prophetnet_seq2seqtrainer.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "prophetnet_seq2seqtrainer.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "ZHMn_DxzU_EL"
      ],
      "authorship_tag": "ABX9TyMIRGYDxCL1ybMZCX+15u2u",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/forest1988/colaboratory/blob/main/prophetnet_seq2seqtrainer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ond9VTc5Zt8d"
      },
      "source": [
        "import torch"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQpqDmBWaESu",
        "outputId": "aa46dce5-a5ea-45a1-feff-c6976cad9907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "torch.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.7.0+cu101'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzxYczZ0aF97",
        "outputId": "3cbf9fb3-b875-4406-e8e1-f64001d68ba6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/huggingface/transformers.git\n",
        "#git clone https://github.com/forest1988/transformers.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 21, done.\u001b[K\n",
            "remote: Counting objects: 100% (21/21), done.\u001b[K\n",
            "remote: Compressing objects: 100% (19/19), done.\u001b[K\n",
            "remote: Total 51115 (delta 3), reused 5 (delta 0), pack-reused 51094\u001b[K\n",
            "Receiving objects: 100% (51115/51115), 37.52 MiB | 27.36 MiB/s, done.\n",
            "Resolving deltas: 100% (35699/35699), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9khHo2A9b1p5",
        "outputId": "033644a7-4aad-428a-8918-85a2e39fcd90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd transformers/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8aOcq_2RJDV",
        "outputId": "7f2cea75-b236-4573-f1c0-8d82f124d572",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install -e ."
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Obtaining file:///content/transformers\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (3.0.12)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (0.7)\n",
            "Collecting tokenizers==0.9.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/34/b39eb9994bc3c999270b69c9eea40ecc6f0e97991dba28282b9fd32d44ee/tokenizers-0.9.3-cp36-cp36m-manylinux1_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 12.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (1.18.5)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (3.12.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (20.4)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 47.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (4.41.1)\n",
            "Collecting sentencepiece==0.1.91\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 46.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (2019.12.20)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from protobuf->transformers==3.5.0) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->transformers==3.5.0) (50.3.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.5.0) (2.4.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.5.0) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.5.0) (0.17.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (1.24.3)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=02b38251fd3d4281477f42842fd9ea990b2b85cba16a6a870e0f3b72258e46d7\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, sentencepiece, transformers\n",
            "  Running setup.py develop for transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.9.3 transformers\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wpt5LV-MMlG",
        "outputId": "8808108f-e671-40ab-a7ab-1aed33052e47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd /content/transformers/examples/seq2seq/"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers/examples/seq2seq\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKqwk3OnZZfr",
        "outputId": "da2730ec-b62e-48ce-f322-fc235dea13fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# to avoid the \"ModuleNotFoundError\" \n",
        "import sys\n",
        "print(sys.path)\n",
        "sys.path.append('/content/transformers/src')\n",
        "print(sys.path)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['', '/env/python', '/usr/lib/python36.zip', '/usr/lib/python3.6', '/usr/lib/python3.6/lib-dynload', '/usr/local/lib/python3.6/dist-packages', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.6/dist-packages/IPython/extensions', '/root/.ipython']\n",
            "['', '/env/python', '/usr/lib/python36.zip', '/usr/lib/python3.6', '/usr/lib/python3.6/lib-dynload', '/usr/local/lib/python3.6/dist-packages', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.6/dist-packages/IPython/extensions', '/root/.ipython', '/content/transformers/src']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmKBOl0cg7yK"
      },
      "source": [
        "# If \"ModuleNotFoundError\" occurs in this cell, please re-run the runtime. \n",
        "# (The first time Colab runtime runs, it seems the sys.path is not correctly updated when we use `pip install -e .`)\n",
        "\n",
        "import transformers"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iK_fsLQg_dP",
        "outputId": "6acdba72-b334-497c-c2b0-1ddc350d3387",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "transformers.__version__"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'3.5.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iawDc5qpcSpj"
      },
      "source": [
        "## dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwGCB7HucRhf",
        "outputId": "0c121545-1421-4231-930b-fe4b2432803f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget https://cdn-datasets.huggingface.co/summarization/xsum.tar.gz\n",
        "!tar -xzvf xsum.tar.gz\n",
        "# !export XSUM_DIR=${PWD}/xsum\n",
        "# %env XSUM_DIR=${PWD}/xsum"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-13 08:22:17--  https://cdn-datasets.huggingface.co/summarization/xsum.tar.gz\n",
            "Resolving cdn-datasets.huggingface.co (cdn-datasets.huggingface.co)... 99.84.178.19, 99.84.178.4, 99.84.178.12, ...\n",
            "Connecting to cdn-datasets.huggingface.co (cdn-datasets.huggingface.co)|99.84.178.19|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 204844092 (195M) [application/x-tar]\n",
            "Saving to: ‘xsum.tar.gz’\n",
            "\n",
            "xsum.tar.gz         100%[===================>] 195.35M  68.3MB/s    in 2.9s    \n",
            "\n",
            "2020-11-13 08:22:21 (68.3 MB/s) - ‘xsum.tar.gz’ saved [204844092/204844092]\n",
            "\n",
            "xsum/\n",
            "xsum/train.target\n",
            "xsum/train.source\n",
            "xsum/val.source\n",
            "xsum/val.target\n",
            "xsum/test.source\n",
            "xsum/test.target\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlDoDzJMfVQy"
      },
      "source": [
        "import os\n",
        "os.environ['XSUM_DIR']=os.path.join(os.getcwd(), 'xsum')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqUue_HQgFQt",
        "outputId": "0a4dc406-0b39-41d8-b7c7-ef85f7e59d07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!echo $XSUM_DIR"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers/examples/seq2seq/xsum\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtDHrviaNM7R",
        "outputId": "fd683311-3cf0-4789-fa64-7b1a1c64ab0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls $XSUM_DIR"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test.source  test.target  train.source\ttrain.target  val.source  val.target\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8WqDU4YdJlu"
      },
      "source": [
        "### Run BART-base"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryygCHtEdXdY"
      },
      "source": [
        "%%capture\n",
        "!pip install gitpython\n",
        "!pip install rouge_score\n",
        "!pip install sacrebleu"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9-NwSpEaKlZ",
        "outputId": "f5b9b783-dc6f-4e69-a389-dea70523b831",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!python finetune_trainer.py \\\n",
        "    --learning_rate=3e-5 \\\n",
        "    --do_train --do_eval --evaluate_during_training \\\n",
        "    --predict_with_generate \\\n",
        "    --n_train 20 \\\n",
        "    --n_val 10 \\\n",
        "    --model_name_or_path facebook/bart-base \\\n",
        "    --data_dir $XSUM_DIR \\\n",
        "    --output_dir bart-base-tmp \\\n",
