            "GPU available: True, used: True\n",
            "TPU available: None, using: 0 TPU cores\n",
            "Using native 16bit precision.\n",
            "/content/multivers/lib/python3.9/site-packages/pytorch_lightning/utilities/distributed.py:50: UserWarning: Experiment logs directory ./checkpoints_user/covidfact exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
            "  warnings.warn(*args, **kwargs)\n",
            "\n",
            "  | Name                 | Type            | Params\n",
            "---------------------------------------------------------\n",
            "0 | encoder              | LongformerModel | 434 M \n",
            "1 | dropout              | Dropout         | 0     \n",
            "2 | label_classifier     | FeedForward     | 1.1 M \n",
            "3 | rationale_classifier | FeedForward     | 2.1 M \n",
            "4 | metrics              | ModuleDict      | 0     \n",
            "---------------------------------------------------------\n",
            "437 M     Trainable params\n",
            "0         Non-trainable params\n",
            "437 M     Total params\n",
            "1,751.052 Total estimated model params size (MB)\n",
            "Validation sanity check:   0% 0/2 [00:00<?, ?it/s]/content/multivers/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
            "Epoch 0:  97% 3520/3624 [2:16:41<04:02,  2.33s/it, loss=0.35, v_num=fact]\n",
            "Validating: 0it [00:00, ?it/s]\u001b[A\n",
            "Validating:   0% 0/110 [00:00<?, ?it/s]\u001b[A\n",
            "Epoch 0:  98% 3540/3624 [2:17:22<03:15,  2.33s/it, loss=0.35, v_num=fact]\n",
            "Epoch 0:  98% 3560/3624 [2:17:46<02:28,  2.32s/it, loss=0.35, v_num=fact]\n",
            "Epoch 0:  99% 3580/3624 [2:18:17<01:41,  2.32s/it, loss=0.35, v_num=fact]\n",
            "Epoch 0:  99% 3600/3624 [2:18:49<00:55,  2.31s/it, loss=0.35, v_num=fact]\n",
            "Epoch 0: 100% 3620/3624 [2:19:25<00:09,  2.31s/it, loss=0.35, v_num=fact]\n",
            "Epoch 0: 100% 3624/3624 [2:19:43<00:00,  2.31s/it, loss=0.338, v_num=fact]\n",
            "                                                 \u001b[A/content/multivers/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n",
            "Epoch 1:   1% 20/3624 [00:39<1:58:29,  1.97s/it, loss=0.338, v_num=fact]Traceback (most recent call last):\n",
            "  File \"/content/multivers/script/train_target.py\", line 103, in <module>\n",
            "    main()\n",
            "  File \"/content/multivers/script/train_target.py\", line 99, in main\n",
            "    subprocess.call(map(str, cmd))\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 351, in call\n",
            "    return p.wait(timeout=timeout)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1189, in wait\n",
            "    return self._wait(timeout=timeout)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1917, in _wait\n",
            "    (pid, sts) = self._try_wait(0)\n",
            "  File \"/usr/lib/python3.9/subprocess.py\", line 1875, in _try_wait\n",
            "    (pid, sts) = os.waitpid(self.pid, wait_flags)\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!source /content/multivers/bin/activate; python /content/multivers/script/train_target.py \\\n",
        "        --dataset=covidfact --gpus=1 --gradient_checkpointing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuNNkIEkq_xQ",
        "outputId": "759e3b2e-1fe9-4f74-9b25-366b38323754"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Global seed set to 76\n",
            "fatal: not a git repository (or any of the parent directories): .git\n",
            "Downloading: 100% 803/803 [00:00<00:00, 676kB/s]\n",