        "    --overwrite_output_dir "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-13 08:22:36.316189: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "/content/transformers/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/13/2020 08:22:39 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "11/13/2020 08:22:39 - INFO - __main__ -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='bart-base-tmp', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov13_08-22-39_632355d25b45', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='bart-base-tmp', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "11/13/2020 08:22:39 - INFO - filelock -   Lock 140564227445312 acquired on /root/.cache/torch/transformers/f5310d276a6d1648d00c32fadc8bf7b4607e0fbd5b404fc4a0045960aa2bdfdb.3c4e938c08a46506ff9a4be12e0f858cd714b9a5c76090d571e7a4aa95cae853.lock\n",
            "[INFO|file_utils.py:1162] 2020-11-13 08:22:39,520 >> https://huggingface.co/facebook/bart-base/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpu5o8md51\n",
            "Downloading: 100% 1.55k/1.55k [00:00<00:00, 1.19MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:22:39,600 >> storing https://huggingface.co/facebook/bart-base/resolve/main/config.json in cache at /root/.cache/torch/transformers/f5310d276a6d1648d00c32fadc8bf7b4607e0fbd5b404fc4a0045960aa2bdfdb.3c4e938c08a46506ff9a4be12e0f858cd714b9a5c76090d571e7a4aa95cae853\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:22:39,601 >> creating metadata file for /root/.cache/torch/transformers/f5310d276a6d1648d00c32fadc8bf7b4607e0fbd5b404fc4a0045960aa2bdfdb.3c4e938c08a46506ff9a4be12e0f858cd714b9a5c76090d571e7a4aa95cae853\n",
            "11/13/2020 08:22:39 - INFO - filelock -   Lock 140564227445312 released on /root/.cache/torch/transformers/f5310d276a6d1648d00c32fadc8bf7b4607e0fbd5b404fc4a0045960aa2bdfdb.3c4e938c08a46506ff9a4be12e0f858cd714b9a5c76090d571e7a4aa95cae853.lock\n",
            "[INFO|configuration_utils.py:413] 2020-11-13 08:22:39,602 >> loading configuration file https://huggingface.co/facebook/bart-base/resolve/main/config.json from cache at /root/.cache/torch/transformers/f5310d276a6d1648d00c32fadc8bf7b4607e0fbd5b404fc4a0045960aa2bdfdb.3c4e938c08a46506ff9a4be12e0f858cd714b9a5c76090d571e7a4aa95cae853\n",
            "[INFO|configuration_utils.py:449] 2020-11-13 08:22:39,602 >> Model config BartConfig {\n",
            "  \"activation_dropout\": 0.1,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"add_bias_logits\": false,\n",
            "  \"add_final_layer_norm\": false,\n",
            "  \"architectures\": [\n",
            "    \"BartModel\",\n",
            "    \"BartForConditionalGeneration\",\n",
            "    \"BartForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classif_dropout\": 0.1,\n",
            "  \"classifier_dropout\": 0.0,\n",
            "  \"d_model\": 768,\n",
            "  \"decoder_attention_heads\": 12,\n",
            "  \"decoder_ffn_dim\": 3072,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 6,\n",
            "  \"decoder_start_token_id\": 2,\n",
            "  \"do_blenderbot_90_layernorm\": false,\n",
            "  \"dropout\": 0.1,\n",
            "  \"early_stopping\": true,\n",
            "  \"encoder_attention_heads\": 12,\n",
            "  \"encoder_ffn_dim\": 3072,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 6,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"extra_pos_embeddings\": 2,\n",
            "  \"force_bos_token_to_be_generated\": false,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"max_position_embeddings\": 1024,\n",
            "  \"model_type\": \"bart\",\n",
            "  \"no_repeat_ngram_size\": 3,\n",
            "  \"normalize_before\": false,\n",
            "  \"normalize_embedding\": true,\n",
            "  \"num_beams\": 4,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"scale_embedding\": false,\n",
            "  \"static_position_embeddings\": false,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"length_penalty\": 1.0,\n",
            "      \"max_length\": 128,\n",
            "      \"min_length\": 12,\n",
            "      \"num_beams\": 4\n",
            "    },\n",
            "    \"summarization_cnn\": {\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"max_length\": 142,\n",
            "      \"min_length\": 56,\n",
            "      \"num_beams\": 4\n",
            "    },\n",
            "    \"summarization_xsum\": {\n",
            "      \"length_penalty\": 1.0,\n",
            "      \"max_length\": 62,\n",
            "      \"min_length\": 11,\n",
            "      \"num_beams\": 6\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50265\n",
            "}\n",
            "\n",
            "[INFO|configuration_utils.py:413] 2020-11-13 08:22:39,680 >> loading configuration file https://huggingface.co/facebook/bart-base/resolve/main/config.json from cache at /root/.cache/torch/transformers/f5310d276a6d1648d00c32fadc8bf7b4607e0fbd5b404fc4a0045960aa2bdfdb.3c4e938c08a46506ff9a4be12e0f858cd714b9a5c76090d571e7a4aa95cae853\n",
            "[INFO|configuration_utils.py:449] 2020-11-13 08:22:39,681 >> Model config BartConfig {\n",
            "  \"activation_dropout\": 0.1,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"add_bias_logits\": false,\n",
            "  \"add_final_layer_norm\": false,\n",
            "  \"architectures\": [\n",
            "    \"BartModel\",\n",
            "    \"BartForConditionalGeneration\",\n",
            "    \"BartForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classif_dropout\": 0.1,\n",
            "  \"classifier_dropout\": 0.0,\n",
            "  \"d_model\": 768,\n",
            "  \"decoder_attention_heads\": 12,\n",
            "  \"decoder_ffn_dim\": 3072,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 6,\n",
            "  \"decoder_start_token_id\": 2,\n",
            "  \"do_blenderbot_90_layernorm\": false,\n",
            "  \"dropout\": 0.1,\n",
            "  \"early_stopping\": true,\n",
            "  \"encoder_attention_heads\": 12,\n",
            "  \"encoder_ffn_dim\": 3072,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 6,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"extra_pos_embeddings\": 2,\n",
            "  \"force_bos_token_to_be_generated\": false,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"max_position_embeddings\": 1024,\n",
            "  \"model_type\": \"bart\",\n",
            "  \"no_repeat_ngram_size\": 3,\n",
            "  \"normalize_before\": false,\n",
            "  \"normalize_embedding\": true,\n",
            "  \"num_beams\": 4,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"scale_embedding\": false,\n",
            "  \"static_position_embeddings\": false,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"length_penalty\": 1.0,\n",
            "      \"max_length\": 128,\n",
            "      \"min_length\": 12,\n",
            "      \"num_beams\": 4\n",
            "    },\n",
            "    \"summarization_cnn\": {\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"max_length\": 142,\n",
            "      \"min_length\": 56,\n",
            "      \"num_beams\": 4\n",
            "    },\n",
            "    \"summarization_xsum\": {\n",
            "      \"length_penalty\": 1.0,\n",
            "      \"max_length\": 62,\n",
            "      \"min_length\": 11,\n",
            "      \"num_beams\": 6\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50265\n",
            "}\n",
            "\n",
            "11/13/2020 08:22:39 - INFO - filelock -   Lock 140564227070048 acquired on /root/.cache/torch/transformers/7c1ba2435b05451bc3b4da073c8dec9630b22024a65f6c41053caccf2880eb8f.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
            "[INFO|file_utils.py:1162] 2020-11-13 08:22:39,780 >> https://huggingface.co/roberta-large/resolve/main/vocab.json not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmp_yhdtstp\n",
            "Downloading: 100% 899k/899k [00:00<00:00, 9.26MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:22:39,973 >> storing https://huggingface.co/roberta-large/resolve/main/vocab.json in cache at /root/.cache/torch/transformers/7c1ba2435b05451bc3b4da073c8dec9630b22024a65f6c41053caccf2880eb8f.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:22:39,973 >> creating metadata file for /root/.cache/torch/transformers/7c1ba2435b05451bc3b4da073c8dec9630b22024a65f6c41053caccf2880eb8f.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab\n",
            "11/13/2020 08:22:39 - INFO - filelock -   Lock 140564227070048 released on /root/.cache/torch/transformers/7c1ba2435b05451bc3b4da073c8dec9630b22024a65f6c41053caccf2880eb8f.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
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            "[INFO|file_utils.py:1162] 2020-11-13 08:22:40,063 >> https://huggingface.co/roberta-large/resolve/main/merges.txt not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpfsxwgjgd\n",
            "Downloading: 100% 456k/456k [00:00<00:00, 5.87MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:22:40,233 >> storing https://huggingface.co/roberta-large/resolve/main/merges.txt in cache at /root/.cache/torch/transformers/20b5a00a80e27ae9accbe25672aba42ad2d4d4cb2c4b9359b50ca8e34e107d6d.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:22:40,233 >> creating metadata file for /root/.cache/torch/transformers/20b5a00a80e27ae9accbe25672aba42ad2d4d4cb2c4b9359b50ca8e34e107d6d.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b\n",
            "11/13/2020 08:22:40 - INFO - filelock -   Lock 140564227071336 released on /root/.cache/torch/transformers/20b5a00a80e27ae9accbe25672aba42ad2d4d4cb2c4b9359b50ca8e34e107d6d.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
            "[INFO|tokenization_utils_base.py:1650] 2020-11-13 08:22:40,234 >> loading file https://huggingface.co/roberta-large/resolve/main/vocab.json from cache at /root/.cache/torch/transformers/7c1ba2435b05451bc3b4da073c8dec9630b22024a65f6c41053caccf2880eb8f.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab\n",
            "[INFO|tokenization_utils_base.py:1650] 2020-11-13 08:22:40,234 >> loading file https://huggingface.co/roberta-large/resolve/main/merges.txt from cache at /root/.cache/torch/transformers/20b5a00a80e27ae9accbe25672aba42ad2d4d4cb2c4b9359b50ca8e34e107d6d.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b\n",
            "11/13/2020 08:22:40 - INFO - filelock -   Lock 140564227445648 acquired on /root/.cache/torch/transformers/486355ec722ef05fd480e999d4c763be56549ae930f6a3742ee721a5d2a05647.9faea28a6782a9589c09b1942c039943df02232d83d2ac288a69ddfa928eae22.lock\n",
            "[INFO|file_utils.py:1162] 2020-11-13 08:22:40,387 >> https://huggingface.co/facebook/bart-base/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmp0w5je5s8\n",
            "Downloading: 100% 558M/558M [00:15<00:00, 35.7MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:22:56,357 >> storing https://huggingface.co/facebook/bart-base/resolve/main/pytorch_model.bin in cache at /root/.cache/torch/transformers/486355ec722ef05fd480e999d4c763be56549ae930f6a3742ee721a5d2a05647.9faea28a6782a9589c09b1942c039943df02232d83d2ac288a69ddfa928eae22\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:22:56,357 >> creating metadata file for /root/.cache/torch/transformers/486355ec722ef05fd480e999d4c763be56549ae930f6a3742ee721a5d2a05647.9faea28a6782a9589c09b1942c039943df02232d83d2ac288a69ddfa928eae22\n",
            "11/13/2020 08:22:56 - INFO - filelock -   Lock 140564227445648 released on /root/.cache/torch/transformers/486355ec722ef05fd480e999d4c763be56549ae930f6a3742ee721a5d2a05647.9faea28a6782a9589c09b1942c039943df02232d83d2ac288a69ddfa928eae22.lock\n",
            "[INFO|modeling_utils.py:940] 2020-11-13 08:22:56,357 >> loading weights file https://huggingface.co/facebook/bart-base/resolve/main/pytorch_model.bin from cache at /root/.cache/torch/transformers/486355ec722ef05fd480e999d4c763be56549ae930f6a3742ee721a5d2a05647.9faea28a6782a9589c09b1942c039943df02232d83d2ac288a69ddfa928eae22\n",
            "[INFO|modeling_utils.py:1056] 2020-11-13 08:23:00,621 >> All model checkpoint weights were used when initializing BartForConditionalGeneration.\n",
            "\n",
            "[INFO|modeling_utils.py:1065] 2020-11-13 08:23:00,622 >> All the weights of BartForConditionalGeneration were initialized from the model checkpoint at facebook/bart-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.\n",
            "11/13/2020 08:23:00 - INFO - utils -   using task specific params for summarization: {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4}\n",
            "[INFO|trainer.py:693] 2020-11-13 08:23:16,249 >> ***** Running training *****\n",
            "[INFO|trainer.py:694] 2020-11-13 08:23:16,250 >>   Num examples = 20\n",
            "[INFO|trainer.py:695] 2020-11-13 08:23:16,250 >>   Num Epochs = 3\n",
            "[INFO|trainer.py:696] 2020-11-13 08:23:16,250 >>   Instantaneous batch size per device = 8\n",
            "[INFO|trainer.py:697] 2020-11-13 08:23:16,250 >>   Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "[INFO|trainer.py:698] 2020-11-13 08:23:16,250 >>   Gradient Accumulation steps = 1\n",
            "[INFO|trainer.py:699] 2020-11-13 08:23:16,250 >>   Total optimization steps = 9\n",
            "100% 9/9 [00:09<00:00,  1.03it/s][INFO|trainer.py:829] 2020-11-13 08:23:26,099 >> \n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "{'epoch': 3.0}\n",
            "100% 9/9 [00:09<00:00,  1.09s/it]\n",
            "[INFO|trainer.py:1222] 2020-11-13 08:23:26,101 >> Saving model checkpoint to bart-base-tmp\n",
            "[INFO|configuration_utils.py:282] 2020-11-13 08:23:26,103 >> Configuration saved in bart-base-tmp/config.json\n",
            "[INFO|modeling_utils.py:740] 2020-11-13 08:23:28,022 >> Model weights saved in bart-base-tmp/pytorch_model.bin\n",
            "11/13/2020 08:23:28 - INFO - __main__ -   *** Evaluate ***\n",
            "[INFO|trainer.py:1387] 2020-11-13 08:23:28,364 >> ***** Running Evaluation *****\n",
            "[INFO|trainer.py:1388] 2020-11-13 08:23:28,364 >>   Num examples = 10\n",
            "[INFO|trainer.py:1389] 2020-11-13 08:23:28,364 >>   Batch size = 8\n",
            "100% 2/2 [00:02<00:00,  1.22s/it]\n",
            "11/13/2020 08:23:36 - INFO - __main__ -   ***** Eval results *****\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     eval_loss = 2.2726950645446777\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     eval_rouge1 = 19.9194\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     eval_rouge2 = 4.0053\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     eval_rougeL = 13.1934\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     eval_rougeLsum = 13.8909\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     eval_gen_len = 87.1\n",
            "11/13/2020 08:23:36 - INFO - __main__ -     epoch = 3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSEDjh1cbVm7",
        "outputId": "d59ff1f2-5186-497e-ff41-03987268eac6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Nov 13 08:23:37 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.32.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   63C    P0    46W /  70W |     10MiB / 15079MiB |     49%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40hjr62hgfSi"
      },
      "source": [
        "## Run with ProphetNet (Error occurs) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeRujgRxgeiB"
      },
      "source": [
        "from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration #, ProphetNetForCausalLM\n",
        "\n",
        "# --- commeented out to avoid CUDA memory error --- \n",
        "\n",
        "# tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')\n",
        "# model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased')\n",
        "\n",
        "# input_ids = tokenizer(\"Studies have been shown that owning a dog is good for you\", return_tensors=\"pt\").input_ids  # Batch size 1\n",
        "# decoder_input_ids = tokenizer(\"Studies show that\", return_tensors=\"pt\").input_ids  # Batch size 1\n",
        "# outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)\n",
        "\n",
        "# logits_next_token = outputs.logits  # logits to predict next token as usual\n",
        "# logits_ngram_next_tokens = outputs.logits_ngram  # logits to predict 2nd, 3rd, ... next tokens"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZIGb-UmTepq"
      },
      "source": [
        "# test how tokeinzer.decoder works \n",
        "\n",
        "# print(tokenizer.decode(input_ids[0]))\n",
        "# print(tokenizer.decode(input_ids[0], skip_special_tokens=True))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmcaKYecSBXW"
      },
      "source": [
        "# show the style of the output\n",
        "\n",
        "# print(type(outputs))\n",
        "# outputs.keys()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ol7nMTdSmrU"
      },
      "source": [
        "# outputs.logits.shape, outputs.logits_ngram.shape"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6Be8gaxS6_Z"
      },
      "source": [