            "Downloading: 100% 899k/899k [00:00<00:00, 3.45MB/s]\n",
            "Downloading: 100% 456k/456k [00:00<00:00, 4.93MB/s]\n",
            "Downloading: 100% 1.36M/1.36M [00:00<00:00, 7.24MB/s]\n",
            "Downloading: 100% 1.74G/1.74G [00:33<00:00, 52.1MB/s]\n",
            "GPU available: True, used: True\n",
            "TPU available: None, using: 0 TPU cores\n",
            "Using native 16bit precision.\n",
            "/content/multivers/lib/python3.9/site-packages/pytorch_lightning/utilities/distributed.py:50: UserWarning: Experiment logs directory ./checkpoints_user/covidfact exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
            "  warnings.warn(*args, **kwargs)\n",
            "\n",
            "  | Name                 | Type            | Params\n",
            "---------------------------------------------------------\n",
            "0 | encoder              | LongformerModel | 434 M \n",
            "1 | dropout              | Dropout         | 0     \n",
            "2 | label_classifier     | FeedForward     | 1.1 M \n",
            "3 | rationale_classifier | FeedForward     | 2.1 M \n",
            "4 | metrics              | ModuleDict      | 0     \n",
            "---------------------------------------------------------\n",
            "437 M     Trainable params\n",
            "0         Non-trainable params\n",
            "437 M     Total params\n",
            "1,751.052 Total estimated model params size (MB)\n",
            "Validation sanity check:   0% 0/2 [00:00<?, ?it/s]/content/multivers/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
            "Epoch 0:  97% 3520/3624 [2:17:31<04:03,  2.34s/it, loss=0.146, v_num=fact]\n",
            "Validating: 0it [00:00, ?it/s]\u001b[A\n",
            "Validating:   0% 0/110 [00:00<?, ?it/s]\u001b[A\n",
            "Epoch 0:  98% 3540/3624 [2:18:12<03:16,  2.34s/it, loss=0.146, v_num=fact]\n",
            "Epoch 0:  98% 3560/3624 [2:18:37<02:29,  2.34s/it, loss=0.146, v_num=fact]\n",
            "Epoch 0:  99% 3580/3624 [2:19:08<01:42,  2.33s/it, loss=0.146, v_num=fact]\n",
            "Epoch 0:  99% 3600/3624 [2:19:40<00:55,  2.33s/it, loss=0.146, v_num=fact]\n",
            "Epoch 0: 100% 3620/3624 [2:20:17<00:09,  2.33s/it, loss=0.146, v_num=fact]\n",
            "Epoch 0: 100% 3624/3624 [2:20:36<00:00,  2.33s/it, loss=0.131, v_num=fact]\n",
            "                                                 \u001b[A/content/multivers/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n",
            "Epoch 1:  97% 3520/3624 [2:17:47<04:04,  2.35s/it, loss=1.02, v_num=fact]\n",
            "Validating: 0it [00:00, ?it/s]\u001b[A\n",
            "Validating:   0% 0/110 [00:00<?, ?it/s]\u001b[A\n",
            "Epoch 1:  98% 3540/3624 [2:18:29<03:17,  2.35s/it, loss=1.02, v_num=fact]\n",
            "Epoch 1:  98% 3560/3624 [2:18:53<02:29,  2.34s/it, loss=1.02, v_num=fact]\n",
            "Epoch 1:  99% 3580/3624 [2:19:25<01:42,  2.34s/it, loss=1.02, v_num=fact]\n",
            "Epoch 1:  99% 3600/3624 [2:19:57<00:55,  2.33s/it, loss=1.02, v_num=fact]\n",
            "Epoch 1: 100% 3620/3624 [2:20:34<00:09,  2.33s/it, loss=1.02, v_num=fact]\n",
            "Epoch 1: 100% 3624/3624 [2:20:52<00:00,  2.33s/it, loss=1.04, v_num=fact]\n",
            "Epoch 2:  97% 3520/3624 [2:17:56<04:04,  2.35s/it, loss=0.497, v_num=fact]\n",
            "Validating: 0it [00:00, ?it/s]\u001b[A\n",
            "Validating:   0% 0/110 [00:00<?, ?it/s]\u001b[A\n",
            "Epoch 2:  98% 3540/3624 [2:18:37<03:17,  2.35s/it, loss=0.497, v_num=fact]\n",
            "Epoch 2:  98% 3560/3624 [2:19:02<02:29,  2.34s/it, loss=0.497, v_num=fact]\n",
            "Epoch 2:  99% 3580/3624 [2:19:34<01:42,  2.34s/it, loss=0.497, v_num=fact]\n",