        "# out_generate = model.generate(input_ids, num_beams=4, max_length=20, early_stopping=True)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsG1KABATFBY"
      },
      "source": [
        "# out_generate.shape, out_generate[0]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwKTqbERTJG4"
      },
      "source": [
        "# tokenizer.decode(out_generate.shape[0], skip_special_tokens=True)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJB3GanOfF0N",
        "outputId": "ada830c1-c1f0-46bc-b55f-78c2ef1dea88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# --- NotImplementedError occurs here --- \n",
        "\n",
        "!python finetune_trainer.py \\\n",
        "    --learning_rate=3e-5 \\\n",
        "    --do_train --do_eval --evaluate_during_training \\\n",
        "    --predict_with_generate \\\n",
        "    --n_train 100 \\\n",
        "    --n_val 10 \\\n",
        "    --model_name_or_path microsoft/prophetnet-large-uncased \\\n",
        "    --data_dir $XSUM_DIR \\\n",
        "    --output_dir tmp \\\n",
        "    --overwrite_output_dir"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-13 08:23:38.862478: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "/content/transformers/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/13/2020 08:23:41 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "11/13/2020 08:23:41 - INFO - __main__ -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov13_08-23-41_632355d25b45', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "11/13/2020 08:23:41 - INFO - filelock -   Lock 140303074720960 acquired on /root/.cache/torch/transformers/5af1cdf83ab91efb16c4b84e954521820508cf6de67834aba5c8d360056ebd81.007d7f4c78db8e2eed8b0afcf7fbeb3cd1ec12a204131bf92811fa10c45947b4.lock\n",
            "[INFO|file_utils.py:1162] 2020-11-13 08:23:41,197 >> https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpkh1i6rdk\n",
            "Downloading: 100% 1.24k/1.24k [00:00<00:00, 1.07MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:23:41,278 >> storing https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json in cache at /root/.cache/torch/transformers/5af1cdf83ab91efb16c4b84e954521820508cf6de67834aba5c8d360056ebd81.007d7f4c78db8e2eed8b0afcf7fbeb3cd1ec12a204131bf92811fa10c45947b4\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:23:41,278 >> creating metadata file for /root/.cache/torch/transformers/5af1cdf83ab91efb16c4b84e954521820508cf6de67834aba5c8d360056ebd81.007d7f4c78db8e2eed8b0afcf7fbeb3cd1ec12a204131bf92811fa10c45947b4\n",
            "11/13/2020 08:23:41 - INFO - filelock -   Lock 140303074720960 released on /root/.cache/torch/transformers/5af1cdf83ab91efb16c4b84e954521820508cf6de67834aba5c8d360056ebd81.007d7f4c78db8e2eed8b0afcf7fbeb3cd1ec12a204131bf92811fa10c45947b4.lock\n",
            "[INFO|configuration_utils.py:413] 2020-11-13 08:23:41,279 >> loading configuration file https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json from cache at /root/.cache/torch/transformers/5af1cdf83ab91efb16c4b84e954521820508cf6de67834aba5c8d360056ebd81.007d7f4c78db8e2eed8b0afcf7fbeb3cd1ec12a204131bf92811fa10c45947b4\n",
            "[INFO|configuration_utils.py:449] 2020-11-13 08:23:41,280 >> Model config ProphetNetConfig {\n",
            "  \"activation_dropout\": 0.1,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"add_cross_attention\": true,\n",
            "  \"architectures\": [\n",
            "    \"ProphetNetForConditionalGeneration\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 102,\n",
            "  \"decoder_ffn_dim\": 4096,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_max_position_embeddings\": 514,\n",
            "  \"decoder_start_token_id\": 102,\n",
            "  \"disable_ngram_loss\": false,\n",
            "  \"dropout\": 0.1,\n",
            "  \"encoder_ffn_dim\": 4096,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_max_position_embeddings\": 513,\n",
            "  \"eos_token_id\": 102,\n",
            "  \"eps\": 0.0,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"prophetnet\",\n",
            "  \"ngram\": 2,\n",
            "  \"num_buckets\": 32,\n",
            "  \"num_decoder_attention_heads\": 16,\n",
            "  \"num_decoder_layers\": 12,\n",
            "  \"num_encoder_attention_heads\": 16,\n",
            "  \"num_encoder_layers\": 12,\n",
            "  \"output_past\": false,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"prefix\": \" \",\n",
            "  \"relative_max_distance\": 128,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "[INFO|configuration_utils.py:413] 2020-11-13 08:23:41,358 >> loading configuration file https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json from cache at /root/.cache/torch/transformers/5af1cdf83ab91efb16c4b84e954521820508cf6de67834aba5c8d360056ebd81.007d7f4c78db8e2eed8b0afcf7fbeb3cd1ec12a204131bf92811fa10c45947b4\n",
            "[INFO|configuration_utils.py:449] 2020-11-13 08:23:41,358 >> Model config ProphetNetConfig {\n",
            "  \"activation_dropout\": 0.1,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"add_cross_attention\": true,\n",
            "  \"architectures\": [\n",
            "    \"ProphetNetForConditionalGeneration\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 102,\n",
            "  \"decoder_ffn_dim\": 4096,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_max_position_embeddings\": 514,\n",
            "  \"decoder_start_token_id\": 102,\n",
            "  \"disable_ngram_loss\": false,\n",
            "  \"dropout\": 0.1,\n",
            "  \"encoder_ffn_dim\": 4096,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_max_position_embeddings\": 513,\n",
            "  \"eos_token_id\": 102,\n",
            "  \"eps\": 0.0,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"prophetnet\",\n",
            "  \"ngram\": 2,\n",
            "  \"num_buckets\": 32,\n",
            "  \"num_decoder_attention_heads\": 16,\n",
            "  \"num_decoder_layers\": 12,\n",
            "  \"num_encoder_attention_heads\": 16,\n",
            "  \"num_encoder_layers\": 12,\n",
            "  \"output_past\": false,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"prefix\": \" \",\n",
            "  \"relative_max_distance\": 128,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "11/13/2020 08:23:41 - INFO - filelock -   Lock 140303074877280 acquired on /root/.cache/torch/transformers/3b5ecfc96b12e126f8589d998af18d8e17970efd33f511b9b1d212cb418a7aa6.bfccaaa55dc78254b9fdcf3c43f1799da93d9e66f1cead1d46d464130f9e9649.lock\n",
            "[INFO|file_utils.py:1162] 2020-11-13 08:23:41,444 >> https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/prophetnet.tokenizer not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpzwhi3gm3\n",
            "Downloading: 100% 232k/232k [00:00<00:00, 3.57MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:23:41,593 >> storing https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/prophetnet.tokenizer in cache at /root/.cache/torch/transformers/3b5ecfc96b12e126f8589d998af18d8e17970efd33f511b9b1d212cb418a7aa6.bfccaaa55dc78254b9fdcf3c43f1799da93d9e66f1cead1d46d464130f9e9649\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:23:41,593 >> creating metadata file for /root/.cache/torch/transformers/3b5ecfc96b12e126f8589d998af18d8e17970efd33f511b9b1d212cb418a7aa6.bfccaaa55dc78254b9fdcf3c43f1799da93d9e66f1cead1d46d464130f9e9649\n",
            "11/13/2020 08:23:41 - INFO - filelock -   Lock 140303074877280 released on /root/.cache/torch/transformers/3b5ecfc96b12e126f8589d998af18d8e17970efd33f511b9b1d212cb418a7aa6.bfccaaa55dc78254b9fdcf3c43f1799da93d9e66f1cead1d46d464130f9e9649.lock\n",
            "[INFO|tokenization_utils_base.py:1650] 2020-11-13 08:23:41,594 >> loading file https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/prophetnet.tokenizer from cache at /root/.cache/torch/transformers/3b5ecfc96b12e126f8589d998af18d8e17970efd33f511b9b1d212cb418a7aa6.bfccaaa55dc78254b9fdcf3c43f1799da93d9e66f1cead1d46d464130f9e9649\n",
            "11/13/2020 08:23:41 - INFO - filelock -   Lock 140303074795416 acquired on /root/.cache/torch/transformers/3d0f611b9f59504ec6dc841d79edeeef72f013b046b9aeec430882d215480a27.ab2e9c0807ddfec82dd02c95d416e2e2db96bc2a83f2b8a3c45d729831a78030.lock\n",
            "[INFO|file_utils.py:1162] 2020-11-13 08:23:41,704 >> https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmp0f10pklg\n",
            "Downloading: 100% 1.57G/1.57G [00:49<00:00, 31.8MB/s]\n",
            "[INFO|file_utils.py:1166] 2020-11-13 08:24:31,333 >> storing https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/pytorch_model.bin in cache at /root/.cache/torch/transformers/3d0f611b9f59504ec6dc841d79edeeef72f013b046b9aeec430882d215480a27.ab2e9c0807ddfec82dd02c95d416e2e2db96bc2a83f2b8a3c45d729831a78030\n",
            "[INFO|file_utils.py:1169] 2020-11-13 08:24:31,333 >> creating metadata file for /root/.cache/torch/transformers/3d0f611b9f59504ec6dc841d79edeeef72f013b046b9aeec430882d215480a27.ab2e9c0807ddfec82dd02c95d416e2e2db96bc2a83f2b8a3c45d729831a78030\n",
            "11/13/2020 08:24:31 - INFO - filelock -   Lock 140303074795416 released on /root/.cache/torch/transformers/3d0f611b9f59504ec6dc841d79edeeef72f013b046b9aeec430882d215480a27.ab2e9c0807ddfec82dd02c95d416e2e2db96bc2a83f2b8a3c45d729831a78030.lock\n",
            "[INFO|modeling_utils.py:940] 2020-11-13 08:24:31,334 >> loading weights file https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/torch/transformers/3d0f611b9f59504ec6dc841d79edeeef72f013b046b9aeec430882d215480a27.ab2e9c0807ddfec82dd02c95d416e2e2db96bc2a83f2b8a3c45d729831a78030\n",
            "[INFO|modeling_utils.py:1056] 2020-11-13 08:24:50,402 >> All model checkpoint weights were used when initializing ProphetNetForConditionalGeneration.\n",
            "\n",
            "[INFO|modeling_utils.py:1065] 2020-11-13 08:24:50,402 >> All the weights of ProphetNetForConditionalGeneration were initialized from the model checkpoint at microsoft/prophetnet-large-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use ProphetNetForConditionalGeneration for predictions without further training.\n",
            "11/13/2020 08:24:50 - INFO - utils -   using task specific params for summarization: {'early_stopping': True, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3, 'num_beams': 4}\n",
            "[INFO|trainer.py:693] 2020-11-13 08:25:02,864 >> ***** Running training *****\n",
            "[INFO|trainer.py:694] 2020-11-13 08:25:02,864 >>   Num examples = 100\n",
            "[INFO|trainer.py:695] 2020-11-13 08:25:02,865 >>   Num Epochs = 3\n",
            "[INFO|trainer.py:696] 2020-11-13 08:25:02,865 >>   Instantaneous batch size per device = 8\n",
            "[INFO|trainer.py:697] 2020-11-13 08:25:02,865 >>   Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "[INFO|trainer.py:698] 2020-11-13 08:25:02,865 >>   Gradient Accumulation steps = 1\n",
            "[INFO|trainer.py:699] 2020-11-13 08:25:02,865 >>   Total optimization steps = 39\n",
            "  0% 0/39 [00:00<?, ?it/s]Traceback (most recent call last):\n",
            "  File \"finetune_trainer.py\", line 309, in <module>\n",
            "    main()\n",
            "  File \"finetune_trainer.py\", line 254, in main\n",
            "    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\n",
            "  File \"/content/transformers/src/transformers/trainer.py\", line 757, in train\n",
            "    for step, inputs in enumerate(epoch_iterator):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\", line 435, in __next__\n",
            "    data = self._next_data()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\", line 475, in _next_data\n",
            "    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 47, in fetch\n",
            "    return self.collate_fn(data)\n",
            "  File \"/content/transformers/examples/seq2seq/utils.py\", line 280, in __call__\n",
            "    batch = self._encode(batch)\n",
            "  File \"/content/transformers/examples/seq2seq/utils.py\", line 322, in _encode\n",
            "    **self.dataset_kwargs,\n",
            "  File \"/content/transformers/src/transformers/tokenization_utils.py\", line 835, in prepare_seq2seq_batch\n",
            "    \"If your model requires more than input_ids for a typical forward pass, you should implement this method. \"\n",
            "NotImplementedError: If your model requires more than input_ids for a typical forward pass, you should implement this method. Returned keys should be [input_ids, attention_mask, labels]. See MarianTokenizer or T5Tokenizer for a reference implementation.\n",
            "  0% 0/39 [00:01<?, ?it/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHMn_DxzU_EL"
      },
      "source": [
        "### Refer to T5Tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_u3k1GCVo1Z"
      },
      "source": [
        "``` python\n",
        "    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)\n",
        "    def prepare_seq2seq_batch(\n",
        "        self,\n",
        "        src_texts: List[str],\n",
        "        tgt_texts: Optional[List[str]] = None,\n",
        "        max_length: Optional[int] = None,\n",
        "        max_target_length: Optional[int] = None,\n",
        "        padding: str = \"longest\",\n",
        "        return_tensors: str = None,\n",
        "        truncation: bool = True,\n",
        "        **kwargs,\n",
        "    ) -> BatchEncoding:\n",
        "        if max_length is None:\n",
        "            max_length = self.max_len\n",
        "        model_inputs = self(\n",
        "            src_texts,\n",
        "            add_special_tokens=True,\n",
        "            return_tensors=return_tensors,\n",
        "            max_length=max_length,\n",
        "            padding=padding,\n",
        "            truncation=truncation,\n",
        "            **kwargs,\n",
        "        )\n",
        "        if tgt_texts is None:\n",
        "            return model_inputs\n",
        "        # Process tgt_texts\n",
        "        if max_target_length is None:\n",
        "            max_target_length = max_length\n",
        "        labels_and_decoder_mask = self(\n",
        "            tgt_texts,\n",
        "            add_special_tokens=True,\n",
        "            return_tensors=return_tensors,\n",
        "            padding=padding,\n",
        "            max_length=max_target_length,\n",
        "            truncation=truncation,\n",
        "            **kwargs,\n",
        "        )\n",
        "        model_inputs[\"labels\"] = labels_and_decoder_mask[\"input_ids\"]\n",
        "        return model_inputs\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Me6KG7SbSz5"
      },
      "source": [
        "## Try modified version of ProphetNet Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weFcd1mtVkTS",
        "outputId": "e36d1fea-97c7-42fe-dd33-bccd169d0b90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd /content/"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNO39XpFY7KB",
        "outputId": "467ec4f7-0378-47ff-b6dd-688ac8d06f22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data  transformers  transformers_modified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljE2NiA-boRJ"
      },
      "source": [
        "import shutil\n",
        "\n",
        "if os.path.exists(\"transformers_modified\"):\n",
        "  print(\"The repository is already cloned. Remove and re-clone it.\")\n",
        "  shutil.rmtree(\"transformers_modified\")"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKRkCX1_Y7xl",
        "outputId": "0f5430b5-17b6-483d-f9d3-f30892446b75",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone -b forest1988-prophetnet-prepare-seq2seq-batch https://github.com/forest1988/transformers.git transformers_modified"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'transformers_modified'...\n",
            "remote: Enumerating objects: 19, done.\u001b[K\n",
            "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 49984 (delta 4), reused 11 (delta 4), pack-reused 49965\u001b[K\n",
            "Receiving objects: 100% (49984/49984), 36.84 MiB | 27.26 MiB/s, done.\n",
            "Resolving deltas: 100% (34887/34887), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_Jck3hWZIcE",
        "outputId": "035948c4-1e97-492b-86ce-ea458585d70d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd transformers_modified/"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers_modified/transformers_modified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNcmGGvSZi1V",
        "outputId": "bb22d9e7-eb09-42f9-aa7b-8d282682d7ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        }
      },
      "source": [
        "!pip install -e . --upgrade"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Obtaining file:///content/transformers_modified/transformers_modified\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied, skipping upgrade: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (3.0.12)\n",