            "Epoch 2:  99% 3600/3624 [2:20:06<00:56,  2.34s/it, loss=0.497, v_num=fact]\n",
            "Epoch 2: 100% 3620/3624 [2:20:43<00:09,  2.33s/it, loss=0.497, v_num=fact]\n",
            "Epoch 2: 100% 3624/3624 [2:21:01<00:00,  2.33s/it, loss=0.433, v_num=fact]\n",
            "                                                 \u001b[ASaving latest checkpoint...\n",
            "Epoch 2: 100% 3624/3624 [2:22:07<00:00,  2.35s/it, loss=0.433, v_num=fact]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gcloud auth login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMQ-_Un6Dca8",
        "outputId": "306b012b-e822-4042-f2ff-76787a7b20f7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Go to the following link in your browser:\n",
            "\n",
            "    https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=32555940559.apps.googleusercontent.com&redirect_uri=https%3A%2F%2Fsdk.cloud.google.com%2Fauthcode.html&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fappengine.admin+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fsqlservice.login+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcompute+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Faccounts.reauth&state=Ib63w28hFX3RdVhCdvT8WZEQ38iOzh&prompt=consent&access_type=offline&code_challenge=C-9-QEu4aCPtlW2ohuVvo0c64RMTNnvNEt9vHP6mh68&code_challenge_method=S256\n",
            "\n",
            "Enter authorization code: 4/0AVHEtk5gRAQUvM9Rhs2Juz6_zidy0Z9KNgTDmauACy5rxLndxhpj_1Xvv1LOEcoPigQ9Tg\n",
            "\n",
            "You are now logged in as [aaalex.lit@gmail.com].\n",
            "Your current project is [None].  You can change this setting by running:\n",
            "  $ gcloud config set project PROJECT_ID\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gcloud config set project cc-evidence-service"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "if62L-flDm3a",
        "outputId": "182f38dc-e430-4996-cd10-8b06a826bf4e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated property [core/project].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gcloud storage cp --recursive /content/checkpoints_user/covidfact gs://cc-evidences-data/multivers_checkpoints"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11Hhk_DkR6uu",
        "outputId": "282a7103-1975-4cb3-d115-50f7f131048e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Copying file:///content/checkpoints_user/covidfact/metrics.csv to gs://cc-evidences-data/multivers_checkpoints/covidfact/metrics.csv\n",
            "Copying file:///content/checkpoints_user/covidfact/checkpoint/epoch=2-step=1319.ckpt to gs://cc-evidences-data/multivers_checkpoints/covidfact/checkpoint/epoch=2-step=1319.ckpt\n",
            "\u001b[1;33mWARNING:\u001b[0m Destination has a default storage class other than \"STANDARD\", hence parallel composite upload will not be performed. If you would like to disable this check, run: gcloud config set storage/parallel_composite_upload_compatibility_check=False\n",
            "Copying file:///content/checkpoints_user/covidfact/checkpoint/last.ckpt to gs://cc-evidences-data/multivers_checkpoints/covidfact/checkpoint/last.ckpt\n",
            "Copying file:///content/checkpoints_user/covidfact/events.out.tfevents.1682397872.c1ef10dbf42a.6926.0 to gs://cc-evidences-data/multivers_checkpoints/covidfact/events.out.tfevents.1682397872.c1ef10dbf42a.6926.0\n",
            "Copying file:///content/checkpoints_user/covidfact/hparams.yaml to gs://cc-evidences-data/multivers_checkpoints/covidfact/hparams.yaml\n",
            "\n",
            "Average throughput: 55.1MiB/s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q6AGtziGDVjo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}