            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (20.4)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (2019.12.20)\n",
            "Requirement already satisfied, skipping upgrade: protobuf in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (3.12.4)\n",
            "Requirement already satisfied, skipping upgrade: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (0.7)\n",
            "Requirement already satisfied, skipping upgrade: sentencepiece==0.1.91 in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (0.1.91)\n",
            "Collecting tokenizers==0.9.3\n",
            "  Using cached https://files.pythonhosted.org/packages/4c/34/b39eb9994bc3c999270b69c9eea40ecc6f0e97991dba28282b9fd32d44ee/tokenizers-0.9.3-cp36-cp36m-manylinux1_x86_64.whl\n",
            "Requirement already satisfied, skipping upgrade: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers==3.5.0) (0.0.43)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.5.0) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.5.0) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.5.0) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->transformers==3.5.0) (50.3.2)\n",
            "Requirement already satisfied, skipping upgrade: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.5.0) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.5.0) (0.17.0)\n",
            "Installing collected packages: tokenizers, transformers\n",
            "  Found existing installation: tokenizers 0.9.2\n",
            "    Uninstalling tokenizers-0.9.2:\n",
            "      Successfully uninstalled tokenizers-0.9.2\n",
            "  Found existing installation: transformers 3.4.0\n",
            "    Can't uninstall 'transformers'. No files were found to uninstall.\n",
            "  Running setup.py develop for transformers\n",
            "Successfully installed tokenizers-0.9.3 transformers\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tokenizers"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7t_HQtdZnMQ",
        "outputId": "c81cd9a2-1272-47b4-989e-df78a18236c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd /content/transformers_modified/examples/seq2seq/\n",
        "\n",
        "# Use already downloaded XSUM in /content/transformers/examples/seq2seq/xsum/"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers_modified/examples/seq2seq\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bP1-eKJewAXT"
      },
      "source": [
        "### Run on GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTUypp1QaKNM",
        "outputId": "5d500c7a-c98d-4ff1-f2c2-25b01d34f570",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# For GPU (if runtime is set to TPU, the code below is run on CPU.)\n",
        "# max_source_lenght 510\n",
        "\n",
        "!python finetune_trainer.py \\\n",
        "    --learning_rate=3e-5 \\\n",
        "    --do_train --do_eval --evaluate_during_training \\\n",
        "    --max_source_length 510 \\\n",
        "    --per_device_train_batch_size 2 \\\n",
        "    --predict_with_generate \\\n",
        "    --n_train 300 \\\n",
        "    --n_val 100 \\\n",
        "    --model_name_or_path microsoft/prophetnet-large-uncased \\\n",
        "    --data_dir $XSUM_DIR \\\n",
        "    --output_dir tmp_gpu \\\n",
        "    --overwrite_output_dir"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-13 08:31:59.397701: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "/content/transformers_modified/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/13/2020 08:32:01 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "11/13/2020 08:32:01 - INFO - __main__ -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_gpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=2, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov13_08-32-01_632355d25b45', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_gpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "11/13/2020 08:33:02 - INFO - utils -   using task specific params for summarization: {'early_stopping': True, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3, 'num_beams': 4}\n",
            "{'epoch': 3.0}\n",
            "100% 450/450 [05:29<00:00,  1.36it/s]\n",
            "11/13/2020 08:38:46 - INFO - __main__ -   *** Evaluate ***\n",
            "100% 13/13 [00:47<00:00,  3.65s/it]\n",
            "11/13/2020 08:39:38 - INFO - __main__ -   ***** Eval results *****\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     eval_loss = 2.2797653675079346\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     eval_rouge1 = 33.6794\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     eval_rouge2 = 11.7373\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     eval_rougeL = 26.6086\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     eval_rougeLsum = 26.6507\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     eval_gen_len = 25.5\n",
            "11/13/2020 08:39:38 - INFO - __main__ -     epoch = 3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XADhtrpPxIC3",
        "outputId": "36474907-d261-422c-c034-300fdb12eec8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# For GPU (if runtime is set to TPU, the code below is run on CPU.)\n",
        "# max_source_lenght 511\n",
        "\n",
        "!python finetune_trainer.py \\\n",
        "    --learning_rate=3e-5 \\\n",
        "    --do_train --do_eval --evaluate_during_training \\\n",
        "    --max_source_length 511 \\\n",
        "    --per_device_train_batch_size 2 \\\n",
        "    --predict_with_generate \\\n",
        "    --n_train 300 \\\n",
        "    --n_val 100 \\\n",
        "    --model_name_or_path microsoft/prophetnet-large-uncased \\\n",
        "    --data_dir $XSUM_DIR \\\n",
        "    --output_dir tmp_gpu \\\n",
        "    --overwrite_output_dir"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-13 08:42:59.987235: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "/content/transformers_modified/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/13/2020 08:43:02 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "11/13/2020 08:43:02 - INFO - __main__ -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_gpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=2, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov13_08-43-02_632355d25b45', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_gpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "11/13/2020 08:43:21 - INFO - utils -   using task specific params for summarization: {'early_stopping': True, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3, 'num_beams': 4}\n",
            "{'epoch': 3.0}\n",
            "100% 450/450 [05:30<00:00,  1.36it/s]\n",
            "11/13/2020 08:49:06 - INFO - __main__ -   *** Evaluate ***\n",
            "100% 13/13 [00:48<00:00,  3.70s/it]\n",
            "11/13/2020 08:49:59 - INFO - __main__ -   ***** Eval results *****\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     eval_loss = 2.243151903152466\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     eval_rouge1 = 35.9087\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     eval_rouge2 = 11.1399\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     eval_rougeL = 26.8134\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     eval_rougeLsum = 26.8874\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     eval_gen_len = 26.0\n",
            "11/13/2020 08:49:59 - INFO - __main__ -     epoch = 3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFiO-nkX0smW",
        "outputId": "cc517763-54c5-4190-e6df-a7ab7361b51a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# For GPU (if runtime is set to TPU, the code below is run on CPU.)\n",
        "# max_source_lenght 512\n",
        "\n",
        "!python finetune_trainer.py \\\n",
        "    --learning_rate=3e-5 \\\n",
        "    --do_train --do_eval --evaluate_during_training \\\n",
        "    --max_source_length 512 \\\n",
        "    --per_device_train_batch_size 2 \\\n",
        "    --predict_with_generate \\\n",
        "    --n_train 300 \\\n",
        "    --n_val 100 \\\n",
        "    --model_name_or_path microsoft/prophetnet-large-uncased \\\n",
        "    --data_dir $XSUM_DIR \\\n",
        "    --output_dir tmp_gpu \\\n",
        "    --overwrite_output_dir"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-13 08:57:26.671954: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "/content/transformers_modified/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/13/2020 08:57:29 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "11/13/2020 08:57:29 - INFO - __main__ -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_gpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=2, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov13_08-57-29_632355d25b45', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_gpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "11/13/2020 08:57:47 - INFO - utils -   using task specific params for summarization: {'early_stopping': True, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3, 'num_beams': 4}\n",
            "  0% 0/450 [00:00<?, ?it/s]/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [34,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [35,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [36,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [37,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [38,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [39,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [40,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [41,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [42,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [43,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
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            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [45,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
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            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [51,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [52,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [53,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [54,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [55,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [56,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [255,0,0], thread: [57,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
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            "/pytorch/aten/src/ATen/native/cuda/Indexing.cu:658: indexSelectLargeIndex: block: [184,0,0], thread: [31,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n",
            "Traceback (most recent call last):\n",
            "  File \"finetune_trainer.py\", line 303, in <module>\n",
            "    main()\n",
            "  File \"finetune_trainer.py\", line 248, in main\n",
            "    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/trainer.py\", line 775, in train\n",
            "    tr_loss += self.training_step(model, inputs)\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/trainer.py\", line 1112, in training_step\n",
            "    loss = self.compute_loss(model, inputs)\n",
            "  File \"/content/transformers_modified/examples/seq2seq/seq2seq_trainer.py\", line 154, in compute_loss\n",
            "    loss, _ = self._compute_loss(model, inputs, labels)\n",
            "  File \"/content/transformers_modified/examples/seq2seq/seq2seq_trainer.py\", line 138, in _compute_loss\n",
            "    logits = model(**inputs, use_cache=False)[0]\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 727, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/modeling_prophetnet.py\", line 1757, in forward\n",
            "    return_dict=return_dict,\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 727, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/modeling_prophetnet.py\", line 1644, in forward\n",
            "    return_dict=return_dict,\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 727, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/modeling_prophetnet.py\", line 1225, in forward\n",
            "    hidden_states, attn_probs = encoder_layer(hidden_states, attention_mask=extended_attention_mask)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 727, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/modeling_prophetnet.py\", line 1051, in forward\n",
            "    attention_mask=attention_mask,\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 727, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/content/transformers_modified/transformers_modified/src/transformers/modeling_prophetnet.py\", line 652, in forward\n",
            "    query_states = self.query_proj(hidden_states) / (self.head_dim ** 0.5)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 727, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py\", line 93, in forward\n",
            "    return F.linear(input, self.weight, self.bias)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\", line 1692, in linear\n",
            "    output = input.matmul(weight.t())\n",
            "RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`\n",
            "  0% 0/450 [00:00<?, ?it/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAEkasp4wFJ3"
      },
      "source": [
        "### Run on TPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "es8nwoWN8ii_",
        "outputId": "9f904f52-d395-431c-bc27-99bb5f61afe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        }
      },
      "source": [
        "# install torch_xla with PyTorch\n",
        "!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.7-cp36-cp36m-linux_x86_64.whl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting cloud-tpu-client==0.10\n",
            "  Downloading https://files.pythonhosted.org/packages/56/9f/7b1958c2886db06feb5de5b2c191096f9e619914b6c31fdf93999fdbbd8b/cloud_tpu_client-0.10-py3-none-any.whl\n",
            "Collecting torch-xla==1.7\n",
            "\u001b[?25l  Downloading https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.7-cp36-cp36m-linux_x86_64.whl (133.6MB)\n",
            "\u001b[K     |████████████████████████████████| 133.6MB 93kB/s \n",
            "\u001b[?25hRequirement already satisfied: oauth2client in /usr/local/lib/python3.6/dist-packages (from cloud-tpu-client==0.10) (4.1.3)\n",
            "Collecting google-api-python-client==1.8.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9a/b4/a955f393b838bc47cbb6ae4643b9d0f90333d3b4db4dc1e819f36aad18cc/google_api_python_client-1.8.0-py3-none-any.whl (57kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 2.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client->cloud-tpu-client==0.10) (0.2.8)\n",
            "Requirement already satisfied: httplib2>=0.9.1 in /usr/local/lib/python3.6/dist-packages (from oauth2client->cloud-tpu-client==0.10) (0.17.4)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client->cloud-tpu-client==0.10) (0.4.8)\n",
            "Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from oauth2client->cloud-tpu-client==0.10) (1.15.0)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client->cloud-tpu-client==0.10) (4.6)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.0.1)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (0.0.4)\n",
            "Requirement already satisfied: google-api-core<2dev,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.16.0)\n",
            "Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.17.2)\n",
            "Requirement already satisfied: setuptools>=34.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (50.3.2)\n",
            "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.12.4)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.52.0)\n",
            "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.6/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2.23.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2018.9)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (4.1.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2020.6.20)\n",
            "Installing collected packages: google-api-python-client, cloud-tpu-client, torch-xla\n",
            "  Found existing installation: google-api-python-client 1.7.12\n",
            "    Uninstalling google-api-python-client-1.7.12:\n",
            "      Successfully uninstalled google-api-python-client-1.7.12\n",
            "Successfully installed cloud-tpu-client-0.10 google-api-python-client-1.8.0 torch-xla-1.7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "googleapiclient"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZT3I3CA4sDy9",
        "outputId": "7732b235-feb8-4fb8-8294-4c399aa2de26",
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# For TPU\n",
        "\n",
        "!python xla_spawn.py --num_core 1 \\\n",
        "    finetune_trainer.py \\\n",
        "    --learning_rate=3e-5 \\\n",
        "    --do_train --do_eval --evaluate_during_training \\\n",
        "    --max_source_length 200 \\\n",
        "    --per_device_train_batch_size 32 \\\n",
        "    --prediction_loss_only \\\n",
        "    --model_name_or_path microsoft/prophetnet-large-uncased \\\n",
        "    --data_dir $XSUM_DIR \\\n",
        "    --output_dir tmp_tpu \\\n",
        "    --overwrite_output_dir"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:TPU has started up successfully with version pytorch-1.7\n",
            "2020-11-07 07:35:03.804088: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:root:TPU has started up successfully with version pytorch-1.7\n",
            "2020-11-07 07:35:18.033957: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.035996: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.036775: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.044493: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.195867: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.206995: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.323494: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-07 07:35:18.406330: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:43 - WARNING - finetune_trainer -   Process rank: -1, device: xla:1, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:43 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-43_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:43 - WARNING - finetune_trainer -   Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:43 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-43_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:44 - WARNING - finetune_trainer -   Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:44 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-44_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:44 - WARNING - finetune_trainer -   Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:44 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-44_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:44 - WARNING - finetune_trainer -   Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:44 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-44_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:45 - WARNING - finetune_trainer -   Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:45 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-45_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "WARNING:finetune_trainer:Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "/content/transformers_modified/src/transformers/training_args.py:347: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "11/07/2020 07:35:45 - WARNING - finetune_trainer -   Process rank: -1, device: xla:0, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "11/07/2020 07:35:45 - INFO - finetune_trainer -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='tmp_tpu', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=True, per_device_train_batch_size=16, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=3e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Nov07_07-35-45_29e81c0d403c', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=8, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='tmp_tpu', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.0, sortish_sampler=False, predict_with_generate=False, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear')\n",
            "Traceback (most recent call last):\n",
            "  File \"xla_spawn.py\", line 72, in <module>\n",
            "    main()\n",
            "  File \"xla_spawn.py\", line 68, in main\n",
            "    xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch_xla/distributed/xla_multiprocessing.py\", line 395, in spawn\n",
            "    start_method=start_method)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/multiprocessing/spawn.py\", line 157, in start_processes\n",
            "    while not context.join():\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/multiprocessing/spawn.py\", line 107, in join\n",
            "    (error_index, name)\n",
            "Exception: process 0 terminated with signal SIGKILL\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgYk8GSt0yI0",
        "outputId": "a4a3e1e8-f21d-4096-a5c7-051c08e8acd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%ls tmp_tpu"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "config.json           pytorch_model.bin        trainer_state.json\n",
            "eval_results.json     special_tokens_map.json  training_args.bin\n",
            "prophetnet.tokenizer  tokenizer_config.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clGOk_SC01U8",
        "outputId": "8888c9c2-8877-408b-9e06-1ab4b15d6f1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "all_results.json\t\trun_distributed_eval.py\n",
            "bertabs\t\t\t\trun_eval.py\n",
            "builtin_trainer\t\t\trun_eval_search.py\n",
            "callbacks.py\t\t\truns\n",
            "convert_model_to_fp16.py\tsave_len_file.py\n",
            "convert_pl_checkpoint_to_hf.py\tsave_randomly_initialized_model.py\n",
            "distillation.py\t\t\tsentence_splitter.py\n",
            "distil_marian_enro_teacher.sh\tseq2seq_trainer.py\n",
            "distil_marian_no_teacher.sh\tseq2seq_training_args.py\n",
            "download_wmt.py\t\t\ttest_bash_script.py\n",
            "dynamic_bs_example.sh\t\ttest_calculate_rouge.py\n",
            "finetune_bart_tiny.sh\t\ttest_data\n",
            "finetune_pegasus_xsum.sh\ttest_datasets.py\n",
            "finetune.py\t\t\ttest_finetune_trainer.py\n",
            "finetune.sh\t\t\ttest_fsmt_bleu_score.py\n",
            "finetune_t5.sh\t\t\ttest_make_student.py\n",
            "finetune_trainer.py\t\ttest_seq2seq_examples_multi_gpu.py\n",
            "__init__.py\t\t\ttest_seq2seq_examples.py\n",
            "make_student.py\t\t\ttest_tatoeba_conversion.py\n",
            "minify_dataset.py\t\ttmp_gpu\n",
            "pack_dataset.py\t\t\ttmp_tpu\n",
            "precomputed_pseudo_labels.md\ttrain_distilbart_cnn.sh\n",
            "__pycache__\t\t\ttrain_distilbart_xsum.sh\n",
            "README.md\t\t\ttrain_mbart_cc25_enro.sh\n",
            "romanian_postprocessing.md\tutils.py\n",
            "rouge_cli.py\t\t\txla_spawn.py\n",
            "run_distiller.sh\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dDDFI5f1V_L",
        "outputId": "30bfc72a-7b6c-4d5b-bee9-4a056de8a2af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!cat tmp_tpu/eval_results.json"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"eval_loss\": 2.2827608585357666,\n",
            "    \"epoch\": 3.0\n",
            "}"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUy2fHMOWvpz",
        "outputId": "2af514b5-54aa-4019-cb16-743dc342408b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!cat tmp_tpu/config.json"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"_name_or_path\": \"microsoft/prophetnet-large-uncased\",\n",
            "  \"activation_dropout\": 0.1,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"add_cross_attention\": true,\n",
            "  \"architectures\": [\n",
            "    \"ProphetNetForConditionalGeneration\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 102,\n",
            "  \"decoder_ffn_dim\": 4096,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_max_position_embeddings\": 514,\n",
            "  \"decoder_start_token_id\": 102,\n",
            "  \"disable_ngram_loss\": false,\n",
            "  \"dropout\": 0.1,\n",
            "  \"early_stopping\": true,\n",
            "  \"encoder_ffn_dim\": 4096,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_max_position_embeddings\": 513,\n",
            "  \"eos_token_id\": 102,\n",
            "  \"eps\": 0.0,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"length_penalty\": 2.0,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"prophetnet\",\n",
            "  \"ngram\": 2,\n",
            "  \"no_repeat_ngram_size\": 3,\n",
            "  \"num_beams\": 4,\n",
            "  \"num_buckets\": 32,\n",
            "  \"num_decoder_attention_heads\": 16,\n",
            "  \"num_decoder_layers\": 12,\n",
            "  \"num_encoder_attention_heads\": 16,\n",
            "  \"num_encoder_layers\": 12,\n",
            "  \"output_past\": false,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"prefix\": \" \",\n",
            "  \"relative_max_distance\": 128,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 30522,\n",
            "  \"xla_device\": true\n",
            "}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TqUwvnhW5zU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}